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Sigmanomics runs a 7-model statistical ensemble across forex, crypto, stocks, commodities, and indices. Every forecast is logged at generation time, scored against actual prices, and published with full transparency. No cherry-picking. No retroactive edits. Just data.
Verified performance across all instruments and timeframes
83%+
Zone Containment (CI80)
1M+
Forecasts Scored
1,456+
Instruments Tracked
5
Asset Classes Covered
Seven statistical models run independently, then combine into a consensus forecast with calibrated confidence intervals.
Baseline linear extrapolation
Regime-aware dynamic weighting
Mean-reversion with drift correction
Momentum and trend-following
Seasonal and cyclical patterns
Volatility breakout detection
Optimized theta decomposition
Each forecast includes Q1-Q3 interquartile range zones and CI80 calibrated bounds. Zone multipliers are tuned per market so the expected zone captures ~80% of actual outcomes.
Every forecast is scored against actual OHLC data. We track directional accuracy (did price move in the predicted direction?) and zone containment (did price land inside the predicted range?). All results are public on the Performance page.
5 asset classes (forex, crypto, stocks, commodities, indices), 6 timeframes (30min through 1D), updated every pipeline run. No instruments excluded from aggregate statistics.
All forecasts are logged at generation time with immutable timestamps. No retroactive edits. Historical forecast data is available for export in CSV and JSON formats.
Quantitative Engineer & Founder
Built Sigmanomics from the ground up — the forecasting pipeline, ensemble engine, zone calibration system, and performance tracking infrastructure. Responsible for model development, data engineering, and platform architecture.
Analyst
Senior analyst whose research has been cited by the U.S. Congress, U.S. Department of Justice, CBOE, Barron's, and Forbes. Brings institutional-grade analysis and editorial credibility to the platform.