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

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.

Let AI read the charts for you. 📈 + 🤖 = Siglens.

This project addresses the real-world portfolio optimization problem, going beyond classical mean-variance models. Actual portfolio construction involves discrete investment decisions, transaction costs, and monitoring constraints, making the problem a Mixed-Integer Optimization (MIO) challenge that is computationally intractable at scale

🔍 Analyze financial data seamlessly with the AutoGen Financial Analysis System, offering risk assessment and quantitative insights through a multi-Agent framework.

Autonomous multi-agent system for Private Equity due diligence. Scrapes, analyses, and synthesises investment memos using LangGraph and Python.

Conducted a case study to develop an investment strategy for car companies, leveraging technical analysis and machine learning for market insights

AI-powered Taiwan Stock Analysis System: Integrating real-time institutional investor tracking (CMoney) and cost-basis modeling (yfinance) with Gemini AI for automated portfolio allocation and risk management.

Implementation of financial optimization models and efficient frontiers

A new package designed to help users navigate the volatile memory market by providing structured, long-term investment strategies. Users input their current financial situation, risk tolerance, and in

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.

📊 Optimize trading decisions for Vietnamese stocks using a multi-agent pipeline that integrates data analysis and risk management for smarter investments.

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.

Smarter investing made simple — an AI-driven portfolio analyzer that blends finance, data science, and intuition.

Market Intelligence & Spatial Analytics Pipeline for Co-Living Arbitrage in Bengaluru. Identifies high-yield investment zones using rental data, transit overlays, and econometric validation.