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.

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.

Time series modeling and comparative forecasting of AAPL and HON stock prices using regression, smoothing, and moving averages in R.

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

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

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.

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.

No description provided.

Analysis of Financial Assets Portfolio over 2 years from 2019 Jan to 2021 Dec

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

Analyzing seasonal performance trends across financial market sectors using Python and historical Yahoo Finance data.

S&P 500 Recovery Pattern Analysis (1998-99 vs 2025-26): Comparative analysis of S&P 500 recovery patterns: 1998 Crisis vs 2025 Market Drop. Includes visualizations, statistical analysis, and trading strategies based on historical patterns.

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.

Systematic Core-Satellite Investment Framework optimized for the Austrian tax environment. Features a rule-based Core and an algorithmic Dual Momentum Satellite for risk-managed wealth accumulation. Rooted in quantitative research and designed for long-term generational growth.

Builds a stock portfolio that tracks a market benchmark under fixed constraints.

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.

A personalized AI-driven investment data pipeline and daily economic briefing system. This repository automates the collection of global market indicators (KOSPI, S&P500, Tech Stocks), calculates technical indicators like RSI, and generates AI-summarized reports to build a foundation for an autonomous investment decision-making model.

🌱 Drive ESG insights with the Ond-ESG-Intelligence-Platform, enhancing sustainability analysis and decision-making for businesses and investors.

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