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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 can 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 :doc:`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 algorith,
``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:: 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 <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>`_.
.. 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.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
base_currency = context.exchanges.values()[0].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 <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``).
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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