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Type de stratégie

Algorithmes de trading par apprentissage automatique

Modèles ML open source pour la finance quantitative

Les algorithmes de trading par apprentissage automatique appliquent des modèles d'apprentissage statistique (gradient boosting, réseaux neuronaux, Transformers) à des séries temporelles financières pour prédire les mouvements de prix ou générer des signaux de trading. Ces implémentations open source couvrent les formulations en régression (cible de prix) et en classification (direction).

12 algorithmes2 bibliothèques
Réseau d'algorithmes

Comment les algorithmes ML se connectent entre les bibliothèques

🧠ML Algorithms
🤖
Freqtrade9 algos
🔬
Microsoft Qlib6 algos
LightGBMRegressorintermediate
LightGBMClassifierintermediate
XGBoostRegressorintermediate
XGBoostClassifierintermediate
PyTorchMLPRegressoradvanced
PyTorchTransformerRegressoradvanced
LGBModelintermediate
XGBModelintermediate
DNNModeladvanced
ALSTMadvanced
TFTModeladvanced
GATsadvanced
Pipeline de décision de trading

Comment les algorithmes ML coopèrent dans un système de trading

1
📊

Market Data Input

Price, volume, indicators

OHLCV price data
Technical indicators
Feature engineering
2
🧠

ML Signal Generation

Model predictions

LightGBM/XGBoost regression
Transformer sequence modeling
MLP classification
3
📈

Entry Decision

Signal threshold filtering

Confidence score > 0.6
Direction: Long/Short
4
📉

Exit Strategy

Profit taking & stop loss

Take profit (fixed % or trailing)
Stop loss (ATR-based)
5
🛡️

Risk Management

Position sizing & portfolio

Position sizing (Kelly criterion)
Portfolio rebalancing
Comparaison des algorithmes

Comparer les algorithmes ML selon les dimensions clés

Matrice de comparaison des algorithmesCliquez sur une colonne pour développer les détails
Métrique
LightGBMRegressorFreqtrade
LightGBMClassifierFreqtrade
XGBoostRegressorFreqtrade
XGBoostClassifierFreqtrade
PyTorchMLPRegressorFreqtrade
PyTorchTransformerRegressorFreqtrade
LGBModelQlib (Microsoft)
XGBModelQlib (Microsoft)
DNNModelQlib (Microsoft)
ALSTMQlib (Microsoft)
TFTModelQlib (Microsoft)
GATsQlib (Microsoft)
🎯Complexité⭐⭐⭐intermediate⭐⭐⭐intermediate⭐⭐⭐intermediate⭐⭐⭐intermediate⭐⭐⭐⭐advanced⭐⭐⭐⭐advanced⭐⭐⭐intermediate⭐⭐⭐intermediate⭐⭐⭐⭐advanced⭐⭐⭐⭐advanced⭐⭐⭐⭐advanced⭐⭐⭐⭐advanced
📈Type de prédictionRégressionClassificationRégressionClassificationRégressionRégressionMixteMixteMixteSéquenceMixteMixte
⚡Vitesse d'entraînement⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡
🎯Précision📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊📊
💡Idéal pourDonnées tabulairesDonnées tabulairesDonnées tabulairesDonnées tabulairesMotifs non linéairesMotifs de séries temporellesUsage généralUsage généralUsage généralDonnées séquentiellesUsage généralRelations graphiques
Complexité :

Freqtrade

LightGBMRegressor
Freqtrade
Machine Learningintermediate

Gradient boosting regression model for price movement prediction using LightGBM.

Source :freqtrade/freqai/prediction_models/LightGBMRegressor.py
LightGBMClassifier
Freqtrade
Machine Learningintermediate

LightGBM classification model for user-defined discrete labels.

Source :freqtrade/freqai/prediction_models/LightGBMClassifier.py
XGBoostRegressor
Freqtrade
Machine Learningintermediate

XGBoost-based regression model for continuous value prediction.

Source :freqtrade/freqai/prediction_models/XGBoostRegressor.py
XGBoostClassifier
Freqtrade
Machine Learningintermediate

XGBoost classification model for user-defined discrete labels.

Source :freqtrade/freqai/prediction_models/XGBoostClassifier.py
PyTorchMLPRegressor
Freqtrade
Machine Learningadvanced

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

Source :freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
PyTorchTransformerRegressor
Freqtrade
Machine Learningadvanced

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

Source :freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py

Qlib (Microsoft)

LGBModel
Qlib (Microsoft)
Machine Learningintermediate

LightGBM model for stock return prediction using technical and fundamental features.

Paramètres clés
num_leaves31Maximum number of leaves
learning_rate0.1Boosting learning rate
Source :qlib/contrib/model/gbdt.py
XGBModel
Qlib (Microsoft)
Machine Learningintermediate

XGBoost model for stock return prediction.

Source :qlib/contrib/model/xgboost.py
DNNModel
Qlib (Microsoft)
Machine Learningadvanced

Deep neural network for nonlinear feature extraction and return prediction.

Paramètres clés
hidden_size256Hidden layer size
Source :qlib/contrib/model/pytorch_nn.py
ALSTM
Qlib (Microsoft)
Machine Learningadvanced

Attention-based LSTM for sequential stock data modeling with attention mechanism.

Paramètres clés
hidden_size64LSTM hidden size
num_layers2Number of LSTM layers
Source :qlib/contrib/model/pytorch_alstm.py
TFTModel
Qlib (Microsoft)
Machine Learningadvanced

Temporal Fusion Transformer combining static and temporal features for multi-horizon prediction.

Source :qlib/contrib/model/pytorch_tft.py
GATs
Qlib (Microsoft)
Machine Learningadvanced

Graph Attention Networks modeling stock relationships for cross-stock prediction.

Source :qlib/contrib/model/pytorch_gats.py
Types de stratégies associés
Apprentissage par renforcement →Facteur / Alpha →Tous les algorithmes →

Algorithmes de trading par apprentissage automatique, référence des algorithmes

LightGBMRegressor (Freqtrade)
Gradient boosting regression model for price movement prediction using LightGBM. Source : https://github.com/freqtrade/freqtrade/blob/c0a991f5632cb900075ff2864b6671ed71a319d7/freqtrade/freqai/prediction_models/LightGBMRegressor.py.
LightGBMClassifier (Freqtrade)
LightGBM classification model for user-defined discrete labels. Source : https://github.com/freqtrade/freqtrade/blob/c0a991f5632cb900075ff2864b6671ed71a319d7/freqtrade/freqai/prediction_models/LightGBMClassifier.py.
XGBoostRegressor (Freqtrade)
XGBoost-based regression model for continuous value prediction. Source : https://github.com/freqtrade/freqtrade/blob/c0a991f5632cb900075ff2864b6671ed71a319d7/freqtrade/freqai/prediction_models/XGBoostRegressor.py.
XGBoostClassifier (Freqtrade)
XGBoost classification model for user-defined discrete labels. Source : https://github.com/freqtrade/freqtrade/blob/c0a991f5632cb900075ff2864b6671ed71a319d7/freqtrade/freqai/prediction_models/XGBoostClassifier.py.
PyTorchMLPRegressor (Freqtrade)
Multi-layer perceptron neural network for regression-based price prediction. Source : https://github.com/freqtrade/freqtrade/blob/c0a991f5632cb900075ff2864b6671ed71a319d7/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py.
PyTorchTransformerRegressor (Freqtrade)
Transformer architecture for time-series regression using self-attention mechanism. Source : https://github.com/freqtrade/freqtrade/blob/c0a991f5632cb900075ff2864b6671ed71a319d7/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py.
LGBModel (Qlib (Microsoft))
LightGBM model for stock return prediction using technical and fundamental features. Paramètres clés : num_leaves (Maximum number of leaves), learning_rate (Boosting learning rate).Source : https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/gbdt.py.
XGBModel (Qlib (Microsoft))
XGBoost model for stock return prediction. Source : https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/xgboost.py.
DNNModel (Qlib (Microsoft))
Deep neural network for nonlinear feature extraction and return prediction. Paramètres clés : hidden_size (Hidden layer size).Source : https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_nn.py.
ALSTM (Qlib (Microsoft))
Attention-based LSTM for sequential stock data modeling with attention mechanism. Paramètres clés : hidden_size (LSTM hidden size), num_layers (Number of LSTM layers).Source : https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_alstm.py.
TFTModel (Qlib (Microsoft))
Temporal Fusion Transformer combining static and temporal features for multi-horizon prediction. Source : https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_tft.py.
GATs (Qlib (Microsoft))
Graph Attention Networks modeling stock relationships for cross-stock prediction. Source : https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_gats.py.