942 lines
46 KiB
XML
942 lines
46 KiB
XML
<?xml version="1.0" encoding="utf-8" ?>
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<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN"
|
||
"JATS-publishing1.dtd">
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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.2" article-type="other">
|
||
<front>
|
||
<journal-meta>
|
||
<journal-id></journal-id>
|
||
<journal-title-group>
|
||
<journal-title>Journal of Open Source Software</journal-title>
|
||
<abbrev-journal-title>JOSS</abbrev-journal-title>
|
||
</journal-title-group>
|
||
<issn publication-format="electronic">2475-9066</issn>
|
||
<publisher>
|
||
<publisher-name>Open Journals</publisher-name>
|
||
</publisher>
|
||
</journal-meta>
|
||
<article-meta>
|
||
<article-id pub-id-type="publisher-id">0</article-id>
|
||
<article-id pub-id-type="doi">N/A</article-id>
|
||
<title-group>
|
||
<article-title><monospace>FreqAI</monospace>: generalizing adaptive
|
||
modeling for chaotic time-series market forecasts</article-title>
|
||
</title-group>
|
||
<contrib-group>
|
||
<contrib contrib-type="author">
|
||
<contrib-id contrib-id-type="orcid">0000-0001-5618-8629</contrib-id>
|
||
<name>
|
||
<surname>Ph.D</surname>
|
||
<given-names>Robert A. Caulk</given-names>
|
||
</name>
|
||
<xref ref-type="aff" rid="aff-1"/>
|
||
<xref ref-type="aff" rid="aff-2"/>
|
||
</contrib>
|
||
<contrib contrib-type="author">
|
||
<contrib-id contrib-id-type="orcid">0000-0003-3289-8604</contrib-id>
|
||
<name>
|
||
<surname>Ph.D</surname>
|
||
<given-names>Elin Törnquist</given-names>
|
||
</name>
|
||
<xref ref-type="aff" rid="aff-1"/>
|
||
<xref ref-type="aff" rid="aff-2"/>
|
||
</contrib>
|
||
<contrib contrib-type="author">
|
||
<name>
|
||
<surname>Voppichler</surname>
|
||
<given-names>Matthias</given-names>
|
||
</name>
|
||
<xref ref-type="aff" rid="aff-2"/>
|
||
</contrib>
|
||
<contrib contrib-type="author">
|
||
<name>
|
||
<surname>Lawless</surname>
|
||
<given-names>Andrew R.</given-names>
|
||
</name>
|
||
<xref ref-type="aff" rid="aff-2"/>
|
||
</contrib>
|
||
<contrib contrib-type="author">
|
||
<name>
|
||
<surname>McMullan</surname>
|
||
<given-names>Ryan</given-names>
|
||
</name>
|
||
<xref ref-type="aff" rid="aff-2"/>
|
||
</contrib>
|
||
<contrib contrib-type="author">
|
||
<name>
|
||
<surname>Santos</surname>
|
||
<given-names>Wagner Costa</given-names>
|
||
</name>
|
||
<xref ref-type="aff" rid="aff-1"/>
|
||
<xref ref-type="aff" rid="aff-2"/>
|
||
</contrib>
|
||
<contrib contrib-type="author">
|
||
<name>
|
||
<surname>Pogue</surname>
|
||
<given-names>Timothy C.</given-names>
|
||
</name>
|
||
<xref ref-type="aff" rid="aff-1"/>
|
||
<xref ref-type="aff" rid="aff-2"/>
|
||
</contrib>
|
||
<contrib contrib-type="author">
|
||
<name>
|
||
<surname>van der Vlugt</surname>
|
||
<given-names>Johan</given-names>
|
||
</name>
|
||
<xref ref-type="aff" rid="aff-2"/>
|
||
</contrib>
|
||
<contrib contrib-type="author">
|
||
<name>
|
||
<surname>Gehring</surname>
|
||
<given-names>Stefan P.</given-names>
|
||
</name>
|
||
<xref ref-type="aff" rid="aff-2"/>
|
||
</contrib>
|
||
<contrib contrib-type="author">
|
||
<name>
|
||
<surname>Schmidt</surname>
|
||
<given-names>Pascal</given-names>
|
||
</name>
|
||
<xref ref-type="aff" rid="aff-2"/>
|
||
</contrib>
|
||
<aff id="aff-1">
|
||
<institution-wrap>
|
||
<institution>Emergent Methods LLC, Arvada Colorado, 80005,
|
||
USA</institution>
|
||
</institution-wrap>
|
||
</aff>
|
||
<aff id="aff-2">
|
||
<institution-wrap>
|
||
<institution>Freqtrade open source project</institution>
|
||
</institution-wrap>
|
||
</aff>
|
||
</contrib-group>
|
||
<volume>¿VOL?</volume>
|
||
<issue>¿ISSUE?</issue>
|
||
<fpage>¿PAGE?</fpage>
|
||
<permissions>
|
||
<copyright-statement>Authors of papers retain copyright and release the
|
||
work under a Creative Commons Attribution 4.0 International License (CC
|
||
BY 4.0)</copyright-statement>
|
||
<copyright-year>2022</copyright-year>
|
||
<copyright-holder>The article authors</copyright-holder>
|
||
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
|
||
<license-p>Authors of papers retain copyright and release the work under
|
||
a Creative Commons Attribution 4.0 International License (CC BY
|
||
4.0)</license-p>
|
||
</license>
|
||
</permissions>
|
||
<kwd-group kwd-group-type="author">
|
||
<kwd>Python</kwd>
|
||
<kwd>Machine Learning</kwd>
|
||
<kwd>adaptive modeling</kwd>
|
||
<kwd>chaotic systems</kwd>
|
||
<kwd>time-series forecasting</kwd>
|
||
</kwd-group>
|
||
</article-meta>
|
||
</front>
|
||
<body>
|
||
<sec id="statement-of-need">
|
||
<title>Statement of need</title>
|
||
<p>Forecasting chaotic time-series based systems, such as
|
||
equity/cryptocurrency markets, requires a broad set of tools geared
|
||
toward testing a wide range of hypotheses. Fortunately, a recent
|
||
maturation of robust machine learning libraries
|
||
(e.g. <monospace>scikit-learn</monospace>), has opened up a wide range
|
||
of research possibilities. Scientists from a diverse range of fields
|
||
can now easily prototype their studies on an abundance of established
|
||
machine learning algorithms. Similarly, these user-friendly libraries
|
||
enable “citzen scientists” to use their basic Python skills for
|
||
data-exploration. However, leveraging these machine learning libraries
|
||
on historical and live chaotic data sources can be logistically
|
||
difficult and expensive. Additionally, robust data-collection,
|
||
storage, and handling presents a disparate challenge.
|
||
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
|
||
aims to provide a generalized and extensible open-sourced framework
|
||
geared toward live deployments of adaptive modeling for market
|
||
forecasting. The <monospace>FreqAI</monospace> framework is
|
||
effectively a sandbox for the rich world of open-source machine
|
||
learning libraries. Inside the <monospace>FreqAI</monospace> sandbox,
|
||
users find they can combine a wide variety of third-party libraries to
|
||
test creative hypotheses on a free live 24/7 chaotic data source -
|
||
cryptocurrency exchange data.</p>
|
||
</sec>
|
||
<sec id="summary">
|
||
<title>Summary</title>
|
||
<p><ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
|
||
evolved from a desire to test and compare a range of adaptive
|
||
time-series forecasting methods on chaotic data. Cryptocurrency
|
||
markets provide a unique data source since they are operational 24/7
|
||
and the data is freely available. Luckily, an existing open-source
|
||
software,
|
||
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/stable/"><monospace>Freqtrade</monospace></ext-link>,
|
||
had already matured under a range of talented developers to support
|
||
robust data collection/storage, as well as robust live environmental
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||
interactions for standard algorithmic trading.
|
||
<monospace>Freqtrade</monospace> also provides a set of data
|
||
analysis/visualization tools for the evaluation of historical
|
||
performance as well as live environmental feedback.
|
||
<monospace>FreqAI</monospace> builds on top of
|
||
<monospace>Freqtrade</monospace> to include a user-friendly well
|
||
tested interface for integrating external machine learning libraries
|
||
for adaptive time-series forecasting. Beyond enabling the integration
|
||
of existing libraries, <monospace>FreqAI</monospace> hosts a range of
|
||
custom algorithms and methodologies aimed at improving computational
|
||
and predictive performances. Thus, <monospace>FreqAI</monospace>
|
||
contains a range of unique features which can be easily tested in
|
||
combination with all the existing Python-accessible machine learning
|
||
libraries to generate novel research on live and historical data.</p>
|
||
<p>The high-level overview of the software is depicted in Figure
|
||
1.</p>
|
||
<p><named-content content-type="image">freqai-algo</named-content>
|
||
<italic>Abstracted overview of FreqAI algorithm</italic></p>
|
||
<sec id="connecting-machine-learning-libraries">
|
||
<title>Connecting machine learning libraries</title>
|
||
<p>Although the <monospace>FreqAI</monospace> framework is designed
|
||
to accommodate any Python library in the “Model training” and
|
||
“Feature set engineering” portions of the software (Figure 1), it
|
||
already boasts a wide range of well documented examples based on
|
||
various combinations of:</p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>scikit-learn
|
||
(<xref alt="Pedregosa et al., 2011" rid="ref-scikit-learn" ref-type="bibr">Pedregosa
|
||
et al., 2011</xref>), Catboost
|
||
(<xref alt="Prokhorenkova et al., 2018" rid="ref-catboost" ref-type="bibr">Prokhorenkova
|
||
et al., 2018</xref>), LightGBM
|
||
(<xref alt="Ke et al., 2017" rid="ref-lightgbm" ref-type="bibr">Ke
|
||
et al., 2017</xref>), XGBoost
|
||
(<xref alt="Chen & Guestrin, 2016" rid="ref-xgboost" ref-type="bibr">Chen
|
||
& Guestrin, 2016</xref>), stable_baselines3
|
||
(<xref alt="Raffin et al., 2021" rid="ref-stable-baselines3" ref-type="bibr">Raffin
|
||
et al., 2021</xref>), openai gym
|
||
(<xref alt="Brockman et al., 2016" rid="ref-openai" ref-type="bibr">Brockman
|
||
et al., 2016</xref>), tensorflow
|
||
(<xref alt="Abadi et al., 2015" rid="ref-tensorflow" ref-type="bibr">Abadi
|
||
et al., 2015</xref>), pytorch
|
||
(<xref alt="Paszke et al., 2019" rid="ref-pytorch" ref-type="bibr">Paszke
|
||
et al., 2019</xref>), Scipy
|
||
(<xref alt="Virtanen et al., 2020" rid="ref-scipy" ref-type="bibr">Virtanen
|
||
et al., 2020</xref>), Numpy
|
||
(<xref alt="Harris et al., 2020" rid="ref-numpy" ref-type="bibr">Harris
|
||
et al., 2020</xref>), and pandas
|
||
(<xref alt="McKinney & others, 2010" rid="ref-pandas" ref-type="bibr">McKinney
|
||
& others, 2010</xref>).</p>
|
||
</list-item>
|
||
</list>
|
||
<p>These mature projects contain a wide range of peer-reviewed and
|
||
industry standard methods, including:</p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>Regression, Classification, Neural Networks, Reinforcement
|
||
Learning, Support Vector Machines, Principal Component Analysis,
|
||
point clustering, and much more.</p>
|
||
</list-item>
|
||
</list>
|
||
<p>which are all leveraged in <monospace>FreqAI</monospace> for
|
||
users to use as templates or extend with their own methods.</p>
|
||
</sec>
|
||
<sec id="furnishing-novel-methods-and-features">
|
||
<title>Furnishing novel methods and features</title>
|
||
<p>Beyond the industry standard methods available through external
|
||
libraries - <monospace>FreqAI</monospace> includes novel methods
|
||
which are not available anywhere else in the open-source (or
|
||
scientific) world. For example, <monospace>FreqAI</monospace>
|
||
provides :</p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>a custom algorithm/methodology for adaptive modeling</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>rapid and self-monitored feature engineering tools</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>unique model features/indicators</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>optimized data collection algorithms</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>safely integrated outlier detection methods</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>websocket communicated forecasts</p>
|
||
</list-item>
|
||
</list>
|
||
<p>Of particular interest for researchers,
|
||
<monospace>FreqAI</monospace> provides the option of large scale
|
||
experimentation via an optimized websocket communications
|
||
interface.</p>
|
||
</sec>
|
||
<sec id="optimizing-the-back-end">
|
||
<title>Optimizing the back-end</title>
|
||
<p><monospace>FreqAI</monospace> aims to make it simple for users to
|
||
combine all the above tools to run studies based in two distinct
|
||
modules:</p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>backtesting studies</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>live-deployments</p>
|
||
</list-item>
|
||
</list>
|
||
<p>Both of these modules and their respective data management
|
||
systems are built on top of
|
||
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/"><monospace>Freqtrade</monospace></ext-link>,
|
||
a mature and actively developed cryptocurrency trading software.
|
||
This means that <monospace>FreqAI</monospace> benefits from a wide
|
||
range of tangential/disparate feature developments such as:</p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>FreqUI, a graphical interface for backtesting and live
|
||
monitoring</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>telegram control</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>robust database handling</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>futures/leverage trading</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>dollar cost averaging</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>trading strategy handling</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>a variety of free data sources via CCXT (FTX, Binance, Kucoin
|
||
etc.)</p>
|
||
</list-item>
|
||
</list>
|
||
<p>These features derive from a strong external developer community
|
||
that shares in the benefit and stability of a communal CI
|
||
(Continuous Integration) system. Beyond the developer community,
|
||
<monospace>FreqAI</monospace> benefits strongly from the userbase of
|
||
<monospace>Freqtrade</monospace>, where most
|
||
<monospace>FreqAI</monospace> beta-testers/developers originated.
|
||
This symbiotic relationship between <monospace>Freqtrade</monospace>
|
||
and <monospace>FreqAI</monospace> ignited a thoroughly tested
|
||
<ext-link ext-link-type="uri" xlink:href="https://github.com/freqtrade/freqtrade/pull/6832"><monospace>beta</monospace></ext-link>,
|
||
which demanded a four month beta and
|
||
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/">comprehensive
|
||
documentation</ext-link> containing:</p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>numerous example scripts</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>a full parameter table</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>methodological descriptions</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>high-resolution diagrams/figures</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>detailed parameter setting recommendations</p>
|
||
</list-item>
|
||
</list>
|
||
</sec>
|
||
<sec id="providing-a-reproducible-foundation-for-researchers">
|
||
<title>Providing a reproducible foundation for researchers</title>
|
||
<p><monospace>FreqAI</monospace> provides an extensible, robust,
|
||
framework for researchers and citizen data scientists. The
|
||
<monospace>FreqAI</monospace> sandbox enables rapid conception and
|
||
testing of exotic hypotheses. From a research perspective,
|
||
<monospace>FreqAI</monospace> handles the multitude of logistics
|
||
associated with live deployments, historical backtesting, and
|
||
feature engineering. With <monospace>FreqAI</monospace>, researchers
|
||
can focus on their primary interests of feature engineering and
|
||
hypothesis testing rather than figuring out how to collect and
|
||
handle data. Further - the well maintained and easily installed
|
||
open-source framework of <monospace>FreqAI</monospace> enables
|
||
reproducible scientific studies. This reproducibility component is
|
||
essential to general scientific advancement in time-series
|
||
forecasting for chaotic systems.</p>
|
||
</sec>
|
||
</sec>
|
||
<sec id="technical-details">
|
||
<title>Technical details</title>
|
||
<p>Typical users configure <monospace>FreqAI</monospace> via two
|
||
files:</p>
|
||
<list list-type="order">
|
||
<list-item>
|
||
<p>A <monospace>configuration</monospace> file
|
||
(<monospace>--config</monospace>) which provides access to the
|
||
full parameter list available
|
||
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/">here</ext-link>:</p>
|
||
</list-item>
|
||
</list>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>control high-level feature engineering</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>customize adaptive modeling techniques</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>set any model training parameters available in third-party
|
||
libraries</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>manage adaptive modeling parameters (retrain frequency,
|
||
training window size, continual learning, etc.)</p>
|
||
</list-item>
|
||
</list>
|
||
<list list-type="order">
|
||
<list-item>
|
||
<label>2.</label>
|
||
<p>A strategy file (<monospace>--strategy</monospace>) where
|
||
users:</p>
|
||
</list-item>
|
||
</list>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>list of the base training features</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>set standard technical-analysis strategies</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>control trade entry/exit criteria</p>
|
||
</list-item>
|
||
</list>
|
||
<p>With these two files, most users can exploit a wide range of
|
||
pre-existing integrations in <monospace>Catboost</monospace> and 7
|
||
other libraries with a simple command:</p>
|
||
<preformat>freqtrade trade --config config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel CatboostRegressor</preformat>
|
||
<p>Advanced users will edit one of the existing
|
||
<monospace>--freqaimodel</monospace> files, which are simply an
|
||
children of the <monospace>IFreqaiModel</monospace> (details below).
|
||
Within these files, advanced users can customize training procedures,
|
||
prediction procedures, outlier detection methods, data preparation,
|
||
data saving methods, etc. This is all configured in a way where they
|
||
can customize as little or as much as they want. This flexible
|
||
customization is owed to the foundational architecture in
|
||
<monospace>FreqAI</monospace>, which is comprised of three distinct
|
||
Python objects:</p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p><monospace>IFreqaiModel</monospace></p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>A singular long-lived object containing all the necessary
|
||
logic to collect data, store data, process data, engineer
|
||
features, run training, and inference models.</p>
|
||
</list-item>
|
||
</list>
|
||
</list-item>
|
||
<list-item>
|
||
<p><monospace>FreqaiDataKitchen</monospace></p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>A short-lived object which is uniquely created for each
|
||
asset/model. Beyond metadata, it also contains a variety of
|
||
data processing tools.</p>
|
||
</list-item>
|
||
</list>
|
||
</list-item>
|
||
<list-item>
|
||
<p><monospace>FreqaiDataDrawer</monospace></p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>Singular long-lived object containing all the historical
|
||
predictions, models, and save/load methods.</p>
|
||
</list-item>
|
||
</list>
|
||
</list-item>
|
||
</list>
|
||
<p>These objects interact with one another with one goal in mind - to
|
||
provide a clean data set to machine learning experts/enthusiasts at
|
||
the user endpoint. These power-users interact with an inherited
|
||
<monospace>IFreqaiModel</monospace> that allows them to dig as deep or
|
||
as shallow as they wish into the inheritence tree. Typical power-users
|
||
focus their efforts on customizing training procedures and testing
|
||
exotic functionalities available in third-party libraries. Thus,
|
||
power-users are freed from the algorithmic weight associated with data
|
||
management, and can instead focus their energy on testing creative
|
||
hypotheses. Meanwhile, some users choose to override deeper
|
||
functionalities within <monospace>IFreqaiModel</monospace> to help
|
||
them craft unique data structures and training procedures.</p>
|
||
<p>The class structure and algorithmic details are depicted in the
|
||
following diagram:</p>
|
||
<p><named-content content-type="image">image</named-content>
|
||
<italic>Class diagram summarizing object interactions in
|
||
FreqAI</italic></p>
|
||
</sec>
|
||
<sec id="online-documentation">
|
||
<title>Online documentation</title>
|
||
<p>The documentation for
|
||
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
|
||
is available online at
|
||
<ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/">https://www.freqtrade.io/en/latest/freqai/</ext-link>
|
||
and covers a wide range of materials:</p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>Quick-start with a single command and example files -
|
||
(beginners)</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>Introduction to the feature engineering interface and basic
|
||
configurations - (intermediate users)</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>Parameter table with indepth descriptions and default parameter
|
||
setting recommendations - (intermediate users)</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>Data analysis and post-processing - (advanced users)</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>Methodological considerations complemented by high resolution
|
||
figures - (advanced users)</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>Instructions for integrating third party machine learning
|
||
libraries into custom prediction models - (advanced users)</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>Software architectural description with class diagram -
|
||
(developers)</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>File structure descriptions - (developers)</p>
|
||
</list-item>
|
||
</list>
|
||
<p>The docs direct users to a variety of pre-made examples which
|
||
integrate <monospace>Catboost</monospace>,
|
||
<monospace>LightGBM</monospace>, <monospace>XGBoost</monospace>,
|
||
<monospace>Sklearn</monospace>,
|
||
<monospace>stable_baselines3</monospace>,
|
||
<monospace>torch</monospace>, <monospace>tensorflow</monospace>.
|
||
Meanwhile, developers will also find thorough docstrings and type
|
||
hinting throughout the source code to aid in code readability and
|
||
customization.</p>
|
||
<p><monospace>FreqAI</monospace> also benefits from a strong support
|
||
network of users and developers on the
|
||
<ext-link ext-link-type="uri" xlink:href="https://discord.gg/w6nDM6cM4y"><monospace>Freqtrade</monospace>
|
||
discord</ext-link> as well as on the
|
||
<ext-link ext-link-type="uri" xlink:href="https://discord.gg/xE4RMg4QYw"><monospace>FreqAI</monospace>
|
||
discord</ext-link>. Within the <monospace>FreqAI</monospace> discord,
|
||
users will find a deep and easily searched knowledge base containing
|
||
common errors. But more importantly, users in the
|
||
<monospace>FreqAI</monospace> discord share anectdotal and
|
||
quantitative observations which compare performance between various
|
||
third-party libraries and methods.</p>
|
||
</sec>
|
||
<sec id="state-of-the-field">
|
||
<title>State of the field</title>
|
||
<p>There are two other open-source tools which are geared toward
|
||
helping users build models for time-series forecasts on market based
|
||
data. However, each of these tools suffer from a non-generalized
|
||
frameworks that do not permit comparison of methods and libraries.
|
||
Additionally, they do not permit easy live-deployments or
|
||
adaptive-modeling methods. For example, two open-sourced projects
|
||
called
|
||
<ext-link ext-link-type="uri" xlink:href="https://tensortradex.readthedocs.io/en/latest/"><monospace>tensortrade</monospace></ext-link>
|
||
(<xref alt="Tensortrade, 2022" rid="ref-tensortrade" ref-type="bibr"><italic>Tensortrade</italic>,
|
||
2022</xref>) and
|
||
<ext-link ext-link-type="uri" xlink:href="https://github.com/AI4Finance-Foundation/FinRL"><monospace>FinRL</monospace></ext-link>
|
||
(<xref alt="AI4Finance-Foundation, 2022" rid="ref-finrl" ref-type="bibr"><italic>AI4Finance-Foundation</italic>,
|
||
2022</xref>) limit users to the exploration of reinforcement learning
|
||
on historical data. These softwares also do not provide robust live
|
||
deployments, they do not furnish novel feature engineering algorithms,
|
||
and they do not provide custom data analysis tools.
|
||
<monospace>FreqAI</monospace> fills the gap.</p>
|
||
</sec>
|
||
<sec id="on-going-research">
|
||
<title>On-going research</title>
|
||
<p>Emergent Methods, based in Arvada CO, is actively using
|
||
<monospace>FreqAI</monospace> to perform large scale experiments aimed
|
||
at comparing machine learning libraries in live and historical
|
||
environments. Past projects include backtesting parametric sweeps,
|
||
while active projects include a 3 week live deployment comparison
|
||
between <monospace>CatboosRegressor</monospace>,
|
||
<monospace>LightGBMRegressor</monospace>, and
|
||
<monospace>XGBoostRegressor</monospace>. Results from these studies
|
||
are on track for publication in scientific journals as well as more
|
||
general data science blogs (e.g. Medium).</p>
|
||
</sec>
|
||
<sec id="installing-and-running-freqai">
|
||
<title>Installing and running <monospace>FreqAI</monospace></title>
|
||
<p><monospace>FreqAI</monospace> is automatically installed with
|
||
<monospace>Freqtrade</monospace> using the following commands on linux
|
||
systems:</p>
|
||
<preformat>git clone git@github.com:freqtrade/freqtrade.git
|
||
cd freqtrade
|
||
./setup.sh -i</preformat>
|
||
<p>However, <monospace>FreqAI</monospace> also benefits from
|
||
<monospace>Freqtrade</monospace> docker distributions, and can be run
|
||
with docker by pulling the stable or develop images from
|
||
<monospace>Freqtrade</monospace> distributions.</p>
|
||
</sec>
|
||
<sec id="funding-sources">
|
||
<title>Funding sources</title>
|
||
<p><ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
|
||
has had no official sponsors, and is entirely grass roots. All
|
||
donations into the project (e.g. the GitHub sponsor system) are kept
|
||
inside the project to help support development of open-sourced and
|
||
communally beneficial features.</p>
|
||
</sec>
|
||
<sec id="acknowledgements">
|
||
<title>Acknowledgements</title>
|
||
<p>We would like to acknowledge various beta testers of
|
||
<monospace>FreqAI</monospace>:</p>
|
||
<list list-type="bullet">
|
||
<list-item>
|
||
<p>Richárd Józsa</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>Juha Nykänen</p>
|
||
</list-item>
|
||
<list-item>
|
||
<p>Salah Lamkadem</p>
|
||
</list-item>
|
||
</list>
|
||
<p>As well as various <monospace>Freqtrade</monospace>
|
||
<ext-link ext-link-type="uri" xlink:href="https://github.com/freqtrade/freqtrade/graphs/contributors">developers</ext-link>
|
||
maintaining tangential, yet essential, modules.</p>
|
||
</sec>
|
||
</body>
|
||
<back>
|
||
<ref-list>
|
||
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</ref>
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<element-citation publication-type="article-journal">
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<name><surname>Meng</surname><given-names>Qi</given-names></name>
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<name><surname>Finley</surname><given-names>Thomas</given-names></name>
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<name><surname>Ma</surname><given-names>Weidong</given-names></name>
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<name><surname>Ye</surname><given-names>Qiwei</given-names></name>
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<name><surname>Liu</surname><given-names>Tie-Yan</given-names></name>
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</person-group>
|
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<article-title>Lightgbm: A highly efficient gradient boosting decision tree</article-title>
|
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<source>Advances in neural information processing systems</source>
|
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<year iso-8601-date="2017">2017</year>
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<volume>30</volume>
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<ref id="ref-xgboost">
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<element-citation publication-type="paper-conference">
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<person-group person-group-type="author">
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<name><surname>Guestrin</surname><given-names>Carlos</given-names></name>
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</person-group>
|
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<article-title>XGBoost: A scalable tree boosting system</article-title>
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<source>Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining</source>
|
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<publisher-name>ACM</publisher-name>
|
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<publisher-loc>New York, NY, USA</publisher-loc>
|
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<year iso-8601-date="2016">2016</year>
|
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<isbn>978-1-4503-4232-2</isbn>
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<uri>http://doi.acm.org/10.1145/2939672.2939785</uri>
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<pub-id pub-id-type="doi">10.1145/2939672.2939785</pub-id>
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<lpage>794</lpage>
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<element-citation publication-type="article-journal">
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<person-group person-group-type="author">
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<name><surname>Raffin</surname><given-names>Antonin</given-names></name>
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||
<name><surname>Hill</surname><given-names>Ashley</given-names></name>
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<name><surname>Gleave</surname><given-names>Adam</given-names></name>
|
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<name><surname>Kanervisto</surname><given-names>Anssi</given-names></name>
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<name><surname>Ernestus</surname><given-names>Maximilian</given-names></name>
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<name><surname>Dormann</surname><given-names>Noah</given-names></name>
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</person-group>
|
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|
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<source>Journal of Machine Learning Research</source>
|
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<year iso-8601-date="2021">2021</year>
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<volume>22</volume>
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<issue>268</issue>
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<uri>http://jmlr.org/papers/v22/20-1364.html</uri>
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</element-citation>
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</ref>
|
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<ref id="ref-openai">
|
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<element-citation>
|
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<person-group person-group-type="author">
|
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<name><surname>Brockman</surname><given-names>Greg</given-names></name>
|
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<name><surname>Cheung</surname><given-names>Vicki</given-names></name>
|
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<name><surname>Pettersson</surname><given-names>Ludwig</given-names></name>
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||
<name><surname>Schneider</surname><given-names>Jonas</given-names></name>
|
||
<name><surname>Schulman</surname><given-names>John</given-names></name>
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||
<name><surname>Tang</surname><given-names>Jie</given-names></name>
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<name><surname>Zaremba</surname><given-names>Wojciech</given-names></name>
|
||
</person-group>
|
||
<article-title>OpenAI gym</article-title>
|
||
<year iso-8601-date="2016">2016</year>
|
||
<uri>https://arxiv.org/abs/1606.01540</uri>
|
||
</element-citation>
|
||
</ref>
|
||
<ref id="ref-tensorflow">
|
||
<element-citation>
|
||
<person-group person-group-type="author">
|
||
<name><surname>Abadi</surname><given-names>Martín</given-names></name>
|
||
<name><surname>Agarwal</surname><given-names>Ashish</given-names></name>
|
||
<name><surname>Barham</surname><given-names>Paul</given-names></name>
|
||
<name><surname>Brevdo</surname><given-names>Eugene</given-names></name>
|
||
<name><surname>Chen</surname><given-names>Zhifeng</given-names></name>
|
||
<name><surname>Citro</surname><given-names>Craig</given-names></name>
|
||
<name><surname>Corrado</surname><given-names>Greg S.</given-names></name>
|
||
<name><surname>Davis</surname><given-names>Andy</given-names></name>
|
||
<name><surname>Dean</surname><given-names>Jeffrey</given-names></name>
|
||
<name><surname>Devin</surname><given-names>Matthieu</given-names></name>
|
||
<name><surname>Ghemawat</surname><given-names>Sanjay</given-names></name>
|
||
<name><surname>Goodfellow</surname><given-names>Ian</given-names></name>
|
||
<name><surname>Harp</surname><given-names>Andrew</given-names></name>
|
||
<name><surname>Irving</surname><given-names>Geoffrey</given-names></name>
|
||
<name><surname>Isard</surname><given-names>Michael</given-names></name>
|
||
<name><surname>Jia</surname><given-names>Yangqing</given-names></name>
|
||
<name><surname>Jozefowicz</surname><given-names>Rafal</given-names></name>
|
||
<name><surname>Kaiser</surname><given-names>Lukasz</given-names></name>
|
||
<name><surname>Kudlur</surname><given-names>Manjunath</given-names></name>
|
||
<name><surname>Levenberg</surname><given-names>Josh</given-names></name>
|
||
<name><surname>Mané</surname><given-names>Dandelion</given-names></name>
|
||
<name><surname>Monga</surname><given-names>Rajat</given-names></name>
|
||
<name><surname>Moore</surname><given-names>Sherry</given-names></name>
|
||
<name><surname>Murray</surname><given-names>Derek</given-names></name>
|
||
<name><surname>Olah</surname><given-names>Chris</given-names></name>
|
||
<name><surname>Schuster</surname><given-names>Mike</given-names></name>
|
||
<name><surname>Shlens</surname><given-names>Jonathon</given-names></name>
|
||
<name><surname>Steiner</surname><given-names>Benoit</given-names></name>
|
||
<name><surname>Sutskever</surname><given-names>Ilya</given-names></name>
|
||
<name><surname>Talwar</surname><given-names>Kunal</given-names></name>
|
||
<name><surname>Tucker</surname><given-names>Paul</given-names></name>
|
||
<name><surname>Vanhoucke</surname><given-names>Vincent</given-names></name>
|
||
<name><surname>Vasudevan</surname><given-names>Vijay</given-names></name>
|
||
<name><surname>Viégas</surname><given-names>Fernanda</given-names></name>
|
||
<name><surname>Vinyals</surname><given-names>Oriol</given-names></name>
|
||
<name><surname>Warden</surname><given-names>Pete</given-names></name>
|
||
<name><surname>Wattenberg</surname><given-names>Martin</given-names></name>
|
||
<name><surname>Wicke</surname><given-names>Martin</given-names></name>
|
||
<name><surname>Yu</surname><given-names>Yuan</given-names></name>
|
||
<name><surname>Zheng</surname><given-names>Xiaoqiang</given-names></name>
|
||
</person-group>
|
||
<article-title>TensorFlow: Large-scale machine learning on heterogeneous systems</article-title>
|
||
<year iso-8601-date="2015">2015</year>
|
||
<uri>https://www.tensorflow.org/</uri>
|
||
</element-citation>
|
||
</ref>
|
||
<ref id="ref-pytorch">
|
||
<element-citation publication-type="chapter">
|
||
<person-group person-group-type="author">
|
||
<name><surname>Paszke</surname><given-names>Adam</given-names></name>
|
||
<name><surname>Gross</surname><given-names>Sam</given-names></name>
|
||
<name><surname>Massa</surname><given-names>Francisco</given-names></name>
|
||
<name><surname>Lerer</surname><given-names>Adam</given-names></name>
|
||
<name><surname>Bradbury</surname><given-names>James</given-names></name>
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