A collection of scripts and notebooks to help you get started quickly.
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Updated
Apr 15, 2025 - Jupyter Notebook
A collection of scripts and notebooks to help you get started quickly.
Powerful automatic differentiation in C++ and Python
Quant Option Pricing - Exotic/Vanilla: Barrier, Asian, European, American, Parisian, Lookback, Cliquet, Variance Swap, Swing, Forward Starting, Step, Fader
This is my github repository where I post trading strategies, tutorials and research on quantitative finance with R, C++ and Python. Some of the topics explored include: machine learning, high frequency trading, NLP, technical analysis and more. Hope you enjoy it!
Python Market Simulation Engine Built on top of Generative AI
Quantitative Finance & Statistics Projects. Topics including multiple linear regression, variance and instability estimates, display methodology.
The official example scripts for the Numerai Signals Data Science Tournament
IB Gateway in a headless docker container.
RustyQlib: A quant library for derivative pricing and quantitative finance
I did this project as one of the parts from a Python test for my Master's degree. The objective was to practice the treatment of financial time series.
Engine and UI for tracking trading performance across stocks and derivatives (options, futures, & future options).
High-Performance Automatic Differentiation for Python
Trading Evolved book code
Quant finance side projects: calculation of volatility surfaces from option chain data, LSTM time series prediction
Interactive Streamlit app simulating Geometric and Arithmetic Brownian Motion. Visualize financial asset price movements with adjustable parameters for paths, time steps, volatility, and initial price.
European Options Pricing using Black-Scholes Model with Greeks Calculator and Monte Carlo Simulation. Visualizations and real-world explanations included.
Exotic Option Valuation using Monte Carlo Simulations
Defi: Swap, Deposit, Borrow, Approve ERC20, Borrow & Repay. Verify via Etherscan and Aave UI
A quantitative portfolio optimization script that leverages historical price data, log-normal returns, and covariance analysis to compute optimal asset allocation via Sharpe ratio maximization using the SLSQP method under bounded constraints.
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