output/luxalgo-buyer-assets/pinescripts/prop-open-drive-95.lux.pine
Open Drive Reclaim Prop 95
A 2-minute MES/MNQ NY-open long-only prop-evaluation engine built around strong opening-drive continuation and VWAP/EMA reclaim.
Entry, stop, target, and governor logic
The public page shows the research-grade operating shape. Source code, alert wiring, and platform-specific automation files remain private until unlock.
| Item | Validated rule | Execution note |
|---|---|---|
| Chart | 2-minute execution chart | The research winner is a 2-minute model. Do not assume 1m or 5m parity. |
| Market | MES/MNQ micro futures | Micro sizing is preferred for prop-rule control and live fill drift monitoring. |
| Direction | Long only | No short-side version survived the prop reality filter at comparable quality. |
| Session | NY-open/open-drive window | TradingView session input must be aligned to chart/exchange timezone. |
| Setup | Strong opening drive, then bullish VWAP/EMA reclaim continuation | Requires trend-quality confirmation, not blind first-bar breakout chasing. |
| Entry | Closed-bar signal, next-bar/market style entry in the approximation | Backtest used next-bar execution from the replay engine. |
| Stop | Max of structure risk and 1.25 ATR, with minimum-risk floor | Stop is attached immediately. No widening. |
| Target | Fixed 1.0R target | Fast, bracketed target. Do not stretch target during eval mode. |
| Time exit | 8 bars on the validated 2-minute model | Exit if neither stop nor target resolves inside the modeled horizon. |
| Governor | Daily stop -$500, daily lock +$650, max two model losses | The strategy is only prop-realistic with the governor active. |
Speed versus survival settings
These rows keep the same signal logic and only change risk dollars plus daily lockouts. Faster pass settings raise drawdown failure risk, so they belong in demo first.
| Mode | Risk | Daily stop | Daily lock | Pass | Fail | Median | P75 | Avg DD |
|---|---|---|---|---|---|---|---|---|
| Best survival score | $200 | $500 | $500 | 98.1% | 1.9% | 36d | 49d | $819 |
| Fast-pass frontier 1 | $300 | $500 | $500 | 94.1% | 5.9% | 21d | 30d | $940 |
| Fast-pass frontier 2 | $275 | $650 | $500 | 94.5% | 5.5% | 23d | 32d | $881 |
| Fast-pass frontier 3 | $275 | $650 | $800 | 93.5% | 6.5% | 24d | 35d | $945 |
| Fast-pass frontier 4 | $275 | $500 | $500 | 95.0% | 5.0% | 25d | 34d | $924 |
PineScript approximation staged privately.
Exact source, alert template, automation wiring, and broker-risk governor stay gated until checkout is connected.
Buy arrows, stop line, target line, session controls, and alert conditions are included in the staged script.
The script is private buyer/testing code and has not been published to the TradingView public library.
What this validation is based on
The page is a research summary, not audited live performance. These are the internal files and rule assumptions behind the published metrics.
| Area | Source | Test note |
|---|---|---|
| Market data | Normalized MES/MNQ continuous-contract intraday bars from work/databento_validation_normalized. | Coverage checked from source CSVs: 2023-06-01 through 2026-05-29. |
| Replay engine | work/drawing_board_edge_hunt_20260624.py plus work/prop_alpha_expanded_tournament.py. | 1-minute bars are resampled into the tested 2-minute and 5-minute execution views before signals are replayed. |
| Validation files | work/prop_alpha_expanded_validation_20260625 and work/prop_alpha_portfolio_validation_20260625. | Promoted candidates were rerun through 10,000 Monte Carlo prop-firm paths. |
| Rule template | Topstep-style 50K Combine/XFA simulator, verified from official Topstep help pages on 2026-06-25. | Includes MLL, optional DLL, contract limits, micro ratio, consistency, payout-day, and automation caveats. |
| Costs | $1.24 commission per side and 1 tick slippage per side in the current simulator template. | Live fills must be audited before scaling. If costs drift, the strategy is paused. |
Risk note
Strategy research is educational software and trading research, not financial advice. Backtests and Monte Carlo simulations can fail in live markets due to fees, slippage, latency, platform behavior, regime changes, and user execution.