Machine learning meets markets - algorithms, strategies, and real research
The bible of ML trading. Covers feature engineering, backtesting pitfalls, and production systems. Written by AQR/Guggenheim quant.
Book Info → Code Examples →Complete course covering ML algorithms, market mechanics, and portfolio optimization. Includes Python implementations.
Take Course →Academic papers on algorithmic trading, market microstructure, and ML applications. Where quants publish.
Browse Papers →Practical guide from ex-Morgan Stanley quant. Covers strategy development, backtesting, and risk management.
Author's Blog →Neural networks, RNNs, and transformer models for trading. Python code with TensorFlow/PyTorch examples.
Code Repository → Research Paper →Open-source library for developing RL trading agents. Includes DQN, A2C, PPO implementations.
GitHub → Paper →Advanced time series forecasting with seasonality. Used by quant funds for price prediction.
Prophet Docs → Neural Prophet →200+ technical indicators for feature engineering. Essential for building ML trading features.
Python TA-Lib → Modern Alternative →Python backtesting library with live trading support. Handles complex strategies and portfolio management.
Documentation → GitHub →Cloud-based algorithmic trading platform. Free tier with historical data. Deploy strategies to live trading.
Platform → LEAN Engine →Portfolio and risk analytics. Tear sheets, drawdown analysis, and performance attribution.
GitHub →High-performance datastore for time-series data. Used by Man AHL for tick data storage.
Arctic GitHub →Open-source crypto trading bot with ML support. Backtesting, hyperopt, and live trading on 100+ exchanges.
Documentation → GitHub →On-chain data for ML features. Wallet tracking, smart money flows, and network metrics.
Nansen → Glassnode →Maximum Extractable Value research. Arbitrage, liquidations, and sandwich attacks in DeFi.
Flashbots Docs → MEV Inspect →NLP on crypto Twitter, Reddit, and Telegram. Real-time sentiment indicators for trading signals.
Example Code →Comprehensive guide to HFT strategies, technology, and regulations. Industry standard reference.
Book Info →Open-source market making bot. Connects to centralized and decentralized exchanges.
Platform → GitHub →Building ultra-low latency trading systems. Memory management, lock-free programming, and FPGA.
C++ Jupyter →State-of-the-art time series forecasting. Beats traditional methods on financial data.
Paper → Code →Using graph structures to model market relationships. Captures sector correlations and supply chains.
Research →Using satellite imagery for commodity trading and economic nowcasting. Parking lots, ship tracking, crop yields.
Provider List →Active communities for strategy discussion and code sharing.
r/algotrading → r/quant →Video tutorials on algorithmic trading and quantitative finance.
Sentdex → QuantPy →APIs for historical and real-time market data.
Alpha Vantage → Polygon.io →1. Learn Python: NumPy, Pandas, Scikit-learn basics
2. Understand Markets: Order types, bid/ask, market microstructure
3. Simple Strategies: Moving average crossovers, mean reversion
4. Backtest Everything: Paper trade for 6 months minimum
5. Start Small: $100-500 to test infrastructure
6. Continuous Learning: Markets evolve, strategies decay