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DOC: Features

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Victor Grau Serrat
2017-10-20 08:28:15 -06:00
parent 78351ee879
commit 00df4f1227
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_sources/index.txt
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.. include:: welcome.rst
|
|
Table of Contents
-----------------
.. toctree::
:maxdepth: 1
_sources/welcome.txt
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data-driven investment strategies.
Features
========
========
- Ease of use: Catalyst tries to get out of your way so that you can
focus on algorithm development. See examples provided.
- Support for several of the top crypto-exchanges by trading volume.
- Input of historical pricing data of all crypto-assets by exchange,
with daily and minute resolution.
- Backtesting and live-trading functionality, with a seamless transition
between the two modes.
- Output of performance statistics are based on Pandas DataFrames to
integrate nicely into the existing PyData eco-system.
- Statistic and machine learning libraries like matplotlib, scipy,
statsmodels, and sklearn support development, analysis, and
visualization of state-of-the-art trading systems.
index.html
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data-driven investment strategies.</p>
<div class="section" id="features">
<h1>Features<a class="headerlink" href="#features" title="Permalink to this headline"></a></h1>
<ul class="simple">
<li>Ease of use: Catalyst tries to get out of your way so that you can
focus on algorithm development. See examples provided.</li>
<li>Support for several of the top crypto-exchanges by trading volume.</li>
<li>Input of historical pricing data of all crypto-assets by exchange,
with daily and minute resolution.</li>
<li>Backtesting and live-trading functionality, with a seamless transition
between the two modes.</li>
<li>Output of performance statistics are based on Pandas DataFrames to
integrate nicely into the existing PyData eco-system.</li>
<li>Statistic and machine learning libraries like matplotlib, scipy,
statsmodels, and sklearn support development, analysis, and
visualization of state-of-the-art trading systems.</li>
</ul>
<div class="line-block">
<div class="line"><br /></div>
<div class="line"><br /></div>
</div>
<div class="section" id="table-of-contents">
<h2>Table of Contents<a class="headerlink" href="#table-of-contents" title="Permalink to this headline"></a></h2>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="install.html">Install</a></li>
</ul>
</div>
</div>
</div>
searchindex.js
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welcome.html
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data-driven investment strategies.</p>
<div class="section" id="features">
<h1>Features<a class="headerlink" href="#features" title="Permalink to this headline"></a></h1>
<ul class="simple">
<li>Ease of use: Catalyst tries to get out of your way so that you can
focus on algorithm development. See examples provided.</li>
<li>Support for several of the top crypto-exchanges by trading volume.</li>
<li>Input of historical pricing data of all crypto-assets by exchange,
with daily and minute resolution.</li>
<li>Backtesting and live-trading functionality, with a seamless transition
between the two modes.</li>
<li>Output of performance statistics are based on Pandas DataFrames to
integrate nicely into the existing PyData eco-system.</li>
<li>Statistic and machine learning libraries like matplotlib, scipy,
statsmodels, and sklearn support development, analysis, and
visualization of state-of-the-art trading systems.</li>
</ul>
</div>