Pham The Anh

Deep-Reinforcement-Learning-with-Stock-Trading. This project uses Deep Reinforcement Learning (DRL) to develop and evaluate stock trading strategies. By implementing agents like PPO, A2C, DDPG, SAC, and TD3 in a realistic trading environment with transaction costs, it aims to optimize trading decisions based on return, volatility, and Sharpe ratio.

Statistical-Arbitrage-Bayesian-Optimized-Kappa-Half-life-Pairs-Trading-Engine. This project implements an advanced pairs trading strategy using statistical arbitrage techniques. It leverages Bayesian optimization to fine-tune Kappa and Half-life parameters, enhancing the mean-reversion trading approach. The system includes comprehensive backtesting, risk management, and performance analysis tools.

Momentum-Based-Strategy-Optimization-with-Grid-Search-on-Backtrader. This project backtests an SMA crossover strategy in Python, using Backtrader and yfinance, with optimization through grid search to find the best parameters.

LLM-Augmented-Fundamental-Analysis-for-Algorithmic-Trading. Combines LLMs with fundamental analysis for algorithmic trading. Features: LLM evaluation of S&P 500 income statements Automated stock scoring and selection Strategy backtesting Performance visualization Uses Python, pandas, numpy, matplotlib, Groq API, and yfinance.

Sentiment-Analysis-in-Trading. This project implements a machine learning pipeline for analyzing stock market sentiment based on textual data. It uses various Natural Language Processing (NLP) techniques and machine learning models to predict sentiment (positive or negative) from stock-related text data.

Mean-Reversion-Strategy-with-Hurst-Exponent-backtest-by-QuantConnect. This project showcases a mean reversion trading algorithm on QuantConnect, leveraging the Hurst exponent to identify mean-reverting conditions. The strategy uses statistical measures like mean and standard deviation for buy/sell signals. Included are steps for building, backtesting, and optimizing the algorithm within QuantConnect.

Portfolio-Optimization. Portfolio Optimization of Major Tech Stocks: Markowitz, Sharpe Ratio, VaR & CAPM

Monte-Carlo-Simulation-for-Stock-Market. Jupyter Notebook

Piotrosky-F-Score-with-Stock-Selection. Piotroski F-Score Research Paper Strategy for Stock Selection