MES/MNQ micro futures, referenced against CME product specs for contract size and tick structure.
CME MES specsCME MNQ specs
Range-bar order-flow candidate
MNQ Range Expansion 96
A compact MNQ range-bar research candidate with high expectancy and a smaller sample.
Trades108closed historical signals
Target RR1.5Rminimum acceptable RR 1.15
Win rate59.3%64 wins / 44 losses
Net return+58.7Rnormalized risk units
Profit factor2.24gross wins / gross losses
Max drawdown4.29Rhistorical path
Expectancy+0.544Rper trade
SymbolsMNQ: 108observed trades
StatusResearch shelfpublic evidence
Target/RR Profile
1.5R target profile. minimum acceptable RR 1.15. 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 Preview
public evidence
What this means
Research read
- Strongest expectancy per trade of the current public shelf.
- Smaller sample than the flagship blend, so it stays in research status.
- Best presented as a watchlist candidate until broader forward testing is complete.
Monte Carlo and shelf fit
Public risk evidence
Expectancy+0.544Rper trade
SymbolsMNQ: 108sample split
StatusResearch shelfpublic shelf
Unlock$9-$19planned range
Prop math layer
Open PDF brief
How to read the prop-firm numbers
The PDF turns the strategy stats into a modeled eval-style plan: risk per trade, historical max drawdown in R, Monte Carlo stress drawdown, pass/fail windows, and a forward-test checklist. Free C-tier pages are published as learning research; B/A-tier pages keep exact locked rules private.
TradingView buyer preview
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
Audit trail
This dashboard is a public summary of local historical replay output. The visible stats are generated from closed-trade artifacts; exact rules, raw files, and code are only exposed when the strategy is free/unlocked.
Historical replay artifacts were built from local ATAS/Databento research exports and Databento historical-data pulls where available.
Databento Historical APIStats use R-multiple closed trades, equity-curve reconstruction, Monte Carlo bootstrap paths, and modeled prop-style pass/fail stress tests where enough sample exists.
Not live audited. Results can change with fees, slippage, missed fills, latency, contract rolls, prop-rule changes, or market regime shifts.
Risk note
Strategy research is educational software and trading research, not financial advice. Backtests and simulations can fail in live markets because of fees, slippage, latency, platform behavior, regime changes, and user execution.