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back-testing

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Early prototype exploring Goldman Sachs’ open-source gs-quant toolkit. Structured walkthrough of the library’s core areas — market data, instruments & risk measures, portfolios, backtesting, scenarios, and analytics — via the official example notebooks and source layout. A hands-on starting point for understanding

  • Updated Jul 21, 2026
  • Jupyter Notebook

Quantitative research framework for cross-sectional equity alpha: random matrix theory covariance cleaning, purged combinatorial cross-validation, and Deflated Sharpe Ratio — measuring how much of a backtest survives leakage, multiple testing and market impact. Python + C++.

  • Updated Aug 10, 2026
  • Python

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