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Open Source Library

Freqtrade

freqtrade/freqtrade★ 28k+

Crypto trading bot framework with FreqAI ML extension supporting multiple machine learning and deep learning models.

Language PythonAssets cryptoMarkets spot, derivativesType framework

Indicator-based Templates

SampleStrategy
Freqtrade
Indicatorbeginner

Template strategy demonstrating basic technical indicator usage for entry/exit signals.

Speed⚡⚡
Accuracy📊📊📊
Source:freqtrade/templates/sample_strategy.py

Machine Learning Models (FreqAI)

FreqAI prediction models for machine learning-based price prediction. Each model requires feature engineering via Freqtrade's feature pipeline.

LightGBMRegressor
Freqtrade
Machine Learningintermediate

Gradient boosting regression model for price movement prediction using LightGBM.

Speed⚡⚡⚡
Accuracy📊📊📊
Key Parameters
n_estimators1000Number of boosting rounds
learning_rate0.01Step size shrinkage
Source:freqai/prediction_models/LightGBMRegressor.py
LightGBMClassifier
Freqtrade
Machine Learningintermediate

Gradient boosting classification model for directional prediction (up/down/neutral).

Speed⚡⚡⚡
Accuracy📊📊📊
Key Parameters
n_estimators1000Number of boosting rounds
Source:freqai/prediction_models/LightGBMClassifier.py
XGBoostRegressor
Freqtrade
Machine Learningintermediate

XGBoost-based regression model for continuous value prediction.

Speed⚡⚡⚡
Accuracy📊📊📊
Key Parameters
n_estimators1000Number of boosting rounds
max_depth6Maximum tree depth
Source:freqai/prediction_models/XGBoostRegressor.py
XGBoostClassifier
Freqtrade
Machine Learningintermediate

XGBoost-based classification model for directional prediction.

Speed⚡⚡⚡
Accuracy📊📊📊
Key Parameters
n_estimators1000Number of boosting rounds
Source:freqai/prediction_models/XGBoostClassifier.py
CatboostRegressor
Freqtrade
Machine Learningintermediate

CatBoost gradient boosting model with native categorical feature support.

Speed⚡⚡
Accuracy📊📊📊
Key Parameters
iterations1000Number of boosting iterations
Source:freqai/prediction_models/CatboostRegressor.py
PyTorchMLPRegressor
Freqtrade
Machine Learningadvanced

Multi-layer perceptron neural network for regression-based price prediction.

Speed⚡⚡
Accuracy📊📊📊
Key Parameters
hidden_dim128Hidden layer dimension
dropout_percent0.2Dropout rate
Source:freqai/prediction_models/PyTorchMLPRegressor.py
PyTorchTransformerRegressor
Freqtrade
Machine Learningadvanced

Transformer architecture for time-series regression using self-attention mechanism.

Speed⚡
Accuracy📊📊📊📊
Key Parameters
num_heads8Number of attention heads
num_layers2Number of transformer layers
Source:freqai/prediction_models/PyTorchTransformerRegressor.py

Reinforcement Learning (FreqAI)

Reinforcement learning agents built on Stable Baselines3. The agent learns trading decisions through environment interaction rather than labeled data.

ReinforcementLearner
Freqtrade
Reinforcement Learningadvanced

Reinforcement learning agent using Stable Baselines3 (PPO/A2C/etc.) for trading decisions.

Speed⚡⚡
Accuracy📊📊📊
Key Parameters
model_typePPORL algorithm (PPO, A2C, etc.)
total_timesteps10000Training timesteps
Source:freqai/prediction_models/ReinforcementLearner.py
Related Libraries
Microsoft Qlib →FinRL →All ML Algorithms →All Libraries →

Freqtrade Strategies and FreqAI Models — Algorithm Reference

SampleStrategy (Freqtrade)
Template strategy demonstrating basic technical indicator usage for entry/exit signals. Source: https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py.
LightGBMRegressor (Freqtrade)
Gradient boosting regression model for price movement prediction using LightGBM. Key parameters: n_estimators (Number of boosting rounds), learning_rate (Step size shrinkage). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMRegressor.py.
LightGBMClassifier (Freqtrade)
Gradient boosting classification model for directional prediction (up/down/neutral). Key parameters: n_estimators (Number of boosting rounds). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMClassifier.py.
XGBoostRegressor (Freqtrade)
XGBoost-based regression model for continuous value prediction. Key parameters: n_estimators (Number of boosting rounds), max_depth (Maximum tree depth). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostRegressor.py.
XGBoostClassifier (Freqtrade)
XGBoost-based classification model for directional prediction. Key parameters: n_estimators (Number of boosting rounds). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostClassifier.py.
CatboostRegressor (Freqtrade)
CatBoost gradient boosting model with native categorical feature support. Key parameters: iterations (Number of boosting iterations). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/CatboostRegressor.py.
PyTorchMLPRegressor (Freqtrade)
Multi-layer perceptron neural network for regression-based price prediction. Key parameters: hidden_dim (Hidden layer dimension), dropout_percent (Dropout rate). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/PyTorchMLPRegressor.py.
PyTorchTransformerRegressor (Freqtrade)
Transformer architecture for time-series regression using self-attention mechanism. Key parameters: num_heads (Number of attention heads), num_layers (Number of transformer layers). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/PyTorchTransformerRegressor.py.
ReinforcementLearner (Freqtrade)
Reinforcement learning agent using Stable Baselines3 (PPO/A2C/etc.) for trading decisions. Key parameters: model_type (RL algorithm (PPO, A2C, etc.)), total_timesteps (Training timesteps). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/ReinforcementLearner.py.