hedging-strategy

Automatic Options Hedging and Backtesting

Delta hedging under SABR model

Futures-Spot-Arbitrage-Binance-V1

Python code for a trading strategy based on time value performed in the SSE 50ETF option market.

Excel/Python application of stochastic methods for financial analysis

Simulating different hedging strategies on Apple's stock option data

A decision model build using probability and stochastic process knowledge to mitigate the revenue loss of a company due to unfavorable fluctuations in international Dollar value

Pricing energy options (focus on German power) with MC and jump-diffusion mean-reversion model, including stochastic volatility, seasonality and regime filtering. Model parameters are calibrated on historical ENTSO-e data.

This project analyzes stock market data, implements option pricing models (Binomial Trees, Black-Scholes-Merton and Monte Carlo Simulation), evaluates hedging strategies and constructs an optimized portfolio. It integrates derivative valuation with portfolio analysis to provide insights into risk management and investment decision-making.

Nelson-Siegel yield curve fitting on 25 years of FRED Treasury data, Diebold-Li VAR factor model with Monte Carlo simulation, stress testing a $51.5M bond portfolio across 26 rate scenarios, and a multi-strategy Treasury futures hedging overlay. Live interactive dashboard built with Streamlit.

A package to learn optimal hedges by a deep feed forward neural network, to minimise the terminal error

In this Repository you will find projects, exercises and bibliography related to Risk Hedging Strategies.

Automate your crypto trading with AutoHedge, an AI-driven hedge fund using swarm intelligence for autonomous market analysis, risk management, and execution on Solana.

Solutions for hedging strategy, Automated Market Making strategy and Exotic Options Pricing made as a part of Goldman Sachs India Hackathon 2025.

Black–Scholes powered Python framework for options trading — featuring volatility forecasting, market microstructure analysis, and backtesting tools for building and deploying advanced trading strategies.

Delta hedging of European options in the Black-Scholes framework, with transaction costs and different rebalancing frequencies

Implementation of derivatives hedging using deep reinforcement learning methods based on Cao et al. (2020).

A Python-based quantitative finance tool for pricing European options and calculating first and second-order Greeks. Features vectorized NumPy computations and comprehensive risk dashboards for derivatives analysis

This repository collects models that me and my colleagues developed in practical fulfillment of studyiing exotic option pricing under Black Sholes. Topics covered include Black Sholes option pricing, dynamics of American contingent claims and discrete and continuous time hedging strategies.

QuantHedge-MM implements advanced computational methods for pricing and hedging options in markets with stochastic regime shifts. Built for quants and researchers, it extends Black-Scholes to Markov-modulated models.