Statistical trend quality system. Uses inferential statistics (T-test, R², Hurst exponent, Durbin-Watson) to validate trend strength before any trade is executed.
Retail traders use moving averages and RSI. Inferential Trend uses the same statistical frameworks that economists use to study market trends—T-tests, R², Hurst exponents, autocorrelation analysis. This isn't technical analysis. It's econometrics applied to futures trading.
Borrowed from hypothesis testing. Proves the trend slope is statistically significant—not just a visual pattern on a chart.
Measures explanatory power. If the trend only explains 10% of price variance, it's noise. If it explains 60%, it's the dominant force.
Fractal analysis from physics. Distinguishes between trending markets (persistent behavior) and choppy markets (random walk characteristics).
Tests residual autocorrelation. Confirms the trend is structurally stable—not just short-term momentum that's about to reverse.
While retail traders chase breakouts based on candlestick patterns, Inferential Trend requires four independent statistical tests to all confirm trend quality before entry. The edge is filtering out false signals that look good but have no mathematical validity.
When R² drops or Durbin-Watson detects regime shift, the algorithm exits immediately. It's not hoping the trend continues—it's watching the statistical properties in real-time and bailing when quality deteriorates.
This isn't a scalping system. Trades are less frequent because the bar is high. But when all four tests align, you're entering with statistical confidence that a genuine trend is present—not just price moving.
Linear regression across optimized lookback periods to calculate trend slope significance and confidence intervals.
Rescaled range analysis across multiple sub-periods to determine persistent trending behavior vs random walk characteristics.
ATR-based stops and targets that adapt to volatility. Automatic breakeven protection and intelligent trailing stops to lock in profits.
Continuous monitoring of statistical validity. Exits immediately when trend quality deteriorates beyond acceptable thresholds.
1 NQ contract · Real tick data · Commissions included
| Metric | Value |
|---|---|
| Total Trades | 817 |
| Average Trade | $121 |
| Average Win | $958 |
| Average Loss | -$837 |
| Sharpe Ratio | 1.34 |
| Test Period | Aug 2022 - Jan 2026 |
3.5 years of data across bull runs, bear markets, and everything in between.
Inferential Trend's statistical filters kept it flat during choppy conditions, only entering when T-stat and R² confirmed real trends.
Mixed regime conditions tested the Hurst exponent filter. Statistical validation prevented entries during false breakouts.
Strong persistent trends provided optimal conditions. Statistical tests confirmed high-quality uptrends with strong R² values.
Sharp rotations challenged trend quality. Regime shift detection exited positions when statistical properties deteriorated.
Inferential Trend captured validated trends while avoiding statistical false signals in volatile conditions.
Lifetime access to the flagship algorithm
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Risk Disclaimer: Futures trading involves substantial risk of loss and is not suitable for all investors. Past performance is not indicative of future results. The performance data shown is from backtesting and forward testing (simulated live trading), which has inherent limitations. Actual trading results may differ significantly. Only trade with capital you can afford to lose. NOI Quant Systems provides software tools only and does not provide investment advice.