Makine öğrenmesi tabanlı işlem algoritmaları
Nicel finans için açık kaynak ML modelleri
Makine öğrenmesi tabanlı işlem algoritmaları, finansal zaman serisi verilerine istatistiksel öğrenme modellerini (gradient boosting, sinir ağları, Transformer'lar) uygulayarak fiyat hareketlerini tahmin eder veya işlem sinyalleri üretir. Bu açık kaynak uygulamalar hem regresyon (fiyat hedefi) hem de sınıflandırma (yön) formülasyonlarını kapsar.
ML algoritmalarının kütüphaneler arasında nasıl bağlandığı
ML algoritmalarının bir işlem sisteminde nasıl birlikte çalıştığı
Market Data Input
Price, volume, indicators
ML Signal Generation
Model predictions
Entry Decision
Signal threshold filtering
Exit Strategy
Profit taking & stop loss
Risk Management
Position sizing & portfolio
ML algoritmalarını temel boyutlarda karşılaştırın
| Metrik | LightGBMRegressorFreqtrade | LightGBMClassifierFreqtrade | XGBoostRegressorFreqtrade | XGBoostClassifierFreqtrade | PyTorchMLPRegressorFreqtrade | PyTorchTransformerRegressorFreqtrade | LGBModelQlib (Microsoft) | XGBModelQlib (Microsoft) | DNNModelQlib (Microsoft) | ALSTMQlib (Microsoft) | TFTModelQlib (Microsoft) | GATsQlib (Microsoft) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Karmaşıklık | ⭐⭐⭐intermediate | ⭐⭐⭐intermediate | ⭐⭐⭐intermediate | ⭐⭐⭐intermediate | ⭐⭐⭐⭐advanced | ⭐⭐⭐⭐advanced | ⭐⭐⭐intermediate | ⭐⭐⭐intermediate | ⭐⭐⭐⭐advanced | ⭐⭐⭐⭐advanced | ⭐⭐⭐⭐advanced | ⭐⭐⭐⭐advanced |
| Tahmin Türü | Regresyon | Sınıflandırma | Regresyon | Sınıflandırma | Regresyon | Regresyon | Karışık | Karışık | Karışık | Dizi | Karışık | Karışık |
| Eğitim Hızı | ⚡⚡⚡ | ⚡⚡⚡ | ⚡⚡⚡ | ⚡⚡⚡ | ⚡⚡ | ⚡ | ⚡⚡ | ⚡⚡ | ⚡⚡ | ⚡ | ⚡⚡ | ⚡⚡ |
| Doğruluk | 📊📊📊 | 📊📊📊 | 📊📊📊 | 📊📊📊 | 📊📊📊 | 📊📊📊📊 | 📊📊 | 📊📊 | 📊📊 | 📊📊📊 | 📊📊 | 📊📊📊📊 |
| Şunun için en iyi | Tablo verisi | Tablo verisi | Tablo verisi | Tablo verisi | Doğrusal olmayan desenler | Zaman serisi desenleri | Genel amaçlı | Genel amaçlı | Genel amaçlı | Sıralı veri | Genel amaçlı | Graf ilişkileri |
Freqtrade
Gradient boosting regression model for price movement prediction using LightGBM.
freqtrade/freqai/prediction_models/LightGBMRegressor.pyLightGBM classification model for user-defined discrete labels.
freqtrade/freqai/prediction_models/LightGBMClassifier.pyXGBoost-based regression model for continuous value prediction.
freqtrade/freqai/prediction_models/XGBoostRegressor.pyXGBoost classification model for user-defined discrete labels.
freqtrade/freqai/prediction_models/XGBoostClassifier.pyMulti-layer perceptron neural network for regression-based price prediction.
freqtrade/freqai/prediction_models/PyTorchMLPRegressor.pyQlib (Microsoft)
LightGBM model for stock return prediction using technical and fundamental features.
| num_leaves | 31 | Maximum number of leaves |
| learning_rate | 0.1 | Boosting learning rate |
qlib/contrib/model/gbdt.pyXGBoost model for stock return prediction.
qlib/contrib/model/xgboost.pyDeep neural network for nonlinear feature extraction and return prediction.
| hidden_size | 256 | Hidden layer size |
qlib/contrib/model/pytorch_nn.pyAttention-based LSTM for sequential stock data modeling with attention mechanism.
| hidden_size | 64 | LSTM hidden size |
| num_layers | 2 | Number of LSTM layers |
qlib/contrib/model/pytorch_alstm.pyTemporal Fusion Transformer combining static and temporal features for multi-horizon prediction.
qlib/contrib/model/pytorch_tft.pyMakine öğrenmesi tabanlı işlem algoritmaları, algoritma referansı
- LightGBMRegressor (Freqtrade)
- Gradient boosting regression model for price movement prediction using LightGBM. Kaynak: https://github.com/freqtrade/freqtrade/blob/c0a991f5632cb900075ff2864b6671ed71a319d7/freqtrade/freqai/prediction_models/LightGBMRegressor.py.
- LightGBMClassifier (Freqtrade)
- LightGBM classification model for user-defined discrete labels. Kaynak: https://github.com/freqtrade/freqtrade/blob/c0a991f5632cb900075ff2864b6671ed71a319d7/freqtrade/freqai/prediction_models/LightGBMClassifier.py.
- XGBoostRegressor (Freqtrade)
- XGBoost-based regression model for continuous value prediction. Kaynak: https://github.com/freqtrade/freqtrade/blob/c0a991f5632cb900075ff2864b6671ed71a319d7/freqtrade/freqai/prediction_models/XGBoostRegressor.py.
- XGBoostClassifier (Freqtrade)
- XGBoost classification model for user-defined discrete labels. Kaynak: 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. Kaynak: 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. Kaynak: 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. Temel parametreler: num_leaves (Maximum number of leaves), learning_rate (Boosting learning rate).Kaynak: https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/gbdt.py.
- XGBModel (Qlib (Microsoft))
- XGBoost model for stock return prediction. Kaynak: 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. Temel parametreler: hidden_size (Hidden layer size).Kaynak: 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. Temel parametreler: hidden_size (LSTM hidden size), num_layers (Number of LSTM layers).Kaynak: 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. Kaynak: 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. Kaynak: https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_gats.py.