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Freqtrade

freqtrade/freqtrade★ 28k+

Crypto trading bot framework with FreqAI ML extension supporting multiple machine learning and deep learning models.

Lenguaje PythonActivos cryptoMercados spot, derivativesTipo framework

Plantillas basadas en indicadores

SampleStrategy

Freqtrade

Basado en indicadoresbeginner

Template strategy demonstrating basic technical indicator usage for entry/exit signals.

Velocidad⚡⚡

Precisión📊📊📊

Origen:freqtrade/templates/sample_strategy.py

Modelos de aprendizaje automático (FreqAI)

Modelos de predicción FreqAI para predicción de precios basada en aprendizaje automático. Cada modelo requiere ingeniería de características a través del pipeline de features de Freqtrade.

LightGBMRegressor

Freqtrade

Aprendizaje automáticointermediate

Gradient boosting regression model for price movement prediction using LightGBM.

Velocidad⚡⚡⚡

Precisión📊📊📊

Parámetros clave

n_estimators	1000	Number of boosting rounds
learning_rate	0.01	Step size shrinkage

Origen:freqai/prediction_models/LightGBMRegressor.py

LightGBMClassifier

Freqtrade

Aprendizaje automáticointermediate

Gradient boosting classification model for directional prediction (up/down/neutral).

Velocidad⚡⚡⚡

Precisión📊📊📊

Parámetros clave

n_estimators

1000

Number of boosting rounds

Origen:freqai/prediction_models/LightGBMClassifier.py

XGBoostRegressor

Freqtrade

Aprendizaje automáticointermediate

XGBoost-based regression model for continuous value prediction.

Velocidad⚡⚡⚡

Precisión📊📊📊

Parámetros clave

n_estimators	1000	Number of boosting rounds
max_depth	6	Maximum tree depth

Origen:freqai/prediction_models/XGBoostRegressor.py

XGBoostClassifier

Freqtrade

Aprendizaje automáticointermediate

XGBoost-based classification model for directional prediction.

Velocidad⚡⚡⚡

Precisión📊📊📊

Parámetros clave

n_estimators

1000

Number of boosting rounds

Origen:freqai/prediction_models/XGBoostClassifier.py

CatboostRegressor

Freqtrade

Aprendizaje automáticointermediate

CatBoost gradient boosting model with native categorical feature support.

Velocidad⚡⚡

Precisión📊📊📊

Parámetros clave

iterations

1000

Number of boosting iterations

Origen:freqai/prediction_models/CatboostRegressor.py

PyTorchMLPRegressor

Freqtrade

Aprendizaje automáticoadvanced

Multi-layer perceptron neural network for regression-based price prediction.

Velocidad⚡⚡

Precisión📊📊📊

Parámetros clave

hidden_dim	128	Hidden layer dimension
dropout_percent	0.2	Dropout rate

Origen:freqai/prediction_models/PyTorchMLPRegressor.py

PyTorchTransformerRegressor

Freqtrade

Aprendizaje automáticoadvanced

Transformer architecture for time-series regression using self-attention mechanism.

Velocidad⚡

Precisión📊📊📊📊

Parámetros clave

num_heads	8	Number of attention heads
num_layers	2	Number of transformer layers

Origen:freqai/prediction_models/PyTorchTransformerRegressor.py

Aprendizaje por refuerzo (FreqAI)

Agentes de aprendizaje por refuerzo construidos sobre Stable Baselines3. El agente aprende decisiones de trading mediante interacción con el entorno en lugar de datos etiquetados.

ReinforcementLearner

Freqtrade

Aprendizaje por refuerzoadvanced

Reinforcement learning agent using Stable Baselines3 (PPO/A2C/etc.) for trading decisions.

Velocidad⚡⚡

Precisión📊📊📊

Parámetros clave

model_type	PPO	RL algorithm (PPO, A2C, etc.)
total_timesteps	10000	Training timesteps

Origen:freqai/prediction_models/ReinforcementLearner.py

Estrategias Freqtrade y modelos FreqAI, referencia de algoritmos

SampleStrategy (Freqtrade): Template strategy demonstrating basic technical indicator usage for entry/exit signals. Origen: https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py.
LightGBMRegressor (Freqtrade): Gradient boosting regression model for price movement prediction using LightGBM. Parámetros clave: n_estimators (Number of boosting rounds), learning_rate (Step size shrinkage).Origen: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMRegressor.py.
LightGBMClassifier (Freqtrade): Gradient boosting classification model for directional prediction (up/down/neutral). Parámetros clave: n_estimators (Number of boosting rounds).Origen: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMClassifier.py.
XGBoostRegressor (Freqtrade): XGBoost-based regression model for continuous value prediction. Parámetros clave: n_estimators (Number of boosting rounds), max_depth (Maximum tree depth).Origen: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostRegressor.py.
XGBoostClassifier (Freqtrade): XGBoost-based classification model for directional prediction. Parámetros clave: n_estimators (Number of boosting rounds).Origen: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostClassifier.py.
CatboostRegressor (Freqtrade): CatBoost gradient boosting model with native categorical feature support. Parámetros clave: iterations (Number of boosting iterations).Origen: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/CatboostRegressor.py.
PyTorchMLPRegressor (Freqtrade): Multi-layer perceptron neural network for regression-based price prediction. Parámetros clave: hidden_dim (Hidden layer dimension), dropout_percent (Dropout rate).Origen: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/PyTorchMLPRegressor.py.
PyTorchTransformerRegressor (Freqtrade): Transformer architecture for time-series regression using self-attention mechanism. Parámetros clave: num_heads (Number of attention heads), num_layers (Number of transformer layers).Origen: 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. Parámetros clave: model_type (RL algorithm (PPO, A2C, etc.)), total_timesteps (Training timesteps).Origen: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/ReinforcementLearner.py.