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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 <https://www.enigma.co>`__ 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<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 <https://www.quantopian.com/help#api-toplevel>`.
My first algorithm
~~~~~~~~~~~~~~~~~~
Lets take a look at a very simple algorithm from the ``examples`` directory:
`buy_btc_simple.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/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
.. <https://www.quantopian.com/help#api-order>`__.
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 <https://www.quantopian.com/help#api-event-properties>`__.
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 <example-algos>`
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 <jupyter>` 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: <current-time>]
-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 <https://www.quantopian.com/help#ide-slippage>`__
.. 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 <http://pandas.pydata.org/>`_,
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
<div style="max-height:1000px;max-width:1500px;overflow:auto;">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>algo_volatility</th>
<th>algorithm_period_return</th>
<th>alpha</th>
<th>benchmark_period_return</th>
<th>benchmark_volatility</th>
<th>beta</th>
<th>btc</th>
<th>capital_used</th>
<th>ending_cash</th>
<th>ending_exposure</th>
<th>...</th>
<th>short_exposure</th>
<th>short_value</th>
<th>shorts_count</th>
<th>sortino</th>
<th>starting_cash</th>
<th>starting_exposure</th>
<th>starting_value</th>
<th>trading_days</th>
<th>transactions</th>
<th>treasury_period_return</th>
</tr>
</thead>
<tbody>
<tr>
<th>2016-01-01 23:59:00+00:00</th>
<td>NaN</td>
<td>0.000000e+00</td>
<td>NaN</td>
<td>-0.010937</td>
<td>NaN</td>
<td>NaN</td>
<td>433.979999</td>
<td>0.000000</td>
<td>1.000000e+07</td>
<td>0.00</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>NaN</td>
<td>1.000000e+07</td>
<td>0.00</td>
<td>0.00</td>
<td>1</td>
<td>[]</td>
<td>0.0227</td>
</tr>
<tr>
<th>2016-01-02 23:59:00+00:00</th>
<td>0.000011</td>
<td>-9.536708e-07</td>
<td>-0.000170</td>
<td>-0.006480</td>
<td>0.173338</td>
<td>-0.000062</td>
<td>432.700000</td>
<td>-442.236708</td>
<td>9.999558e+06</td>
<td>432.70</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-11.224972</td>
<td>1.000000e+07</td>
<td>0.00</td>
<td>0.00</td>
<td>2</td>
<td>[{u'order_id': u'7869f7828fa140328eb40477bb7de...</td>
<td>0.0227</td>
</tr>
<tr>
<th>2016-01-03 23:59:00+00:00</th>
<td>0.000011</td>
<td>-2.328842e-06</td>
<td>-0.000176</td>
<td>-0.026512</td>
<td>0.197857</td>
<td>0.000009</td>
<td>428.390000</td>
<td>-437.831716</td>
<td>9.999120e+06</td>
<td>856.78</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-12.754262</td>
<td>9.999558e+06</td>
<td>432.70</td>
<td>432.70</td>
<td>3</td>
<td>[{u'order_id': u'be62ff77760c4599abaac43be9cc9...</td>
<td>0.0227</td>
</tr>
<tr>
<th>2016-01-04 23:59:00+00:00</th>
<td>0.000011</td>
<td>-2.380954e-06</td>
<td>-0.000139</td>
<td>-0.008640</td>
<td>0.269790</td>
<td>0.000020</td>
<td>432.900000</td>
<td>-442.441116</td>
<td>9.998677e+06</td>
<td>1298.70</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-11.287205</td>
<td>9.999120e+06</td>
<td>856.78</td>
<td>856.78</td>
<td>4</td>
<td>[{u'order_id': u'd6dca79513214346a646079213526...</td>
<td>0.0224</td>
</tr>
<tr>
<th>2016-01-05 23:59:00+00:00</th>
<td>0.000011</td>
<td>-3.650729e-06</td>
<td>-0.000158</td>
<td>-0.021426</td>
<td>0.245989</td>
<td>0.000024</td>
<td>431.840000</td>
<td>-441.357754</td>
<td>9.998236e+06</td>
<td>1727.36</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-12.333847</td>
<td>9.998677e+06</td>
<td>1298.70</td>
<td>1298.70</td>
<td>5</td>
<td>[{u'order_id': u'505275d6646a41f3856b22b16678d...</td>
<td>0.0225</td>
</tr>
</tbody>
</table>
</div>
|
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 <https://matplotlib.org/>`_, 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 <install.html#macos-virtualenv-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 <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/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 <http://scikit-learn.org/stable/>`__ 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 <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/running_catalyst_in_jupyter_notebook.ipynb>`_ referenced in the text below)
The `Jupyter Notebook <https://jupyter.org/>`__ 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/<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 07:19:50.097334] 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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2015-03-01 23:59:00+00:00
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NaN
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2015-03-04 23:59:00+00:00
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2015-03-08 23:59:00+00:00
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2015-03-09 23:59:00+00:00
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.. raw:: html
<td>
0.0220
.. raw:: html
</td>
.. raw:: html
<td>
128367
.. raw:: html
</td>
.. raw:: html
<td>
0.0220
.. raw:: html
</td>
.. raw:: html
<td>
-0.015925
.. raw:: html
</td>
.. raw:: html
<td>
0.100000
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-10 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.626552
.. raw:: html
</td>
.. raw:: html
<td>
0.069935
.. raw:: html
</td>
.. raw:: html
<td>
-1.625285
.. raw:: html
</td>
.. raw:: html
<td>
0.212500
.. raw:: html
</td>
.. raw:: html
<td>
0.800983
.. raw:: html
</td>
.. raw:: html
<td>
0.676289
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
92538.000
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
83952.000
.. raw:: html
</td>
.. raw:: html
<td>
83952.000
.. raw:: html
</td>
.. raw:: html
<td>
10
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0214
.. raw:: html
</td>
.. raw:: html
<td>
54961
.. raw:: html
</td>
.. raw:: html
<td>
0.0214
.. raw:: html
</td>
.. raw:: html
<td>
0.069935
.. raw:: html
</td>
.. raw:: html
<td>
0.212500
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-11 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.644515
.. raw:: html
</td>
.. raw:: html
<td>
0.022235
.. raw:: html
</td>
.. raw:: html
<td>
-1.727710
.. raw:: html
</td>
.. raw:: html
<td>
0.150000
.. raw:: html
</td>
.. raw:: html
<td>
0.834650
.. raw:: html
</td>
.. raw:: html
<td>
0.684052
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
87768.000
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
92538.000
.. raw:: html
</td>
.. raw:: html
<td>
92538.000
.. raw:: html
</td>
.. raw:: html
<td>
11
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0211
.. raw:: html
</td>
.. raw:: html
<td>
42511
.. raw:: html
</td>
.. raw:: html
<td>
0.0211
.. raw:: html
</td>
.. raw:: html
<td>
0.022235
.. raw:: html
</td>
.. raw:: html
<td>
0.150000
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-12 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.614650
.. raw:: html
</td>
.. raw:: html
<td>
0.022235
.. raw:: html
</td>
.. raw:: html
<td>
-1.573455
.. raw:: html
</td>
.. raw:: html
<td>
0.150000
.. raw:: html
</td>
.. raw:: html
<td>
0.798403
.. raw:: html
</td>
.. raw:: html
<td>
0.680882
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
87768.000
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
87768.000
.. raw:: html
</td>
.. raw:: html
<td>
87768.000
.. raw:: html
</td>
.. raw:: html
<td>
12
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0210
.. raw:: html
</td>
.. raw:: html
<td>
2909
.. raw:: html
</td>
.. raw:: html
<td>
0.0210
.. raw:: html
</td>
.. raw:: html
<td>
0.022235
.. raw:: html
</td>
.. raw:: html
<td>
0.150000
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-13 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.588942
.. raw:: html
</td>
.. raw:: html
<td>
0.019405
.. raw:: html
</td>
.. raw:: html
<td>
-1.454733
.. raw:: html
</td>
.. raw:: html
<td>
0.146291
.. raw:: html
</td>
.. raw:: html
<td>
0.767688
.. raw:: html
</td>
.. raw:: html
<td>
0.677881
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
87484.980
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
87768.000
.. raw:: html
</td>
.. raw:: html
<td>
87768.000
.. raw:: html
</td>
.. raw:: html
<td>
13
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0213
.. raw:: html
</td>
.. raw:: html
<td>
57613
.. raw:: html
</td>
.. raw:: html
<td>
0.0213
.. raw:: html
</td>
.. raw:: html
<td>
0.019405
.. raw:: html
</td>
.. raw:: html
<td>
0.146291
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-14 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.565911
.. raw:: html
</td>
.. raw:: html
<td>
0.019373
.. raw:: html
</td>
.. raw:: html
<td>
-1.344915
.. raw:: html
</td>
.. raw:: html
<td>
0.146250
.. raw:: html
</td>
.. raw:: html
<td>
0.739230
.. raw:: html
</td>
.. raw:: html
<td>
0.675665
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
87481.800
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
87484.980
.. raw:: html
</td>
.. raw:: html
<td>
87484.980
.. raw:: html
</td>
.. raw:: html
<td>
14
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0213
.. raw:: html
</td>
.. raw:: html
<td>
48310
.. raw:: html
</td>
.. raw:: html
<td>
0.0213
.. raw:: html
</td>
.. raw:: html
<td>
0.019373
.. raw:: html
</td>
.. raw:: html
<td>
0.146250
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-15 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.551394
.. raw:: html
</td>
.. raw:: html
<td>
0.041659
.. raw:: html
</td>
.. raw:: html
<td>
-1.191436
.. raw:: html
</td>
.. raw:: html
<td>
0.175450
.. raw:: html
</td>
.. raw:: html
<td>
0.714876
.. raw:: html
</td>
.. raw:: html
<td>
0.680484
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
87481.800
.. raw:: html
</td>
.. raw:: html
<td>
87481.800
.. raw:: html
</td>
.. raw:: html
<td>
15
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0213
.. raw:: html
</td>
.. raw:: html
<td>
29454
.. raw:: html
</td>
.. raw:: html
<td>
0.0213
.. raw:: html
</td>
.. raw:: html
<td>
0.041659
.. raw:: html
</td>
.. raw:: html
<td>
0.175450
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-16 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.541846
.. raw:: html
</td>
.. raw:: html
<td>
0.019055
.. raw:: html
</td>
.. raw:: html
<td>
-1.188212
.. raw:: html
</td>
.. raw:: html
<td>
0.145833
.. raw:: html
</td>
.. raw:: html
<td>
0.706049
.. raw:: html
</td>
.. raw:: html
<td>
0.680281
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
89710.344
.. raw:: html
</td>
.. raw:: html
<td>
16
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0210
.. raw:: html
</td>
.. raw:: html
<td>
25564
.. raw:: html
</td>
.. raw:: html
<td>
0.0210
.. raw:: html
</td>
.. raw:: html
<td>
0.019055
.. raw:: html
</td>
.. raw:: html
<td>
0.145833
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-17 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.524682
.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
-1.115149
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</td>
.. raw:: html
<td>
0.145833
.. raw:: html
</td>
.. raw:: html
<td>
0.684599
.. raw:: html
</td>
.. raw:: html
<td>
0.678870
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
87450.000
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
87450.000
.. raw:: html
</td>
.. raw:: html
<td>
87450.000
.. raw:: html
</td>
.. raw:: html
<td>
17
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0206
.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
0.0206
.. raw:: html
</td>
.. raw:: html
<td>
0.019055
.. raw:: html
</td>
.. raw:: html
<td>
0.145833
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-18 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.532621
.. raw:: html
</td>
.. raw:: html
<td>
-0.021999
.. raw:: html
</td>
.. raw:: html
<td>
-1.180440
.. raw:: html
</td>
.. raw:: html
<td>
0.092041
.. raw:: html
</td>
.. raw:: html
<td>
0.696261
.. raw:: html
</td>
.. raw:: html
<td>
0.685307
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
83344.620
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
87450.000
.. raw:: html
</td>
.. raw:: html
<td>
87450.000
.. raw:: html
</td>
.. raw:: html
<td>
18
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0193
.. raw:: html
</td>
.. raw:: html
<td>
164911
.. raw:: html
</td>
.. raw:: html
<td>
0.0193
.. raw:: html
</td>
.. raw:: html
<td>
-0.021999
.. raw:: html
</td>
.. raw:: html
<td>
0.092041
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-19 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.518811
.. raw:: html
</td>
.. raw:: html
<td>
-0.013234
.. raw:: html
</td>
.. raw:: html
<td>
-1.096387
.. raw:: html
</td>
.. raw:: html
<td>
0.103526
.. raw:: html
</td>
.. raw:: html
<td>
0.676861
.. raw:: html
</td>
.. raw:: html
<td>
0.686186
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
84221.028
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
83344.620
.. raw:: html
</td>
.. raw:: html
<td>
83344.620
.. raw:: html
</td>
.. raw:: html
<td>
19
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0198
.. raw:: html
</td>
.. raw:: html
<td>
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.. raw:: html
</td>
.. raw:: html
<td>
0.0198
.. raw:: html
</td>
.. raw:: html
<td>
-0.013234
.. raw:: html
</td>
.. raw:: html
<td>
0.103526
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-20 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.505168
.. raw:: html
</td>
.. raw:: html
<td>
-0.017324
.. raw:: html
</td>
.. raw:: html
<td>
-1.050273
.. raw:: html
</td>
.. raw:: html
<td>
0.098170
.. raw:: html
</td>
.. raw:: html
<td>
0.659945
.. raw:: html
</td>
.. raw:: html
<td>
0.685070
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
83812.080
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
84221.028
.. raw:: html
</td>
.. raw:: html
<td>
84221.028
.. raw:: html
</td>
.. raw:: html
<td>
20
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0193
.. raw:: html
</td>
.. raw:: html
<td>
132725
.. raw:: html
</td>
.. raw:: html
<td>
0.0193
.. raw:: html
</td>
.. raw:: html
<td>
-0.017324
.. raw:: html
</td>
.. raw:: html
<td>
0.098170
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-21 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.492384
.. raw:: html
</td>
.. raw:: html
<td>
-0.018494
.. raw:: html
</td>
.. raw:: html
<td>
-1.002051
.. raw:: html
</td>
.. raw:: html
<td>
0.096637
.. raw:: html
</td>
.. raw:: html
<td>
0.643679
.. raw:: html
</td>
.. raw:: html
<td>
0.684283
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
83695.056
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
83812.080
.. raw:: html
</td>
.. raw:: html
<td>
83812.080
.. raw:: html
</td>
.. raw:: html
<td>
21
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0193
.. raw:: html
</td>
.. raw:: html
<td>
201155
.. raw:: html
</td>
.. raw:: html
<td>
0.0193
.. raw:: html
</td>
.. raw:: html
<td>
-0.018494
.. raw:: html
</td>
.. raw:: html
<td>
0.096637
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-22 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.482998
.. raw:: html
</td>
.. raw:: html
<td>
-0.004744
.. raw:: html
</td>
.. raw:: html
<td>
-0.927947
.. raw:: html
</td>
.. raw:: html
<td>
0.114653
.. raw:: html
</td>
.. raw:: html
<td>
0.629319
.. raw:: html
</td>
.. raw:: html
<td>
0.686478
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
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<td>
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<td>
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<td>
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<th>
2015-03-23 23:59:00+00:00
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<td>
0.477523
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<tr>
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<th>
2015-03-24 23:59:00+00:00
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.. raw:: html
<td>
0.504086
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
0.0188
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<td>
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<td>
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<tr>
.. raw:: html
<th>
2015-03-25 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.497690
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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</td>
.. raw:: html
<td>
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<td>
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<td>
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<td>
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<td>
[]
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</td>
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<td>
0.0193
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</td>
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<td>
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0.0193
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<td>
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<td>
0.031148
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.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-26 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.489730
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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</td>
.. raw:: html
<td>
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<td>
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<td>
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<td>
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[]
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<td>
0.0201
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<td>
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<td>
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<td>
0.010523
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</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-27 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.495916
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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</td>
.. raw:: html
<td>
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<td>
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<td>
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<td>
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<td>
[]
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<td>
0.0195
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</td>
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<td>
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<td>
0.0195
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</td>
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<td>
-0.049785
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<td>
0.055636
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</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-28 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.488469
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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</td>
.. raw:: html
<td>
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<td>
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<td>
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<td>
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<td>
0.0195
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<td>
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<td>
0.0195
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</td>
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<td>
-0.064490
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<td>
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</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-29 23:59:00+00:00
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</th>
.. raw:: html
<td>
0.479671
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
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</td>
.. raw:: html
<td>
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<td>
…
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</td>
.. raw:: html
<td>
100000.0
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</td>
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<td>
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</td>
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<td>
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<td>
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<td>
[]
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</td>
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<td>
0.0195
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</td>
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<td>
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<td>
0.0195
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</td>
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<td>
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<td>
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</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-30 23:59:00+00:00
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</th>
.. raw:: html
<td>
0.476306
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<td>
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<td>
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<td>
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<td>
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<td>
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<td>
…
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</td>
.. raw:: html
<td>
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<td>
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<td>
0.0196
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</tr>
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<tr>
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<th>
…
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</th>
.. raw:: html
<td>
…
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</td>
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…
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</td>
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<td>
…
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…
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…
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…
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…
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…
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…
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…
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<td>
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…
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…
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…
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</td>
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<td>
…
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2017-05-30 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.495432
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<td>
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<td>
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<td>
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</td>
.. raw:: html
<td>
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<td>
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<td>
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.. raw:: html
<tr>
.. raw:: html
<th>
2017-05-31 23:59:00+00:00
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.. raw:: html
<td>
0.495243
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<td>
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<tr>
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<th>
2017-06-01 23:59:00+00:00
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<td>
0.495836
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</td>
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<td>
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<tr>
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<th>
2017-06-02 23:59:00+00:00
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<td>
0.495948
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2017-06-03 23:59:00+00:00
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2017-06-04 23:59:00+00:00
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2017-06-05 23:59:00+00:00
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0.496148
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2017-06-06 23:59:00+00:00
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0.497592
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2017-06-07 23:59:00+00:00
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2017-06-08 23:59:00+00:00
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0.499349
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2017-06-09 23:59:00+00:00
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2017-06-10 23:59:00+00:00
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2017-06-11 23:59:00+00:00
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2017-06-12 23:59:00+00:00
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2017-06-13 23:59:00+00:00
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2017-06-14 23:59:00+00:00
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2017-06-15 23:59:00+00:00
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2017-06-16 23:59:00+00:00
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2017-06-17 23:59:00+00:00
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2017-06-18 23:59:00+00:00
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2017-06-19 23:59:00+00:00
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2017-06-20 23:59:00+00:00
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2017-06-21 23:59:00+00:00
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<td>
0.895349
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</td>
.. raw:: html
<td>
0.000000
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</td>
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<td>
14455.525045
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</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
798180.000
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</td>
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<td>
…
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</td>
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<td>
100000.0
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</td>
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<td>
761701.584
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</td>
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<td>
761701.584
.. raw:: html
</td>
.. raw:: html
<td>
850
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
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<td>
0.0221
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</td>
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<td>
62426319778
.. raw:: html
</td>
.. raw:: html
<td>
0.0221
.. raw:: html
</td>
.. raw:: html
<td>
7.126355
.. raw:: html
</td>
.. raw:: html
<td>
9.458331
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2017-06-28 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.507750
.. raw:: html
</td>
.. raw:: html
<td>
7.135895
.. raw:: html
</td>
.. raw:: html
<td>
-0.016340
.. raw:: html
</td>
.. raw:: html
<td>
9.470831
.. raw:: html
</td>
.. raw:: html
<td>
0.564078
.. raw:: html
</td>
.. raw:: html
<td>
0.895349
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
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<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
799134.000
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
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<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
798180.000
.. raw:: html
</td>
.. raw:: html
<td>
798180.000
.. raw:: html
</td>
.. raw:: html
<td>
851
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
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<td>
0.0222
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</td>
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<td>
39676839183
.. raw:: html
</td>
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<td>
0.0222
.. raw:: html
</td>
.. raw:: html
<td>
7.135895
.. raw:: html
</td>
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<td>
9.470831
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</td>
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</tr>
.. raw:: html
</tbody>
.. raw:: html
</table>
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<p>
851 rows × 45 columns
.. raw:: html
</p>
.. raw:: html
</div>
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
<div>
.. raw:: html
<table border="1" class="dataframe">
.. raw:: html
<thead>
.. raw:: html
<tr style="text-align: right;">
.. raw:: html
<th>
.. raw:: html
</th>
.. raw:: html
<th>
algo_volatility
.. raw:: html
</th>
.. raw:: html
<th>
algorithm_period_return
.. raw:: html
</th>
.. raw:: html
<th>
alpha
.. raw:: html
</th>
.. raw:: html
<th>
benchmark_period_return
.. raw:: html
</th>
.. raw:: html
<th>
benchmark_volatility
.. raw:: html
</th>
.. raw:: html
<th>
beta
.. raw:: html
</th>
.. raw:: html
<th>
capital_used
.. raw:: html
</th>
.. raw:: html
<th>
cash
.. raw:: html
</th>
.. raw:: html
<th>
ending_cash
.. raw:: html
</th>
.. raw:: html
<th>
ending_exposure
.. raw:: html
</th>
.. raw:: html
<th>
…
.. raw:: html
</th>
.. raw:: html
<th>
starting_cash
.. raw:: html
</th>
.. raw:: html
<th>
starting_exposure
.. raw:: html
</th>
.. raw:: html
<th>
starting_value
.. raw:: html
</th>
.. raw:: html
<th>
trading_days
.. raw:: html
</th>
.. raw:: html
<th>
transactions
.. raw:: html
</th>
.. raw:: html
<th>
treasury_period_return
.. raw:: html
</th>
.. raw:: html
<th>
volume
.. raw:: html
</th>
.. raw:: html
<th>
treasury
.. raw:: html
</th>
.. raw:: html
<th>
algorithm
.. raw:: html
</th>
.. raw:: html
<th>
benchmark
.. raw:: html
</th>
.. raw:: html
</tr>
.. raw:: html
</thead>
.. raw:: html
<tbody>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-01 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
NaN
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
NaN
.. raw:: html
</td>
.. raw:: html
<td>
0.045833
.. raw:: html
</td>
.. raw:: html
<td>
NaN
.. raw:: html
</td>
.. raw:: html
<td>
NaN
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
100000.000000
.. raw:: html
</td>
.. raw:: html
<td>
100000.000000
.. raw:: html
</td>
.. raw:: html
<td>
0.000
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
0.000
.. raw:: html
</td>
.. raw:: html
<td>
0.000
.. raw:: html
</td>
.. raw:: html
<td>
1
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0200
.. raw:: html
</td>
.. raw:: html
<td>
317
.. raw:: html
</td>
.. raw:: html
<td>
0.0200
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
0.045833
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-02 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.000278
.. raw:: html
</td>
.. raw:: html
<td>
-0.000025
.. raw:: html
</td>
.. raw:: html
<td>
0.011045
.. raw:: html
</td>
.. raw:: html
<td>
0.120833
.. raw:: html
</td>
.. raw:: html
<td>
0.290503
.. raw:: html
</td>
.. raw:: html
<td>
-0.000956
.. raw:: html
</td>
.. raw:: html
<td>
-85544.474955
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
85542.000
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
0.000
.. raw:: html
</td>
.. raw:: html
<td>
0.000
.. raw:: html
</td>
.. raw:: html
<td>
2
.. raw:: html
</td>
.. raw:: html
<td>
[{u’commission’: None, u’amount’: 318, u’sid’:…
.. raw:: html
</td>
.. raw:: html
<td>
0.0208
.. raw:: html
</td>
.. raw:: html
<td>
98063
.. raw:: html
</td>
.. raw:: html
<td>
0.0208
.. raw:: html
</td>
.. raw:: html
<td>
-0.000025
.. raw:: html
</td>
.. raw:: html
<td>
0.120833
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-03 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.051796
.. raw:: html
</td>
.. raw:: html
<td>
-0.005688
.. raw:: html
</td>
.. raw:: html
<td>
-1.197544
.. raw:: html
</td>
.. raw:: html
<td>
0.113416
.. raw:: html
</td>
.. raw:: html
<td>
0.633538
.. raw:: html
</td>
.. raw:: html
<td>
0.077239
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
84975.642
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
85542.000
.. raw:: html
</td>
.. raw:: html
<td>
85542.000
.. raw:: html
</td>
.. raw:: html
<td>
3
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0212
.. raw:: html
</td>
.. raw:: html
<td>
442983
.. raw:: html
</td>
.. raw:: html
<td>
0.0212
.. raw:: html
</td>
.. raw:: html
<td>
-0.005688
.. raw:: html
</td>
.. raw:: html
<td>
0.113416
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-04 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.342118
.. raw:: html
</td>
.. raw:: html
<td>
0.034955
.. raw:: html
</td>
.. raw:: html
<td>
0.401861
.. raw:: html
</td>
.. raw:: html
<td>
0.166666
.. raw:: html
</td>
.. raw:: html
<td>
0.524400
.. raw:: html
</td>
.. raw:: html
<td>
0.181468
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
89040.000
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
84975.642
.. raw:: html
</td>
.. raw:: html
<td>
84975.642
.. raw:: html
</td>
.. raw:: html
<td>
4
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0212
.. raw:: html
</td>
.. raw:: html
<td>
245889
.. raw:: html
</td>
.. raw:: html
<td>
0.0212
.. raw:: html
</td>
.. raw:: html
<td>
0.034955
.. raw:: html
</td>
.. raw:: html
<td>
0.166666
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
<tr>
.. raw:: html
<th>
2015-03-05 23:59:00+00:00
.. raw:: html
</th>
.. raw:: html
<td>
0.637226
.. raw:: html
</td>
.. raw:: html
<td>
-0.038185
.. raw:: html
</td>
.. raw:: html
<td>
-3.914003
.. raw:: html
</td>
.. raw:: html
<td>
0.070834
.. raw:: html
</td>
.. raw:: html
<td>
0.976896
.. raw:: html
</td>
.. raw:: html
<td>
0.550520
.. raw:: html
</td>
.. raw:: html
<td>
0.000000
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
14455.525045
.. raw:: html
</td>
.. raw:: html
<td>
81726.000
.. raw:: html
</td>
.. raw:: html
<td>
…
.. raw:: html
</td>
.. raw:: html
<td>
100000.0
.. raw:: html
</td>
.. raw:: html
<td>
89040.000
.. raw:: html
</td>
.. raw:: html
<td>
89040.000
.. raw:: html
</td>
.. raw:: html
<td>
5
.. raw:: html
</td>
.. raw:: html
<td>
[]
.. raw:: html
</td>
.. raw:: html
<td>
0.0211
.. raw:: html
</td>
.. raw:: html
<td>
117440
.. raw:: html
</td>
.. raw:: html
<td>
0.0211
.. raw:: html
</td>
.. raw:: html
<td>
-0.038185
.. raw:: html
</td>
.. raw:: html
<td>
0.070834
.. raw:: html
</td>
.. raw:: html
</tr>
.. raw:: html
</tbody>
.. raw:: html
</table>
.. raw:: html
<p>
5 rows × 45 columns
.. raw:: html
</p>
.. raw:: html
</div>
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 <https://www.jetbrains.com/pycharm/download/>`__.
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<example-algos>`.
Feel free to ask questions on the ``#catalyst_dev`` channel of our
`Discord group <https://discord.gg/SJK32GY>`__ and report
problems on our `GitHub issue tracker <https://github.com/enigmampc/catalyst/issues>`__.
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