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<?xml version="1.0" encoding="utf-8" ?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN"
"JATS-publishing1.dtd">
<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
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 &amp; Guestrin, 2016" rid="ref-xgboost" ref-type="bibr">Chen
&amp; 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 &amp; others, 2010" rid="ref-pandas" ref-type="bibr">McKinney
&amp; 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>
<ref id="ref-scikit-learn">
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