Master Thesis: Limit order placement with Reinforcement Learning
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Updated
Aug 22, 2018 - Jupyter Notebook
Master Thesis: Limit order placement with Reinforcement Learning
Cross-industry PM: Less process, more progress. PM toolkit bridging theory and practice across industries. Features actionable templates, risk assessment tools, empathy maps, and cultural frameworks. Emphasizes outcome-driven delivery over process overhead. Built for practitioners who execute.
An intelligent Reinforcement Learning based trade execution engine trained on real SPY 1-minute data to minimize market impact and cost. Uses PPO in a custom Gym environment to dynamically decide execution quantities and outperforms traditional TWAP/VWAP strategies.
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