Know if your backtest is a real edge or overfit noise. Runs bar-permutation MCPT, Deflated Sharpe, PBO/CSCV, Hansen SPA, Romano-Wolf StepM, a stationary bootstrap and an out-of-sample split on your own returns — no market feed, no API keys.
Find hidden P&L, cost, and record inconsistencies before you trust your backtest. Get clear findings, evidence, and repair candidates you can verify.
Scan your price data for trading strategies — honestly. It backtests hundreds of classic setups, ranks them into a leaderboard, then does what hype tools won't: it corrects the best Sharpe for how many you tried (Deflated Sharpe), checks out-of-sample survival, and estimates overfit probability — so a great-looking curve is a hypothesis to test, not a strategy to trade. Leave it scanning overnight; it only gets more skeptical the longer it runs. Self-contained HTML report.
Your backtest looks strong. But can you trust the evidence behind it? Check for holdout contamination, time leakage, cost-stress failures, concentration risk, and reproducibility issues — before you rely on the result.
Keep a permanent, hash-chained ledger of public forecasts and see who has actually earned weight. Add calls before they resolve, add outcomes later, and get hit rate with confidence intervals, calibration, and naive versus track-record-weighted consensus on open questions. Edited or removed entries are detected. Offline deterministic script; calls recorded after the answer was known earn no weight.
Computes historical win-rates and expected returns for 2X Leveraged & -2X Inverse ETFs based on 5-year index drawdowns (KOSPI, S&P500, NASDAQ). Eliminates emotional trading with objective statistical backtesting.
Academic sanity-check with interactive HTML dashboard. Detects look-ahead bias, over-parameterization, and benchmarks against 10 canonical blueprints via animated SVG gauges and color-coded KPI cards. Parses natural language, code, or CSV uploads. Output auto-adapts (EN/ZH-TW/ZH-CN). Built on De Prado (2018). For students and strategy developers. Not financial advice.
Discover, backtest and validate US stock trading strategies, pick from any universe, and generate an IBKR paper-trading deployment script. No API keys, free daily data, institutional anti-overfit validation.
Turn local research hypotheses into traceable evidence. Validate daily CSVs, compare predeclared moving-average strategies with chronological holdouts, stress transaction costs, and independently audit results. Includes a Python standard-library lab, visual HTML reports and optional local Vibe-Trading helpers. Analysis and offline simulation only; no access to real user accounts.