Backtest Research
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.
⚠ 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.
// EQUITY CURVE — CUMULATIVE P&L
// YEAR BY YEAR — BACKTEST DATA
Deepest dip is closed-trade, measured inside each year. 2019 begins June 18 and 2026 runs through August 7.
// LONG VS SHORT — SIMULATED
Direction split at the selected risk setting and window.
// PERFORMANCE BY GRADE — SIMULATED
Grade sets position size under the risk ceiling. A+ through C are all traded; D and F are not.
// SYSTEM ANATOMY — SIMULATED
The full stat sheet at the selected setting and window. Every figure is labelled in plain terms so it reads correctly on its own.
The same simulated trades, cut by month, quarter, weekday and week of month. Setups arrive at a similar rate all year — what each setup returned did not. Win rates and trade counts are identical at every risk setting; only the dollar figures rescale.
// AVERAGE RESULT PER TRADE, BY MONTH
Simulated dollars per trade, pooled by calendar month.
// BY QUARTER
// BY DAY OF WEEK
// BY WEEK OF MONTH
Week 5 is a partial bucket — only months with a 29th onward contribute. Read it as a footnote, not a finding.
// EVERY MONTH, EVERY YEAR
Simulated net per calendar month. Values are printed in every cell, so nothing is carried by colour alone.
// WHAT THIS DATA DOES NOT SAY
Seven years is six to eight observations per calendar month, not ninety. Nothing here is tested for statistical significance. 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, and it is not a filter we apply. The strategy takes every qualifying setup in every month. We publish it so a slow stretch reads as a slow stretch instead of a broken system — not so anyone sits out a quarter.
A net figure tells you where the record ended up, not how it got there. This is the shape of the curve — the climbs and the dips, how long each lasted, and how many trades ran back to back in the same direction.
// THE TEN STRONGEST CLIMBS
Trough of one dip up to the peak before the next.
// THE TEN DEEPEST DRAWDOWNS
Peak to trough, then trough back to a new equity high.
// NET BY YEAR
// DEEPEST DIP BY YEAR
// RUNS — HOW OFTEN
Consecutive trades with the same outcome. Identical at every risk setting — sizing changes the dollars, not the sequence.
// BEST MONTHS
// WORST MONTHS
// HOW TO READ THIS
Slow stretches are normal. Here is how normal.
Four questions, answered from seven years of simulated results. If you are in a flat patch right now, this tells you whether the strategy is behaving the way it always has.
The part most people get wrong
Traders assume a working system sits at a new high most of the time. It does not — and neither does this one.
› Show the numbers behind this
How often a stretch finished lower than it started
Take any day in the sample, look ahead 30, 60 or 90 days, and see whether the account is higher or lower. Repeated from every possible start date, one day at a time. These figures shift between risk settings — grade-based sizing changes each trade’s weight, so a stretch can finish green at one setting and red at another.
Stops at 90 days deliberately. Seven years does not contain enough separate year-long stretches for a one-year figure to mean anything, and publishing one would read like a promise the sample cannot support.
Every year had a bad month
The worst 30-day stretch inside each calendar year. Note 2022 — the deepest bad month in the sample landed in the strongest year on the page.
Red days and red weeks
Counted day by day and week by week. The share of red days holds steady across risk settings, but weeks and the time-below-a-high figure shift a little, because grade-based sizing changes how much each trade contributes to a day’s total.
How to read all of this
How the same simulated signals behave against a prop firm evaluation. Pick your account size below and the risk setting above. Evals are modelled on an end-of-day trailing drawdown and run back to back — the moment one passes or fails, the next begins. Each calendar year starts fresh on January 1.
// EVALS BY YEAR
Each year simulated independently from January 1. The last column shows when that year’s final eval completed, which may fall in the following year.
// ALL RISK SETTINGS AT THIS ACCOUNT SIZE
The same account, every risk setting side by side. The row matching your selection is highlighted.
// HOW THE EVAL IS MODELLED
This is the data. The next step is your own chart.
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