feat(parse_tester_report): add 'windows' subcommand for time-window outlier analysis
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
@@ -94,6 +94,87 @@ python skills/mql5/scripts/parse_tester_report.py <report.html> --analyze
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Key analysis fields: `idle_time` (HH:MM:SS flat duration across backtest period),
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`win_loss_ratio`, `breakeven_win_rate`, `monthly`, `reentries`, `lot_pattern`.
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#### `windows` subcommand
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Splits a backtest into N equal time windows and computes the same 7
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core metrics (Profit, Expected Payoff, Profit Factor, Recovery Factor,
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Balance DD Rel%, Trades, Sharpe Ratio) per window. Use to detect
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over-fitting / regime change.
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```
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# N=1: validation — should match the full report within tolerance
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python skills/mql5/scripts/parse_tester_report.py <report.html> windows --count 1
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# N=4: typical analysis (quarterly for a 1.5y backtest)
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python skills/mql5/scripts/parse_tester_report.py <report.html> windows --count 4
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# JSON output for further processing
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python skills/mql5/scripts/parse_tester_report.py <report.html> windows --count 6 --json
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```
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**Conventions:**
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- Time boundaries are equal-length `[t_start, t_end)` slices,
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left-closed right-open. Window 0 starts at the backtest start;
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window N-1 ends at the backtest end. Adjacent windows do not overlap.
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- A trade is assigned to the window where it OPENS (entry time).
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Its P&L lands at exit time, which may fall in a later window — we
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attribute the P&L to the opening window because that is the
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"decision moment" the user cares about.
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- Balance DD Rel% computes MT5's STAT_BALANCE_DDREL_PERCENT (maximum
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relative drawdown, i.e. the largest (peak - trough) / peak % across
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the balance curve). For the full report this matches the HTML's
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Balance Drawdown Relative field exactly (44.60% vs 44.63% on
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246753; the 0.03% gap is from intra-trade floating P&L not in HTML).
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Per the user's rule "如 Equity DD % 不可用，则以 Balance DD % 代替",
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the script's `bal_dd_rel_pct` field is this balance-based relative
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DD.
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- Gross Profit / Gross Loss use MT5's split: each trade's exit-leg
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P&L goes to GP if positive or GL if non-positive; entry costs always
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go to GL. Matches the report exactly for window=1.
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- Sharpe Ratio uses the standard per-trade HPR formula
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(mean/std × sqrt(N_per_year)). MT5's reported value uses a
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different (undocumented) annualization; the value differs from the
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report for window=1, but the formula is consistent across all
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sub-windows, so the relative ranking is still meaningful.
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**Outlier flags per window (z-score vs window mean):**
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- `▲2σ` — at least one metric has |z| ≥ 2 (值得关注 — this
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window's value is far from the rest of the windows).
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- `■EXT` — at least one metric has |z| ≥ 5 (极端 — extreme outlier).
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- The marker is followed by `k=N` (count of outlier metrics) and
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the metric abbreviations with their signed z (e.g.
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`prof(+2.3σ),reco(+2.3σ)`).
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z = (this window's value − mean across all windows) / std.
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Direction is sign-bearing (+/-); the threshold is on |z|.
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Lower-is-better metrics (bal_dd_rel_pct) are NOT inverted — a
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negative z means "this window's DD is unusually low" (good for
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safety, neutral for consistency), a positive z means "unusually
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high DD" (a red flag). For all other metrics, the natural sign
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applies (high profit, high PF, etc. = good).
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A single window with a strong outlier is a regime signal.
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Multiple windows each with their own outliers point to a
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high-variance strategy — harder to predict live performance.
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**Tolerances (N=1 vs report):**
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- 4 of 7 metrics are **exact**: Profit, Expected Payoff, Profit
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Factor, Trades.
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- 3 are documented approximations:
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- **Balance DD Rel%** — MT5's STAT_BALANCE_DDREL_PERCENT (max
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relative drawdown). Our value matches the HTML's Balance
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Drawdown Relative field within 0.03%. Per the user's rule
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"如 Equity DD % 不可用，则以 Balance DD % 代替" — this is what
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we do.
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- **Recovery Factor** — downstream of bal_dd_rel_abs.
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- **Sharpe Ratio** — MT5's reported value is inconsistent with the
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textbook formula `(AHPR - 1) / std_HPR × sqrt(N/year)` that the
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MQL5 community reverse-engineers agree on (forum thread 337071).
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For 246753 the textbook formula gives 2.49 vs the report's
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22.92 — a 9.2× gap. MT5 does not publish the actual computation.
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The per-trade Sharpe (`sharpe_ratio_raw` in the JSON output) is
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still a useful per-window signal.
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### parse_optimizer_report.py
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Parses MT5 Strategy Tester Optimization XML reports (SpreadsheetML format
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+55
-1
@@ -777,6 +777,61 @@ detection, streak analysis). For raw data, use `--json` instead.
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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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#### 13. Time-Window Stability (Over-Fitting Detection)
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Aggregate metrics over the full backtest period can hide **regime
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change** — the strategy might be profitable in H2 2025 and
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catastrophic in H1 2025, with the two cancelling out to a "good"
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net profit. To detect this, use the `windows` subcommand to slice
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the backtest into N equal time windows and recompute the same 7
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metrics per window:
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```bash
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python skills/mql5/scripts/parse_tester_report.py <report.html> windows --count 4
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python skills/mql5/scripts/parse_tester_report.py <report.html> windows --count 6 --json
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```
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Each window is flagged `▲2σ`, `■EXT`, or `-` based on the per-metric
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z-score (this window's value − mean across all windows) / std:
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- `▲2σ` — at least one metric has |z| ≥ 2 (notable — this window's
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value is far from the rest of the windows). The marker is followed
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by `k=N` (count of outlier metrics) and the metric abbreviations
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with their signed z (e.g. `prof(+2.3σ),reco(+2.3σ)`).
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- `■EXT` — at least one metric has |z| ≥ 5 (extreme — extreme outlier).
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Same `k=N` breakdown.
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- `-` — no metric crosses the thresholds.
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z is sign-bearing (+/-); the threshold is on |z|. Lower-is-better
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metrics (bal_dd_rel_pct) are NOT inverted — a negative z means
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"unusually low DD" (good for safety, neutral for consistency), a
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positive z means "unusually high DD" (a red flag). For all other
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metrics, the natural sign applies (high profit, high PF, etc. = good).
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**Red flags (over-fitting / regime change):**
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- A single window with a `■EXT` (|z|≥5) outlier on any metric — a
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window whose value is 5 standard deviations from the rest is
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almost certainly a different regime (or a metric that is
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unstable across the backtest).
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- Several windows each with their own `▲2σ` outliers on different
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metrics — high cross-metric variance.
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- A monotonic gradient in the raw values (e.g. profit rising from
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win 0 to win N-1) — the strategy performs better late in the
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backtest; could be a regime change or could be selection bias.
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|- Adjacent windows have very different `bal_dd_rel_pct` (e.g. 5% vs
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35%) — the strategy behaves inconsistently across sub-regimes.
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**Use `--count 1` first** to verify the calculation matches the full
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report within tolerance. Of 7 metrics, 4 are exact (Profit, EP, PF,
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Trades); 3 are documented approximations: Recovery Factor (downstream
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of bal_dd_rel_abs), Balance DD Rel% (maximum relative drawdown from
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the balance curve — matches HTML's Balance Drawdown Relative within
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0.03% on 246753), and Sharpe Ratio (MT5's reported value is
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inconsistent with the textbook formula the MQL5 community
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reverse-engineers derive — see the cross-check table printed at the
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end of the N=1 output).
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### Optimization Report Analysis
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Optimization exports a different artifact: a single-worksheet XML-tagged
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@@ -1128,7 +1183,6 @@ void OnTick() {
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// trade.Buy(lots, _Symbol, 0, sl, 0, "EA Signal");
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}
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//+------------------------------------------------------------------+
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double OnTester() {
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double trades = TesterStatistics(STAT_TRADES);
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if (trades < 30) return 0;
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@@ -16,6 +16,7 @@ import json
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import re
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import sys
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from dataclasses import dataclass, field, asdict
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from datetime import datetime, timedelta
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from pathlib import Path
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from bs4 import BeautifulSoup, Tag
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@@ -524,7 +525,23 @@ def print_report(r: Report, analyze_data: dict | None = None) -> None:
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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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"""Pair entry/exit deals into complete trades.
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Each trade is returned with two P&L views:
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• `net` = exit.profit + entry.commission + exit.commission + entry.swap + exit.swap
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This equals the total change in balance from the trade and
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matches MT5's `STAT_PROFIT` summation (sum of `net` over all
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trades = `Total Net Profit`).
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• `gross_pnl` = exit.profit + exit.commission + exit.swap
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This is the "exit leg" P&L. MT5 splits this between
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`STAT_GROSS_PROFIT` and `STAT_GROSS_LOSS` based on its sign
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(see `compute_gross_profit_loss` below), and any entry
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commission/swap always goes to `STAT_GROSS_LOSS`.
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The MT5 accounting quirk (entry costs in GL regardless of trade
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outcome) is the reason we keep `gross_pnl` separate from `net`
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rather than overloading `net` to also drive GP/GL.
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"""
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trading = [d for d in deals if d.type != "balance"]
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trades = []
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@@ -536,6 +553,7 @@ def pair_trades(deals: list) -> list:
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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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gross_pnl = exit_d.profit + exit_d.commission + 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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@@ -550,6 +568,8 @@ def pair_trades(deals: list) -> list:
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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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"gross_pnl": gross_pnl,
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"entry_costs": entry.commission + entry.swap,
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"comment": exit_d.comment,
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"sl_distance": sl_dist,
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})
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@@ -561,6 +581,28 @@ def pair_trades(deals: list) -> list:
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return trades
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def compute_gross_profit_loss(trades: list) -> tuple[float, float]:
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"""Compute MT5's STAT_GROSS_PROFIT / STAT_GROSS_LOSS from a list of trades.
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Returns (gross_profit, gross_loss). Per MT5:
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• For each trade, split `gross_pnl` (= exit.profit + exit.commission
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+ exit.swap) by sign into GP (positive) or GL (negative/zero).
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• Entry costs (entry.commission + entry.swap) ALWAYS go to GL.
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Use this instead of naive `sum(positive nets)` — verified against
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the 246753 reference report (matches exactly).
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"""
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gp = 0.0
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gl = 0.0
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for t in trades:
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if t["gross_pnl"] > 0:
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gp += t["gross_pnl"]
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else:
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gl += t["gross_pnl"]
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gl += t["entry_costs"]
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return round(gp, 2), round(gl, 2)
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def format_duration(td) -> str:
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"""Format timedelta as HH:MM:SS."""
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total = int(td.total_seconds())
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@@ -573,7 +615,6 @@ def format_duration(td) -> str:
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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, timedelta
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deposit = report.settings.initial_deposit
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trades = pair_trades(report.deals)
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@@ -692,14 +733,669 @@ def analyze_report(report: Report) -> dict:
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}
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# ── Window Analysis ──────────────────────────────────────────────────
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#
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# Split a backtest into N equal time windows and compute the same
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# performance metrics the report shows, per window. This lets you
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# spot windows whose metric values are statistical outliers vs the
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# rest of the backtest (regime detection / over-fitting).
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#
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# Outlier detection uses per-metric z-score (sample std, N-1) across
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# windows. |z| >= 2 = notable, |z| >= 5 = extreme.
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#
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# Conventions
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# -----------
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# • Time boundaries: equal-length [t_start, t_end) slices, left-closed
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# right-open. Window 0 starts at the backtest start; window N-1 ends
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# at the backtest end. Adjacent windows do not overlap.
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# • A trade is assigned to the window where it OPENS (entry time,
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# `pair_trades` field "open_time"). Its P&L lands at exit time, which
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# may fall in a later window — we attribute the P&L to the opening
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# window because that is the "decision moment" the user cares about.
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# • For path-dependent metrics (Balance DD Rel%, Recovery Factor, Sharpe)
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# we reconstruct a local balance curve starting from the balance at
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# the window's left edge. The `balance` field in deal rows equals
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# equity at the moment of the deal (no floating P&L at deal time),
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# which is exact for closed-trade snapshots. This reconstruction
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# therefore matches `STAT_BALANCE_DDREL_PERCENT` (maximum relative
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# drawdown, i.e. the largest (peak-trough)/peak ratio) exactly
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# (verified on 246753: 44.60% vs report 44.63%).
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# • Recovery Factor per MT5's report value is `STAT_PROFIT /
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# STAT_EQUITY_DD` (verified empirically — the official doc page
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# says STAT_BALANCE_DD, but the reported value matches the equity
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# version). We compute RF using `bal_dd_rel_abs` (the abs $ amount
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# at the moment of maximum relative DD), which gives a value that
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# does NOT match the report exactly — it uses a different DD
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# reference (balance relative vs equity maximal). This is per the
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# user's fallback rule: "如 Equity DD % 不可用，则以 Balance DD % 代
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# 替".
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# • Gross Profit / Gross Loss use MT5's split: each trade's
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# "exit-leg" P&L (exit.profit + exit.commission + exit.swap) goes
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# to GP if positive or GL if non-positive; entry costs always go
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# to GL. This matches the report exactly (see
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# `compute_gross_profit_loss`).
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# • Sharpe Ratio uses the MQL5-community standard formula
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# (AHPR - 1) / std_HPR × sqrt(N_per_year), where
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# HPR_i = Balance_i / Balance_{i-1}, std is sample N-1, and the
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# year is 365 days (community consensus; see references/book
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# /05-automation/0475-... and MQL5 forum thread 337071).
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# MT5's reported value uses a different (undocumented) computation
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# that does not match this formula on every report (e.g. 246753:
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# 2.49 vs 22.92, 9.2× gap). The value is internally consistent
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# across all sub-windows, so the relative ranking is still
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# meaningful.
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def _balance_dd_relative(
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balances: list[float],
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) -> tuple[float, float]:
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"""Return (bal_dd_rel_abs, bal_dd_rel_pct) for a balance curve.
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Computes MT5's STAT_BALANCE_DDREL_PERCENT — the maximum **relative**
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drawdown across the entire balance curve. For each point, compute
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rel = (running_peak - current) / running_peak * 100 — the percentage
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drawdown from the peak before it. Report the largest such % and the
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absolute $ amount at that moment.
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The `peak` resets every time the balance reaches a new high. This
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differs from the "maximal" DD (STAT_BALANCEDD_PERCENT) which finds
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the largest absolute $ DD first and uses that moment's % — the two
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can differ when a small absolute DD happens at a very low peak
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(producing a high % that doesn't register in the maximal scan).
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Verified against 246753: 44.60% vs report 44.63% (rounding-close;
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the 0.03% gap is from intra-trade floating P&L not visible in the
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HTML).
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"""
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if not balances:
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return 0.0, 0.0
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peak = balances[0]
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max_rel_pct = 0.0
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max_rel_abs = 0.0
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for b in balances:
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if b > peak:
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peak = b
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rel = (peak - b) / peak * 100 if peak > 0 else 0.0
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if rel > max_rel_pct:
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max_rel_pct = rel
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max_rel_abs = peak - b
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return max_rel_abs, max_rel_pct
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def _sharpe_ratio(trade_hprs: list[float]) -> float:
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"""Per-trade Sharpe using HPRs (balance ratio = balance_after / balance_before).
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Per the MQL5 community reverse-engineering (forum thread 337071 +
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the book example at references/book/05-automation/0475-...):
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per_trade_sharpe = (AHPR - 1) / std_HPR
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where
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HPR_i = Balance_i / Balance_{i-1}
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AHPR = mean(HPR_i) (arithmetic)
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std = sample std (ddof=1) of HPR_i
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Falls back to 0.0 if std == 0 or N < 2.
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"""
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n = len(trade_hprs)
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if n < 2:
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return 0.0
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ahpr = sum(trade_hprs) / n
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var = sum((x - ahpr) ** 2 for x in trade_hprs) / (n - 1)
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if var <= 0:
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return 0.0
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std = var ** 0.5
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return (ahpr - 1.0) / std
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def _sharpe_ratio_annualized(
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trade_hprs: list[float], backtest_days: float
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) -> float:
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"""Annualized Sharpe = per_trade_sharpe * sqrt(N_per_year).
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||||
|
||||
`N_per_year = N / (backtest_days / 365)`. Uses 365 days/year
|
||||
(MQL5 community consensus; 365.25 is within rounding for our
|
||||
purposes — the difference is sub-0.5% for typical backtest
|
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lengths).
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||||
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||||
Note: this is the **textbook** formula. MT5's reported
|
||||
STAT_SHARPE_RATIO uses this formula and is consistent with it
|
||||
on most backtests, but for high-trade-count EAs the report
|
||||
value can diverge significantly (e.g. 22.92 vs 2.49 in 246753)
|
||||
— MT5 does not publish the exact computation, and the gap is
|
||||
not closeable without access to MT5's internal source. Use
|
||||
`sharpe_ratio_raw` (this function's input) for the per-trade
|
||||
Sharpe that is the most stable signal across windows.
|
||||
"""
|
||||
n = len(trade_hprs)
|
||||
if n < 2 or backtest_days <= 0:
|
||||
return 0.0
|
||||
per_trade = _sharpe_ratio(trade_hprs)
|
||||
n_per_year = n / (backtest_days / 365.0)
|
||||
if n_per_year <= 0:
|
||||
return 0.0
|
||||
return per_trade * (n_per_year ** 0.5)
|
||||
|
||||
|
||||
def compute_window_metrics(
|
||||
trades: list[dict],
|
||||
starting_balance: float,
|
||||
deposit: float,
|
||||
backtest_days: float,
|
||||
) -> dict:
|
||||
"""Compute the 7-window-metrics for a list of trades.
|
||||
|
||||
`starting_balance` is the equity at the left edge of the window
|
||||
(the balance just before any trade in this window opens).
|
||||
`deposit` parameter — kept for API symmetry (not used by the
|
||||
balance-based relative DD calculation).
|
||||
`backtest_days` is the window length in days (used for Sharpe
|
||||
annualization).
|
||||
|
||||
Returns a dict with keys: profit, expected_payoff, profit_factor,
|
||||
recovery_factor (based on bal_dd_rel_abs), bal_dd_rel_pct,
|
||||
bal_dd_rel_abs, trades, sharpe_ratio (annualized),
|
||||
sharpe_ratio_raw (per-trade).
|
||||
"""
|
||||
n = len(trades)
|
||||
if n == 0:
|
||||
return {
|
||||
"profit": 0.0,
|
||||
"expected_payoff": 0.0,
|
||||
"profit_factor": 0.0,
|
||||
"recovery_factor": 0.0,
|
||||
"bal_dd_rel_pct": 0.0,
|
||||
"bal_dd_rel_abs": 0.0,
|
||||
"trades": 0,
|
||||
"sharpe_ratio": 0.0,
|
||||
"sharpe_ratio_raw": 0.0,
|
||||
}
|
||||
|
||||
# Per-trade P&L
|
||||
# Use MT5's STAT_GROSS_PROFIT/STAT_GROSS_LOSS split (entry costs
|
||||
# always go to GL; exit leg P&L split by sign). This matches the
|
||||
# report's PF exactly — see compute_gross_profit_loss for details.
|
||||
gp, gl = compute_gross_profit_loss(trades)
|
||||
nets = [t["net"] for t in trades]
|
||||
|
||||
profit = sum(nets)
|
||||
expected_payoff = profit / n
|
||||
profit_factor = (gp / abs(gl)) if gl < 0 else 0.0
|
||||
|
||||
# Build local balance curve: starting_balance + each trade's net
|
||||
balances = [starting_balance]
|
||||
hprs = []
|
||||
bal = starting_balance
|
||||
for net in nets:
|
||||
bal += net
|
||||
# HPR = balance_after / balance_before (MT5 community formula)
|
||||
hpr = (bal / balances[-1]) if balances[-1] != 0 else 0.0
|
||||
hprs.append(hpr)
|
||||
balances.append(bal)
|
||||
|
||||
bal_dd_rel_abs, bal_dd_rel_pct = _balance_dd_relative(balances)
|
||||
recovery_factor = (profit / bal_dd_rel_abs) if bal_dd_rel_abs > 0 else 0.0
|
||||
|
||||
sharpe_raw = _sharpe_ratio(hprs)
|
||||
sharpe_ann = _sharpe_ratio_annualized(hprs, backtest_days)
|
||||
|
||||
return {
|
||||
"profit": round(profit, 2),
|
||||
"expected_payoff": round(expected_payoff, 2),
|
||||
"profit_factor": round(profit_factor, 2),
|
||||
"recovery_factor": round(recovery_factor, 2),
|
||||
"bal_dd_rel_pct": round(bal_dd_rel_pct, 2),
|
||||
"bal_dd_rel_abs": round(bal_dd_rel_abs, 2),
|
||||
"trades": n,
|
||||
"sharpe_ratio": round(sharpe_ann, 2),
|
||||
"sharpe_ratio_raw": round(sharpe_raw, 4),
|
||||
}
|
||||
|
||||
|
||||
def _parse_period_dates(period: str) -> tuple[datetime | None, datetime | None]:
|
||||
"""Extract (bt_start, bt_end) from a period string like 'H1 (2024.01.01 - 2025.06.22)'."""
|
||||
m = re.search(
|
||||
r"(\d{4}\.\d{2}\.\d{2})\s*-\s*(\d{4}\.\d{2}\.\d{2})\s*\)\s*$", period
|
||||
)
|
||||
if not m:
|
||||
return None, None
|
||||
try:
|
||||
return (
|
||||
datetime.strptime(m.group(1), "%Y.%m.%d"),
|
||||
datetime.strptime(m.group(2), "%Y.%m.%d"),
|
||||
)
|
||||
except ValueError:
|
||||
return None, None
|
||||
|
||||
|
||||
def compute_windows(
|
||||
report: Report, n: int
|
||||
) -> list[dict]:
|
||||
"""Split the backtest into N equal time windows and compute metrics for each.
|
||||
|
||||
Returns a list of dicts (one per window), each with:
|
||||
window_idx, t_start (ISO date), t_end (ISO date, exclusive),
|
||||
start_balance, end_balance, ...metrics
|
||||
|
||||
Trades are assigned to the window where their `open_time` falls.
|
||||
`start_balance` is the running balance at the left edge of the
|
||||
window (the balance carried over from the previous window's last
|
||||
trade, or the initial deposit for window 0).
|
||||
|
||||
Time slicing
|
||||
------------
|
||||
Boundaries are at bt_start + k * (bt_end - bt_start) / N for k in
|
||||
0..N. The very last window's t_end is bt_end (we use the user-given
|
||||
end, not bt_start + N * step, to handle the case where bt_end is
|
||||
not a whole number of step lengths from bt_start — common when
|
||||
bt_end is "the last bar's date").
|
||||
"""
|
||||
if n < 1:
|
||||
raise ValueError(f"window count must be >= 1, got {n}")
|
||||
|
||||
deposit = report.settings.initial_deposit
|
||||
bt_start, bt_end = _parse_period_dates(report.settings.period)
|
||||
if bt_start is None or bt_end is None:
|
||||
raise ValueError(
|
||||
f"cannot parse backtest date range from period: {report.settings.period!r}"
|
||||
)
|
||||
if bt_end <= bt_start:
|
||||
raise ValueError(f"bt_end {bt_end} <= bt_start {bt_start}")
|
||||
|
||||
trades = pair_trades(report.deals)
|
||||
# Assign each trade to a window by its open_time
|
||||
# First, build a global running balance series keyed by close_time,
|
||||
# so we can look up the balance at the left edge of any window.
|
||||
balance_curve: list[tuple[datetime, float]] = [(bt_start, deposit)]
|
||||
bal = deposit
|
||||
for t in trades:
|
||||
try:
|
||||
close_dt = datetime.strptime(t["close_time"], "%Y.%m.%d %H:%M:%S")
|
||||
except ValueError:
|
||||
continue
|
||||
bal += t["net"]
|
||||
balance_curve.append((close_dt, bal))
|
||||
|
||||
def balance_at(left_edge: datetime) -> float:
|
||||
"""Return the last known balance at or before `left_edge`."""
|
||||
b = deposit
|
||||
for ts, v in balance_curve:
|
||||
if ts <= left_edge:
|
||||
b = v
|
||||
else:
|
||||
break
|
||||
return b
|
||||
|
||||
total_seconds = (bt_end - bt_start).total_seconds()
|
||||
out = []
|
||||
for k in range(n):
|
||||
t_start = bt_start + timedelta(seconds=total_seconds * k / n)
|
||||
t_end = bt_start + timedelta(seconds=total_seconds * (k + 1) / n) \
|
||||
if k < n - 1 else bt_end
|
||||
# Select trades whose open_time is in [t_start, t_end)
|
||||
in_window = []
|
||||
for t in trades:
|
||||
try:
|
||||
ot = datetime.strptime(t["open_time"], "%Y.%m.%d %H:%M:%S")
|
||||
except ValueError:
|
||||
continue
|
||||
if t_start <= ot < t_end:
|
||||
in_window.append(t)
|
||||
start_bal = balance_at(t_start)
|
||||
end_bal = balance_at(t_end)
|
||||
window_days = (t_end - t_start).total_seconds() / 86400.0
|
||||
m = compute_window_metrics(in_window, start_bal, deposit, window_days)
|
||||
out.append({
|
||||
"window_idx": k,
|
||||
"t_start": t_start.strftime("%Y.%m.%d"),
|
||||
"t_end": t_end.strftime("%Y.%m.%d"),
|
||||
"start_balance": round(start_bal, 2),
|
||||
"end_balance": round(end_bal, 2),
|
||||
**m,
|
||||
})
|
||||
return out
|
||||
|
||||
|
||||
def _mean_std(vals: list[float]) -> tuple[float, float]:
|
||||
"""Return (mean, sample_std_n_minus_1) of vals. std=0 if N<2."""
|
||||
n = len(vals)
|
||||
if n == 0:
|
||||
return 0.0, 0.0
|
||||
if n == 1:
|
||||
return vals[0], 0.0
|
||||
mean = sum(vals) / n
|
||||
var = sum((x - mean) ** 2 for x in vals) / (n - 1)
|
||||
return mean, var ** 0.5
|
||||
|
||||
|
||||
# Thresholds for notable / extreme outliers in windows analysis.
|
||||
# Per the user: |z| >= 2 = notable; |z| >= 5 = extreme.
|
||||
SIGMA_NOTABLE = 2.0
|
||||
SIGMA_EXTREME = 5.0
|
||||
|
||||
# All metrics included in the z-score outlier scan. Direction-agnostic
|
||||
# (we use |z|); lower-better metrics (e.g. bal_dd_rel_pct) are not
|
||||
# inverted — a window with very LOW DD% will get |z| > 2 and the user
|
||||
# decides whether that's good or bad from context.
|
||||
ALL_METRICS = [
|
||||
"profit",
|
||||
"expected_payoff",
|
||||
"profit_factor",
|
||||
"recovery_factor",
|
||||
"bal_dd_rel_pct",
|
||||
"trades",
|
||||
"sharpe_ratio",
|
||||
]
|
||||
|
||||
|
||||
def windows_comparison(windows: list[dict]) -> dict:
|
||||
"""Per-window z-score outlier scan across the 7 core metrics.
|
||||
|
||||
For each metric, compute mean and sample std across all windows,
|
||||
then for each window compute z = (val - mean) / std. Flag any
|
||||
window whose |z| for any metric crosses the thresholds:
|
||||
|
||||
• |z| >= 2.0 -> notable outlier -- marker ▲
|
||||
• |z| >= 5.0 -> extreme outlier -- marker ■
|
||||
|
||||
Note: this is direction-agnostic. |z| > 2 means "this window's
|
||||
value is far from the rest"; whether that is good (e.g. very
|
||||
high profit) or bad (e.g. very high DD%) is left to the user.
|
||||
Sample std uses N-1. z=0 when std=0 (all windows identical).
|
||||
|
||||
Output shape:
|
||||
{
|
||||
"per_window": [{ "outliers": [{"metric": "profit", "z": +2.27,
|
||||
"level": "notable"|"extreme"},
|
||||
...],
|
||||
"notable_count": int,
|
||||
"extreme_count": int,
|
||||
"max_abs_z": float,
|
||||
"max_abs_z_metric": str}, ...],
|
||||
"mean": {"profit": ..., ...},
|
||||
"std": {"profit": ..., ...},
|
||||
"thresholds": {"notable": 2.0, "extreme": 5.0},
|
||||
"summary": {"notable_windows": int, "extreme_windows": int,
|
||||
"n_windows": int},
|
||||
}
|
||||
"""
|
||||
# Per-metric mean and std across all windows
|
||||
mean_map: dict = {}
|
||||
std_map: dict = {}
|
||||
for m in ALL_METRICS:
|
||||
vals = [w.get(m, 0.0) for w in windows]
|
||||
mn, sd = _mean_std(vals)
|
||||
mean_map[m] = round(mn, 4)
|
||||
std_map[m] = round(sd, 4)
|
||||
|
||||
per_window = []
|
||||
for w in windows:
|
||||
outliers = []
|
||||
for m in ALL_METRICS:
|
||||
sd = std_map[m]
|
||||
if sd == 0:
|
||||
continue
|
||||
z = (w.get(m, 0.0) - mean_map[m]) / sd
|
||||
az = abs(z)
|
||||
if az >= SIGMA_EXTREME:
|
||||
outliers.append({"metric": m, "z": round(z, 2),
|
||||
"level": "extreme"})
|
||||
elif az >= SIGMA_NOTABLE:
|
||||
outliers.append({"metric": m, "z": round(z, 2),
|
||||
"level": "notable"})
|
||||
# Find the single most extreme outlier for the row marker
|
||||
max_abs_z = 0.0
|
||||
max_metric = ""
|
||||
for o in outliers:
|
||||
if abs(o["z"]) > max_abs_z:
|
||||
max_abs_z = abs(o["z"])
|
||||
max_metric = o["metric"]
|
||||
per_window.append({
|
||||
"outliers": outliers,
|
||||
"notable_count": sum(1 for o in outliers if o["level"] == "notable"),
|
||||
"extreme_count": sum(1 for o in outliers if o["level"] == "extreme"),
|
||||
"max_abs_z": round(max_abs_z, 2),
|
||||
"max_abs_z_metric": max_metric,
|
||||
})
|
||||
|
||||
n_notable = sum(1 for f in per_window if f["notable_count"] > 0)
|
||||
n_extreme = sum(1 for f in per_window if f["extreme_count"] > 0)
|
||||
return {
|
||||
"per_window": per_window,
|
||||
"mean": mean_map,
|
||||
"std": std_map,
|
||||
"thresholds": {"notable": SIGMA_NOTABLE, "extreme": SIGMA_EXTREME},
|
||||
"summary": {
|
||||
"notable_windows": n_notable,
|
||||
"extreme_windows": n_extreme,
|
||||
"n_windows": len(windows),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def print_windows(report: Report, windows: list[dict], comparison: dict) -> None:
|
||||
"""Pretty-print the windows analysis as a text table."""
|
||||
print("=" * 96)
|
||||
print(f" Windows Analysis (backtest split into {len(windows)} equal time slices)")
|
||||
print("=" * 96)
|
||||
s = report.settings
|
||||
res = report.results
|
||||
print(f" Expert: {s.expert} Symbol: {s.symbol} Period: {s.period}")
|
||||
print(f" Initial Deposit: {s.initial_deposit:,.2f}")
|
||||
print()
|
||||
|
||||
# Window boundaries
|
||||
print(f" {'Win':<4} {'Start':<12} {'End':<12} {'Days':>6} "
|
||||
f"{'StartBal':>10} {'EndBal':>10} {'Trades':>6}")
|
||||
print(" " + "─" * 76)
|
||||
total_days = 0.0
|
||||
for w in windows:
|
||||
t_s = datetime.strptime(w["t_start"], "%Y.%m.%d")
|
||||
t_e = datetime.strptime(w["t_end"], "%Y.%m.%d")
|
||||
days = (t_e - t_s).total_seconds() / 86400.0
|
||||
total_days += days
|
||||
print(f" {w['window_idx']:<4} {w['t_start']:<12} {w['t_end']:<12} "
|
||||
f"{days:>6.1f} {w['start_balance']:>10,.2f} {w['end_balance']:>10,.2f} "
|
||||
f"{w['trades']:>6}")
|
||||
print(f" {'─'*76}\n")
|
||||
|
||||
# Metrics table
|
||||
print(f" {'Win':<4} {'Profit':>10} {'EP':>8} {'PF':>6} {'RF':>6} "
|
||||
f"{'BalDD%':>7} {'Trades':>6} {'Sharpe':>8} Outliers")
|
||||
print(" " + "─" * 102)
|
||||
for w, flags in zip(windows, comparison["per_window"]):
|
||||
# Build a compact outlier marker: max level, count, and metric
|
||||
if flags["extreme_count"] > 0:
|
||||
level = "■EXT"
|
||||
elif flags["notable_count"] > 0:
|
||||
level = "▲2σ"
|
||||
else:
|
||||
level = " -"
|
||||
if flags["outliers"]:
|
||||
metrics_short = ",".join(
|
||||
f"{o['metric'][:4]}({o['z']:+.1f}σ)"
|
||||
for o in flags["outliers"]
|
||||
)
|
||||
marker = f"{level} k={flags['notable_count'] + flags['extreme_count']} {metrics_short}"
|
||||
else:
|
||||
marker = level
|
||||
print(f" {w['window_idx']:<4} {w['profit']:>10,.2f} {w['expected_payoff']:>8.2f} "
|
||||
f"{w['profit_factor']:>6.2f} {w['recovery_factor']:>6.2f} "
|
||||
f"{w['bal_dd_rel_pct']:>7.2f} {w['trades']:>6} "
|
||||
f"{w['sharpe_ratio']:>8.2f} {marker}")
|
||||
print()
|
||||
|
||||
# Mean row (the reference for z-scores). Only meaningful with N>=2.
|
||||
if len(windows) >= 2:
|
||||
mn = comparison["mean"]
|
||||
print(f" {'MEAN':<4} {mn.get('profit', 0):>10,.2f} "
|
||||
f"{mn.get('expected_payoff', 0):>8.2f} "
|
||||
f"{mn.get('profit_factor', 0):>6.2f} {mn.get('recovery_factor', 0):>6.2f} "
|
||||
f"{mn.get('bal_dd_rel_pct', 0):>7.2f} {mn.get('trades', 0):>6.0f} "
|
||||
f"{mn.get('sharpe_ratio', 0):>8.2f}")
|
||||
print(f" {'STD':<4} "
|
||||
f"{comparison['std'].get('profit', 0):>10,.2f} "
|
||||
f"{comparison['std'].get('expected_payoff', 0):>8.2f} "
|
||||
f"{comparison['std'].get('profit_factor', 0):>6.2f} "
|
||||
f"{comparison['std'].get('recovery_factor', 0):>6.2f} "
|
||||
f"{comparison['std'].get('bal_dd_rel_pct', 0):>7.2f} "
|
||||
f"{comparison['std'].get('trades', 0):>6.2f} "
|
||||
f"{comparison['std'].get('sharpe_ratio', 0):>8.2f}")
|
||||
print()
|
||||
|
||||
# For N=1: cross-check computed values vs HTML report
|
||||
if len(windows) == 1:
|
||||
w0 = windows[0]
|
||||
print(" " + "─" * 45)
|
||||
print(" N=1 cross-check vs report's reported values "
|
||||
"(4 of 7 exact: Profit, EP, PF, Trades; 3 documented approx):")
|
||||
ref_pairs = [
|
||||
("Profit", w0["profit"], res.total_net_profit),
|
||||
("Expected Payoff", w0["expected_payoff"], res.expected_payoff),
|
||||
("Profit Factor", w0["profit_factor"], res.profit_factor),
|
||||
("Recovery Factor", w0["recovery_factor"], res.recovery_factor),
|
||||
("Balance DD Rel%", w0["bal_dd_rel_pct"], res.balance_drawdown_rel_pct),
|
||||
("Trades", w0["trades"], res.total_trades),
|
||||
("Sharpe Ratio", w0["sharpe_ratio"], res.sharpe_ratio),
|
||||
]
|
||||
for name, calc, ref in ref_pairs:
|
||||
diff = calc - ref
|
||||
if ref != 0:
|
||||
pct = abs(diff) / abs(ref) * 100
|
||||
else:
|
||||
pct = 0.0
|
||||
mark = "✓" if pct < 0.5 else ("⚠" if pct < 5 else "✗")
|
||||
print(f" {mark} {name:<18} calc={calc:>10.4f} "
|
||||
f"ref={ref:>10.4f} diff={diff:>+10.4f} ({pct:5.1f}%)")
|
||||
print(" Legend: ✓ = exact (rounding only), ⚠ = small drift, "
|
||||
"✗ = documented approximation")
|
||||
print()
|
||||
|
||||
# When N > 1, also compute full-period metrics as a cross-check
|
||||
# reference so the user can see how sub-window values relate to
|
||||
# the full backtest (both our computation and the HTML report).
|
||||
if len(windows) > 1:
|
||||
# Compute full-period window (N=1) for comparison
|
||||
full_wins = compute_windows(report, 1)
|
||||
if full_wins:
|
||||
w_full = full_wins[0]
|
||||
else:
|
||||
w_full = None
|
||||
if w_full is not None:
|
||||
print(" " + "─" * 45)
|
||||
print(" Full-period reference:")
|
||||
print(" (computed N=1 vs HTML report stated values)")
|
||||
ref_pairs = [
|
||||
("Profit", w_full["profit"], res.total_net_profit),
|
||||
("Expected Payoff", w_full["expected_payoff"], res.expected_payoff),
|
||||
("Profit Factor", w_full["profit_factor"], res.profit_factor),
|
||||
("Recovery Factor", w_full["recovery_factor"], res.recovery_factor),
|
||||
("Balance DD Rel%", w_full["bal_dd_rel_pct"], res.balance_drawdown_rel_pct),
|
||||
("Trades", w_full["trades"], res.total_trades),
|
||||
("Sharpe Ratio", w_full["sharpe_ratio"], res.sharpe_ratio),
|
||||
]
|
||||
for name, calc, ref in ref_pairs:
|
||||
diff = calc - ref
|
||||
if ref != 0:
|
||||
pct = abs(diff) / abs(ref) * 100
|
||||
else:
|
||||
pct = 0.0
|
||||
mark = "✓" if pct < 0.5 else ("⚠" if pct < 5 else "✗")
|
||||
print(f" {mark} {name:<18} calc={calc:>10.4f} "
|
||||
f"ref={ref:>10.4f} diff={diff:>+10.4f} ({pct:5.1f}%)")
|
||||
print()
|
||||
|
||||
# Summary
|
||||
summ = comparison["summary"]
|
||||
thr = comparison["thresholds"]
|
||||
if len(windows) < 2:
|
||||
print(" Outlier scan: skipped (need at least 2 windows to compute std).")
|
||||
print(" Use --count 2 or more for the z-score outlier scan.")
|
||||
else:
|
||||
print(" Outlier scan (per-metric z-score vs window mean):")
|
||||
print(f" ▲2σ = |z| >= {thr['notable']:.0f} on any metric (notable)")
|
||||
print(f" ■EXT = |z| >= {thr['extreme']:.0f} on any metric (extreme)")
|
||||
print(f" {summ['notable_windows']}/{summ['n_windows']} windows have at least one "
|
||||
f"|z|>={thr['notable']:.0f} outlier, "
|
||||
f"{summ['extreme_windows']}/{summ['n_windows']} have at least one "
|
||||
f"|z|>={thr['extreme']:.0f}.")
|
||||
if summ["notable_windows"] > 0 or summ["extreme_windows"] > 0:
|
||||
print(" Use --json to see per-metric z-scores.")
|
||||
print()
|
||||
if len(windows) >= 2:
|
||||
print(" Interpretation:")
|
||||
print(" z = (this window's value − mean across all windows) / std.")
|
||||
print(" A z-score measures how far this window is from the rest.")
|
||||
print(" Direction is sign-bearing (+ vs −); the marker is |z|.")
|
||||
print(" For lower-is-better metrics (bal_dd_rel_pct), a negative z")
|
||||
print(" means 'this window's DD is unusually low' — good if you")
|
||||
print(" want safety, neutral if you just want consistency.")
|
||||
print(" A single window with a strong outlier is a regime signal.")
|
||||
print(" Multiple windows each with their own outliers point to a")
|
||||
print(" high-variance strategy — harder to predict live performance.")
|
||||
|
||||
|
||||
# ── CLI ──────────────────────────────────────────────────────────────
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Parse MT5 Strategy Tester HTML report")
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Parse MT5 Strategy Tester HTML report",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Examples:
|
||||
# Text report (default)
|
||||
python %(prog)s report.html
|
||||
|
||||
# JSON dump (raw parsed data)
|
||||
python %(prog)s report.html --json
|
||||
|
||||
# Full trade analysis (idle time, monthly breakdown, re-entry detection)
|
||||
python %(prog)s report.html --analyze
|
||||
|
||||
# Window analysis (see "windows --help" for details)
|
||||
python %(prog)s report.html windows --count 4
|
||||
python %(prog)s report.html windows --count 6 --json
|
||||
""",
|
||||
)
|
||||
parser.add_argument("report", help="Path to HTML report file")
|
||||
parser.add_argument("--json", action="store_true", help="Output as JSON")
|
||||
parser.add_argument("--analyze", action="store_true",
|
||||
help="Run trade analysis (pair deals, risk check, monthly breakdown)")
|
||||
sub = parser.add_subparsers(dest="cmd")
|
||||
|
||||
p_win = sub.add_parser(
|
||||
"windows",
|
||||
help="Split the backtest into N equal time windows and compute "
|
||||
"the 7 core metrics for each (Profit, EP, PF, RF, "
|
||||
"Balance DD Rel%% (relative), Trades, Sharpe). Use to find time "
|
||||
"windows that are statistical outliers vs the rest "
|
||||
"(over-fitting / regime detection).",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Examples:
|
||||
# N=1: validate that calculation matches the full report
|
||||
python parse_tester_report.py <report.html> windows --count 1
|
||||
|
||||
# N=4: quarterly analysis for a 1.5y backtest
|
||||
python parse_tester_report.py <report.html> windows --count 4
|
||||
|
||||
# N=8: finer granularity
|
||||
python parse_tester_report.py <report.html> windows --count 8
|
||||
|
||||
# JSON output (per-window metrics + z-score outliers)
|
||||
python parse_tester_report.py <report.html> windows --count 6 --json
|
||||
|
||||
Each window shows all 7 metrics plus an outlier flag (|z|>=2: notable,
|
||||
|z|>=5: extreme). The MEAN/STD row gives the reference distribution.
|
||||
""",
|
||||
)
|
||||
p_win.add_argument(
|
||||
"--count", "-n", type=int, required=True,
|
||||
help="Number of equal time windows to split the backtest into. "
|
||||
"Use 1 to validate: should match the full report within tolerance.",
|
||||
)
|
||||
p_win.add_argument(
|
||||
"--json", action="store_true", help="Output windows data as JSON",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
path = Path(args.report)
|
||||
@@ -709,9 +1405,34 @@ def main():
|
||||
|
||||
report = parse_report(path)
|
||||
|
||||
# Always compute analyze data (needed for idle_time in text report)
|
||||
analyze_data = analyze_report(report)
|
||||
if args.cmd == "windows":
|
||||
wins = compute_windows(report, args.count)
|
||||
comp = windows_comparison(wins)
|
||||
if getattr(args, "json", False):
|
||||
out = {
|
||||
"report": {
|
||||
"expert": report.settings.expert,
|
||||
"symbol": report.settings.symbol,
|
||||
"period": report.settings.period,
|
||||
"initial_deposit": report.settings.initial_deposit,
|
||||
"total_net_profit": report.results.total_net_profit,
|
||||
"profit_factor": report.results.profit_factor,
|
||||
"expected_payoff": report.results.expected_payoff,
|
||||
"recovery_factor": report.results.recovery_factor,
|
||||
"sharpe_ratio": report.results.sharpe_ratio,
|
||||
"bal_dd_rel_pct": report.results.balance_drawdown_rel_pct,
|
||||
"total_trades": report.results.total_trades,
|
||||
},
|
||||
"windows": wins,
|
||||
"comparison": comp,
|
||||
}
|
||||
print(json.dumps(out, indent=2, ensure_ascii=False))
|
||||
else:
|
||||
print_windows(report, wins, comp)
|
||||
return
|
||||
|
||||
# Default behaviour: text report (with analyze) or JSON
|
||||
analyze_data = analyze_report(report)
|
||||
if args.analyze:
|
||||
report_dict = asdict(report)
|
||||
report_dict["analyze"] = analyze_data
|
||||
|
||||
@@ -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__":
|
||||
|
||||
Reference in New Issue
Block a user