Strategy Type

Machine Learning Trading Algorithms

Open-Source ML Models for Quantitative Finance

Machine learning trading algorithms apply statistical learning models — gradient boosting, neural networks, and transformers — to financial time-series data to predict price movements or generate trading signals. These open-source implementations cover both regression (price target) and classification (direction) formulations.

13 algorithms2 libraries

Algorithm Network

How ML algorithms connect across libraries

🧠ML Algorithms

🤖

Freqtrade9 algos

🔬

Microsoft Qlib6 algos

LightGBMRegressorintermediate

LightGBMClassifierintermediate

XGBoostRegressorintermediate

XGBoostClassifierintermediate

CatboostRegressorintermediate

PyTorchMLPRegressoradvanced

PyTorchTransformerRegressoradvanced

LGBModelintermediate

XGBModelintermediate

DNNModeladvanced

ALSTMadvanced

TFTModeladvanced

GATsadvanced

Trading Decision Pipeline

How ML algorithms work together in a trading system

📊

Market Data Input

Price, volume, indicators

OHLCV price data

Technical indicators

Feature engineering

🧠

ML Signal Generation

Model predictions

LightGBM/XGBoost regression

Transformer sequence modeling

MLP classification

📈

Entry Decision

Signal threshold filtering

Confidence score > 0.6

Direction: Long/Short

📉

Exit Strategy

Profit taking & stop loss

Take profit (fixed % or trailing)

Stop loss (ATR-based)

🛡️

Risk Management

Position sizing & portfolio

Position sizing (Kelly criterion)

Portfolio rebalancing

Algorithm Comparison

Compare ML algorithms across key dimensions

Algorithm Comparison MatrixClick a column to expand details

Metric	LightGBMRegressorFreqtrade	LightGBMClassifierFreqtrade	XGBoostRegressorFreqtrade	XGBoostClassifierFreqtrade	CatboostRegressorFreqtrade	PyTorchMLPRegressorFreqtrade	PyTorchTransformerRegressorFreqtrade	LGBModelQlib (Microsoft)	XGBModelQlib (Microsoft)	DNNModelQlib (Microsoft)	ALSTMQlib (Microsoft)	TFTModelQlib (Microsoft)	GATsQlib (Microsoft)
🎯Complexity	⭐⭐⭐intermediate	⭐⭐⭐intermediate	⭐⭐⭐intermediate	⭐⭐⭐intermediate	⭐⭐⭐intermediate	⭐⭐⭐⭐advanced	⭐⭐⭐⭐advanced	⭐⭐⭐intermediate	⭐⭐⭐intermediate	⭐⭐⭐⭐advanced	⭐⭐⭐⭐advanced	⭐⭐⭐⭐advanced	⭐⭐⭐⭐advanced
📈Prediction Type	Regression	Classification	Regression	Classification	Regression	Regression	Regression	Mixed	Mixed	Mixed	Sequence	Mixed	Mixed
⚡Training Speed	⚡⚡⚡	⚡⚡⚡	⚡⚡⚡	⚡⚡⚡	⚡⚡	⚡⚡	⚡	⚡⚡	⚡⚡	⚡⚡	⚡	⚡⚡	⚡⚡
🎯Accuracy	📊📊📊	📊📊📊	📊📊📊	📊📊📊	📊📊📊	📊📊📊	📊📊📊📊	📊📊	📊📊	📊📊	📊📊📊	📊📊	📊📊📊📊
💡Best For	Tabular data	Tabular data	Tabular data	Tabular data	General purpose	Non-linear patterns	Time series patterns	General purpose	General purpose	General purpose	Sequential data	General purpose	Graph relationships

Freqtrade

LightGBMRegressor

Freqtrade

Machine Learningintermediate

Gradient boosting regression model for price movement prediction using LightGBM.

Speed⚡⚡⚡

Accuracy📊📊📊

Key Parameters

n_estimators	1000	Number of boosting rounds
learning_rate	0.01	Step size shrinkage

Source:freqai/prediction_models/LightGBMRegressor.py

LightGBMClassifier

Freqtrade

Machine Learningintermediate

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

Speed⚡⚡⚡

Accuracy📊📊📊

Key Parameters

n_estimators

1000

Number of boosting rounds

Source:freqai/prediction_models/LightGBMClassifier.py

XGBoostRegressor

Freqtrade

Machine Learningintermediate

XGBoost-based regression model for continuous value prediction.

Speed⚡⚡⚡

Accuracy📊📊📊

Key Parameters

n_estimators	1000	Number of boosting rounds
max_depth	6	Maximum tree depth

Source:freqai/prediction_models/XGBoostRegressor.py

XGBoostClassifier

Freqtrade

Machine Learningintermediate

XGBoost-based classification model for directional prediction.

Speed⚡⚡⚡

Accuracy📊📊📊

Key Parameters

n_estimators

1000

Number of boosting rounds

Source:freqai/prediction_models/XGBoostClassifier.py

CatboostRegressor

Freqtrade

Machine Learningintermediate

CatBoost gradient boosting model with native categorical feature support.

Speed⚡⚡

Accuracy📊📊📊

Key Parameters

iterations

1000

Number of boosting iterations

Source:freqai/prediction_models/CatboostRegressor.py

PyTorchMLPRegressor

Freqtrade

Machine Learningadvanced

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

Speed⚡⚡

Accuracy📊📊📊

Key Parameters

hidden_dim	128	Hidden layer dimension
dropout_percent	0.2	Dropout rate

Source:freqai/prediction_models/PyTorchMLPRegressor.py

PyTorchTransformerRegressor

Freqtrade

Machine Learningadvanced

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

Speed⚡

Accuracy📊📊📊📊

Key Parameters

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

Source:freqai/prediction_models/PyTorchTransformerRegressor.py

Qlib (Microsoft)

LGBModel

Qlib (Microsoft)

Machine Learningintermediate

LightGBM model for stock return prediction using technical and fundamental features.

Speed⚡⚡

Accuracy📊📊📊

Key Parameters

num_leaves	31	Maximum number of leaves
learning_rate	0.1	Boosting learning rate

Source:qlib/contrib/model/gbdt.py

XGBModel

Qlib (Microsoft)

Machine Learningintermediate

XGBoost model for stock return prediction.

Speed⚡⚡

Accuracy📊📊📊

Source:qlib/contrib/model/xgboost.py

DNNModel

Qlib (Microsoft)

Machine Learningadvanced

Deep neural network for nonlinear feature extraction and return prediction.

Speed⚡⚡

Accuracy📊📊📊

Key Parameters

hidden_size

256

Hidden layer size

Source:qlib/contrib/model/pytorch_nn.py

ALSTM

Qlib (Microsoft)

Machine Learningadvanced

Attention-based LSTM for sequential stock data modeling with attention mechanism.

Speed⚡

Accuracy📊📊📊

Key Parameters

hidden_size	64	LSTM hidden size
num_layers	2	Number of LSTM layers

Source:qlib/contrib/model/pytorch_alstm.py

TFTModel

Qlib (Microsoft)

Machine Learningadvanced

Temporal Fusion Transformer combining static and temporal features for multi-horizon prediction.

Speed⚡⚡

Accuracy📊📊📊

Source:qlib/contrib/model/pytorch_tft.py

GATs

Qlib (Microsoft)

Machine Learningadvanced

Graph Attention Networks modeling stock relationships for cross-stock prediction.

Speed⚡⚡

Accuracy📊📊📊

Source:qlib/contrib/model/pytorch_gats.py

Machine Learning Trading Algorithms — Algorithm Reference

LightGBMRegressor (Freqtrade): Gradient boosting regression model for price movement prediction using LightGBM. Key parameters: n_estimators (Number of boosting rounds), learning_rate (Step size shrinkage). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMRegressor.py.
LightGBMClassifier (Freqtrade): Gradient boosting classification model for directional prediction (up/down/neutral). Key parameters: n_estimators (Number of boosting rounds). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/LightGBMClassifier.py.
XGBoostRegressor (Freqtrade): XGBoost-based regression model for continuous value prediction. Key parameters: n_estimators (Number of boosting rounds), max_depth (Maximum tree depth). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostRegressor.py.
XGBoostClassifier (Freqtrade): XGBoost-based classification model for directional prediction. Key parameters: n_estimators (Number of boosting rounds). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/XGBoostClassifier.py.
CatboostRegressor (Freqtrade): CatBoost gradient boosting model with native categorical feature support. Key parameters: iterations (Number of boosting iterations). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/CatboostRegressor.py.
PyTorchMLPRegressor (Freqtrade): Multi-layer perceptron neural network for regression-based price prediction. Key parameters: hidden_dim (Hidden layer dimension), dropout_percent (Dropout rate). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/PyTorchMLPRegressor.py.
PyTorchTransformerRegressor (Freqtrade): Transformer architecture for time-series regression using self-attention mechanism. Key parameters: num_heads (Number of attention heads), num_layers (Number of transformer layers). Source: https://github.com/freqtrade/freqtrade/blob/develop/freqai/prediction_models/PyTorchTransformerRegressor.py.
LGBModel (Qlib (Microsoft)): LightGBM model for stock return prediction using technical and fundamental features. Key parameters: num_leaves (Maximum number of leaves), learning_rate (Boosting learning rate). Source: https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/gbdt.py.
XGBModel (Qlib (Microsoft)): XGBoost model for stock return prediction. Source: 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. Key parameters: hidden_size (Hidden layer size). Source: 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. Key parameters: hidden_size (LSTM hidden size), num_layers (Number of LSTM layers). Source: 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. Source: 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. Source: https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_gats.py.