stefan-jansen/machine-learning-for-trading

Code for Machine Learning for Algorithmic Trading, 2nd edition. ML for Trading - 2 nd Edition This book aims to show how ML can add value to algorithmic trading strategies in a practical yet comprehensive way. It covers a broad range of ML techniques from linear regression to deep reinforcement learning and demonstrates how to build, backtest, and evaluate a trading strategy driven by model predictions. In four parts with 23 chapters plus an appendix , it covers on over 800 pages : important aspects of data sourcing, financial feature engineering , and portfolio management, the design and evaluation of long-short strategies based on supervised and unsupervised ML algorithms , how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news, using deep learning models like CNN and RNN with market and alternative data, how to generate synthetic data with generative adversarial networks, and training a trading agent using deep reinforcement learning This repo contains over 150 notebooks that put the concepts, algorithms, and use cases discussed in the book into action. They provide numerous examples that show: how to work with and…

📋 本文为 GitHub Trending Daily RSS 的 RSS 摘要原文，未经 AI 整理。完整上下文请以 原文 为准。