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Freqtrade

freqtrade/freqtrade★ 28k+

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

Langage PythonActifs cryptoMarchés spot, derivativesType framework

Templates basés sur indicateurs

SampleStrategy

Freqtrade

Basé sur indicateursbeginner

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

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Précision📊📊📊

Source :freqtrade/templates/sample_strategy.py

Modèles d'apprentissage automatique (FreqAI)

Modèles de prédiction FreqAI pour la prédiction de prix basée sur l'apprentissage automatique. Chaque modèle nécessite un feature engineering via le pipeline de features de Freqtrade.

LightGBMRegressor

Freqtrade

Machine Learningintermediate

Gradient boosting regression model for price movement prediction using LightGBM.

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Paramètres clés

n_estimators	1000	Number of boosting rounds
learning_rate	0.01	Step size shrinkage

Source :freqai/prediction_models/LightGBMRegressor.py

LightGBMClassifier

Freqtrade

Machine Learningintermediate

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

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Paramètres clés

n_estimators

1000

Number of boosting rounds

Source :freqai/prediction_models/LightGBMClassifier.py

XGBoostRegressor

Freqtrade

Machine Learningintermediate

XGBoost-based regression model for continuous value prediction.

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Paramètres clés

n_estimators	1000	Number of boosting rounds
max_depth	6	Maximum tree depth

Source :freqai/prediction_models/XGBoostRegressor.py

XGBoostClassifier

Freqtrade

Machine Learningintermediate

XGBoost-based classification model for directional prediction.

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Paramètres clés

n_estimators

1000

Number of boosting rounds

Source :freqai/prediction_models/XGBoostClassifier.py

CatboostRegressor

Freqtrade

Machine Learningintermediate

CatBoost gradient boosting model with native categorical feature support.

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Paramètres clés

iterations

1000

Number of boosting iterations

Source :freqai/prediction_models/CatboostRegressor.py

PyTorchMLPRegressor

Freqtrade

Machine Learningadvanced

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

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Paramètres clés

hidden_dim	128	Hidden layer dimension
dropout_percent	0.2	Dropout rate

Source :freqai/prediction_models/PyTorchMLPRegressor.py

PyTorchTransformerRegressor

Freqtrade

Machine Learningadvanced

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

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Paramètres clés

num_heads	8	Number of attention heads
num_layers	2	Number of transformer layers

Source :freqai/prediction_models/PyTorchTransformerRegressor.py

Apprentissage par renforcement (FreqAI)

Agents d'apprentissage par renforcement construits sur Stable Baselines3. L'agent apprend des décisions de trading par interaction avec l'environnement plutôt que par données étiquetées.

ReinforcementLearner

Freqtrade

Apprentissage par renforcementadvanced

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

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Précision📊📊📊

Paramètres clés

model_type	PPO	RL algorithm (PPO, A2C, etc.)
total_timesteps	10000	Training timesteps

Source :freqai/prediction_models/ReinforcementLearner.py

Stratégies Freqtrade et modèles FreqAI, référence des algorithmes

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. Paramètres clés : 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). Paramètres clés : 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. Paramètres clés : 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. Paramètres clés : 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. Paramètres clés : 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. Paramètres clés : 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. Paramètres clés : 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. Paramètres clés : model_type (RL algorithm (PPO, A2C, etc.)), total_timesteps (Training timesteps).Source : https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/ReinforcementLearner.py.