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<li class="toctree-l1 current"><a class="current reference internal" href="">Catalyst Beginner Tutorial</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="#basics">Basics</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#my-first-algorithm">My first algorithm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#running-the-algorithm">Running the algorithm</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="#ingesting-data">Ingesting data</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#working-example-dual-moving-average-cross-over">Working example: Dual Moving Average Cross-Over</a></li>
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<div class="section" id="catalyst-beginner-tutorial">
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<h1>Catalyst Beginner Tutorial<a class="headerlink" href="#catalyst-beginner-tutorial" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="basics">
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<h2>Basics<a class="headerlink" href="#basics" title="Permalink to this headline">¶</a></h2>
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<p>Catalyst is an open-source algorithmic trading simulator for crypto
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assets written in Python.</p>
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<p>The source can be found at: <a class="reference external" href="https://github.com/enigmampc/catalyst">https://github.com/enigmampc/catalyst</a></p>
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<p>Some benefits include:</p>
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<ul class="simple">
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<li>Support for several of the top crypto-exchanges by trading volume.</li>
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<li>Realistic: slippage, transaction costs, order delays.</li>
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<li>Stream-based: Process each event individually, avoids look-ahead
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bias.</li>
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<li>Batteries included: Common transforms (moving average) as well as
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common risk calculations (Sharpe).</li>
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<li>Developed and continuously updated by
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<a class="reference external" href="https://www.enigma.co">Enigma MPC</a> which is building the Enigma
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data marketplace protocol as well as Catalyst, the first application
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that will run on our protocol. Powered by our financial data
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marketplace, Catalyst empowers users to share and curate data and
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build profitable, data-driven investment strategies.</li>
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</ul>
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<p>This tutorial assumes that you have Catalyst correctly installed, see the
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<a class="reference internal" href="install.html"><em>installation instructions</em></a> if you haven’t set up
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Catalyst yet.</p>
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<p>Every <code class="docutils literal"><span class="pre">catalyst</span></code> algorithm consists of at least two functions you have to
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define:</p>
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<ul class="simple">
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<li><code class="docutils literal"><span class="pre">initialize(context)</span></code></li>
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<li><code class="docutils literal"><span class="pre">handle_data(context,</span> <span class="pre">data)</span></code></li>
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</ul>
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<p>Before the start of the algorithm, <code class="docutils literal"><span class="pre">catalyst</span></code> calls the
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<code class="docutils literal"><span class="pre">initialize()</span></code> function and passes in a <code class="docutils literal"><span class="pre">context</span></code> variable.
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<code class="docutils literal"><span class="pre">context</span></code> is a persistent namespace for you to store variables you
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need to access from one algorithm iteration to the next.</p>
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<p>After the algorithm has been initialized, <code class="docutils literal"><span class="pre">catalyst</span></code> calls the
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<code class="docutils literal"><span class="pre">handle_data()</span></code> function once for each event. At every call, it passes
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the same <code class="docutils literal"><span class="pre">context</span></code> variable and an event-frame called <code class="docutils literal"><span class="pre">data</span></code>
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containing the current trading bar with open, high, low, and close
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(OHLC) prices as well as volume for each crypto asset in your universe.</p>
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</div>
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<div class="section" id="my-first-algorithm">
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<h2>My first algorithm<a class="headerlink" href="#my-first-algorithm" title="Permalink to this headline">¶</a></h2>
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<p>Lets take a look at a very simple algorithm from the <code class="docutils literal"><span class="pre">examples</span></code>
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directory, <code class="docutils literal"><span class="pre">buy_btc.py</span></code>:</p>
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<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">catalyst.api</span> <span class="kn">import</span> <span class="n">order</span><span class="p">,</span> <span class="n">record</span><span class="p">,</span> <span class="n">symbol</span>
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<span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="n">context</span><span class="p">):</span>
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<span class="n">context</span><span class="o">.</span><span class="n">asset</span> <span class="o">=</span> <span class="n">symbol</span><span class="p">(</span><span class="s">'btc_usd'</span><span class="p">)</span>
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<span class="k">def</span> <span class="nf">handle_data</span><span class="p">(</span><span class="n">context</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
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<span class="n">order</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
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<span class="n">record</span><span class="p">(</span><span class="n">btc</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">current</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="s">'price'</span><span class="p">))</span>
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</pre></div>
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</div>
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<p>As you can see, we first have to import some functions we would like to
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use. All functions commonly used in your algorithm can be found in
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<code class="docutils literal"><span class="pre">catalyst.api</span></code>. Here we are using <code class="xref py py-func docutils literal"><span class="pre">order()</span></code> which takes two
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arguments: a cryptoasset object, and a number specifying how many assets you would
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like to order (if negative, <code class="xref py py-func docutils literal"><span class="pre">order()</span></code> will sell/short
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assets). In this case we want to order 1 bitcoin at each iteration.</p>
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<p>Finally, the <code class="xref py py-func docutils literal"><span class="pre">record()</span></code> function allows you to save the value
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of a variable at each iteration. You provide it with a name for the variable
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together with the variable itself: <code class="docutils literal"><span class="pre">varname=var</span></code>. After the algorithm
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finished running you will have access to each variable value you tracked
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with <code class="xref py py-func docutils literal"><span class="pre">record()</span></code> under the name you provided (we will see this
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further below). You also see how we can access the current price data of
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a bitcoin in the <code class="docutils literal"><span class="pre">data</span></code> event frame.</p>
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</div>
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<div class="section" id="running-the-algorithm">
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<h2>Running the algorithm<a class="headerlink" href="#running-the-algorithm" title="Permalink to this headline">¶</a></h2>
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<p>To can now test this algorithm on crypto data, <code class="docutils literal"><span class="pre">catalyst</span></code> provides three
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interfaces:</p>
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<ul class="simple">
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<li>A command-line interface,</li>
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<li><code class="docutils literal"><span class="pre">IPython</span> <span class="pre">Notebook</span></code> magic,</li>
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<li>and <code class="xref py py-func docutils literal"><span class="pre">run_algorithm()</span></code>.</li>
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</ul>
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<div class="section" id="ingesting-data">
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<h3>Ingesting data<a class="headerlink" href="#ingesting-data" title="Permalink to this headline">¶</a></h3>
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<p>In previous versions of Catalyst you needed to manually ingest data before running
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your algorithm to make it available at runtime. Starting with version 0.3, the
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algorithm will automagically ingest the data it needs the first time that encounters
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a data request for data that it doesn’t have.</p>
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<p>Still, we believe it is important for you to have a high-level understanding
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of how data is managed:</p>
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<ul class="simple">
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<li>Pricing data is split and packaged into <code class="docutils literal"><span class="pre">bundles</span></code>: chunks of data organized
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as time series that are kept up to date daily on Enigma’s servers. Catalyst
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downloads the bundles that needs at any given time, and reconstructs the whole
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dataset in your hard drive.</li>
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<li>Pricing data is provided in <code class="docutils literal"><span class="pre">daily</span></code> and <code class="docutils literal"><span class="pre">minute</span></code> resolution. Those are different
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bundle datasets, and are managed separately.</li>
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<li>Bundles are exchange-specific, as the pricing data is specific to the trades that
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happen in each exchange. You can optionally specify which exchange you want pricing
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data from.</li>
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<li>Catalyst keeps track of all the downloaded bundles, so that it only has to download
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them once, and will do incremental updates as needed.</li>
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<li>When running in <code class="docutils literal"><span class="pre">live</span> <span class="pre">trading</span></code> mode, Catalyst will first look for historical
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pricing data in the locally stored bundles. If there is anything missing, Catalyst will
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hit the exchange for the most recent data, and merge it with the local bundle to make
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it available for future iterations.</li>
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</ul>
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<p>If you want to learn more, check out the <a class="reference internal" href="bundles.html#ingesting-data"><span>ingesting data</span></a> section
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for more detail.</p>
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</div>
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<div class="section" id="command-line-interface">
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<h3>Command line interface<a class="headerlink" href="#command-line-interface" title="Permalink to this headline">¶</a></h3>
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<p>After you installed Catalyst you should be able to execute the following
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from your command line (e.g. <code class="docutils literal"><span class="pre">cmd.exe</span></code> on Windows, or the Terminal app
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on OSX). Displaying here a simplified output for eductional purposes:</p>
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<div class="highlight-bash"><div class="highlight"><pre><span class="nv">$ </span>catalyst --help
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</pre></div>
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</div>
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<div class="highlight-python"><div class="highlight"><pre>Usage: catalyst [OPTIONS] COMMAND [ARGS]...
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Top level catalyst entry point.
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Options:
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--version Show the version and exit.
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--help Show this message and exit.
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Commands:
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ingest-exchange Ingest data for the given exchange.
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live Trade live with the given algorithm.
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run Run a backtest for the given algorithm.
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</pre></div>
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</div>
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<p>There are three main modes you can run on Catalyst. The first being <code class="docutils literal"><span class="pre">ingest-exchange</span></code>
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for data ingestion, which we have summarized in the previous section. The second
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is <code class="docutils literal"><span class="pre">live</span></code> to use your algorithm to trade live against a given exchange, and the
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third mode <code class="docutils literal"><span class="pre">run</span></code> is to backtest your algorithm before trading live with it.</p>
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<p>Let’s start with backtesting, so run this other command to learn more about
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the available options:</p>
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<div class="highlight-bash"><div class="highlight"><pre><span class="nv">$ </span>catalyst run --help
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</pre></div>
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</div>
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<div class="highlight-python"><div class="highlight"><pre>Usage: catalyst run [OPTIONS]
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Run a backtest for the given algorithm.
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Options:
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-f, --algofile FILENAME The file that contains the algorithm to run.
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-t, --algotext TEXT The algorithm script to run.
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-D, --define TEXT Define a name to be bound in the namespace
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before executing the algotext. For example
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'-Dname=value'. The value may be any python
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expression. These are evaluated in order so
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they may refer to previously defined names.
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--data-frequency [daily|minute]
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The data frequency of the simulation.
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[default: daily]
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--capital-base FLOAT The starting capital for the simulation.
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[default: 10000000.0]
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-b, --bundle BUNDLE-NAME The data bundle to use for the simulation.
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[default: poloniex]
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--bundle-timestamp TIMESTAMP The date to lookup data on or before.
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[default: <current-time>]
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-s, --start DATE The start date of the simulation.
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-e, --end DATE The end date of the simulation.
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-o, --output FILENAME The location to write the perf data. If this
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is '-' the perf will be written to stdout.
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[default: -]
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--print-algo / --no-print-algo Print the algorithm to stdout.
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-x, --exchange-name [poloniex|bitfinex|bittrex]
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The name of the targeted exchange
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(supported: bitfinex, bittrex, poloniex).
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-n, --algo-namespace TEXT A label assigned to the algorithm for data
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storage purposes.
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-c, --base-currency TEXT The base currency used to calculate
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statistics (e.g. usd, btc, eth).
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--help Show this message and exit.
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</pre></div>
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</div>
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<p>As you can see there are a couple of flags that specify where to find your
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algorithm (<code class="docutils literal"><span class="pre">-f</span></code>) as well as a parameter to specify which exchange to use.
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There are also arguments for the date range to run the algorithm over
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(<code class="docutils literal"><span class="pre">--start</span></code> and <code class="docutils literal"><span class="pre">--end</span></code>). Finally, you’ll want to save the performance
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metrics of your algorithm so that you can analyze how it performed. This is
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done via the <code class="docutils literal"><span class="pre">--output</span></code> flag and will cause it to write the performance
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<code class="docutils literal"><span class="pre">DataFrame</span></code> in the pickle Python file format. Note that you can also define
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a configuration file with these parameters that you can then conveniently pass
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to the <code class="docutils literal"><span class="pre">-c</span></code> option so that you don’t have to supply the command line args
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all the time (see the .conf files in the examples directory).</p>
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<p>Thus, to execute our algorithm from above and save the results to
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<code class="docutils literal"><span class="pre">buy_btc_simple_out.pickle</span></code> we would call <code class="docutils literal"><span class="pre">catalyst</span> <span class="pre">run</span></code> as follows:</p>
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<div class="highlight-python"><div class="highlight"><pre><span class="n">catalyst</span> <span class="n">run</span> <span class="o">-</span><span class="n">f</span> <span class="n">buy_btc_simple</span><span class="o">.</span><span class="n">py</span> <span class="o">-</span><span class="n">x</span> <span class="n">bitfinex</span> <span class="o">--</span><span class="n">start</span> <span class="mi">2016</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="mi">1</span> <span class="o">--</span><span class="n">end</span> <span class="mi">2016</span><span class="o">-</span><span class="mi">9</span><span class="o">-</span><span class="mi">29</span> <span class="o">-</span><span class="n">o</span> <span class="n">buy_simple_btc_out</span><span class="o">.</span><span class="n">pickle</span>
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</pre></div>
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</div>
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<p><code class="docutils literal"><span class="pre">run</span></code> first calls the <code class="docutils literal"><span class="pre">initialize()</span></code> function, and then
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streams the historical asset price day-by-day through <code class="docutils literal"><span class="pre">handle_data()</span></code>.
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After each call to <code class="docutils literal"><span class="pre">handle_data()</span></code> we instruct <code class="docutils literal"><span class="pre">catalyst</span></code> to order 1
|
|
bitcoin. After the call of the <code class="docutils literal"><span class="pre">order()</span></code> function, <code class="docutils literal"><span class="pre">catalyst</span></code>
|
|
enters the ordered stock and amount in the order book. After the
|
|
<code class="docutils literal"><span class="pre">handle_data()</span></code> function has finished, <code class="docutils literal"><span class="pre">catalyst</span></code> 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 <code class="docutils literal"><span class="pre">catalyst</span></code> uses).</p>
|
|
<p>Let’s take a quick look at the performance <code class="docutils literal"><span class="pre">DataFrame</span></code>. For this, we
|
|
use <code class="docutils literal"><span class="pre">pandas</span></code> from inside the IPython Notebook and print the first ten
|
|
rows. Note that <code class="docutils literal"><span class="pre">catalyst</span></code> makes heavy usage of
|
|
<a class="reference external" href="http://pandas.pydata.org/">pandas</a>, especially for data input and
|
|
outputting so it’s worth spending some time to learn it.</p>
|
|
<div class="highlight-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
|
|
<span class="n">perf</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_pickle</span><span class="p">(</span><span class="s">'buy_btc_simple_out.pickle'</span><span class="p">)</span> <span class="c"># read in perf DataFrame</span>
|
|
<span class="n">perf</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
|
|
</pre></div>
|
|
</div>
|
|
<p>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 very first column
|
|
<code class="docutils literal"><span class="pre">btc</span></code> was placed there by the <code class="docutils literal"><span class="pre">record()</span></code> 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.</p>
|
|
<p>Our algorithm performance as assessed by the
|
|
<code class="docutils literal"><span class="pre">portfolio_value</span></code> closely matches that of the bitcoin price. This
|
|
is not surprising as our algorithm only bought bitcoin every chance it got.</p>
|
|
</div>
|
|
</div>
|
|
<div class="section" id="access-to-previous-prices-using-history">
|
|
<h2>Access to previous prices using <code class="docutils literal"><span class="pre">history</span></code><a class="headerlink" href="#access-to-previous-prices-using-history" title="Permalink to this headline">¶</a></h2>
|
|
<div class="section" id="working-example-dual-moving-average-cross-over">
|
|
<h3>Working example: Dual Moving Average Cross-Over<a class="headerlink" href="#working-example-dual-moving-average-cross-over" title="Permalink to this headline">¶</a></h3>
|
|
<p>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.</p>
|
|
<p>As we need to have access to previous prices to implement this strategy
|
|
we need a new concept: History</p>
|
|
<p><code class="docutils literal"><span class="pre">data.history()</span></code> 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 <code class="docutils literal"><span class="pre">'1d'</span></code> for <code class="docutils literal"><span class="pre">'1m'</span></code>
|
|
but note that you need to have minute-level data for using <code class="docutils literal"><span class="pre">1m</span></code>). This is
|
|
a function we use in the <code class="docutils literal"><span class="pre">handle_data()</span></code> section:</p>
|
|
<div class="highlight-python"><div class="highlight"><pre> <span class="kn">from</span> <span class="nn">catalyst.api</span> <span class="kn">import</span> <span class="n">order</span><span class="p">,</span> <span class="n">record</span><span class="p">,</span> <span class="n">symbol</span>
|
|
|
|
<span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="n">context</span><span class="p">):</span>
|
|
<span class="n">context</span><span class="o">.</span><span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
|
|
<span class="n">context</span><span class="o">.</span><span class="n">asset</span> <span class="o">=</span> <span class="n">symbol</span><span class="p">(</span><span class="s">'btc_usd'</span><span class="p">)</span>
|
|
|
|
<span class="k">def</span> <span class="nf">handle_data</span><span class="p">(</span><span class="n">context</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
|
|
<span class="c"># Skip first 300 days to get full windows</span>
|
|
<span class="n">context</span><span class="o">.</span><span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
|
|
<span class="k">if</span> <span class="n">context</span><span class="o">.</span><span class="n">i</span> <span class="o"><</span> <span class="mi">300</span><span class="p">:</span>
|
|
<span class="k">return</span>
|
|
|
|
<span class="c"># Compute averages</span>
|
|
<span class="c"># data.history() has to be called with the same params</span>
|
|
<span class="c"># from above and returns a pandas dataframe.</span>
|
|
<span class="n">short_mavg</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">history</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="s">'price'</span><span class="p">,</span> <span class="n">bar_count</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="s">"1d"</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
|
|
<span class="n">long_mavg</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">history</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="s">'price'</span><span class="p">,</span> <span class="n">bar_count</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="s">"1d"</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
|
|
|
|
<span class="c"># Trading logic</span>
|
|
<span class="k">if</span> <span class="n">short_mavg</span> <span class="o">></span> <span class="n">long_mavg</span><span class="p">:</span>
|
|
<span class="c"># order_target orders as many shares as needed to</span>
|
|
<span class="c"># achieve the desired number of shares.</span>
|
|
<span class="n">order_target</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
|
|
<span class="k">elif</span> <span class="n">short_mavg</span> <span class="o"><</span> <span class="n">long_mavg</span><span class="p">:</span>
|
|
<span class="n">order_target</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
|
|
|
<span class="c"># Save values for later inspection</span>
|
|
<span class="n">record</span><span class="p">(</span><span class="n">btc</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">current</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="s">'price'</span><span class="p">),</span>
|
|
<span class="n">short_mavg</span><span class="o">=</span><span class="n">short_mavg</span><span class="p">,</span>
|
|
<span class="n">long_mavg</span><span class="o">=</span><span class="n">long_mavg</span><span class="p">)</span>
|
|
</pre></div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
<div class="section" id="conclusions">
|
|
<h2>Conclusions<a class="headerlink" href="#conclusions" title="Permalink to this headline">¶</a></h2>
|
|
<p>We hope that this tutorial gave you a little insight into the
|
|
architecture, API, and features of <code class="docutils literal"><span class="pre">catalyst</span></code>. For next steps, check
|
|
out some of the
|
|
<a class="reference external" href="https://github.com/enigmampc/catalyst/tree/master/catalyst/examples">examples</a>.
|
|
The natural next step would be too look into the
|
|
<a class="reference external" href="https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_and_hodl.py">buy_and_hodl</a>
|
|
example, which is a more elaborated and realistic version of the <code class="docutils literal"><span class="pre">buy_btc_simple</span></code> example presented in this tutorial.</p>
|
|
<p>Feel free to ask questions on the <code class="docutils literal"><span class="pre">#catalyst_dev</span></code> channel of our
|
|
<a class="reference external" href="https://discord.gg/SJK32GY">Discord group</a> and report
|
|
problems on our <a class="reference external" href="https://github.com/enigmampc/catalyst/issues">GitHub issue tracker</a>.</p>
|
|
</div>
|
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