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. scikit-learn), 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. FreqAI aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The FreqAI framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the FreqAI 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.

FreqAI 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, Freqtrade, 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. Freqtrade also provides a set of data analysis/visualization tools for the evaluation of historical performance as well as live environmental feedback. FreqAI builds on top of Freqtrade 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, FreqAI hosts a range of custom algorithms and methodologies aimed at improving computational and predictive performances. Thus, FreqAI 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.

The high-level overview of the software is depicted in Figure 1.

freqai-algo Abstracted overview of FreqAI algorithm

Typical users configure FreqAI via two files:

With these two files, most users can exploit a wide range of pre-existing integrations in Catboost and 7 other libraries with a simple command:

Advanced users will edit one of the existing --freqaimodel files, which are simply an children of the IFreqaiModel (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 FreqAI, which is comprised of three distinct Python objects:

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 IFreqaiModel 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 IFreqaiModel to help them craft unique data structures and training procedures.

The class structure and algorithmic details are depicted in the following diagram:

image Class diagram summarizing object interactions in FreqAI

The documentation for FreqAI is available online at https://www.freqtrade.io/en/latest/freqai/ and covers a wide range of materials:

The docs direct users to a variety of pre-made examples which integrate Catboost, LightGBM, XGBoost, Sklearn, stable_baselines3, torch, tensorflow. Meanwhile, developers will also find thorough docstrings and type hinting throughout the source code to aid in code readability and customization.

FreqAI also benefits from a strong support network of users and developers on the Freqtrade discord as well as on the FreqAI discord. Within the FreqAI discord, users will find a deep and easily searched knowledge base containing common errors. But more importantly, users in the FreqAI discord share anectdotal and quantitative observations which compare performance between various third-party libraries and methods.

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 tensortrade (Tensortrade, 2022) and FinRL (AI4Finance-Foundation, 2022) 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. FreqAI fills the gap.

Emergent Methods, based in Arvada CO, is actively using FreqAI 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 CatboosRegressor, LightGBMRegressor, and XGBoostRegressor. Results from these studies are on track for publication in scientific journals as well as more general data science blogs (e.g. Medium).

FreqAI is automatically installed with Freqtrade using the following commands on linux systems:

However, FreqAI also benefits from Freqtrade docker distributions, and can be run with docker by pulling the stable or develop images from Freqtrade distributions.

FreqAI 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.

We would like to acknowledge various beta testers of FreqAI:

As well as various Freqtrade developers maintaining tangential, yet essential, modules.