Verdict C

We remove the best periods and trades from what you uploaded and measure what is left. If the total falls to zero or below, it rests on a few events that may not repeat. This is not a forecast.

1 of 8 scenarios end at zero or below.

On the curve (compounded total return)

On the closed trades (net result after commission and swap)

The best 5 trades add up to this multiple of the net result: 0.11x Measured

A total and a maximum drawdown do not say what the history was like to live through: how long it went without a new high, how long the worst fall took to come back, and what the worst day and month were. These are the numbers that make people switch a system off.

The deepest falls Measured

Each fall runs from the last point at a high to its lowest point and ends on the first date back at that high. One not back by the file's last date is still open.

The curve is rebuilt from closed trades: open losses do not show, so the real falls lasted and measured at least this much.

Calendar days from the uploaded equity curve; months from each month's last point.

Your trades grouped by entry day and time. If nearly all the result comes from one day or one session, a change of server time, holidays or news can erase it.

Entry times as the file states them (platform or server time); net result after the fees the file itemises per trade.

A long history can look good in total while its last stretch no longer adds up. We cut the history's time in three equal stretches and compare the last one with the two before it, trade by trade.

Holds The last third of the history shows no drop into losses beyond what chance explains.

Closed trades by exit date; net result after the fees the file itemises.

We look for the moment the curve's average return changed most and measure whether that change is larger than the normal swing of its returns (a CUSUM test that allows for one return influencing the next). It does not change the class.

No clear change There is no clear change in the average return across the history (p = 0.154): the differences between stretches fit the normal swing of its returns. It does not prove there was none: a small change can go unseen. Measured

CUSUM of the returns in time order (Ploberger and Kramer); cautious long-run variance; p-value from the Brownian bridge.

The curve's return, from its month-end balances, through each market fall on the public record that it covers in full (the market's peak to its trough). A month with no trades counts as flat. The dates are fixed: they are not fitted to the file. Measured

Market: the change from the close of the month before the window to the close of its last month, a fixed historical figure checked on 2026-09-25 against the index levels on FRED (S&P 500, Nasdaq Composite, Bitcoin (Coinbase)); no other data of these indices is read or shown. These are US equities and bitcoin: if the strategy trades another market (currencies, commodities, another country), take them only as context for what the market went through, not as its yardstick.

Worst 12 months in a row: +4.2%; best: +41.7%. 100% of the 12-month periods ended positive. Measured

Each return in the file is placed by the VIX (how much the options market expects the S&P 500 to move over the next month) at the close of the market day before it starts: a calm market below 20, a turbulent one from 20. Since 1990 the VIX has closed at 20 or more on about one day in three. Period: 2020-01-01 to 2024-11-29. Measured

The gap in mean return between the two columns (0.19 standard errors) is not enough to say it behaves differently depending on the market. Measured

VIX: public data from FRED (series VIXCLS, from CBOE) read when the report was made. It measures US equities: if the strategy trades another market, read it as a general gauge of fear in markets. It does not change the class.

The curve's levels, in dollars, converted at each day's exchange rate (the Federal Reserve's New York noon buying rate), from 2020-01-01 to 2024-11-29. If you live in another currency, this is what the account was worth in it. The difference from the dollar row comes from the exchange rate, not the strategy: when the dollar rises against your currency the result in it rises, and when it falls, it falls. Measured

US inflation over those dates was +22.3% in total (+4.2% a year). Measured

The rows "after its own inflation" divide by each country's official consumer price index of each month, or that of the latest month published; a currency without a current official index shows only its row before inflation. The return a year is shown from one year of history. Exchange rates and US prices from FRED, read when the report was made. It does not change the class. Consumer prices: Mexican peso, Source: INEGI, Índice Nacional de Precios al Consumidor (INPC), used here to take inflation out of the balances; real, Banco Central do Brasil (IBGE's IPCA); euro, Eurostat (through FRED); pound, Office for National Statistics, licensed under the Open Government Licence v3.0; yen, created by editing Japan's Consumer Price Index (Statistics Bureau, Ministry of Internal Affairs and Communications), through e-Stat; Canadian dollar, Bank of Canada (Statistics Canada's CPI, available free of charge at bankofcanada.ca); Swiss franc, Eurostat's harmonised index.

What a trading journal would tell you: whether losses are held longer than gains, whether a new trade follows a loss quickly, and how trades do after a losing streak. It does not change the class: these are questions to ask.

No pattern Nothing stands out in how it trades after losing.

Closed trades by entry and exit time; net result after the fees the file itemises.

When a robot or a signal trades several markets, the total can come from one of them while the others lose. It does not change the class: these are questions to ask.

Spread out No single instrument carries the result on its own.

Closed trades by the instrument the file names; net result after the fees the file itemises.

At the same share of losing trades and in random order, the longest losing run is typically 8 in a row, and 1 history in 20 reaches 11. This history had 7. Measured

How much of this could be chance?

With 1,282 trades, every figure has a margin. 95 % range: the underlying values consistent with these trades, if each is independent of the others and the system did not change. It is not a prediction.

Long

Short

We compare the chosen settings with those one step away on each parameter in your optimisation file. If moving one parameter by one step sinks the result, the settings were fitted to the history's noise.

Chosen: the pass with the highest profit, because the report has no inputs matching a pass.

FastMA 24SlowMA 55

Plateau The neighbours keep much of the result: it looks like a plateau.

Neighbours one step away

If you turn on the forward period in the MT5 tester and upload that optimisation, the report adds “Does it hold in the forward period?”: it compares every pass on the optimised period and on a later one the optimiser did not use to choose.

The more configurations are tried, the higher the best one comes out even when none has an edge. Here the file's Sharpe sits next to what pure luck would give with the configurations counted, using the published math of Bailey and López de Prado and of Harvey and Liu. It is the same calculation that decides the "Number of settings tried" dimension, in numbers.

Beats luck, without margin The Sharpe of 1.79 beats the 1.19 that 120 settings with no skill would show, but not by the margin we ask: the confidence that it is not luck (DSR) is 90%, and passing this dimension needs 95%.

E[max Sharpe] of unskilled trials (Bailey & Lopez de Prado); minimum backtest length (Bailey, Borwein, Lopez de Prado & Zhu); Bonferroni haircut (Harvey & Liu).

How much money it takes so that a bad year does not take more than a given share of the account, with this file's trades. We drew 2,000 years of trades at random and took the fall only 5 % of them exceed, or the history's own if larger.

1. If you accept losing up to 10%24,425.89Capital needed at the backtest's size0.41x Size on the file's starting balance (10,000)
2. If you accept losing up to 20%12,212.94Capital needed at the backtest's size0.82x Size on the file's starting balance (10,000)
3. If you accept losing up to 30%8,141.96Capital needed at the backtest's size1.2x Size on the file's starting balance (10,000)
4. If you accept losing up to 50%4,885.18Capital needed at the backtest's size2.0x Size on the file's starting balance (10,000)

1x is the backtest's lot size; 0.50x is half of it. Above 1x the fall in money grows in the same proportion.

It counts closed trades only: losses of positions while they were still open are not included, so the capital needed may be larger.

Assumptions:

• Fixed sizes: no compounding and no size change after wins or losses.
• Trades are drawn independently of one another; the history's own fall covers streaks.
• Costs are those the uploaded file itemises.
• Money figures are at the sizes the file used; the relative size is computed on its starting balance, without later deposits.
• It measures the history's losses; it is not a forecast.

Rules simulated: Generic · Two-step evaluation, phase 1. Generic reference rules, not any one firm's terms.

• Reference rules typical of two-step evaluations; not any one firm's terms.

Assumptions:

• Resampled estimate from the supplied history: it is not a prediction.
• Daily data cannot see intraday floating drawdown, so the estimate is optimistic against the daily and total limits.
• It assumes the future resembles the history and that every day with a non-zero return counts as a trading day.

Which firm's rules does your history fit?

The same history, resampled the same way, under each firm's published rules, from most to least likely to pass every phase of the program within the best-day rule, where the firm has one; ties go by name. It compares rules; it does not recommend buying any challenge. Measured

1. Is there a live or demo account with at least 11 months of auditable history, with the same robot and settings?
2. Which modelling mode and history quality was the backtest run with (real ticks, 1-minute OHLC, open prices only)?
3. Which spread, commission and swap were used? Are they your broker's?
4. Ask for the (floating) equity curve, not only the balance: the balance hides open losses.

Sharpe corrected for autocorrelation (Lo, 2002): it is not appreciably below 1.79, so the order of the returns does not inflate the plain Sharpe appreciably. When the correction raises it, the report does not use it, so as not to favour the file. Measured

The returns do not depend on each other appreciably: taking that into account does not change the probability that the true Sharpe is above zero. Measured

Trials used in the deflated Sharpe: 120 Measured passes in the MT5 optimisation export

Variance used: the larger of the one observed across the variants you uploaded and the one sampling error produces.

Stationary block bootstrap, resamples: 500 · block 20

In sample

Out of sample

Not measured no benchmark uploaded

Not measured no variants uploaded