Natural Language Processing on Stocks' Earnings Call Transcripts: An Investment Strategy Backtest Based on S&P Global Papers.
This repository contains a collection of functions to evaluate investment strategies regarding multiple testing concerns.
Replication data and code for "Strategic Asset Allocation Revisited" published on Substack: https://policytensor.substack.com/p/strategic-asset-allocation-revisited.
Quantitative platform for investment strategies with real-time data integration, supporting flexible portfolio management via UI/GUI
Implementation of financial optimization models and efficient frontiers
This project uses stochastic approximation algorithms to optimize investment strategies and queueing systems. Techniques include the Simultaneous Perturbation Stochastic Approximation (SPSA) for maximizing the Sharpe ratio and Stochastic Approximation (SA) for minimizing waiting times in a GI/GI/1 queue.
Time series modeling and comparative forecasting of AAPL and HON stock prices using regression, smoothing, and moving averages in R.
This project constructs an optimized portfolio for trading stocks using Capital Assets Pricing Model. Build by Quadratic Programming Techniques and apply constraints using Shape Ratio.
Conducted a case study to develop an investment strategy for car companies, leveraging technical analysis and machine learning for market insights
This project looks at the performance of the stock market Tech sector on the Nasdaq index, analyzing the relationship between Operating Cash Flow and Capex, and it’s effect on overall returns.
Multi-agent AI system for actionable stock insights using LLMs (DeepSeek), SEC filings, yFinance, LangSearch, and Ollama.
Portfolio Analyzer is a modular toolkit for advanced portfolio construction, optimization, and risk analytics. It features Black-Litterman blending, robust statistical estimation, Monte Carlo simulation, and interactive Jupyter workflows for quantitative investment research.
A simple trading algorithm for SPY ETF using a moving average crossover strategy. This project analyzes SPY weekly price data, implements a buy/sell algorithm, and tracks performance metrics to evaluate profitability and risk. Ideal for learning algorithmic trading basics and financial data analysis.
Stock Market Clustering & Predictive Analysis | Leverage PCA & DBSCAN, K-Means, Hierarchical Clustering to uncover investment insights. Identify market segments, high-risk outliers (NVDA, TSLA, NFLX), and portfolio optimization strategies using S&P 500 data.
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