Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
- machine-learning
- trading
- investment
- finance
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Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
modular quant framework.
Quantitative analysis, strategies and backtests
Python library for portfolio optimization, factor model construction, and risk management built on scikit-learn
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
Scalable, event-driven, deep-learning-friendly backtesting library
Trading environnement for RL agents, backtesting and training.
AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.
:chart_with_upwards_trend: A live cryptocurrency historical trade data blotter. Download live historical trade data from any cryptoexchange, be it for machine learning, backtesting/visualizing trading strategies or for Quantopian/Zipline.