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feat(parse_tester_report): add 'windows' subcommand for time-window outlier analysis

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Split backtest into N equal time slices (left-closed right-open) and
compute the 7 core metrics per window: Profit, EP, PF, RF, Balance DD
Rel%, Trades, Sharpe. Each window gets an outlier flag based on per-
metric z-score (|z|>=2 = notable, |z|>=5 = extreme). N=1 runs a full-
period cross-check vs the HTML report.

Key changes:
- Add compute_windows / compute_window_metrics / print_windows /
  windows_comparison functions, CLI subcommand 'windows'
- pair_trades now exports gross_pnl/entry_costs for MT5 GP/GL split
- compute_gross_profit_loss: MT5 accounting (entry costs always to GL)
- _balance_dd_relative: max relative DD (STAT_BALANCE_DDREL_PERCENT)
- _sharpe_ratio: textbook (AHPR-1)/std_HPR formula, 365-day year
- Help text with examples for both --help and windows --help
- verify_sl_tp_formulas.py: localize all output labels to English
- AGENTS.md / SKILL.md: document windows subcommand conventions

Docs: 5 of 7 metrics exact for N=1 (Profit, EP, PF, Trades exact;
RF/BalDD% are approximations due to balance-only reconstruction;
Sharpe uses textbook formula diverging from MT5's 22.92)
This commit is contained in:
ZhijuCen
2026-07-04 01:18:33 +08:00
parent 835186faa9
commit 061e879638
4 changed files with 879 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skills/mql5/scripts/verify_sl_tp_formulas.py
+17 -17
View File
@@ -213,10 +213,10 @@ def run_tests(
sl_buy = round(bid - sl_dist_price, spec.digits)
loss_buy = calc_profit(spec, lots, bid, sl_buy)
print(f" SL距离={sl_distance_pts:>5} pts "
f"→ 价格距离={sl_dist_price} "
print(f" SL dist={sl_distance_pts:>5} pts "
f"-> price dist={sl_dist_price} "
f"BUY SL={fmt(sl_buy, spec.digits)} "
f"亏损={fmt(loss_buy, spec.digits)} {pc}")
f"loss={fmt(loss_buy, spec.digits)} {pc}")
# Also verify with Ask = Bid + spread
spread_pts = 12 if spec.name == "XAUUSD" else 3
@@ -239,9 +239,9 @@ def run_tests(
print(f" Risk={risk_pct}% {direction:4s} "
f"SL={fmt(sl, spec.digits)} "
f"目标亏损={fmt(ml, 2)} {pc} "
f"实际亏损={fmt(actual_loss, 2)} {pc} "
f"差={fmt(abs(actual_loss) - ml, 6)}")
f"target loss={fmt(ml, 2)} {pc} "
f"actual loss={fmt(actual_loss, 2)} {pc} "
f"diff={fmt(abs(actual_loss) - ml, 6)}")
print()
# ─────────────────────────────────────────────────────────────────
@@ -261,11 +261,11 @@ def run_tests(
else:
actual_loss = 0.0
print(f" SL距离={sl_distance_pts:>5} pts "
print(f" SL dist={sl_distance_pts:>5} pts "
f"SL={fmt(sl_buy, spec.digits)} "
f"计算手数={lots_calc:.4f} "
f"实际亏损={fmt(actual_loss, 2)} {pc} "
f"差={fmt(abs(actual_loss) - ml, 6)}")
f"lots calc={lots_calc:.4f} "
f"actual loss={fmt(actual_loss, 2)} {pc} "
f"diff={fmt(abs(actual_loss) - ml, 6)}")
print()
# ─────────────────────────────────────────────────────────────────
@@ -295,7 +295,7 @@ def run_tests(
# Reverse loss check
loss_rev = calc_profit(spec, lots_rev, bid, sl_buy)
print(f" Risk={risk_pct}% SL距离={sl_pts}pts budget={fmt(ml, 2)} {pc}")
print(f" Risk={risk_pct}% SL dist={sl_pts}pts budget={fmt(ml, 2)} {pc}")
print(f" Forward: SL={fmt(sl_fwd, spec.digits)} lots={lots_fwd:.2f} "
f"loss={fmt(loss_fwd, 2)} {pc} (budget={fmt(ml, 2)})")
print(f" Reverse: lots={lots_rev:.4f} "
@@ -358,7 +358,7 @@ def main():
print(f" Step 2: convert to JPY = {fmt(target_usd, 2)} × {jpy_rate} = {fmt(target_jpy, 2)} JPY")
print(f" Step 3: points = budget / (PointValue × Lots)")
print(f" = {fmt(target_jpy, 2)} / ({point_value(jpy_spec):.1f} × {lots}) = {target_jpy / (point_value(jpy_spec) * lots):.1f} pts")
print(f" SL距离 = {target_jpy / (point_value(jpy_spec) * lots):.1f} × {jpy_spec.point} = "
print(f" SL dist = {target_jpy / (point_value(jpy_spec) * lots):.1f} x {jpy_spec.point} = "
f"{target_jpy / (point_value(jpy_spec) * lots) * jpy_spec.point:.4f} price")
print(f" SL = {bids['USDJPY']} - {target_jpy / (point_value(jpy_spec) * lots) * jpy_spec.point:.4f} = "
f"{fmt(sl_fwd, jpy_spec.digits)}")
@@ -367,8 +367,8 @@ def main():
print(f" Step 5: loss in USD = {fmt(loss_jpy, 2)} / {jpy_rate} = {fmt(loss_usd, 2)} USD")
print()
print(f" Result: target {fmt(target_usd, 2)} USD ≈ actual {fmt(loss_usd, 2)} USD "
f"(差={fmt(abs(loss_usd) - target_usd, 4)} USD, "
f"来自 NormalizeDouble 四舍五入)")
f"(diff={fmt(abs(loss_usd) - target_usd, 4)} USD, "
f"from NormalizeDouble rounding)")
print()
# ─────────────────────────────────────────────────────────────────
@@ -390,10 +390,10 @@ def main():
actual_loss = calc_profit(xau, lots, bids["XAUUSD"], sl)
print(f" Lots={lots:>5.2f} "
f"SL距离={sl_dist_pts:>6} pts ({sl_dist_price:.2f} price) "
f"SL dist={sl_dist_pts:>6} pts ({sl_dist_price:.2f} price) "
f"SL={fmt(sl, xau.digits)} "
f"亏损={fmt(actual_loss, 2)} USD "
f"差={fmt(abs(actual_loss) - ml_usd, 6)}")
f"loss={fmt(actual_loss, 2)} USD "
f"diff={fmt(abs(actual_loss) - ml_usd, 6)}")
if __name__ == "__main__":