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add some changes recommended by @shagunsodhani

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@ -58,7 +58,7 @@ Forecasting chaotic time-series based systems, such as equity/cryptocurrency mar
# Summary
[`FreqAI`](https://www.freqtrade.io/en/latest/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`](https://www.freqtrade.io/en/stable/), 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.
[`FreqAI`](https://www.freqtrade.io/en/latest/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 via a variety of open-sourced [exchange APIs](https://docs.ccxt.com/en/latest/manual.html#exchange-structure). Luckily, an existing open-source software, [`Freqtrade`](https://www.freqtrade.io/en/stable/), 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.
@ -81,14 +81,14 @@ which are all leveraged in `FreqAI` for users to use as templates or extend with
Beyond the industry standard methods available through external libraries - `FreqAI` includes novel methods which are not available anywhere else in the open-source (or scientific) world. For example, `FreqAI` provides :
* a custom algorithm/methodology for adaptive modeling
* rapid and self-monitored feature engineering tools
* unique model features/indicators
* optimized data collection algorithms
* safely integrated outlier detection methods
* websocket communicated forecasts
* a custom algorithm/methodology for adaptive modeling details [here](https://www.freqtrade.io/en/stable/freqai/#general-approach) and [here](https://www.freqtrade.io/en/stable/freqai-developers/#project-architecture)
* rapid and self-monitored feature engineering tools, details [here](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#feature-engineering)
* unique model features/indicators, such as the [inlier metric](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#inlier-metric)
* optimized data collection/storage algorithms, all code shown [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/freqai/data_drawer.py)
* safely integrated outlier detection methods, details [here](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#outlier-detection)
* websocket communicated forecasts, details [here](https://www.freqtrade.io/en/stable/producer-consumer/)
Of particular interest for researchers, `FreqAI` provides the option of large scale experimentation via an optimized websocket communications interface.
Of particular interest for researchers, `FreqAI` provides the option of large scale experimentation via an optimized [websocket communications interface](https://www.freqtrade.io/en/stable/producer-consumer/).
## Optimizing the back-end
@ -105,7 +105,7 @@ Both of these modules and their respective data management systems are built on
* futures/leverage trading
* dollar cost averaging
* trading strategy handling
* a variety of free data sources via CCXT (FTX, Binance, Kucoin etc.)
* a variety of free data sources via [CCXT](https://docs.ccxt.com/en/latest/manual.html#exchange-structure) (FTX, Binance, Kucoin etc.)
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, `FreqAI` benefits strongly from the userbase of `Freqtrade`, where most `FreqAI` beta-testers/developers originated. This symbiotic relationship between `Freqtrade` and `FreqAI` ignited a thoroughly tested [`beta`](https://github.com/freqtrade/freqtrade/pull/6832), which demanded a four month beta and [comprehensive documentation](https://www.freqtrade.io/en/latest/freqai/) containing:
@ -179,7 +179,7 @@ There are two other open-source tools which are geared toward helping users buil
# On-going research
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).
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 planned for submission to scientific journals as well as more general data science blogs (e.g. Medium).
# Installing and running `FreqAI`