Pinned Loading

1. Quantum-Pricing-of-Derivatives-through-the-Heston-Model-and-Quantum-Neural-Networks Quantum-Pricing-of-Derivatives-through-the-Heston-Model-and-Quantum-Neural-Networks Public

   Calculate a fair price for European options under the Heston stochastic volatility model using a quantum neural network and quantum amplitude estimation.

   Python

2. Quantum-Risk-Parity-Optimization-with-Entanglement-Based-Covariance-Estimation Quantum-Risk-Parity-Optimization-with-Entanglement-Based-Covariance-Estimation Public

   Portfolio optimization through quantum risk parity, meaning that a quantum kernel estimates the covariance matrix of multiple assets with full entanglement and portfolio weights are chosen to alloc…

   Python

3. Stochastic-Portfolio-Optimization-under-Jump-Diffusion Stochastic-Portfolio-Optimization-under-Jump-Diffusion Public

   Optimize portfolio allocation in a market where asset prices follow Merton’s Jump-Diffusion model, instead of Geometric Brownian Motion (GBM). or an Ornstein Uhlenbeck process.

   Python

4. synthetic-financial-data synthetic-financial-data Public

   Use models like GBM, merton's jump diffusion, and stochastic volatility (heston) to generate synthetic OHLCV and fundamentals data

   Python

5. Monte-Carlo-Simulations-using-the-Bates-Model Monte-Carlo-Simulations-using-the-Bates-Model Public

   A bates model is calibrated on current options data and using its parameters a monte carlo simulation of price paths is performed.

   Python

6. welc welc Public

   A stacking ensemble of classifiers that are used to predict movements of stocks. The data I used for mine I have not put here, but the model architecture and backtesting framework I have, for other…

   Python