From engineer to quant: a free, in-depth, open course (100 illustrated sessions + Python labs) on markets, math, research discipline, portfolios, derivatives, ML and production trading.
Repository for pair trading research using python
Research notebooks for MASI downside-risk modeling, VaR/ES backtesting, GARCH benchmarks, and hybrid LSTM risk forecasting.
Companion notebook for the post “Algo Backtests Are Lying to You.”
Python notebooks for Varve Data: point-in-time SEC fundamentals and filing events
Educational ETH buy-and-hold backtest notebook with return, risk, and performance analysis.
Mean reversion signal backtests and analysis with a Jupyter notebook and CSV signal data.
Exploratory index-rebalancing data collection and backtesting research with Python, notebooks, and historical S&P materials.
Validacao de estrategias sistematicas: as 153 metricas do Manual Wizz em codigo aberto, com notebooks no Colab.
Time-series momentum backtest on a diversified ETF basket, with statistical analysis and a Jupyter notebook walkthrough.
Cross-sectional research study of vanilla technical indicator strategies across Indian equities using Python and Jupyter notebooks.
Is your backtest overfit? CSCV/PBO, Deflated Sharpe Ratio and MinBTL implemented and verified in executed Jupyter notebooks
Indian equity momentum research across 180 configurations, with trading costs, tax overlays, risk comparisons, executed notebooks and a local dashboard.
Equity and options research notebooks: risk analytics, Black-Scholes pricing with numerically verified Greeks, walk-forward signal evaluation, and backtests that report negative results.
103,561 table tennis matches with the Elo ratings of both players as they stood BEFORE the match. Free 2,000-row sample + calibration notebook.
A modular quantitative trading environment for designing, backtesting, and simulating strategies (Mean Reversion, Momentum, Pairs Trading) in real-time. Features an event-driven backtesting engine, statistical research notebooks, a FastAPI backend, a dynamic frontend dashboard, and full Docker containerization.
Six quantitative-finance projects covering the questions an institutional allocation team must answer: where risk sits, why assets co-move, what weights to hold, risk parity, macro regime, and the rates side. Python, 68 offline tests, executed Colab notebooks, free data only.
Python tool that simulates an EM FX NDF trading book, identifies fixing date risk spikes, recommends cost-optimal hedges using scipy.optimize, monetizes client flow imbalances, and backtests the strategy on real historical FX data. Built in Jupyter Notebook with pandas, numpy, matplotlib, and yfinance.
2026 - 2025 - Multi-Broker Algorithmic Trading Platform - Juypter Notebook Learning
Notebooks on crypto EDA, strategy backtesting, RSI and ML-based stock price prediction.
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