diff --git a/_sources/appendix.rst.txt b/_sources/appendix.rst.txt new file mode 100644 index 00000000..6b3590a7 --- /dev/null +++ b/_sources/appendix.rst.txt @@ -0,0 +1,371 @@ +API Reference +------------- + +Running a Backtest +~~~~~~~~~~~~~~~~~~ + +.. autofunction:: catalyst.run_algorithm(...) + +Algorithm API +~~~~~~~~~~~~~ + +The following methods are available for use in the ``initialize``, +``handle_data``, and ``before_trading_start`` API functions. + +In all listed functions, the ``self`` argument is implicitly the +currently-executing :class:`~zipline.algorithm.TradingAlgorithm` instance. + +Data Object +``````````` + +.. autoclass:: catalyst.protocol.BarData + :members: + +Scheduling Functions +```````````````````` + +.. autofunction:: catalyst.api.schedule_function + +.. autoclass:: catalyst.api.date_rules + :members: + :undoc-members: + +.. autoclass:: catalyst.api.time_rules + :members: + +Orders +`````` + +.. autofunction:: catalyst.api.order + +.. autofunction:: catalyst.api.order_value + +.. autofunction:: catalyst.api.order_percent + +.. autofunction:: catalyst.api.order_target + +.. autofunction:: catalyst.api.order_target_value + +.. autofunction:: catalyst.api.order_target_percent + +.. autoclass:: catalyst.finance.execution.ExecutionStyle + :members: + +.. autoclass:: catalyst.finance.execution.MarketOrder + +.. autoclass:: catalyst.finance.execution.LimitOrder + +.. autoclass:: catalyst.finance.execution.StopOrder + +.. autoclass:: catalyst.finance.execution.StopLimitOrder + +.. autofunction:: catalyst.api.get_order + +.. autofunction:: catalyst.api.get_open_orders + +.. autofunction:: catalyst.api.cancel_order + +Order Cancellation Policies +''''''''''''''''''''''''''' + +.. autofunction:: catalyst.api.set_cancel_policy + +.. autoclass:: catalyst.finance.cancel_policy.CancelPolicy + :members: + +.. autofunction:: catalyst.api.EODCancel + +.. autofunction:: catalyst.api.NeverCancel + + +Assets +`````` + +.. autofunction:: catalyst.api.symbol + +.. autofunction:: catalyst.api.symbols + +.. autofunction:: catalyst.api.set_symbol_lookup_date + +.. autofunction:: catalyst.api.sid + + +Trading Controls +```````````````` + +zipline provides trading controls to help ensure that the algorithm is +performing as expected. The functions help protect the algorithm from certian +bugs that could cause undesirable behavior when trading with real money. + +.. autofunction:: catalyst.api.set_do_not_order_list + +.. autofunction:: catalyst.api.set_long_only + +.. autofunction:: catalyst.api.set_max_leverage + +.. autofunction:: catalyst.api.set_max_order_count + +.. autofunction:: catalyst.api.set_max_order_size + +.. autofunction:: catalyst.api.set_max_position_size + + +Simulation Parameters +````````````````````` + +.. autofunction:: catalyst.api.set_benchmark + +Commission Models +''''''''''''''''' + +.. autofunction:: catalyst.api.set_commission + +.. autoclass:: catalyst.finance.commission.CommissionModel + :members: + +.. autoclass:: catalyst.finance.commission.PerShare + +.. autoclass:: catalyst.finance.commission.PerTrade + +.. autoclass:: catalyst.finance.commission.PerDollar + +Slippage Models +''''''''''''''' + +.. autofunction:: catalyst.api.set_slippage + +.. autoclass:: catalyst.finance.slippage.SlippageModel + :members: + +.. autoclass:: catalyst.finance.slippage.FixedSlippage + +.. autoclass:: catalyst.finance.slippage.VolumeShareSlippage + +Pipeline +```````` + +Not supported yet. + +.. For more information, see :ref:`pipeline-api` + +.. .. autofunction:: catalyst.api.attach_pipeline + +.. .. autofunction:: catalyst.api.pipeline_output + + +Miscellaneous +````````````` + +.. autofunction:: catalyst.api.record + +.. autofunction:: catalyst.api.get_environment + +.. autofunction:: catalyst.api.fetch_csv + + +.. _pipeline-api: + +.. Pipeline API +.. ~~~~~~~~~~~~ + +.. .. autoclass:: zipline.pipeline.Pipeline +.. :members: +.. :member-order: groupwise + +.. .. autoclass:: zipline.pipeline.CustomFactor +.. :members: +.. :member-order: groupwise + +.. .. autoclass:: zipline.pipeline.filters.Filter +.. :members: __and__, __or__ +.. :exclude-members: dtype + +.. .. autoclass:: zipline.pipeline.factors.Factor +.. :members: bottom, deciles, demean, linear_regression, pearsonr, +.. percentile_between, quantiles, quartiles, quintiles, rank, +.. spearmanr, top, winsorize, zscore, isnan, notnan, isfinite, eq, +.. \__add__, \__sub__, \__mul__, \__div__, \__mod__, \__pow__, +.. \__lt__, \__le__, \__ne__, \__ge__, \__gt__ +.. :exclude-members: dtype +.. :member-order: bysource + +.. .. autoclass:: zipline.pipeline.term.Term +.. :members: +.. :exclude-members: compute_extra_rows, dependencies, inputs, mask, windowed + +.. .. autoclass:: zipline.pipeline.data.USEquityPricing +.. :members: open, high, low, close, volume +.. :undoc-members: + +.. Built-in Factors +.. ```````````````` + +.. .. autoclass:: zipline.pipeline.factors.AverageDollarVolume +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.BollingerBands +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.BusinessDaysSincePreviousEvent +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.BusinessDaysUntilNextEvent +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingAverage +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingStdDev +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.Latest +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.MaxDrawdown +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.Returns +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.RollingLinearRegressionOfReturns +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.RollingPearsonOfReturns +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.RollingSpearmanOfReturns +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.RSI +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.SimpleMovingAverage +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.VWAP +.. :members: + +.. .. autoclass:: zipline.pipeline.factors.WeightedAverageValue +.. :members: + +.. Pipeline Engine +.. ``````````````` + +.. .. autoclass:: zipline.pipeline.engine.PipelineEngine +.. :members: run_pipeline, run_chunked_pipeline +.. :member-order: bysource + +.. .. autoclass:: zipline.pipeline.engine.SimplePipelineEngine +.. :members: __init__, run_pipeline, run_chunked_pipeline +.. :member-order: bysource + +.. .. autofunction:: zipline.pipeline.engine.default_populate_initial_workspace + +.. Data Loaders +.. ```````````` + +.. .. autoclass:: zipline.pipeline.loaders.equity_pricing_loader.USEquityPricingLoader +.. :members: __init__, from_files, load_adjusted_array +.. :member-order: bysource + +Asset Metadata +~~~~~~~~~~~~~~ + +.. autoclass:: catalyst.assets.Asset + :members: + +.. autoclass:: catalyst.assets.AssetConvertible + :members: + + +Trading Calendar API +~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: catalyst.utils.calendars.get_calendar + +.. autoclass:: catalyst.utils.calendars.TradingCalendar + :members: + +.. autofunction:: catalyst.utils.calendars.register_calendar + +.. autofunction:: catalyst.utils.calendars.register_calendar_type + +.. autofunction:: catalyst.utils.calendars.deregister_calendar + +.. autofunction:: catalyst.utils.calendars.clear_calendars + + +Data API +~~~~~~~~ + +.. Writers +.. ``````` +.. .. autoclass:: zipline.data.minute_bars.BcolzMinuteBarWriter +.. :members: + +.. .. autoclass:: zipline.data.us_equity_pricing.BcolzDailyBarWriter +.. :members: + +.. .. autoclass:: zipline.data.us_equity_pricing.SQLiteAdjustmentWriter +.. :members: + +.. .. autoclass:: zipline.assets.AssetDBWriter +.. :members: + +.. Readers +.. ``````` +.. .. autoclass:: zipline.data.minute_bars.BcolzMinuteBarReader +.. :members: + +.. .. autoclass:: zipline.data.us_equity_pricing.BcolzDailyBarReader +.. :members: + +.. .. autoclass:: zipline.data.us_equity_pricing.SQLiteAdjustmentReader +.. :members: + +.. .. autoclass:: zipline.assets.AssetFinder +.. :members: + +.. .. autoclass:: zipline.data.data_portal.DataPortal +.. :members: + +.. Bundles +.. ``````` +.. .. autofunction:: zipline.data.bundles.register + +.. .. autofunction:: zipline.data.bundles.ingest(name, environ=os.environ, date=None, show_progress=True) + +.. .. autofunction:: zipline.data.bundles.load(name, environ=os.environ, date=None) + +.. .. autofunction:: zipline.data.bundles.unregister + +.. .. data:: zipline.data.bundles.bundles + +.. The bundles that have been registered as a mapping from bundle name to bundle +.. data. This mapping is immutable and should only be updated through +.. :func:`~zipline.data.bundles.register` or +.. :func:`~zipline.data.bundles.unregister`. + +.. .. autofunction:: zipline.data.bundles.yahoo_equities + + + +Utilities +~~~~~~~~~ + +Caching +``````` + +.. autoclass:: catalyst.utils.cache.CachedObject + +.. autoclass:: catalyst.utils.cache.ExpiringCache + +.. autoclass:: catalyst.utils.cache.dataframe_cache + +.. autoclass:: catalyst.utils.cache.working_file + +.. autoclass:: catalyst.utils.cache.working_dir + +Command Line +```````````` +.. autofunction:: catalyst.utils.cli.maybe_show_progress diff --git a/_sources/beginner-tutorial.rst.txt b/_sources/beginner-tutorial.rst.txt new file mode 100644 index 00000000..291dd205 --- /dev/null +++ b/_sources/beginner-tutorial.rst.txt @@ -0,0 +1,16511 @@ +Catalyst Beginner Tutorial +-------------------------- + +Basics +~~~~~~ + +Catalyst is an open-source algorithmic trading simulator for crypto +assets written in Python. The source code can be found at: +https://github.com/enigmampc/catalyst + +Some benefits include: + +- Support for several of the top crypto-exchanges by trading volume. +- Realistic: slippage, transaction costs, order delays. +- Stream-based: Process each event individually, avoids look-ahead + bias. +- Batteries included: Common transforms (moving average) as well as + common risk calculations (Sharpe). +- Developed and continuously updated by + `Enigma MPC `__ which is building the Enigma + data marketplace protocol as well as Catalyst, the first application + that will run on our protocol. Powered by our financial data + marketplace, Catalyst empowers users to share and curate data and + build profitable, data-driven investment strategies. + +This tutorial assumes that you have Catalyst correctly installed, see the +:doc:`Install` section if you haven't set up Catalyst yet. + +Every ``catalyst`` algorithm consists of at least two functions you have to +define: + +* ``initialize(context)`` +* ``handle_data(context, data)`` + +Before the start of the algorithm, ``catalyst`` calls the +``initialize()`` function and passes in a ``context`` variable. +``context`` is a persistent namespace for you to store variables you +need to access from one algorithm iteration to the next. + +After the algorithm has been initialized, ``catalyst`` calls the +``handle_data()`` function on each iteration, that's one per day (daily) or +once every minute (minute), depending on the frequency we choose to run our +simulation. On every iteration, ``handle_data()`` passes the same ``context`` +variable and an event-frame called ``data`` containing the current trading bar +with open, high, low, and close (OHLC) prices as well as volume for each +crypto asset in your universe. + +.. For more information on these functions, see the `relevant part of the +.. Quantopian docs `. + +My first algorithm +~~~~~~~~~~~~~~~~~~ + +Lets take a look at a very simple algorithm from the ``examples`` directory: +`buy_btc_simple.py `_: + +.. code-block:: python + + from catalyst.api import order, record, symbol + + + def initialize(context): + context.asset = symbol('btc_usd') + + + def handle_data(context, data): + order(context.asset, 1) + record(btc = data.current(context.asset, 'price')) + + +As you can see, we first have to import some functions we would like to +use. All functions commonly used in your algorithm can be found in +``catalyst.api``. Here we are using :func:`~catalyst.api.order()` which takes +twoarguments: a cryptoasset object, and a number specifying how many assets you +wouldlike to order (if negative, :func:`~catalyst.api.order()` will sell/short +assets). In this case we want to order 1 bitcoin at each iteration. + +.. For more documentation on ``order()``, see the `Quantopian docs +.. `__. + +Finally, the :func:`~catalyst.api.record` function allows you to save the value +of a variable at each iteration. You provide it with a name for the variable +together with the variable itself: ``varname=var``. After the algorithm +finished running you will have access to each variable value you tracked +with :func:`~catalyst.api.record` under the name you provided (we will see this +further below). You also see how we can access the current price data of +a bitcoin in the ``data`` event frame. + +.. (for more information see `here `__. + +Ingesting data +~~~~~~~~~~~~~~ + +Before you can backtest your algorithm, you first need to load the historical +pricing data that Catalyst needs to run your simulation through a process called +``ingestion``. When you ingest data, Catalyst downloads that data in compressed +form from the Enigma servers (which eventually will migrate to the Enigma Data +Marketplace), and stores it locally to make it available at runtime. + +In order to ingest data, you need to run a command like the following: + +.. code-block:: bash + + catalyst ingest-exchange -x bitfinex -i btc_usd + +This instructs Catalyst to download pricing data from the ``Bitfinex`` exchange +for the ``btc_usd`` currency pair (this follows from the simple algorithm +presented above where we want to trade ``btc_usd``), and we're choosing to test +our algorithm using historical pricing data from the Bitfinex exchange. By +default, Catalyst assumes that you want data with ``daily`` frequency (one candle +bar per day). If you want instead ``minute`` frequency (one candle bar for every +minute), you would need to specify it as follows: + +.. code-block:: bash + + catalyst ingest-exchange -x bitfinex -i btc_usd -f minute + +.. parsed-literal:: + + Ingesting exchange bundle bitfinex... + [====================================] Ingesting daily price data on bitfinex: 100% + +We believe it is important for you to have a high-level understanding of how +data is managed, hence the following overview: + +- Pricing data is split and packaged into ``bundles``: chunks of data organized + as time series that are kept up to date daily on Enigma's servers. Catalyst + downloads the requested bundles and reconstructs the full dataset in your + hard drive. + +- Pricing data is provided in ``daily`` and ``minute`` resolution. Those are + different bundle datasets, and are managed separately. + +- Bundles are exchange-specific, as the pricing data is specific to the trades + that happen in each exchange. As a result, you must specify which + exchange you want pricing data from when ingesting data. + +- Catalyst keeps track of all the downloaded bundles, so that it only has to + download them once, and will do incremental updates as needed. + +- When running in ``live trading`` mode, Catalyst will first look for + historical pricing data in the locally stored bundles. If there is anything + missing, Catalyst will hit the exchange for the most recent data, and merge + it with the local bundle to optimize the number of requests it needs to make + to the exchange. + +The ``ingest-exchange`` command in catalyst offers additional parameters to +further tweak the data ingestion process. You can learn more by running the +following from the command line: + +.. code-block:: bash + + catalyst ingest-exchange --help + +Running the algorithm +~~~~~~~~~~~~~~~~~~~~~ + +You can now test your algorithm using cryptoassets' historical pricing data, +``catalyst`` provides three interfaces: + +- A command-line interface (CLI), +- a :func:`~catalyst.run_algorithm()` that you can call from other + Python scripts, +- and the ``Jupyter Notebook`` magic. + + +We'll start with the CLI, and introduce the ``run_algorithm()`` in the last +example of this tutorial. Some of the :doc:`example algorithms ` +provide instructions on how to run them both from the CLI, and using the +:func:`~catalyst.run_algorithm` function. For the third method, refer to the +corresponding section on :ref:`Catalyst & Jupyter Notebook ` after you +have assimilated the contents of this tutorial. + +Command line interface +^^^^^^^^^^^^^^^^^^^^^^ + +After you installed Catalyst, you should be able to execute the following +from your command line (e.g. ``cmd.exe`` or the ``Anaconda Prompt`` on Windows, +or the Terminal application on MacOS). + +.. code-block:: bash + + $ catalyst --help + +This is the resulting output, simplified for eductional purposes: + +.. parsed-literal:: + + Usage: catalyst [OPTIONS] COMMAND [ARGS]... + + Top level catalyst entry point. + + Options: + --version Show the version and exit. + --help Show this message and exit. + + Commands: + ingest-exchange Ingest data for the given exchange. + live Trade live with the given algorithm. + run Run a backtest for the given algorithm. + +There are three main modes you can run on Catalyst. The first being +``ingest-exchange`` for data ingestion, which we have covered in the previous +section. The second is ``live`` to use your algorithm to trade live against a +given exchange, and the third mode ``run`` is to backtest your algorithm before +trading live with it. + +Let's start with backtesting, so run this other command to learn more about +the available options: + +.. code-block:: bash + + $ catalyst run --help + +.. parsed-literal:: + + Usage: catalyst run [OPTIONS] + + Run a backtest for the given algorithm. + + Options: + -f, --algofile FILENAME The file that contains the algorithm to run. + -t, --algotext TEXT The algorithm script to run. + -D, --define TEXT Define a name to be bound in the namespace + before executing the algotext. For example + '-Dname=value'. The value may be any python + expression. These are evaluated in order so + they may refer to previously defined names. + --data-frequency [daily|minute] + The data frequency of the simulation. + [default: daily] + --capital-base FLOAT The starting capital for the simulation. + [default: 10000000.0] + -b, --bundle BUNDLE-NAME The data bundle to use for the simulation. + [default: poloniex] + --bundle-timestamp TIMESTAMP The date to lookup data on or before. + [default: ] + -s, --start DATE The start date of the simulation. + -e, --end DATE The end date of the simulation. + -o, --output FILENAME The location to write the perf data. If this + is '-' the perf will be written to stdout. + [default: -] + --print-algo / --no-print-algo Print the algorithm to stdout. + -x, --exchange-name [poloniex|bitfinex|bittrex] + The name of the targeted exchange + (supported: bitfinex, bittrex, poloniex). + -n, --algo-namespace TEXT A label assigned to the algorithm for data + storage purposes. + -c, --base-currency TEXT The base currency used to calculate + statistics (e.g. usd, btc, eth). + --help Show this message and exit. + + +As you can see there are a couple of flags that specify where to find your +algorithm (``-f``) as well as a the ``-x`` flag to specify which exchange to +use. There are also arguments for the date range to run the algorithm over +(``--start`` and ``--end``). You also need to set the base currency for your +algorithm through the ``-c`` flag, and the ``--capital_base``. All the +aforementioned parameters are required. Optionally, you will want to save the +performance metrics of your algorithm so that you can analyze how it performed. +This is done via the ``--output`` flag and will cause it to write the +performance ``DataFrame`` in the pickle Python file format. Note that you can +also define a configuration file with these parameters that you can then +conveniently pass to the ``-c`` option so that you don't have to supply the +command line args all the time. + +Thus, to execute our algorithm from above and save the results to +``buy_btc_simple_out.pickle`` we would call ``catalyst run`` as follows: + +.. code-block:: bash + + catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -c usd --capital-base 100000 -o buy_btc_simple_out.pickle + + +.. parsed-literal:: + + INFO: run_algo: running algo in backtest mode + INFO: exchange_algorithm: initialized trading algorithm in backtest mode + INFO: Performance: Simulated 639 trading days out of 639. + INFO: Performance: first open: 2016-01-01 00:00:00+00:00 + INFO: Performance: last close: 2017-09-30 23:59:00+00:00 + + +``run`` first calls the ``initialize()`` function, and then +streams the historical asset price day-by-day through ``handle_data()``. +After each call to ``handle_data()`` we instruct ``catalyst`` to order 1 +bitcoin. After the call of the ``order()`` function, ``catalyst`` +enters the ordered stock and amount in the order book. After the +``handle_data()`` function has finished, ``catalyst`` looks for any open +orders and tries to fill them. If the trading volume is high enough for +this asset, the order is executed after adding the commission and +applying the slippage model which models the influence of your order on +the stock price, so your algorithm will be charged more than just the +asset price. (Note, that you can also change the commission and +slippage model that ``catalyst`` uses). + +.. see the `Quantopian docs `__ +.. for more information). + + +Let's take a quick look at the performance ``DataFrame``. For this, we write +different Python script--let's call it ``print_results.py``--and we make use of +the fantastic ``pandas`` library to print the first ten rows. Note that +``catalyst`` makes heavy usage of `pandas `_, +especially for data analysis and outputting so it's worth spending some time to +learn it. + +.. code-block:: python + + import pandas as pd + perf = pd.read_pickle('buy_btc_simple_out.pickle') # read in perf DataFrame + print(perf.head()) + +Which we execute by running: + +.. code-block:: bash + + $ python print_results.py + +.. raw:: html + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
algo_volatilityalgorithm_period_returnalphabenchmark_period_returnbenchmark_volatilitybetabtccapital_usedending_cashending_exposure...short_exposureshort_valueshorts_countsortinostarting_cashstarting_exposurestarting_valuetrading_daystransactionstreasury_period_return
2016-01-01 23:59:00+00:00NaN0.000000e+00NaN-0.010937NaNNaN433.9799990.0000001.000000e+070.00...000NaN1.000000e+070.000.001[]0.0227
2016-01-02 23:59:00+00:000.000011-9.536708e-07-0.000170-0.0064800.173338-0.000062432.700000-442.2367089.999558e+06432.70...000-11.2249721.000000e+070.000.002[{u'order_id': u'7869f7828fa140328eb40477bb7de...0.0227
2016-01-03 23:59:00+00:000.000011-2.328842e-06-0.000176-0.0265120.1978570.000009428.390000-437.8317169.999120e+06856.78...000-12.7542629.999558e+06432.70432.703[{u'order_id': u'be62ff77760c4599abaac43be9cc9...0.0227
2016-01-04 23:59:00+00:000.000011-2.380954e-06-0.000139-0.0086400.2697900.000020432.900000-442.4411169.998677e+061298.70...000-11.2872059.999120e+06856.78856.784[{u'order_id': u'd6dca79513214346a646079213526...0.0224
2016-01-05 23:59:00+00:000.000011-3.650729e-06-0.000158-0.0214260.2459890.000024431.840000-441.3577549.998236e+061727.36...000-12.3338479.998677e+061298.701298.705[{u'order_id': u'505275d6646a41f3856b22b16678d...0.0225
+
+ +| + +There is a row for each trading day, starting on the first day of our +simulation Jan 1st, 2016. In the columns you can find various +information about the state of your algorithm. The column +``btc`` was placed there by the ``record()`` function mentioned earlier +and allows us to plot the price of bitcoin. For example, we could easily +examine now how our portfolio value changed over time compared to the +bitcoin price. + +Now we will run the simulation again, but this time we extend our original +algorithm with the addition of the ``analyze()`` function. Somewhat analogously +as how ``initialize()`` gets called once before the start of the algorithm, +``analyze()`` gets called once at the end of the algorithm, and receives two +variables: ``context``, which we discussed at the very beginning, and ``perf``, +which is the pandas dataframe containing the performance data for our algorithm +that we reviewed above. Inside the ``analyze()`` function is where we can +analyze and visualize the results of our strategy. Here's the revised simple +algorithm (note the addition of Line 1, and Lines 11-18) + +.. code-block:: python + + import matplotlib.pyplot as plt + from catalyst.api import order, record, symbol + + def initialize(context): + context.asset = symbol('btc_usd') + + def handle_data(context, data): + order(context.asset, 1) + record(btc = data.current(context.asset, 'price')) + + def analyze(context, perf): + ax1 = plt.subplot(211) + perf.portfolio_value.plot(ax=ax1) + ax1.set_ylabel('portfolio value') + ax2 = plt.subplot(212, sharex=ax1) + perf.btc.plot(ax=ax2) + ax2.set_ylabel('bitcoin price') + plt.show() + +Here we make use of the external visualization library called +`matplotlib `_, which you might recall we installed +alongside enigma-catalyst (with the exception of the ``Conda`` install, where it +was included by default inside the conda environment we created). If for any +reason you don't have it installed, you can add it by running: + +.. code-block:: bash + + (catalyst)$ pip install matplotlib + +If everything works well, you'll see the following chart: + +.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/buy_btc_simple_graph.png + +Our algorithm performance as assessed by the ``portfolio_value`` closely +matches that of the bitcoin price. This is not surprising as our algorithm +only bought bitcoin every chance it got. + + If you get an error when invoking matplotlib to visualize the performance + results refer to `MacOS + Matplotlib `_. + Alternatively, some users have reported the following error when running an algo + in a Linux environment: + + .. parsed-literal:: + + ImportError: No module named _tkinter, please install the python-tk package + + Which can easily solved by running (in Ubuntu/Debian-based systems): + + .. code-block:: python + + sudo apt install python-tk + + +.. _history: + +Access to previous prices using ``history`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Working example: Dual Moving Average Cross-Over +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The Dual Moving Average (DMA) is a classic momentum strategy. It's +probably not used by any serious trader anymore but is still very +instructive. The basic idea is that we compute two rolling or moving +averages (mavg) -- one with a longer window that is supposed to capture +long-term trends and one shorter window that is supposed to capture +short-term trends. Once the short-mavg crosses the long-mavg from below +we assume that the stock price has upwards momentum and long the stock. +If the short-mavg crosses from above we exit the positions as we assume +the stock to go down further. + +As we need to have access to previous prices to implement this strategy +we need a new concept: History. ``data.history()`` is a convenience function +that keeps a rolling window of data for you. The first argument is the number +of bars you want to collect, the second argument is the unit (either ``'1d'`` +for daily or ``'1m'`` for minute frequency, but note that you need to have +minute-level data when using ``1m``). This is a function we use in the +``handle_data()`` section. + +You will note that the code below is substantially longer than the previous +examples. Don't get overwhelmed by it as the logic is fairly simple and easy to +follow. Most of the added some complexity has been added to beautify the output, +which you can skim through for now. A copy of this algorithm is available in +the ``examples`` directory: +`dual_moving_average.py `_. + +.. literalinclude:: ../../catalyst/examples/dual_moving_average.py + :language: python + +In order to run the code above, you have to ingest the needed data first: + +.. code-block:: bash + + catalyst ingest-exchange -x bitfinex -f minute -i ltc_usd + +And then run the code above with the following command: + +.. code-block:: bash + + catalyst run -f dual_moving_average.py -x bitfinex -s 2017-9-22 -e 2017-9-23 --capital-base 1000 --base-currency usd --data-frequency minute -o out.pickle + +Alternatively, we can make use of the ``run_algorithm()`` function included at +the end of the file, where we can specify all the simulation parameters, and +execute this file as a Python script: + +.. code-block:: bash + + python dual_moving_average.py + +Either way, we obtain the following charts: + +.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/tutorial_dual_moving_average.png + + +A few comments on the code above: + + At the beginning of our code, we import a number of Python libraries that we + will be using in different parts of our script. It's good practice to keep all + imports at the beginning of the file, as they are available globally + throughout our script. All the libraries imported in this example are already + present in your environment since they are prerequisites for the Catalyst + installation. + + Focus on the code that is inside ``handle_data()`` that is where all the + trading logic occurs. You can safely dismiss most of the code in the + ``analyze()`` section, which is mostly to customize the visualization of the + performance of our algorithm using the matplotlib library. You can copy and + paste this whole section into other algorithms to obtain a similar display. + + Inside the ``handle_data()``, we also used the ``order_target_percent()`` + function above. This and other functions like it can make order management + and portfolio rebalancing much easier. + + The ``ltc_usd`` asset was arbitrarily chosen. The values of 50 and 200 for the + ``short_window`` and ``long_window`` parameters are fairly common for a dual + moving average crossover strategy from the world of traditional stocks (but + bear in mind that they are usually used with daily bars instead of minute + bars). The ``start`` and ``end`` dates have been chosen so as to demonstrate + how our strategy can both perform better (blue line above green line on the + ``Percent Change`` chart) and worse (green line above blue line towards the end) than the + price of the asset we are trading. + + You can change any of these parameters: ``asset``, ``short_window``, + ``long_window``, ``start_date`` and ``end_date`` and compare the results, and + you will see that in most cases, the performance is either worse than the + price of the asset, or you are overfitting to one specific case. As we said + at the beginning of this section, this strategy is probably not used by any + serious trader anymore, but its educational purpose. + +Although it might not be directly apparent, the power of ``history()`` +(pun intended) can not be under-estimated as most algorithms make use of +prior market developments in one form or another. You could easily +devise a strategy that trains a classifier with +`scikit-learn `__ which tries to +predict future market movements based on past prices (note, that most of +the ``scikit-learn`` functions require ``numpy.ndarray``\ s rather than +``pandas.DataFrame``\ s, so you can simply pass the underlying +``ndarray`` of a ``DataFrame`` via ``.values``). + +.. _jupyter: + +Jupyter Notebook +~~~~~~~~~~~~~~~~ + +(`This is actual Notebook `_ referenced in the text below) + +The `Jupyter Notebook `__ is a very powerful +browser-based interface to a Python interpreter. As it is already the +de-facto interface for most quantitative researchers, ``catalyst`` +provides an easy way to run your algorithm inside the Notebook without +requiring you to use the CLI. We include this section here as an alternative to +running algorithms through the command line. + +Install +^^^^^^^ + +In order to use Jupyter Notebook, you first have to install it inside your +environment. It's available as ``pip`` package, so regardless of how you +installed Catalyst, go inside your catalyst environemnt and run: + +.. code-block:: bash + + (catalyst)$ pip install jupyter + +Once you have Jupyter Notebook installed, every time you want to use it run: + +.. code-block:: bash + + (catalyst)$ jupyter notebook + +A local server will launch, and will open a new window on your browser. That's +the interface through which you will interact with Jupyter Notebook. + +Running Algorithms +^^^^^^^^^^^^^^^^^^ + +Before running your algorithms inside the Jupyter Notebook, remember to ingest +the data from the command line interface (CLI). In the example below, you would +need to run first: + +.. code-block:: bash + + catalyst ingest-exchange -x bitfinex -i btc_usd + +To use Catalyst inside a Jupyter Noebook, you have to write your algorithm in a +cell and let the Jupyter know that it is supposed to execute this algorithm with +Catalyst. This is done via the ``%%catalyst`` IPython magic command that is +available after you import ``catalyst`` from within the Notebook. This magic +takes the same arguments as the command line interface. Thus to run the +algorithm just supply the same parameters as the CLI but without the -f +and -o arguments. We just have to execute the following cell after +importing ``catalyst`` to register the magic. + +.. code:: python + + # Register the catalyst magic + %load_ext catalyst + +.. code:: python + + # Setup matplotlib to display graphs inline in this Notebook + %matplotlib inline + +Note below that we do not have to specify an input file (-f) since the +magic will use the contents of the cell and look for your algorithm +functions. + +.. code:: python + + %%catalyst --start 2015-3-2 --end 2017-6-28 --capital-base 100000 -x bitfinex -c usd + + from catalyst.finance.slippage import VolumeShareSlippage + + from catalyst.api import ( + order_target_value, + symbol, + record, + cancel_order, + get_open_orders, + ) + + def initialize(context): + context.ASSET_NAME = 'btc_usd' + context.TARGET_HODL_RATIO = 0.8 + context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO + + # For all trading pairs in the poloniex bundle, the default denomination + # currently supported by Catalyst is 1/1000th of a full coin. Use this + # constant to scale the price of up to that of a full coin if desired. + context.TICK_SIZE = 1000.0 + + context.is_buying = True + context.asset = symbol(context.ASSET_NAME) + + context.i = 0 + + def handle_data(context, data): + context.i += 1 + + starting_cash = context.portfolio.starting_cash + target_hodl_value = context.TARGET_HODL_RATIO * starting_cash + reserve_value = context.RESERVE_RATIO * starting_cash + + # Cancel any outstanding orders + orders = get_open_orders(context.asset) or [] + for order in orders: + cancel_order(order) + + # Stop buying after passing the reserve threshold + cash = context.portfolio.cash + if cash <= reserve_value: + context.is_buying = False + + # Retrieve current asset price from pricing data + price = data.current(context.asset, 'price') + + # Check if still buying and could (approximately) afford another purchase + if context.is_buying and cash > price: + # Place order to make position in asset equal to target_hodl_value + order_target_value( + context.asset, + target_hodl_value, + limit_price=price*1.1, + ) + + record( + price=price, + volume=data.current(context.asset, 'volume'), + cash=cash, + starting_cash=context.portfolio.starting_cash, + leverage=context.account.leverage, + ) + + def analyze(context=None, results=None): + import matplotlib.pyplot as plt + + # Plot the portfolio and asset data. + ax1 = plt.subplot(611) + results[['portfolio_value']].plot(ax=ax1) + ax1.set_ylabel('Portfolio Value (USD)') + + ax2 = plt.subplot(612, sharex=ax1) + ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME)) + (context.TICK_SIZE * results[['price']]).plot(ax=ax2) + + trans = results.ix[[t != [] for t in results.transactions]] + buys = trans.ix[ + [t[0]['amount'] > 0 for t in trans.transactions] + ] + ax2.plot( + buys.index, + context.TICK_SIZE * results.price[buys.index], + '^', + markersize=10, + color='g', + ) + + ax3 = plt.subplot(613, sharex=ax1) + results[['leverage', 'alpha', 'beta']].plot(ax=ax3) + ax3.set_ylabel('Leverage ') + + ax4 = plt.subplot(614, sharex=ax1) + results[['starting_cash', 'cash']].plot(ax=ax4) + ax4.set_ylabel('Cash (USD)') + + results[[ + 'treasury', + 'algorithm', + 'benchmark', + ]] = results[[ + 'treasury_period_return', + 'algorithm_period_return', + 'benchmark_period_return', + ]] + + ax5 = plt.subplot(615, sharex=ax1) + results[[ + 'treasury', + 'algorithm', + 'benchmark', + ]].plot(ax=ax5) + ax5.set_ylabel('Percent Change') + + ax6 = plt.subplot(616, sharex=ax1) + results[['volume']].plot(ax=ax6) + ax6.set_ylabel('Volume (mCoins/5min)') + + plt.legend(loc=3) + + # Show the plot. + plt.gcf().set_size_inches(18, 8) + plt.show() + +:: + + [2017-08-11 07:19:46.411748] INFO: Loader: Loading benchmark data for 'USDT_BTC' from 1989-12-31 00:00:00+00:00 to 2017-08-09 00:00:00+00:00 + [2017-08-11 07:19:46.418983] INFO: Loader: Loading data for /Users//.catalyst/data/USDT_BTC_benchmark.csv failed with error [Unknown string format]. + [2017-08-11 07:19:46.419740] INFO: Loader: Cache at /Users//.catalyst/data/USDT_BTC_benchmark.csv does not have data from 1990-01-01 00:00:00+00:00 to 2017-08-09 00:00:00+00:00. + + [2017-08-11 07:19:46.420770] INFO: Loader: Downloading benchmark data for 'USDT_BTC' from 1989-12-31 00:00:00+00:00 to 2017-08-09 00:00:00+00:00 + [2017-08-11 07:19:50.060244] WARNING: Loader: Still don't have expected data after redownload! + [2017-08-11 07:19:50.097334] WARNING: Loader: Refusing to download new treasury data because a download succeeded at 2017-08-11 06:56:49+00:00. + [2017-08-11 07:19:54.618399] INFO: Performance: Simulated 851 trading days out of 851. + [2017-08-11 07:19:54.619301] INFO: Performance: first open: 2015-03-01 00:00:00+00:00 + [2017-08-11 07:19:54.620430] INFO: Performance: last close: 2017-06-28 23:59:00+00:00 + +.. figure:: https://i.imgur.com/DS5w47q.png + :alt: png + +.. raw:: html + +
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+ +2015-03-02 23:59:00+00:00 + +.. raw:: html + + + +0.000278 + +.. raw:: html + + + +-0.000025 + +.. raw:: html + + + +0.011045 + +.. raw:: html + + + +0.120833 + +.. raw:: html + + + +0.290503 + +.. raw:: html + + + +-0.000956 + +.. raw:: html + + + +-85544.474955 + +.. raw:: html + + + +14455.525045 + +.. raw:: html + + + +14455.525045 + +.. raw:: html + + + +85542.000 + +.. raw:: html + + + +… + +.. raw:: html + + + +100000.0 + +.. raw:: html + + + +0.000 + +.. raw:: html + + + +0.000 + +.. raw:: html + + + +2 + +.. raw:: html + + + +[{u’commission’: None, u’amount’: 318, u’sid’:… + +.. raw:: html + + + +0.0208 + +.. raw:: html + + + +98063 + +.. raw:: html + + + +0.0208 + +.. raw:: html + + + +-0.000025 + +.. raw:: html + + + +0.120833 + +.. raw:: html + +
+ +2015-03-03 23:59:00+00:00 + +.. raw:: html + + + +0.051796 + +.. raw:: html + + + +-0.005688 + +.. raw:: html + + + +-1.197544 + +.. raw:: html + + + +0.113416 + +.. raw:: html + + + +0.633538 + +.. raw:: html + + + +0.077239 + +.. raw:: html + + + +0.000000 + +.. raw:: html + + + +14455.525045 + +.. raw:: html + + + +14455.525045 + +.. raw:: html + + + +84975.642 + +.. raw:: html + + + +… + +.. raw:: html + + + +100000.0 + +.. raw:: html + + + +85542.000 + +.. raw:: html + + + +85542.000 + +.. raw:: html + + + +3 + +.. raw:: html + + + +[] + +.. raw:: html + + + +0.0212 + +.. raw:: html + + + +442983 + +.. raw:: html + + + +0.0212 + +.. raw:: html + + + +-0.005688 + +.. raw:: html + + + +0.113416 + +.. raw:: html + +
+ +2015-03-04 23:59:00+00:00 + +.. raw:: html + + + +0.342118 + +.. raw:: html + + + +0.034955 + +.. raw:: html + + + +0.401861 + +.. raw:: html + + + +0.166666 + +.. raw:: html + + + +0.524400 + +.. raw:: html + + + +0.181468 + +.. raw:: html + + + +0.000000 + +.. raw:: html + + + +14455.525045 + +.. raw:: html + + + +14455.525045 + +.. raw:: html + + + +89040.000 + +.. raw:: html + + + +… + +.. raw:: html + + + +100000.0 + +.. raw:: html + + + +84975.642 + +.. raw:: html + + + +84975.642 + +.. raw:: html + + + +4 + +.. raw:: html + + + +[] + +.. raw:: html + + + +0.0212 + +.. raw:: html + + + +245889 + +.. raw:: html + + + +0.0212 + +.. raw:: html + + + +0.034955 + +.. raw:: html + + + +0.166666 + +.. raw:: html + +
+ +2015-03-05 23:59:00+00:00 + +.. raw:: html + + + +0.637226 + +.. raw:: html + + + +-0.038185 + +.. raw:: html + + + +-3.914003 + +.. raw:: html + + + +0.070834 + +.. raw:: html + + + +0.976896 + +.. raw:: html + + + +0.550520 + +.. raw:: html + + + +0.000000 + +.. raw:: html + + + +14455.525045 + +.. raw:: html + + + +14455.525045 + +.. raw:: html + + + +81726.000 + +.. raw:: html + + + +… + +.. raw:: html + + + +100000.0 + +.. raw:: html + + + +89040.000 + +.. raw:: html + + + +89040.000 + +.. raw:: html + + + +5 + +.. raw:: html + + + +[] + +.. raw:: html + + + +0.0211 + +.. raw:: html + + + +117440 + +.. raw:: html + + + +0.0211 + +.. raw:: html + + + +-0.038185 + +.. raw:: html + + + +0.070834 + +.. raw:: html + +
+ +.. raw:: html + +

+ +5 rows × 45 columns + +.. raw:: html + +

+ +.. raw:: html + +
+ +PyCharm IDE +~~~~~~~~~~~ + +PyCharm is an Integrated Development Environment (IDE) used in computer +programming, specifically for the Python language. It streamlines the continuos +development of Python code, and among other things includes a debugger that +comes in handy to see the inner workings of Catalyst, and your trading +algorithms. + +Install +^^^^^^^ +Install PyCharm from their `Website `__. +There is a free and open-source **Community** version. + +Setup +^^^^^ + +1. When creating a new project in PyCharm, right under you specify the Location, + click on **Project Interpreter** to display a drop down menu + +2. Select **Existing interpreter**, click the gear box right next to it and + select 'add local'. Depending on your installation, select either + "*Virtual Environemnt*" or "*Conda Environment" and click the '...' button to + navigate to your catalyst env and select the Python binary file: + ``bin/python`` for Linux/MacOS installations or 'python.exe' for Windows + installs (for example: 'C:\\Users\\user\\Anaconda2\\envs\\catalyst\\python.exe'). + Select OK. You may want to click on *Make available to all projects* for your + future reference. Click OK again, and create your new environment using the + set up of your virtual environment. + +Alternatively, if you already have your project created, in Windows do: + +1. File -> Default Settings -> Project Interpreter. Click the gear box next to + the project interpreter and select ‘add local’, and follow the steps from the + second step above. + +On MacOS: + +1. PyCharm -> Preferences -> Settings -> Project:’NAME_OF_PROJECT’ -> + Project Interpreter. Click the gear box next to the project interpreter + and select ‘add local’, and follow the steps from the second step above. + +You should now be able to run your project/scripts in PyCharm. + +Next steps +~~~~~~~~~~ + +We hope that this tutorial gave you a little insight into the +architecture, API, and features of Catalyst. For next steps, check +out some of the other :doc:`example algorithms`. + +Feel free to ask questions on the ``#catalyst_dev`` channel of our +`Discord group `__ and report +problems on our `GitHub issue tracker `__. diff --git a/_sources/bundles.rst.txt b/_sources/bundles.rst.txt new file mode 100644 index 00000000..6fd8c1fb --- /dev/null +++ b/_sources/bundles.rst.txt @@ -0,0 +1,354 @@ +.. _data-bundles: + +Data Bundles +------------ + +A data bundle is a collection of pricing data, adjustment data, and an asset +database. Bundles allow us to preload all of the data we will need to run +backtests and store the data for future runs. + +.. _bundles-command: + +Discovering Available Bundles +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Zipline comes with a few bundles by default as well as the ability to register +new bundles. To see which bundles we have have available, we may run the +``bundles`` command, for example: + +.. code-block:: bash + + $ zipline bundles + my-custom-bundle 2016-05-05 20:35:19.809398 + my-custom-bundle 2016-05-05 20:34:53.654082 + my-custom-bundle 2016-05-05 20:34:48.401767 + quandl + quantopian-quandl 2016-05-05 20:06:40.894956 + +The output here shows that there are 3 bundles available: + +- ``my-custom-bundle`` (added by the user) +- ``quandl`` (provided by zipline) +- ``quantopian-quandl`` (provided by zipline) + +The dates and times next to the name show the times when the data for this +bundle was ingested. We have run three different ingestions for +``my-custom-bundle``. We have never ingested any data for the ``quandl`` bundle +so it just shows ```` instead. Finally, there is only one +ingestion for ``quantopian-quandl``. + +.. _ingesting-data: + +Ingesting Data +~~~~~~~~~~~~~~ + +The first step to using a data bundle is to ingest the data. The ingestion +process will invoke some custom bundle command and then write the data to a +standard location that zipline can find. By default the location where ingested +data will be written is ``$ZIPLINE_ROOT/data/`` where by default +``ZIPLINE_ROOT=~/.zipline``. The ingestion step may take some time as it could +involve downloading and processing a lot of data. This can be run with: + +.. code-block:: bash + + $ zipline ingest [-b ] + + +where ```` is the name of the bundle to ingest, defaulting to +:ref:`quantopian-quandl `. + +Old Data +~~~~~~~~ + +When the ``ingest`` command is used it will write the new data to a subdirectory +of ``$ZIPLINE_ROOT/data/`` which is named with the current date. This +makes it possible to look at older data or even run backtests with the older +copies. Running a backtest with an old ingestion makes it easier to reproduce +backtest results later. + +One drawback of saving all of the data by default is that the data directory +may grow quite large even if you do not want to use the data. As shown earlier, +we can list all of the ingestions with the :ref:`bundles command +`. To solve the problem of leaking old data there is another +command: ``clean``, which will clear data bundles based on some time +constraints. + +For example: + +.. code-block:: bash + + # clean everything older than + $ zipline clean [-b ] --before + + # clean everything newer than + $ zipline clean [-b ] --after + + # keep everything in the range of [before, after] and delete the rest + $ zipline clean [-b ] --before --after + + # clean all but the last runs + $ zipline clean [-b ] --keep-last + + +Running Backtests with Data Bundles +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now that the data has been ingested we can use it to run backtests with the +``run`` command. The bundle to use can be specified with the ``--bundle`` option +like: + +.. code-block:: bash + + $ zipline run --bundle --algofile algo.py ... + + +We may also specify the date to use to look up the bundle data with the +``--bundle-date`` option. Setting the ``--bundle-date`` will cause run to use +the most recent bundle ingestion that is less than or equal to the +``bundle-date``. This is how we can run backtests with older data. The reason +that ``-bundle-date`` uses a less than or equal to relationship is that we can +specify the date that we ran an old backtest and get the same data that would +have been available to us on that date. The ``bundle-date`` defaults to the +current day to use the most recent data. + +Default Data Bundles +~~~~~~~~~~~~~~~~~~~~ + +.. _quandl-data-bundle: + +Quandl WIKI Bundle +`````````````````` + +By default zipline comes with the ``quandl`` data bundle which uses quandl's +`WIKI dataset `_. The quandl data bundle +includes daily pricing data, splits, cash dividends, and asset metadata. To +ingest the ``quandl`` data bundle we recommend creating an account on quandl.com +to get an API key to be able to make more API requests per day. Once we have an +API key we may run: + +.. code-block:: bash + + $ QUANDL_API_KEY= zipline ingest -b quandl + +though we may still run ``ingest`` as an anonymous quandl user (with no API +key). We may also set the ``QUANDL_DOWNLOAD_ATTEMPTS`` environment variable to +an integer which is the number of attempts that should be made to download data +from quandls servers. By default ``QUANDL_DOWNLOAD_ATTEMPTS`` will be 5, meaning +that we will retry each attempt 5 times. + +.. note:: + + ``QUANDL_DOWNLOAD_ATTEMPTS`` is not the total number of allowed failures, + just the number of allowed failures per request. The quandl loader will make + one request per 100 equities for the metadata followed by one request per + equity. + + +.. _quantopian-quandl-mirror: + +Quantopian Quandl WIKI Mirror +''''''''''''''''''''''''''''' + +Quantopian provides a mirror of the quandl WIKI dataset with the data in the +formats that zipline expects. This is available under the name: +``quantopian-quandl`` and is the default bundle for zipline. + +Yahoo Bundle Factories +`````````````````````` + +Zipline also ships with a factory function for creating a data bundle out of a +set of tickers from yahoo: :func:`~zipline.data.bundles.yahoo_equities`. +:func:`~zipline.data.bundles.yahoo_equities` makes it easy to pre-download and +cache the data for a set of equities from yahoo. The yahoo bundles include daily +pricing data along with splits, cash dividends, and inferred asset metadata. To +create a bundle from a set of equities, add the following to your +``~/.zipline/extensions.py`` file: + +.. code-block:: python + + from zipline.data.bundles import register, yahoo_equities + + # these are the tickers you would like data for + equities = { + 'AAPL', + 'MSFT', + 'GOOG', + } + register( + 'my-yahoo-equities-bundle', # name this whatever you like + yahoo_equities(equities), + ) + + +This may now be used like: + +.. code-block:: bash + + $ zipline ingest -b my-yahoo-equities-bundle + $ zipline run -f algo.py --bundle my-yahoo-equities-bundle + + +More than one yahoo equities bundle may be registered as long as they use +different names. + +Writing a New Bundle +~~~~~~~~~~~~~~~~~~~~ + +Data bundles exist to make it easy to use different data sources with +zipline. To add a new bundle, one must implement an ``ingest`` function. + +The ``ingest`` function is responsible for loading the data into memory and +passing it to a set of writer objects provided by zipline to convert the data to +zipline's internal format. The ingest function may work by downloading data from +a remote location like the ``quandl`` bundle or yahoo bundles or it may just +load files that are already on the machine. The function is provided with +writers that will write the data to the correct location transactionally. If an +ingestion fails part way through the bundle will not be written in an incomplete +state. + +The signature of the ingest function should be: + +.. code-block:: python + + ingest(environ, + asset_db_writer, + minute_bar_writer, + daily_bar_writer, + adjustment_writer, + calendar, + start_session, + end_session, + cache, + show_progress, + output_dir) + +``environ`` +``````````` + +``environ`` is a mapping representing the environment variables to use. This is +where any custom arguments needed for the ingestion should be passed, for +example: the ``quandl`` bundle uses the enviornment to pass the API key and the +download retry attempt count. + +``asset_db_writer`` +``````````````````` + +``asset_db_writer`` is an instance of :class:`~zipline.assets.AssetDBWriter`. +This is the writer for the asset metadata which provides the asset lifetimes and +the symbol to asset id (sid) mapping. This may also contain the asset name, +exchange and a few other columns. To write data, invoke +:meth:`~zipline.assets.AssetDBWriter.write` with dataframes for the various +pieces of metadata. More information about the format of the data exists in the +docs for write. + +``minute_bar_writer`` +````````````````````` + +``minute_bar_writer`` is an instance of +:class:`~zipline.data.minute_bars.BcolzMinuteBarWriter`. This writer is used to +convert data to zipline's internal bcolz format to later be read by a +:class:`~zipline.data.minute_bars.BcolzMinuteBarReader`. If minute data is +provided, users should call +:meth:`~zipline.data.minute_bars.BcolzMinuteBarWriter.write` with an iterable of +(sid, dataframe) tuples. The ``show_progress`` argument should also be forwarded +to this method. If the data source does not provide minute level data, then +there is no need to call the write method. It is also acceptable to pass an +empty iterator to :meth:`~zipline.data.minute_bars.BcolzMinuteBarWriter.write` +to signal that there is no minutely data. + +.. note:: + + The data passed to + :meth:`~zipline.data.minute_bars.BcolzMinuteBarWriter.write` may be a lazy + iterator or generator to avoid loading all of the minute data into memory at + a single time. A given sid may also appear multiple times in the data as long + as the dates are strictly increasing. + +``daily_bar_writer`` +```````````````````` + +``daily_bar_writer`` is an instance of +:class:`~zipline.data.us_equity_pricing.BcolzDailyBarWriter`. This writer is +used to convert data into zipline's internal bcolz format to later be read by a +:class:`~zipline.data.us_equity_pricing.BcolzDailyBarReader`. If daily data is +provided, users should call +:meth:`~zipline.data.minute_bars.BcolzDailyBarWriter.write` with an iterable of +(sid dataframe) tuples. The ``show_progress`` argument should also be forwarded +to this method. If the data shource does not provide daily data, then there is +no need to call the write method. It is also acceptable to pass an empty +iterable to :meth:`~zipline.data.minute_bars.BcolzMinuteBarWriter.write` to +signal that there is no daily data. If no daily data is provided but minute data +is provided, a daily rollup will happen to service daily history requests. + +.. note:: + + Like the ``minute_bar_writer``, the data passed to + :meth:`~zipline.data.minute_bars.BcolzMinuteBarWriter.write` may be a lazy + iterable or generator to avoid loading all of the data into memory at once. + Unlike the ``minute_bar_writer``, a sid may only appear once in the data + iterable. + +``adjustment_writer`` +````````````````````` + +``adjustment_writer`` is an instance of +:class:`~zipline.data.us_equity_pricing.SQLiteAdjustmentWriter`. This writer is +used to store splits, mergers, dividends, and stock dividends. The data should +be provided as dataframes and passed to +:meth:`~zipline.data.us_equity_pricing.SQLiteAdjustmentWriter.write`. Each of +these fields are optional, but the writer can accept as much of the data as you +have. + +``calendar`` +```````````` + +``calendar`` is an instance of +:class:`zipline.utils.calendars.TradingCalendar`. The calendar is provided to +help some bundles generate queries for the days needed. + +``start_session`` +````````````````` + +``start_session`` is a :class:`pandas.Timestamp` object indicating the first +day that the bundle should load data for. + +``end_session`` +``````````````` + +``end_session`` is a :class:`pandas.Timestamp` object indicating the last day +that the bundle should load data for. + +``cache`` +````````` + +``cache`` is an instance of :class:`~zipline.utils.cache.dataframe_cache`. This +object is a mapping from strings to dataframes. This object is provided in case +an ingestion crashes part way through. The idea is that the ingest function +should check the cache for raw data, if it doesn't exist in the cache, it should +acquire it and then store it in the cache. Then it can parse and write the +data. The cache will be cleared only after a successful load, this prevents the +ingest function from needing to redownload all the data if there is some bug in +the parsing. If it is very fast to get the data, for example if it is coming +from another local file, then there is no need to use this cache. + +``show_progress`` +````````````````` + +``show_progress`` is a boolean indicating that the user would like to receive +feedback about the ingest function's progress fetching and writing the +data. Some examples for where to show how many files you have downloaded out of +the total needed, or how far into some data conversion the ingest function +is. One tool that may help with implementing ``show_progress`` for a loop is +:class:`~zipline.utils.cli.maybe_show_progress`. This argument should always be +forwarded to ``minute_bar_writer.write`` and ``daily_bar_writer.write``. + + +``output_dir`` +`````````````` + +``output_dir`` is a string representing the file path where all the data will be +written. ``output_dir`` will be some subdirectory of ``$ZIPLINE_ROOT`` and will +contain the time of the start of the current ingestion. This can be used to +directly move resources here if for some reason your ingest function can produce +it's own outputs without the writers. For example, the ``quantopian:quandl`` +bundle uses this to directly untar the bundle into the ``output_dir``. diff --git a/_sources/development-guidelines.rst.txt b/_sources/development-guidelines.rst.txt new file mode 100644 index 00000000..3adf2a43 --- /dev/null +++ b/_sources/development-guidelines.rst.txt @@ -0,0 +1,144 @@ +Development Guidelines +====================== +This page is intended for developers of Catalyst, people who want to contribute to the Catalyst codebase or documentation, or people who want to install from source and make local changes to their copy of Catalyst. + +All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We `track issues `_ on `GitHub `_ and also have a `discord group `_ where you can ask questions. + +Creating a Development Environment +---------------------------------- + +First, you'll need to clone Catalyst by running: + +.. code-block:: bash + + $ git clone git@github.com:enigmampc/catalyst.git + +Then check out to a new branch where you can make your changes: + +.. code-block:: bash + + $ git checkout -b some-short-descriptive-name + +If you don't already have them, you'll need some C library dependencies. You can follow the `install guide `_ to get the appropriate dependencies. + +The following section assumes you already have virtualenvwrapper and pip installed on your system. Suggested installation of Python library dependencies used for development: + +.. code-block:: bash + + $ mkvirtualenv catalyst + $ ./etc/ordered_pip.sh ./etc/requirements.txt + $ pip install -r ./etc/requirements_dev.txt + $ pip install -r ./etc/requirements_blaze.txt + +Finally, you can build the C extensions by running: + +.. code-block:: bash + + $ python setup.py build_ext --inplace + +Development with Docker +----------------------- + +If you want to work with zipline using a `Docker`__ container, you'll need to +build the ``Dockerfile`` in the Zipline root directory, and then build +``Dockerfile-dev``. Instructions for building both containers can be found in +``Dockerfile`` and ``Dockerfile-dev``, respectively. + +__ https://docs.docker.com/get-started/ + +Git Branching Structure +----------------------- + +If you want to contribute to the codebase of Catalyst, familiarize yourself with our branching structure, a fairly standardized one for that matter, that follows what is documented in the following article: `A successful Git branching model `_. To contribute, create your local branch and submit a Pull Request (PR) to the **develop** branch. + +.. image:: https://camo.githubusercontent.com/9bde6fb64a9542a572e0e2017cbb58d9d2c440ac/687474703a2f2f6e7669652e636f6d2f696d672f6769742d6d6f64656c4032782e706e67 + +Contributing to the Docs +------------------------ + +If you'd like to contribute to the documentation on enigmampc.github.io, you can navigate to ``docs/source/`` where each `reStructuredText `_ file is a separate section there. To add a section, create a new file called ``some-descriptive-name.rst`` and add ``some-descriptive-name`` to ``index.rst``. To edit a section, simply open up one of the existing files, make your changes, and save them. + +We use `Sphinx `_ to generate documentation for Catalyst, which you will need to install by running: + +.. code-block:: bash + + $ pip install -r ./etc/requirements_docs.txt + +To build and view the docs locally, run: + +.. code-block:: bash + + # assuming you're in the Catalyst root directory + $ cd docs + $ make html + $ {BROWSER} build/html/index.html + + +There is a `documented issue `_ +with ``sphinx`` and ``docutils`` that causes the error below when trying to build +the docs. + +.. code-block:: text + + Exception occurred: + File "(...)/env-c/lib/python2.7/site-packages/docutils/writers/_html_base.py", line 671, in depart_document + assert not self.context, 'len(context) = %s' % len(self.context) + AssertionError: len(context) = 3 + +If you get this error, you need to downgrade your version of ``docutils`` as +follows, and build the docs again: + +.. code-block:: bash + + $ pip install docutils==0.12 + + +Commit messages +--------------- + +Standard prefixes to start a commit message: + +.. code-block:: text + + BLD: change related to building Catalyst + BUG: bug fix + DEP: deprecate something, or remove a deprecated object + DEV: development tool or utility + DOC: documentation + ENH: enhancement + MAINT: maintenance commit (refactoring, typos, etc) + REV: revert an earlier commit + STY: style fix (whitespace, PEP8, flake8, etc) + TST: addition or modification of tests + REL: related to releasing Catalyst + PERF: performance enhancements + + +Some commit style guidelines: + +Commit lines should be no longer than `72 characters `_. The first line of the commit should include one of the above prefixes. There should be an empty line between the commit subject and the body of the commit. In general, the message should be in the imperative tense. Best practice is to include not only what the change is, but why the change was made. + +**Example:** + +.. code-block:: text + + MAINT: Remove unused calculations of max_leverage, et al. + + In the performance period the max_leverage, max_capital_used, + cumulative_capital_used were calculated but not used. + + At least one of those calculations, max_leverage, was causing a + divide by zero error. + + Instead of papering over that error, the entire calculation was + a bit suspect so removing, with possibility of adding it back in + later with handling the case (or raising appropriate errors) when + the algorithm has little cash on hand. + + +Formatting Docstrings +--------------------- + +When adding or editing docstrings for classes, functions, etc, we use `numpy `_ as the canonical reference. + + diff --git a/_sources/example-algos.rst.txt b/_sources/example-algos.rst.txt new file mode 100644 index 00000000..ec5b74a0 --- /dev/null +++ b/_sources/example-algos.rst.txt @@ -0,0 +1,213 @@ +| + +Example Algorithms +================== + +This section documents a number of example algorithms to complement the +beginner tutorial, and show how other trading algorithms can be implemented +using Catalyst. + +Overview +~~~~~~~~ + +- :ref:`Buy BTC Simple`: The simplest algorithm that introduces + the ``initialize()`` and ``handle_data()`` functions, and is used in the + :doc:`beginner tutorial` to show how to run catalyst + for the first time. + +- :ref:`Buy and Hodl `: A very straightforward *buy and hold* that + makes one single buy at the very beginning. Introduces the notions of + ``cash``, management of outstanding ``orders``, and ``order_target_value`` + to place orders. It also introduces the ``analyze()`` function to visualize + the performance of our strategy using the external library ``matplotlib``. + +- :ref:`Dual Moving Average Crossover`: A classic momentum + strategy used in the second part of the + `beginner tutorial `_ to introduce the + ``data.history()`` function. It makes a heavy use of ``matplotlib`` library + in the ``analyze()`` function to chart the performance of the algorithm. + +- :ref:`Mean Reversion Algorithm `: Another simple momentum + strategy that is used in our + `two-part video tutorial `_ to show how + to get started in backtesting and live trading with Catalyst. + +- :ref:`Simple Universe `: This code provides the 'universe' + of available trading pairs on a given exchange on any given day. You can use + this code to dynamically select which currency pairs you want to trade each + day of your strategy. This example does not make any trades. + +- :ref:`Portfolio Optimization `: Use this code to + execute a portfolio optimization model. This strategy will select the + portfolio with the maximum Sharpe Ratio. The parameters are set to use 180 + days of historical data and rebalance every 30 days. This code was used in + writting the following article: + `Markowitz Portfolio Optimization for Cryptocurrencies `_. + + +.. _buy_btc_simple: + +Buy BTC Simple Algorithm +~~~~~~~~~~~~~~~~~~~~~~~~ + +Source code: `examples/buy_btc_simple.py `_ + +.. literalinclude:: ../../catalyst/examples/buy_btc_simple.py + :language: python + +This simple algorithm does not produce any output nor displays any chart. + + +.. _buy_and_hodl: + +Buy and Hodl Algorithm +~~~~~~~~~~~~~~~~~~~~~~ + +First ingest the historical pricing data needed to run this algorithm: + +.. code-block:: bash + + catalyst ingest-exchange -x bitfinex -f daily -i btc_usd + +Then, you can run the code below with the following command: + +.. code-block:: bash + + catalyst run -f buy_and_hodl.py --start 2015-3-1 --end 2017-10-31 --capital-base 100000 -x bitfinex -c btc -o bah.pickle + +or using the same parameters specified in the run_algorithm() function at the +end of the file: + +.. code-block:: bash + + python buy_and_hodl.py + + +This command will run the trading algorithm in the specified time range and +plot the resulting performance using the matplotlib library. You can choose any +date interval with the ``--start`` and ``--end`` parameters, but bear in mind +that 2015-3-1 is the earliest date that Catalyst supports (if you choose an +earlier date, you'll get an error), and the most recent date you can choose is +one day prior to the current date. + +Source code: `examples/buy_and_hodl.py `_ + +.. literalinclude:: ../../catalyst/examples/buy_and_hodl.py + :language: python + +.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/example_buy_and_hodl.png + +.. _dual_moving_average: + +Dual Moving Average Crossover +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This strategy is covered in detail in the last part of +`this tutorial `_. + +Source Code: `examples/dual_moving_average.py `_ + +.. literalinclude:: ../../catalyst/examples/dual_moving_average.py + :language: python + +.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/tutorial_dual_moving_average.png + + +.. _mean_reversion: + +Mean Reversion Algorithm +~~~~~~~~~~~~~~~~~~~~~~~~ + +This algorithm is based on a simple momentum strategy. When the cryptoasset goes +up quickly, we're going to buy; when it goes down quickly, we're going to sell. +Hopefully, we'll ride the waves. + +We are choosing to backtest this trading algorithm with the ``neo_usd`` currency +pairon the ``Bitfinex`` exchange. Thus, first ingest the historical pricing data +that we need, with minute resolution: + +.. code-block:: bash + + catalyst ingest-exchange -x bitfinex -f minute -i neo_usd + +To run this algorithm, we are opting for the Python interpreter, instead of the +command line (CLI). All of the parameters for the simulation are specified in +lines 218-245, so in order to run the algorithm we just type: + +.. code-block:: bash + + python mean_reversion_simple.py + +Source code: `examples/mean_reversion_simple.py `_ + +.. literalinclude:: ../../catalyst/examples/mean_reversion_simple.py + :language: python + +.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/example_mean_reversion_simple.png + +Notice the difference in performance between the charts above and those seen on +`this video tutorial `_ at +minute 8:10. The buy and sell orders are triggered at the same exact times, but +the differences result from a more realistic slippage model +implemented after the video was recorded, which executes the orders at slighlty +different prices, but resulting in significant changes in performance of our +strategy. + +.. _simple_universe: + +Simple Universe +~~~~~~~~~~~~~~~ + +This example aims to provide an easy way for users to learn how to +collect data from any given exchange and select a subset of the available +currency pairs for trading. You simply need to specify the exchange and +the market (base_currency) that you want to focus on. You will then see +how to create a universe of assets, and filter it based the market you +desire. + +The example prints out the closing price of all the pairs for a given +market in a given exchange every 30 minutes. The example also contains +the OHLCV data with minute-resolution for the past seven days which +could be used to create indicators. Use this code as the backbone to +create your own trading strategy. + +The lookback_date variable is used to ensure data for a coin existed on +the lookback period specified. + +To run, execute the following two commands in a terminal (inside catalyst +environment). The first one retrieves all the pricing data needed for this +script to run (only needs to be run once), and the second one executes this +script with the parameters specified in the run_algorithm() call at the end +of the file: + +.. code-block:: bash + + catalyst ingest-exchange -x bitfinex -f minute + +Source code: `examples/simple_universe.py `_ + +.. literalinclude:: ../../catalyst/examples/simple_universe.py + :language: python + + +.. _portfolio_optimization: + +Portfolio Optimization +~~~~~~~~~~~~~~~~~~~~~~ + +Use this code to execute a portfolio optimization model. This strategy will +select the portfolio with the maximum Sharpe Ratio. The parameters are set to +use 180 days of historical data and rebalance every 30 days. This code was used +in writting the following article: +`Markowitz Portfolio Optimization for Cryptocurrencies `_. + +Source code: `examples/simple_universe.py `_ + +.. literalinclude:: ../../catalyst/examples/portfolio_optimization.py + :language: python + +.. image:: https://cdn-images-1.medium.com/max/1600/0*EjjiKZHlYF3sn7yQ. + :align: center + + + diff --git a/_sources/features.rst.txt b/_sources/features.rst.txt new file mode 100644 index 00000000..79b02583 --- /dev/null +++ b/_sources/features.rst.txt @@ -0,0 +1,122 @@ +Features +======== + +This page describes the features that Catalyst provides in the current version, +and what is planned for future releases. + +Current Functionality +~~~~~~~~~~~~~~~~~~~~~ + +* Backtesting and live-trading modes to run your trading algorithms, with a + seamless transition between the two. +* Paper trading simulates order in live-trading mode. +* Support for 3 exchanges: Bitfinex, Bittrex and Poloniex in both modes + (backtesting and live-trading). Historical data for backtesting is provided + with daily resolution for all three exchanges, and minute resolution for + Bitfinex and Poloniex. No minute-resolution data is currently available for + Bittrex. Refer to + `Catalyst Market Coverage `_ for + details. +* Interface with over 90 exchanges available in live and paper trading modes. +* Granular commission models which closely simulates each exchange fee + structure in backtesting and paper trading. +* Standardized naming convention for all asset pairs trading on any exchange in + the form ``{market_currency}_{base_currency}``. See + :ref:`naming`. +* Output of performance statistics based on Pandas DataFrames to integrate + nicely into the existing PyData ecosystem. +* Support for accessing multiple exchanges per algorithm, which opens the door + to cross-exchange arbitrage opportunities. +* Support for running multiple algorithms on the same exchange independently of + one another. Catalyst performance tracker stores just enough data to allow + algorithms to run independently while still sharing critical data through + exchanges. +* Benchmark defaults to Bitcoin price (btc_usdt in Poloniex exchange) for the + purpose of comparing performance across trading algorithms. A custom benchmark + can be specified through ``set_benchmark()`` (but see + `issue #86 `_). +* Support for MacOS, Linux and Windows installations. +* Support for Python2 and Python3. + +For additional details on the functionality added on recent releases, see the +:doc:`Release Notes`. + +Upcoming features +~~~~~~~~~~~~~~~~~ + +* Additional datasets beyond pricing data (Q1 2018) +* API documentation (Q1 2018) +* Support for decentralized exchanges (Q1 2018) +* Support for data ingestion of community-contributed data sets (Q1 2018) +* Pipeline support (Q1 2018) +* Web UI (Q2 2018) + + + .. _naming: + +Naming Convention +~~~~~~~~~~~~~~~~~ + +Catalyst introduces a standardized naming convention for all asset pairs +trading on any exchange in the following form: + + + **{market_currency}_{base_currency}** + +Where {market_currency} is the asset to be traded using {base_currency} as +the reference, both written in lowercase and separated with an underscore. + +This standardization is needed to overcome the lack of consistency in the +naming of assets across different exchanges, and making it easier to the user +to refer to the asset pairs that you want to trade. + +Catalyst maintains a `Market Coverage Overview `_ +where you can check the mapping between Catalyst naming pairs and that of each +exchange. Catalyst will always expect in all its functions that you will refer to +the asset pairs by using the Catalyst naming convention. + +If at any point, you input the wrong name for an asset pair, you will get an error +of that pair not found in the given exchange, and a list of pairs available on that exchange: + +.. code-block:: bash + + $ catalyst ingest-exchange -x poloniex -i btc_usd + +.. parsed-literal:: + + Ingesting exchange bundle poloniex... + Error traceback: /Volumes/Data/Users/victoris/Desktop/Enigma/user-install/catalyst-dev/catalyst/exchange/exchange.py (line 175) + SymbolNotFoundOnExchange: Symbol btc_usd not found on exchange Poloniex. + Choose from: ['rep_usdt', 'gno_btc', 'xvc_btc', 'pink_btc', 'sys_btc', + 'emc2_btc', 'rads_btc', 'note_btc', 'maid_btc', 'bch_btc', 'gnt_btc', + 'bcn_btc', 'rep_btc', 'bcy_btc', 'cvc_btc', 'nxt_xmr', 'zec_usdt', + 'fct_btc', 'gas_btc', 'pot_btc', 'eth_usdt', 'btc_usdt', 'lbc_btc', + 'dcr_btc', 'etc_usdt', 'omg_eth', 'amp_btc', 'xpm_btc', 'nxt_btc', + 'vtc_btc', 'steem_eth', 'blk_xmr', 'pasc_btc', 'zec_xmr', 'grc_btc', + 'nxc_btc', 'btcd_btc', 'ltc_btc', 'dash_btc', 'naut_btc', 'zec_eth', + 'zec_btc', 'burst_btc', 'zrx_eth', 'bela_btc', 'steem_btc', 'etc_btc', + 'eth_btc', 'huc_btc', 'strat_btc', 'lsk_btc', 'exp_btc', 'clam_btc', + 'rep_eth', 'dash_xmr', 'cvc_eth', 'bch_usdt', 'zrx_btc', 'dash_usdt', + 'blk_btc', 'xrp_btc', 'nxt_usdt', 'neos_btc', 'omg_btc', 'bts_btc', + 'doge_btc', 'gnt_eth', 'sbd_btc', 'gno_eth', 'xcp_btc', 'ltc_usdt', + 'btm_btc', 'xmr_usdt', 'lsk_eth', 'omni_btc', 'nav_btc', 'fldc_btc', + 'ppc_btc', 'xbc_btc', 'dgb_btc', 'sc_btc', 'btcd_xmr', 'vrc_btc', + 'ric_btc', 'str_btc', 'maid_xmr', 'xmr_btc', 'sjcx_btc', 'via_btc', + 'xem_btc', 'nmc_btc', 'etc_eth', 'ltc_xmr', 'ardr_btc', 'gas_eth', + 'flo_btc', 'xrp_usdt', 'game_btc', 'bch_eth', 'bcn_xmr', 'str_usdt'] + +In the example above, exchange Poloniex does not use USD, but uses instead the +USDT cryptocurrency asset that is issued on the Bitcoin blockchain via the Omni +Layer Protocol. Each USDT unit is backed by a U.S Dollar held in the reserves of +Tether Limited. USDT can be transferred, stored, and spent, just like bitcoins +or any other cryptocurrency. Given its 1:1 mapping to the USD, is a viable alternative. + +.. code-block:: bash + + $ catalyst ingest-exchange -x poloniex -i btc_usdt + +.. parsed-literal:: + + Ingesting exchange bundle poloniex... + [====================================] Fetching poloniex daily candles: : 100% + diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 00000000..5681e3b6 --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,25 @@ +.. include:: ../../README.rst + +| +| + +Table of Contents +----------------- + +.. toctree:: + :maxdepth: 1 + + install + beginner-tutorial + live-trading + features + example-algos + utilities + videos + resources + development-guidelines + releases +.. bundles +.. appendix +.. release-process + diff --git a/_sources/install.rst.txt b/_sources/install.rst.txt new file mode 100644 index 00000000..30d408a2 --- /dev/null +++ b/_sources/install.rst.txt @@ -0,0 +1,588 @@ +Install +======= + +To get started with Catalyst, you will need to install it in your computer. +Like any other piece of software, Catalyst has a number of dependencies +(other software on which it depends to run) that you will need to install, as +well. We recommend using a software named ``Conda`` that will manage all +these dependencies for you, and set up the environment needed to get you up +and running as easily as possible. This is the recommended installation method +for Windows, MacOS and Linux. See :ref:`Installing with Conda `. + +What conda does is create a pre-configured environment, and inside that +environment install Catalyst using ``pip``, Python's package manager. Thus, +as an alternative installation method for MacOS and Linux, you can install +Catalyst directly with ``pip`` (we recommend in combination with a virtual +environemnt). See :ref:`Installing with pip `. + +Alternatively you can install Catalyst using ``pipenv`` which is a mix of pip +and virtualenv. See :ref:`Installing with pipenv `. + +Regardless of the method, each operating system (OS), has its own +prerequisites, make sure to review the corresponding sections for your system: +:ref:`Linux `, :ref:`MacOS ` and :ref:`Windows `. + +.. _conda: + +Installing with ``conda`` +------------------------- + +The preferred method to install Catalyst is via the ``conda`` package manager, +which comes as part of Continuum Analytics' `Anaconda +`_ distribution. + +The primary advantage of using Conda over ``pip`` is that conda natively +understands the complex binary dependencies of packages like ``numpy`` and +``scipy``. This means that ``conda`` can install Catalyst and its +dependencies without requiring the use of a second tool to acquire Catalyst's +non-Python dependencies. + + For Windows, you will first need to install the *Microsoft Visual C++ + Compiler for Python 2.7*. Follow the instructions on the :ref:`Windows + ` section and come back here. + +For instructions on how to install ``conda``, see the `Conda Installation +Documentation `_. Alternatively, +you can install MiniConda, which is a smaller footprint (fewer packages and +smaller size) than its big brother Anaconda, but it still contains all the +main packages needed. To install MiniConda, you can follow these steps: + +1. Download `MiniConda `_. Select either + Python 3.6 (recommended) or Python 2.7 for your Operating System. The + `Enigma Data Marketplace `_ will + require Python3, that's why we are recommending to opt for the newer version. +2. Install MiniConda. See the `Installation Instructions + `_ if you need help. +3. Ensure the correct installation by running ``conda list`` in a Terminal + window, which should print the list of packages installed with Conda. + + For Windows, if you accepted the default installation options, you didn't + check an option to add Conda to the PATH, so trying to run ``conda`` from + a regular ``Command Prompt`` will result in the following error: ``'conda' + is no recognized as an internal or external command, operatble program or + batch file``. That's to be expected. You will nee to launch an ``Anaconda + Prompt`` that was added at installation time to your list of programs + available from the Start menu. + +Once either Conda or MiniConda has been set up you can install Catalyst: + +1. Download the file `python3.6-environment.yml + `_ + (recommended) or `python2.7-environment.yml + `_ + matching your Conda installation from step #1 above. + + To download, simply click on the 'Raw' button and save the file locally + to a folder you can remember. Make sure that the file gets saved with the + ``.yml`` extension, and nothing like a ``.txt`` file or anything else. + +2. Open a Terminal window and enter [``cd/dir``] into the directory where you + saved the above ``.yml`` file. + +3. Install using this file. This step can take about 5-10 minutes to install. + + .. code-block:: bash + + conda env create -f python3.6-environment.yml + + or + + .. code-block:: bash + + conda env create -f python2.7-environment.yml + +4. Activate the environment (which you need to do every time you start a new + session to run Catalyst): + + **Linux or MacOS:** + + .. code-block:: bash + + source activate catalyst + + **Windows:** + + .. code-block:: bash + + activate catalyst + +5. Verify that Catalyst is install correctly: + + .. code-block:: bash + + catalyst --version + + which should display the current version. + +Congratulations! You now have Catalyst installed. + +Troubleshooting ``conda`` Install +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If the command ``conda env create -f python2.7-environment.yml`` in step 3 +above failed for any reason, you can try setting up the environment manually +with the following steps: + +1. If the above installation failed, and you have a partially set up catalyst + environment, remove it first. If you are starting from scratch, proceed to + step #2: + + .. code-block:: bash + + conda env remove --name catalyst + +2. Create the environment: + + for python 2.7: + + .. code-block:: bash + + conda create --name catalyst python=2.7 scipy zlib + + or for python 3.6: + + .. code-block:: bash + + conda create --name catalyst python=3.6 scipy zlib + +3. Activate the environment: + + **Linux or MacOS:** + + .. code-block:: bash + + source activate catalyst + + **Windows:** + + .. code-block:: bash + + activate catalyst + +4. Install the Catalyst inside the environment: + + .. code-block:: bash + + pip install enigma-catalyst matplotlib + +5. Verify that Catalyst is installed correctly: + + .. code-block:: bash + + catalyst --version + + which should display the current version. + +Congratulations! You now have Catalyst properly installed. + +.. _pip: + +Installing with ``pip`` +----------------------- + +Installing Catalyst via ``pip`` is slightly more involved than the average +Python package. + +There are two reasons for the additional complexity: + +1. Catalyst ships several C extensions that require access to the CPython C + API. In order to build the C extensions, ``pip`` needs access to the + CPython header files for your Python installation. + +2. Catalyst depends on `numpy `_, the core library for + numerical array computing in Python. Numpy depends on having the `LAPACK + `_ linear algebra routines available. + +Because LAPACK and the CPython headers are non-Python dependencies, the +correctway to install them varies from platform to platform. If you'd rather +use a single tool to install Python and non-Python dependencies, or if you're +already using `Anaconda `_ as your Python +distribution, refer to the :ref:`Installing with Conda ` section. + +If you use Python for anything other than Catalyst, we **strongly** recommend +that you install in a `virtualenv +`_. The `Hitchhiker's Guide to +Python`_ provides an `excellent tutorial on virtualenv +`_. Here's a +summarized version: + +.. code-block:: bash + + $ pip install virtualenv + $ virtualenv catalyst-venv + $ source ./catalyst-venv/bin/activate + +Once you've installed the necessary additional dependencies for your system +(:ref:`Linux`, :ref:`MacOS` or :ref:`Windows`) **and have activated your virtualenv**, you should be able to simply run + +.. code-block:: bash + + $ pip install enigma-catalyst matplotlib + +Note that in the command above we install two different packages. The second +one, ``matplotlib`` is a visualization library. While it's not strictly +required to run catalyst simulations or live trading, it comes in very handy +to visualize the performance of your algorithms, and for this reason we +recommend you install it, as well. + + +Troubleshooting ``pip`` Install +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +**Issue**: + Package enigma-catalyst cannot be found + +**Solution**: + Make sure you have the most up-to-date version of pip installed, by running: + + .. code-block:: bash + + $ pip install --upgrade pip + + On Windows, the recommended command is: + + .. code-block:: bash + + $ python -m pip install --upgrade pip + +---- + +**Issue**: + Package enigma-catalyst cannot still be found, even after upgrading pip + (see above), with an error similar to: + + .. code-block:: bash + + Downloading/unpacking enigma-catalyst + Could not find a version that satisfies the requirement enigma-catalyst + (from versions: 0.1.dev9, 0.2.dev2, 0.1.dev4, 0.1.dev5, 0.1.dev3, + 0.2.dev1, 0.1.dev8, 0.1.dev6) + Cleaning up... + No distributions matching the version for enigma-catalyst + +**Solution**: + In some systems (this error has been reported in Ubuntu), pip is configured + to only find stable versions by default. Since Catalyst is in alpha + version, pip cannot find a matching version that satisfies the installation + requirements. The solution is to include the `--pre` flag to include + pre-release and development versions: + + .. code-block:: bash + + $ pip install --pre enigma-catalyst + +---- + +**Issue**: + Package enigma-catalyst fails to install because of outdated setuptools + +**Solution**: + Upgrade to the most up-to-date setuptools package by running: + + .. code-block:: bash + + $ pip install --upgrade pip setuptools + +---- + +**Issue**: + Missing required packages + +**Solution**: + Download `requirements.txt + `_ + (click on the *Raw* button and Right click -> Save As...) and use it to + install all the required dependencies by running: + + .. code-block:: bash + + $ pip install -r requirements.txt + +---- + +**Issue**: + Installation fails with error: + ``fatal error: Python.h: No such file or directory`` + +**Solution**: + Some systems (this issue has been reported in Ubuntu) require `python-dev` + for the proper build and installation of package dependencies. The solution + is to install python-dev, which is independent of the virtual environment. + In Ubuntu, you would need to run: + + .. code-block:: bash + + $ sudo apt-get install python-dev + +---- + +**Issue**: + Missing TA_Lib + +**Solution**: + Follow `these instructions + `_ to install the TA_Lib Python wrapper + (and if needed, its underlying C library as well). + +.. _pipenv: + +Installing with ``pipenv`` +-------------------------- + +Installing Catalyst via ``pipenv`` is perhaps easier that installing it via +``pip`` itself but you need to install ``pipenv`` first via ``pip``. + +.. code-block:: bash + + $ pip install pipenv + +Once ``pipenv`` is installed you can proceed by creating a project folder and +installing Catalyst on that project automagically as follows: + +.. code-block:: bash + + $ mkdir project + $ cd project + $ pipenv --two + $ pipenv install enigma-catalyst matplotlib + +Until now the workflow compared to ``pip`` is almost identical, the difference +is that you don't need to load manually any virtualenv however you need to use +the `pipenv run` prefix to run the `catalyst` command as follows: + +.. code-block:: bash + + $ pipenv run catalyst --version + +If you want to know more about ``pipenv`` go to the `pipenv github repo`_ + +.. _`pipenv github repo`: https://github.com/pypa/pipenv + +.. _linux: + +GNU/Linux Requirements +---------------------- + +On `Debian-derived`_ Linux distributions, you can acquire all the necessary +binary dependencies from ``apt`` by running: + +.. code-block:: bash + + $ sudo apt-get install libatlas-base-dev python-dev gfortran pkg-config libfreetype6-dev + +On recent `RHEL-derived`_ derived Linux distributions (e.g. Fedora), the +following should be sufficient to acquire the necessary additional +dependencies: + +.. code-block:: bash + + $ sudo dnf install atlas-devel gcc-c++ gcc-gfortran libgfortran python-devel redhat-rep-config + +On `Arch Linux`_, you can acquire the additional dependencies via ``pacman``: + +.. code-block:: bash + + $ pacman -S lapack gcc gcc-fortran pkg-config + +.. Commenting it out until Catalyst fully supports Python 3.X +.. +.. There are also AUR packages available for installing `Python 3.4 +.. `_ (Arch's default python is now +.. 3.5, but Catalyst only currently supports 3.4), and `ta-lib +.. `_, an optional Catalyst dependency. +.. Python 2 is also installable via: + +.. + +.. $ pacman -S python2 + +Amazon Linux AMI Notes +~~~~~~~~~~~~~~~~~~~~~~ + +The packages ``pip`` and ``setuptools`` that come shipped by default are very +outdated. Thus, you first need to run: + +.. code-block:: bash + + $ pip install --upgrade pip setuptools + +The default installation is also missing the C and C++ compilers, which you +install by: + +.. code-block:: bash + + $ sudo yum install gcc gcc-c++ + +Then you should follow the regular installation instructions outlined at the +beginning of this page. + + +.. _MacOS: + +MacOS Requirements +------------------ + +The version of Python shipped with MacOS by default is generally out of date, +and has a number of quirks because it's used directly by the operating system. +For these reasons, many developers choose to install and use a separate Python +installation. The `Hitchhiker's Guide to Python`_ provides an excellent guide +to `Installing Python on MacOS `_, +which explains how to install Python with the `Homebrew`_ manager. + +Assuming you've installed Python with Homebrew, you'll also likely need the +following brew packages: + +.. code-block:: bash + + $ brew install freetype pkg-config gcc openssl + +MacOS + virtualenv/conda + matplotlib +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The first time that you try to run an algorithm that loads the ``matplotlib`` +library, you may get the following error: + +.. code-block:: text + + RuntimeError: Python is not installed as a framework. The Mac OS X backend + will not be able to function correctly if Python is not installed as a + framework. See the Python documentation for more information on installing + Python as a framework on Mac OS X. Please either reinstall Python as a + framework, or try one of the other backends. If you are using (Ana)Conda + please install python.app and replace the use of 'python' with 'pythonw'. + See 'Working with Matplotlib on OSX' in the Matplotlib FAQ for more + information. + +This is a ``matplotlib``-specific error, that will go away once you run the +following command: + +.. code-block:: bash + + $ echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc + +in order to override the default ``MacOS`` backend for your system, which +may not be accessible from inside the virtual or conda environment. This will +allow Catalyst to open matplotlib charts from within a virtual environment, +which is useful for displaying the performance of your backtests. To learn more +about matplotlib backends, please refer to the +`matplotlib backend documentation `_. + +.. _windows: + +Windows Requirements +-------------------- + +In Windows, you will first need to install the Microsoft Visual C++ Compiler, +which is different depending on the version of Python that you plan to use: + +* Python 3.5, 3.6: `Visual C++ 2015 Build Tools + `_, + which installs Visual C++ version 14.0. **This is the recommended version** + +* Python 2.7: `Microsoft Visual C++ Compiler for Python 2.7 + `_, which + installs version Visual C++ version 9.0 + +This package contains the compiler and the set of system headers necessary for +producing binary wheels for Python packages. If it's not already in your +system, download it and install it before proceeding to the next step. If you +need additional help, or are looking for other versions of Visual C++ for +Windows (only advanced users), follow `this link `_. + +Once you have the above compiler installed, the easiest and best supported way +to install Catalyst in Windows is to use :ref:`Conda `. If you didn't +any problems installing the compiler, jump to the :ref:`Conda ` section, +otherwise keep on reading to troubleshoot the C++ compiler installtion. + +Some problems we have encountered installing the **Visual C++ Compiler** +mentioned above are as follows: + +- **The system administrator has set policies to prevent this installation**. + + In some systems, there is a default *Windows Software Restriction* policy + that prevents the installation of some software packages like this one. + You'll have to change the Registry to circumvent this: + + - Click ``Start``, and search for ``regedit`` and launch the + ``Registry Editor`` + - Navigate to the following folder: + ``HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\Windows\Installer`` + - If the last folder does not exist, create it by right-clicking on the + parent folder and choosing -> ``New`` -> ``Key`` and typing ``Installer`` + - If there is an entry for ``DisableMSI``, set the Value data to 0. + - If there is no such entry, click on the ``Edit`` menu -> ``New`` -> + ``DWORD (32-bit) Value`` and enter ``DisableMSI`` as the Name (and by + default you get 0 as the Value Data) + +| + +- **The installer has encountered an unexpected error installing this package. + This may indicate a problem with this package. The error code is 2503.** + + We have observed this when trying to install a package without enough + administrator permissions. Even when you are logged in as an Administrator, + you have to explictily install this package with administrator privileges: + + - Click ``Start`` and find ``CMD`` or ``Command Prompt`` + - Right click on it and choose ``Run as administrator`` + - ``cd`` into the folder where you downloaded ``VCForPython27.msi`` + - Run ``msiexec /i VCForPython27.msi`` + +Updating Catalyst +----------------- + +Catalyst is currently in alpha and in under very active development. We release +new minor versions every few days in response to the thorough battle testing +that our user community puts Catalyst in. As a result, you should expect to +update Catalyst frequently. Once installed, Catalyst can easily be updated as a +``pip`` package regardless of the environemnt used for installation. Make sure +you activate your environment first as you did in your first install, and then +execute: + +.. code-block:: bash + + $ pip uninstall enigma-catalyst + $ pip install enigma-catalyst + +Alternatively, you could update Catalyst issuing the following command: + +.. code-block:: bash + + $ pip install -U enigma-catalyst + +but this command will also upgrade all the Catalyst dependencies to the latest +versions available, and may have unexpected side effects if a newer version of a +dependency inadvertently breaks some functionality that Catalyst relies on. +Thus, the first method is the recommended one. + +Getting Help +------------ + +If after following the instructions above, and going through the +*Troubleshooting* sections, you still experience problems installing Catalyst, +you can seek additional help through the following channels: + +- Join our `Catalyst Forum `_, and browse a variety + of topics and conversations around common issues that others face when using + Catalyst, and how to resolve them. And join the conversation! + +- Join our `Discord community `_, and head over + the #catalyst_dev channel where many other users (as well as the project + developers) hang out, and can assist you with your particular issue. The + more descriptive and the more information you can provide, the easiest will + be for others to help you out. + +- Report the problem you are experiencing on our + `GitHub repository `_ + following the guidelines provided therein. Before you do so, take a moment + to browse through all `previous reported issues + `_ + in the likely case that someone else experienced that same issue before, + and you get a hint on how to solve it. + + +.. _`Debian-derived`: https://www.debian.org/misc/children-distros +.. _`RHEL-derived`: https://en.wikipedia.org/wiki/Red_Hat_Enterprise_Linux_derivatives +.. _`Arch Linux` : https://www.archlinux.org/ +.. _`Hitchhiker's Guide to Python` : http://docs.python-guide.org/en/latest/ +.. _`Homebrew` : http://brew.sh diff --git a/_sources/live-trading.rst.txt b/_sources/live-trading.rst.txt new file mode 100644 index 00000000..7d5f2394 --- /dev/null +++ b/_sources/live-trading.rst.txt @@ -0,0 +1,197 @@ +Live Trading +============ +This document explains how to get started with live trading. + +Supported Exchanges +^^^^^^^^^^^^^^^^^^^ + +Since version 0.4, Catalyst integrated with `CCXT `_, +a cryptocurrency trading library with support for more than 90 exchanges. The +range of CCXT and Catalyst support for each of those exchanges varies greatly. +The most supported exchanges are as follows: + +The exchanges available for backtesting are fully supported in live mode: + +- Bitfinex, id = ``bitfinex`` +- Bittrex, id = ``bittrex`` +- Poloniex, id = ``poloniex`` + +Additionally, we have successfully tested the following exchanges: + +- Binance, id = ``binance`` +- Bitmex, id = ``bitmex`` +- GDAX, id = ``gdax`` + +As Catalyst is currently in Alpha and in under active development, you are +encouraged to throughly test any exchange in *paper trading* mode before trading +*live* with it. + +Paper Trading vs Live Trading modes +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Catalyst currently supports three different modes in which you can execute your +trading algorithm. The first is **backtesting**, which is covered extensively in +the tutorial, and uses historical data to run your algorithm. There is no +interaction with the exchange in backtesting mode, and this is the first mode +that you should test any new algorithm. + +Once you are confident with the simulations that you have obtained with your +algorithm in backtesting, you may switch to live trading, where you have two +different modes: + +* **Paper Trading**: The simulated algorithm runs in real time, and fetches + pricing data in real time from the exchange, but the orders never reach the + exchange, and are instead kept within Catalyst and simulated. No real currency + is bought or sold. Think of it as a `backtesting happening in real time`. + +* **Live Trading**: This is the proper live trading mode in which an algorithm + runs in real time, fetching pricing data from live exchanges and placing + orders against the exchange. Real currency is transacted on the exchange + driven by the algorithm. + +These three modes are controlled by the following variables: + ++---------------+-------------------------+ +| Mode | Parameters | ++ +-------+-----------------+ +| | live | simulate_orders | ++---------------+-------+-----------------+ +| backtesting | False | True (default) | ++---------------+-------+-----------------+ +| paper trading | True | True | ++---------------+-------+-----------------+ +| live trading | True | False | ++---------------+-------+-----------------+ + + +Authentication +^^^^^^^^^^^^^^ +Most exchanges require token key/secret combination for authentication. By +convention, Catalyst uses an ``auth.json`` file to hold this data. + +This example illustrates the convention using the *Bitfinex* exchange. +Here is how to generate key and secret values for the Bitfinex exchange: +https://docs.bitfinex.com/v1/docs/api-access. Most exchanges follow +a similar process. + +The auth.json file: + +.. code-block:: json + + { + "name": "bitfinex", + "key": "my-key", + "secret": "my-secret" + } + + +The file goes here: ``~/.catalyst/data/exchanges/bitfinex/auth.json`` + +Note that the `bitfinex` part in the directory above corresponds to the id of the Bitfinex +exchange as defined in the "Supported Exchanges" section above. +Attempting to run an algorithm where the targeted exchange is missing +its ``auth.json`` file will create the directory structure and create an empty +auth.json file, but will result in an error. + +Currency Symbols +^^^^^^^^^^^^^^^^ +Catalyst introduces a universal convention to reference +trading pairs and individual currencies. This +is required to ensure that the ``symbol()`` api predictably +returns the correct asset regardless of the targeted exchange. + +Exchanges tend to use their own convention to represent currencies +(e.g. XBT and BTC both represent Bitcoin on different exchanges). +Trading pairs are also inconsistent. For example, Bitfinex +puts the market currency before the base currency without a +separator, Bittrex puts the base currency first and uses a dash +seperator. + +Here is the Catalyst convention: + +*[Market Currency]_[Base Currency]* all lowercase. + +Currency symbols (e.g. btc, eth, ltc) follow the Bittrex convention. + +Here are some examples: + +.. code:: python + + # With Bitfinex + bitcoin_usd_asset = symbol('btc_usd') + ethereum_bitcoin_asset = symbol('eth_btc') + + # With Bittrex + ethereum_bitcoin_asset = symbol('eth_btc') + neo_ethereum_asset = symbol('neo_eth) + +Note that the trading pairs are always referenced in the same manner. +However, not all trading pairs are available on all exchanges. An +error will occur if the specified trading pair is not trading +on the exchange. To check which currency pairs are available on each +of the supported exchanges, see +`Catalyst Market Coverage `_. + +Trading an Algorithm +^^^^^^^^^^^^^^^^^^^^ +There is no special convention to follow when writing an +algorithm for live trading. The same algorithm should work in +backtest and live execution mode without modification. + +What differs are the arguments provided to the catalyst client or +`run_algorithm()` interface. Here is the same example in both interfaces: + +.. code-block:: bash + + catalyst live -f my_algo_code -x bitfinex -c btc -n my_algo_name + +.. code-block:: python + + run_algorithm( + initialize=initialize, + handle_data=handle_data, + analyze=analyze, + exchange_name='bitfinex', + live=True, + algo_namespace='my_algo_name', + base_currency='btc' + ) + + +Here is the breakdown of the new arguments: + +- ``live``: Boolean flag which enables live trading. It defaults to ``False``. +- ``capital_base``: The amount of base_currency assigned to the strategy. + It has to be lower or equal to the amount of base currency available for + trading on the exchange. For illustration, order_target_percent(asset, 1) + will order the capital_base amount specified here of the specified asset. +- ``exchange_name``: The name of the targeted exchange. See the + `CCXT Supported Exchanges `_ + for the full list. +- ``algo_namespace``: A arbitrary label assigned to your algorithm for + data storage purposes. +- ``base_currency``: The base currency used to calculate the + statistics of your algorithm. Currently, the base currency of all + trading pairs of your algorithm must match this value. +- ``simulate_orders``: Enables the paper trading mode, in which orders are + simulated in Catalyst instead of processed on the exchange. It defaults to + ``True``. +- ``end_date``: When setting the end_date to a time in the **future**, + it will schedule the live algo to finish gracefully at the specified date. +- ``start_date``: (**Will be implemented in the future**) + The live algo starts by default in the present, as mentioned above. + by setting the start_date to a time in the future, the algorithm would + essentially sleep and when the predefined time comes, it would start executing. + + + +The `catalyst live` command offers additional parameters. +You can learn more by running the following from the command line: + +.. code-block:: bash + + catalyst live --help + + +Here is a complete algorithm for reference: +`Buy Low and Sell High `_ diff --git a/_sources/release-process.rst.txt b/_sources/release-process.rst.txt new file mode 100644 index 00000000..02e9f419 --- /dev/null +++ b/_sources/release-process.rst.txt @@ -0,0 +1,263 @@ +Release Process +--------------- + +.. include:: dev-doc-message.txt + + +Updating the Release Notes +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +When we are ready to ship a new release of zipline, edit the :doc:`releases` +page. We will have been maintaining a whatsnew file while working on the release +with the new version. First, find that file in: +``docs/source/whatsnew/.txt``. It will be the highest version number. +Edit the release date field to be today's date in the format: + +:: + + , + + +for example, November 6, 2015. +Remove the active development warning from the whatsnew, since it will no +longer be pending release. +Update the title of the release from "Development" to "Release x.x.x" and +update the underline of the title to match the title's width. + +If you are renaming the release at this point, you'll need to git mv the file +and also update releases.rst to reference the renamed file. + +To build and view the docs locally, run: + +.. code-block:: bash + + $ cd docs + $ make html + $ {BROWSER} build/html/index.html + +Updating the Python stub files +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +PyCharm and other linters and type checkers can use `Python stub files +`__ for type hinting. For +example, we generate stub files for the :mod:`~zipline.api` namespace, since that +namespace is populated at import time by decorators on TradingAlgorithm +methods. Those functions are therefore hidden from static analysis tools, but +we can generate static files to make them available. Under **Python 3**, run +the following to generate any stub files: + +.. code-block:: bash + + $ python etc/gen_type_stubs.py + +.. note:: + + In order to make stub consumers aware of the classes referred to in the + stub, the stub file should import those classes. However, since + ``... import *`` and ``... import ... as ...`` in a stub file will export + those imports, we import the names explicitly. For the stub for + ``zipline.api``, this is done in a header string in the + ``gen_type_stubs.py`` script mentioned above. If new classes are added as + parameters or return types of ``zipline.api`` functions, then new imports + should be added to that header. + +Updating the ``__version__`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We use `versioneer `__ to +manage the ``__version__`` and ``setup.py`` version. This means that we pull +this information from our version control's tags to ensure that they stay in +sync and to have very fine grained version strings for development installs. + +To upgrade the version use the git tag command like: + +.. code-block:: bash + + $ git tag .. + $ git push && git push --tags + + +This will push the the code and the tag information. + +Next, click the "Draft a new release" button on the `zipline releases page +`__. For the new release, +choose the tag you just pushed, and publish the release. + +Uploading PyPI packages +~~~~~~~~~~~~~~~~~~~~~~~ + +``sdist`` +^^^^^^^^^ + +To build the ``sdist`` (source distribution) run: + +.. code-block:: bash + + $ python setup.py sdist + + +from the zipline root. This will create a gzipped tarball that includes all the +python, cython, and miscellaneous files needed to install zipline. To test that +the source dist worked correctly, ``cd`` into an empty directory, create a new +virtualenv and then run: + + +.. code-block:: bash + + $ pip install /dist/zipline-...tar.gz + $ python -c 'import zipline;print(zipline.__version__)' + +This should print the version we are expecting to release. + +.. note:: + + It is very important to both ``cd`` into a clean directory and make a clean + virtualenv. Changing directories ensures that we have included all the needed + files in the manifest. Using a clean virtualenv ensures that we have listed + all the required packages. + +Now that we have tested the package locally, it should be tested using the test +PyPI server. + +Edit your ``~/.pypirc`` file to look like: + +:: + + [distutils] + index-servers = + pypi + pypitest + + [pypi] + username: + password: + + [pypitest] + repository: https://testpypi.python.org/pypi + username: + password: + +after that, run: + +.. code-block:: bash + + $ python setup.py sdist upload -r pypitest + + +.. note:: + + If the package version has been taken: locally update your setup.py to + override the version with a new number. Do not use the next version, just + append a ``.`` section to the current version. PyPI prevents the same + package version from appearing twice, so we need to work around this when + debugging packaging problems on the test server. + + .. warning:: + + Do not commit the temporary version change. + + +This will upload zipline to the pypi test server. To test installing from pypi, +create a new virtualenv, ``cd`` into a clean directory and then run: + +.. code-block:: bash + + $ pip install --extra-index-url https://testpypi.python.org/pypi zipline + $ python -c 'import zipline;print(zipline.__version__)' + + +This should pull the package you just uploaded and then print the version +number. + +Now that we have tested locally and on PyPI test, it is time to upload to PyPI: + +.. code-block:: bash + + $ python setup.py sdist upload + +``bdist`` +^^^^^^^^^ + +Because zipline now supports multiple versions of numpy, we're not building +binary wheels, since they are not tagged with the version of numpy with which +they were compiled. + +Documentation +~~~~~~~~~~~~~ + +To update `zipline.io `__, checkout the +latest master and run: + +.. code-block:: python + + python /docs/deploy.py + +This will build the documentation, checkout a fresh copy of the ``gh-pages`` +git branch, and copy the built docs into the zipline root. + +.. note:: + + The docs should always be built with **Python 3**. Many of our api functions + are wrapped by preprocessing functions which accept \*args and \**kwargs. In + Python 3, sphinx will respect the ``__wrapped__`` attribute and display the + correct arguments. + +Now, using our browser of choice, view the ``index.html`` page and verify that +the docs look correct. + +Once we are happy, push the updated docs to the GitHub ``gh-pages`` branch. + +.. code-block:: bash + + $ git add . + $ git commit -m "DOC: update zipline.io" + $ git push origin gh-pages + +`zipline.io `__ will update in a few moments. + +Uploading conda packages +~~~~~~~~~~~~~~~~~~~~~~~~ + +Travis and AppVeyor build zipline conda packages for us. Once they have built +and uploaded to anaconda.org the packages (and their dependencies) for the +release commit to master, we should move those packages from the "ci" label to +the "main" label. You can do this from the anaconda.org web interface. This +is also a good time to remove all the old "ci" packages from anaconda. + +Travis and AppVeyor only build and upload linux-64 and win-64 packages. We'll +need to build and upload osx-64 packages manually on an OSX machine. + +To build the conda packages for zipline locally, run: + +.. code-block:: bash + + $ python etc/conda_build_matrix.py + +If all of the builds succeed, then this will not print anything and exit with +``EXIT_SUCCESS``. If there are build issues, we must address them and decide +what to do. + +Once all of the builds in the matrix pass, we can upload them to anaconda with: + +.. code-block:: bash + + $ python etc/conda_build_matrix.py --upload + +If you would like to test this command by uploading to a different user, this +may be specified with the ``--user`` flag. + +Next Commit +~~~~~~~~~~~ + +Push a new commit post-release that adds the whatsnew for the next release, +which should be titled according to a micro version increment. If that next +release turns out to be a major/minor version increment, the file can be +renamed when that's decided. You can use ``docs/source/whatsnew/skeleton.txt`` +as a template for the new file. + +Include the whatsnew file in ``docs/source/releases.rst``. New releases should +appear at the top. The syntax for this is: + +:: + + .. include:: whatsnew/.txt diff --git a/_sources/releases.rst.txt b/_sources/releases.rst.txt new file mode 100644 index 00000000..b50bba04 --- /dev/null +++ b/_sources/releases.rst.txt @@ -0,0 +1,485 @@ +============= +Release Notes +============= + +Version 0.5.8 +^^^^^^^^^^^^^ +**Release Date**: 2018-03-29 + +Bug Fixes +~~~~~~~~~ +- Fix Data Marketplace release on mainnet + +Version 0.5.7 +^^^^^^^^^^^^^ +**Release Date**: 2018-03-29 + +Build +~~~~~ +- Data Marketplace deployed on mainnet. +- Added progress indicators for publishing data, and made the data publishing + synchronous to provide feedback to the publisher. + +Bug Fixes +~~~~~~~~~ +- fixes in storing and loading the state :issue:`214`, + :issue:`287` + +Version 0.5.6 +^^^^^^^^^^^^^ +**Release Date**: 2018-03-22 + +Build +~~~~~ +- Data Marketplace: ensures compatibility across wallets, now fully supporting + ``ledger``, ``trezor``, ``keystore``, ``private key``. Partial support for + ``metamask`` (includes sign_msg, but not sign_tx). Current support for + ``Digital Bitbox`` is unknown, but believed to be supported. +- Data Marketplace: Switched online provider from MyEtherWallet to MyCrypto. +- Data Marketplace: Added progress indicator for data ingestion. + +Bug Fixes +~~~~~~~~~ +- Changed benchmark to be constant, so it doesn't ingest data at all. Temporary + fix for :issue:`271`, :issue:`285` + +Version 0.5.5 +^^^^^^^^^^^^^ +**Release Date**: 2018-03-19 + +Bug Fixes +~~~~~~~~~ +- Fixed an issue with the data history in daily frequency :issue:`274` +- Fix hourly frequency issues :issue:`227` and :issue:`114` + +Version 0.5.4 +^^^^^^^^^^^^^ +**Release Date**: 2018-03-14 + +Build +~~~~~ +- Switched Data Marketplace from Ropstein testnet to Rinkeby testnet after + incorporating changes resulting from the marketplace contract audit +- Several usability improvements of the Data Marketplace that make the + `--dataset` parameter optional. If it is not included in the command line, + will list available datasets, and let you choose interactively. + +Bug Fixes +~~~~~~~~~ +- Fix Binance requirement of symbol to be included in the cancelled order + :issue:`204` +- Fix `notenoughcasherror` when an open order is filled minutes later + :issue:`237` +- Properly handle of empty candles received from exchanges :issue:`236` +- Added a function to reduce open orders amount from calculated target/amount + for target orders :issue:`243` +- Fix missing file in live trading mode on date change :issue:`252`, + :issue:`253` +- Upgraded Data Marketplace to Web3==4.0.0b11, which was breaking some + functionality from prior version 4.0.0b7 :issue:`257` +- Always request more data to avoid empty bars and always give the exact bar + number :issue:`260` + +Documentation +~~~~~~~~~~~~~ +- PyCharm documentation :issue:`195` +- Added TA-Lib troubleshooting instructions +- Added instructions on how to create a Conda environment for Python 3.6, and + updated Visual C++ instructions for Windows and Python 3 +- Linking example algorithms in the documentation to their sources + + +Version 0.5.3 +^^^^^^^^^^^^^ +**Release Date**: 2018-02-09 + +Bug Fixes +~~~~~~~~~ +- Fixed an issue with last candle in backtesting :issue:`219` + +Version 0.5.2 +^^^^^^^^^^^^^ +**Release Date**: 2018-02-08 + +Bug Fixes +~~~~~~~~~ +- Fixed an issue with live candle values :issue:`216` and :issue:`199` + +Version 0.5.1 +^^^^^^^^^^^^^ +**Release Date**: 2018-02-07 + +Bug Fixes +~~~~~~~~~ +- Fixed an issue with orders that stay open :issue:`211` +- Fixed Jupyter issues :issue:`179` +- Fetching multiple tickers in one call to minimize rate limit risks :issue:`174` +- Improved live state presentation :issue:`171` + + +Build +~~~~~ +- Introducing the Enigma Marketplace + +Version 0.4.7 +^^^^^^^^^^^^^ +**Release Date**: 2018-01-19 + +Bug Fixes +~~~~~~~~~ +- Fixing issue :issue:`137` impacting the CLI + +Build +~~~~~ +- Implemented authentication aliases (:issue:`60`) + +Version 0.4.6 +^^^^^^^^^^^^^ +**Release Date**: 2018-01-18 + +Bug Fixes +~~~~~~~~~ +- Fixed some Python3 issues +- Reading the trade log to get executed order prices on exchanges like Binance (:issue:`151`) +- Fixed issue with market order executing price (:issue:`150` and :issue:`111`) +- Implemented standardized symbol mapping (:issue:`157`) +- Improved error handling for unsupported timeframes (:issue:`159`) +- Using Bitfinex instead of Poloniex to fetch btc_usdt benchmark (:issue:`161`) + + +Build +~~~~~ +- Added a `context.state` dict to keep arbitrary state values between runs +- Added ability to stop live algo at specified end date + +Version 0.4.5 +^^^^^^^^^^^^^ +**Release Date**: 2018-01-12 + +Bug Fixes +~~~~~~~~~ +- Improved order execution for exchanges supporting trade lists (:issue:`151`) +- Fixed an issue where requesting history of multiple assets repeats values +- Raising an error for order amounts smaller than exchange lots +- Handling multiple req errors with tickers more gracefully (:issue:`160`) + +Version 0.4.4 +^^^^^^^^^^^^^ +**Release Date**: 2018-01-09 + +Bug Fixes +~~~~~~~~~ +- Removed redundant capital_base validation (:issue:`142`) +- Fixed portfolio update issue with restored state (:issue:`111`) +- Skipping cash validation where there are open orders (:issue:`144`) + +Version 0.4.3 +^^^^^^^^^^^^^ +**Release Date**: 2018-01-05 + +Bug Fixes +~~~~~~~~~ +- Fixed CLI issue (:issue:`137`) +- Upgraded CCXT + +Version 0.4.2 +^^^^^^^^^^^^^ +**Release Date**: 2018-01-03 + +Bug Fixes +~~~~~~~~~ +- Fixed cash synchronization issue (:issue:`133`) +- Fixed positions synchronization issue (:issue:`132`) +- Patched empyrical to resolve a np.log1p issue (:issue:`126`) +- Fixed a paper trading issue (:issue:`124`) +- Fixed a commission issue (:issue:`104`) +- Fixed a poloniex specific issue in live trading (:issue:`103`) + +Build +~~~~~ +- Caching CCXT market info to limit round-trips (:issue:`99`) +- Tentative support for Pipeline (:issue:`96`) + +Version 0.4.0 +^^^^^^^^^^^^^ +**Release Date**: 2017-12-12 + +Bug Fixes +~~~~~~~~~ + +- Changed Poloniex interface (should solve :issue:`95` and :issue:`94`) +- Solved issue with overriding commission and slippage (:issue:`87`) +- Fixed inefficiency with Bittrex current prices (:issue:`76`) + +Build +~~~~~ +- Integrated with CCXT +- Added paper trading capability (`simulate_orders=True` param in live mode) +- More granular commissions (:issue:`82`) +- Added market orders in live mode (:issue:`81`) + +Version 0.3.10 +~~~~~~~~~~~~~~ +**Release Date**: 2017-11-28 + +Bug Fixes +~~~~~~~~~ + +- Fixed issue with fetching assets with daily frequency + +Version 0.3.9 +^^^^^^^^^^^^^ +**Release Date**: 2017-11-28 + +Bug Fixes +~~~~~~~~~ + +- Fixed sortino warning issues (:issue:`77`) +- Adjusted computation of last candle of data.history (:issue:`71`) + +Build +~~~~~ +- Added capital_base parameter to live mode to limit cash (:issue:`79`) +- Added support for csv ingestion (:issue:`65`) +- Improved cash display in running stats (:issue:`80`) + + +Version 0.3.8 +^^^^^^^^^^^^^ +**Release Date**: 2017-11-14 + +Bug Fixes +~~~~~~~~~ + +- Fixed a warning filter issue introduced with the latest release + +Version 0.3.7 +^^^^^^^^^^^^^ +**Release Date**: 2017-11-14 + +Bug Fixes +~~~~~~~~~ + +- Fixed an SSL cert issue (:issue:`64`) +- Fixed cumulative stats warnings (:issue:`63`) +- Disabled auto-ingestion because of unresolved caching issues (:issue:`47`) +- Standardized live-trading stats (:issue:`61`) + +Build +~~~~~ + +- Added a mean-reversion sample algo +- Added minutely stats in the analyze() function (:issue:`62`) +- Added specificity to some error messages + +Version 0.3.6 +^^^^^^^^^^^^^ +**Release Date**: 2017-11-4 + +Bug Fixes +~~~~~~~~~ + +- Fixed an issue with single bar data.history() (:issue:`55`) + +Version 0.3.5 +^^^^^^^^^^^^^ +**Release Date**: 2017-11-4 + +Bug Fixes +~~~~~~~~~ + +- Added workaround for: KeyError: Timestamp error (:issue:`53`) + +Version 0.3.4 +^^^^^^^^^^^^^ +**Release Date**: 2017-11-2 + +Bug Fixes +~~~~~~~~~ + +- Fixed issue with auto-ingestion of minute data (:issue:`47`) +- Fixed issue with sell orders in backtesting +- Fixed data frequency issues with data.history() in backtesting +- Fixed an issue with can_trade() +- Reduced the commission and slippage values to account for lower volume + transactions + +Build +~~~~~ + +- Added more unit tests + +Documentation +~~~~~~~~~~~~~ + +- Improved installation notes for Windows C++ compiler and Conda +- Addition of + `Jupyter Notebook guide `_ +- Addition of + `Live Trading page `_ +- Addition of + `Videos page `_ +- Addition of + `Resources page `_ +- Addition of `Development Guidelines + `_ +- Addition of + `Release Notes `_ +- Updated code docstrings + + +Version 0.3.3 +^^^^^^^^^^^^^ +**Release Date**: 2017-10-26 + +Bug Fixes +~~~~~~~~~ + +- Fix missing -x in ingest-exchange +- Fix issue with daily chunks end date (data bundles) +- Fix issue in the prepare_chunk logic (data bundles) + +Build +~~~~~ + +- Added data validation unit tests + + +Version 0.3.2 +^^^^^^^^^^^^^ +**Release Date**: 2017-10-25 + +Bug Fixes +~~~~~~~~~ + +- Fix to work with empty data bundles +- Fix Windows path of ``$HOME/.catalyst`` folder +- Fix ``etc/python2.7-environment.yml`` for Windows Conda install +- Fix hash method to create sid numbers compatible across platforms +- Fix an issue with asset date in chunks + +Build +~~~~~ + +- Python3 adjustments +- Added method to clean bundle folders, and remove symbols.json +- Implemented and improved unit tests + + +Version 0.3.1 +^^^^^^^^^^^^^ +**Release Date**: 2017-10-22 + +Bug Fixes +~~~~~~~~~ + +- Fixed OS-dependent path issue in data bundle +- Changed handling of empty ``auth.json``, instead of throwing an error for + missing file +- Updated ``etc/python2.7-environment.yml`` to work with Catalyst version 0.3 +- Updated ``catalyst/examples/buy_and_hodl.py`` and + ``catalyst/examples/buy_low_sell_high.py`` to work with Catalyst version 0.3 + + +Version 0.3 +^^^^^^^^^^^ +**Release Date**: 2017-10-20 + +- Standardized live and backtesting syntax +- Added a repository for historical data +- Added supported for multiple exchanges per algorithm +- Added a standardized dictionary of symbols for each exchange +- Added auto-ingestion of bundle data while backtesting +- Bug fixes + + +Version 0.2.dev5 +^^^^^^^^^^^^^^^^ +**Release Date**: 2017-10-03 + +- Fixes bug in data.history function that was formatting 'volume' data as + integers, now they are returned as floats with up to 9 decimals of precision. + Data bundles redone. + +Version 0.2.dev4 +^^^^^^^^^^^^^^^^ + +**Release Date**: 2017-09-20 + +- Fixes bug in the pricing resolution of 1-minute data, now set to 8 decimal + places. Pricing resolution of daily data remains set to 9 decimal places. +- The current data bundle takes 340MB compressed for download, and 460MB + uncompressed on disk for Catalyst to use. + +Version 0.2.dev3 +^^^^^^^^^^^^^^^^ + +**Release Date**: 2017-09-20 + +- 1-minute resolution OHLCV data bundle for backtesting from Poloniex exchange +- Implementation of trading of fractional crypto assets (i.e. 0.01 BTC) +- Minimum trade size of a coin can be configured on a per-coin basis, defaults + to 0.00000001 in backtesting (most exchanges set the minimum trade to larger + amounts, which will impact live trading) +- Increased pricing resolution from 3 to 9 decimal places +- The current data bundle takes 40MB compressed for download, and 99MB + uncompressed on disk for Catalyst to use. + +Version 0.2.dev2 +^^^^^^^^^^^^^^^^ + +**Release Date**: 2017-09-07 + +- Fix path issue + +Version 0.2.dev1 +^^^^^^^^^^^^^^^^ + +**Release Date**: 2017-09-03 + +- Implementation of live trading: + + - Comprehensive trading functionality against exchanges Bitfinex and Bittrex. + - Support for all trading pairs available on each exchange. + - Multiple algorithms can trade simultaneously against a single exchange + using the same account. + - Each algorithm has a persisted state (i.e. algorithm can be stopped and + restarted preserving the state without data loss) that tracks all open + orders, executed transactions and portfolio positions. + +- Minute by minute portfolio performance metrics. + + - Daily summary performance statistics compatible with pyfolio, a Python + library for performance and risk analysis of financial portfolios + +Version 0.1.dev9 +^^^^^^^^^^^^^^^^ + +**Release Date**: 2017-08-28 + +- Retrieval of crypto benchmark from bundle, instead of hitting Poloniex + exchange directly +- Change of bundle storage provider from Dropbox to AWS +- Fix issue with 1/1000 scaling issue of prices in bundle + +Version 0.1.dev8 +^^^^^^^^^^^^^^^^ + +**Release Date**: 2017-08-18 + +- Fixes issue in the creation of bundles (:issue:`27`) + + +Version 0.1.dev7 +^^^^^^^^^^^^^^^^ +- Fixes issues in empty benchmark (:issue:`16`) +- Fixes issue of normalizing timestamps before comparison (:issue:`24`) +- Generic data bundles +- CLI UI improvements + +Version 0.1.dev6 +^^^^^^^^^^^^^^^^ + +**Release Date**: 2017-07-13 + +- Initial public release diff --git a/_sources/resources.rst.txt b/_sources/resources.rst.txt new file mode 100644 index 00000000..eb1580a5 --- /dev/null +++ b/_sources/resources.rst.txt @@ -0,0 +1,26 @@ +Resources +========= + +- `Catalyst Whitepaper `_ + + +Related 3rd Party APIs +^^^^^^^^^^^^^^^^^^^^^^ + +- `Zipline `_ is a Pythonic Algorithmic + Trading Library, and the project Catalyst forked off in the spring of 2017. +- `Quantopian `_ provides a platform for + freelance quantitative analysts develop, test, and use trading algorithms to + buy and sell securities. They aim to create a crowd-sourced hedge fund by + fostering their community of freelance traders. Quantopian's backtesting and + live-trading engine is powered by *Zipline*. +- `Pandas `_ is a Python + library providing high-performance, easy-to-use data structures and data + analysis tools. Catalyst relies heavily on pandas, and many API functions + return data as Pandas dataframes. +- `Numpy `_ is the fundamental + package for scientific computing with Python. Some of the data computation + that your algorithms will need, will be optimized leveraging Numpy. +- `Matplotlib `_ is a Python 2D + plotting library that many of examples rely on to plot the performance of + trading algorithms diff --git a/_sources/unit-tests.rst.txt b/_sources/unit-tests.rst.txt new file mode 100644 index 00000000..1da822f1 --- /dev/null +++ b/_sources/unit-tests.rst.txt @@ -0,0 +1,88 @@ +========== +Unit Tests +========== + +Exchanges +~~~~~~~~~ + +Markets +------- +Sample: + All markets in 3 random exchanges +Test: + Fetch all TradingPair instances +Assert: + No error + +Current Ticker +------------------ +Sample: + 3 random markets in each of the 3 random exchanges +Test: + Fetch current price and volume +Assert: + Not null and no error + +Historical Price Data +--------------------- +Sample: + - 3 random markets for each of the 3 random exchanges supporting historical data + - For each market, randomly select one supported frequency +Test: + Fetch historical data for each market using the selected frequency +Assert: + - No error and not blank + - Date of each candle is consistent with the Catalyst desired pattern, + - All candle start at fix intervals + - Last candle partial and forward looking from the end date + +Authentication and Orders +------------------------- +Sample: + 1 random market for each of 3 random authenticated exchanges +Test: + - Create one limit order randomly buying or selling at least 10% out from the current price + - Retrieve the open order from the exchange + - Cancel the open order +Assert: + No error + + +Bundles +~~~~~~~ + +Validate Bundle Data +-------------------- +Sample: + - 3 random market in bundles for exchanges supporting historical data + - For each market, randomly selected data range available in the exchange historical data +Test: + - Clean the target exchange bundle + - Ingest the selected market data for the selected data range + - Retrieve the bundle data into a dataframe + - Retrieve the equivalent OHLCV data from the exchange into a dataframe +Assert: + Matching data for the bundle and exchange + + +Algo Stats +---------- +Sample: + - 2 sample algorithms with built-in stats calculator + - 2 KPIs both calculated by each algo and by Catalyst +Test: + - Run each algorithm + - Compare the results of the two methods or calculating stats +Assert: + - Matching stats + +CSV Ingestion +------------- +Sample: + 3 random CSV files containing price data +Test: + - Ingest each CSV files + - Validate with the exchange like in the 'Validate Bundle Data' test +Assert: + Matching data between the bundle and the exchange + diff --git a/_sources/utilities.rst.txt b/_sources/utilities.rst.txt new file mode 100644 index 00000000..8c89aa52 --- /dev/null +++ b/_sources/utilities.rst.txt @@ -0,0 +1,149 @@ +Utilities +========= + +This section covers a variety of utilites that provide complimentary +functionality to your trading algorithms. These are code snippets that you can +add to any algorithm to add the desired functionality. + +If you are looking for example trading algorithms, see the corresponding section. + +Output to CSV file +~~~~~~~~~~~~~~~~~~ + +Add this script to the analyze method to create and save a CSV file with the +results from the trading algorithm. This file will include the default +parameters of the results DataFrame plus any recorded variables and will be +saved in the same location where your trading algorithm is saved. The exact +script that you need to use depends on the interface that you are using to run +your trading algorithm, which could be the CLI or a Python Interpreter. + +1. Script to use with CLI: + + .. code-block:: python + + def analyze(context=None, results=None): + import sys + import os + from os.path import basename + + # Save results in CSV file + filename = os.path.splitext(basename(sys.argv[3]))[0] + results.to_csv(filename + '.csv') + +2. Script to use with Python Interpreter: + + .. code-block:: python + + def analyze(context=None, results=None): + import os + from os.path import basename + + # Save results in CSV file + filename = os.path.splitext(os.path.basename(__file__))[0] + results.to_csv(filename + '.csv') + +Extracting market data +~~~~~~~~~~~~~~~~~~~~~~ + +Use this script to save the price and volume data of one cryptoasset in a CSV +file, which will be saved in the same location and with the same name as your +Python file. To get custom data, simply modify the asset's symbol and the dates. +Run this script directly from your development environment: python scriptname.py, +where the contents of 'scriptname.py' are as follows. Two different version are +provided as an example for daily- and minute-resolution data respectively: + +Simpler case for daily data + +.. code-block:: python + + import os + import pytz + from datetime import datetime + + from catalyst.api import record, symbol, symbols + from catalyst.utils.run_algo import run_algorithm + + def initialize(context): + # Portfolio assets list + context.asset = symbol('btc_usdt') # Bitcoin on Poloniex + + def handle_data(context, data): + # Variables to record for a given asset: price and volume + price = data.current(context.asset, 'price') + volume = data.current(context.asset, 'volume') + record(price=price, volume=volume) + + def analyze(context=None, results=None): + + # Generate DataFrame with Price and Volume only + data = results[['price','volume']] + + # Save results in CSV file + filename = os.path.splitext(os.path.basename(__file__))[0] + data.to_csv(filename + '.csv') + + ''' Bitcoin data is available on Poloniex since 2015-3-1. + Dates vary for other tokens. In the example below, we choose the + full month of July of 2017. + ''' + start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc) + end = datetime(2017, 7, 31, 0, 0, 0, 0, pytz.utc) + results = run_algorithm(initialize=initialize, + handle_data=handle_data, + analyze=analyze, + start=start, + end=end, + exchange_name='poloniex', + capital_base=10000, + base_currency = 'usdt') + +More versatile case for minute data + +.. code-block:: python + + import os + import csv + import pytz + from datetime import datetime + + from catalyst.api import record, symbol, symbols + from catalyst.utils.run_algo import run_algorithm + + + def initialize(context): + # Portfolio assets list + context.asset = symbol('btc_usdt') # Bitcoin on Poloniex + + # Creates a .CSV file with the same name as this script to store results + context.csvfile = open(os.path.splitext( + os.path.basename(__file__))[0]+'.csv', 'w+') + context.csvwriter = csv.writer(context.csvfile) + + def handle_data(context, data): + # Variables to record for a given asset: price and volume + # Other options include 'open', 'high', 'open', 'close' + # Please note that 'price' equals 'close' + date = context.blotter.current_dt # current time in each iteration + price = data.current(context.asset, 'price') + volume = data.current(context.asset, 'volume') + + # Writes one line to CSV on each iteration with the chosen variables + context.csvwriter.writerow([date,price,volume]) + + def analyze(context=None, results=None): + # Close open file properly at the end + context.csvfile.close() + + + # Bitcoin data is available from 2015-3-2. Dates vary for other tokens. + start = datetime(2017, 7, 30, 0, 0, 0, 0, pytz.utc) + end = datetime(2017, 7, 31, 0, 0, 0, 0, pytz.utc) + results = run_algorithm(initialize=initialize, + handle_data=handle_data, + analyze=analyze, + start=start, + end=end, + exchange_name='poloniex', + data_frequency='minute', + base_currency ='usdt', + capital_base=10000 ) \ No newline at end of file diff --git a/_sources/videos.rst.txt b/_sources/videos.rst.txt new file mode 100644 index 00000000..0beb291b --- /dev/null +++ b/_sources/videos.rst.txt @@ -0,0 +1,63 @@ +Videos +====== + + +Installation: MacOS +------------------- + +.. raw:: html + + + +| +| + +Installation: Windows +--------------------- + +Where things go smoothly: + +.. raw:: html + + + +| + +Where things don't: + +.. raw:: html + + + +| +| + +Backtesting a Strategy +---------------------- + +This is the first video of a two-part series on using Catalyst for algorithmic +trading. This video implements a simple momentum strategy based on +`mean reversion `_: when the cryptoasset +goes up quickly, we’re going to buy; when it goes down quickly, we’re going to +sell. Hopefully, we’ll ride the waves. + +.. raw:: html + + + +| +| + +Live Trading a Strategy +----------------------- + +This is the second part of the two-part series on using Catalyst for algorithmic +trading. Having backtested `our strategy `_ +in the previous video, we now take it to trade live against the Bittrex exchange. + +.. raw:: html + + + +| +| diff --git a/_static/css/badge_only.css b/_static/css/badge_only.css index 7e17fb14..6362912b 100644 --- a/_static/css/badge_only.css +++ b/_static/css/badge_only.css @@ -1,2 +1,2 @@ -.fa:before{-webkit-font-smoothing:antialiased}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-weight:normal;font-style:normal;src:url("../font/fontawesome_webfont.eot");src:url("../font/fontawesome_webfont.eot?#iefix") format("embedded-opentype"),url("../font/fontawesome_webfont.woff") format("woff"),url("../font/fontawesome_webfont.ttf") format("truetype"),url("../font/fontawesome_webfont.svg#FontAwesome") format("svg")}.fa:before{display:inline-block;font-family:FontAwesome;font-style:normal;font-weight:normal;line-height:1;text-decoration:inherit}a 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+ @@ -143,17 +144,17 @@ - - + + - + - + - + @@ -168,7 +169,7 @@ - + @@ -176,14 +177,14 @@ - - + + - + @@ -218,8 +219,8 @@ - - + + @@ -247,10 +248,10 @@ - + - + @@ -274,7 +275,7 @@ - + @@ -345,8 +346,8 @@ - - + + @@ -361,14 +362,14 @@ - - + + - - + + @@ -379,7 +380,7 @@ - + @@ -398,17 +399,287 @@ - + - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_static/fonts/fontawesome-webfont.ttf b/_static/fonts/fontawesome-webfont.ttf index 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toctree). So let's find + // the closest header in the document and try with that one. + if (link.length === 0) { + var doc_link = $('.document a[href="' + anchor + '"]'); + var closest_section = doc_link.closest('div.section'); + // Try again with the closest section entry. + link = $('.wy-menu-vertical') + .find('[href="#' + closest_section.attr("id") + '"]'); + + } + $('.wy-menu-vertical li.toctree-l1 li.current') + .removeClass('current'); + link.closest('li.toctree-l2').addClass('current'); + link.closest('li.toctree-l3').addClass('current'); + link.closest('li.toctree-l4').addClass('current'); + } + catch (err) { + console.log("Error expanding nav for anchor", err); + } + } + }; + + nav.onScroll = function () { + this.winScroll = false; + var newWinPosition = this.win.scrollTop(), + winBottom = newWinPosition + this.winHeight, + navPosition = this.navBar.scrollTop(), + newNavPosition = navPosition + (newWinPosition - this.winPosition); + if (newWinPosition < 0 || winBottom > this.docHeight) { + return; + } + this.navBar.scrollTop(newNavPosition); + this.winPosition = newWinPosition; + }; + + nav.onResize = function () { + this.winResize = false; + this.winHeight = this.win.height(); + this.docHeight = $(document).height(); + }; + + nav.hashChange = function () { + this.linkScroll = true; + this.win.one('hashchange', function () { + this.linkScroll = false; + }); + }; + + nav.toggleCurrent = function (elem) { + var parent_li = elem.closest('li'); + parent_li.siblings('li.current').removeClass('current'); + parent_li.siblings().find('li.current').removeClass('current'); + parent_li.find('> ul li.current').removeClass('current'); + parent_li.toggleClass('current'); + } + + return nav; +}; + +module.exports.ThemeNav = ThemeNav(); + +if (typeof(window) != 'undefined') { + window.SphinxRtdTheme = { StickyNav: module.exports.ThemeNav }; } -$(document).ready(function() { - // Shift nav in mobile when clicking the menu. - $(document).on('click', "[data-toggle='wy-nav-top']", function() { - $("[data-toggle='wy-nav-shift']").toggleClass("shift"); - $("[data-toggle='rst-versions']").toggleClass("shift"); - }); - // Nav menu link click operations - $(document).on('click', ".wy-menu-vertical .current ul li a", function() { - var target = $(this); - // Close menu when you click a link. - $("[data-toggle='wy-nav-shift']").removeClass("shift"); - $("[data-toggle='rst-versions']").toggleClass("shift"); - // Handle dynamic display of l3 and l4 nav lists - toggleCurrent(target); - if (typeof(window.SphinxRtdTheme) != 'undefined') { - window.SphinxRtdTheme.StickyNav.hashChange(); - } - }); - $(document).on('click', "[data-toggle='rst-current-version']", function() { - $("[data-toggle='rst-versions']").toggleClass("shift-up"); - }); - // Make tables responsive - $("table.docutils:not(.field-list)").wrap("
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-
+ + + + + + + + + + + + + + + + +
+ + +
-
+
+

API Reference

@@ -491,6 +320,168 @@ backwards compatibility with older API concepts.

+
+
+can_trade()
+

For the given asset or iterable of assets, returns true if all of the +following are true: +1) the asset is alive for the session of the current simulation time

+
+
(if current simulation time is not a market minute, we use the next +session)
+
    +
  1. (if we are in minute mode) the asset’s exchange is open at the
    2. +
+
+
current simulation time or at the simulation calendar’s next market +minute
+
    +
  1. there is a known last price for the asset.
    2. +
+ +++ + + + + + +
Parameters:assets: Asset or iterable of assets
Returns:can_trade : bool or pd.Series[bool] indexed by asset.
+

Notes

+

The second condition above warrants some further explanation. +- If the asset’s exchange calendar is identical to the simulation +calendar, then this condition always returns True. +- If there are market minutes in the simulation calendar outside of +this asset’s exchange’s trading hours (for example, if the simulation +is running on the CME calendar but the asset is MSFT, which trades on +the NYSE), during those minutes, this condition will return false +(for example, 3:15 am Eastern on a weekday, during which the CME is +open but the NYSE is closed).

+
+ +
+
+current()
+

Returns the current value of the given assets for the given fields +at the current simulation time. Current values are the as-traded price +and are usually not adjusted for events like splits or dividends (see +notes for more information).

+ +++ + + + + + +
Parameters:

assets : Asset or iterable of Assets

+

fields : str or iterable[str].

+
+

Valid values are: “price”, +“last_traded”, “open”, “high”, “low”, “close”, “volume”, or column +names in files read by fetch_csv.

+
+
Returns:

current_value : Scalar, pandas Series, or pandas DataFrame.

+
+

See notes below.

+
+
+

Notes

+

If a single asset and a single field are passed in, a scalar float +value is returned.

+

If a single asset and a list of fields are passed in, a pandas Series +is returned whose indices are the fields, and whose values are scalar +values for this asset for each field.

+

If a list of assets and a single field are passed in, a pandas Series +is returned whose indices are the assets, and whose values are scalar +values for each asset for the given field.

+

If a list of assets and a list of fields are passed in, a pandas +DataFrame is returned, indexed by asset. The columns are the requested +fields, filled with the scalar values for each asset for each field.

+

If the current simulation time is not a valid market time, we use the +last market close instead.

+

“price” returns the last known close price of the asset. If there is +no last known value (either because the asset has never traded, or +because it has delisted) NaN is returned. If a value is found, and we +had to cross an adjustment boundary (split, dividend, etc) to get it, +the value is adjusted before being returned.

+

“last_traded” returns the date of the last trade event of the asset, +even if the asset has stopped trading. If there is no last known value, +pd.NaT is returned.

+

“volume” returns the trade volume for the current simulation time. If +there is no trade this minute, 0 is returned.

+

“open”, “high”, “low”, and “close” return the relevant information for +the current trade bar. If there is no current trade bar, NaN is +returned.

+
+ +
+
+history()
+

Returns a window of data for the given assets and fields.

+

This data is adjusted for splits, dividends, and mergers as of the +current algorithm time.

+

The semantics of missing data are identical to the ones described in +the notes for get_spot_value.

+ +++ + + + + + +
Parameters:

assets: Asset or iterable of Asset

+

fields: string or iterable of string. Valid values are “open”, “high”,

+
+

“low”, “close”, “volume”, “price”, and “last_traded”.

+
+

bar_count: integer number of bars of trade data

+

frequency: string. “1m” for minutely data or “1d” for daily date

+
Returns:

history : Series or DataFrame or Panel

+
+

Return type depends on the dimensionality of the ‘assets’ and +‘fields’ parameters.

+

If single asset and field are passed in, the returned Series is +indexed by dt.

+

If multiple assets and single field are passed in, the returned +DataFrame is indexed by dt, and has assets as columns.

+

If a single asset and multiple fields are passed in, the returned +DataFrame is indexed by dt, and has fields as columns.

+

If multiple assets and multiple fields are passed in, the returned +Panel is indexed by field, has dt as the major axis, and assets +as the minor axis.

+
+
+

Notes

+

If the current simulation time is not a valid market time, we use the +last market close instead.

+
+ +
+
+is_stale()
+

For the given asset or iterable of assets, returns true if the asset +is alive and there is no trade data for the current simulation time.

+

If the asset has never traded, returns False.

+

If the current simulation time is not a valid market time, we use the +current time to check if the asset is alive, but we use the last +market minute/day for the trade data check.

+ +++ + + + + + +
Parameters:assets: Asset or iterable of assets
Returns:boolean or Series of booleans, indexed by asset.
+
+
@@ -498,7 +489,7 @@ backwards compatibility with older API concepts.

Scheduling Functions

-catalyst.api.schedule_function(*args, **kwargs)
+catalyst.api.schedule_function(self, func, date_rule=None, time_rule=None, half_days=True, calendar=None)

Schedules a function to be called according to some timed rules.

@@ -593,7 +584,7 @@ backwards compatibility with older API concepts.

Orders

-catalyst.api.order(*args, **kwargs)
+catalyst.api.order(self, asset, amount, limit_price=None, stop_price=None, style=None)

Place an order.

@@ -648,7 +639,7 @@ and limit_price o
-catalyst.api.order_value(*args, **kwargs)
+catalyst.api.order_value(self, asset, value, limit_price=None, stop_price=None, style=None)

Place an order by desired value rather than desired number of shares.

@@ -701,7 +692,7 @@ value < 0 :: Sell/Short

-catalyst.api.order_percent(*args, **kwargs)
+catalyst.api.order_percent(self, asset, percent, limit_price=None, stop_price=None, style=None)

Place an order in the specified asset corresponding to the given percent of the current portfolio value.

@@ -750,7 +741,7 @@ This is specified as a decimal, for example: 0.50 means 50%.

-catalyst.api.order_target(*args, **kwargs)
+catalyst.api.order_target(self, asset, target, limit_price=None, stop_price=None, style=None)

Place an order to adjust a position to a target number of shares. If the position doesn’t already exist, this is equivalent to placing a new order. If the position does exist, this is equivalent to placing an @@ -797,7 +788,7 @@ current number of shares.

Notes

order_target does not take into account any open orders. For example:

-
order_target(sid(0), 10)
+
order_target(sid(0), 10)
 order_target(sid(0), 10)
 

 

@@ -810,7 +801,7 @@ call to order_target
 

-catalyst.api.order_target_value(*args, **kwargs)

+catalyst.api.order_target_value(self, asset, target, limit_price=None, stop_price=None, style=None)
 
Place an order to adjust a position to a target value. If
 the position doesn’t already exist, this is equivalent to placing a new
 order. If the position does exist, this is equivalent to placing an
@@ -859,7 +850,7 @@ is actually the target exposure, as Futures have no ‘value’.

 
Notes

 
order_target_value does not take into account any open orders. For
 example:

-
order_target_value(sid(0), 10)
+
order_target_value(sid(0), 10)
 order_target_value(sid(0), 10)
 

 

@@ -872,7 +863,7 @@ second order_target_value
 

-catalyst.api.order_target_percent(*args, **kwargs)

+catalyst.api.order_target_percent(self, asset, target, limit_price=None, stop_price=None, style=None)
 
Place an order to adjust a position to a target percent of the
 current portfolio value. If the position doesn’t already exist, this is
 equivalent to placing a new order. If the position does exist, this is
@@ -921,7 +912,7 @@ percent and the current percent.

 
Notes

 
order_target_value does not take into account any open orders. For
 example:

-
order_target_percent(sid(0), 10)
+
order_target_percent(sid(0), 10)
 order_target_percent(sid(0), 10)
 

 

@@ -987,7 +978,7 @@ limit price once the market reaches a specified stop price.

 
 

 

-catalyst.api.get_order(*args, **kwargs)

+catalyst.api.get_order(self, order_id, exchange_name)
 
Lookup an order based on the order id returned from one of the
 order functions.
@@ -1016,7 +1007,7 @@ order functions.

-catalyst.api.get_open_orders(*args, **kwargs)
+catalyst.api.get_open_orders(self, asset=None)

Retrieve all of the current open orders.

@@ -1044,7 +1035,7 @@ orders for this asset.

-catalyst.api.cancel_order(*args, **kwargs)
+catalyst.api.cancel_order(self, order_param, exchange_name, symbol=None, params={})

Cancel an open order.

@@ -1070,7 +1061,7 @@ orders for this asset.

Order Cancellation Policies

-catalyst.api.set_cancel_policy(*args, **kwargs)
+catalyst.api.set_cancel_policy(self, cancel_policy)

Sets the order cancellation policy for the simulation.

@@ -1160,7 +1151,7 @@ Zipline will only apply this policy to minutely simulations.

Assets

-catalyst.api.symbol(*args, **kwargs)
+catalyst.api.symbol(self, symbol_str, exchange_name=None)

Lookup an Equity by its ticker symbol.

@@ -1199,7 +1190,7 @@ symbol lookup date.

-catalyst.api.symbols(*args, **kwargs)
+catalyst.api.symbols(self, *args)

Lookup multuple Equities as a list.

@@ -1235,7 +1226,7 @@ lookup date.

-catalyst.api.set_symbol_lookup_date(*args, **kwargs)
+catalyst.api.set_symbol_lookup_date(self, dt)

Set the date for which symbols will be resolved to their assets (symbols may map to different firms or underlying assets at different times)

@@ -1255,7 +1246,7 @@ different times)

-catalyst.api.sid(*args, **kwargs)
+catalyst.api.sid(self, sid)

Lookup an Asset by its unique asset identifier.

@@ -1291,7 +1282,7 @@ performing as expected. The functions help protect the algorithm from certian bugs that could cause undesirable behavior when trading with real money.

-catalyst.api.set_do_not_order_list(*args, **kwargs)
+catalyst.api.set_do_not_order_list(self, restricted_list, on_error='fail')

Set a restriction on which assets can be ordered.

@@ -1309,14 +1300,14 @@ bugs that could cause undesirable behavior when trading with real money.

-catalyst.api.set_long_only(*args, **kwargs)
+catalyst.api.set_long_only(self, on_error='fail')

Set a rule specifying that this algorithm cannot take short positions.

-catalyst.api.set_max_leverage(*args, **kwargs)
+catalyst.api.set_max_leverage(self, max_leverage)

Set a limit on the maximum leverage of the algorithm.

@@ -1335,7 +1326,7 @@ be no maximum.

-catalyst.api.set_max_order_count(*args, **kwargs)
+catalyst.api.set_max_order_count(self, max_count, on_error='fail')

Set a limit on the number of orders that can be placed in a single day.

@@ -1354,7 +1345,7 @@ day.

-catalyst.api.set_max_order_size(*args, **kwargs)
+catalyst.api.set_max_order_size(self, asset=None, max_shares=None, max_notional=None, on_error='fail')

Set a limit on the number of shares and/or dollar value of any single order placed for sid. Limits are treated as absolute values and are enforced at the time that the algo attempts to place an order for sid.

@@ -1385,7 +1376,7 @@ asset.

-catalyst.api.set_max_position_size(*args, **kwargs)
+catalyst.api.set_max_position_size(self, asset=None, max_shares=None, max_notional=None, on_error='fail')

Set a limit on the number of shares and/or dollar value held for the given sid. Limits are treated as absolute values and are enforced at the time that the algo attempts to place an order for sid. This means @@ -1423,7 +1414,7 @@ asset.

Simulation Parameters

-catalyst.api.set_benchmark(*args, **kwargs)
+catalyst.api.set_benchmark(self, benchmark)

Set the benchmark asset.

@@ -1446,7 +1437,7 @@ automatically reinvested.

Commission Models

-catalyst.api.set_commission(*args, **kwargs)
+catalyst.api.set_commission(self, maker=None, taker=None)
@@ -1556,7 +1547,7 @@ an optional minimum cost per trade.

Slippage Models

-catalyst.api.set_slippage(*args, **kwargs)
+catalyst.api.set_slippage(self, spread=None)
@@ -1633,7 +1624,7 @@ multiple bars.

Miscellaneous

-catalyst.api.record(*args, **kwargs)
+catalyst.api.record(self, *args, **kwargs)

Track and record values each day.

@@ -1655,7 +1646,7 @@ dataframe passed to analyze
-catalyst.api.get_environment(*args, **kwargs)
+catalyst.api.get_environment(self, field='platform')

Query the execution environment.

@@ -1719,7 +1710,7 @@ know if they are running on the Quantopian platform instead.

-catalyst.api.fetch_csv(*args, **kwargs)
+catalyst.api.fetch_csv(self, url, pre_func=None, post_func=None, date_column='date', date_format=None, timezone='UTC', symbol=None, mask=True, symbol_column=None, special_params_checker=None, **kwargs)

Fetch a csv from a remote url and register the data so that it is queryable from the data object.

@@ -1795,7 +1786,7 @@ information to map the sids in the asset finder.

Asset Metadata

-class catalyst.assets.Asset
+class catalyst.assets.Asset(int sid, exchange, symbol='', asset_name='', start_date=None, end_date=None, first_traded=None, auto_close_date=None, exchange_full=None, min_trade_size=None)
first_traded
@@ -1870,7 +1861,7 @@ Assets.

Trading Calendar API

-catalyst.utils.calendars.get_calendar(self, name)
+catalyst.utils.calendars.get_calendar(name)

Retrieves an instance of an TradingCalendar whose name is given.

@@ -1894,7 +1885,7 @@ Assets.

-class catalyst.utils.calendars.TradingCalendar(start=Timestamp('1990-01-01 00:00:00+0000', tz='UTC'), end=Timestamp('2019-03-29 17:27:04.135959+0000', tz='UTC'))[source]
+class catalyst.utils.calendars.TradingCalendar(start=Timestamp('1990-01-01 00:00:00+0000', tz='UTC'), end=Timestamp('2019-03-29 20:15:07.045083+0000', tz='UTC'))[source]

An TradingCalendar represents the timing information of a single market exchange.

The timing information is made up of two parts: sessions, and opens/closes.

@@ -2517,7 +2508,7 @@ negative).

-catalyst.utils.calendars.register_calendar(self, name, calendar, force=False)
+catalyst.utils.calendars.register_calendar(name, calendar, force=False)

Registers a calendar for retrieval by the get_calendar method.

@@ -2551,7 +2542,7 @@ Default is False.

-catalyst.utils.calendars.register_calendar_type(self, name, calendar_type, force=False)
+catalyst.utils.calendars.register_calendar_type(name, calendar_type, force=False)

Registers a calendar by type.

This is useful for registering a new calendar to be lazily instantiated at some future point in time.

@@ -2587,7 +2578,7 @@ Default is False.

-catalyst.utils.calendars.deregister_calendar(self, name)
+catalyst.utils.calendars.deregister_calendar(name)

If a calendar is registered with the given name, it is de-registered.

@@ -2605,7 +2596,7 @@ Default is False.

-catalyst.utils.calendars.clear_calendars(self)
+catalyst.utils.calendars.clear_calendars()

Deregisters all current registered calendars

@@ -2639,14 +2630,14 @@ Default is False.

Examples

-
>>> from pandas import Timestamp, Timedelta
->>> expires = Timestamp('2014', tz='UTC')
+
>>> from pandas import Timestamp, Timedelta
+>>> expires = Timestamp('2014', tz='UTC')
 >>> obj = CachedObject(1, expires)
->>> obj.unwrap(expires - Timedelta('1 minute'))
+>>> obj.unwrap(expires - Timedelta('1 minute'))
 1
 >>> obj.unwrap(expires)
 1
->>> obj.unwrap(expires + Timedelta('1 minute'))
+>>> obj.unwrap(expires + Timedelta('1 minute'))
 ... 
 Traceback (most recent call last):
     ...
@@ -2675,14 +2666,14 @@ If None, than a dict is used as a default.

 
 
 
Examples

-
>>> from pandas import Timestamp, Timedelta
->>> expires = Timestamp('2014', tz='UTC')
+
>>> from pandas import Timestamp, Timedelta
+>>> expires = Timestamp('2014', tz='UTC')
 >>> value = 1
 >>> cache = ExpiringCache()
->>> cache.set('foo', value, expires)
->>> cache.get('foo', expires - Timedelta('1 minute'))
+>>> cache.set('foo', value, expires)
+>>> cache.get('foo', expires - Timedelta('1 minute'))
 1
->>> cache.get('foo', expires + Timedelta('1 minute'))
+>>> cache.get('foo', expires + Timedelta('1 minute'))
 Traceback (most recent call last):
     ...
 KeyError: 'foo'
@@ -2822,7 +2813,7 @@ meaning it has as strong of guarantees as Examples

-
with maybe_show_progress([1, 2, 3], True) as ns:
+
with maybe_show_progress([1, 2, 3], True) as ns:
      for n in ns:
          ...
 

@@ -2834,6 +2825,10 @@ meaning it has as strong of guarantees as 
+            
+           

           

           

   
@@ -2843,9 +2838,10 @@ meaning it has as strong of guarantees as 
     

         © Copyright 2018, Enigma MPC, Inc..
+
     

   

-  Built with Sphinx using a theme provided by Read the Docs.
+  Built with Sphinx using a theme provided by Read the Docs. 
 
 
 
@@ -2866,7 +2862,8 @@ meaning it has as strong of guarantees as 
diff --git a/beginner-tutorial.html b/beginner-tutorial.html
index 5fe21e5d..3739b524 100644
--- a/beginner-tutorial.html
+++ b/beginner-tutorial.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -44,21 +46,28 @@
 
 
 
+   
   

 
     
     
 
     

 
       
       
 
 
       
       

         

-          

+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+
   
+
+  
   

 

-          

+          

+           

             
   

 
Catalyst Beginner Tutorial

@@ -377,16 +230,16 @@ crypto asset in your universe.

 
My first algorithm

 
Lets take a look at a very simple algorithm from the examples directory:
 buy_btc_simple.py:

-
from catalyst.api import order, record, symbol
+
from catalyst.api import order, record, symbol
 
 
 def initialize(context):
-    context.asset = symbol('btc_usd')
+    context.asset = symbol('btc_usd')
 
 
 def handle_data(context, data):
     order(context.asset, 1)
-    record(btc = data.current(context.asset, 'price'))
+    record(btc = data.current(context.asset, 'price'))
 

 

 
As you can see, we first have to import some functions we would like to
@@ -411,7 +264,7 @@ pricing data that Catalyst needs to run your simulation through a process called
 form from the Enigma servers (which eventually will migrate to the Enigma Data
 Marketplace), and stores it locally to make it available at runtime.

 
In order to ingest data, you need to run a command like the following:

-
catalyst ingest-exchange -x bitfinex -i btc_usd
+
catalyst ingest-exchange -x bitfinex -i btc_usd
 

 

 
This instructs Catalyst to download pricing data from the Bitfinex exchange
@@ -421,10 +274,10 @@ our algorithm using historical pricing data from the Bitfinex exchange. By
 default, Catalyst assumes that you want data with daily frequency (one candle
 bar per day). If you want instead minute frequency (one candle bar for every
 minute), you would need to specify it as follows:

-
catalyst ingest-exchange -x bitfinex -i btc_usd -f minute
+
catalyst ingest-exchange -x bitfinex -i btc_usd -f minute
 

 

-
Ingesting exchange bundle bitfinex...
+
Ingesting exchange bundle bitfinex...
   [====================================]  Ingesting daily price data on bitfinex:  100%
 

 

@@ -451,7 +304,7 @@ to the exchange.
 
The ingest-exchange command in catalyst offers additional parameters to
 further tweak the data ingestion process. You can learn more by running the
 following from the command line:

-
catalyst ingest-exchange --help
+
catalyst ingest-exchange --help
 

 

 

@@ -476,11 +329,11 @@ have assimilated the contents of this tutorial.

 
After you installed Catalyst, you should be able to execute the following
 from your command line (e.g. cmd.exe or the Anaconda Prompt on Windows,
 or the Terminal application on MacOS).

-
$ catalyst --help
+
$ catalyst --help
 

 

 
This is the resulting output, simplified for eductional purposes:

-
Usage: catalyst [OPTIONS] COMMAND [ARGS]...
+
Usage: catalyst [OPTIONS] COMMAND [ARGS]...
 
   Top level catalyst entry point.
 
@@ -501,10 +354,10 @@ given exchange, and the third mode 
$ catalyst run --help
+
$ catalyst run --help
 

 

-
Usage: catalyst run [OPTIONS]
+
Usage: catalyst run [OPTIONS]
 
   Run a backtest for the given algorithm.
 
@@ -513,7 +366,7 @@ the available options:

   -t, --algotext TEXT             The algorithm script to run.
   -D, --define TEXT               Define a name to be bound in the namespace
                                   before executing the algotext. For example
-                                  '-Dname=value'. The value may be any python
+                                  '-Dname=value'. The value may be any python
                                   expression. These are evaluated in order so
                                   they may refer to previously defined names.
   --data-frequency [daily|minute]
@@ -528,7 +381,7 @@ the available options:

   -s, --start DATE                The start date of the simulation.
   -e, --end DATE                  The end date of the simulation.
   -o, --output FILENAME           The location to write the perf data. If this
-                                  is '-' the perf will be written to stdout.
+                                  is '-' the perf will be written to stdout.
                                   [default: -]
   --print-algo / --no-print-algo  Print the algorithm to stdout.
   -x, --exchange-name [poloniex|bitfinex|bittrex]
@@ -555,10 +408,10 @@ conveniently pass to the -c
 
Thus, to execute our algorithm from above and save the results to
 buy_btc_simple_out.pickle we would call catalyst run as follows:

-
catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -c usd --capital-base 100000 -o buy_btc_simple_out.pickle
+
catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -c usd --capital-base 100000 -o buy_btc_simple_out.pickle
 

 

-
INFO: run_algo: running algo in backtest mode
+
INFO: run_algo: running algo in backtest mode
 INFO: exchange_algorithm: initialized trading algorithm in backtest mode
 INFO: Performance: Simulated 639 trading days out of 639.
 INFO: Performance: first open: 2016-01-01 00:00:00+00:00
@@ -583,13 +436,13 @@ the fantastic pandascatalyst makes heavy usage of pandas,
 especially for data analysis and outputting so it’s worth spending some time to
 learn it.

-
import pandas as pd
-perf = pd.read_pickle('buy_btc_simple_out.pickle') # read in perf DataFrame
+
import pandas as pd
+perf = pd.read_pickle('buy_btc_simple_out.pickle') # read in perf DataFrame
 print(perf.head())
 

 

 
Which we execute by running:

-
$ python print_results.py
+
$ python print_results.py
 

 

 

@@ -762,23 +615,23 @@ which is the pandas dataframe containing the performance data for our algorithm
 that we reviewed above. Inside the analyze() function is where we can
 analyze and visualize the results of our strategy. Here’s the revised simple
 algorithm (note the addition of Line 1, and Lines 11-18)

-
import matplotlib.pyplot as plt
+
import matplotlib.pyplot as plt
 from catalyst.api import order, record, symbol
 
 def initialize(context):
-    context.asset = symbol('btc_usd')
+    context.asset = symbol('btc_usd')
 
 def handle_data(context, data):
     order(context.asset, 1)
-    record(btc = data.current(context.asset, 'price'))
+    record(btc = data.current(context.asset, 'price'))
 
 def analyze(context, perf):
     ax1 = plt.subplot(211)
     perf.portfolio_value.plot(ax=ax1)
-    ax1.set_ylabel('portfolio value')
+    ax1.set_ylabel('portfolio value')
     ax2 = plt.subplot(212, sharex=ax1)
     perf.btc.plot(ax=ax2)
-    ax2.set_ylabel('bitcoin price')
+    ax2.set_ylabel('bitcoin price')
     plt.show()
 

 

@@ -787,7 +640,7 @@ algorithm (note the addition of Line 1, and Lines 11-18)

 alongside enigma-catalyst (with the exception of the Conda install, where it
 was included by default inside the conda environment we created). If for any
 reason you don’t have it installed, you can add it by running:

-
(catalyst)$ pip install matplotlib
+
(catalyst)$ pip install matplotlib
 

 

 
If everything works well, you’ll see the following chart:

@@ -800,11 +653,11 @@ only bought bitcoin every chance it got.

 results refer to MacOS + Matplotlib.
 Alternatively, some users have reported the following error when running an algo
 in a Linux environment:

-
ImportError: No module named _tkinter, please install the python-tk package
+
ImportError: No module named _tkinter, please install the python-tk package
 

 

 
Which can easily solved by running (in Ubuntu/Debian-based systems):

-
sudo apt install python-tk
+
sudo apt install python-tk
 

 

 

@@ -836,7 +689,7 @@ follow. Most of the added some complexity has been added to beautify the output,
 which you can skim through for now. A copy of this algorithm is available in
 the examples directory:
 dual_moving_average.py.

-
import matplotlib.pyplot as plt
+
import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 from logbook import Logger
@@ -845,101 +698,101 @@ the examples dire
 from catalyst.api import (record, symbol, order_target_percent,)
 from catalyst.exchange.utils.stats_utils import extract_transactions
 
-NAMESPACE = 'dual_moving_average'
+NAMESPACE = 'dual_moving_average'
 log = Logger(NAMESPACE)
 
 
 def initialize(context):
     context.i = 0
-    context.asset = symbol('ltc_usd')
+    context.asset = symbol('ltc_usd')
     context.base_price = None
 
 
 def handle_data(context, data):
-    # define the windows for the moving averages
+    # define the windows for the moving averages
     short_window = 50
     long_window = 200
 
-    # Skip as many bars as long_window to properly compute the average
+    # Skip as many bars as long_window to properly compute the average
     context.i += 1
     if context.i < long_window:
         return
 
-    # Compute moving averages calling data.history() for each
-    # moving average with the appropriate parameters. We choose to use
-    # minute bars for this simulation -> freq="1m"
-    # Returns a pandas dataframe.
+    # Compute moving averages calling data.history() for each
+    # moving average with the appropriate parameters. We choose to use
+    # minute bars for this simulation -> freq="1m"
+    # Returns a pandas dataframe.
     short_data = data.history(context.asset,
-                              'price',
+                              'price',
                               bar_count=short_window,
-                              frequency="1T",
+                              frequency="1T",
                               )
     short_mavg = short_data.mean()
     long_data = data.history(context.asset,
-                             'price',
+                             'price',
                              bar_count=long_window,
-                             frequency="1T",
+                             frequency="1T",
                              )
     long_mavg = long_data.mean()
 
-    # Let's keep the price of our asset in a more handy variable
-    price = data.current(context.asset, 'price')
+    # Let's keep the price of our asset in a more handy variable
+    price = data.current(context.asset, 'price')
 
-    # If base_price is not set, we use the current value. This is the
-    # price at the first bar which we reference to calculate price_change.
+    # If base_price is not set, we use the current value. This is the
+    # price at the first bar which we reference to calculate price_change.
     if context.base_price is None:
         context.base_price = price
     price_change = (price - context.base_price) / context.base_price
 
-    # Save values for later inspection
+    # Save values for later inspection
     record(price=price,
            cash=context.portfolio.cash,
            price_change=price_change,
            short_mavg=short_mavg,
            long_mavg=long_mavg)
 
-    # Since we are using limit orders, some orders may not execute immediately
-    # we wait until all orders are executed before considering more trades.
+    # Since we are using limit orders, some orders may not execute immediately
+    # we wait until all orders are executed before considering more trades.
     orders = context.blotter.open_orders
     if len(orders) > 0:
         return
 
-    # Exit if we cannot trade
+    # Exit if we cannot trade
     if not data.can_trade(context.asset):
         return
 
-    # We check what's our position on our portfolio and trade accordingly
+    # We check what's our position on our portfolio and trade accordingly
     pos_amount = context.portfolio.positions[context.asset].amount
 
-    # Trading logic
+    # Trading logic
     if short_mavg > long_mavg and pos_amount == 0:
-        # we buy 100% of our portfolio for this asset
+        # we buy 100% of our portfolio for this asset
         order_target_percent(context.asset, 1)
     elif short_mavg < long_mavg and pos_amount > 0:
-        # we sell all our positions for this asset
+        # we sell all our positions for this asset
         order_target_percent(context.asset, 0)
 
 
 def analyze(context, perf):
-    # Get the base_currency that was passed as a parameter to the simulation
+    # Get the base_currency that was passed as a parameter to the simulation
     exchange = list(context.exchanges.values())[0]
     base_currency = exchange.base_currency.upper()
 
-    # First chart: Plot portfolio value using base_currency
+    # First chart: Plot portfolio value using base_currency
     ax1 = plt.subplot(411)
-    perf.loc[:, ['portfolio_value']].plot(ax=ax1)
+    perf.loc[:, ['portfolio_value']].plot(ax=ax1)
     ax1.legend_.remove()
-    ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
+    ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
     start, end = ax1.get_ylim()
     ax1.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
-    # Second chart: Plot asset price, moving averages and buys/sells
+    # Second chart: Plot asset price, moving averages and buys/sells
     ax2 = plt.subplot(412, sharex=ax1)
-    perf.loc[:, ['price', 'short_mavg', 'long_mavg']].plot(
+    perf.loc[:, ['price', 'short_mavg', 'long_mavg']].plot(
         ax=ax2,
-        label='Price')
+        label='Price')
     ax2.legend_.remove()
-    ax2.set_ylabel('{asset}\n({base})'.format(
+    ax2.set_ylabel('{asset}\n({base})'.format(
         asset=context.asset.symbol,
         base=base_currency
     ))
@@ -948,72 +801,72 @@ the examples dire
 
     transaction_df = extract_transactions(perf)
     if not transaction_df.empty:
-        buy_df = transaction_df[transaction_df['amount'] > 0]
-        sell_df = transaction_df[transaction_df['amount'] < 0]
+        buy_df = transaction_df[transaction_df['amount'] > 0]
+        sell_df = transaction_df[transaction_df['amount'] < 0]
         ax2.scatter(
             buy_df.index.to_pydatetime(),
-            perf.loc[buy_df.index, 'price'],
-            marker='^',
+            perf.loc[buy_df.index, 'price'],
+            marker='^',
             s=100,
-            c='green',
-            label=''
+            c='green',
+            label=''
         )
         ax2.scatter(
             sell_df.index.to_pydatetime(),
-            perf.loc[sell_df.index, 'price'],
-            marker='v',
+            perf.loc[sell_df.index, 'price'],
+            marker='v',
             s=100,
-            c='red',
-            label=''
+            c='red',
+            label=''
         )
 
-    # Third chart: Compare percentage change between our portfolio
-    # and the price of the asset
+    # Third chart: Compare percentage change between our portfolio
+    # and the price of the asset
     ax3 = plt.subplot(413, sharex=ax1)
-    perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3)
+    perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3)
     ax3.legend_.remove()
-    ax3.set_ylabel('Percent Change')
+    ax3.set_ylabel('Percent Change')
     start, end = ax3.get_ylim()
     ax3.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
-    # Fourth chart: Plot our cash
+    # Fourth chart: Plot our cash
     ax4 = plt.subplot(414, sharex=ax1)
     perf.cash.plot(ax=ax4)
-    ax4.set_ylabel('Cash\n({})'.format(base_currency))
+    ax4.set_ylabel('Cash\n({})'.format(base_currency))
     start, end = ax4.get_ylim()
     ax4.yaxis.set_ticks(np.arange(0, end, end / 5))
 
     plt.show()
 
 
-if __name__ == '__main__':
+if __name__ == '__main__':
     
     run_algorithm(
             capital_base=1000,
-            data_frequency='minute',
+            data_frequency='minute',
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='bitfinex',
+            exchange_name='bitfinex',
             algo_namespace=NAMESPACE,
-            base_currency='usd',
-            start=pd.to_datetime('2017-9-22', utc=True),
-            end=pd.to_datetime('2017-9-23', utc=True),
+            base_currency='usd',
+            start=pd.to_datetime('2017-9-22', utc=True),
+            end=pd.to_datetime('2017-9-23', utc=True),
         )
 

 

 
In order to run the code above, you have to ingest the needed data first:

-
catalyst ingest-exchange -x bitfinex -f minute -i ltc_usd
+
catalyst ingest-exchange -x bitfinex -f minute -i ltc_usd
 

 

 
And then run the code above with the following command:

-
catalyst run -f dual_moving_average.py -x bitfinex -s 2017-9-22 -e 2017-9-23 --capital-base 1000 --base-currency usd --data-frequency minute -o out.pickle
+
catalyst run -f dual_moving_average.py -x bitfinex -s 2017-9-22 -e 2017-9-23 --capital-base 1000 --base-currency usd --data-frequency minute -o out.pickle
 

 

 
Alternatively, we can make use of the run_algorithm() function included at
 the end of the file, where we can specify all the simulation parameters, and
 execute this file as a Python script:

-
python dual_moving_average.py
+
python dual_moving_average.py
 

 

 
Either way, we obtain the following charts:

@@ -1074,11 +927,11 @@ running algorithms through the command line.

 
In order to use Jupyter Notebook, you first have to install it inside your
 environment. It’s available as pip package, so regardless of how you
 installed Catalyst, go inside your catalyst environemnt and run:

-
(catalyst)$ pip install jupyter
+
(catalyst)$ pip install jupyter
 

 

 
Once you have Jupyter Notebook installed, every time you want to use it run:

-
(catalyst)$ jupyter notebook
+
(catalyst)$ jupyter notebook
 

 

 
A local server will launch, and will open a new window on your browser. That’s
@@ -1089,7 +942,7 @@ the interface through which you will interact with Jupyter Notebook.

 
Before running your algorithms inside the Jupyter Notebook, remember to ingest
 the data from the command line interface (CLI). In the example below, you would
 need to run first:

-
catalyst ingest-exchange -x bitfinex -i btc_usd
+
catalyst ingest-exchange -x bitfinex -i btc_usd
 

 

 
To use Catalyst inside a Jupyter Noebook, you have to write your algorithm in a
@@ -1100,18 +953,18 @@ takes the same arguments as the command line interface. Thus to run the
 algorithm just supply the same parameters as the CLI but without the -f
 and -o arguments. We just have to execute the following cell after
 importing catalyst to register the magic.

-
# Register the catalyst magic
+
# Register the catalyst magic
 %load_ext catalyst
 

 

-
# Setup matplotlib to display graphs inline in this Notebook
+
# Setup matplotlib to display graphs inline in this Notebook
 %matplotlib inline
 

 

 
Note below that we do not have to specify an input file (-f) since the
 magic will use the contents of the cell and look for your algorithm
 functions.

-
%%catalyst --start 2015-3-2 --end 2017-6-28 --capital-base 100000 -x bitfinex -c usd
+
%%catalyst --start 2015-3-2 --end 2017-6-28 --capital-base 100000 -x bitfinex -c usd
 
 from catalyst.finance.slippage import VolumeShareSlippage
 
@@ -1124,13 +977,13 @@ functions.

 )
 
 def initialize(context):
-    context.ASSET_NAME = 'btc_usd'
+    context.ASSET_NAME = 'btc_usd'
     context.TARGET_HODL_RATIO = 0.8
     context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO
 
-    # For all trading pairs in the poloniex bundle, the default denomination
-    # currently supported by Catalyst is 1/1000th of a full coin. Use this
-    # constant to scale the price of up to that of a full coin if desired.
+    # For all trading pairs in the poloniex bundle, the default denomination
+    # currently supported by Catalyst is 1/1000th of a full coin. Use this
+    # constant to scale the price of up to that of a full coin if desired.
     context.TICK_SIZE = 1000.0
 
     context.is_buying = True
@@ -1145,22 +998,22 @@ functions.

     target_hodl_value = context.TARGET_HODL_RATIO * starting_cash
     reserve_value = context.RESERVE_RATIO * starting_cash
 
-    # Cancel any outstanding orders
+    # Cancel any outstanding orders
     orders = get_open_orders(context.asset) or []
     for order in orders:
         cancel_order(order)
 
-    # Stop buying after passing the reserve threshold
+    # Stop buying after passing the reserve threshold
     cash = context.portfolio.cash
     if cash <= reserve_value:
         context.is_buying = False
 
-    # Retrieve current asset price from pricing data
-    price = data.current(context.asset, 'price')
+    # Retrieve current asset price from pricing data
+    price = data.current(context.asset, 'price')
 
-    # Check if still buying and could (approximately) afford another purchase
+    # Check if still buying and could (approximately) afford another purchase
     if context.is_buying and cash > price:
-        # Place order to make position in asset equal to target_hodl_value
+        # Place order to make position in asset equal to target_hodl_value
         order_target_value(
             context.asset,
             target_hodl_value,
@@ -1169,7 +1022,7 @@ functions.

 
     record(
         price=price,
-        volume=data.current(context.asset, 'volume'),
+        volume=data.current(context.asset, 'volume'),
         cash=cash,
         starting_cash=context.portfolio.starting_cash,
         leverage=context.account.leverage,
@@ -1178,70 +1031,70 @@ functions.

 def analyze(context=None, results=None):
     import matplotlib.pyplot as plt
 
-    # Plot the portfolio and asset data.
+    # Plot the portfolio and asset data.
     ax1 = plt.subplot(611)
-    results[['portfolio_value']].plot(ax=ax1)
-    ax1.set_ylabel('Portfolio Value (USD)')
+    results[['portfolio_value']].plot(ax=ax1)
+    ax1.set_ylabel('Portfolio Value (USD)')
 
     ax2 = plt.subplot(612, sharex=ax1)
-    ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
-    (context.TICK_SIZE * results[['price']]).plot(ax=ax2)
+    ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
+    (context.TICK_SIZE * results[['price']]).plot(ax=ax2)
 
     trans = results.ix[[t != [] for t in results.transactions]]
     buys = trans.ix[
-        [t[0]['amount'] > 0 for t in trans.transactions]
+        [t[0]['amount'] > 0 for t in trans.transactions]
     ]
     ax2.plot(
         buys.index,
         context.TICK_SIZE * results.price[buys.index],
-        '^',
+        '^',
         markersize=10,
-        color='g',
+        color='g',
     )
 
     ax3 = plt.subplot(613, sharex=ax1)
-    results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
-    ax3.set_ylabel('Leverage ')
+    results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
+    ax3.set_ylabel('Leverage ')
 
     ax4 = plt.subplot(614, sharex=ax1)
-    results[['starting_cash', 'cash']].plot(ax=ax4)
-    ax4.set_ylabel('Cash (USD)')
+    results[['starting_cash', 'cash']].plot(ax=ax4)
+    ax4.set_ylabel('Cash (USD)')
 
     results[[
-        'treasury',
-        'algorithm',
-        'benchmark',
+        'treasury',
+        'algorithm',
+        'benchmark',
     ]] = results[[
-        'treasury_period_return',
-        'algorithm_period_return',
-        'benchmark_period_return',
+        'treasury_period_return',
+        'algorithm_period_return',
+        'benchmark_period_return',
     ]]
 
     ax5 = plt.subplot(615, sharex=ax1)
     results[[
-        'treasury',
-        'algorithm',
-        'benchmark',
+        'treasury',
+        'algorithm',
+        'benchmark',
     ]].plot(ax=ax5)
-    ax5.set_ylabel('Percent Change')
+    ax5.set_ylabel('Percent Change')
 
     ax6 = plt.subplot(616, sharex=ax1)
-    results[['volume']].plot(ax=ax6)
-    ax6.set_ylabel('Volume (mCoins/5min)')
+    results[['volume']].plot(ax=ax6)
+    ax6.set_ylabel('Volume (mCoins/5min)')
 
     plt.legend(loc=3)
 
-    # Show the plot.
+    # Show the plot.
     plt.gcf().set_size_inches(18, 8)
     plt.show()
 

 

-
[2017-08-11 07:19:46.411748] INFO: Loader: Loading benchmark data for 'USDT_BTC' from 1989-12-31 00:00:00+00:00 to 2017-08-09 00:00:00+00:00
+
[2017-08-11 07:19:46.411748] INFO: Loader: Loading benchmark data for 'USDT_BTC' from 1989-12-31 00:00:00+00:00 to 2017-08-09 00:00:00+00:00
 [2017-08-11 07:19:46.418983] INFO: Loader: Loading data for /Users/<snipped>/.catalyst/data/USDT_BTC_benchmark.csv failed with error [Unknown string format].
 [2017-08-11 07:19:46.419740] INFO: Loader: Cache at /Users/<snipped>/.catalyst/data/USDT_BTC_benchmark.csv does not have data from 1990-01-01 00:00:00+00:00 to 2017-08-09 00:00:00+00:00.
 
-[2017-08-11 07:19:46.420770] INFO: Loader: Downloading benchmark data for 'USDT_BTC' from 1989-12-31 00:00:00+00:00 to 2017-08-09 00:00:00+00:00
-[2017-08-11 07:19:50.060244] WARNING: Loader: Still don't have expected data after redownload!
+[2017-08-11 07:19:46.420770] INFO: Loader: Downloading benchmark data for 'USDT_BTC' from 1989-12-31 00:00:00+00:00 to 2017-08-09 00:00:00+00:00
+[2017-08-11 07:19:50.060244] WARNING: Loader: Still don't have expected data after redownload!
 [2017-08-11 07:19:50.097334] WARNING: Loader: Refusing to download new treasury data because a download succeeded at 2017-08-11 06:56:49+00:00.
 [2017-08-11 07:19:54.618399] INFO: Performance: Simulated 851 trading days out of 851.
 [2017-08-11 07:19:54.619301] INFO: Performance: first open: 2015-03-01 00:00:00+00:00
@@ -2618,7 +2471,7 @@ functions.

 
Also, instead of defining an output file we are accessing it via the “_”
 variable that will be created in the name space and contain the
 performance DataFrame.

-
_.head()
+
_.head()
 

 

 
algo_volatility

@@ -2809,15 +2662,19 @@ problems on our 
+            
+           
           
           
 
@@ -2850,7 +2708,8 @@ problems on our 
diff --git a/bundles.html b/bundles.html
index e7f275a7..78785407 100644
--- a/bundles.html
+++ b/bundles.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -42,21 +44,28 @@
 
 
 
+   
   

 
     
     
 
     

 
       
       
 
 
       
       

         

-          

+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+
   
+
+  
   

 

-          

+          

+           

             
   

 
Data Bundles

@@ -339,12 +168,12 @@ backtests and store the data for future runs.

 
Zipline comes with a few bundles by default as well as the ability to register
 new bundles. To see which bundles we have have available, we may run the
 bundles command, for example:

-
$ zipline bundles
-my-custom-bundle 2016-05-05 20:35:19.809398
-my-custom-bundle 2016-05-05 20:34:53.654082
-my-custom-bundle 2016-05-05 20:34:48.401767
+
$ zipline bundles
+my-custom-bundle 2016-05-05 20:35:19.809398
+my-custom-bundle 2016-05-05 20:34:53.654082
+my-custom-bundle 2016-05-05 20:34:48.401767
 quandl <no ingestions>
-quantopian-quandl 2016-05-05 20:06:40.894956
+quantopian-quandl 2016-05-05 20:06:40.894956
 

 

 
The output here shows that there are 3 bundles available:

@@ -367,7 +196,7 @@ standard location that zipline can find. By default the location where ingested
 data will be written is $ZIPLINE_ROOT/data/<bundle> where by default
 ZIPLINE_ROOT=~/.zipline. The ingestion step may take some time as it could
 involve downloading and processing a lot of data. This can be run with:

-
$ zipline ingest [-b <bundle>]
+
$ zipline ingest [-b <bundle>]
 

 

 
where <bundle> is the name of the bundle to ingest, defaulting to
@@ -386,17 +215,17 @@ we can list all of the ingestions with the clean, which will clear data bundles based on some time
 constraints.

 
For example:

-
# clean everything older than <date>
-$ zipline clean [-b <bundle>] --before <date>
+
# clean everything older than <date>
+$ zipline clean [-b <bundle>] --before <date>
 
-# clean everything newer than <date>
-$ zipline clean [-b <bundle>] --after <date>
+# clean everything newer than <date>
+$ zipline clean [-b <bundle>] --after <date>
 
-# keep everything in the range of [before, after] and delete the rest
-$ zipline clean [-b <bundle>] --before <date> --after <after>
+# keep everything in the range of [before, after] and delete the rest
+$ zipline clean [-b <bundle>] --before <date> --after <after>
 
-# clean all but the last <int> runs
-$ zipline clean [-b <bundle>] --keep-last <int>
+# clean all but the last <int> runs
+$ zipline clean [-b <bundle>] --keep-last <int>
 

 

 

@@ -405,7 +234,7 @@ constraints.

 
Now that the data has been ingested we can use it to run backtests with the
 run command. The bundle to use can be specified with the --bundle option
 like:

-
$ zipline run --bundle <bundle> --algofile algo.py ...
+
$ zipline run --bundle <bundle> --algofile algo.py ...
 

 

 
We may also specify the date to use to look up the bundle data with the
@@ -427,7 +256,7 @@ includes daily pricing data, splits, cash dividends, and asset metadata. To
 ingest the quandl data bundle we recommend creating an account on quandl.com
 to get an API key to be able to make more API requests per day. Once we have an
 API key we may run:

-
$ QUANDL_API_KEY=<api-key> zipline ingest -b quandl
+
$ QUANDL_API_KEY=<api-key> zipline ingest -b quandl
 

 

 
though we may still run ingest as an anonymous quandl user (with no API
@@ -458,23 +287,23 @@ cache the data for a set of equities from yahoo. The yahoo bundles include daily
 pricing data along with splits, cash dividends, and inferred asset metadata. To
 create a bundle from a set of equities, add the following to your
 ~/.zipline/extensions.py file:

-
from zipline.data.bundles import register, yahoo_equities
+
from zipline.data.bundles import register, yahoo_equities
 
-# these are the tickers you would like data for
+# these are the tickers you would like data for
 equities = {
-    'AAPL',
-    'MSFT',
-    'GOOG',
+    'AAPL',
+    'MSFT',
+    'GOOG',
 }
 register(
-    'my-yahoo-equities-bundle',  # name this whatever you like
+    'my-yahoo-equities-bundle',  # name this whatever you like
     yahoo_equities(equities),
 )
 

 

 
This may now be used like:

-
$ zipline ingest -b my-yahoo-equities-bundle
-$ zipline run -f algo.py --bundle my-yahoo-equities-bundle
+
$ zipline ingest -b my-yahoo-equities-bundle
+$ zipline run -f algo.py --bundle my-yahoo-equities-bundle
 

 

 
More than one yahoo equities bundle may be registered as long as they use
@@ -494,7 +323,7 @@ writers that will write the data to the correct location transactionally. If an
 ingestion fails part way through the bundle will not be written in an incomplete
 state.

 
The signature of the ingest function should be:

-
ingest(environ,
+
ingest(environ,
        asset_db_writer,
        minute_bar_writer,
        daily_bar_writer,
@@ -630,6 +459,10 @@ bundle uses this to directly untar the bundle into the 
+            
+           

           

           

   
@@ -639,9 +472,10 @@ bundle uses this to directly untar the bundle into the 
     

         © Copyright 2018, Enigma MPC, Inc..
+
     

   

-  Built with Sphinx using a theme provided by Read the Docs.
+  Built with Sphinx using a theme provided by Read the Docs. 
 
 
 
@@ -662,7 +496,8 @@ bundle uses this to directly untar the bundle into the 
diff --git a/development-guidelines.html b/development-guidelines.html
index de33724b..0194487b 100644
--- a/development-guidelines.html
+++ b/development-guidelines.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -44,21 +46,28 @@
 
 
 
+   
   

 
     
     
 
     

 
       
       
 
 
       
       

         

-          

+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+
   
+
+  
   

 

-          

+          

+           

             
   

 
Development Guidelines

@@ -338,23 +175,23 @@
 

 
Creating a Development Environment

 
First, you’ll need to clone Catalyst by running:

-
$ git clone git@github.com:enigmampc/catalyst.git
+
$ git clone git@github.com:enigmampc/catalyst.git
 

 

 
Then check out to a new branch where you can make your changes:

-
$ git checkout -b some-short-descriptive-name
+
$ git checkout -b some-short-descriptive-name
 

 

 
If you don’t already have them, you’ll need some C library dependencies. You can follow the install guide to get the appropriate dependencies.

 
The following section assumes you already have virtualenvwrapper and pip installed on your system. Suggested installation of Python library dependencies used for development:

-
$ mkvirtualenv catalyst
-$ ./etc/ordered_pip.sh ./etc/requirements.txt
-$ pip install -r ./etc/requirements_dev.txt
-$ pip install -r ./etc/requirements_blaze.txt
+
$ mkvirtualenv catalyst
+$ ./etc/ordered_pip.sh ./etc/requirements.txt
+$ pip install -r ./etc/requirements_dev.txt
+$ pip install -r ./etc/requirements_blaze.txt
 

 

 
Finally, you can build the C extensions by running:

-
$ python setup.py build_ext --inplace
+
$ python setup.py build_ext --inplace
 

 

 

@@ -374,20 +211,20 @@ build the DockerfileContributing to the Docs
 
If you’d like to contribute to the documentation on enigmampc.github.io, you can navigate to docs/source/ where each reStructuredText file is a separate section there. To add a section, create a new file called some-descriptive-name.rst and add some-descriptive-name to index.rst. To edit a section, simply open up one of the existing files, make your changes, and save them.

 
We use Sphinx to generate documentation for Catalyst, which you will need to install by running:

-
$ pip install -r ./etc/requirements_docs.txt
+
$ pip install -r ./etc/requirements_docs.txt
 

 

 
To build and view the docs locally, run:

-
# assuming you're in the Catalyst root directory
-$ cd docs
-$ make html
-$ {BROWSER} build/html/index.html
+
# assuming you're in the Catalyst root directory
+$ cd docs
+$ make html
+$ {BROWSER} build/html/index.html
 

 

 
There is a documented issue
 with sphinx and docutils that causes the error below when trying to build
 the docs.

-
Exception occurred:
+
Exception occurred:
   File "(...)/env-c/lib/python2.7/site-packages/docutils/writers/_html_base.py", line 671, in depart_document
     assert not self.context, 'len(context) = %s' % len(self.context)
 AssertionError: len(context) = 3
@@ -395,14 +232,14 @@ AssertionError: len(context) = 3
 

 
If you get this error, you need to downgrade your version of docutils as
 follows, and build the docs again:

-
$ pip install docutils==0.12
+
$ pip install docutils==0.12
 

 

 

 

 
Commit messages

 
Standard prefixes to start a commit message:

-
BLD: change related to building Catalyst
+
BLD: change related to building Catalyst
 BUG: bug fix
 DEP: deprecate something, or remove a deprecated object
 DEV: development tool or utility
@@ -419,7 +256,7 @@ PERF: performance enhancements
 
Some commit style guidelines:

 
Commit lines should be no longer than 72 characters. The first line of the commit should include one of the above prefixes. There should be an empty line between the commit subject and the body of the commit. In general, the message should be in the imperative tense. Best practice is to include not only what the change is, but why the change was made.

 
Example:

-
MAINT: Remove unused calculations of max_leverage, et al.
+
MAINT: Remove unused calculations of max_leverage, et al.
 
 In the performance period the max_leverage, max_capital_used,
 cumulative_capital_used were calculated but not used.
@@ -441,15 +278,19 @@ the algorithm has little cash on hand.
 

 
 
+           

+           

+            
+           

           

           
 
@@ -482,7 +324,8 @@ the algorithm has little cash on hand.
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/example-algos.html b/example-algos.html
index 3209018a..796bac13 100644
--- a/example-algos.html
+++ b/example-algos.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -44,21 +46,28 @@
 
 
 
+   
   

 
     
     
 
     

 
       
       
 
 
       
       

         

-          

+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+
   
+
+  
   

 

-          

+          

+           

             
   

 


@@ -375,7 +213,7 @@ writting the following article:
 

 
Buy BTC Simple Algorithm

 
Source code: examples/buy_btc_simple.py

-
'''
+
'''
     This is a very simple example referenced in the beginner's tutorial:
     https://enigmampc.github.io/catalyst/beginner-tutorial.html
 
@@ -404,25 +242,25 @@ writting the following article:
 
 
 def initialize(context):
-    context.asset = symbol('btc_usdt')
+    context.asset = symbol('btc_usdt')
 
 
 def handle_data(context, data):
     order(context.asset, 1)
-    record(btc=data.current(context.asset, 'price'))
+    record(btc=data.current(context.asset, 'price'))
 
 
-if __name__ == '__main__':
+if __name__ == '__main__':
     run_algorithm(
         capital_base=10000,
-        data_frequency='daily',
+        data_frequency='daily',
         initialize=initialize,
         handle_data=handle_data,
-        exchange_name='poloniex',
-        algo_namespace='buy_and_hodl',
-        base_currency='usdt',
-        start=pd.to_datetime('2015-03-01', utc=True),
-        end=pd.to_datetime('2017-10-31', utc=True),
+        exchange_name='poloniex',
+        algo_namespace='buy_and_hodl',
+        base_currency='usdt',
+        start=pd.to_datetime('2015-03-01', utc=True),
+        end=pd.to_datetime('2017-10-31', utc=True),
     )
 

 

@@ -431,16 +269,16 @@ writting the following article:
 

 
Buy and Hodl Algorithm

 
First ingest the historical pricing data needed to run this algorithm:

-
catalyst ingest-exchange -x bitfinex -f daily -i btc_usd
+
catalyst ingest-exchange -x bitfinex -f daily -i btc_usd
 

 

 
Then, you can run the code below with the following command:

-
catalyst run -f buy_and_hodl.py --start 2015-3-1 --end 2017-10-31 --capital-base 100000 -x bitfinex -c btc -o bah.pickle
+
catalyst run -f buy_and_hodl.py --start 2015-3-1 --end 2017-10-31 --capital-base 100000 -x bitfinex -c btc -o bah.pickle
 

 

 
or using the same parameters specified in the run_algorithm() function at the
 end of the file:

-
python buy_and_hodl.py
+
python buy_and_hodl.py
 

 

 
This command will run the trading algorithm in the specified time range and
@@ -450,22 +288,22 @@ that 2015-3-1 is the earliest date that Catalyst supports (if you choose an
 earlier date, you’ll get an error), and the most recent date you can choose is
 one day prior to the current date.

 
Source code: examples/buy_and_hodl.py

-
#!/usr/bin/env python
-#
-# Copyright 2017 Enigma MPC, Inc.
-# Copyright 2015 Quantopian, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
+
#!/usr/bin/env python
+#
+# Copyright 2017 Enigma MPC, Inc.
+# Copyright 2015 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import pandas as pd
 import matplotlib.pyplot as plt
 
@@ -475,7 +313,7 @@ one day prior to the current date.

 
 
 def initialize(context):
-    context.ASSET_NAME = 'btc_usdt'
+    context.ASSET_NAME = 'btc_usdt'
     context.TARGET_HODL_RATIO = 0.8
     context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO
 
@@ -492,23 +330,23 @@ one day prior to the current date.

     target_hodl_value = context.TARGET_HODL_RATIO * starting_cash
     reserve_value = context.RESERVE_RATIO * starting_cash
 
-    # Cancel any outstanding orders
+    # Cancel any outstanding orders
     orders = get_open_orders(context.asset) or []
     for order in orders:
         cancel_order(order)
 
-    # Stop buying after passing the reserve threshold
+    # Stop buying after passing the reserve threshold
     cash = context.portfolio.cash
     if cash <= reserve_value:
         context.is_buying = False
 
-    # Retrieve current asset price from pricing data
-    price = data.current(context.asset, 'price')
+    # Retrieve current asset price from pricing data
+    price = data.current(context.asset, 'price')
 
-    # Check if still buying and could (approximately) afford another purchase
+    # Check if still buying and could (approximately) afford another purchase
     if context.is_buying and cash > price:
-        print('buying')
-        # Place order to make position in asset equal to target_hodl_value
+        print('buying')
+        # Place order to make position in asset equal to target_hodl_value
         order_target_value(
             context.asset,
             target_hodl_value,
@@ -517,7 +355,7 @@ one day prior to the current date.

 
     record(
         price=price,
-        volume=data.current(context.asset, 'volume'),
+        volume=data.current(context.asset, 'volume'),
         cash=cash,
         starting_cash=context.portfolio.starting_cash,
         leverage=context.account.leverage,
@@ -526,77 +364,77 @@ one day prior to the current date.

 
 def analyze(context=None, results=None):
 
-    # Plot the portfolio and asset data.
+    # Plot the portfolio and asset data.
     ax1 = plt.subplot(611)
-    results[['portfolio_value']].plot(ax=ax1)
-    ax1.set_ylabel('Portfolio\nValue\n(USD)')
+    results[['portfolio_value']].plot(ax=ax1)
+    ax1.set_ylabel('Portfolio\nValue\n(USD)')
 
     ax2 = plt.subplot(612, sharex=ax1)
-    ax2.set_ylabel('{asset}\n(USD)'.format(asset=context.ASSET_NAME))
-    results[['price']].plot(ax=ax2)
+    ax2.set_ylabel('{asset}\n(USD)'.format(asset=context.ASSET_NAME))
+    results[['price']].plot(ax=ax2)
 
     trans = results.ix[[t != [] for t in results.transactions]]
     buys = trans.ix[
-        [t[0]['amount'] > 0 for t in trans.transactions]
+        [t[0]['amount'] > 0 for t in trans.transactions]
     ]
     ax2.scatter(
         buys.index.to_pydatetime(),
         results.price[buys.index],
-        marker='^',
+        marker='^',
         s=100,
-        c='g',
-        label=''
+        c='g',
+        label=''
     )
 
     ax3 = plt.subplot(613, sharex=ax1)
-    results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
-    ax3.set_ylabel('Leverage ')
+    results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
+    ax3.set_ylabel('Leverage ')
 
     ax4 = plt.subplot(614, sharex=ax1)
-    results[['starting_cash', 'cash']].plot(ax=ax4)
-    ax4.set_ylabel('Cash (USD)')
+    results[['starting_cash', 'cash']].plot(ax=ax4)
+    ax4.set_ylabel('Cash (USD)')
 
     results[[
-        'treasury',
-        'algorithm',
-        'benchmark',
+        'treasury',
+        'algorithm',
+        'benchmark',
     ]] = results[[
-        'treasury_period_return',
-        'algorithm_period_return',
-        'benchmark_period_return',
+        'treasury_period_return',
+        'algorithm_period_return',
+        'benchmark_period_return',
     ]]
 
     ax5 = plt.subplot(615, sharex=ax1)
     results[[
-        'treasury',
-        'algorithm',
-        'benchmark',
+        'treasury',
+        'algorithm',
+        'benchmark',
     ]].plot(ax=ax5)
-    ax5.set_ylabel('Percent\nChange')
+    ax5.set_ylabel('Percent\nChange')
 
     ax6 = plt.subplot(616, sharex=ax1)
-    results[['volume']].plot(ax=ax6)
-    ax6.set_ylabel('Volume')
+    results[['volume']].plot(ax=ax6)
+    ax6.set_ylabel('Volume')
 
     plt.legend(loc=3)
 
-    # Show the plot.
+    # Show the plot.
     plt.gcf().set_size_inches(18, 8)
     plt.show()
 
 
-if __name__ == '__main__':
+if __name__ == '__main__':
     run_algorithm(
         capital_base=10000,
-        data_frequency='daily',
+        data_frequency='daily',
         initialize=initialize,
         handle_data=handle_data,
         analyze=analyze,
-        exchange_name='poloniex',
-        algo_namespace='buy_and_hodl',
-        base_currency='usdt',
-        start=pd.to_datetime('2015-03-01', utc=True),
-        end=pd.to_datetime('2017-10-31', utc=True),
+        exchange_name='poloniex',
+        algo_namespace='buy_and_hodl',
+        base_currency='usdt',
+        start=pd.to_datetime('2015-03-01', utc=True),
+        end=pd.to_datetime('2017-10-31', utc=True),
     )
 

 

@@ -607,7 +445,7 @@ one day prior to the current date.

 
This strategy is covered in detail in the last part of
 this tutorial.

 
Source Code: examples/dual_moving_average.py

-
import matplotlib.pyplot as plt
+
import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 from logbook import Logger
@@ -616,101 +454,101 @@ one day prior to the current date.

 from catalyst.api import (record, symbol, order_target_percent,)
 from catalyst.exchange.utils.stats_utils import extract_transactions
 
-NAMESPACE = 'dual_moving_average'
+NAMESPACE = 'dual_moving_average'
 log = Logger(NAMESPACE)
 
 
 def initialize(context):
     context.i = 0
-    context.asset = symbol('ltc_usd')
+    context.asset = symbol('ltc_usd')
     context.base_price = None
 
 
 def handle_data(context, data):
-    # define the windows for the moving averages
+    # define the windows for the moving averages
     short_window = 50
     long_window = 200
 
-    # Skip as many bars as long_window to properly compute the average
+    # Skip as many bars as long_window to properly compute the average
     context.i += 1
     if context.i < long_window:
         return
 
-    # Compute moving averages calling data.history() for each
-    # moving average with the appropriate parameters. We choose to use
-    # minute bars for this simulation -> freq="1m"
-    # Returns a pandas dataframe.
+    # Compute moving averages calling data.history() for each
+    # moving average with the appropriate parameters. We choose to use
+    # minute bars for this simulation -> freq="1m"
+    # Returns a pandas dataframe.
     short_data = data.history(context.asset,
-                              'price',
+                              'price',
                               bar_count=short_window,
-                              frequency="1T",
+                              frequency="1T",
                               )
     short_mavg = short_data.mean()
     long_data = data.history(context.asset,
-                             'price',
+                             'price',
                              bar_count=long_window,
-                             frequency="1T",
+                             frequency="1T",
                              )
     long_mavg = long_data.mean()
 
-    # Let's keep the price of our asset in a more handy variable
-    price = data.current(context.asset, 'price')
+    # Let's keep the price of our asset in a more handy variable
+    price = data.current(context.asset, 'price')
 
-    # If base_price is not set, we use the current value. This is the
-    # price at the first bar which we reference to calculate price_change.
+    # If base_price is not set, we use the current value. This is the
+    # price at the first bar which we reference to calculate price_change.
     if context.base_price is None:
         context.base_price = price
     price_change = (price - context.base_price) / context.base_price
 
-    # Save values for later inspection
+    # Save values for later inspection
     record(price=price,
            cash=context.portfolio.cash,
            price_change=price_change,
            short_mavg=short_mavg,
            long_mavg=long_mavg)
 
-    # Since we are using limit orders, some orders may not execute immediately
-    # we wait until all orders are executed before considering more trades.
+    # Since we are using limit orders, some orders may not execute immediately
+    # we wait until all orders are executed before considering more trades.
     orders = context.blotter.open_orders
     if len(orders) > 0:
         return
 
-    # Exit if we cannot trade
+    # Exit if we cannot trade
     if not data.can_trade(context.asset):
         return
 
-    # We check what's our position on our portfolio and trade accordingly
+    # We check what's our position on our portfolio and trade accordingly
     pos_amount = context.portfolio.positions[context.asset].amount
 
-    # Trading logic
+    # Trading logic
     if short_mavg > long_mavg and pos_amount == 0:
-        # we buy 100% of our portfolio for this asset
+        # we buy 100% of our portfolio for this asset
         order_target_percent(context.asset, 1)
     elif short_mavg < long_mavg and pos_amount > 0:
-        # we sell all our positions for this asset
+        # we sell all our positions for this asset
         order_target_percent(context.asset, 0)
 
 
 def analyze(context, perf):
-    # Get the base_currency that was passed as a parameter to the simulation
+    # Get the base_currency that was passed as a parameter to the simulation
     exchange = list(context.exchanges.values())[0]
     base_currency = exchange.base_currency.upper()
 
-    # First chart: Plot portfolio value using base_currency
+    # First chart: Plot portfolio value using base_currency
     ax1 = plt.subplot(411)
-    perf.loc[:, ['portfolio_value']].plot(ax=ax1)
+    perf.loc[:, ['portfolio_value']].plot(ax=ax1)
     ax1.legend_.remove()
-    ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
+    ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
     start, end = ax1.get_ylim()
     ax1.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
-    # Second chart: Plot asset price, moving averages and buys/sells
+    # Second chart: Plot asset price, moving averages and buys/sells
     ax2 = plt.subplot(412, sharex=ax1)
-    perf.loc[:, ['price', 'short_mavg', 'long_mavg']].plot(
+    perf.loc[:, ['price', 'short_mavg', 'long_mavg']].plot(
         ax=ax2,
-        label='Price')
+        label='Price')
     ax2.legend_.remove()
-    ax2.set_ylabel('{asset}\n({base})'.format(
+    ax2.set_ylabel('{asset}\n({base})'.format(
         asset=context.asset.symbol,
         base=base_currency
     ))
@@ -719,57 +557,57 @@ one day prior to the current date.

 
     transaction_df = extract_transactions(perf)
     if not transaction_df.empty:
-        buy_df = transaction_df[transaction_df['amount'] > 0]
-        sell_df = transaction_df[transaction_df['amount'] < 0]
+        buy_df = transaction_df[transaction_df['amount'] > 0]
+        sell_df = transaction_df[transaction_df['amount'] < 0]
         ax2.scatter(
             buy_df.index.to_pydatetime(),
-            perf.loc[buy_df.index, 'price'],
-            marker='^',
+            perf.loc[buy_df.index, 'price'],
+            marker='^',
             s=100,
-            c='green',
-            label=''
+            c='green',
+            label=''
         )
         ax2.scatter(
             sell_df.index.to_pydatetime(),
-            perf.loc[sell_df.index, 'price'],
-            marker='v',
+            perf.loc[sell_df.index, 'price'],
+            marker='v',
             s=100,
-            c='red',
-            label=''
+            c='red',
+            label=''
         )
 
-    # Third chart: Compare percentage change between our portfolio
-    # and the price of the asset
+    # Third chart: Compare percentage change between our portfolio
+    # and the price of the asset
     ax3 = plt.subplot(413, sharex=ax1)
-    perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3)
+    perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3)
     ax3.legend_.remove()
-    ax3.set_ylabel('Percent Change')
+    ax3.set_ylabel('Percent Change')
     start, end = ax3.get_ylim()
     ax3.yaxis.set_ticks(np.arange(start, end, (end - start) / 5))
 
-    # Fourth chart: Plot our cash
+    # Fourth chart: Plot our cash
     ax4 = plt.subplot(414, sharex=ax1)
     perf.cash.plot(ax=ax4)
-    ax4.set_ylabel('Cash\n({})'.format(base_currency))
+    ax4.set_ylabel('Cash\n({})'.format(base_currency))
     start, end = ax4.get_ylim()
     ax4.yaxis.set_ticks(np.arange(0, end, end / 5))
 
     plt.show()
 
 
-if __name__ == '__main__':
+if __name__ == '__main__':
     
     run_algorithm(
             capital_base=1000,
-            data_frequency='minute',
+            data_frequency='minute',
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='bitfinex',
+            exchange_name='bitfinex',
             algo_namespace=NAMESPACE,
-            base_currency='usd',
-            start=pd.to_datetime('2017-9-22', utc=True),
-            end=pd.to_datetime('2017-9-23', utc=True),
+            base_currency='usd',
+            start=pd.to_datetime('2017-9-22', utc=True),
+            end=pd.to_datetime('2017-9-23', utc=True),
         )
 

 

@@ -783,19 +621,19 @@ Hopefully, we’ll ride the waves.

 
We are choosing to backtest this trading algorithm with the neo_usd currency
 pairon the Bitfinex exchange. Thus, first ingest the historical pricing data
 that we need, with minute resolution:

-
catalyst ingest-exchange -x bitfinex -f minute -i neo_usd
+
catalyst ingest-exchange -x bitfinex -f minute -i neo_usd
 

 

 
To run this algorithm, we are opting for the Python interpreter, instead of the
 command line (CLI). All of the parameters for the simulation are specified in
 lines 218-245, so in order to run the algorithm we just type:

-
python mean_reversion_simple.py
+
python mean_reversion_simple.py
 

 

 
Source code: examples/mean_reversion_simple.py

-
# For this example, we're going to write a simple momentum script.  When the
-# stock goes up quickly, we're going to buy; when it goes down quickly, we're
-# going to sell.  Hopefully we'll ride the waves.
+
# For this example, we're going to write a simple momentum script.  When the
+# stock goes up quickly, we're going to buy; when it goes down quickly, we're
+# going to sell.  Hopefully we'll ride the waves.
 import os
 import tempfile
 import time
@@ -808,33 +646,33 @@ lines 218-245, so in order to run the algorithm we just type:

 from catalyst import run_algorithm
 from catalyst.api import symbol, record, order_target_percent, get_open_orders
 from catalyst.exchange.utils.stats_utils import extract_transactions
-# We give a name to the algorithm which Catalyst will use to persist its state.
-# In this example, Catalyst will create the `.catalyst/data/live_algos`
-# directory. If we stop and start the algorithm, Catalyst will resume its
-# state using the files included in the folder.
+# We give a name to the algorithm which Catalyst will use to persist its state.
+# In this example, Catalyst will create the `.catalyst/data/live_algos`
+# directory. If we stop and start the algorithm, Catalyst will resume its
+# state using the files included in the folder.
 from catalyst.utils.paths import ensure_directory
 
-NAMESPACE = 'mean_reversion_simple'
+NAMESPACE = 'mean_reversion_simple'
 log = Logger(NAMESPACE)
 
 
-# To run an algorithm in Catalyst, you need two functions: initialize and
-# handle_data.
+# To run an algorithm in Catalyst, you need two functions: initialize and
+# handle_data.
 
 def initialize(context):
-    # This initialize function sets any data or variables that you'll use in
-    # your algorithm.  For instance, you'll want to define the trading pair (or
-    # trading pairs) you want to backtest.  You'll also want to define any
-    # parameters or values you're going to use.
+    # This initialize function sets any data or variables that you'll use in
+    # your algorithm.  For instance, you'll want to define the trading pair (or
+    # trading pairs) you want to backtest.  You'll also want to define any
+    # parameters or values you're going to use.
 
-    # In our example, we're looking at Neo in Ether.
-    context.market = symbol('bnb_eth')
+    # In our example, we're looking at Neo in Ether.
+    context.market = symbol('bnb_eth')
     context.base_price = None
     context.current_day = None
 
     context.RSI_OVERSOLD = 60
     context.RSI_OVERBOUGHT = 70
-    context.CANDLE_SIZE = '15T'
+    context.CANDLE_SIZE = '15T'
 
     context.start_time = time.time()
 
@@ -843,94 +681,94 @@ lines 218-245, so in order to run the algorithm we just type:

 
 
 def handle_data(context, data):
-    # This handle_data function is where the real work is done.  Our data is
-    # minute-level tick data, and each minute is called a frame.  This function
-    # runs on each frame of the data.
+    # This handle_data function is where the real work is done.  Our data is
+    # minute-level tick data, and each minute is called a frame.  This function
+    # runs on each frame of the data.
 
-    # We flag the first period of each day.
-    # Since cryptocurrencies trade 24/7 the `before_trading_starts` handle
-    # would only execute once. This method works with minute and daily
-    # frequencies.
-    today = data.current_dt.floor('1D')
+    # We flag the first period of each day.
+    # Since cryptocurrencies trade 24/7 the `before_trading_starts` handle
+    # would only execute once. This method works with minute and daily
+    # frequencies.
+    today = data.current_dt.floor('1D')
     if today != context.current_day:
         context.traded_today = False
         context.current_day = today
 
-    # We're computing the volume-weighted-average-price of the security
-    # defined above, in the context.market variable.  For this example, we're
-    # using three bars on the 15 min bars.
+    # We're computing the volume-weighted-average-price of the security
+    # defined above, in the context.market variable.  For this example, we're
+    # using three bars on the 15 min bars.
 
-    # The frequency attribute determine the bar size. We use this convention
-    # for the frequency alias:
-    # http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
+    # The frequency attribute determine the bar size. We use this convention
+    # for the frequency alias:
+    # http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
     prices = data.history(
         context.market,
-        fields='close',
+        fields='close',
         bar_count=50,
         frequency=context.CANDLE_SIZE
     )
 
-    # Ta-lib calculates various technical indicator based on price and
-    # volume arrays.
+    # Ta-lib calculates various technical indicator based on price and
+    # volume arrays.
 
-    # In this example, we are comp
+    # In this example, we are comp
     rsi = talib.RSI(prices.values, timeperiod=14)
 
-    # We need a variable for the current price of the security to compare to
-    # the average. Since we are requesting two fields, data.current()
-    # returns a DataFrame with
-    current = data.current(context.market, fields=['close', 'volume'])
-    price = current['close']
+    # We need a variable for the current price of the security to compare to
+    # the average. Since we are requesting two fields, data.current()
+    # returns a DataFrame with
+    current = data.current(context.market, fields=['close', 'volume'])
+    price = current['close']
 
-    # If base_price is not set, we use the current value. This is the
-    # price at the first bar which we reference to calculate price_change.
+    # If base_price is not set, we use the current value. This is the
+    # price at the first bar which we reference to calculate price_change.
     if context.base_price is None:
         context.base_price = price
 
     price_change = (price - context.base_price) / context.base_price
     cash = context.portfolio.cash
 
-    # Now that we've collected all current data for this frame, we use
-    # the record() method to save it. This data will be available as
-    # a parameter of the analyze() function for further analysis.
+    # Now that we've collected all current data for this frame, we use
+    # the record() method to save it. This data will be available as
+    # a parameter of the analyze() function for further analysis.
 
     record(
-        volume=current['volume'],
+        volume=current['volume'],
         price=price,
         price_change=price_change,
         rsi=rsi[-1],
         cash=cash
     )
-    # We are trying to avoid over-trading by limiting our trades to
-    # one per day.
+    # We are trying to avoid over-trading by limiting our trades to
+    # one per day.
     if context.traded_today:
         return
 
-    # TODO: retest with open orders
-    # Since we are using limit orders, some orders may not execute immediately
-    # we wait until all orders are executed before considering more trades.
+    # TODO: retest with open orders
+    # Since we are using limit orders, some orders may not execute immediately
+    # we wait until all orders are executed before considering more trades.
     orders = context.blotter.open_orders
     if len(orders) > 0:
-        log.info('exiting because orders are open: {}'.format(orders))
+        log.info('exiting because orders are open: {}'.format(orders))
         return
 
-    # Exit if we cannot trade
+    # Exit if we cannot trade
     if not data.can_trade(context.market):
         return
 
-    # Another powerful built-in feature of the Catalyst backtester is the
-    # portfolio object.  The portfolio object tracks your positions, cash,
-    # cost basis of specific holdings, and more.  In this line, we calculate
-    # how long or short our position is at this minute.
+    # Another powerful built-in feature of the Catalyst backtester is the
+    # portfolio object.  The portfolio object tracks your positions, cash,
+    # cost basis of specific holdings, and more.  In this line, we calculate
+    # how long or short our position is at this minute.
     pos_amount = context.portfolio.positions[context.market].amount
 
     if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
         log.info(
-            '{}: buying - price: {}, rsi: {}'.format(
+            '{}: buying - price: {}, rsi: {}'.format(
                 data.current_dt, price, rsi[-1]
             )
         )
-        # Set a style for limit orders,
+        # Set a style for limit orders,
         limit_price = price * 1.005
         order_target_percent(
             context.market, 1, limit_price=limit_price
@@ -939,7 +777,7 @@ lines 218-245, so in order to run the algorithm we just type:

 
     elif rsi[-1] >= context.RSI_OVERBOUGHT and pos_amount > 0:
         log.info(
-            '{}: selling - price: {}, rsi: {}'.format(
+            '{}: selling - price: {}, rsi: {}'.format(
                 data.current_dt, price, rsi[-1]
             )
         )
@@ -952,93 +790,93 @@ lines 218-245, so in order to run the algorithm we just type:

 
 def analyze(context=None, perf=None):
     end = time.time()
-    log.info('elapsed time: {}'.format(end - context.start_time))
+    log.info('elapsed time: {}'.format(end - context.start_time))
 
     import matplotlib.pyplot as plt
-    # The base currency of the algo exchange
+    # The base currency of the algo exchange
     base_currency = list(context.exchanges.values())[0].base_currency.upper()
 
-    # Plot the portfolio value over time.
+    # Plot the portfolio value over time.
     ax1 = plt.subplot(611)
-    perf.loc[:, 'portfolio_value'].plot(ax=ax1)
-    ax1.set_ylabel('Portfolio\nValue\n({})'.format(base_currency))
+    perf.loc[:, 'portfolio_value'].plot(ax=ax1)
+    ax1.set_ylabel('Portfolio\nValue\n({})'.format(base_currency))
 
-    # Plot the price increase or decrease over time.
+    # Plot the price increase or decrease over time.
     ax2 = plt.subplot(612, sharex=ax1)
-    perf.loc[:, 'price'].plot(ax=ax2, label='Price')
+    perf.loc[:, 'price'].plot(ax=ax2, label='Price')
 
-    ax2.set_ylabel('{asset}\n({base})'.format(
+    ax2.set_ylabel('{asset}\n({base})'.format(
         asset=context.market.symbol, base=base_currency
     ))
 
     transaction_df = extract_transactions(perf)
     if not transaction_df.empty:
-        buy_df = transaction_df[transaction_df['amount'] > 0]
-        sell_df = transaction_df[transaction_df['amount'] < 0]
+        buy_df = transaction_df[transaction_df['amount'] > 0]
+        sell_df = transaction_df[transaction_df['amount'] < 0]
         ax2.scatter(
             buy_df.index.to_pydatetime(),
-            perf.loc[buy_df.index.floor('1 min'), 'price'],
-            marker='^',
+            perf.loc[buy_df.index.floor('1 min'), 'price'],
+            marker='^',
             s=100,
-            c='green',
-            label=''
+            c='green',
+            label=''
         )
         ax2.scatter(
             sell_df.index.to_pydatetime(),
-            perf.loc[sell_df.index.floor('1 min'), 'price'],
-            marker='v',
+            perf.loc[sell_df.index.floor('1 min'), 'price'],
+            marker='v',
             s=100,
-            c='red',
-            label=''
+            c='red',
+            label=''
         )
 
     ax4 = plt.subplot(613, sharex=ax1)
-    perf.loc[:, 'cash'].plot(
-        ax=ax4, label='Base Currency ({})'.format(base_currency)
+    perf.loc[:, 'cash'].plot(
+        ax=ax4, label='Base Currency ({})'.format(base_currency)
     )
-    ax4.set_ylabel('Cash\n({})'.format(base_currency))
+    ax4.set_ylabel('Cash\n({})'.format(base_currency))
 
-    perf['algorithm'] = perf.loc[:, 'algorithm_period_return']
+    perf['algorithm'] = perf.loc[:, 'algorithm_period_return']
 
     ax5 = plt.subplot(614, sharex=ax1)
-    perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
-    ax5.set_ylabel('Percent\nChange')
+    perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
+    ax5.set_ylabel('Percent\nChange')
 
     ax6 = plt.subplot(615, sharex=ax1)
-    perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
-    ax6.set_ylabel('RSI')
-    ax6.axhline(context.RSI_OVERBOUGHT, color='darkgoldenrod')
-    ax6.axhline(context.RSI_OVERSOLD, color='darkgoldenrod')
+    perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
+    ax6.set_ylabel('RSI')
+    ax6.axhline(context.RSI_OVERBOUGHT, color='darkgoldenrod')
+    ax6.axhline(context.RSI_OVERSOLD, color='darkgoldenrod')
 
     if not transaction_df.empty:
         ax6.scatter(
             buy_df.index.to_pydatetime(),
-            perf.loc[buy_df.index.floor('1 min'), 'rsi'],
-            marker='^',
+            perf.loc[buy_df.index.floor('1 min'), 'rsi'],
+            marker='^',
             s=100,
-            c='green',
-            label=''
+            c='green',
+            label=''
         )
         ax6.scatter(
             sell_df.index.to_pydatetime(),
-            perf.loc[sell_df.index.floor('1 min'), 'rsi'],
-            marker='v',
+            perf.loc[sell_df.index.floor('1 min'), 'rsi'],
+            marker='v',
             s=100,
-            c='red',
-            label=''
+            c='red',
+            label=''
         )
     plt.legend(loc=3)
     start, end = ax6.get_ylim()
     ax6.yaxis.set_ticks(np.arange(0, end, end / 5))
 
-    # Show the plot.
+    # Show the plot.
     plt.gcf().set_size_inches(18, 8)
     plt.show()
     pass
 
 
-if __name__ == '__main__':
-    # The execution mode: backtest or live
+if __name__ == '__main__':
+    # The execution mode: backtest or live
     live = True
 
     if live:
@@ -1047,41 +885,41 @@ lines 218-245, so in order to run the algorithm we just type:

             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='binance',
+            exchange_name='binance',
             live=True,
             algo_namespace=NAMESPACE,
-            base_currency='eth',
+            base_currency='eth',
             live_graph=False,
             simulate_orders=False,
             stats_output=None,
-            # auth_aliases=dict(poloniex='auth2')
+            # auth_aliases=dict(poloniex='auth2')
         )
 
     else:
         folder = os.path.join(
-            tempfile.gettempdir(), 'catalyst', NAMESPACE
+            tempfile.gettempdir(), 'catalyst', NAMESPACE
         )
         ensure_directory(folder)
 
-        timestr = time.strftime('%Y%m%d-%H%M%S')
-        out = os.path.join(folder, '{}.p'.format(timestr))
-        # catalyst run -f catalyst/examples/mean_reversion_simple.py \
-        #    -x bitfinex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion \
-        #   --data-frequency minute --capital-base 10000
+        timestr = time.strftime('%Y%m%d-%H%M%S')
+        out = os.path.join(folder, '{}.p'.format(timestr))
+        # catalyst run -f catalyst/examples/mean_reversion_simple.py \
+        #    -x bitfinex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion \
+        #   --data-frequency minute --capital-base 10000
         run_algorithm(
             capital_base=0.035,
-            data_frequency='minute',
+            data_frequency='minute',
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,
-            exchange_name='bitfinex',
+            exchange_name='bitfinex',
             algo_namespace=NAMESPACE,
-            base_currency='btc',
-            start=pd.to_datetime('2017-10-01', utc=True),
-            end=pd.to_datetime('2017-11-10', utc=True),
+            base_currency='btc',
+            start=pd.to_datetime('2017-10-01', utc=True),
+            end=pd.to_datetime('2017-11-10', utc=True),
             output=out
         )
-        log.info('saved perf stats: {}'.format(out))
+        log.info('saved perf stats: {}'.format(out))
 

 

 https://s3.amazonaws.com/enigmaco-docs/github.io/example_mean_reversion_simple.png
@@ -1113,11 +951,11 @@ environment). The first one retrieves all the pricing data needed for this
 script to run (only needs to be run once), and the second one executes this
 script with the parameters specified in the run_algorithm() call at the end
 of the file:

-
catalyst ingest-exchange -x bitfinex -f minute
+
catalyst ingest-exchange -x bitfinex -f minute
 

 

 
Source code: examples/simple_universe.py

-
"""
+
"""
 Requires Catalyst version 0.3.0 or above
 Tested on Catalyst version 0.3.3
 
@@ -1159,67 +997,67 @@ of the file:

 
 
 def initialize(context):
-    context.i = -1  # minute counter
+    context.i = -1  # minute counter
     context.exchange = list(context.exchanges.values())[0].name.lower()
     context.base_currency = list(context.exchanges.values())[0].base_currency.lower()
 
 
 def handle_data(context, data):
     context.i += 1
-    lookback_days = 7  # 7 days
+    lookback_days = 7  # 7 days
 
-    # current date & time in each iteration formatted into a string
+    # current date & time in each iteration formatted into a string
     now = data.current_dt
-    date, time = now.strftime('%Y-%m-%d %H:%M:%S').split(' ')
+    date, time = now.strftime('%Y-%m-%d %H:%M:%S').split(' ')
     lookback_date = now - timedelta(days=lookback_days)
-    # keep only the date as a string, discard the time
-    lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[0]
+    # keep only the date as a string, discard the time
+    lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[0]
 
-    one_day_in_minutes = 1440  # 60 * 24 assumes data_frequency='minute'
-    # update universe everyday at midnight
+    one_day_in_minutes = 1440  # 60 * 24 assumes data_frequency='minute'
+    # update universe everyday at midnight
     if not context.i % one_day_in_minutes:
         context.universe = universe(context, lookback_date, date)
 
-    # get data every 30 minutes
+    # get data every 30 minutes
     minutes = 30
 
-    # get lookback_days of history data: that is 'lookback' number of bins
+    # get lookback_days of history data: that is 'lookback' number of bins
     lookback = int(one_day_in_minutes / minutes * lookback_days)
     if not context.i % minutes and context.universe:
-        # we iterate for every pair in the current universe
+        # we iterate for every pair in the current universe
         for coin in context.coins:
             pair = str(coin.symbol)
 
-            # Get 30 minute interval OHLCV data. This is the standard data
-            # required for candlestick or indicators/signals. Return Pandas
-            # DataFrames. 30T means 30-minute re-sampling of one minute data.
-            # Adjust it to your desired time interval as needed.
+            # Get 30 minute interval OHLCV data. This is the standard data
+            # required for candlestick or indicators/signals. Return Pandas
+            # DataFrames. 30T means 30-minute re-sampling of one minute data.
+            # Adjust it to your desired time interval as needed.
             opened = fill(data.history(coin,
-                                       'open',
+                                       'open',
                                        bar_count=lookback,
-                                       frequency='30T')).values
+                                       frequency='30T')).values
             high = fill(data.history(coin,
-                                     'high',
+                                     'high',
                                      bar_count=lookback,
-                                     frequency='30T')).values
+                                     frequency='30T')).values
             low = fill(data.history(coin,
-                                    'low',
+                                    'low',
                                     bar_count=lookback,
-                                    frequency='30T')).values
+                                    frequency='30T')).values
             close = fill(data.history(coin,
-                                      'price',
+                                      'price',
                                       bar_count=lookback,
-                                      frequency='30T')).values
+                                      frequency='30T')).values
             volume = fill(data.history(coin,
-                                       'volume',
+                                       'volume',
                                        bar_count=lookback,
-                                       frequency='30T')).values
+                                       frequency='30T')).values
 
-            # close[-1] is the last value in the set, which is the equivalent
-            # to current price (as in the most recent value)
-            # displays the minute price for each pair every 30 minutes
-            print('{now}: {pair} -\tO:{o},\tH:{h},\tL:{c},\tC{c},'
-                  '\tV:{v}'.format(
+            # close[-1] is the last value in the set, which is the equivalent
+            # to current price (as in the most recent value)
+            # displays the minute price for each pair every 30 minutes
+            print('{now}: {pair} -\tO:{o},\tH:{h},\tL:{c},\tC{c},'
+                  '\tV:{v}'.format(
                     now=now,
                     pair=pair,
                     o=opened[-1],
@@ -1229,39 +1067,39 @@ of the file:

                     v=volume[-1],
                   ))
 
-            # -------------------------------------------------------------
-            # --------------- Insert Your Strategy Here -------------------
-            # -------------------------------------------------------------
+            # -------------------------------------------------------------
+            # --------------- Insert Your Strategy Here -------------------
+            # -------------------------------------------------------------
 
 
 def analyze(context=None, results=None):
     pass
 
 
-# Get the universe for a given exchange and a given base_currency market
-# Example: Poloniex BTC Market
+# Get the universe for a given exchange and a given base_currency market
+# Example: Poloniex BTC Market
 def universe(context, lookback_date, current_date):
-    # get all the pairs for the given exchange
+    # get all the pairs for the given exchange
     json_symbols = get_exchange_symbols(context.exchange)
-    # convert into a DataFrame for easier processing
+    # convert into a DataFrame for easier processing
     df = pd.DataFrame.from_dict(json_symbols).transpose().astype(str)
-    df['base_currency'] = df.apply(lambda row: row.symbol.split('_')[1],
+    df['base_currency'] = df.apply(lambda row: row.symbol.split('_')[1],
                                    axis=1)
-    df['market_currency'] = df.apply(lambda row: row.symbol.split('_')[0],
+    df['market_currency'] = df.apply(lambda row: row.symbol.split('_')[0],
                                      axis=1)
 
-    # Filter all the pairs to get only the ones for a given base_currency
-    df = df[df['base_currency'] == context.base_currency]
+    # Filter all the pairs to get only the ones for a given base_currency
+    df = df[df['base_currency'] == context.base_currency]
 
-    # Filter all pairs to ensure that pair existed in the current date range
+    # Filter all pairs to ensure that pair existed in the current date range
     df = df[df.start_date < lookback_date]
     df = df[df.end_daily >= current_date]
-    context.coins = symbols(*df.symbol)  # convert all the pairs to symbols
+    context.coins = symbols(*df.symbol)  # convert all the pairs to symbols
 
     return df.symbol.tolist()
 
 
-# Replace all NA, NAN or infinite values with its nearest value
+# Replace all NA, NAN or infinite values with its nearest value
 def fill(series):
     if isinstance(series, pd.Series):
         return series.replace([np.inf, -np.inf], np.nan).ffill().bfill()
@@ -1273,21 +1111,21 @@ of the file:

         return series
 
 
-if __name__ == '__main__':
-    start_date = pd.to_datetime('2017-11-10', utc=True)
-    end_date = pd.to_datetime('2017-11-13', utc=True)
+if __name__ == '__main__':
+    start_date = pd.to_datetime('2017-11-10', utc=True)
+    end_date = pd.to_datetime('2017-11-13', utc=True)
 
     performance = run_algorithm(start=start_date, end=end_date,
-                                capital_base=100.0,  # amount of base_currency
+                                capital_base=100.0,  # amount of base_currency
                                 initialize=initialize,
                                 handle_data=handle_data,
                                 analyze=analyze,
-                                exchange_name='poloniex',
-                                data_frequency='minute',
-                                base_currency='btc',
+                                exchange_name='poloniex',
+                                data_frequency='minute',
+                                base_currency='btc',
                                 live=False,
                                 live_graph=False,
-                                algo_namespace='simple_universe')
+                                algo_namespace='simple_universe')
 

 

 

@@ -1299,7 +1137,7 @@ use 180 days of historical data and rebalance every 30 days. This code was used
 in writting the following article:
 Markowitz Portfolio Optimization for Cryptocurrencies.

 
Source code: examples/simple_universe.py

-
'''Use this code to execute a portfolio optimization model. This code
+
'''Use this code to execute a portfolio optimization model. This code
    will select the portfolio with the maximum Sharpe Ratio. The parameters
    are set to use 180 days of historical data and rebalance every 30 days.
 
@@ -1322,49 +1160,49 @@ in writting the following article:
 from catalyst.api import record, symbols, order_target_percent
 from catalyst.utils.run_algo import run_algorithm
 
-np.set_printoptions(threshold='nan', suppress=True)
+np.set_printoptions(threshold='nan', suppress=True)
 
 
 def initialize(context):
-    # Portfolio assets list
-    context.assets = symbols('btc_usdt', 'eth_usdt', 'ltc_usdt', 'dash_usdt',
-                             'xmr_usdt')
+    # Portfolio assets list
+    context.assets = symbols('btc_usdt', 'eth_usdt', 'ltc_usdt', 'dash_usdt',
+                             'xmr_usdt')
     context.nassets = len(context.assets)
-    # Set the time window that will be used to compute expected return
-    # and asset correlations
+    # Set the time window that will be used to compute expected return
+    # and asset correlations
     context.window = 180
-    # Set the number of days between each portfolio rebalancing
+    # Set the number of days between each portfolio rebalancing
     context.rebalance_period = 30
     context.i = 0
 
 
 def handle_data(context, data):
-    # Only rebalance at the beggining of the algorithm execution and
-    # every multiple of the rebalance period
+    # Only rebalance at the beggining of the algorithm execution and
+    # every multiple of the rebalance period
     if context.i == 0 or context.i % context.rebalance_period == 0:
         n = context.window
-        prices = data.history(context.assets, fields='price',
-                              bar_count=n + 1, frequency='1d')
+        prices = data.history(context.assets, fields='price',
+                              bar_count=n + 1, frequency='1d')
         pr = np.asmatrix(prices)
         t_prices = prices.iloc[1:n + 1]
         t_val = t_prices.values
         tminus_prices = prices.iloc[0:n]
         tminus_val = tminus_prices.values
-        # Compute daily returns (r)
+        # Compute daily returns (r)
         r = np.asmatrix(t_val / tminus_val - 1)
-        # Compute the expected returns of each asset with the average
-        # daily return for the selected time window
+        # Compute the expected returns of each asset with the average
+        # daily return for the selected time window
         m = np.asmatrix(np.mean(r, axis=0))
-        # ###
+        # ###
         stds = np.std(r, axis=0)
-        # Compute excess returns matrix (xr)
+        # Compute excess returns matrix (xr)
         xr = r - m
-        # Matrix algebra to get variance-covariance matrix
+        # Matrix algebra to get variance-covariance matrix
         cov_m = np.dot(np.transpose(xr), xr) / n
-        # Compute asset correlation matrix (informative only)
+        # Compute asset correlation matrix (informative only)
         corr_m = cov_m / np.dot(np.transpose(stds), stds)
 
-        # Define portfolio optimization parameters
+        # Define portfolio optimization parameters
         n_portfolios = 50000
         results_array = np.zeros((3 + context.nassets, n_portfolios))
         for p in range(n_portfolios):
@@ -1375,47 +1213,47 @@ in writting the following article:
             p_std = np.sqrt(np.dot(np.dot(w, cov_m),
                                    np.transpose(w))) * np.sqrt(365)
 
-            # store results in results array
+            # store results in results array
             results_array[0, p] = p_r
             results_array[1, p] = p_std
-            # store Sharpe Ratio (return / volatility) - risk free rate element
-            # excluded for simplicity
+            # store Sharpe Ratio (return / volatility) - risk free rate element
+            # excluded for simplicity
             results_array[2, p] = results_array[0, p] / results_array[1, p]
             i = 0
             for iw in weights:
                 results_array[3 + i, p] = weights[i]
                 i += 1
 
-        # convert results array to Pandas DataFrame
+        # convert results array to Pandas DataFrame
         results_frame = pd.DataFrame(np.transpose(results_array),
-                                     columns=['r', 'stdev', 'sharpe']
+                                     columns=['r', 'stdev', 'sharpe']
                                              + context.assets)
-        # locate position of portfolio with highest Sharpe Ratio
-        max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
-        # locate positon of portfolio with minimum standard deviation
-        # min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
+        # locate position of portfolio with highest Sharpe Ratio
+        max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
+        # locate positon of portfolio with minimum standard deviation
+        # min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
 
-        # order optimal weights for each asset
+        # order optimal weights for each asset
         for asset in context.assets:
             if data.can_trade(asset):
                 order_target_percent(asset, max_sharpe_port[asset])
 
-        # create scatter plot coloured by Sharpe Ratio
+        # create scatter plot coloured by Sharpe Ratio
         plt.scatter(results_frame.stdev,
                     results_frame.r,
                     c=results_frame.sharpe,
-                    cmap='RdYlGn')
-        plt.xlabel('Volatility')
-        plt.ylabel('Returns')
+                    cmap='RdYlGn')
+        plt.xlabel('Volatility')
+        plt.ylabel('Returns')
         plt.colorbar()
-        # plot red star to highlight position of portfolio
-        # with highest Sharpe Ratio
+        # plot red star to highlight position of portfolio
+        # with highest Sharpe Ratio
         plt.scatter(max_sharpe_port[1],
                     max_sharpe_port[0],
-                    marker='o',
-                    color='b',
+                    marker='o',
+                    color='b',
                     s=200)
-        # plot green star to highlight position of minimum variance portfolio
+        # plot green star to highlight position of minimum variance portfolio
         plt.show()
         print(max_sharpe_port)
         record(pr=pr,
@@ -1428,17 +1266,17 @@ in writting the following article:
 
 
 def analyze(context=None, results=None):
-    # Form DataFrame with selected data
-    data = results[['pr', 'r', 'm', 'stds', 'max_sharpe_port', 'corr_m',
-                    'portfolio_value']]
+    # Form DataFrame with selected data
+    data = results[['pr', 'r', 'm', 'stds', 'max_sharpe_port', 'corr_m',
+                    'portfolio_value']]
 
-    # Save results in CSV file
-    filename = os.path.splitext(os.path.basename(__file__))[0]
-    data.to_csv(filename + '.csv')
+    # Save results in CSV file
+    filename = os.path.splitext(os.path.basename(__file__))[0]
+    data.to_csv(filename + '.csv')
 
 
-if __name__ == '__main__':
-    # Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
+if __name__ == '__main__':
+    # Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
     start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
     end = datetime(2017, 8, 16, 0, 0, 0, 0, pytz.utc)
     results = run_algorithm(initialize=initialize,
@@ -1446,9 +1284,9 @@ in writting the following article:
                             analyze=analyze,
                             start=start,
                             end=end,
-                            exchange_name='poloniex',
+                            exchange_name='poloniex',
                             capital_base=100000,
-                            base_currency='usdt', )
+                            base_currency='usdt', )
 

 

 https://cdn-images-1.medium.com/max/1600/0*EjjiKZHlYF3sn7yQ.
@@ -1456,15 +1294,19 @@ in writting the following article:
 

 
 
+           

+           

+            
+           

           

           
 
@@ -1497,7 +1340,8 @@ in writting the following article:
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/features.html b/features.html
index 8a9ba6fd..708dc073 100644
--- a/features.html
+++ b/features.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -44,21 +46,28 @@
 
 
 
+   
   

 
     
     
 
     

 
       
       
 
 
       
       

         

-          

+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+
   
+
+  
   

 

-          

+          

+           

             
   

 
Features

@@ -402,29 +236,29 @@ exchange. Catalyst will always expect in all its functions that you will refer t
 the asset pairs by using the Catalyst naming convention.

 
If at any point, you input the wrong name for an asset pair, you will get an error
 of that pair not found in the given exchange, and a list of pairs available on that exchange:

-
$ catalyst ingest-exchange -x poloniex -i btc_usd
+
$ catalyst ingest-exchange -x poloniex -i btc_usd
 

 

-
Ingesting exchange bundle poloniex...
+
Ingesting exchange bundle poloniex...
 Error traceback: /Volumes/Data/Users/victoris/Desktop/Enigma/user-install/catalyst-dev/catalyst/exchange/exchange.py (line 175)
 SymbolNotFoundOnExchange:  Symbol btc_usd not found on exchange Poloniex.
-Choose from: ['rep_usdt', 'gno_btc', 'xvc_btc', 'pink_btc', 'sys_btc',
-'emc2_btc', 'rads_btc', 'note_btc', 'maid_btc', 'bch_btc', 'gnt_btc',
-'bcn_btc', 'rep_btc', 'bcy_btc', 'cvc_btc', 'nxt_xmr', 'zec_usdt',
-'fct_btc', 'gas_btc', 'pot_btc', 'eth_usdt', 'btc_usdt', 'lbc_btc',
-'dcr_btc', 'etc_usdt', 'omg_eth', 'amp_btc', 'xpm_btc', 'nxt_btc',
-'vtc_btc', 'steem_eth', 'blk_xmr', 'pasc_btc', 'zec_xmr', 'grc_btc',
-'nxc_btc', 'btcd_btc', 'ltc_btc', 'dash_btc', 'naut_btc', 'zec_eth',
-'zec_btc', 'burst_btc', 'zrx_eth', 'bela_btc', 'steem_btc', 'etc_btc',
-'eth_btc', 'huc_btc', 'strat_btc', 'lsk_btc', 'exp_btc', 'clam_btc',
-'rep_eth', 'dash_xmr', 'cvc_eth', 'bch_usdt', 'zrx_btc', 'dash_usdt',
-'blk_btc', 'xrp_btc', 'nxt_usdt', 'neos_btc', 'omg_btc', 'bts_btc',
-'doge_btc', 'gnt_eth', 'sbd_btc', 'gno_eth', 'xcp_btc', 'ltc_usdt',
-'btm_btc', 'xmr_usdt', 'lsk_eth', 'omni_btc', 'nav_btc', 'fldc_btc',
-'ppc_btc', 'xbc_btc', 'dgb_btc', 'sc_btc', 'btcd_xmr', 'vrc_btc',
-'ric_btc', 'str_btc', 'maid_xmr', 'xmr_btc', 'sjcx_btc', 'via_btc',
-'xem_btc', 'nmc_btc', 'etc_eth', 'ltc_xmr', 'ardr_btc', 'gas_eth',
-'flo_btc', 'xrp_usdt', 'game_btc', 'bch_eth', 'bcn_xmr', 'str_usdt']
+Choose from: ['rep_usdt', 'gno_btc', 'xvc_btc', 'pink_btc', 'sys_btc',
+'emc2_btc', 'rads_btc', 'note_btc', 'maid_btc', 'bch_btc', 'gnt_btc',
+'bcn_btc', 'rep_btc', 'bcy_btc', 'cvc_btc', 'nxt_xmr', 'zec_usdt',
+'fct_btc', 'gas_btc', 'pot_btc', 'eth_usdt', 'btc_usdt', 'lbc_btc',
+'dcr_btc', 'etc_usdt', 'omg_eth', 'amp_btc', 'xpm_btc', 'nxt_btc',
+'vtc_btc', 'steem_eth', 'blk_xmr', 'pasc_btc', 'zec_xmr', 'grc_btc',
+'nxc_btc', 'btcd_btc', 'ltc_btc', 'dash_btc', 'naut_btc', 'zec_eth',
+'zec_btc', 'burst_btc', 'zrx_eth', 'bela_btc', 'steem_btc', 'etc_btc',
+'eth_btc', 'huc_btc', 'strat_btc', 'lsk_btc', 'exp_btc', 'clam_btc',
+'rep_eth', 'dash_xmr', 'cvc_eth', 'bch_usdt', 'zrx_btc', 'dash_usdt',
+'blk_btc', 'xrp_btc', 'nxt_usdt', 'neos_btc', 'omg_btc', 'bts_btc',
+'doge_btc', 'gnt_eth', 'sbd_btc', 'gno_eth', 'xcp_btc', 'ltc_usdt',
+'btm_btc', 'xmr_usdt', 'lsk_eth', 'omni_btc', 'nav_btc', 'fldc_btc',
+'ppc_btc', 'xbc_btc', 'dgb_btc', 'sc_btc', 'btcd_xmr', 'vrc_btc',
+'ric_btc', 'str_btc', 'maid_xmr', 'xmr_btc', 'sjcx_btc', 'via_btc',
+'xem_btc', 'nmc_btc', 'etc_eth', 'ltc_xmr', 'ardr_btc', 'gas_eth',
+'flo_btc', 'xrp_usdt', 'game_btc', 'bch_eth', 'bcn_xmr', 'str_usdt']
 

 

 
In the example above, exchange Poloniex does not use USD, but uses instead the
@@ -432,10 +266,10 @@ USDT cryptocurrency asset that is issued on the Bitcoin blockchain via the Omni
 Layer Protocol. Each USDT unit is backed by a U.S Dollar held in the reserves of
 Tether Limited. USDT can be transferred, stored, and spent, just like bitcoins
 or any other cryptocurrency. Given its 1:1 mapping to the USD, is a viable alternative.

-
$ catalyst ingest-exchange -x poloniex -i btc_usdt
+
$ catalyst ingest-exchange -x poloniex -i btc_usdt
 

 

-
Ingesting exchange bundle poloniex...
+
Ingesting exchange bundle poloniex...
     [====================================]  Fetching poloniex daily candles: :  100%
 

 

@@ -443,15 +277,19 @@ or any other cryptocurrency. Given its 1:1 mapping to the USD, is a viable alter
 

 
 
+           

+           

+            
+           

           

           
 
@@ -484,7 +323,8 @@ or any other cryptocurrency. Given its 1:1 mapping to the USD, is a viable alter
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/genindex.html b/genindex.html
index 5ee9c11e..cfcf457c 100644
--- a/genindex.html
+++ b/genindex.html
@@ -14,6 +14,8 @@
 
   
   
+  
+  
 
   
 
@@ -43,21 +45,28 @@
 
 
 
+   
   

 
     
     
 
     

 
       
       
 
 
       
       

         

-          

@@ -462,6 +296,14 @@
   
 
 
+
H

+
+  
+

+
 
I

 

   
 

@@ -700,6 +544,10 @@
 
 
 
+           

+           

+            
+           

           

           
 
@@ -732,7 +581,8 @@
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/index.html b/index.html
index 56cabf5d..11fd576f 100644
--- a/index.html
+++ b/index.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -43,21 +45,28 @@
 
 
 
+   
   

 
     
     
 
     

 
       
       
 
 
       
       

         

-          

+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+
   
+
+  
   

 

-          

+          

+           

             
   Enigma | Catalyst
 
version tag
@@ -404,12 +233,16 @@ performance across trading algorithms.
 

 
 
+           

+           

+            
+           

           

           
 
@@ -443,7 +277,8 @@ performance across trading algorithms.
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/install.html b/install.html
index ad1b48de..31c7d3ec 100644
--- a/install.html
+++ b/install.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -44,21 +46,28 @@
 
 
 
+   
   

 
     
     
 
     

 
       
       
 
 
       
       

         

-          

+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+
   
+
+  
   

 

-          

+          

+           

             
   

 
Install

@@ -399,7 +250,7 @@ to a folder you can remember. Make sure that the file gets saved with the
 saved the above .yml file.

 
 
  • Install using this file. This step can take about 5-10 minutes to install.
    
-
    conda env create -f python3.6-environment.yml
+
    conda env create -f python3.6-environment.yml
 
    
 
    
 
    • 
@@ -407,7 +258,7 @@ saved the above .yml
 
    or
    
 
    
-
    conda env create -f python2.7-environment.yml
+
    conda env create -f python2.7-environment.yml
 
    
 
    
 
    
@@ -416,16 +267,16 @@ saved the above .yml
    Activate the environment (which you need to do every time you start a new
 session to run Catalyst):
    
 
    Linux or MacOS:
    
-
    source activate catalyst
+
    source activate catalyst
 
    
 
    
 
    Windows:
    
-
    activate catalyst
+
    activate catalyst
 
    
 
    
 
 
  • Verify that Catalyst is install correctly:
    
-
    catalyst --version
+
    catalyst --version
 
    
 
    
 
    which should display the current version.
    
@@ -441,13 +292,13 @@ with the following steps:
    
 
  • If the above installation failed, and you have a partially set up catalyst
 environment, remove it first. If you are starting from scratch, proceed to
 step #2:
    
-
    conda env remove --name catalyst
+
    conda env remove --name catalyst
 
    
 
    
 
    • 
 
  • Create the environment:
    
 
    for python 2.7:
    
-
    conda create --name catalyst python=2.7 scipy zlib
+
    conda create --name catalyst python=2.7 scipy zlib
 
    
 
    
 
    • 
@@ -455,7 +306,7 @@ step #2:
    
 
    
 
    or for python 3.6:
    
 
    
-
    conda create --name catalyst python=3.6 scipy zlib
+
    conda create --name catalyst python=3.6 scipy zlib
 
    
 
    
 
    
@@ -463,21 +314,21 @@ step #2:
    
 
      
 
    1. Activate the environment:
      
 
      Linux or MacOS:
      
-
      source activate catalyst
+
      source activate catalyst
 
      
 
      
 
      Windows:
      
-
      activate catalyst
+
      activate catalyst
 
      
 
      
 
      2. 
 
    2. Install the Catalyst inside the environment:
      
-
      pip install enigma-catalyst matplotlib
+
      pip install enigma-catalyst matplotlib
 
      
 
      
 
      3. 
 
    3. Verify that Catalyst is installed correctly:
      
-
      catalyst --version
+
      catalyst --version
 
      
 
      
 
      which should display the current version.
      
@@ -507,14 +358,14 @@ distribution, refer to the virtualenv.  The Hitchhiker’s Guide to
 Python provides an excellent tutorial on virtualenv. Here’s a
 summarized version:
      
-
      $ pip install virtualenv
-$ virtualenv catalyst-venv
-$ source ./catalyst-venv/bin/activate
+
      $ pip install virtualenv
+$ virtualenv catalyst-venv
+$ source ./catalyst-venv/bin/activate
 
      
 
      
 
      Once you’ve installed the necessary additional dependencies for your system
 (GNU/Linux Requirements, MacOS Requirements or Windows Requirements) and have activated your virtualenv, you should be able to simply run
      
-
      $ pip install enigma-catalyst matplotlib
+
      $ pip install enigma-catalyst matplotlib
 
      
 
      
 
      Note that in the command above we install two different packages. The second
@@ -529,11 +380,11 @@ recommend you install it, as well.
      
 
      Package enigma-catalyst cannot be found
      
 
      Solution:
      
 
      Make sure you have the most up-to-date version of pip installed, by running:
      
-
      $ pip install --upgrade pip
+
      $ pip install --upgrade pip
 
      
 
      
 
      On Windows, the recommended command is:
      
-
      $ python -m pip install --upgrade pip
+
      $ python -m pip install --upgrade pip
 
      
 
      
 
      
@@ -543,10 +394,10 @@ recommend you install it, as well.
      
 
      Issue:
      
 
      Package enigma-catalyst cannot still be found, even after upgrading pip
 (see above), with an error similar to:
      
-
      Downloading/unpacking enigma-catalyst
+
      Downloading/unpacking enigma-catalyst
 Could not find a version that satisfies the requirement enigma-catalyst
-(from versions: 0.1.dev9, 0.2.dev2, 0.1.dev4, 0.1.dev5, 0.1.dev3,
-0.2.dev1, 0.1.dev8, 0.1.dev6)
+(from versions: 0.1.dev9, 0.2.dev2, 0.1.dev4, 0.1.dev5, 0.1.dev3,
+0.2.dev1, 0.1.dev8, 0.1.dev6)
 Cleaning up...
 No distributions matching the version for enigma-catalyst
 
      
@@ -558,7 +409,7 @@ to only find stable versions by default. Since Catalyst is in alpha
 version, pip cannot find a matching version that satisfies the installation
 requirements. The solution is to include the –pre flag to include
 pre-release and development versions:
      
-
      $ pip install --pre enigma-catalyst
+
      $ pip install --pre enigma-catalyst
 
      
 
      
 
      
@@ -569,7 +420,7 @@ pre-release and development versions:
      
 
      Package enigma-catalyst fails to install because of outdated setuptools
      
 
      Solution:
      
 
      Upgrade to the most up-to-date setuptools package by running:
      
-
      $ pip install --upgrade pip setuptools
+
      $ pip install --upgrade pip setuptools
 
      
 
      
 
      
@@ -582,7 +433,7 @@ pre-release and development versions:
      
 
      Download requirements.txt
 (click on the Raw button and Right click -> Save As…) and use it to
 install all the required dependencies by running:
      
-
      $ pip install -r requirements.txt
+
      $ pip install -r requirements.txt
 
      
 
      
 
      
@@ -597,7 +448,7 @@ install all the required dependencies by running:
      
 for the proper build and installation of package dependencies. The solution
 is to install python-dev, which is independent of the virtual environment.
 In Ubuntu, you would need to run:
      
-
      $ sudo apt-get install python-dev
+
      $ sudo apt-get install python-dev
    • @@ -616,21 +467,21 @@ In Ubuntu, you would need to run:

    Installing with pipenv

    Installing Catalyst via pipenv is perhaps easier that installing it via pip itself but you need to install pipenv first via pip.

    -
    $ pip install pipenv
+
    $ pip install pipenv
 
    
 
    
 
    Once pipenv is installed you can proceed by creating a project folder and
 installing Catalyst on that project automagically as follows:
    
-
    $ mkdir project
-$ cd project
-$ pipenv --two
-$ pipenv install enigma-catalyst matplotlib
+
    $ mkdir project
+$ cd project
+$ pipenv --two
+$ pipenv install enigma-catalyst matplotlib
 
    
 
    
 
    Until now the workflow compared to pip is almost identical, the difference
 is that you don’t need to load manually any virtualenv however you need to use
 the pipenv run prefix to run the catalyst command as follows:
    
-
    $ pipenv run catalyst --version
+
    $ pipenv run catalyst --version
 
    
 
    
 
    If you want to know more about pipenv go to the pipenv github repo
    
@@ -639,29 +490,29 @@ the pipenv run prefix to run the catalyst command as f
 
    GNU/Linux Requirements
    
 
    On Debian-derived Linux distributions, you can acquire all the necessary
 binary dependencies from apt by running:
    
-
    $ sudo apt-get install libatlas-base-dev python-dev gfortran pkg-config libfreetype6-dev
+
    $ sudo apt-get install libatlas-base-dev python-dev gfortran pkg-config libfreetype6-dev
 
    
 
    
 
    On recent RHEL-derived derived Linux distributions (e.g. Fedora), the
 following should be sufficient to acquire the necessary additional
 dependencies:
    
-
    $ sudo dnf install atlas-devel gcc-c++ gcc-gfortran libgfortran python-devel redhat-rep-config
+
    $ sudo dnf install atlas-devel gcc-c++ gcc-gfortran libgfortran python-devel redhat-rep-config
 
    
 
    
 
    On Arch Linux, you can acquire the additional dependencies via pacman:
    
-
    $ pacman -S lapack gcc gcc-fortran pkg-config
+
    $ pacman -S lapack gcc gcc-fortran pkg-config
 
    
 
    
 
    
 
    Amazon Linux AMI Notes
    
 
    The packages pip and setuptools that come shipped by default are very
 outdated. Thus, you first need to run:
    
-
    $ pip install --upgrade pip setuptools
+
    $ pip install --upgrade pip setuptools
 
    
 
    
 
    The default installation is also missing the C and C++ compilers, which you
 install by:
    
-
    $ sudo yum install gcc gcc-c++
+
    $ sudo yum install gcc gcc-c++
 
    
 
    
 
    Then you should follow the regular installation instructions outlined at the
@@ -678,14 +529,14 @@ to 
 which explains how to install Python with the Homebrew manager.
    
 
    Assuming you’ve installed Python with Homebrew, you’ll also likely need the
 following brew packages:
    
-
    $ brew install freetype pkg-config gcc openssl
+
    $ brew install freetype pkg-config gcc openssl
 
    
 
    
 
    
 
    MacOS + virtualenv/conda + matplotlib
    
 
    The first time that you try to run an algorithm that loads the matplotlib
 library, you may get the following error:
    
-
    RuntimeError: Python is not installed as a framework. The Mac OS X backend
+
    RuntimeError: Python is not installed as a framework. The Mac OS X backend
 will not be able to function correctly if Python is not installed as a
 framework. See the Python documentation for more information on installing
 Python as a framework on Mac OS X. Please either reinstall Python as a
@@ -697,7 +548,7 @@ information.
 
    
 
    This is a matplotlib-specific error, that will go away once you run the
 following command:
    
-
    $ echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
+
    $ echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
 
    
 
    
 
    in order to override the default MacOS backend for your system, which
@@ -775,12 +626,12 @@ update Catalyst frequently. Once installed, Catalyst can easily be updated as a
 pip package regardless of the environemnt used for installation. Make sure
 you activate your environment first as you did in your first install, and then
 execute:
    
-
    $ pip uninstall enigma-catalyst
-$ pip install enigma-catalyst
+
    $ pip uninstall enigma-catalyst
+$ pip install enigma-catalyst
 
    
 
    
 
    Alternatively, you could update Catalyst issuing the following command:
    
-
    $ pip install -U enigma-catalyst
+
    $ pip install -U enigma-catalyst
 
    
 
    
 
    but this command will also upgrade all the Catalyst dependencies to the latest
@@ -813,15 +664,19 @@ and you get a hint on how to solve it.
 
    
 
 
+           
    
+           
    
+            
+           
    
           
    
           
 
@@ -854,7 +710,8 @@ and you get a hint on how to solve it.
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/live-trading.html b/live-trading.html
index 37c1faaf..4fbf5316 100644
--- a/live-trading.html
+++ b/live-trading.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -44,21 +46,28 @@
 
 
 
+   
   
    
 
     
     
 
     
    
 
       
       
 
 
       
       
    
         
    
-          
    
+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    
+
   
+
+  
   
    
 
    
-          
    
+          
    
+           
    
             
   
    
 
    Live Trading
    
@@ -414,7 +250,7 @@ Here is how to generate key and secret values for the Bitfinex exchange:
 https://docs.bitfinex.com/v1/docs/api-access. Most exchanges follow
 a similar process.
    
 
    The auth.json file:
    
-
    {
+
    {
   "name": "bitfinex",
   "key": "my-key",
   "secret": "my-secret"
@@ -444,13 +280,13 @@ seperator.
    
 
    [Market Currency]_[Base Currency] all lowercase.
    
 
    Currency symbols (e.g. btc, eth, ltc) follow the Bittrex convention.
    
 
    Here are some examples:
    
-
    # With Bitfinex
-bitcoin_usd_asset = symbol('btc_usd')
-ethereum_bitcoin_asset = symbol('eth_btc')
+
    # With Bitfinex
+bitcoin_usd_asset = symbol('btc_usd')
+ethereum_bitcoin_asset = symbol('eth_btc')
 
-# With Bittrex
-ethereum_bitcoin_asset = symbol('eth_btc')
-neo_ethereum_asset = symbol('neo_eth)
+# With Bittrex
+ethereum_bitcoin_asset = symbol('eth_btc')
+neo_ethereum_asset = symbol('neo_eth)
 
    
 
    
 
    Note that the trading pairs are always referenced in the same manner.
@@ -467,17 +303,17 @@ algorithm for live trading. The same algorithm should work in
 backtest and live execution mode without modification.
    
 
    What differs are the arguments provided to the catalyst client or
 run_algorithm() interface. Here is the same example in both interfaces:
    
-
    catalyst live -f my_algo_code -x bitfinex -c btc -n my_algo_name
+
    catalyst live -f my_algo_code -x bitfinex -c btc -n my_algo_name
 
    
 
    
-
    run_algorithm(
+
    run_algorithm(
     initialize=initialize,
     handle_data=handle_data,
     analyze=analyze,
-    exchange_name='bitfinex',
+    exchange_name='bitfinex',
     live=True,
-    algo_namespace='my_algo_name',
-    base_currency='btc'
+    algo_namespace='my_algo_name',
+    base_currency='btc'
 )
 
    
 
    
@@ -508,7 +344,7 @@ essentially sleep and when the predefined time comes, it would start executing.<
 
 
    The catalyst live command offers additional parameters.
 You can learn more by running the following from the command line:
    
-
    catalyst live --help
+
    catalyst live --help
 
    
 
    
 
    Here is a complete algorithm for reference:
@@ -517,15 +353,19 @@ You can learn more by running the following from the command line:
    
 
    
 
 
+           
    
+           
    
+            
+           
    
           
    
           
 
@@ -558,7 +399,8 @@ You can learn more by running the following from the command line:
    
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/objects.inv b/objects.inv
index b91e801a..e43090a3 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/release-process.html b/release-process.html
index e56f5201..ce5fd64a 100644
--- a/release-process.html
+++ b/release-process.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -42,21 +44,28 @@
 
 
 
+   
   
    
 
     
     
 
     
    
 
       
       
 
 
       
       
    
         
    
-          
    
+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    
+
   
+
+  
   
    
 
    
-          
    
+          
    
+           
    
             
   
    
 
    Release Process
    
@@ -342,7 +171,7 @@ page. We will have been maintaining a whatsnew file while working on the release
 with the new version. First, find that file in:
 docs/source/whatsnew/<version>.txt. It will be the highest version number.
 Edit the release date field to be today’s date in the format:
    
-
    <month> <day>, <year>
+
    <month> <day>, <year>
 
    
 
    
 
    for example, November 6, 2015.
@@ -353,9 +182,9 @@ update the underline of the title to match the title’s width.
    
 
    If you are renaming the release at this point, you’ll need to git mv the file
 and also update releases.rst to reference the renamed file.
    
 
    To build and view the docs locally, run:
    
-
    $ cd docs
-$ make html
-$ {BROWSER} build/html/index.html
+
    $ cd docs
+$ make html
+$ {BROWSER} build/html/index.html
 
    
 
    
 
    
@@ -367,7 +196,7 @@ namespace is populated at import time by decorators on TradingAlgorithm
 methods. Those functions are therefore hidden from static analysis tools, but
 we can generate static files to make them available. Under Python 3, run
 the following to generate any stub files:
    
-
    $ python etc/gen_type_stubs.py
+
    $ python etc/gen_type_stubs.py
 
    
 
    
 
    
@@ -389,8 +218,8 @@ manage the __version__
 
    To upgrade the version use the git tag command like:
    
-
    $ git tag <major>.<minor>.<micro>
-$ git push && git push --tags
+
    $ git tag <major>.<minor>.<micro>
+$ git push && git push --tags
 
    
 
    
 
    This will push the the code and the tag information.
    
@@ -402,15 +231,15 @@ choose the tag you just pushed, and publish the release.
    
 
    
 
    sdist
    
 
    To build the sdist (source distribution) run:
    
-
    $ python setup.py sdist
+
    $ python setup.py sdist
 
    
 
    
 
    from the zipline root. This will create a gzipped tarball that includes all the
 python, cython, and miscellaneous files needed to install zipline. To test that
 the source dist worked correctly, cd into an empty directory, create a new
 virtualenv and then run:
    
-
    $ pip install <zipline-root>/dist/zipline-<major>.<minor>.<micro>.tar.gz
-$ python -c 'import zipline;print(zipline.__version__)'
+
    $ pip install <zipline-root>/dist/zipline-<major>.<minor>.<micro>.tar.gz
+$ python -c 'import zipline;print(zipline.__version__)'
 
    
 
    
 
    This should print the version we are expecting to release.
    
@@ -424,7 +253,7 @@ all the required packages.
    
 
    Now that we have tested the package locally, it should be tested using the test
 PyPI server.
    
 
    Edit your ~/.pypirc file to look like:
    
-
    [distutils]
+
    [distutils]
 index-servers =
     pypi
     pypitest
@@ -440,7 +269,7 @@ PyPI server.
    
 
    
 
    
 
    after that, run:
    
-
    $ python setup.py sdist upload -r pypitest
+
    $ python setup.py sdist upload -r pypitest
 
    
 
    
 
    
@@ -457,14 +286,14 @@ debugging packaging problems on the test server.
    
 
    
 
    This will upload zipline to the pypi test server. To test installing from pypi,
 create a new virtualenv, cd into a clean directory and then run:
    
-
    $ pip install --extra-index-url https://testpypi.python.org/pypi zipline
-$ python -c 'import zipline;print(zipline.__version__)'
+
    $ pip install --extra-index-url https://testpypi.python.org/pypi zipline
+$ python -c 'import zipline;print(zipline.__version__)'
 
    
 
    
 
    This should pull the package you just uploaded and then print the version
 number.
    
 
    Now that we have tested locally and on PyPI test, it is time to upload to PyPI:
    
-
    $ python setup.py sdist upload
+
    $ python setup.py sdist upload
 
    
 
    
 
    
@@ -479,7 +308,7 @@ they were compiled.
    
 
    Documentation
    
 
    To update zipline.io, checkout the
 latest master and run:
    
-
    python <zipline_root>/docs/deploy.py
+
    python <zipline_root>/docs/deploy.py
 
    
 
    
 
    This will build the documentation, checkout a fresh copy of the gh-pages
@@ -494,9 +323,9 @@ correct arguments.
    
 
    Now, using our browser of choice, view the index.html page and verify that
 the docs look correct.
    
 
    Once we are happy, push the updated docs to the GitHub gh-pages branch.
    
-
    $ git add .
-$ git commit -m "DOC: update zipline.io"
-$ git push origin gh-pages
+
    $ git add .
+$ git commit -m "DOC: update zipline.io"
+$ git push origin gh-pages
 
    
 
    
 
    zipline.io will update in a few moments.
    
@@ -511,14 +340,14 @@ is also a good time to remove all the old “ci” packages from anaconda.
    
 
    Travis and AppVeyor only build and upload linux-64 and win-64 packages.  We’ll
 need to build and upload osx-64 packages manually on an OSX machine.
    
 
    To build the conda packages for zipline locally, run:
    
-
    $ python etc/conda_build_matrix.py
+
    $ python etc/conda_build_matrix.py
 
    
 
    
 
    If all of the builds succeed, then this will not print anything and exit with
 EXIT_SUCCESS. If there are build issues, we must address them and decide
 what to do.
    
 
    Once all of the builds in the matrix pass, we can upload them to anaconda with:
    
-
    $ python etc/conda_build_matrix.py --upload
+
    $ python etc/conda_build_matrix.py --upload
 
    
 
    
 
    If you would like to test this command by uploading to a different user, this
@@ -533,13 +362,17 @@ renamed when that’s decided. You can use 
 
    Include the whatsnew file in docs/source/releases.rst. New releases should
 appear at the top. The syntax for this is:
    
-
    .. include:: whatsnew/<version>.txt
+
    .. include:: whatsnew/<version>.txt
 
    
 
    
 
    
 
    
 
 
+           
    
+           
    
+            
+           
    
           
    
           
 
@@ -572,7 +406,8 @@ appear at the top. The syntax for this is:
    
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/releases.html b/releases.html
index e738adb0..cfdb4155 100644
--- a/releases.html
+++ b/releases.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -43,21 +45,28 @@
 
 
 
+   
   
    
 
     
     
 
     
    
 
       
       
 
 
       
       
    
         
    
-          
    
+          
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+
    
+
   
+
+  
   
    
 
    
-          
    
+          
    
+           
    
             
   
    
 
    Release Notes
    
+
    
+
    Version 0.5.8
    
+
    Release Date: 2018-03-29
    
+
    
+
    Bug Fixes
    
+
      
+
    • Fix Data Marketplace release on mainnet
      • 
+
    
+
    
+
    
 
    
 
    Version 0.5.7
    
 
    Release Date: 2018-03-29
    
@@ -343,8 +307,8 @@
 synchronous to provide feedback to the publisher.
 
 
    
-
    
-
    Bug Fixes
    
+
    
+
    Bug Fixes
    
 
      
 
    • fixes in storing and loading the state #214,
 #287
      • 
@@ -354,8 +318,8 @@ synchronous to provide feedback to the publisher.
 
      
 
      Version 0.5.6
      
 
      Release Date: 2018-03-22
      
-
      
-
      Build
      
+
      
+
      Build
      
 
        
 
      • Data Marketplace: ensures compatibility across wallets, now fully supporting
 ledger, trezor, keystore, private key. Partial support for
@@ -365,8 +329,8 @@ synchronous to provide feedback to the publisher.
        • 
 
      • Data Marketplace: Added progress indicator for data ingestion.
        • 
 
      
 
      
-
      
-
      Bug Fixes
      
+
      
+
      Bug Fixes
      
 
        
 
      • Changed benchmark to be constant, so it doesn’t ingest data at all. Temporary
 fix for #271, #285
        • 
@@ -376,8 +340,8 @@ fix for 
 
        Version 0.5.5
        
 
        Release Date: 2018-03-19
        
-
        
-
        Bug Fixes
        
+
        
+
        Bug Fixes
        
 
          
 
        • Fixed an issue with the data history in daily frequency #274
          • 
 
        • Fix hourly frequency issues #227 and #114
          • 
@@ -387,8 +351,8 @@ fix for 
 
          Version 0.5.4
          
 
          Release Date: 2018-03-14
          
-
          
-
          Build
          
+
          
+
          Build
          
 
            
 
          • Switched Data Marketplace from Ropstein testnet to Rinkeby testnet after
 incorporating changes resulting from the marketplace contract audit
            • 
@@ -397,8 +361,8 @@ incorporating changes resulting from the marketplace contract audit
 will list available datasets, and let you choose interactively.
 
          
 
          
-
          
-
          Bug Fixes
          
+
          
+
          Bug Fixes
          
 
            
 
          • Fix Binance requirement of symbol to be included in the cancelled order
 #204
            • 
@@ -429,8 +393,8 @@ updated Visual C++ instructions for Windows and Python 3
 
            
 
            Version 0.5.3
            
 
            Release Date: 2018-02-09
            
-
            
-
            Bug Fixes
            
+
            
+
            Bug Fixes
            
 
              
 
            • Fixed an issue with last candle in backtesting #219
              • 
 
            
@@ -439,8 +403,8 @@ updated Visual C++ instructions for Windows and Python 3
 
            
 
            Version 0.5.2
            
 
            Release Date: 2018-02-08
            
-
            
-
            Bug Fixes
            
+
            
+
            Bug Fixes
            
 
              
 
            • Fixed an issue with live candle values #216 and #199
              • 
 
            
@@ -449,8 +413,8 @@ updated Visual C++ instructions for Windows and Python 3
 
            
 
            Version 0.5.1
            
 
            Release Date: 2018-02-07
            
-
            
-
            Bug Fixes
            
+
            
+
            Bug Fixes
            
 
              
 
            • Fixed an issue with orders that stay open #211
              • 
 
            • Fixed Jupyter issues #179
              • 
@@ -458,8 +422,8 @@ updated Visual C++ instructions for Windows and Python 3
 
            • Improved live state presentation #171
              • 
 
            
 
            
-
            
-
            Build
            
+
            
+
            Build
            
 
              
 
            • Introducing the Enigma Marketplace
              • 
 
            
@@ -468,14 +432,14 @@ updated Visual C++ instructions for Windows and Python 3
 
            
 
            Version 0.4.7
            
 
            Release Date: 2018-01-19
            
-
            
-
            Bug Fixes
            
+
            
+
            Bug Fixes
            
 
              
 
            • Fixing issue #137 impacting the CLI
              • 
 
            
 
            
-
            
-
            Build
            
+
            
+
            Build
            
 
              
 
            • Implemented authentication aliases (#60)
              • 
 
            
@@ -484,8 +448,8 @@ updated Visual C++ instructions for Windows and Python 3
 
            
 
            Version 0.4.6
            
 
            Release Date: 2018-01-18
            
-
            
-
            Bug Fixes
            
+
            
+
            Bug Fixes
            
 
              
 
            • Fixed some Python3 issues
              • 
 
            • Reading the trade log to get executed order prices on exchanges like Binance (#151)
              • 
@@ -495,8 +459,8 @@ updated Visual C++ instructions for Windows and Python 3
 
            • Using Bitfinex instead of Poloniex to fetch btc_usdt benchmark (#161)
              • 
 
            
 
            
-
            
-
            Build
            
+
            
+
            Build
            
 
              
 
            • Added a context.state dict to keep arbitrary state values between runs
              • 
 
            • Added ability to stop live algo at specified end date
              • 
@@ -506,8 +470,8 @@ updated Visual C++ instructions for Windows and Python 3
 
              
 
              Version 0.4.5
              
 
              Release Date: 2018-01-12
              
-
              
-
              Bug Fixes
              
+
              
+
              Bug Fixes
              
 
                
 
              • Improved order execution for exchanges supporting trade lists (#151)
                • 
 
              • Fixed an issue where requesting history of multiple assets repeats values
                • 
@@ -519,8 +483,8 @@ updated Visual C++ instructions for Windows and Python 3
 
                
 
                Version 0.4.4
                
 
                Release Date: 2018-01-09
                
-
                
-
                Bug Fixes
                
+
                
+
                Bug Fixes
                
 
                  
 
                • Removed redundant capital_base validation (#142)
                  • 
 
                • Fixed portfolio update issue with restored state (#111)
                  • 
@@ -531,8 +495,8 @@ updated Visual C++ instructions for Windows and Python 3
 
                  
 
                  Version 0.4.3
                  
 
                  Release Date: 2018-01-05
                  
-
                  
-
                  Bug Fixes
                  
+
                  
+
                  Bug Fixes
                  
 
                    
 
                  • Fixed CLI issue (#137)
                    • 
 
                  • Upgraded CCXT
                    • 
@@ -542,8 +506,8 @@ updated Visual C++ instructions for Windows and Python 3
 
                    
 
                    Version 0.4.2
                    
 
                    Release Date: 2018-01-03
                    
-
                    
-
                    Bug Fixes
                    
+
                    
+
                    Bug Fixes
                    
 
                      
 
                    • Fixed cash synchronization issue (#133)
                      • 
 
                    • Fixed positions synchronization issue (#132)
                      • 
@@ -553,8 +517,8 @@ updated Visual C++ instructions for Windows and Python 3
 
                    • Fixed a poloniex specific issue in live trading (#103)
                      • 
 
                    
 
                    
-
                    
-
                    Build
                    
+
                    
+
                    Build
                    
 
                      
 
                    • Caching CCXT market info to limit round-trips (#99)
                      • 
 
                    • Tentative support for Pipeline (#96)
                      • 
@@ -564,16 +528,16 @@ updated Visual C++ instructions for Windows and Python 3
 
                      
 
                      Version 0.4.0
                      
 
                      Release Date: 2017-12-12
                      
-
                      
-
                      Bug Fixes
                      
+
                      
+
                      Bug Fixes
                      
 
                        
 
                      • Changed Poloniex interface (should solve #95 and #94)
                        • 
 
                      • Solved issue with overriding commission and slippage (#87)
                        • 
 
                      • Fixed inefficiency with Bittrex current prices (#76)
                        • 
 
                      
 
                      
-
                      
-
                      Build
                      
+
                      
+
                      Build
                      
 
                        
 
                      • Integrated with CCXT
                        • 
 
                      • Added paper trading capability (simulate_orders=True param in live mode)
                        • 
@@ -585,8 +549,8 @@ updated Visual C++ instructions for Windows and Python 3
 
                        Version 0.3.10
                        
 
                        Release Date: 2017-11-28
                        
 
                      
-
                      
-
                      Bug Fixes
                      
+
                      
+
                      Bug Fixes
                      
 
                        
 
                      • Fixed issue with fetching assets with daily frequency
                        • 
 
                      
@@ -595,15 +559,15 @@ updated Visual C++ instructions for Windows and Python 3
 
                      
 
                      Version 0.3.9
                      
 
                      Release Date: 2017-11-28
                      
-
                      
-
                      Bug Fixes
                      
+
                      
+
                      Bug Fixes
                      
 
                        
 
                      • Fixed sortino warning issues (#77)
                        • 
 
                      • Adjusted computation of last candle of data.history (#71)
                        • 
 
                      
 
                      
-
                      
-
                      Build
                      
+
                      
+
                      Build
                      
 
                        
 
                      • Added capital_base parameter to live mode to limit cash (#79)
                        • 
 
                      • Added support for csv ingestion (#65)
                        • 
@@ -614,8 +578,8 @@ updated Visual C++ instructions for Windows and Python 3
 
                        
 
                        Version 0.3.8
                        
 
                        Release Date: 2017-11-14
                        
-
                        
-
                        Bug Fixes
                        
+
                        
+
                        Bug Fixes
                        
 
                          
 
                        • Fixed a warning filter issue introduced with the latest release
                          • 
 
                        
@@ -624,8 +588,8 @@ updated Visual C++ instructions for Windows and Python 3
 
                        
 
                        Version 0.3.7
                        
 
                        Release Date: 2017-11-14
                        
-
                        
-
                        Bug Fixes
                        
+
                        
+
                        Bug Fixes
                        
 
                          
 
                        • Fixed an SSL cert issue (#64)
                          • 
 
                        • Fixed cumulative stats warnings (#63)
                          • 
@@ -633,8 +597,8 @@ updated Visual C++ instructions for Windows and Python 3
 
                        • Standardized live-trading stats (#61)
                          • 
 
                        
 
                        
-
                        
-
                        Build
                        
+
                        
+
                        Build
                        
 
                          
 
                        • Added a mean-reversion sample algo
                          • 
 
                        • Added minutely stats in the analyze() function (#62)
                          • 
@@ -645,8 +609,8 @@ updated Visual C++ instructions for Windows and Python 3
 
                          
 
                          Version 0.3.6
                          
 
                          Release Date: 2017-11-4
                          
-
                          
-
                          Bug Fixes
                          
+
                          
+
                          Bug Fixes
                          
 
                            
 
                          • Fixed an issue with single bar data.history() (#55)
                            • 
 
                          
@@ -655,8 +619,8 @@ updated Visual C++ instructions for Windows and Python 3
 
                          
 
                          Version 0.3.5
                          
 
                          Release Date: 2017-11-4
                          
-
                          
-
                          Bug Fixes
                          
+
                          
+
                          Bug Fixes
                          
 
                            
 
                          • Added workaround for: KeyError: Timestamp error (#53)
                            • 
 
                          
@@ -665,8 +629,8 @@ updated Visual C++ instructions for Windows and Python 3
 
                          
 
                          Version 0.3.4
                          
 
                          Release Date: 2017-11-2
                          
-
                          
-
                          Bug Fixes
                          
+
                          
+
                          Bug Fixes
                          
 
                            
 
                          • Fixed issue with auto-ingestion of minute data (#47)
                            • 
 
                          • Fixed issue with sell orders in backtesting
                            • 
@@ -676,14 +640,14 @@ updated Visual C++ instructions for Windows and Python 3
 transactions
 
                          
 
                          
-
                          
-
                          Build
                          
+
                          
+
                          Build
                          
 
                            
 
                          • Added more unit tests
                            • 
 
                          
 
                          
-
                          
-
                          Documentation
                          
+
                          
+
                          Documentation
                          
 
                            
 
                          • Improved installation notes for Windows C++ compiler and Conda
                            • 
 
                          • Addition of
@@ -704,16 +668,16 @@ transactions
                            • 
 
                            
 
                            Version 0.3.3
                            
 
                            Release Date: 2017-10-26
                            
-
                            
-
                            Bug Fixes
                            
+
                            
+
                            Bug Fixes
                            
 
                              
 
                            • Fix missing -x in ingest-exchange
                              • 
 
                            • Fix issue with daily chunks end date (data bundles)
                              • 
 
                            • Fix issue in the prepare_chunk logic (data bundles)
                              • 
 
                            
 
                            
-
                            
-
                            Build
                            
+
                            
+
                            Build
                            
 
                              
 
                            • Added data validation unit tests
                              • 
 
                            
@@ -722,8 +686,8 @@ transactions
 
                            
 
                            Version 0.3.2
                            
 
                            Release Date: 2017-10-25
                            
-
                            
-
                            Bug Fixes
                            
+
                            
+
                            Bug Fixes
                            
 
                              
 
                            • Fix to work with empty data bundles
                              • 
 
                            • Fix Windows path of $HOME/.catalyst folder
                              • 
@@ -732,8 +696,8 @@ transactions
 
                            • Fix an issue with asset date in chunks
                              • 
 
                            
 
                            
-
                            
-
                            Build
                            
+
                            
+
                            Build
                            
 
                              
 
                            • Python3 adjustments
                              • 
 
                            • Added method to clean bundle folders, and remove symbols.json
                              • 
@@ -744,8 +708,8 @@ transactions
 
                              
 
                              Version 0.3.1
                              
 
                              Release Date: 2017-10-22
                              
-
                              
-
                              Bug Fixes
                              
+
                              
+
                              Bug Fixes
                              
 
                                
 
                              • Fixed OS-dependent path issue in data bundle
                                • 
 
                              • Changed handling of empty auth.json, instead of throwing an error for
@@ -865,13 +829,17 @@ exchange directly
                                • 
 
                              
 
 
+           
                              
+           
                              
+            
+           
                              
           
                              
           
 
@@ -904,7 +873,8 @@ exchange directly
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/resources.html b/resources.html
index e6af4ae1..8fce9b8f 100644
--- a/resources.html
+++ b/resources.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -44,21 +46,28 @@
 
 
 
+   
   
                              
 
     
     
 
     
                              
 
       
       
 
 
       
       
                              
         
                              
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+          
+
+
+
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+
+
+
                              
+
   
+
+  
   
                              
 
                              
-          
                              
+          
                              
+           
                              
             
   
                              
 
                              Resources
                              
@@ -361,15 +193,19 @@ trading algorithms
 
                              
 
 
+           
                              
+           
                              
+            
+           
                              
           
                              
           
 
@@ -402,7 +239,8 @@ trading algorithms
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/search.html b/search.html
index 62290c03..42ae0647 100644
--- a/search.html
+++ b/search.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -42,21 +44,28 @@
 
 
 
+   
   
                              
 
     
     
 
     
                              
 
       
       
 
 
       
       
                              
         
                              
-          
                              
+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
                              
+
   
                                
-    
                              • Docs »
                                • 
-      
-    
                              • 
+    
+      
                              • Docs »
                                • 
+        
+      
                              • Search
                                • 
+    
+    
       
                              • 
         
       
                                • 
+    
   
                              
+
+  
   
                              
 
                              
-          
                              
+          
                              
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@@ -371,7 +205,8 @@
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/searchindex.js b/searchindex.js
index 130d1123..e228f550 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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:[3,9],split:[0,1,2,4],splitext:[4,13],spot:0,spread:[0,4],spring:11,sqliteadjustmentwrit:2,sqrt:4,ssl:10,stabl:[4,7],stai:[9,10],standard:[0,2,3,4,5,10],star:4,start:[0,1,2,3,4,6,7,8,12,13],start_dat:[0,1,4,8],start_minut:0,start_sess:0,start_session_label:0,start_tim:4,starting_cash:[1,4],starting_exposur:1,starting_valu:1,stat:[4,10],state:[0,1,2,4,6,10],statist:[1,5,6,8,10],stats_output:4,stats_util:[1,4],statsmodel:6,statu:4,std:4,stdev:4,stdout:1,steem_btc:5,steem_eth:5,step:[2,7],still:[1,2,4,5,7],stock:[1,2,4],stop:[0,1,4,10],stop_pric:0,stoplimitord:0,stopord:0,storag:[1,8,10],store:[0,1,2,4,5,10,13],str:[0,4],str_btc:5,str_usdt:5,straightforward:4,strat_btc:5,strategi:[1,4,6,8],stream:1,streamlin:1,strftime:4,strict_extens:0,strictli:[0,2,7],string:[0,1,2,4,9],string_typ:0,strong:0,strongli:7,struct:0,structur:[5,6,8,11],sty:3,style:[0,3,4],subdirectori:2,subject:[0,3],submit:3,subplot:[1,4],subset:4,substanti:1,subtract:0,succe:9,succeed:1,success:[2,3],successfulli:8,sudo:[1,7],suffici:7,suggest:3,sum:4,summar:7,summari:10,suppli:1,support:[0,1,4,5,6,7,9,10,12],suppos:1,suppress:4,sure:[4,7],surpris:1,suspect:3,symbol:[0,1,2,4,5,10,13],symbol_column:0,symbol_str:0,symbolnotfound:0,symbolnotfoundonexchang:5,sync:9,synchron:10,syntax:[0,9,10],sys:13,sys_btc:5,system:[0,1,3,6,7],t_price:4,t_val:4,ta_lib:7,tag:9,take:[0,1,2,7,10,14],taken:9,taker:[0,4],talib:4,tar:9,tarbal:9,target:[0,1,8,10,12],target_hodl_ratio:[1,4],target_hodl_valu:[1,4],technic:[4,6],tell:0,tempfil:4,templat:9,temporari:[0,9,10],ten:1,tend:8,tens:3,tent:10,term:1,termin:[1,4,7],test:[0,1,3,4,7,8,9,10,11],testnet:10,testpypi:9,tether:5,text:1,than:[0,1,2,3,7,8,10],thei:[0,1,2,9,10,11],them:[0,1,3,7,9],therefor:9,therein:7,thi:[0,1,2,3,4,5,7,8,9,13,14],thing:[1,14],think:8,third:[1,4],thorough:7,those:[0,1,3,4,8,9],though:2,thread:0,three:[0,1,2,4,5,6,8],threshold:[1,4],through:[1,2,5,7],throughli:8,throughout:1,thu:[1,4,7],tick:4,tick_siz:1,ticker:[0,2,10],time:[0,1,2,4,6,7,8,9,13],time_rul:0,timedelta:[0,4],timefram:10,timeperiod:4,timeseri:4,timestamp:[0,1,2,10],timestr:4,timezon:0,titl:9,tkagg:7,tminus_pric:4,tminus_v:4,tmp_dir:0,to_csv:[4,13],to_datetim:[1,4],to_dict:0,to_pydatetim:[1,4],todai:[4,9],todo:4,togeth:1,token:[4,8,13],tolist:4,tool:[2,3,7,9,11],top:[1,6,9],topic:7,total:[0,2],toward:1,traceback:[0,5],track:[0,1,3,4,10],tracker:[1,5],trade:[1,4,5,6,7,10,11,13],traded_todai:4,trader:[1,11],trading_dai:1,tradingalgorithm:[0,9],tradingcalendar:[0,2],tradingcontrolexcept:0,tradingpair:12,tradingsimul:0,tradit:1,train:1,tran:[1,4],transact:[0,1,4,8,10],transaction:2,transaction_df:[1,4],transfer:5,transform:1,transit:[5,6],transpos:4,travi:9,treasuri:[1,4],treasury_period_return:[1,4],treat:0,trend:1,trezor:10,tri:[1,6],trigger:[0,4],trip:10,troubleshoot:10,tst:3,tue:0,tupl:[0,2],turn:9,tutori:[4,6,7,8],tweak:1,twice:9,two:[0,1,4,5,6,7,8,12,13,14],twoargument:1,txt:[3,7,9],type:[0,4,7,9],typo:3,tzinfo:0,ubuntu:[1,7],uncompress:10,under:[1,2,4,7,8,9],underli:[0,1,7],underlin:9,underscor:5,understand:[1,7],undesir:0,unexpect:7,uninstal:7,uniqu:0,unit:[1,5,10],univers:[0,1,8],universe_func:0,unknown:[1,10],unless:4,unlik:2,unpack:7,unresolv:10,unsupport:10,untar:2,until:[1,4,7],unus:3,unwrap:0,updat:[1,4,10],upgrad:[7,9,10],upper:[1,4],upward:1,url:[0,9],usabl:10,usag:1,usd:[1,4,5],usdt:[4,5,13],usdt_btc:1,usdt_btc_benchmark:1,use:[0,1,2,3,4,5,6,7,8,9,10,11,13],used:[0,1,2,3,4,7,8],useful:[0,7],user:[1,2,4,5,6,7,9],usernam:9,uses:[0,1,2,5,8],using:[2,3,4,5,7,8,9,10,12,13,14],usr:4,usual:[0,1],utc:[0,1,4,13],util:[1,2,3,4,6],utilit:13,val:0,valid:[0,10],valu:[0,1,4,7,8,10],valueerror:0,vari:[4,7,8,13],variabl:[1,2,4,8,13],varianc:4,varieti:[7,13],variou:[1,2,4],varnam:1,vcforpython27:7,venv:7,veri:[0,1,2,4,7,9],verifi:[7,9],versatil:13,version:[0,1,3,4,5,7,8,9,13],via:[1,5,7],via_btc:5,viabl:5,victori:5,video:[4,6,10],view:[3,9],virtual:[1,7],virtualenv:9,virtualenvwrapp:3,visit:[4,6],visual:[1,4,6,7,10],vix:0,volatil:4,volum:[0,1,4,5,6,10,12,13],volume_limit:0,volumeshareslippag:[0,1],vrc_btc:5,vtc_btc:5,wai:[1,2,4,6,7],wait:[1,4],wallet:10,want:[0,1,2,3,4,5,7],warn:[0,1,9,10],warn_on_cancel:0,warrant:0,warranti:4,wave:[4,14],web3:10,web:[5,9],websit:[1,6],wed:0,week_end:0,week_start:0,weekdai:0,weight:4,welcom:3,well:[1,2,6,7],were:[3,4,9],what:[0,1,3,4,5,7,8,9],whatev:2,whatsnew:9,wheel:[7,9],when:[0,1,2,3,4,7,8,9,10,14],where:[1,2,3,4,5,7,8,10,13,14],whether:0,which:[0,1,2,3,4,5,7,8,9,10,13],whitepap:11,whitespac:3,who:3,whole:1,whose:0,why:[3,7],width:9,win:9,window:[0,1,4,5,10],within:[1,7,8],without:[1,2,4,7,8,10],work:[2,3,4,7,8,9,10],workaround:10,workflow:7,working_dir:0,working_fil:0,world:1,wors:1,worth:1,would:[0,1,2,4,7,8,9],wouldlik:1,wrap:[0,9],wrapper:7,writ:4,write:[1,4,8,13],writer:[2,3,13],writerow:13,written:[0,1,2,5,6],wrong:5,www:4,xbc_btc:5,xbt:8,xcp_btc:5,xem_btc:5,xlabel:4,xmr_btc:5,xmr_usdt:[4,5],xpm_btc:5,xrp_btc:5,xrp_usdt:5,xvc_btc:5,yahoo_equ:2,yaxi:[1,4],year:9,yet:[0,1],ylabel:4,yml:[7,10],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\ No newline at end of file
diff --git a/utilities.html b/utilities.html
index 148209de..2c826593 100644
--- a/utilities.html
+++ b/utilities.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -44,21 +46,28 @@
 
 
 
+   
   
                              
 
     
     
 
     
                              
 
       
       
 
 
       
       
                              
         
                              
-          
                              
+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
                              
+
   
+
+  
   
                              
 
                              
-          
                              
+          
                              
+           
                              
             
   
                              
 
                              Utilities
                              
@@ -347,25 +180,25 @@ script that you need to use depends on the interface that you are using to run
 your trading algorithm, which could be the CLI or a Python Interpreter.
                              
 
                                
 
                              1. Script to use with CLI:
                                
-
                                def analyze(context=None, results=None):
+
                                def analyze(context=None, results=None):
     import sys
     import os
     from os.path import basename
 
-    # Save results in CSV file
+    # Save results in CSV file
     filename = os.path.splitext(basename(sys.argv[3]))[0]
-    results.to_csv(filename + '.csv')
+    results.to_csv(filename + '.csv')
 
                                
 
                                
 
                                2. 
 
                              2. Script to use with Python Interpreter:
                                
-
                                def analyze(context=None, results=None):
+
                                def analyze(context=None, results=None):
     import os
     from os.path import basename
 
-    # Save results in CSV file
-    filename = os.path.splitext(os.path.basename(__file__))[0]
-    results.to_csv(filename + '.csv')
+    # Save results in CSV file
+    filename = os.path.splitext(os.path.basename(__file__))[0]
+    results.to_csv(filename + '.csv')
 
                                
 
                                
 
                                3. 
@@ -380,7 +213,7 @@ Run this script directly from your development environment: python scriptname.py
 where the contents of ‘scriptname.py’ are as follows. Two different version are
 provided as an example for daily- and minute-resolution data respectively:
                                
 
                                Simpler case for daily data
                                
-
                                import os
+
                                import os
 import pytz
 from datetime import datetime
 
@@ -388,23 +221,23 @@ provided as an example for daily- and minute-resolution data respectively:
                                
 from catalyst.utils.run_algo import run_algorithm
 
 def initialize(context):
-   # Portfolio assets list
-   context.asset = symbol('btc_usdt') # Bitcoin on Poloniex
+   # Portfolio assets list
+   context.asset = symbol('btc_usdt') # Bitcoin on Poloniex
 
 def handle_data(context, data):
-   # Variables to record for a given asset: price and volume
-   price = data.current(context.asset, 'price')
-   volume = data.current(context.asset, 'volume')
+   # Variables to record for a given asset: price and volume
+   price = data.current(context.asset, 'price')
+   volume = data.current(context.asset, 'volume')
    record(price=price, volume=volume)
 
 def analyze(context=None, results=None):
 
-   # Generate DataFrame with Price and Volume only
-   data = results[['price','volume']]
+   # Generate DataFrame with Price and Volume only
+   data = results[['price','volume']]
 
-   # Save results in CSV file
-   filename = os.path.splitext(os.path.basename(__file__))[0]
-   data.to_csv(filename + '.csv')
+   # Save results in CSV file
+   filename = os.path.splitext(os.path.basename(__file__))[0]
+   data.to_csv(filename + '.csv')
 
 ''' Bitcoin data is available on Poloniex since 2015-3-1.
     Dates vary for other tokens. In the example below, we choose the
@@ -417,13 +250,13 @@ provided as an example for daily- and minute-resolution data respectively:
                                
                         analyze=analyze,
                         start=start,
                         end=end,
-                        exchange_name='poloniex',
+                        exchange_name='poloniex',
                         capital_base=10000,
-                        base_currency = 'usdt')
+                        base_currency = 'usdt')
 
                                
 
                                
 
                                More versatile case for minute data
                                
-
                                import os
+
                                import os
 import csv
 import pytz
 from datetime import datetime
@@ -433,31 +266,31 @@ provided as an example for daily- and minute-resolution data respectively:
                                
 
 
 def initialize(context):
-# Portfolio assets list
-context.asset     = symbol('btc_usdt') # Bitcoin on Poloniex
+# Portfolio assets list
+context.asset     = symbol('btc_usdt') # Bitcoin on Poloniex
 
-# Creates a .CSV file with the same name as this script to store results
+# Creates a .CSV file with the same name as this script to store results
 context.csvfile   = open(os.path.splitext(
-                         os.path.basename(__file__))[0]+'.csv', 'w+')
+                         os.path.basename(__file__))[0]+'.csv', 'w+')
 context.csvwriter = csv.writer(context.csvfile)
 
 def handle_data(context, data):
-# Variables to record for a given asset: price and volume
-# Other options include 'open', 'high', 'open', 'close'
-# Please note that 'price' equals 'close'
-date   = context.blotter.current_dt     # current time in each iteration
-price  = data.current(context.asset, 'price')
-volume = data.current(context.asset, 'volume')
+# Variables to record for a given asset: price and volume
+# Other options include 'open', 'high', 'open', 'close'
+# Please note that 'price' equals 'close'
+date   = context.blotter.current_dt     # current time in each iteration
+price  = data.current(context.asset, 'price')
+volume = data.current(context.asset, 'volume')
 
-# Writes one line to CSV on each iteration with the chosen variables
+# Writes one line to CSV on each iteration with the chosen variables
 context.csvwriter.writerow([date,price,volume])
 
 def analyze(context=None, results=None):
-# Close open file properly at the end
+# Close open file properly at the end
 context.csvfile.close()
 
 
-# Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
+# Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
 start = datetime(2017, 7, 30, 0, 0, 0, 0, pytz.utc)
 end = datetime(2017, 7, 31, 0, 0, 0, 0, pytz.utc)
 results = run_algorithm(initialize=initialize,
@@ -465,9 +298,9 @@ provided as an example for daily- and minute-resolution data respectively:
                                
                         analyze=analyze,
                         start=start,
                         end=end,
-                        exchange_name='poloniex',
-                        data_frequency='minute',
-                        base_currency ='usdt',
+                        exchange_name='poloniex',
+                        data_frequency='minute',
+                        base_currency ='usdt',
                         capital_base=10000 )
 
                                
 
                                
@@ -475,15 +308,19 @@ provided as an example for daily- and minute-resolution data respectively:
                                
 
                                
 
 
+           
                                
+           
                                
+            
+           
                                
           
                                
           
 
@@ -516,7 +354,8 @@ provided as an example for daily- and minute-resolution data respectively:
                                
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };
     
       
diff --git a/videos.html b/videos.html
index 8d280d00..89b15c9a 100644
--- a/videos.html
+++ b/videos.html
@@ -13,6 +13,8 @@
 
   
   
+  
+  
 
   
 
@@ -44,21 +46,28 @@
 
 
 
+   
   
                                
 
     
     
 
     
                                
 
       
       
 
 
       
       
                                
         
                                
-          
                                
+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
                                
+
   
+
+  
   
                                
 
                                
-          
                                
+          
                                
+           
                                
             
   
                                
 
                                Videos
                                
@@ -377,15 +212,19 @@ in the previous video, we now take it to trade live against the Bittrex exchange
 
                                
 
 
+           
                                
+           
                                
+            
+           
                                
           
                                
           
 
@@ -418,7 +258,8 @@ in the previous video, we now take it to trade live against the Bittrex exchange
             VERSION:'0.4',
             COLLAPSE_INDEX:false,
             FILE_SUFFIX:'.html',
-            HAS_SOURCE:  true
+            HAS_SOURCE:  true,
+            SOURCELINK_SUFFIX: '.txt'
         };