Quantitative systems / Jan – Feb 2025
Algorithmic Trading System
Market regimes, sentiment & trading simulation
Combines market-regime classification, news sentiment, and CNN-LSTM modeling in a stock-trading simulation.
34%regime detection accuracy improvement
30%simulated return improvement
What I built
- Developed a stock trading simulation platform combining market trends, news sentiment, and ML models to mimic real-world trading—enabling informed, data-driven strategies.
- Constructed a hybrid model using K-Means, Agglomerative Clustering, and logistic regression to classify market regimes—improving detection accuracy by 34%, enabling faster strategy shifts and boosting simulated returns.
- Designed a CNN-LSTM model with reinforcement-based position sizing, increasing prediction accuracy by 12% and boosting simulated returns by 30% during backtesting.
- Built a sentiment-analysis pipeline from RSS feeds to generate real-time trading signals and enhanced predictive insight.