MES/MNQ micro futures are referenced against CME contract specs for size and tick structure.
CME MES specsCME MNQ specsPrior Sweep Momentum 80
Opening Range Baseline plus a guarded prior-sweep add-on. This is designed as a faster discretionary signal stream and possible demo-automation branch, while still staying near the 80% win-rate profile. Historical replay only, not a live-funded guarantee.
Target/RR Profile
0.3-1.25R target profile. short-target base plus guarded sweep add-on. The displayed RR is the modeled take-profit multiple from the research artifacts; live fills can vary with slippage, fees, and platform execution.
Equity Curve
Prop Bootstrap
Risk-normalized 50K-style bootstrap at $350 risk/trade, 5,000 runs. Pass/fail assumes historical day resampling and prop drawdown mechanics.
Module Split
| Module | Trades | Win | Net R | PF |
|---|---|---|---|---|
| Opening Range Baseline | 353 | 86.1% | +46.0 | 1.87 |
| Guarded prior-sweep add-on | 124 | 62.9% | +40.0 | 1.79 |
Quality Read
| Sharpe approx | 4.41 |
|---|---|
| Sortino approx | 7.30 |
| Ulcer index | 1.44R |
| Positive active days | 79.8% |
| Monte Carlo final R p10/p50/p90 | 65.6 / 86.3 / 105.1 |
Execution Rules Tested
Add-on filter: MES only, risk <= 48 ticks, max 3 trades per day, stop adding trades after roughly -2R realized day loss. This profile is a candidate for paper/demo first, not a claim of live profitability.
PineScript chart proof
LuxAlgo Quant AI baseline compiled in TradingView Pine Editor on the CME_MINI:ES1! chart. The public page shows the compiled chart preview; the Pine source stays in the private buyer deliverable packet.
Testing Method and Sources
This page is a public validation summary generated from local historical replay artifacts. Exact rules, source code, and raw signal files stay private unless the strategy is free/unlocked.
Replay files came from local futures research exports and Databento historical pulls where available.
Databento Historical APIStats use closed trades, R-multiple expectancy, equity reconstruction, Monte Carlo bootstrap, and modeled prop-style pass/fail stress tests.
Not live audited. Fees, slippage, latency, missed fills, roll handling, platform behavior, and market regime changes can alter results.