MES/MNQ micro futures, referenced against CME product specs for contract size and tick structure.
CME MES specsCME MNQ specs
Market-structure short candidate
Failed Auction Short Research
A broader MES-focused short-side research branch with a larger sample and moderate edge.
Trades199closed historical signals
Target RR~1.25Rshort-side failed-auction target
Win rate56.8%113 wins / 86 losses
Net return+32.9Rnormalized risk units
Profit factor1.34gross wins / gross losses
Max drawdown8.27Rhistorical path
Expectancy+0.165Rper trade
SymbolsMES: 175, MNQ: 24observed trades
StatusResearch shelfpublic evidence
Target/RR Profile
~1.25R target profile. short-side failed-auction target. 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
- Good larger-sample proof object for the public shelf.
- Drawdown behavior is rougher than the flagship blend, so this should not be positioned as prop-ready.
- Useful as a raw ingredient for future filtered variants.
Monte Carlo and shelf fit
Public risk evidence
MC p05 final+6.9Rbootstrap downside
MC p50 final+32.5Rmedian simulation
MC p95 DD17.5Rstress drawdown
Below zero2.2%simulated paths
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; Pine delivery is kept out of public_html so the site does not leak scripts accidentally.
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.