How to Read MT5 Trading Statistics (Profit Factor, Sharpe, Drawdown)

चरण-दर-चरण निर्देश

1. चरण 1: Profit Factor — the single most useful metric

   Profit Factor = gross_profit / gross_loss. Ignores trade count, time, and equity progression — purely a ratio of winning gross to losing gross.

   Interpretation: • PF < 1.0 — EA loses money. Reject. • 1.0–1.3 — barely profitable. After accounting for spread/commission slippage in live, likely unprofitable. Marginal. • 1.3–1.5 — viable but tight. Live performance needs to closely match backtest to remain profitable. High risk. • 1.5–2.0 — robust. Modest live-vs-backtest divergence still leaves the EA profitable. • 2.0–3.0 — strong. Reasonable cushion against execution friction. • 3.0+ — suspicious. Either the backtest period was unusually favourable, the EA was curve-fit, or the data window was too short. Investigate.

   Profit Factor is symmetric across long/short and across symbols, so it's the right metric for comparing EAs that trade differently. Use as the primary filter after Drawdown.

2. चरण 2: Maximum Drawdown — the loss you have to survive

   Drawdown is the peak-to-trough percentage equity decline during the backtest. MT5 reports both Balance Drawdown (closed P&L) and Equity Drawdown (mark-to-market including open positions). For EA evaluation, use Equity Drawdown — it captures the open-position pain you'd actually feel.

   Acceptable ranges by EA type: • Conservative trend-follower — 5–10% max DD • Standard EA — 10–20% max DD • Aggressive scalper — 15–25% max DD • Grid / martingale — 30%+ max DD (often blows up in live despite favourable backtest) • Retail EA above 30% DD — high blow-up risk; not suitable for live capital • Above 50% — likely blows up on a single bad month

   If your tolerance is 'never lose more than 10%', size positions so the EA's backtest DD scales to 10%. An EA with 25% backtest DD trading 1.0 lots becomes a 10% DD EA trading 0.4 lots — at the cost of 40% of the absolute profit.

   Also check the Drawdown Duration — how long the EA stayed below its previous equity peak. A 15% DD that recovers in 2 weeks is fine; a 15% DD that takes 18 months to recover is psychological torture and most traders quit before the recovery.

3. चरण 3: Sharpe Ratio — risk-adjusted return

   Sharpe = (average return - risk-free rate) / standard deviation of returns. Higher is better. MT5 build 4000+ reports Sharpe in the Results tab; older builds require manual computation from the equity curve.

   Interpretation: • Sharpe < 0.5 — return barely beats variance. The EA's profit is mostly luck. • 0.5–1.0 — modest. Tolerable for conservative strategies. • 1.0–2.0 — good. Most professional managers target this range. • 2.0–3.0 — excellent. • 3.0+ — exceptional but rare. Verify the calculation; many short backtests inflate Sharpe.

   Caveat: Sharpe penalises upside volatility the same as downside volatility, which is mathematically wrong for trading (you want upside volatility). Sortino Ratio is the fix — only penalises downside variance. MT5 doesn't report Sortino natively; export the equity curve to Excel/Python and compute it for serious analysis.

   Also: Sharpe is only meaningful over 1+ year of data. Short backtests give Sharpe values that look impressive but reflect noise.

4. चरण 4: Recovery Factor — return per unit of drawdown

   Recovery Factor = net_profit / max_drawdown. Conceptually: 'how many times did the EA recover from its worst drawdown'.

   Interpretation: • RF < 1.0 — the EA's worst drawdown exceeds its total profit. Unacceptable. • 1.0–2.0 — marginal. The EA spends most of its life recovering from drawdowns. • 2.0–5.0 — acceptable. Reasonable cushion. • 5.0–10.0 — strong. • 10.0+ — excellent.

   Recovery Factor is the metric that captures 'how comfortable would I be running this EA'. An EA with 50% profit and 25% drawdown (RF 2.0) and an EA with 30% profit and 5% drawdown (RF 6.0) — most traders prefer the latter despite the lower absolute profit.

   For multi-EA portfolios, use Recovery Factor as the position-sizing metric: allocate capital proportional to RF, not to absolute profit.

5. चरण 5: Win Rate alone is misleading — pair with Avg Win/Loss

   Win Rate = winning_trades / total_trades. By itself, win rate tells you nothing about profitability — an EA with 90% win rate and 1-pip wins and 50-pip losses still loses money.

   The correct pair: Win Rate × Average Win/Loss Ratio.

   Avg Win/Loss Ratio = average_winning_trade / average_losing_trade. Sometimes called Payoff Ratio.

   Breakeven combinations: • 30% win × 2.5 R:R (Avg Win/Loss) = profitable • 50% win × 1.0 R:R = breakeven • 70% win × 0.5 R:R = profitable • 80% win × 0.3 R:R = barely breakeven

   Trend-following EAs typically: 30–40% win × 2.5–4.0 R:R. Big winners, frequent small losers. Scalping EAs typically: 60–80% win × 0.5–1.0 R:R. Many small winners, occasional large losers. Both profiles can be profitable; neither is inherently better.

   Treat Win Rate as a personality trait of the EA, not a quality metric. Some traders psychologically need high win rate (scalpers); others tolerate low win rate (trend followers).

6. चरण 6: Expected Payoff — average $ per trade

   Expected Payoff = net_profit / total_trades. The average $ profit (or loss) per trade across the entire backtest.

   Use this to estimate live profitability: if Expected Payoff is $4.50 and your EA averages 200 trades per month, you can expect roughly $900/month gross profit in live (assuming live execution matches backtest, which usually it doesn't perfectly).

   More importantly, compare Expected Payoff to spread + commission cost per round-turn trade. If Expected Payoff is $5 but your broker charges $4 in commission and 1 pip spread (≈$10 on a 0.1-lot trade), the EA is unprofitable after costs even though the backtest shows positive expected payoff. MT5 backtests can include broker costs if you configured them correctly (see backtest-ea-mt5 guide); always verify.

   Negative Expected Payoff with positive Net Profit is impossible mathematically — they have the same sign by definition. If you see this in a report, you're looking at two different backtests.

7. चरण 7: Total Trades — statistical credibility floor

   Total Trades determines the statistical reliability of every other metric. Rules of thumb:

   • < 30 trades — meaningless. Win Rate could be 50% true and 80% in the backtest by pure chance. • 30–100 — directional but not reliable. Use for early validation only. • 100–500 — credible. Most metrics are within ±20% of their true value. • 500–2000 — robust. Metrics close to true values. • 2000+ — bulletproof. Any divergence between backtest and live now strongly suggests an execution or environmental difference, not statistical noise.

   For a serious live-money decision, target 500+ trades over 3+ years. If your EA's natural trade frequency is too low to hit 500 trades in 3 years (e.g. quarterly swing strategy), use a longer backtest period or accept that you have lower statistical confidence.

   This is also why Strategy Tester's 'optimization on 6 months' for a low-frequency EA is dangerous: 30–50 trades in 6 months produces metrics that are essentially noise.

8. चरण 8: Always visually inspect the equity curve

   Summary metrics can hide bad behaviour. The Strategy Tester's Graph tab shows the equity curve over time — look at it. Things to spot:

   • Stair-step pattern with sudden drops — the EA has cliff-risk trades (martingale style: many small wins, occasional huge loss). Even if metrics look OK, the cliff trades will eventually take an outsized hit live.

   • Equity flat-line for months — the EA stopped trading for a regime it doesn't handle (e.g. low volatility). Live, this is fine, but you need to budget for these dead periods.

   • Equity rapidly climbs then crashes — late in the test period the EA's edge degraded. Look for what changed: structural market shift, EA logic flaw, broker condition change.

   • Equity grows linearly with low volatility — likely a healthy EA. Confirm with metrics.

   • Equity grows exponentially — usually a sizing artifact (compounding aggressive risk). Re-run with fixed-lot sizing to see the un-compounded version.

   An equity curve a trader would feel comfortable holding through is the real validation. If the chart makes you nervous looking at it, that's signal even when the metrics check out.

बचने के लिए सामान्य गलतियाँ

• ✗ Treating Net Profit as the primary metricठीक करें: Net Profit scales with starting balance and lot size. Use Profit Factor and Recovery Factor as primary, Net Profit as a sanity check.
• ✗ Comparing two EAs with different starting balances by absolute profitठीक करें: Convert to percentage. $5000 profit on $10k starting = 50%; on $100k = 5%. The metric is return %, not dollars.
• ✗ Trusting metrics computed on < 100 tradesठीक करें: Statistical noise floor. Either backtest longer or accept lower confidence.
• ✗ Optimizing for Win Rate without checking Avg Win/Lossठीक करें: A 90% win rate with 10:1 negative skew (every loser is 10× a winner) loses money. Use Profit Factor instead.
• ✗ Ignoring Drawdown Durationठीक करें: Max DD says how deep; Duration says how long. A 15% DD that lasts 18 months is much harder to hold through than a 25% DD that recovers in 2 months.
• ✗ Believing Sharpe > 5ठीक करें: Sharpe > 3 on retail forex EAs is almost always backtest artifact (short period, low trade count). Re-test with longer history before believing.

अक्सर पूछे जाने वाले प्रश्न