Freqtrade
freqtrade/freqtrade★ 28k+
Crypto trading bot framework with FreqAI ML extension supporting multiple machine learning and deep learning models.
Indikator-basierte Templates
Maschinelle Lernmodelle (FreqAI)
FreqAI-Vorhersagemodelle für ML-basierte Preisprognose. Jedes Modell erfordert Feature Engineering über die Feature-Pipeline von Freqtrade.
Gradient boosting regression model for price movement prediction using LightGBM.
| n_estimators | 1000 | Number of boosting rounds |
| learning_rate | 0.01 | Step size shrinkage |
freqai/prediction_models/LightGBMRegressor.pyGradient boosting classification model for directional prediction (up/down/neutral).
| n_estimators | 1000 | Number of boosting rounds |
freqai/prediction_models/LightGBMClassifier.pyXGBoost-based regression model for continuous value prediction.
| n_estimators | 1000 | Number of boosting rounds |
| max_depth | 6 | Maximum tree depth |
freqai/prediction_models/XGBoostRegressor.pyXGBoost-based classification model for directional prediction.
| n_estimators | 1000 | Number of boosting rounds |
freqai/prediction_models/XGBoostClassifier.pyCatBoost gradient boosting model with native categorical feature support.
| iterations | 1000 | Number of boosting iterations |
freqai/prediction_models/CatboostRegressor.pyMulti-layer perceptron neural network for regression-based price prediction.
| hidden_dim | 128 | Hidden layer dimension |
| dropout_percent | 0.2 | Dropout rate |
freqai/prediction_models/PyTorchMLPRegressor.pyTransformer architecture for time-series regression using self-attention mechanism.
| num_heads | 8 | Number of attention heads |
| num_layers | 2 | Number of transformer layers |
freqai/prediction_models/PyTorchTransformerRegressor.pyReinforcement Learning (FreqAI)
Reinforcement-Learning-Agenten auf Basis von Stable Baselines3. Der Agent lernt Handelsentscheidungen durch Umgebungsinteraktion statt durch gelabelte Daten.
Reinforcement learning agent using Stable Baselines3 (PPO/A2C/etc.) for trading decisions.
| model_type | PPO | RL algorithm (PPO, A2C, etc.) |
| total_timesteps | 10000 | Training timesteps |
freqai/prediction_models/ReinforcementLearner.pyFreqtrade-Strategien und FreqAI-Modelle, Algorithmus-Referenz
- SampleStrategy (Freqtrade)
- Template strategy demonstrating basic technical indicator usage for entry/exit signals. Quelle: https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py.
- LightGBMRegressor (Freqtrade)
- Gradient boosting regression model for price movement prediction using LightGBM. Schlüsselparameter: n_estimators (Number of boosting rounds), learning_rate (Step size shrinkage).Quelle: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMRegressor.py.
- LightGBMClassifier (Freqtrade)
- Gradient boosting classification model for directional prediction (up/down/neutral). Schlüsselparameter: n_estimators (Number of boosting rounds).Quelle: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMClassifier.py.
- XGBoostRegressor (Freqtrade)
- XGBoost-based regression model for continuous value prediction. Schlüsselparameter: n_estimators (Number of boosting rounds), max_depth (Maximum tree depth).Quelle: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostRegressor.py.
- XGBoostClassifier (Freqtrade)
- XGBoost-based classification model for directional prediction. Schlüsselparameter: n_estimators (Number of boosting rounds).Quelle: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostClassifier.py.
- CatboostRegressor (Freqtrade)
- CatBoost gradient boosting model with native categorical feature support. Schlüsselparameter: iterations (Number of boosting iterations).Quelle: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/CatboostRegressor.py.
- PyTorchMLPRegressor (Freqtrade)
- Multi-layer perceptron neural network for regression-based price prediction. Schlüsselparameter: hidden_dim (Hidden layer dimension), dropout_percent (Dropout rate).Quelle: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/PyTorchMLPRegressor.py.
- PyTorchTransformerRegressor (Freqtrade)
- Transformer architecture for time-series regression using self-attention mechanism. Schlüsselparameter: num_heads (Number of attention heads), num_layers (Number of transformer layers).Quelle: 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. Schlüsselparameter: model_type (RL algorithm (PPO, A2C, etc.)), total_timesteps (Training timesteps).Quelle: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/ReinforcementLearner.py.