EdgeLab runs institutional-grade statistics on your trading ideas — FVGs, session bias, opening range breakouts, HP filter trends — and tells you if the edge is real before you risk a dollar.
No spam. Notified when EdgeLab opens to the public.
How It Works
EdgeLab follows the same workflow institutional quant desks use — validate the idea statistically before building the strategy.
Tell EdgeLab what you think works — "XAUUSD fair value gaps fill reliably in the London session" or "AUDJPY has afternoon bullish bias after ADR exhaustion." No jargon required.
pre-screen →A fast statistical test (1–5 seconds, no AI involved) runs against years of price data. Binomial tests, t-tests, Mann-Whitney U. Real statistics, not vibes. If the edge is there, you proceed.
p < 0.05 →The quant AI designs a strategy around your confirmed edge, runs in-sample and out-of-sample backtests with OOS validation, and reports whether the result holds up to scrutiny.
oos pass →What You Can Test
Not indicator templates. Rigorous statistical tests grounded in market microstructure and quantitative finance research.
Tests FVG fill rates, path dependency (MAE distribution), and whether entering at adverse excursion levels creates a measurable edge across 1h and 15m timeframes.
Tests all four standard OR windows simultaneously (5m, 15m, 30m, 60m). Breakout must align with the opening candle direction — the key filter documented in Zarattini et al. 2024.
Tests whether a market moves directionally during a specific trading session — London open momentum, NY session reversals, Tokyo range behavior — across hundreds of sessions.
The most fundamental question in strategy design. Tests autocorrelation structure to determine whether an instrument trends or mean-reverts at a given timeframe — and how strongly.
When the morning session consumes ≥70% of the 20-day ADR before the afternoon opens, tests whether a statistically significant directional bias emerges.
Applies the Hodrick-Prescott filter (λ=100×n²) to extract the trend component from noise. Tests whether MA crossovers on the clean trend signal have statistically significant directional accuracy.
Tests whether one market period predicts the next. If the first hour of NY open is bullish, does the second hour continue? Reveals sequential market structure that can be traded systematically.
Finds which specific hours carry the strongest directional edge. Markets are not uniformly random throughout the day — liquidity patterns and institutional participation create repeatable hourly tendencies.
Tests whether an instrument has a statistically significant directional tendency on specific days — Monday reversals, Wednesday trend days, Friday liquidation patterns.
Tests whether price statistically respects key historical levels — prior day high/low/close, prior week levels. Measures bounce rate, break rate, and average forward move at each level type.
Tests the statistical relationship between two instruments — AUD and gold, CAD and oil. Measures Pearson correlation, cointegration, and Ornstein-Uhlenbeck half-life to determine if the relationship is fast enough to trade.
Splits historical data into volatility regimes using ATR percentile rank. Tests whether returns and Sharpe ratios are regime-dependent — telling you whether a volatility filter should be part of every strategy you build.
Identifies pin bars, hammers, and shooting stars at 20-bar swing highs and lows. Tests whether follow-through in the rejection direction is statistically reliable.
Tests whether a currency pair exhibits persistent directional drift consistent with the carry premium — the UIRP violation empirically documented since the 1980s. Tests across 5, 20, and 60-bar horizons.
Crabel's Narrowest Range in 7 bars. Tests whether NR7 compression predicts directional expansion, and whether the NR7 bar's own direction predicts which way the expansion goes.
Tests whether the ratio of 5-day to 20-day realized volatility creates statistically distinguishable behavioral regimes with predictable directional bias.
Research Library
When the AI designs a strategy around your edge, it doesn't guess — it searches a curated library of quantitative finance papers and cites the relevant research. Every strategy recommendation is grounded in published, peer-reviewed work.
The same papers institutional quant desks use. Accessible to any trader with a hypothesis.
The difference
Real results from live research sessions
Join the waitlist. Be first when EdgeLab opens to the public. We'll send you early access and research walkthroughs from the YouTube channel.
No spam. Unsubscribe anytime.