output/luxalgo-buyer-assets/pinescripts/open-drive-or-boost.lux.pine
Tier A/B higher-frequency portfolio
Open Drive OR Boost Portfolio
A higher-frequency portfolio combining Open Drive Prop 95 with a reduced-risk opening-range failed-break add-on.
Trades4162.67 per week
Win rate58.2%10k validated
Target RR1.0R, OR add-on at 0.5x riskdefault model
Expectancy+0.106Rper trade
Profit factor1.41gross wins / losses
Max DD6.83Rhistorical path
Eval pass93.1%10,000 paths
Eval fail4.7%trailing/DD fail
Median pass46 daysp25 33 / p75 63
Funded payout99.6%modeled eligibility
Historical Equity Curveclosed-trade R path
Exact operating profile
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 open-drive plus 5-minute OR add-on context | Use as a portfolio checklist, not as one loose signal. |
| Market | MES/MNQ micro futures | Micro sizing first; add-on risk is intentionally reduced. |
| Module 1 | Open Drive Prop 95 at 1.0x risk | Primary edge and pass engine. |
| Module 2 | OR failed-break long add-on at 0.5x synthetic risk | Adds signals but should not replace the main engine. |
| Entry | Closed-bar signals only | No intrabar hindsight arrows. |
| Stop | Structure/ATR bracket stop | Both modules require stop at entry. |
| Target | Fixed 1.0R target on each module | Add-on is risk-scaled, not target-stretched. |
| Portfolio rule | Do not stack simultaneous correlated positions | If both modules fire together, prioritize the main open-drive signal. |
| Evaluation mode | Use solo engine first unless frequency is required | Portfolio passes slower in median days than the solo model. |
| Funded mode | Better fit for steady payout-day accumulation | More signals help discretion, but only with risk-weighting. |
Fast-pass governor frontier
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 | $300 | $500 | $650 | 93.5% | 6.3% | 35d | 50d | $952 |
TradingView package
PineScript approximation staged privately.
Buyer package includes the solo script plus the OR add-on approximation and risk-weighted portfolio checklist.
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.
Sources and testing protocol
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.