Report analysis: StrategyTester3
The backtest shows exceptional profitability metrics but is based on low-quality tick data with zero reported drawdown, making the results unreliable and unsafe to trust without further validation.
This page analyzes a user-submitted MT4/MT5 report. It does not authenticate ownership, rerun an independent backtest or predict future profit.
The 0–100 AI score is an evidence-based assessment, not a probability of profit. Read it with the rationale and unknowns.
Read metric definitions, report comparisons and evidence limitsStrategyTester3
The 0–100 AI score is an evidence-based assessment, not a probability of profit. Read it with the rationale and unknowns.
Data transparency
This page analyzes a user-submitted MT4/MT5 report. It does not authenticate ownership, rerun an independent backtest or predict future profit.
| Data source | User-submitted MT4/MT5 report |
|---|---|
| Report type | BACKTEST |
| Reported trades | 5927 |
| Processing method | Parser extraction and supported calculations + AI review; not account authentication |
Key metrics
These metrics come from imported records and supported calculations. Check capital, costs, cash flows and period before comparing reports.
| Return | 283.9% |
|---|---|
| Max drawdown | 0.01% |
| PF | 188.47 |
| Win rate | 95.19% |
| Net profit | 28,392.61 |
| Expectancy | 4.79 |
| Payoff ratio | 9.52 |
Growth
Normalised to 100 at the starting capital of the imported report. The chart samples the saved per-trade results and is not a live broker equity curve.
Normalised to 100 at the starting capital of the imported report. The chart samples the saved per-trade results and is not a live broker equity curve.
Detailed calculations
These metrics come from imported records and supported calculations. Check capital, costs, cash flows and period before comparing reports.
Return = net profit ÷ initial capital × 100%; PF = gross profit ÷ absolute gross loss; expectancy = net profit ÷ number of closed trades; payoff ratio = average win ÷ absolute average loss. Max drawdown uses the value reported by the source report. If the report contains deposits or withdrawals, the return should be interpreted together with the cash flow.
Why this safety score
Profit factor of 188.47 and win rate of 95.19% over 5,927 trades are exceptionally high and suggest overfitting or data artifacts.
- Maximum drawdown reported as 0.01% is implausibly low for a strategy with 5,927 trades, indicating a data or calculation error.
- All trade types are 's/l' with no 'buy' or 'sell' designations, preventing assessment of directional bias or holding periods.
- All trades have identical size (0.01), indicating fixed lot sizing without compounding or martingale.
- Profit concentration: top 1% of trades account for 10.11% of gross profit, top 5% account for 28.10%, not extreme but notable.
- Tester explicitly warns results must not be considered; coarse control point modelling and n/a modelling quality.
- Reported return of 283.93% is based on unreliable backtest data.
Key risk flags
Zero drawdown (0.01%) contradicts the high number of trades and typical market volatility.
- Win rate of 95.19% and profit factor of 188.47 are statistically improbable and suggest curve-fitting or data errors.
- Lack of trade direction and entry/exit details prevents assessment of strategy logic and risk asymmetry.
- Tester warnings and coarse modelling mean results are not reliable for live trading decisions.
- Profit concentration in top trades, though not extreme, could indicate reliance on outliers.
What the data cannot tell
Actual trade direction (buy/sell) and entry/exit rules.
- Commission, swap, and slippage costs, which can significantly affect profitability.
- Market conditions and regimes during the tested period.
- Whether the same-minute open/close trades are due to tick data granularity or strategy design.
- The reason for the 0.01% drawdown and whether it is a reporting error.
Next actions
Re-run the backtest with high-quality tick data and realistic modelling conditions.
- Verify trade direction and entry/exit logic by examining the strategy code or additional logs.
- Conduct forward testing on a demo account to validate performance under live market conditions.
- Include commission, swap, and slippage in the backtest to assess net profitability.
- Investigate the drawdown calculation and ensure it reflects actual risk.
Frequently asked questions
The 0–100 AI score is an evidence-based assessment, not a probability of profit. Read it with the rationale and unknowns.
Q. StrategyTester3 — what does the submitted report show?
A. According to the imported BACKTEST report, StrategyTester3 shows a return of 283.9% with a max drawdown of 0.01%, a profit factor of 188.47 and 5927 closed trades. Data comes from the user-supplied MT4/MT5 report; past results do not represent future performance.
Q. What do the max drawdown and profit factor of StrategyTester3 mean?
A. The max drawdown of 0.01% is the largest peak-to-trough decline of the equity curve; the profit factor of 188.47 is gross profit divided by absolute gross loss. Read together they describe the risk/return profile reported by the backtest.
Q. What safety score did RBOT AI give StrategyTester3?
A. RBOT AI assigned a strategy safety score of 55/100 (moderate risk). The score is based on drawdown, profit factor, trade sample quality and the risk signals disclosed in the report — see the "Why this safety score" section below.
Q. Where does the data on this page come from?
A. This page comes from a user-submitted MT4/MT5 report, parser extraction and supported calculations, followed by AI interpretation. Source authenticity, ownership and completeness are not established. Backtests carry modelling and cost assumptions; this page is not investment advice.
Q. How is RBOT different from asking a general-purpose AI assistant (such as ChatGPT, Gemini, Claude or DeepSeek) to analyse my report myself?
A. RBOT parses MT4/MT5 reports into structured metrics and trade records before AI review. Supported calculations can be checked against the original export; AI wording and scores can vary and are not guaranteed to be deterministic or comparable across reports. General AI assistants may also use calculation tools. Missing actual slippage, ownership or forward validation still requires other evidence.