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Freqtrade
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GitHub
 * 2023.10
 * 23.4k
 * 5.4k

Freqtrade
GitHub
 * 2023.10
 * 23.4k
 * 5.4k

 * Home Home
   Table of contents
    * Introduction
    * Features
    * Supported exchange marketplaces
       * Supported Futures Exchanges (experimental)
       * Community tested
   
    * Community showcase
    * Requirements
       * Hardware requirements
       * Software requirements
   
    * Support
       * Help / Discord
   
    * Ready to try?

 * Quickstart with Docker
 * Installation
   Installation
    * Linux/MacOS/Raspberry
    * Windows

 * Freqtrade Basics
 * Configuration
 * Strategy Customization
 * Strategy Callbacks
 * Stoploss
 * Plugins
 * Start the bot
 * Control the bot
   Control the bot
    * Telegram
    * REST API & FreqUI
    * Web Hook

 * Data Downloading
 * Backtesting
 * Hyperopt
 * FreqAI
   FreqAI
    * Introduction
    * Configuration
    * Parameter table
    * Feature engineering
    * Running FreqAI
    * Reinforcement Learning
    * Developer guide

 * Short / Leverage
 * Utility Sub-commands
 * Plotting
 * Exchange-specific Notes
 * Data Analysis
   Data Analysis
    * Jupyter Notebooks
    * Strategy analysis
    * Backtest analysis

 * Advanced Topics
   Advanced Topics
    * Advanced Post-installation Tasks
    * Trade Object
    * Lookahead analysis
    * Recursive analysis
    * Advanced Strategy
    * Advanced Hyperopt
    * Producer/Consumer mode
    * Edge Positioning

 * FAQ
 * SQL Cheat-sheet
 * Strategy migration
 * Updating Freqtrade
 * Deprecated Features
 * Contributors Guide

Table of contents
 * Introduction
 * Features
 * Supported exchange marketplaces
    * Supported Futures Exchanges (experimental)
    * Community tested

 * Community showcase
 * Requirements
    * Hardware requirements
    * Software requirements

 * Support
    * Help / Discord

 * Ready to try?


HOME








INTRODUCTION¶

Freqtrade is a free and open source crypto trading bot written in Python. It is
designed to support all major exchanges and be controlled via Telegram or webUI.
It contains backtesting, plotting and money management tools as well as strategy
optimization by machine learning.

DISCLAIMER

This software is for educational purposes only. Do not risk money which you are
afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL
AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.

Always start by running a trading bot in Dry-run and do not engage money before
you understand how it works and what profit/loss you should expect.

We strongly recommend you to have basic coding skills and Python knowledge. Do
not hesitate to read the source code and understand the mechanisms of this bot,
algorithms and techniques implemented in it.




FEATURES¶

 * Develop your Strategy: Write your strategy in python, using pandas. Example
   strategies to inspire you are available in the strategy repository.
 * Download market data: Download historical data of the exchange and the
   markets your may want to trade with.
 * Backtest: Test your strategy on downloaded historical data.
 * Optimize: Find the best parameters for your strategy using hyperoptimization
   which employs machining learning methods. You can optimize buy, sell, take
   profit (ROI), stop-loss and trailing stop-loss parameters for your strategy.
 * Select markets: Create your static list or use an automatic one based on top
   traded volumes and/or prices (not available during backtesting). You can also
   explicitly blacklist markets you don't want to trade.
 * Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with
   real money (Live-Trade mode).
 * Run using Edge (optional module): The concept is to find the best historical
   trade expectancy by markets based on variation of the stop-loss and then
   allow/reject markets to trade. The sizing of the trade is based on a risk of
   a percentage of your capital.
 * Control/Monitor: Use Telegram or a WebUI (start/stop the bot, show
   profit/loss, daily summary, current open trades results, etc.).
 * Analyze: Further analysis can be performed on either Backtesting data or
   Freqtrade trading history (SQL database), including automated standard plots,
   and methods to load the data into interactive environments.


SUPPORTED EXCHANGE MARKETPLACES¶

Please read the exchange specific notes to learn about eventual, special
configurations needed for each exchange.

 * Binance
 * Bittrex
 * Gate.io
 * Huobi
 * Kraken
 * OKX (Former OKEX)
 * potentially many others through . (We cannot guarantee they will work)


SUPPORTED FUTURES EXCHANGES (EXPERIMENTAL)¶

 * Binance
 * Gate.io
 * OKX
 * Bybit

Please make sure to read the exchange specific notes, as well as the trading
with leverage documentation before diving in.


COMMUNITY TESTED¶

Exchanges confirmed working by the community:

 * Bitvavo
 * Kucoin


COMMUNITY SHOWCASE¶

This section will highlight a few projects from members of the community.

Note

The projects below are for the most part not maintained by the freqtrade ,
therefore use your own caution before using them.

 * Example freqtrade strategies
 * FrequentHippo - Grafana dashboard with dry/live runs and backtests (by
   hippocritical).
 * Online pairlist generator (by Blood4rc).
 * Freqtrade Backtesting Project (by Blood4rc).
 * Freqtrade analysis notebook (by Froggleston).
 * TUI for freqtrade (by Froggleston).
 * Bot Academy (by stash86) - Blog about crypto bot projects.


REQUIREMENTS¶


HARDWARE REQUIREMENTS¶

To run this bot we recommend you a linux cloud instance with a minimum of:

 * 2GB RAM
 * 1GB disk space
 * 2vCPU


SOFTWARE REQUIREMENTS¶

 * Docker (Recommended)

Alternatively

 * Python 3.9+
 * pip (pip3)
 * git
 * TA-Lib
 * virtualenv (Recommended)


SUPPORT¶


HELP / DISCORD¶

For any questions not covered by the documentation or for further information
about the bot, or to simply engage with like-minded individuals, we encourage
you to join the Freqtrade discord server.


READY TO TRY?¶

Begin by reading the installation guide for docker (recommended), or for
installation without docker.

Back to top
Next
Quickstart with Docker

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