FSD-X
// Backtest Research Data

Backtest Research

Knightfall MK1 Last updated August 8, 2026

Simulated parameter analysis for FSD-X ORB PRO (Knightfall MK1) tracking 1,109 total execution cycles from June 18, 2019 to August 7, 2026. Calculated utilizing a grade-based risk model — risk is scaled dynamically by setup grade. Hypothetical results — past performance is not indicative of future results.

↗ View Full Trade Log — 1,109 Trades · Updated August 2026

⚠ All data on this page reflects hypothetical simulated backtest results, not actual trading. Past performance is not necessarily indicative of future results. Figures are gross of costs — they exclude commissions, fees, and slippage, which vary by broker and reduce net performance. Results shown are from a specific risk profile and are not typical.

Hypothetical Net (MNQ)
$104,230
Dynamic risk by grade · $400 indicator setting
Simulated Win Rate
63.03%
A through C included
699 of 1,109 trades profitable (TradingView Strategy Tester)
Profit Factor
1.73
Positive math expectancy
TradingView Tester shows 1.729 — same figure, just unrounded.
Max Drawdown
-$2,250
Peak-to-trough capital
TradingView Tester shows -$2,276 (0.23%) — TV measures max intraday equity (counts open-trade dips); ours is closed-trade drawdown.
Equity Curve — Cumulative P&L
MNQ · 1,109 trades · Jun 18, 2019 – Aug 7, 2026 · $400 base risk
+$104,230
Final cumulative P&L

⚠ Hypothetical, simulated backtest results — not actual trading. Figures are gross of costs (exclude commissions, fees, and slippage). Past performance is not necessarily indicative of future results.

// Risk Profile Planner — Interactive

See the numbers for your account size

Pick your account size and base risk to see the full picture for each profile: how it performs getting funded (eval pass rates) and trading funded (7-year backtest). This is the same planner our members use — now open, so you can check it against the account you actually trade.

Knightfall MK1 Data updated August 8, 2026
Account Size
Base Risk Per Trade
Backtest Window
Daily Loss Limit — some EOD accounts add a per-day cap
Evals are modeled on an end-of-day (EOD) trailing drawdown — the rule set that best fits this strategy. The max-loss threshold is recalculated once per day from the end-of-day closing balance — it trails the highest EOD balance, never moves down, is fixed during the next session, and is enforced in real time: touching it intraday fails the eval. The Daily Loss Limit toggle models EOD accounts that also carry a per-day cap (50K $1,200 · 100K $1,800 · 150K $2,700): hitting it liquidates and pauses trading for the rest of that day without failing the account. “No DLL” models EOD accounts without a daily cap. Each calendar year is simulated independently, starting fresh on January 1 — evals run back-to-back, and the moment one passes or fails the next begins, so each year carries its own standalone count. A year's final eval is allowed to finish even if it runs into the following year; those years are marked with an asterisk (*) showing when the last eval completed. Targets / EOD trailing drawdown by account: 50K +$3,000 / $2,000 · 100K +$6,000 / $3,000 · 150K +$9,000 / $4,500. Assumes every signal taken mechanically at the selected base risk (an idealized ceiling; real execution varies). Hypothetical/simulated results, not actual trading. Past performance is not indicative of future results. Figures are gross of costs — they exclude commissions, fees, and slippage, which vary by broker and reduce net performance. Results shown are from a specific risk profile and are not typical.
This planner is yours to explore for free. Members get it built into the Playbook — synced next to a live journal, backtest logger, and the full auto-graded ORB suite — so you plan risk against your own trades, not just the backtest.
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// GRADING LAYER METRICS — SIMULATED

Hypothetical backtest · $400 risk profile · grade-based dynamic sizing · gross of costs

GradeTradesWin RateProfit FactorNet P&L
Grade A+143 65.7%2.09 $21,112
Grade A339 64.0%1.79 $37,088
Grade B+205 60.0%1.44 $13,980
Grade B300 62.0%1.66 $22,785
Grade C+38 71.1%2.84 $4,296
Grade C84 61.9%1.65 $4,968
// SYSTEM ANATOMY — SIMULATED
Total Trade Count:1,109
Wins / Losses:699 / 410
Expected Value:$93.99 / trade
Avg Win / Avg Loss:$354 / -$348
Max Consec. Wins:12
Max Consec. Losses:5
Profitable Months:73 / 84 (87%)(of months with trades)
Avg Monthly Return:$1,241 (simulated)
Monthly Sharpe:1.091
Recovery Factor:46.3x
Max Drawdown:-$2,250(-$2,276 TV intraday)
Risk Per Trade:Grade-Scaled($400 indicator setting)
Ecosystem Status:Hypothetical Backtest

// MULTI-YEAR ANNUAL LEDGER — BACKTEST DATA

YearTradesWinsLossesWin RateNet Return
2019 PARTIAL862 75.0%$764
20201307951 60.8%$7,364
20211469749 66.4%$14,662
202219512669 64.6%$20,192
202317310370 59.5%$14,312
202417310964 63.0%$16,135
202518711770 62.6%$18,931
2026 PARTIAL976235 63.9%$11,870
2019 and 2026 are partial years — the sample begins June 18, 2019 and ends August 7, 2026. Every row is simulated on the $400 risk profile with grade-based dynamic sizing; the eight calendar years it spans sum to 1,109 trades and $104,230. 2026 overlaps our live tracking period — see the live track record for what the strategy actually signalled since May 1, 2026. The two are tracked separately and are never combined into a single figure.

The same 1,109 simulated trades, cut by month, quarter, weekday and week of month. Setups arrived at a similar rate in every month — about 11 to 15 a year-month across the sample. What each setup returned did not.

Strongest month
$138
March · 66.0% win on 97 trades
Slowest month
$49
May · 57.4% win on 108 trades
Strongest weekday
$127
Thursday · 68.7% win on 246 trades
Full-sample average
$94
All 1,109 simulated trades · 63.03% win

Hover any bar or cell for detail. Every chart has a table twin.

// AVERAGE RESULT PER TRADE, BY MONTH — SIMULATED

Simulated dollars per trade, all 1,109 trades pooled by calendar month. Setups arrive at a similar rate all year — about 11 to 15 a month in any given year. The return per setup does not stay similar.

Swipe the chart sideways — or use the table view.

// BY QUARTER

Simulated dollars per trade. Q1 to Q3 is the widest of the four.

// BY DAY OF WEEK

Simulated dollars per trade on comparable volume — 209 to 246 trades per day.

// BY WEEK OF MONTH — SIMULATED

Simulated dollars per trade. Week 5 is a partial bucket — only months with a 29th through 31st contribute to it, so it holds 70 trades against 240-odd in each of the others. Read Week 5 as a footnote, not a finding.

Swipe the chart sideways — or use the table view.

// EVERY MONTH, EVERY YEAR — SIMULATED

Simulated net result per calendar month. 2019 begins June 18 and 2026 ends August 7 — both are partial years. Empty cells are months with no simulated trades. Values are printed in every cell, so nothing here is carried by colour alone.

Swipe the grid sideways — or use the table view.

// WHAT THIS DATA DOES NOT SAY

Seven years is six to eight observations per calendar month, not ninety. Each monthly bar averages a handful of samples — six Novembers, eight Junes. Nothing here is tested for statistical significance, and none of these gaps is wide enough relative to the sample to rule out chance. The quarterly and day-of-week splits pool the most trades and are the most dependable; Week 5 pools the fewest and is the least.

This is not a forecast. Nothing here predicts what any month will do next. A seven-year average describes what the simulation did, not what the market will do.

It is not a filter we apply. The strategy takes every qualifying setup in every month. We publish this so members recognise a slow stretch as a slow stretch instead of a broken system — not so anyone sits out a quarter.

Dollars, not points. These figures come from the grade-based dynamic sizing model, so month-to-month differences blend how the setups performed with how many contracts the model allocated. A size-neutral points breakdown is not published here.

2026 Overlaps Our Live Tracking

2026 is a partial year here, and it covers the same stretch as our live tracking, which began May 1, 2026. Over the shared window (May 1 – August 7, 2026) this backtest shows $2,730 across 50 simulated trades at 58.0%. The live page shows what the strategy actually signalled over the same days, tracked independently.

The two figures are produced separately and are never combined into one total. They live on separate pages on purpose. Worth noting when you compare them: that window covers May through August — several of the slower months in this seven-year sample — and the simulated win rate over it (58.0%) sits below the full-sample 63.03%.

See the live track record →

Method

Derived from the same TradingView Strategy Tester export that produces the equity curve on the Overview tab: 1,109 simulated trades on MNQ 5-minute, June 18, 2019 through August 7, 2026, production defaults, grade-based dynamic sizing at the $400 indicator setting. Trades are bucketed by close date. Win rate counts a trade as a win when its simulated result is above zero. “Week 1” is the 1st through 7th of a month, “Week 2” the 8th through 14th, and so on; “Week 5” is the 29th onward. Every bucket on this tab sums back to $104,230 across 1,109 trades. The gain/loss pair on the heatmap sits in the colour-vision-deficiency warning band, which is why every cell carries its printed value and a table twin rather than relying on green versus red.

This is the data. The next step is your own chart.

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