开源库
Freqtrade
freqtrade/freqtrade★ 28k+
Crypto trading bot framework with FreqAI ML extension supporting multiple machine learning and deep learning models.
基于指标的模板
机器学习模型（FreqAI）
FreqAI 预测模型用于基于机器学习的价格预测。每个模型都需要通过 Freqtrade 的特征流水线进行特征工程。
Gradient boosting regression model for price movement prediction using LightGBM.
速度⚡⚡⚡
准确度📊📊📊
关键参数
| n_estimators | 1000 | Number of boosting rounds |
| learning_rate | 0.01 | Step size shrinkage |
源代码：
freqai/prediction_models/LightGBMRegressor.pyGradient boosting classification model for directional prediction (up/down/neutral).
速度⚡⚡⚡
准确度📊📊📊
关键参数
| n_estimators | 1000 | Number of boosting rounds |
源代码：
freqai/prediction_models/LightGBMClassifier.pyXGBoost-based regression model for continuous value prediction.
速度⚡⚡⚡
准确度📊📊📊
关键参数
| n_estimators | 1000 | Number of boosting rounds |
| max_depth | 6 | Maximum tree depth |
源代码：
freqai/prediction_models/XGBoostRegressor.pyXGBoost-based classification model for directional prediction.
速度⚡⚡⚡
准确度📊📊📊
关键参数
| n_estimators | 1000 | Number of boosting rounds |
源代码：
freqai/prediction_models/XGBoostClassifier.pyCatBoost gradient boosting model with native categorical feature support.
速度⚡⚡
准确度📊📊📊
关键参数
| iterations | 1000 | Number of boosting iterations |
源代码：
freqai/prediction_models/CatboostRegressor.pyMulti-layer perceptron neural network for regression-based price prediction.
速度⚡⚡
准确度📊📊📊
关键参数
| hidden_dim | 128 | Hidden layer dimension |
| dropout_percent | 0.2 | Dropout rate |
源代码：
freqai/prediction_models/PyTorchMLPRegressor.py强化学习（FreqAI）
基于 Stable Baselines3 构建的强化学习智能体。智能体通过与环境交互而非标注数据来学习交易决策。
Freqtrade 策略与 FreqAI 模型，算法参考
- SampleStrategy (Freqtrade)
- Template strategy demonstrating basic technical indicator usage for entry/exit signals. 源代码： https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py.
- LightGBMRegressor (Freqtrade)
- Gradient boosting regression model for price movement prediction using LightGBM. 关键参数： n_estimators (Number of boosting rounds), learning_rate (Step size shrinkage).源代码： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMRegressor.py.
- LightGBMClassifier (Freqtrade)
- Gradient boosting classification model for directional prediction (up/down/neutral). 关键参数： n_estimators (Number of boosting rounds).源代码： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMClassifier.py.
- XGBoostRegressor (Freqtrade)
- XGBoost-based regression model for continuous value prediction. 关键参数： n_estimators (Number of boosting rounds), max_depth (Maximum tree depth).源代码： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostRegressor.py.
- XGBoostClassifier (Freqtrade)
- XGBoost-based classification model for directional prediction. 关键参数： n_estimators (Number of boosting rounds).源代码： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostClassifier.py.
- CatboostRegressor (Freqtrade)
- CatBoost gradient boosting model with native categorical feature support. 关键参数： iterations (Number of boosting iterations).源代码： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/CatboostRegressor.py.
- PyTorchMLPRegressor (Freqtrade)
- Multi-layer perceptron neural network for regression-based price prediction. 关键参数： hidden_dim (Hidden layer dimension), dropout_percent (Dropout rate).源代码： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/PyTorchMLPRegressor.py.
- PyTorchTransformerRegressor (Freqtrade)
- Transformer architecture for time-series regression using self-attention mechanism. 关键参数： num_heads (Number of attention heads), num_layers (Number of transformer layers).源代码： 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. 关键参数： model_type (RL algorithm (PPO, A2C, etc.)), total_timesteps (Training timesteps).源代码： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/ReinforcementLearner.py.