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.
掃描你的價格資料、找出候選策略——而且對每一個講真話。它回測上百個經典組合、排成 leaderboard，然後做 hype 工具不敢做的事：照你試的個數幫最佳 Sharpe 打折（Deflated Sharpe）、檢查樣本外還剩多少、估過擬合機率——漂亮的曲線一律當成待驗證的假設、不是能交易的策略。睡前設好讓它整夜掃，掃越久對你越懷疑。自包含 HTML 報告（中／英）、跑在你自己的 CSV 上。
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.