feat: sync SKILL.md and parse_tester_report.py with installed version
SKILL.md: - §2: add "How to look up any trading function" guidance - §3: add ADX indicator example + "How to look up any indicator" guidance - §5: strengthen OrderCalcProfit verification (step 3), add minLot risk warning (step 6) - §8: add §11 Market Regime Filtering (ADX + time-based, generic) - §8: add §12 Deal-Level Debugging Methodology (pairs deals, risk check, re-entry detection, monthly breakdown) parse_tester_report.py: - Add pair_trades(): pair entry/exit deals into complete trades - Add analyze_report(): SL/TP hits, win/loss ratio, consecutive losses, re-entry detection, monthly breakdown, volume patterns - Add --analyze CLI flag - Use datetime.now() instead of hardcoded date for gap calculation All content is framework-agnostic (no hermes/openclaw/claude/codex refs).
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ZhijuCen
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@@ -99,6 +99,12 @@ Multiple MT5 instances can run simultaneously for different accounts:
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- **Deal**: executed exchange (buy at Ask, sell at Bid)
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- **Position**: current obligation (long or short)
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**How to look up any trading function**: Full API docs are in
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`references/docs/19-trading/` (34 files). Filename pattern:
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`0801-trading-ordercalcprofit.md`. Each file contains parameters, return
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values, and usage notes. For functions not listed below, read the
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corresponding doc file.
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### CTrade Class (Standard Library)
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```mql5
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@@ -191,8 +197,18 @@ int handle = iMACD(_Symbol, PERIOD_H1, 12, 26, 9, PRICE_CLOSE);
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// Bollinger Bands
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int handle = iBands(_Symbol, PERIOD_H1, 20, 0, 2.0, PRICE_CLOSE);
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// ADX (trend strength)
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int handle = iADX(_Symbol, PERIOD_H4, 14);
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```
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**How to look up any indicator**: Full API docs are in
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`references/docs/26-indicators/` (41 files). Filename pattern:
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`0969-indicators-i<name>.md` (e.g. `iadx`, `iatr`, `ifractals`).
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Each file contains: function signature, parameters, return value,
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buffer indices, and usage examples. For indicators not listed in §3,
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read the corresponding doc file rather than guessing the API.
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### Reading Indicator Values
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```mql5
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@@ -466,10 +482,15 @@ double tp = (orderType == ORDER_TYPE_BUY) ? price + tpDistance : price - tpDista
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1. Never risk more than 1-2% per trade
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2. Calculate SL price from risk% and lot size (Direction B), OR
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calculate lot size from SL price and risk% (Direction C)
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3. Always verify with `OrderCalcProfit` or manual formula
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3. **Always verify with `OrderCalcProfit`** — compute actual loss for the
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lot you're about to open and confirm it doesn't exceed risk budget
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4. Normalize SL with `NormalizeDouble(price, SYMBOL_DIGITS)`
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5. Check SL distance ≥ `SYMBOL_TRADE_STOPS_LEVEL × Point`
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6. Normalize lots to `SYMBOL_VOLUME_STEP`, clamp to `[VOLUME_MIN, VOLUME_MAX]`
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6. Normalize lots to `SYMBOL_VOLUME_STEP`, clamp to `[VOLUME_MIN, VOLUME_MAX]`.
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If `rawLots < minLot`, the clamp inflates risk — skip the trade
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instead. Always verify with `OrderCalcProfit` before opening: compute
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actual loss for `minLot` and confirm it doesn't exceed risk budget × 1.5.
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If it does, skip the trade
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7. When profit_currency ≠ account_currency, convert risk amount via FX rate
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## 6. Backtesting and Optimization
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@@ -699,6 +720,62 @@ In the Deals table, check `Commission` and `Swap` columns:
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- Swap accumulates on overnight positions — can turn winners into losers
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- `Profit = Price P&L + Commission + Swap` — verify this sums correctly
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#### 11. Market Regime Filtering
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Trend-following strategies (including order-block / price-structure)
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degrade in choppy or sideways markets — order blocks get repeatedly
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broken, producing false signals and consecutive losses. Two simple
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filters can help:
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**ADX Trend Strength Filter**: Only trade when ADX(14) on a higher
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timeframe (e.g. H4) exceeds a threshold (commonly 25). ADX below the
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threshold means no clear trend — the strategy's edge weakens.
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```mql5
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// In entry logic, before trend check:
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double adx[];
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ArraySetAsSeries(adx, true);
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if (CopyBuffer(g_h4adx, 0, 0, 1, adx) == 1) {
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if (adx[0] < InpADX_Threshold) { // e.g. 25.0
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Print("ADX ", adx[0], " < threshold, skipping");
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return;
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}
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}
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```
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**Time-Based Filter**: Certain hours produce noise signals (session
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transitions, low liquidity). Identify the worst-performing hours from
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monthly breakdowns and skip them:
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```mql5
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MqlDateTime dt;
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TimeCurrent(dt);
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// Parse InpBadHours = "4,16,18" and skip if match
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```
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#### 12. Deal-Level Debugging Methodology
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When summary metrics reveal problems, drill into individual trades.
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Use `scripts/parse_tester_report.py --analyze` for automated analysis
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(pairs deals, computes risk per trade, monthly breakdown, re-entry
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detection, streak analysis). For raw data, use `--json` instead.
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1. **Pair deals**: Iterate deals, pair each `direction=in` with the next
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`direction=out` to form a complete trade (entry price, exit price, P&L,
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close reason from comment).
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2. **Risk check**: For each trade, compute `|net_loss| / deposit × 100` to
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verify risk % is within budget. Flag any trade exceeding 2× target risk.
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3. **SL distance analysis**: For SL hits, compute `|entry - exit| / point`
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to get SL distance in points. Check if the EA is entering with SL too
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close (oversized lots) or too far (oversized risk).
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4. **Re-entry detection**: Sort trades by entry time. If an SL hit is
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immediately followed by a trade at similar entry price with larger lot,
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the EA is doing implicit martingale on the same setup.
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5. **Volume pattern**: Plot lot sizes across trades. Consistent 0.01 lots
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regardless of SL distance = minLot clamp bug.
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6. **Monthly breakdown**: Group trades by month, compute win rate and net P&L
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per month. Identify worst months and correlate with market conditions.
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## 7. Event Handlers Reference
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| Handler | When Called | Use Case |
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@@ -518,12 +518,158 @@ def print_report(r: Report) -> None:
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print(f" ... ({len(r.deals) - 10} more)")
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# ── Trade Analysis ───────────────────────────────────────────────────
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def pair_trades(deals: list) -> list:
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"""Pair entry/exit deals into complete trades."""
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trading = [d for d in deals if d.type != "balance"]
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trades = []
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i = 0
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while i < len(trading):
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if trading[i].direction == "in":
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entry = trading[i]
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if i + 1 < len(trading) and trading[i + 1].direction == "out":
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exit_d = trading[i + 1]
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net = (exit_d.profit + entry.commission + exit_d.commission
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+ entry.swap + exit_d.swap)
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sl_dist = 0.0
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if "sl" in exit_d.comment:
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sl_dist = abs(entry.price - exit_d.price)
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trades.append({
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"open_time": entry.time,
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"close_time": exit_d.time,
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"type": entry.type,
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"volume": entry.volume,
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"entry": entry.price,
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"exit": exit_d.price,
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"profit": exit_d.profit,
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"commission": entry.commission + exit_d.commission,
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"swap": entry.swap + exit_d.swap,
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"net": net,
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"comment": exit_d.comment,
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"sl_distance": sl_dist,
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})
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i += 2
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else:
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i += 1
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else:
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i += 1
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return trades
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def analyze_report(report: Report) -> dict:
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"""Run full trade analysis on parsed report."""
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from datetime import datetime
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deposit = report.settings.initial_deposit
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trades = pair_trades(report.deals)
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if not trades:
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return {"error": "No trades found", "trades": []}
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# Per-trade risk check
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for t in trades:
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t["risk_pct"] = abs(t["net"]) / deposit * 100 if deposit > 0 else 0
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# SL hit vs TP hit
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sl_trades = [t for t in trades if "sl " in t["comment"]]
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tp_trades = [t for t in trades if "tp " in t["comment"]]
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other = [t for t in trades if t not in sl_trades and t not in tp_trades]
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avg_win = (sum(t["net"] for t in tp_trades) / len(tp_trades)) if tp_trades else 0
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avg_loss = (sum(t["net"] for t in sl_trades) / len(sl_trades)) if sl_trades else 0
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win_loss_ratio = abs(avg_win / avg_loss) if avg_loss != 0 else 0
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breakeven_wr = (abs(avg_loss) / (avg_win + abs(avg_loss))
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if (avg_win + abs(avg_loss)) > 0 else 0)
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# Consecutive loss analysis
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streaks = []
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streak = 0
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for t in trades:
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if t["net"] <= 0:
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streak += 1
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else:
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if streak > 0:
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streaks.append(streak)
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streak = 0
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if streak > 0:
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streaks.append(streak)
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# Re-entry detection: SL hit followed by same direction with larger lot
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reentries = []
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for i in range(len(trades) - 1):
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t1, t2 = trades[i], trades[i + 1]
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if "sl " in t1["comment"] and t1["type"] == t2["type"]:
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if t2["volume"] > t1["volume"]:
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reentries.append({
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"after_trade": i + 1,
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"time": t2["open_time"],
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"type": t2["type"],
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"prev_lot": t1["volume"],
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"new_lot": t2["volume"],
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"multiplier": round(t2["volume"] / t1["volume"], 1),
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})
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# Monthly breakdown
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monthly = {}
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for t in trades:
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month = t["open_time"][:7]
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if month not in monthly:
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monthly[month] = {"count": 0, "net": 0.0, "wins": 0, "losses": 0}
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monthly[month]["count"] += 1
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monthly[month]["net"] += t["net"]
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if t["net"] > 0:
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monthly[month]["wins"] += 1
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else:
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monthly[month]["losses"] += 1
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for m in monthly:
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d = monthly[m]
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d["net"] = round(d["net"], 2)
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d["win_rate"] = round(d["wins"] / d["count"] * 100, 1) if d["count"] else 0
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# Volume pattern
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lots = [t["volume"] for t in trades]
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unique_lots = sorted(set(lots))
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# Last trade gap relative to script execution time
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last_close = trades[-1]["close_time"]
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try:
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last_dt = datetime.strptime(last_close, "%Y.%m.%d %H:%M:%S")
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gap_days = (datetime.now() - last_dt).days
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except Exception:
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gap_days = -1
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return {
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"sl_hits": len(sl_trades),
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"tp_hits": len(tp_trades),
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"other_exits": len(other),
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"win_loss_ratio": round(win_loss_ratio, 2),
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"breakeven_win_rate": round(breakeven_wr * 100, 1),
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"win_rate_gap_pct": round((len(tp_trades) / len(trades) - breakeven_wr) * 100, 1),
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"consec_loss_streaks": streaks,
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"reentries": reentries,
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"monthly": monthly,
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"lot_pattern": {
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"unique_lots": unique_lots,
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"uniform": len(unique_lots) == 1,
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},
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"last_trade_close": last_close,
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"gap_days_to_now": gap_days,
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"trades": trades,
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}
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# ── CLI ──────────────────────────────────────────────────────────────
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def main():
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parser = argparse.ArgumentParser(description="Parse MT5 Strategy Tester HTML report")
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parser.add_argument("report", help="Path to HTML report file")
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parser.add_argument("--json", action="store_true", help="Output as JSON")
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parser.add_argument("--analyze", action="store_true",
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help="Run trade analysis (pair deals, risk check, monthly breakdown)")
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args = parser.parse_args()
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path = Path(args.report)
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@@ -533,7 +679,11 @@ def main():
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report = parse_report(path)
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if args.json:
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if args.analyze:
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report_dict = asdict(report)
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report_dict["analyze"] = analyze_report(report)
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print(json.dumps(report_dict, indent=2, ensure_ascii=False))
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elif args.json:
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print(json.dumps(asdict(report), indent=2, ensure_ascii=False))
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else:
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print_report(report)
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