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Three former DeepMind researchers who built the poker AI DeepStack have applied reinforcement learning to stock trading through their startup EquiLibre Technologies, which is now valued at $500M after a Series A led by Creandum. The AI has achieved zero negative months since inception.
The author explains how to build a self-improving quant trading system using AI loop engineering, where the AI runs loops to prompt, verify, and act autonomously, contrasting with manual prompting.
An open-source Python quant trading system leveraging AI, real-time data processing, and risk management has been released for free.
Introducing QuantDinger, an open-source AI quantitative trading platform that supports local deployment, full-chain connectivity for crypto, US stocks, and forex, integrating AI analysis, strategy generation, backtesting, and live trading integration.
A Cornell lecture by Marcos Lopez de Prado shares the quant trading framework using neural networks that Jane Street quants use, with potential earnings of $750k/year.
Jane Street offers $750k/year for quants who can apply Stochastic Processes and Markov Chains in trading, and a free MIT lecture covers similar material.