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 :doc:`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_volatility algorithm_period_return alpha benchmark_period_return benchmark_volatility beta btc capital_used ending_cash ending_exposure ... short_exposure short_value shorts_count sortino starting_cash starting_exposure starting_value trading_days transactions treasury_period_return
2016-01-01 23:59:00+00:00 NaN 0.000000e+00 NaN -0.010937 NaN NaN 433.979999 0.000000 1.000000e+07 0.00 ... 0 0 0 NaN 1.000000e+07 0.00 0.00 1 [] 0.0227
2016-01-02 23:59:00+00:00 0.000011 -9.536708e-07 -0.000170 -0.006480 0.173338 -0.000062 432.700000 -442.236708 9.999558e+06 432.70 ... 0 0 0 -11.224972 1.000000e+07 0.00 0.00 2 [{u'order_id': u'7869f7828fa140328eb40477bb7de... 0.0227
2016-01-03 23:59:00+00:00 0.000011 -2.328842e-06 -0.000176 -0.026512 0.197857 0.000009 428.390000 -437.831716 9.999120e+06 856.78 ... 0 0 0 -12.754262 9.999558e+06 432.70 432.70 3 [{u'order_id': u'be62ff77760c4599abaac43be9cc9... 0.0227
2016-01-04 23:59:00+00:00 0.000011 -2.380954e-06 -0.000139 -0.008640 0.269790 0.000020 432.900000 -442.441116 9.998677e+06 1298.70 ... 0 0 0 -11.287205 9.999120e+06 856.78 856.78 4 [{u'order_id': u'd6dca79513214346a646079213526... 0.0224
2016-01-05 23:59:00+00:00 0.000011 -3.650729e-06 -0.000158 -0.021426 0.245989 0.000024 431.840000 -441.357754 9.998236e+06 1727.36 ... 0 0 0 -12.333847 9.998677e+06 1298.70 1298.70 5 [{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:: python (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 `_. .. code-block:: python import numpy as np import pandas as pd from logbook import Logger import matplotlib.pyplot as plt from catalyst import run_algorithm from catalyst.api import (order, record, symbol, order_target_percent, get_open_orders) from catalyst.exchange.utils.stats_utils import extract_transactions NAMESPACE = 'dual_moving_average' log = Logger(NAMESPACE) def initialize(context): context.i = 0 context.asset = symbol('ltc_usd') context.base_price = None def handle_data(context, data): # 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 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. short_mavg = data.history(context.asset, 'price', bar_count=short_window, frequency="1m").mean() long_mavg = data.history(context.asset, 'price', bar_count=long_window, frequency="1m").mean() # 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 context.base_price is None: context.base_price = price price_change = (price - context.base_price) / context.base_price # 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. orders = get_open_orders(context.asset) if len(orders) > 0: return # 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 pos_amount = context.portfolio.positions[context.asset].amount # Trading logic if short_mavg > long_mavg and pos_amount == 0: # 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 order_target_percent(context.asset, 0) def analyze(context, perf): # 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 ax1 = plt.subplot(411) perf.loc[:, ['portfolio_value']].plot(ax=ax1) ax1.legend_.remove() 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 ax2 = plt.subplot(412, sharex=ax1) perf.loc[:, ['price','short_mavg','long_mavg']].plot(ax=ax2, label='Price') ax2.legend_.remove() ax2.set_ylabel('{asset}\n({base})'.format( asset = context.asset.symbol, base = base_currency )) start, end = ax2.get_ylim() ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5)) 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] ax2.scatter( buy_df.index.to_pydatetime(), perf.loc[buy_df.index, 'price'], marker='^', s=100, c='green', label='' ) ax2.scatter( sell_df.index.to_pydatetime(), perf.loc[sell_df.index, 'price'], marker='v', s=100, c='red', label='' ) # 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) ax3.legend_.remove() 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 ax4 = plt.subplot(414, sharex=ax1) perf.cash.plot(ax=ax4) 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__': run_algorithm( capital_base=1000, data_frequency='minute', initialize=initialize, handle_data=handle_data, analyze=analyze, 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), ) 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 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:: bash (catalyst)$ pip install jupyter Once you have Jupyter Notebook installed, every time you want to use it run: .. code:: 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:: 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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.. raw:: html algo_volatility .. raw:: html algorithm_period_return .. raw:: html alpha .. raw:: html benchmark_period_return .. raw:: html benchmark_volatility .. raw:: html beta .. raw:: html capital_used .. raw:: html cash .. raw:: html ending_cash .. raw:: html ending_exposure .. raw:: html … .. raw:: html starting_cash .. raw:: html starting_exposure .. raw:: html starting_value .. raw:: html trading_days .. raw:: html transactions .. raw:: html treasury_period_return .. raw:: html volume .. raw:: html treasury .. raw:: html algorithm .. raw:: html benchmark .. raw:: html
2015-03-01 23:59:00+00:00 .. raw:: html NaN .. raw:: html 0.000000 .. raw:: html NaN .. raw:: html 0.045833 .. raw:: html NaN .. raw:: html NaN .. raw:: html 0.000000 .. raw:: html 100000.000000 .. raw:: html 100000.000000 .. raw:: html 0.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 0.000 .. raw:: html 0.000 .. raw:: html 1 .. raw:: html [] .. raw:: html 0.0200 .. raw:: html 317 .. raw:: html 0.0200 .. raw:: html 0.000000 .. raw:: html 0.045833 .. raw:: html
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
2015-03-06 23:59:00+00:00 .. raw:: html 0.580521 .. raw:: html -0.028645 .. raw:: html -3.100822 .. raw:: html 0.083333 .. raw:: html 0.874082 .. raw:: html 0.546703 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 82680.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 81726.000 .. raw:: html 81726.000 .. raw:: html 6 .. raw:: html [] .. raw:: html 0.0224 .. raw:: html 84197 .. raw:: html 0.0224 .. raw:: html -0.028645 .. raw:: html 0.083333 .. raw:: html
2015-03-07 23:59:00+00:00 .. raw:: html 0.530557 .. raw:: html -0.028645 .. raw:: html -2.625704 .. raw:: html 0.083333 .. raw:: html 0.802793 .. raw:: html 0.536589 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 82680.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 82680.000 .. raw:: html 82680.000 .. raw:: html 7 .. raw:: html [] .. raw:: html 0.0224 .. raw:: html 181 .. raw:: html 0.0224 .. raw:: html -0.028645 .. raw:: html 0.083333 .. raw:: html
2015-03-08 23:59:00+00:00 .. raw:: html 0.491628 .. raw:: html -0.028645 .. raw:: html -2.276841 .. raw:: html 0.083333 .. raw:: html 0.746605 .. raw:: html 0.529163 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 82680.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 82680.000 .. raw:: html 82680.000 .. raw:: html 8 .. raw:: html [] .. raw:: html 0.0224 .. raw:: html 30900 .. raw:: html 0.0224 .. raw:: html -0.028645 .. raw:: html 0.083333 .. raw:: html
2015-03-09 23:59:00+00:00 .. raw:: html 0.467885 .. raw:: html -0.015925 .. raw:: html -1.895269 .. raw:: html 0.100000 .. raw:: html 0.698764 .. raw:: html 0.532652 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 83952.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 82680.000 .. raw:: html 82680.000 .. raw:: html 9 .. raw:: html [] .. raw:: html 0.0220 .. raw:: html 128367 .. raw:: html 0.0220 .. raw:: html -0.015925 .. raw:: html 0.100000 .. raw:: html
2015-03-10 23:59:00+00:00 .. raw:: html 0.626552 .. raw:: html 0.069935 .. raw:: html -1.625285 .. raw:: html 0.212500 .. raw:: html 0.800983 .. raw:: html 0.676289 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 92538.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 83952.000 .. raw:: html 83952.000 .. raw:: html 10 .. raw:: html [] .. raw:: html 0.0214 .. raw:: html 54961 .. raw:: html 0.0214 .. raw:: html 0.069935 .. raw:: html 0.212500 .. raw:: html
2015-03-11 23:59:00+00:00 .. raw:: html 0.644515 .. raw:: html 0.022235 .. raw:: html -1.727710 .. raw:: html 0.150000 .. raw:: html 0.834650 .. raw:: html 0.684052 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 87768.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 92538.000 .. raw:: html 92538.000 .. raw:: html 11 .. raw:: html [] .. raw:: html 0.0211 .. raw:: html 42511 .. raw:: html 0.0211 .. raw:: html 0.022235 .. raw:: html 0.150000 .. raw:: html
2015-03-12 23:59:00+00:00 .. raw:: html 0.614650 .. raw:: html 0.022235 .. raw:: html -1.573455 .. raw:: html 0.150000 .. raw:: html 0.798403 .. raw:: html 0.680882 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 87768.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 87768.000 .. raw:: html 87768.000 .. raw:: html 12 .. raw:: html [] .. raw:: html 0.0210 .. raw:: html 2909 .. raw:: html 0.0210 .. raw:: html 0.022235 .. raw:: html 0.150000 .. raw:: html
2015-03-13 23:59:00+00:00 .. raw:: html 0.588942 .. raw:: html 0.019405 .. raw:: html -1.454733 .. raw:: html 0.146291 .. raw:: html 0.767688 .. raw:: html 0.677881 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 87484.980 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 87768.000 .. raw:: html 87768.000 .. raw:: html 13 .. raw:: html [] .. raw:: html 0.0213 .. raw:: html 57613 .. raw:: html 0.0213 .. raw:: html 0.019405 .. raw:: html 0.146291 .. raw:: html
2015-03-14 23:59:00+00:00 .. raw:: html 0.565911 .. raw:: html 0.019373 .. raw:: html -1.344915 .. raw:: html 0.146250 .. raw:: html 0.739230 .. raw:: html 0.675665 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 87481.800 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 87484.980 .. raw:: html 87484.980 .. raw:: html 14 .. raw:: html [] .. raw:: html 0.0213 .. raw:: html 48310 .. raw:: html 0.0213 .. raw:: html 0.019373 .. raw:: html 0.146250 .. raw:: html
2015-03-15 23:59:00+00:00 .. raw:: html 0.551394 .. raw:: html 0.041659 .. raw:: html -1.191436 .. raw:: html 0.175450 .. raw:: html 0.714876 .. raw:: html 0.680484 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 89710.344 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 87481.800 .. raw:: html 87481.800 .. raw:: html 15 .. raw:: html [] .. raw:: html 0.0213 .. raw:: html 29454 .. raw:: html 0.0213 .. raw:: html 0.041659 .. raw:: html 0.175450 .. raw:: html
2015-03-16 23:59:00+00:00 .. raw:: html 0.541846 .. raw:: html 0.019055 .. raw:: html -1.188212 .. raw:: html 0.145833 .. raw:: html 0.706049 .. raw:: html 0.680281 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 87450.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 89710.344 .. raw:: html 89710.344 .. raw:: html 16 .. raw:: html [] .. raw:: html 0.0210 .. raw:: html 25564 .. raw:: html 0.0210 .. raw:: html 0.019055 .. raw:: html 0.145833 .. raw:: html
2015-03-17 23:59:00+00:00 .. raw:: html 0.524682 .. raw:: html 0.019055 .. raw:: html -1.115149 .. raw:: html 0.145833 .. raw:: html 0.684599 .. raw:: html 0.678870 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 87450.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 87450.000 .. raw:: html 87450.000 .. raw:: html 17 .. raw:: html [] .. raw:: html 0.0206 .. raw:: html 9 .. raw:: html 0.0206 .. raw:: html 0.019055 .. raw:: html 0.145833 .. raw:: html
2015-03-18 23:59:00+00:00 .. raw:: html 0.532621 .. raw:: html -0.021999 .. raw:: html -1.180440 .. raw:: html 0.092041 .. raw:: html 0.696261 .. raw:: html 0.685307 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 83344.620 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 87450.000 .. raw:: html 87450.000 .. raw:: html 18 .. raw:: html [] .. raw:: html 0.0193 .. raw:: html 164911 .. raw:: html 0.0193 .. raw:: html -0.021999 .. raw:: html 0.092041 .. raw:: html
2015-03-19 23:59:00+00:00 .. raw:: html 0.518811 .. raw:: html -0.013234 .. raw:: html -1.096387 .. raw:: html 0.103526 .. raw:: html 0.676861 .. raw:: html 0.686186 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 84221.028 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 83344.620 .. raw:: html 83344.620 .. raw:: html 19 .. raw:: html [] .. raw:: html 0.0198 .. raw:: html 713904 .. raw:: html 0.0198 .. raw:: html -0.013234 .. raw:: html 0.103526 .. raw:: html
2015-03-20 23:59:00+00:00 .. raw:: html 0.505168 .. raw:: html -0.017324 .. raw:: html -1.050273 .. raw:: html 0.098170 .. raw:: html 0.659945 .. raw:: html 0.685070 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 83812.080 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 84221.028 .. raw:: html 84221.028 .. raw:: html 20 .. raw:: html [] .. raw:: html 0.0193 .. raw:: html 132725 .. raw:: html 0.0193 .. raw:: html -0.017324 .. raw:: html 0.098170 .. raw:: html
2015-03-21 23:59:00+00:00 .. raw:: html 0.492384 .. raw:: html -0.018494 .. raw:: html -1.002051 .. raw:: html 0.096637 .. raw:: html 0.643679 .. raw:: html 0.684283 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 83695.056 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 83812.080 .. raw:: html 83812.080 .. raw:: html 21 .. raw:: html [] .. raw:: html 0.0193 .. raw:: html 201155 .. raw:: html 0.0193 .. raw:: html -0.018494 .. raw:: html 0.096637 .. raw:: html
2015-03-22 23:59:00+00:00 .. raw:: html 0.482998 .. raw:: html -0.004744 .. raw:: html -0.927947 .. raw:: html 0.114653 .. raw:: html 0.629319 .. raw:: html 0.686478 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 85070.088 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 83695.056 .. raw:: html 83695.056 .. raw:: html 22 .. raw:: html [] .. raw:: html 0.0193 .. raw:: html 64378 .. raw:: html 0.0193 .. raw:: html -0.004744 .. raw:: html 0.114653 .. raw:: html
2015-03-23 23:59:00+00:00 .. raw:: html 0.477523 .. raw:: html -0.026505 .. raw:: html -0.935352 .. raw:: html 0.086139 .. raw:: html 0.623502 .. raw:: html 0.687025 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 82894.014 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 85070.088 .. raw:: html 85070.088 .. raw:: html 23 .. raw:: html [] .. raw:: html 0.0192 .. raw:: html 61850 .. raw:: html 0.0192 .. raw:: html -0.026505 .. raw:: html 0.086139 .. raw:: html
2015-03-24 23:59:00+00:00 .. raw:: html 0.504086 .. raw:: html -0.084215 .. raw:: html -1.021023 .. raw:: html 0.010523 .. raw:: html 0.655188 .. raw:: html 0.701025 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 77122.950 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 82894.014 .. raw:: html 82894.014 .. raw:: html 24 .. raw:: html [] .. raw:: html 0.0188 .. raw:: html 490180 .. raw:: html 0.0188 .. raw:: html -0.084215 .. raw:: html 0.010523 .. raw:: html
2015-03-25 23:59:00+00:00 .. raw:: html 0.497690 .. raw:: html -0.068474 .. raw:: html -0.952786 .. raw:: html 0.031148 .. raw:: html 0.644272 .. raw:: html 0.704251 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 78697.050 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 77122.950 .. raw:: html 77122.950 .. raw:: html 25 .. raw:: html [] .. raw:: html 0.0193 .. raw:: html 90862 .. raw:: html 0.0193 .. raw:: html -0.068474 .. raw:: html 0.031148 .. raw:: html
2015-03-26 23:59:00+00:00 .. raw:: html 0.489730 .. raw:: html -0.084215 .. raw:: html -0.943240 .. raw:: html 0.010523 .. raw:: html 0.634965 .. raw:: html 0.703738 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 77122.950 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 78697.050 .. raw:: html 78697.050 .. raw:: html 26 .. raw:: html [] .. raw:: html 0.0201 .. raw:: html 2299 .. raw:: html 0.0201 .. raw:: html -0.084215 .. raw:: html 0.010523 .. raw:: html
2015-03-27 23:59:00+00:00 .. raw:: html 0.495916 .. raw:: html -0.049785 .. raw:: html -0.857592 .. raw:: html 0.055636 .. raw:: html 0.636644 .. raw:: html 0.713671 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 80565.936 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 77122.950 .. raw:: html 77122.950 .. raw:: html 27 .. raw:: html [] .. raw:: html 0.0195 .. raw:: html 663 .. raw:: html 0.0195 .. raw:: html -0.049785 .. raw:: html 0.055636 .. raw:: html
2015-03-28 23:59:00+00:00 .. raw:: html 0.488469 .. raw:: html -0.064490 .. raw:: html -0.848769 .. raw:: html 0.036368 .. raw:: html 0.627920 .. raw:: html 0.713212 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 79095.504 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 80565.936 .. raw:: html 80565.936 .. raw:: html 28 .. raw:: html [] .. raw:: html 0.0195 .. raw:: html 7061 .. raw:: html 0.0195 .. raw:: html -0.064490 .. raw:: html 0.036368 .. raw:: html
2015-03-29 23:59:00+00:00 .. raw:: html 0.479671 .. raw:: html -0.066903 .. raw:: html -0.822844 .. raw:: html 0.033205 .. raw:: html 0.616787 .. raw:: html 0.712868 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 78854.142 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 79095.504 .. raw:: html 79095.504 .. raw:: html 29 .. raw:: html [] .. raw:: html 0.0195 .. raw:: html 8526 .. raw:: html 0.0195 .. raw:: html -0.066903 .. raw:: html 0.033205 .. raw:: html
2015-03-30 23:59:00+00:00 .. raw:: html 0.476306 .. raw:: html -0.046605 .. raw:: html -0.769239 .. raw:: html 0.059803 .. raw:: html 0.610002 .. raw:: html 0.716464 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 80883.936 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 78854.142 .. raw:: html 78854.142 .. raw:: html 30 .. raw:: html [] .. raw:: html 0.0196 .. raw:: html 29654 .. raw:: html 0.0196 .. raw:: html -0.046605 .. raw:: html 0.059803 .. raw:: html
… .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html … .. raw:: html
2017-05-30 23:59:00+00:00 .. raw:: html 0.495432 .. raw:: html 5.949752 .. raw:: html -0.016611 .. raw:: html 7.916664 .. raw:: html 0.554369 .. raw:: html 0.888883 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 680519.682 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 701826.000 .. raw:: html 701826.000 .. raw:: html 822 .. raw:: html [] .. raw:: html 0.0221 .. raw:: html 40157964723 .. raw:: html 0.0221 .. raw:: html 5.949752 .. raw:: html 7.916664 .. raw:: html
2017-05-31 23:59:00+00:00 .. raw:: html 0.495243 .. raw:: html 6.102328 .. raw:: html -0.017086 .. raw:: html 8.154164 .. raw:: html 0.554182 .. raw:: html 0.888844 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 695777.322 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 680519.682 .. raw:: html 680519.682 .. raw:: html 823 .. raw:: html [] .. raw:: html 0.0221 .. raw:: html 31098652109 .. raw:: html 0.0221 .. raw:: html 6.102328 .. raw:: html 8.154164 .. raw:: html
2017-06-01 23:59:00+00:00 .. raw:: html 0.495836 .. raw:: html 6.504967 .. raw:: html -0.014668 .. raw:: html 8.644144 .. raw:: html 0.554541 .. raw:: html 0.889303 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 736041.210 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 695777.322 .. raw:: html 695777.322 .. raw:: html 824 .. raw:: html [] .. raw:: html 0.0221 .. raw:: html 40944880757 .. raw:: html 0.0221 .. raw:: html 6.504967 .. raw:: html 8.644144 .. raw:: html
2017-06-02 23:59:00+00:00 .. raw:: html 0.495948 .. raw:: html 6.801995 .. raw:: html -0.013641 .. raw:: html 9.033331 .. raw:: html 0.554581 .. raw:: html 0.889440 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 765744.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 736041.210 .. raw:: html 736041.210 .. raw:: html 825 .. raw:: html [] .. raw:: html 0.0215 .. raw:: html 22364557424 .. raw:: html 0.0215 .. raw:: html 6.801995 .. raw:: html 9.033331 .. raw:: html
2017-06-03 23:59:00+00:00 .. raw:: html 0.495729 .. raw:: html 6.952409 .. raw:: html -0.013100 .. raw:: html 9.230418 .. raw:: html 0.554317 .. raw:: html 0.889470 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 780785.400 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 765744.000 .. raw:: html 765744.000 .. raw:: html 826 .. raw:: html [] .. raw:: html 0.0215 .. raw:: html 23687278961 .. raw:: html 0.0215 .. raw:: html 6.952409 .. raw:: html 9.230418 .. raw:: html
2017-06-04 23:59:00+00:00 .. raw:: html 0.495450 .. raw:: html 7.042244 .. raw:: html -0.012768 .. raw:: html 9.348122 .. raw:: html 0.553999 .. raw:: html 0.889479 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 789768.900 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 780785.400 .. raw:: html 780785.400 .. raw:: html 827 .. raw:: html [] .. raw:: html 0.0215 .. raw:: html 21332021248 .. raw:: html 0.0215 .. raw:: html 7.042244 .. raw:: html 9.348122 .. raw:: html
2017-06-05 23:59:00+00:00 .. raw:: html 0.496148 .. raw:: html 7.524987 .. raw:: html -0.011320 .. raw:: html 9.980649 .. raw:: html 0.554578 .. raw:: html 0.889805 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 838043.208 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 789768.900 .. raw:: html 789768.900 .. raw:: html 828 .. raw:: html [] .. raw:: html 0.0218 .. raw:: html 22372229837 .. raw:: html 0.0218 .. raw:: html 7.524987 .. raw:: html 9.980649 .. raw:: html
2017-06-06 23:59:00+00:00 .. raw:: html 0.497592 .. raw:: html 8.194835 .. raw:: html -0.009554 .. raw:: html 10.858330 .. raw:: html 0.555841 .. raw:: html 0.890368 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 905028.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 838043.208 .. raw:: html 838043.208 .. raw:: html 829 .. raw:: html [] .. raw:: html 0.0214 .. raw:: html 81923184446 .. raw:: html 0.0214 .. raw:: html 8.194835 .. raw:: html 10.858330 .. raw:: html
2017-06-07 23:59:00+00:00 .. raw:: html 0.498895 .. raw:: html 7.557258 .. raw:: html -0.011975 .. raw:: html 10.022932 .. raw:: html 0.557003 .. raw:: html 0.890845 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 841270.272 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 905028.000 .. raw:: html 905028.000 .. raw:: html 830 .. raw:: html [] .. raw:: html 0.0218 .. raw:: html 49070430356 .. raw:: html 0.0218 .. raw:: html 7.557258 .. raw:: html 10.022932 .. raw:: html
2017-06-08 23:59:00+00:00 .. raw:: html 0.499349 .. raw:: html 8.010395 .. raw:: html -0.010676 .. raw:: html 10.616664 .. raw:: html 0.557357 .. raw:: html 0.891092 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 886584.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 841270.272 .. raw:: html 841270.272 .. raw:: html 831 .. raw:: html [] .. raw:: html 0.0219 .. raw:: html 34013412940 .. raw:: html 0.0219 .. raw:: html 8.010395 .. raw:: html 10.616664 .. raw:: html
2017-06-09 23:59:00+00:00 .. raw:: html 0.499063 .. raw:: html 8.099750 .. raw:: html -0.010386 .. raw:: html 10.733746 .. raw:: html 0.557033 .. raw:: html 0.891098 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 895519.482 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 886584.000 .. raw:: html 886584.000 .. raw:: html 832 .. raw:: html [] .. raw:: html 0.0221 .. raw:: html 25275425996 .. raw:: html 0.0221 .. raw:: html 8.099750 .. raw:: html 10.733746 .. raw:: html
2017-06-10 23:59:00+00:00 .. raw:: html 0.498769 .. raw:: html 8.086143 .. raw:: html -0.010416 .. raw:: html 10.715915 .. raw:: html 0.556705 .. raw:: html 0.891098 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 894158.760 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 895519.482 .. raw:: html 895519.482 .. raw:: html 833 .. raw:: html [] .. raw:: html 0.0221 .. raw:: html 30620792046 .. raw:: html 0.0221 .. raw:: html 8.086143 .. raw:: html 10.715915 .. raw:: html
2017-06-11 23:59:00+00:00 .. raw:: html 0.498971 .. raw:: html 8.484533 .. raw:: html -0.009305 .. raw:: html 11.237914 .. raw:: html 0.556827 .. raw:: html 0.891266 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 933997.800 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 894158.760 .. raw:: html 894158.760 .. raw:: html 834 .. raw:: html [] .. raw:: html 0.0221 .. raw:: html 30830678595 .. raw:: html 0.0221 .. raw:: html 8.484533 .. raw:: html 11.237914 .. raw:: html
2017-06-12 23:59:00+00:00 .. raw:: html 0.503448 .. raw:: html 7.320494 .. raw:: html -0.014065 .. raw:: html 9.712706 .. raw:: html 0.560936 .. raw:: html 0.892695 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 817593.900 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 933997.800 .. raw:: html 933997.800 .. raw:: html 835 .. raw:: html [] .. raw:: html 0.0221 .. raw:: html 88704710635 .. raw:: html 0.0221 .. raw:: html 7.320494 .. raw:: html 9.712706 .. raw:: html
2017-06-13 23:59:00+00:00 .. raw:: html 0.503565 .. raw:: html 7.656697 .. raw:: html -0.013054 .. raw:: html 10.153225 .. raw:: html 0.560981 .. raw:: html 0.892830 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 851214.132 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 817593.900 .. raw:: html 817593.900 .. raw:: html 836 .. raw:: html [] .. raw:: html 0.0221 .. raw:: html 42251296767 .. raw:: html 0.0221 .. raw:: html 7.656697 .. raw:: html 10.153225 .. raw:: html
2017-06-14 23:59:00+00:00 .. raw:: html 0.506845 .. raw:: html 6.734516 .. raw:: html -0.016873 .. raw:: html 8.944917 .. raw:: html 0.563995 .. raw:: html 0.893862 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 758996.040 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 851214.132 .. raw:: html 851214.132 .. raw:: html 837 .. raw:: html [] .. raw:: html 0.0215 .. raw:: html 63183088135 .. raw:: html 0.0215 .. raw:: html 6.734516 .. raw:: html 8.944917 .. raw:: html
2017-06-15 23:59:00+00:00 .. raw:: html 0.506562 .. raw:: html 6.695367 .. raw:: html -0.016991 .. raw:: html 8.893622 .. raw:: html 0.563678 .. raw:: html 0.893865 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 755081.142 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 758996.040 .. raw:: html 758996.040 .. raw:: html 838 .. raw:: html [] .. raw:: html 0.0216 .. raw:: html 104677533974 .. raw:: html 0.0216 .. raw:: html 6.695367 .. raw:: html 8.893622 .. raw:: html
2017-06-16 23:59:00+00:00 .. raw:: html 0.506404 .. raw:: html 6.887855 .. raw:: html -0.016343 .. raw:: html 9.145831 .. raw:: html 0.563472 .. raw:: html 0.893913 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 774330.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 755081.142 .. raw:: html 755081.142 .. raw:: html 839 .. raw:: html [] .. raw:: html 0.0216 .. raw:: html 43479966625 .. raw:: html 0.0216 .. raw:: html 6.887855 .. raw:: html 9.145831 .. raw:: html
2017-06-17 23:59:00+00:00 .. raw:: html 0.507407 .. raw:: html 7.435283 .. raw:: html -0.014812 .. raw:: html 9.863113 .. raw:: html 0.564341 .. raw:: html 0.894311 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 829072.746 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 774330.000 .. raw:: html 774330.000 .. raw:: html 840 .. raw:: html [] .. raw:: html 0.0216 .. raw:: html 36800919715 .. raw:: html 0.0216 .. raw:: html 7.435283 .. raw:: html 9.863113 .. raw:: html
2017-06-18 23:59:00+00:00 .. raw:: html 0.507740 .. raw:: html 7.070069 .. raw:: html -0.016112 .. raw:: html 9.384581 .. raw:: html 0.564605 .. raw:: html 0.894482 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 792551.400 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 829072.746 .. raw:: html 829072.746 .. raw:: html 841 .. raw:: html [] .. raw:: html 0.0216 .. raw:: html 46411759478 .. raw:: html 0.0216 .. raw:: html 7.070069 .. raw:: html 9.384581 .. raw:: html
2017-06-19 23:59:00+00:00 .. raw:: html 0.507754 .. raw:: html 7.358645 .. raw:: html -0.015226 .. raw:: html 9.762694 .. raw:: html 0.564557 .. raw:: html 0.894583 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 821408.946 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 792551.400 .. raw:: html 792551.400 .. raw:: html 842 .. raw:: html [] .. raw:: html 0.0219 .. raw:: html 28294406623 .. raw:: html 0.0219 .. raw:: html 7.358645 .. raw:: html 9.762694 .. raw:: html
2017-06-20 23:59:00+00:00 .. raw:: html 0.507705 .. raw:: html 7.628795 .. raw:: html -0.014414 .. raw:: html 10.116664 .. raw:: html 0.564451 .. raw:: html 0.894665 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 848424.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 821408.946 .. raw:: html 821408.946 .. raw:: html 843 .. raw:: html [] .. raw:: html 0.0216 .. raw:: html 36903854052 .. raw:: html 0.0216 .. raw:: html 7.628795 .. raw:: html 10.116664 .. raw:: html
2017-06-21 23:59:00+00:00 .. raw:: html 0.507531 .. raw:: html 7.476155 .. raw:: html -0.014900 .. raw:: html 9.916664 .. raw:: html 0.564238 .. raw:: html 0.894696 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 833160.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 848424.000 .. raw:: html 848424.000 .. raw:: html 844 .. raw:: html [] .. raw:: html 0.0216 .. raw:: html 43815656010 .. raw:: html 0.0216 .. raw:: html 7.476155 .. raw:: html 9.916664 .. raw:: html
2017-06-22 23:59:00+00:00 .. raw:: html 0.507315 .. raw:: html 7.645891 .. raw:: html -0.014372 .. raw:: html 10.139065 .. raw:: html 0.563979 .. raw:: html 0.894725 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 850133.568 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 833160.000 .. raw:: html 833160.000 .. raw:: html 845 .. raw:: html [] .. raw:: html 0.0215 .. raw:: html 22304647568 .. raw:: html 0.0215 .. raw:: html 7.645891 .. raw:: html 10.139065 .. raw:: html
2017-06-23 23:59:00+00:00 .. raw:: html 0.507020 .. raw:: html 7.635155 .. raw:: html -0.014388 .. raw:: html 10.124997 .. raw:: html 0.563652 .. raw:: html 0.894725 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 849060.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 850133.568 .. raw:: html 850133.568 .. raw:: html 846 .. raw:: html [] .. raw:: html 0.0215 .. raw:: html 13090231864 .. raw:: html 0.0215 .. raw:: html 7.635155 .. raw:: html 10.124997 .. raw:: html
2017-06-24 23:59:00+00:00 .. raw:: html 0.507936 .. raw:: html 7.105628 .. raw:: html -0.016304 .. raw:: html 9.431173 .. raw:: html 0.564463 .. raw:: html 0.895061 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 796107.276 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 849060.000 .. raw:: html 849060.000 .. raw:: html 847 .. raw:: html [] .. raw:: html 0.0215 .. raw:: html 34088563732 .. raw:: html 0.0215 .. raw:: html 7.105628 .. raw:: html 9.431173 .. raw:: html
2017-06-25 23:59:00+00:00 .. raw:: html 0.507675 .. raw:: html 7.036714 .. raw:: html -0.016515 .. raw:: html 9.340880 .. raw:: html 0.564168 .. raw:: html 0.895069 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 789215.898 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 796107.276 .. raw:: html 796107.276 .. raw:: html 848 .. raw:: html [] .. raw:: html 0.0215 .. raw:: html 41560204433 .. raw:: html 0.0215 .. raw:: html 7.036714 .. raw:: html 9.340880 .. raw:: html
2017-06-26 23:59:00+00:00 .. raw:: html 0.507780 .. raw:: html 6.761571 .. raw:: html -0.017485 .. raw:: html 8.980368 .. raw:: html 0.564221 .. raw:: html 0.895175 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 761701.584 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 789215.898 .. raw:: html 789215.898 .. raw:: html 849 .. raw:: html [] .. raw:: html 0.0214 .. raw:: html 73840480752 .. raw:: html 0.0214 .. raw:: html 6.761571 .. raw:: html 8.980368 .. raw:: html
2017-06-27 23:59:00+00:00 .. raw:: html 0.508048 .. raw:: html 7.126355 .. raw:: html -0.016390 .. raw:: html 9.458331 .. raw:: html 0.564409 .. raw:: html 0.895349 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 798180.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 761701.584 .. raw:: html 761701.584 .. raw:: html 850 .. raw:: html [] .. raw:: html 0.0221 .. raw:: html 62426319778 .. raw:: html 0.0221 .. raw:: html 7.126355 .. raw:: html 9.458331 .. raw:: html
2017-06-28 23:59:00+00:00 .. raw:: html 0.507750 .. raw:: html 7.135895 .. raw:: html -0.016340 .. raw:: html 9.470831 .. raw:: html 0.564078 .. raw:: html 0.895349 .. raw:: html 0.000000 .. raw:: html 14455.525045 .. raw:: html 14455.525045 .. raw:: html 799134.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 798180.000 .. raw:: html 798180.000 .. raw:: html 851 .. raw:: html [] .. raw:: html 0.0222 .. raw:: html 39676839183 .. raw:: html 0.0222 .. raw:: html 7.135895 .. raw:: html 9.470831 .. raw:: html
.. raw:: html

851 rows × 45 columns .. raw:: html

.. raw:: html
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. .. code:: python _.head() .. raw:: html
.. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html .. raw:: html
.. raw:: html algo_volatility .. raw:: html algorithm_period_return .. raw:: html alpha .. raw:: html benchmark_period_return .. raw:: html benchmark_volatility .. raw:: html beta .. raw:: html capital_used .. raw:: html cash .. raw:: html ending_cash .. raw:: html ending_exposure .. raw:: html … .. raw:: html starting_cash .. raw:: html starting_exposure .. raw:: html starting_value .. raw:: html trading_days .. raw:: html transactions .. raw:: html treasury_period_return .. raw:: html volume .. raw:: html treasury .. raw:: html algorithm .. raw:: html benchmark .. raw:: html
2015-03-01 23:59:00+00:00 .. raw:: html NaN .. raw:: html 0.000000 .. raw:: html NaN .. raw:: html 0.045833 .. raw:: html NaN .. raw:: html NaN .. raw:: html 0.000000 .. raw:: html 100000.000000 .. raw:: html 100000.000000 .. raw:: html 0.000 .. raw:: html … .. raw:: html 100000.0 .. raw:: html 0.000 .. raw:: html 0.000 .. raw:: html 1 .. raw:: html [] .. raw:: html 0.0200 .. raw:: html 317 .. raw:: html 0.0200 .. raw:: html 0.000000 .. raw:: html 0.045833 .. raw:: html
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
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 `__.