Averaging into adverse moves (grid/DCA signature). Long and short held simultaneously. Floating drawdown (14.3%) far exceeds balance drawdown (0.3%). At least one dimension is rated red; treat the marketed performance with corresponding distrust.
What we measured
Trades reconstructed
530
Pairing confidence
exact
Balance reconstruction
exact
History quality
13% real ticks
Real ticks from
2025.09.19 00:00:00
Execution delay
10 ms
Tester log provided
yes
Tester log files
20260813.log
Randomizer prints
0
Report SHA-256
f0f2dea70414e8f4…
Note: the tester journal contained 2 test runs; only the last one was used. Clearing the journal before a run avoids this.
Reconstructed floating equity
Floating drawdown (sampled)
14.3 %
Balance drawdown (deal-wise)
0.3 %
Report-head equity DD (tick-based)
254.69 (24.24%)
Equity samples (deal timestamps)
1060
sampled at deal timestamps; intrabar floating equity between deals is unobservable from a report, so the tester-head equity DD (tick-based) is the upper reference
Findings
INFO Only 13% real ticks
Most of the window uses generated ticks; fill realism is limited before the real-tick start date.
CAUTION Floating drawdown (14.3%) far exceeds balance drawdown (0.3%)
Open positions ran materially deeper underwater than the closed-trade curve shows: hidden intratrade risk, and exactly what intraday prop rules punish.
CAUTION Martingale-like sizing
Volume increases after losses in 41.7% of cases vs 25.7% after wins.
RED Averaging into adverse moves (grid/DCA signature)
193 entries were added against an open losing basket vs 112 with the move, up to 11 simultaneous positions; median size of adverse adds is 1.0x the open basket's average. Losses concentrate exactly when exposure peaks.
RED Long and short held simultaneously
235 moments with open positions on both sides.
INFO 2026 carries 75% of total profit
Remove one year and the picture changes materially.
CAUTION [iqcapital_classic] Position-loss limit breached even at 1/3 sizing
2 trades exceed the per-position loss limit at one-third size (7 at full size).
Sub-strategies
Entry-comment prefix: "Waka". The split is based on entry comments; MT5 tester artifacts do not carry magic numbers, so magic-only multi-strategy EAs appear as one group here (behavioral clustering is on the roadmap).
Key
Trades
PnL
Win %
Long/Short
Median hold (h)
default
374
-8
74.3
185/189
7.03
#1
96
23
66.7
44/52
27.77
#2
34
18
70.6
15/19
23.77
#3
16
32
75.0
4/12
20.92
#4
5
1
80.0
2/3
32.2
#5
3
21
66.7
1/2
73.88
#6
1
16
100.0
0/1
40.36
#7
1
64
100.0
0/1
36.11
Underlying vs. strategy
Symbol
Trades
Entry-price move
Traded span
Long/Short trades
Long PnL
Short PnL
NZDCAD.ls
173
+1.6 %
2025-09-29 … 2026-08-07
84/89
33
37
AUDCAD.ls
198
+7.0 %
2025-09-30 … 2026-08-07
86/112
25
32
AUDNZD.ls
159
+5.2 %
2025-09-29 … 2026-08-05
81/78
21
19
approximated from entry prices; a tester report carries no independent price series. Price-percent and account-percent are not directly comparable without sizing; the move contextualizes the long/short split, it is not a benchmark return.
Year by year
Year
Trades
PnL
Win %
Long PnL
Short PnL
2025
134
42
78.4
20
21
2026
396
125
71.0
58
67
Costs and honest metrics
Net profit
166.44
Commission
-22.14
Swap
-19.52
Gross before costs
208.10
Cost share of gross
20.0 %
EOD Sharpe (annualized)
14.23
Report-head 'Sharpe'
0.67
CAGR
18.8 %
Max drawdown (EOD)
0.3 %
Cost fragility and outlier dependence
Break-even cost shock: +0.31 USD per trade. Add that much cost to every trade (worse spread, slippage, commission) and the whole result is gone. Average position size: 0.077 lots.
Extra cost per trade
Net profit
Profit factor
Win %
+0.50 USD
-99
0.716
28.3
+1.00 USD
-364
0.355
11.9
+2.00 USD
-894
0.152
5.8
+5.00 USD
-2484
0.04
2.1
Leave-best-N-out: remove the N most profitable trades:
Best trades removed
Their PnL
Share of gross wins
Net without them
1
64
15.4 %
103
5
119
28.6 %
48
10
153
37.0 %
13
20
195
47.1 %
-29
flat USD shock per trade, not lot-scaled -- judge it against the average lot size; leave-best-out removes the N most profitable trades from the observed set.
Exit profile
Exit type
Count
TP
528
end_of_test
2
Prop-firm fit: IQ Capital Classic (funded)
Rules used for this simulation (as of 2026-08-13):
Monte-Carlo resampling: stress on the reconstructed trades
1000 paths per method, seed 42, block length 5 trading days; additive resampling at the tested sizing; drawdowns as % of start balance. Paths are not stopped at account death -- drawdowns beyond 100% mean repeated wipeouts at this sizing.
Max-drawdown distribution (percent of start balance):
Method
Median
P90
P95
P99
Permutation (order only)
3.3 %
4.87 %
5.31 %
6.39 %
Bootstrap (IID)
3.28 %
5.65 %
6.44 %
8.83 %
Block bootstrap (daily blocks)
0.35 %
0.35 %
0.35 %
0.36 %
Observed EOD max drawdown of this backtest: 0.3 %. Compare it against the percentiles above.
Equity fan: cumulative PnL of resampled paths vs. the observed backtest (% of start balance):
Bootstrap intervals (5th … 95th percentile):
Metric
P5
Median
P95
Profit factor
1.144
1.673
2.499
Expectancy per trade (USD)
0.08
0.31
0.59
Share of resampled paths ending at or below zero net profit: 1.1 %.
Streaks and recovery (block-bootstrap paths):
Metric
Median
P90
P95
P99
Max losing streak (days)
1
1
1
1
Time under water (days)
6
9
10
13
Ruin probability (floor only) per prop profile and sizing. The safe-sizing answer is the first column at or below 10 %:
Profile
Floor %
1/1
1/1.5
1/2
1/3
1/4
1/6
1/8
Safe at
iqcapital_classic
6.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1/1
ftmo_challenge
10.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1/1
generic_6pct_trailing
6.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1/1
Cells are breach probabilities in percent at each sizing (1/2 = half the tested lots). Floor only: the full rule set is stricter, so the truly safe sizing is at most the bold one.
Challenge pass probability, ftmo_challenge (at the tested sizing; daily and floor checks based on: intraday-sampled equity vs. day anchor):
Stage
Target
P(pass)
P(fail: floor)
P(fail: daily)
Undecided
Median days to pass
phase1
10 %
52.6 %
0.0 %
47.4 %
0.0 %
139
phase2
5 %
74.0 %
0.0 %
26.0 %
0.0 %
70
Both phases passed (independent-resample approximation): 38.9 %.
Attempt economics: expected attempts to pass phase 1: 1.9; for a 90 % chance of at least one pass: 4 attempts; probability of 5 consecutive fails: 2.4 %. multiply attempts by your challenge fee for the expected cost to fund.
Does reducing risk raise the pass chance? Phase 1 at each sizing (targets stay fixed, trading scales down):
Sizing
1/1
1/1.5
1/2
1/3
1/4
1/6
1/8
P(pass)
52.6 %
31.1 %
0.5 %
0.0 %
0.0 %
0.0 %
0.0 %
Median days
139
205
225
n/a
n/a
n/a
n/a
What these numbers can and cannot say:
Monte Carlo resamples the SIMULATED trades of this backtest. It inherits every weakness of the simulation (in-sample bias, regime luck, execution gap) and cannot add information -- it only reveals how fragile the observed path is.
Same trades, different order: answers only how path-dependent the drawdown is. Treats trades as exchangeable -- grid and martingale sequences are not (their order is caused by the market path), so read this as a diagnostic, not a forecast.
Draws trades independently with replacement (IID assumption). Real EAs violate IID through clustering and position chains; intervals are tighter than reality for such systems.
Resamples 5-trading-day blocks of the end-of-day PnL series (circular: blocks wrap at the series end, so every day carries equal weight -- without wrapping, a crash on the final day would be undersampled): preserves short-range clustering and losing streaks up to the block length; longer regime shifts remain unmodeled.
Floor breaches only, on block-bootstrap paths over a horizon equal to the tested history, profits above start swept (same convention as the prop-fit sweep). The firms' full rule sets (daily loss, consistency, position loss) are stricter -- treat every probability as a lower bound, and the safe-sizing answer as an upper bound on the truly safe size.
The fan bands are pointwise percentiles across resampled paths -- the envelope is not a single achievable path. Paths use the tested sizing and are not stopped at account death.
Challenge-pass probabilities run each resampled path until profit target, max-loss floor or a daily-loss day is hit (checked in that day's order: daily first, then floor). Where the audit has reconstructed equity, daily-loss and floor checks use the intraday equity drop below the day anchor (sampled at deal timestamps, server-day boundary) -- still a lower bound on breaches, but far closer to the firms' equity-based rules than EOD deltas. Phases are treated as independent resamples for the combined figure; the sizing curve keeps the targets fixed while scaling the trading, which is why less risk can also mean more undecided paths.
Withdrawal replay and Capital what-if
Lot policy detected: mixed_or_unknown (lot-size CV 1.515, lots/balance CV 1.613). Read the matching row below.
Monthly withdrawal replay (monthly sweep to start balance), covering the active span 2025-09-29 … 2026-08-10. An EA can sit out most of the tested window, and all monthly and per-annum figures refer to this span:
Sizing model
Months paid
Total withdrawn
Withdrawn %/a
Median paid month
Best month
Dry streak (months)
fixed_lots
12/12
166
18.59
16
23
0
balance_scaled
12/12
167
18.74
16
23
0
Monthly PnL heatmap: the dry-streak figure, visible at a glance:
Across 1000 resampled paths (fixed lots), total withdrawn spans 145 … 165 … 187 USD (P5/median/P95); share of paths paying nothing at all: 0.0 %.
Capital what-if (fixed lots: identical trades, different account):
Capital
MaxDD %
P(breach) iqcapital_classic
P(breach) ftmo_challenge
P(breach) generic_6pct_trailing
1000 (tested)
0.35
0.0 %
0.0 %
0.0 %
2500
0.14
0.0 %
0.0 %
0.0 %
5000
0.07
0.0 %
0.0 %
0.0 %
10000
0.03
0.0 %
0.0 %
0.0 %
25000
0.01
0.0 %
0.0 %
0.0 %
100000
0.0
0.0 %
0.0 %
0.0 %
fixed-lots USD path re-expressed per capital; margin not modeled. Safe sizing at another capital follows the MC ruin matrix scaled by capital/tested.
Assumptions and their direction:
Withdrawals are simulated monthly on the last trading day: everything above the start balance is taken out. A month without surplus pays nothing -- the dry-streak figure shows how long that can last. Paths are the OBSERVED backtest days; the withdrawal distribution adds resampled paths (same circular block bootstrap as the MC section, own seed).
balance_scaled multiplies each day's PnL by balance/start -- an approximation: min-lot granularity and balance-coupled EA defaults do not scale linearly. Direction: optimistic for small accounts.
Capital what-if keeps the observed USD path (fixed lots) and re-expresses risk against each capital. Margin requirements are NOT modeled -- a small account may be unable to hold the positions at all. Direction: optimistic. Under balance_scaled sizing, percent metrics are unchanged by construction; only the min-lot limit differs, which is why no table is shown.
Glossary
Pairing confidence
How entry and exit deals were matched into trades: 'exact' = taken from the tester log; 'validated' = FIFO/LIFO reproduced the report's holding-time figures; 'heuristic' = unconfirmed FIFO assumption.
EOD Sharpe (annualized)
Sharpe ratio computed from end-of-day balance returns, annualized with √252. Comparable across systems, unlike the report-head 'Sharpe', which is per-trade.
Cost share of gross
Commission plus swap as a share of gross profit before costs. High values mean the edge is eaten by fees and financing.
Max drawdown (EOD)
Largest peak-to-trough loss of the end-of-day balance curve.
Floating drawdown (sampled)
Largest drawdown of reconstructed equity (balance plus open-position value), sampled at deal timestamps. Between deals equity is unobservable from a report; the tester-head equity DD is tick-based.
Martingale
Position sizing that grows after losses. Looks smooth for months, then loses the account in one streak.
Consistency rule
Prop-firm rule capping the best day's share of total profit; punishes concentrated profit profiles.
Account deaths
Number of times the simulated account breached the profile's drawdown floor over this history (account is then reset and the simulation continues).
Withdrawal %/a
Yearly withdrawal as percent of account size in the sweep simulation (profits above start are swept daily).
Z-Score
Serial correlation of the win/loss sequence. Strongly negative values often just reflect several sub-strategies interleaving, not necessarily a defect.
Monte-Carlo resampling
Re-arranging or re-drawing the audited trades many times to see the range of drawdowns and streaks the same trading could have produced. It cannot add information; it reveals path fragility, not future returns.
Block bootstrap
Bootstrap that draws whole multi-day blocks of the daily PnL series instead of single trades, preserving short-range clustering (losing weeks stay losing weeks).
Ruin probability (floor only)
Share of resampled paths that breach the profile's drawdown floor at least once, with profits above start swept. The firm's full rule set is stricter, so this is a lower bound.
Time under water
Longest stretch of trading days a path spends below its previous balance peak.
Withdrawal replay
Re-plays the backtest with a monthly payout: on the last trading day of each month, everything above the start balance is withdrawn. Shows what the strategy pays a trader who lives off it, instead of compounding like a backtest.
Dry streak
Longest run of consecutive months in which the monthly withdrawal was zero: months a trader living off the account would have earned nothing.
Lot policy
Whether the EA trades fixed lot sizes or scales them with the balance, detected from the variation of lot sizes across the deal list.
Capital what-if
The observed USD path re-expressed against a different account size (fixed lots): drawdown percentages and floor-breach risk change with capital even though the trades are identical. Margin limits are not modeled.
Underlying vs. strategy
The symbol's own price move over the traded span, approximated from entry prices. If most profit is long-side while the symbol itself rose strongly, part of the result is the market's tailwind, not the mechanics. The tester cannot separate the two.
Cost fragility
How quickly the result dies as per-trade costs rise. The break-even shock is the extra cost per trade (spread, slippage, commission) at which net profit reaches zero; high-frequency systems often die at cents.
What this audit cannot tell you
A good backtest cannot prove an edge. This audit dissects the
simulation you gave it. It can expose structural risks (martingale,
grids, hidden concentration, cost fragility, rule conflicts), but it
cannot tell you that the strategy will make money. Three limits are
fundamental:
In-sample bias: commercial EAs are typically released and
updated after most of the tested window. A strong report may
replay what the vendor optimized against, not what the market will do.
Regime dependence: a profitable window can reward almost any
mechanic aligned with the prevailing trend. Year slices and long/short
splits above hint at this, but cannot settle it.
Simulation gap: even with real ticks and execution delay, the
tester does not model live spreads, slippage beyond the next tick, or
broker-specific behavior.
This audit is software analysis, not investment advice, and contains
no recommendation to buy or trade anything.