開源函式庫
Freqtrade
freqtrade/freqtrade★ 28k+
Crypto trading bot framework with FreqAI ML extension supporting multiple machine learning and deep learning models.
基於指標的模板
機器學習模型（FreqAI）
FreqAI 預測模型用於基於機器學習的價格預測。每個模型都需要透過 Freqtrade 的特徵流水線進行特徵工程。
Gradient boosting regression model for price movement prediction using LightGBM.
速度⚡⚡⚡
準確度📊📊📊
關鍵參數
| n_estimators | 1000 | Number of boosting rounds |
| learning_rate | 0.01 | Step size shrinkage |
原始碼：
freqai/prediction_models/LightGBMRegressor.pyGradient boosting classification model for directional prediction (up/down/neutral).
速度⚡⚡⚡
準確度📊📊📊
關鍵參數
| n_estimators | 1000 | Number of boosting rounds |
原始碼：
freqai/prediction_models/LightGBMClassifier.pyXGBoost-based regression model for continuous value prediction.
速度⚡⚡⚡
準確度📊📊📊
關鍵參數
| n_estimators | 1000 | Number of boosting rounds |
| max_depth | 6 | Maximum tree depth |
原始碼：
freqai/prediction_models/XGBoostRegressor.pyXGBoost-based classification model for directional prediction.
速度⚡⚡⚡
準確度📊📊📊
關鍵參數
| n_estimators | 1000 | Number of boosting rounds |
原始碼：
freqai/prediction_models/XGBoostClassifier.pyCatBoost gradient boosting model with native categorical feature support.
速度⚡⚡
準確度📊📊📊
關鍵參數
| iterations | 1000 | Number of boosting iterations |
原始碼：
freqai/prediction_models/CatboostRegressor.pyMulti-layer perceptron neural network for regression-based price prediction.
速度⚡⚡
準確度📊📊📊
關鍵參數
| hidden_dim | 128 | Hidden layer dimension |
| dropout_percent | 0.2 | Dropout rate |
原始碼：
freqai/prediction_models/PyTorchMLPRegressor.py強化學習（FreqAI）
基於 Stable Baselines3 構建的強化學習代理。代理透過與環境互動而非標註資料來學習交易決策。
Freqtrade 策略與 FreqAI 模型，演算法參考
- SampleStrategy (Freqtrade)
- Template strategy demonstrating basic technical indicator usage for entry/exit signals. 原始碼： https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py.
- LightGBMRegressor (Freqtrade)
- Gradient boosting regression model for price movement prediction using LightGBM. 關鍵參數： n_estimators (Number of boosting rounds), learning_rate (Step size shrinkage).原始碼： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMRegressor.py.
- LightGBMClassifier (Freqtrade)
- Gradient boosting classification model for directional prediction (up/down/neutral). 關鍵參數： n_estimators (Number of boosting rounds).原始碼： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMClassifier.py.
- XGBoostRegressor (Freqtrade)
- XGBoost-based regression model for continuous value prediction. 關鍵參數： n_estimators (Number of boosting rounds), max_depth (Maximum tree depth).原始碼： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostRegressor.py.
- XGBoostClassifier (Freqtrade)
- XGBoost-based classification model for directional prediction. 關鍵參數： n_estimators (Number of boosting rounds).原始碼： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostClassifier.py.
- CatboostRegressor (Freqtrade)
- CatBoost gradient boosting model with native categorical feature support. 關鍵參數： iterations (Number of boosting iterations).原始碼： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/CatboostRegressor.py.
- PyTorchMLPRegressor (Freqtrade)
- Multi-layer perceptron neural network for regression-based price prediction. 關鍵參數： hidden_dim (Hidden layer dimension), dropout_percent (Dropout rate).原始碼： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/PyTorchMLPRegressor.py.
- PyTorchTransformerRegressor (Freqtrade)
- Transformer architecture for time-series regression using self-attention mechanism. 關鍵參數： num_heads (Number of attention heads), num_layers (Number of transformer layers).原始碼： 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. 關鍵參數： model_type (RL algorithm (PPO, A2C, etc.)), total_timesteps (Training timesteps).原始碼： https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/ReinforcementLearner.py.