크롱

The quant stack that took institutions years to build is sitting on github right now 5 repositories. free. open source. more useful than any course 1. freqtrade/freqtrade https://github.com/freqtrade/freqtrade 50k+ stars. crypto bot with a built-in ml module called freqai trains models on live data, reoptimizes automatically, supports 30+ exchanges. telegram integration out of the box. the community has been battle-testing this since 2018 and the issues tab alone is worth reading 2. hummingbot/hummingbot https://github.com/hummingbot/hummingbot 19k stars. market making and cross-exchange arbitrage engine. manages bid/ask placement, spread adjustment, and inventory hedging across 50+ cex and dex simultaneously. used by actual liquidity providers in production. the architecture docs explain things no course will tell you 3. AI4Finance-Foundation/FinRL https://github.com/AI4Finance-Foundation/FinRL 12k stars. reinforcement learning applied to trading. agents trained with PPO, DQN, DDPG on real market environments. the repo includes crypto data pipelines via binance api and ccxt. not a toy researchers publish papers using this codebase 4. nautechsystems/nautilus_trader https://github.com/nautechsystems/nautilus_trader 9k stars. python on the surface, rust underneath. the backtesting engine is byte-for-byte identical to the live trading engine what you test is what you deploy. built for latency-sensitive strategies where milliseconds matter 5. ranaroussi/quantstats https://github.com/ranaroussi/quantstats 7k stars. performance analytics library. sharpe, sortino, calmar, max drawdown, monthly return heatmaps, monte carlo one function call generates a full tearsheet. the standard tool for evaluating whether a strategy is real or just lucky you don't need a course to understand how systematic trading works you need to read code written by people who actually do it