A market simulator and a set of trading strategies built from technical indicators, supervised learning, and reinforcement learning, then evaluated against historical equities data.
Coursework covering the intersection of machine learning and quantitative finance — building the infrastructure to compute technical indicators, backtest trading strategies against a market simulator, and compare rule-based, supervised, and reinforcement-learning approaches on the same data.
Implemented a market simulator that models order execution, commissions, and market impact. Built a manual rule-based strategy from technical indicators (Bollinger Bands, RSI, momentum), then trained a random-forest learner and a Q-learning agent on the same indicator set, using in-sample and out-of-sample splits to guard against overfitting.
The reinforcement-learning strategy outperformed the manual and supervised baselines on out-of-sample data, at the cost of longer training time and more sensitivity to hyperparameters.