DrTrAdE™ Quant Lab
Research · Optimisation & Validation Platform
DEEPER INSIGHTS
HIGHER CONVICTION
01Backtest Configuration Configure your SweepFlow™ research run.
Initial planned 1R risk: $1,000
02Strategy Settings Choose settings in plain English. All 40 SweepFlow inputs are still available.
Set your entry, stops, trading sessions and targets below. Your backtest uses these precise settings. No optimisation boxes to worry about.
⚙ Inputs40 SweepFlow settings · tap to expand
Edit all SweepFlow settings here. In AUTO, tick any input to compare different values. Unticked inputs remain fixed.
FIXED runs use precisely the entered inputs.
10,000 combinations maximum per job. Large grids may be slow even below the safety limit.
👁 Review exactly what this backtest will use — see all settings on this page
Open to review every SweepFlow input and AUTO comparison choice, no file download needed.
03Backtest Results Run a backtest or open a saved result
Top-ranked settings from the Python optimisation
Winning AUTO settings — optimised inputs only
Ranked by Python: net R after costs, then lower drawdown, then trade count. Loading the best settings does not launch a job.
Waiting for the Python trade ledger.
Monthly P&L Calendar
Awaiting trade history
Saved Results
Institutional Balance · Equity · Drawdown
Built only from actual sequential R outcomes. No fabricated performance data.
Account Balance · Recorded / Modelled
Modelled on the chosen starting balance and fixed or compounded percentage risk. Risk % controls the monetary reporting model, not the Python engine's trade size.
Monthly Performance
| Month (New York) | Closed Trades | Modelled P&L |
|---|
Monthly P&L Heatmap
Daily P&L in New York time. Select a closed trading day to inspect its total.
Trade-by-Trade Execution Ledger
Entry and exit times are New York local time (EST/EDT automatically). Exit time is the candle timestamp recorded by Python, not a tick-by-tick broker execution time. An exit marked “(level)” is the calculated SL/TP target, not a confirmed fill.
| # | Entry (NY) | Side | Entry Price | SL | TP1 | TP2 | TP1 Hit | Exit (NY) | Exit Type | Exit Price / Level | Realised R | Modelled P&L |
|---|
Trade Intelligence · Sessions, Days & Hours
Real trade-ledger analysis in New York entry time (DST-aware). These are descriptive historical groupings, not verified future edges. Small samples are flagged.
| Group | Trades | Net R | Avg R | Win % | Closed DD (R) | Sample | Inspect |
|---|
All Inputs Used in This Run
Snapshot taken at submission time; includes all 40 SweepFlow parameters, date range, execution mode, optimisation axes and balance/risk model.
Saved Backtest Results
Saved snapshots currently remain in this browser (up to 24). Permanent server-side saved history is pending its Python endpoint. Original completed server job files remain untouched.
Compare Saved Backtests
Select two to four browser-saved runs. Compare real performance metrics and every SweepFlow input directly, without downloading JSON.
Prop Firm Laboratory
Dedicated challenge research, multiple attempts and payout scenarios — no live firm account or payment implied.
Select a real completed trade ledger, then simulate one or more historical challenge attempts.
Rule thresholds can be modelled from historical closed trades. Full pass/fail depends on floating-equity and compliance data not currently in GitHub.
Choose strategy backtest
Reuse a completed SweepFlow trade ledger, or launch a new FIXED/AUTO run using the existing Python engine.
New runs use the exact strategy settings configured in the Backtests section. The Python engine remains the only trading calculation source.
Repeated challenges after failure
Each failed attempt starts fresh on the next unused historical trade; this is a closed-trade diagnostic, not an actual challenge purchase.
Optional fee is only used for transparent hypothetical attempt costs.
FX Prop Firm Challenge & Payout Scenario
Conservative historical scenario only. Closed trades cannot prove real-world PASS because intratrade floating equity, firm restrictions and approval data are not available. Real funding and payouts are never guaranteed.
Select a completed backtest with an actual UTC-timestamped trade ledger, then run the simulator.
Firm profiles are dated reference snapshots. Confirm your specific purchased product, restrictions and payout policy from the linked official firm rules.
Saved Challenge Simulations
Stored in this browser only. They are research snapshots, not firm results or certified passes.
Compare Prop Firm Rule Profiles
Same historical ledger, different dated firm rule snapshots. Always verify specific program rules before purchase.
Choose a run and start a comparison to populate this table.
Floating Risk · Real M1 Diagnostic
Review modelled intratrade equity using a JSON report generated on your Windows server. The selected illustrative thresholds are NOT verified FTMO, FundingPips or FundedNext rules; no result is a certified challenge pass or payout.
No diagnostic loaded. Choose a local JSON report generated by the Windows M1 diagnostic.
Import is browser-local and does not alter the Python engine, create a funded account, or upload this report to GitHub. Automatic server-side job integration is still pending.
Funded-Stage & Reward Eligibility
Passing evaluation profit milestones does not mean a trader is funded or can receive money. Reward schedules, restrictions and actual approval must be satisfied separately.