A quantitative trading strategy backtester with an interactive dashboard. Enables users to implement, test, and visualise trading strategies using historical market data, featuring customisable parameters and key performance metrics. Developed with Python and Polars.

2026 FIFA World Cup prediction research system — 7-component fusion (Dixon-Coles, Elo, Pi-Rating, Weibull, Market, Stacking, Conformal), auditable walk-forward backtesting, bilingual reports

Production multi-agent trading platform with rigorous walk-forward validation. TSMOM momentum (1.097 Sharpe) + GEX regime filtering. Interactive CLI, autonomous trade lifecycle, daily scheduler. Alpaca integration. Built on Microsoft AutoGen. Research-driven approach with statistical validation. Educational - not financial advice.

Kiploks Trading Robustness Engine is an open-source TypeScript engine for deterministic backtest and walk-forward analysis (WFA) of algorithmic trading strategies, published as @kiploks/engine-* packages under Apache 2.0.

Reproducible Python time-series forecasting benchmark with walk-forward validation, leakage-safe backtesting, classical models, lag-based machine learning, tests, and CI.

End-to-end ML system for prediction market trading — 521K markets, 78 features, 7 model architectures, walk-forward validation, live VPS A/B across 7 configs. Honest research-stop on alpha decay (NO-GO verdict). AFML methodology: Purged K-Fold, Deflated Sharpe Ratio, meta-labeling, focal loss.

Quantum Geometry and Topological Regime Detection in Financial Markets

NexaBask — Production-grade basket trading system with risk-first architecture, MT5 integration, walk-forward validation, and ML/LLM roadmap

Temporal partitioning and backtesting for time-correlated datasets

Multi-sport AI forecasting platform built solo by directing Claude agent fleets: CV tracking from broadcast video, calibrated win-prob models across 4 sports, leak-free walk-forward validation, pre-registered claims ledger, honest reject graveyard. Calibration rigor, not edge claims.

ML investigation into whether public data can generate profitable betting signals on European football markets

ML-based edge extraction framework for MT5 — mean reversion, pairs, volatility edges. Walk-forward + bootstrap validation. HTML reports.

Multi-model time-series forecasting with Bayesian Optimisation (Optuna TPE): SARIMA, Random Forest, XGBoost, LightGBM, Prophet, LSTM, and QuantileML probabilistic forecasts behind a unified ModelSpec protocol. Walk-forward validated; supports monthly, weekly, daily, and hourly data.

Motor de decisión ML para trading cuantitativo con validación walk-forward anti-leakage, triple-barrier labeling, XGBoost + Optuna, risk management para cuentas micro, human-in-the-loop y paper trading. Sistema completo Python 3.11+ de producción para NAS100 M5 con gating de modelos, costos realistas y kill switch.

Rigorous ML framework for alpha generation research. Demonstrates that apparent alpha from public market data is primarily overfitting. Key finding: IC flips from +0.036 to -0.084 when regularization is increased 100x.

Companion code for the SSRN paper Reinforcement Learning for Volatility Hedging: A Dual-Asset Strategy Using Bitcoin and Gold.

Quant research pipeline for XAUUSD regime classification using macroeconomic features, Random Forest, Temporal Convolutional Networks (TCN), walk-forward validation, and trading-system backtesting.

Statistical arbitrage engine that screens S&P 500 pairs using Engle-Granger and Johansen cointegration tests, fits Ornstein-Uhlenbeck dynamics via MLE, and trades spreads with a Kalman filter hedge ratio. Includes a walk-forward backtest with monthly pair re-screening, continuous position carry-over, and a full performance dashboard.

End-to-end ML research pipeline on OHLC time-series: feature engineering, walk-forward validation with Random Forest & XGBoost, and vectorized backtesting on public data. Built to demonstrate leakage-free validation methodology.

Independent replication and overfitting audit for an ML-gated NQ futures research strategy