Algoritmi di trading con machine learning

Algoritmi di trading con machine learning, riferimento algoritmi

LightGBMRegressor (Freqtrade)

Gradient boosting regression model for price movement prediction using LightGBM. Parametri chiave: n_estimators (Number of boosting rounds), learning_rate (Step size shrinkage).Sorgente: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMRegressor.py.

LightGBMClassifier (Freqtrade)

Gradient boosting classification model for directional prediction (up/down/neutral). Parametri chiave: n_estimators (Number of boosting rounds).Sorgente: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMClassifier.py.

XGBoostRegressor (Freqtrade)

XGBoost-based regression model for continuous value prediction. Parametri chiave: n_estimators (Number of boosting rounds), max_depth (Maximum tree depth).Sorgente: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostRegressor.py.

XGBoostClassifier (Freqtrade)

XGBoost-based classification model for directional prediction. Parametri chiave: n_estimators (Number of boosting rounds).Sorgente: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostClassifier.py.

CatboostRegressor (Freqtrade)

CatBoost gradient boosting model with native categorical feature support. Parametri chiave: iterations (Number of boosting iterations).Sorgente: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/CatboostRegressor.py.

PyTorchMLPRegressor (Freqtrade)

Multi-layer perceptron neural network for regression-based price prediction. Parametri chiave: hidden_dim (Hidden layer dimension), dropout_percent (Dropout rate).Sorgente: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/PyTorchMLPRegressor.py.

PyTorchTransformerRegressor (Freqtrade)

Transformer architecture for time-series regression using self-attention mechanism. Parametri chiave: num_heads (Number of attention heads), num_layers (Number of transformer layers).Sorgente: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/PyTorchTransformerRegressor.py.

LGBModel (Qlib (Microsoft))

LightGBM model for stock return prediction using technical and fundamental features. Parametri chiave: num_leaves (Maximum number of leaves), learning_rate (Boosting learning rate).Sorgente: https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/gbdt.py.

XGBModel (Qlib (Microsoft))

XGBoost model for stock return prediction. Sorgente: 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. Parametri chiave: hidden_size (Hidden layer size).Sorgente: 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. Parametri chiave: hidden_size (LSTM hidden size), num_layers (Number of LSTM layers).Sorgente: 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. Sorgente: 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. Sorgente: https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_gats.py.