CodeMarketLabs
8 years of systematic trading, and these 5 mistakes I made all of them. From biased backtests and survivorship bias to ghost orders, infrastructure failures, and crash recovery — a complete breakdown of what goes wrong and exactly how to fix it.
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A complete breakdown of the Python backtesting framework I built with Claude Code — and how you can backtest any strategy from a single prompt. Covers the cash-flow engine architecture, the CLAUDE.md context file, overfitting prevention, and a live demo: MA Crossing 20/50 on SPY (2000–2026), +17.0% total return, Sharpe -0.08, 138 trades — including the full AI audit of why the result is bad and what to fix.
A complete breakdown of long/short equity strategies as used in institutional trading: mean reversion, relative value, divergence, beta hedging, and two Python backtests — JPMorgan vs Goldman Sachs and SPY vs FEZ. Includes IBKR execution and an honest assessment of the risks nobody talks about.
A full Iron Condor backtest on ES Futures from 2005 to 2026: how to reconstruct a volatility surface without options data, how to model the VRP, and what the five Greeks actually mean for a short-vol position. Includes five reinvestment scenarios with real metrics (CAGR, Sharpe 1.68, Sortino 1.46, max drawdown −9.4%), an honest breakdown of the model's limits (flat skew, fixed VRP), and a description of how the strategy runs in production with dynamic delta hedging, vega hedging, and charm monitoring.
How to reconstruct a complete historical volatility surface from scratch using only daily price data — no options data subscription required. We cover the Variance Risk Premium (VRP), GARCH(1,1) conditional volatility estimation with Student-t innovations, and the SVI parameterization for arbitrage-free volatility smiles. Includes regime-dependent skew calibration (crisis vs calm), the complete QuantSkewEngine Python class, and a skew timeline visualization across 20 years of market regimes.
The CFTC publishes 30 years of futures positioning data every Friday — buried in a legacy ZIP file with 300 columns and no API. We built one. A REST API that ingests, normalizes, and exposes COT data across 398 futures contracts with Z-scores, pressure metrics, COT Index, and divergence signals computed on the fly. Full architecture walkthrough: Django ORM, incremental sync pipeline, API key authentication, and a live Z-score snapshot across all mainstream contracts.
A complete 20-year backtest of Turtle System 2 across 26 diversified futures markets — currencies, rates, energies, metals, grains, softs, and livestock. We analyze the system's performance from 2006 to 2026, expose its weaknesses (6 years of stagnation, low Sharpe in bull markets), and introduce the Turtle Overlay: a portfolio construction approach that combines a 75% SPX / 25% Cash base with the Turtle's uncorrelated P&L on top. Full Python implementation included, downloadable Jupyter Notebook.
quantkit is a Python library for quantitative analysts and derivatives traders. It covers the full pricing stack — seven stochastic models calibrated via FFT on real options surfaces, a complete Black-Scholes Greeks engine, multi-leg portfolio management with interactive dashboards, and a rates toolkit spanning Nelson-Siegel, Hull-White, and Vasicek models.
marketconnect-exec is a Python library for quant traders and portfolio managers. It provides a unified interface to Deribit and Interactive Brokers — covering order management, positions, account data, market data, and derivatives analytics through a single, consistent API.
equity-curve is a Python library for quantitative analysts and portfolio managers. It covers the full strategy analysis pipeline — from raw NAV data to interactive dashboards — with 30+ risk metrics, econometric tests, and Plotly visualisations.