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.

Calibrated multi-sport forecasting, audited in public. Live analytics site + a claim-by-claim evidence index with the rejects and retractions included. Calibration rigor, not edge claims.

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.

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

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.

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

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

Historical RV15 forecasts for six U.S. stocks. Option state improves both families; the mixed B2 block improves the linear model only. Source-time assumptions, placebo and final-window limits remain material. Public aggregate checks are not an independent licensed-data replication.

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

Walk-forward-validated ML pipeline for 21-day swing-trade selection across AI/semiconductor stocks, with an options-strategy overlay — benchmarked honestly: the edge is sector beta, not stock-selection alpha.

Pre-game NFL win probability, spread and total forecasts: walk-forward models, a leakage-proof day-before information lock, pre-registered evaluation, and a broadcast-style FastAPI + HTMX dashboard.

F1 pole, winner and podium predictions — published before every race, scored in public, and measured against baselines a one-line rule could beat.

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.

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.

Research framework testing whether market regimes make systematic strategies more robust out of sample: causal features, purged walk-forward validation, cost-aware backtesting and significance testing.