The purpose of this project is to measure how much of the performance of a diversified quantitative investment portfolio is significantly impacted by random market behavior, if at all. If successful, the results of this analysis will lay the groundwork for a broader analysis pertaining to the separation of alpha and beta across the investment portfolio. If the "luck" portion of the portfolio can be measured dynamically (accounting for lags etc) then a hedging tool could potentially eliminate random market risk without eroding portfolio returns in times of erratic market behavior. The methodology is to obtain historical performance data from 11 different trading models (mean reversion, pairs, market making, momentum, statistical arbitrage, etc) that together form a diversified investment portfolio over a particularly volatile trading period. I will explore the data by analyzing the distribution of performance across symbols and across time periods to reveal the structure of the performance data and how it relates to and is impacted by market behavior. I will then model the data to measure how much of the performance is explained by the market and market volatility, its clustering tendencies and its correlation to the predictor variables. Finally, I will interpret the results and reconcile the results with my original hypothesis to determine if it makes sense to continue work to create a hedging instrument for the portfolio.

Pair Trading with Dynamic Hedge Ratio

Binance Futures Trading Bot 📈🤖: Automated trading, Telegram control, Hedge Mode, Dynamic Pair Scanner, AI Mode (Rule-based/Optional Gemini AI for strategy optimization), Take Profit/Stop Loss, Risk Management. For advanced users. High risk involved.

A quantitative pairs trading strategy using dynamic hedge ratios estimated via Kalman filtering and cointegration techniques. Designed for statistical arbitrage and mean reversion in equities.

Pair trading and Kalman Filter descriptions; Use of Kalman Filter to dynamically update the hedge ratio of pair trading

A pairs trading strategy that uses the Kalman Filter to dynamically estimate the hedge ratio for cointegrated assets.

This project implements a cointegrated pair trading strategy using kalman filter based dynamic hedging, where asset pairs with a statistically significant long-term relationship are identified via cointegration tests. It dynamically adjusts the hedge ratio based on updated spread behaviour to maintain market neutrality.

This project implements a dynamic pair trading strategy using a Kalman Filter to estimate time-varying hedge ratios between co-integrated stock pairs. Unlike traditional static linear regression, this approach adapts to changing market regimes using a state-space model, enhancing responsiveness and trading signal accuracy.

Pairs trading strategy using Kalman Filter applied on E-mini S&P 500 and NASDAQ-100 futures. This project models the time-varying hedge ratio dynamically to identify mean-reverting opportunities in highly correlated assets.

High-frequency pairs trading framework built from first principles on 10-second order book data. Covers microstructure-aware data cleaning, VWAP microprice construction, Engle-Granger cointegration with HAC-robust inference, and a dynamic hedge ratio via rolling OLS with time-decay EMA smoothing.

No description available

State-Space Modeling for Adaptive Statistical Arbitrage in European Markets

Pairs trading strategy with dynamically estimated hedge ratios using the Kalman Filter for adaptive statistical arbitrage.

Real-time market-neutral pairs trading engine with dynamic hedge ratios, event-aware risk controls, and live monitoring dashboard.

Creating a pairs trading strategy using the co-integrated time series approach, implementing a Kalman filter to dynamically calculate the hedge ratio.

This project implements a statistical arbitrage strategy using pairs trading that identifies cointegrated asset pairs in financial markets. The strategy employs Kalman filters to estimate dynamic hedge ratios and generate trading signals based on mean reversion opportunities.

Automated Pairs Trading system using Kalman Filter for dynamic hedge ratio estimation. Implements a mean-reversion strategy on SPY/QQQ residuals with real-time risk management and position tracking.

Develop a statistical arbitrage strategy using pairs trading. The strategy identifies cointegrated asset pairs, estimates dynamic hedge ratios using Kalman filters, and generates trading signals based on mean reversion opportunities. The Kalman filter is formulated as a sequential decision process following Powell's Sequential Decision Analysis.

A real-time Statistical Arbitrage dashboard for pairs trading (BTC/ETH). Built with Python, Streamlit, and SQLite. Features live WebSocket ingestion, OLS regression for dynamic Hedge Ratios, Z-Score alerting, and interactive analytics.

Quantitative trading system built with Backtrader and Pandas. Implements trend-following and mean-reversion strategies with trailing stop-losses. Features a statistical arbitrage engine for Pairs Trading (PEP/KO) utilizing real-time OLS regression to calculate dynamic hedge ratios and trade price spreads.

This statistical arbitrage engine is a Python-based backtesting platform that identifies cointegrated asset pairs and executes mean-reversion trading strategies using dynamic hedge ratios, Z-score signals, and volatility-targeted position sizing.

A pipeline for China A/H cross-market pair trading with statistical filtering of companies, dynamic hedge ratio estimation and backtesting using vectorbt and realistic transaction costs.

A real-time crypto pairs trading analytics system using Binance WebSocket data. Includes live price streaming, resampling, spread & z-score computation, Kalman filter–based dynamic hedge ratio, and an interactive Streamlit dashboard with CSV export support.

A professional EOD pairs trading terminal for Nifty 50 using machine learning, clustering, and cointegration. Includes dynamic spread signals, backtesting, and live alerts. Advanced version adds Kalman hedge ratios, half-life, regime detection, VaR-based sizing, and detailed PnL analytics.

An implementation of a regime-adaptive pairs trading framework for commodity-linked equities (EWA/EWC). The system utilizes a Kalman Filter for dynamic hedge-ratio estimation and a Random Forest classifier to gate entries using Copper and Oil leads. Designed to mitigate beta-drift and structural decoupling through non-linear macro filtering.