walk-forward-validation

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

Multi-sport forecasting platform: CV player tracking from broadcast video, calibrated win-probability models across 4 sports, leak-guarded walk-forward validation, pre-registered claims ledger, and a documented reject graveyard. Calibration rigor, not edge claims.

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

Machine-learning trading research and paper-inference engine for ETH market-data experimentation.

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

Temporal partitioning and backtesting for time-correlated datasets

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.

Sistema de ML para predecir precio diario del cobre con Ridge Regression. Walk-forward R2=0.96 promedio, MAPE 1.16%.

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.

ML + LLM agent system for stock price direction and volatility prediction, with automated news sentiment analysis and rigorous data leakage prevention (walk-forward validation, look-ahead bias testing).

A systematic intraday long-only strategy on XAU/USD using volume profile Point of Control (POC) retest signals, validated on 8 years of 5-minute MT5 data.

I trained reinforcement learning policies to hedge Bitcoin with gold.