ZhijuCen
aa0909764f
- Remove gap_days_to_end and last_trade_close from analyze_report output - Add idle_time: total backtest duration minus all position holding times (includes flat time before first trade and after last trade) - Add format_duration() helper for timedelta -> HH:MM:SS formatting - Parse both start/end dates from period string for accurate calculation - Update print_report() to display idle_time in Holding Times section - main() now always computes analyze data for text report - Update AGENTS.md: add scripts docs, jobs/resources dirs, verification method
727 lines
28 KiB
Python
727 lines
28 KiB
Python
#!/usr/bin/env python3
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"""
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Parse MT5 Strategy Tester HTML report.
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Extracts: account properties, EA parameters, P&L metrics, orders, deals.
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Usage:
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python skills/mql5/scripts/parse_tester_report.py <report.html>
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python skills/mql5/scripts/parse_tester_report.py <report.html> --json
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"""
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from __future__ import annotations
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import argparse
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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 pathlib import Path
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from bs4 import BeautifulSoup, Tag
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# ── Data classes ─────────────────────────────────────────────────────
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@dataclass
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class Settings:
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expert: str = ""
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symbol: str = ""
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period: str = ""
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company: str = ""
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currency: str = ""
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initial_deposit: float = 0.0
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leverage: str = ""
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inputs: dict[str, str] = field(default_factory=dict)
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@dataclass
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class Results:
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history_quality: str = ""
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bars: int = 0
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ticks: int = 0
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symbols: int = 0
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total_net_profit: float = 0.0
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gross_profit: float = 0.0
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gross_loss: float = 0.0
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balance_drawdown_abs: float = 0.0
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balance_drawdown_max: float = 0.0
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balance_drawdown_max_pct: float = 0.0
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balance_drawdown_rel: float = 0.0
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balance_drawdown_rel_pct: float = 0.0
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equity_drawdown_abs: float = 0.0
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equity_drawdown_max: float = 0.0
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equity_drawdown_max_pct: float = 0.0
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equity_drawdown_rel: float = 0.0
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equity_drawdown_rel_pct: float = 0.0
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profit_factor: float = 0.0
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expected_payoff: float = 0.0
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margin_level: float = 0.0
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recovery_factor: float = 0.0
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sharpe_ratio: float = 0.0
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z_score: float = 0.0
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z_score_pct: float = 0.0
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ahpr: float = 0.0
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ahpr_pct: float = 0.0
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ghpr: float = 0.0
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ghpr_pct: float = 0.0
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lr_correlation: float = 0.0
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lr_standard_error: float = 0.0
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on_tester_result: float = 0.0
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total_trades: int = 0
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total_deals: int = 0
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short_trades: int = 0
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short_won_pct: float = 0.0
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long_trades: int = 0
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long_won_pct: float = 0.0
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profit_trades: int = 0
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profit_trades_pct: float = 0.0
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loss_trades: int = 0
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loss_trades_pct: float = 0.0
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largest_profit_trade: float = 0.0
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largest_loss_trade: float = 0.0
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avg_profit_trade: float = 0.0
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avg_loss_trade: float = 0.0
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max_consec_wins: int = 0
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max_consec_wins_amt: float = 0.0
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max_consec_losses: int = 0
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max_consec_losses_amt: float = 0.0
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max_consec_profit: float = 0.0
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max_consec_profit_count: int = 0
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max_consec_loss: float = 0.0
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max_consec_loss_count: int = 0
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avg_consec_wins: int = 0
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avg_consec_losses: int = 0
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min_hold_time: str = ""
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max_hold_time: str = ""
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avg_hold_time: str = ""
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# MFE/MAE
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corr_profit_mfe: float = 0.0
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corr_profit_mae: float = 0.0
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corr_mfe_mae: float = 0.0
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@dataclass
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class Order:
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open_time: str = ""
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order: int = 0
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symbol: str = ""
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type: str = ""
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volume: str = ""
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price: float = 0.0
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sl: float = 0.0
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tp: float = 0.0
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close_time: str = ""
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state: str = ""
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comment: str = ""
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@dataclass
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class Deal:
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time: str = ""
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deal: int = 0
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symbol: str = ""
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type: str = ""
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direction: str = ""
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volume: float = 0.0
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price: float = 0.0
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order: int = 0
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commission: float = 0.0
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swap: float = 0.0
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profit: float = 0.0
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balance: float = 0.0
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comment: str = ""
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@dataclass
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class Report:
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settings: Settings = field(default_factory=Settings)
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results: Results = field(default_factory=Results)
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orders: list[Order] = field(default_factory=list)
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deals: list[Deal] = field(default_factory=list)
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# ── Parsing helpers ──────────────────────────────────────────────────
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def decode_html(path: Path) -> str:
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"""Read MT5 report (UTF-16LE) and return UTF-8 string."""
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raw = path.read_bytes()
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# Detect BOM
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if raw[:2] == b"\xff\xfe":
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return raw.decode("utf-16-le")
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if raw[:2] == b"\xfe\xff":
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return raw.decode("utf-16-be")
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# Try utf-16-le without BOM
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try:
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return raw.decode("utf-16-le")
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except UnicodeDecodeError:
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return raw.decode("utf-8", errors="replace")
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def parse_number(text: str) -> float:
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"""Parse number from MT5 report format: '1 305.90' → 1305.90, '-201.39' → -201.39"""
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text = text.strip()
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if not text:
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return 0.0
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# Remove spaces used as thousand separators
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text = text.replace(" ", "")
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# Extract first number-like token (may include %, parentheses)
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m = re.search(r"[-\d][\d,.]*", text)
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if not m:
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return 0.0
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num_str = m.group().replace(",", "")
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try:
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return float(num_str)
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except ValueError:
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return 0.0
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def parse_pct(text: str) -> float:
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"""Extract percentage value: '100.27% (516.89)' → 100.27"""
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m = re.search(r"([\d.]+)%", text)
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return float(m.group(1)) if m else 0.0
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def td_text(td: Tag) -> str:
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"""Get text content of a <td>, stripping whitespace."""
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return td.get_text(strip=True)
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# ── Main parser ──────────────────────────────────────────────────────
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def parse_report(html_path: Path) -> Report:
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html = decode_html(html_path)
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soup = BeautifulSoup(html, "html.parser")
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report = Report()
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tables = soup.find_all("table")
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if not tables:
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print("Error: no tables found in HTML", file=sys.stderr)
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return report
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# ── Table 0: Settings + Results ──────────────────────────────────
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main_table = tables[0]
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rows = main_table.find_all("tr")
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section = "settings"
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stats_map: dict[str, str] = {}
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for row in rows:
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cells = row.find_all(["td", "th"])
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if not cells:
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continue
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# Detect section headers
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text_all = " ".join(td_text(c) for c in cells)
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if "Settings" in text_all and len(cells) <= 3:
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section = "settings"
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continue
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if "Results" in text_all and len(cells) <= 3:
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section = "results"
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continue
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if section == "settings":
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# Settings rows: label in col 0-2, value in col 3+
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if len(cells) < 2:
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continue
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label = td_text(cells[0])
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# Input parameters: label is empty, value is in the next cell
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if not label and len(cells) >= 2:
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val = td_text(cells[-1])
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if val.startswith("==="):
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continue # group header
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if "=" in val:
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k, v = val.split("=", 1)
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report.settings.inputs[k.strip()] = v.strip()
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continue
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# Standard settings fields
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if label.endswith(":"):
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label = label[:-1]
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val = td_text(cells[-1]) if len(cells) >= 2 else ""
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if label == "Expert":
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report.settings.expert = val
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elif label == "Symbol":
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report.settings.symbol = val
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elif label == "Period":
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report.settings.period = val
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elif label == "Company":
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report.settings.company = val
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elif label == "Currency":
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report.settings.currency = val
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elif label == "Initial Deposit":
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report.settings.initial_deposit = parse_number(val)
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elif label == "Leverage":
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report.settings.leverage = val
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elif section == "results":
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# Results: find label cells (ending with ":") and pair with next cell
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for i, cell in enumerate(cells):
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lbl = td_text(cell)
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if not lbl.endswith(":") or not lbl:
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continue
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lbl = lbl.rstrip(":")
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# Value is the next cell
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if i + 1 < len(cells):
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val = td_text(cells[i + 1])
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else:
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val = ""
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stats_map[lbl] = val
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# ── Map stats_map to Results fields ──────────────────────────────
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r = report.results
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r.history_quality = stats_map.get("History Quality", "")
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r.bars = int(parse_number(stats_map.get("Bars", "0")))
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r.ticks = int(parse_number(stats_map.get("Ticks", "0")))
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r.symbols = int(parse_number(stats_map.get("Symbols", "0")))
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r.total_net_profit = parse_number(stats_map.get("Total Net Profit", "0"))
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r.gross_profit = parse_number(stats_map.get("Gross Profit", "0"))
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r.gross_loss = parse_number(stats_map.get("Gross Loss", "0"))
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r.balance_drawdown_abs = parse_number(stats_map.get("Balance Drawdown Absolute", "0"))
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r.balance_drawdown_max = parse_number(stats_map.get("Balance Drawdown Maximal", "0"))
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r.balance_drawdown_max_pct = parse_pct(stats_map.get("Balance Drawdown Maximal", "0"))
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r.balance_drawdown_rel = parse_number(stats_map.get("Balance Drawdown Relative", "0"))
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r.balance_drawdown_rel_pct = parse_pct(stats_map.get("Balance Drawdown Relative", "0"))
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r.equity_drawdown_abs = parse_number(stats_map.get("Equity Drawdown Absolute", "0"))
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r.equity_drawdown_max = parse_number(stats_map.get("Equity Drawdown Maximal", "0"))
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r.equity_drawdown_max_pct = parse_pct(stats_map.get("Equity Drawdown Maximal", "0"))
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r.equity_drawdown_rel = parse_number(stats_map.get("Equity Drawdown Relative", "0"))
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r.equity_drawdown_rel_pct = parse_pct(stats_map.get("Equity Drawdown Relative", "0"))
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r.profit_factor = parse_number(stats_map.get("Profit Factor", "0"))
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r.expected_payoff = parse_number(stats_map.get("Expected Payoff", "0"))
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r.margin_level = parse_pct(stats_map.get("Margin Level", "0"))
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r.recovery_factor = parse_number(stats_map.get("Recovery Factor", "0"))
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r.sharpe_ratio = parse_number(stats_map.get("Sharpe Ratio", "0"))
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z = stats_map.get("Z-Score", "0")
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r.z_score = parse_number(z)
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r.z_score_pct = parse_pct(z)
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ahpr = stats_map.get("AHPR", "0")
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r.ahpr = parse_number(ahpr)
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r.ahpr_pct = parse_pct(ahpr)
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ghpr = stats_map.get("GHPR", "0")
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r.ghpr = parse_number(ghpr)
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r.ghpr_pct = parse_pct(ghpr)
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r.lr_correlation = parse_number(stats_map.get("LR Correlation", "0"))
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r.lr_standard_error = parse_number(stats_map.get("LR Standard Error", "0"))
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r.on_tester_result = parse_number(stats_map.get("OnTester result", "0"))
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r.total_trades = int(parse_number(stats_map.get("Total Trades", "0")))
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r.total_deals = int(parse_number(stats_map.get("Total Deals", "0")))
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# Parse Short/Long Trades: "5 (20.00%)"
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short = stats_map.get("Short Trades (won %)", "0")
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r.short_trades = int(parse_number(short))
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r.short_won_pct = parse_pct(short)
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long = stats_map.get("Long Trades (won %)", "0")
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r.long_trades = int(parse_number(long))
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r.long_won_pct = parse_pct(long)
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pt = stats_map.get("Profit Trades (% of total)", "0")
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r.profit_trades = int(parse_number(pt))
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r.profit_trades_pct = parse_pct(pt)
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lt = stats_map.get("Loss Trades (% of total)", "0")
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r.loss_trades = int(parse_number(lt))
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r.loss_trades_pct = parse_pct(lt)
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r.largest_profit_trade = parse_number(stats_map.get("Largest profit trade", "0"))
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r.largest_loss_trade = parse_number(stats_map.get("Largest loss trade", "0"))
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r.avg_profit_trade = parse_number(stats_map.get("Average profit trade", "0"))
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r.avg_loss_trade = parse_number(stats_map.get("Average loss trade", "0"))
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# Consecutive: "3 (85.31)" or "1"
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mcw = stats_map.get("Maximum consecutive wins ($)", "0")
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r.max_consec_wins = int(parse_number(mcw))
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m = re.search(r"\(([-\d.]+)\)", mcw)
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r.max_consec_wins_amt = float(m.group(1)) if m else 0.0
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mcl = stats_map.get("Maximum consecutive losses ($)", "0")
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r.max_consec_losses = int(parse_number(mcl))
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m = re.search(r"\(([-\d.]+)\)", mcl)
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r.max_consec_losses_amt = float(m.group(1)) if m else 0.0
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# "361.91 (2)"
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mcp = stats_map.get("Maximal consecutive profit (count)", "0")
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r.max_consec_profit = parse_number(mcp)
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m = re.search(r"\((\d+)\)", mcp)
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r.max_consec_profit_count = int(m.group(1)) if m else 0
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mcl2 = stats_map.get("Maximal consecutive loss (count)", "0")
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r.max_consec_loss = parse_number(mcl2)
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m = re.search(r"\((\d+)\)", mcl2)
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r.max_consec_loss_count = int(m.group(1)) if m else 0
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r.avg_consec_wins = int(parse_number(stats_map.get("Average consecutive wins", "0")))
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r.avg_consec_losses = int(parse_number(stats_map.get("Average consecutive losses", "0")))
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r.min_hold_time = stats_map.get("Minimal position holding time", "")
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r.max_hold_time = stats_map.get("Maximal position holding time", "")
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r.avg_hold_time = stats_map.get("Average position holding time", "")
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r.corr_profit_mfe = parse_number(stats_map.get("Correlation (Profits,MFE)", "0"))
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r.corr_profit_mae = parse_number(stats_map.get("Correlation (Profits,MAE)", "0"))
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r.corr_mfe_mae = parse_number(stats_map.get("Correlation (MFE,MAE)", "0"))
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# ── Table 1+: Orders and Deals ───────────────────────────────────
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# The second table contains both Orders and Deals sections,
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# each with their own header row (bgcolor=#E5F0FC)
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for tbl in tables[1:]:
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header_rows = tbl.find_all("tr", bgcolor=re.compile(r"#E5F0FC"))
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for header_row in header_rows:
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headers = [td_text(th) for th in header_row.find_all(["td", "th"])]
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# Find data rows that follow this header (until next header or end)
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all_rows = tbl.find_all("tr")
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hdr_idx = all_rows.index(header_row)
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data_rows = []
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for r in all_rows[hdr_idx + 1:]:
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bg = r.get("bgcolor", "")
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if re.match(r"#(FFFFFF|F7F7F7)", str(bg)):
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data_rows.append(r)
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elif r.find("th") and ("Deals" in td_text(r) or "Orders" in td_text(r)):
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break # next section header
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if "Open Time" in headers and "Order" in headers:
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# Orders table — cells are in order, colspan only affects visual layout
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for dr in data_rows:
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cells = dr.find_all("td")
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if len(cells) < 10:
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continue
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vals = [td_text(c) for c in cells]
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order = Order(
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open_time=vals[0],
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order=int(parse_number(vals[1])),
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symbol=vals[2],
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type=vals[3],
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volume=vals[4],
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price=parse_number(vals[5]),
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sl=parse_number(vals[6]),
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tp=parse_number(vals[7]),
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close_time=vals[8],
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state=vals[9],
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comment=vals[10] if len(vals) > 10 else "",
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)
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report.orders.append(order)
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elif "Deal" in headers and "Direction" in headers:
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# Deals table
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for dr in data_rows:
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cells = dr.find_all("td")
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if len(cells) < 10:
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continue
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vals = [td_text(c) for c in cells]
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deal = Deal(
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time=vals[0],
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deal=int(parse_number(vals[1])),
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symbol=vals[2],
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type=vals[3],
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direction=vals[4],
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volume=parse_number(vals[5]),
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price=parse_number(vals[6]),
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order=int(parse_number(vals[7])),
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commission=parse_number(vals[8]),
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swap=parse_number(vals[9]),
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profit=parse_number(vals[10]),
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balance=parse_number(vals[11]),
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comment=vals[12] if len(vals) > 12 else "",
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)
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report.deals.append(deal)
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return report
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# ── Pretty print ─────────────────────────────────────────────────────
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def print_report(r: Report, analyze_data: dict | None = None) -> None:
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s = r.settings
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res = r.results
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print("=" * 72)
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print(" MT5 Strategy Tester Report")
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print("=" * 72)
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print(f"\n Expert: {s.expert}")
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print(f" Symbol: {s.symbol}")
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print(f" Period: {s.period}")
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print(f" Company: {s.company}")
|
|
print(f" Currency: {s.currency}")
|
|
print(f" Deposit: {s.initial_deposit:,.2f}")
|
|
print(f" Leverage: {s.leverage}")
|
|
|
|
if s.inputs:
|
|
print(f"\n EA Parameters ({len(s.inputs)}):")
|
|
for k, v in s.inputs.items():
|
|
print(f" {k} = {v}")
|
|
|
|
print(f"\n{'─' * 72}")
|
|
print(" Data Quality")
|
|
print(f"{'─' * 72}")
|
|
print(f" History Quality: {res.history_quality}")
|
|
print(f" Bars: {res.bars:,}")
|
|
print(f" Ticks: {res.ticks:,}")
|
|
print(f" Symbols: {res.symbols}")
|
|
|
|
print(f"\n{'─' * 72}")
|
|
print(" P&L Summary")
|
|
print(f"{'─' * 72}")
|
|
print(f" Net Profit: {res.total_net_profit:>12,.2f}")
|
|
print(f" Gross Profit: {res.gross_profit:>12,.2f}")
|
|
print(f" Gross Loss: {res.gross_loss:>12,.2f}")
|
|
print(f" Profit Factor: {res.profit_factor:>12.2f}")
|
|
print(f" Expected Payoff: {res.expected_payoff:>12.2f}")
|
|
print(f" Recovery Factor: {res.recovery_factor:>12.2f}")
|
|
print(f" Sharpe Ratio: {res.sharpe_ratio:>12.2f}")
|
|
|
|
print(f"\n{'─' * 72}")
|
|
print(" Drawdown")
|
|
print(f"{'─' * 72}")
|
|
print(f" Balance Abs: {res.balance_drawdown_abs:>12,.2f}")
|
|
print(f" Balance Max: {res.balance_drawdown_max:>12,.2f} ({res.balance_drawdown_max_pct:.2f}%)")
|
|
print(f" Balance Rel: {res.balance_drawdown_rel_pct:.2f}% ({res.balance_drawdown_rel:,.2f})")
|
|
print(f" Equity Abs: {res.equity_drawdown_abs:>12,.2f}")
|
|
print(f" Equity Max: {res.equity_drawdown_max:>12,.2f} ({res.equity_drawdown_max_pct:.2f}%)")
|
|
print(f" Equity Rel: {res.equity_drawdown_rel_pct:.2f}% ({res.equity_drawdown_rel:,.2f})")
|
|
|
|
print(f"\n{'─' * 72}")
|
|
print(" Trade Statistics")
|
|
print(f"{'─' * 72}")
|
|
print(f" Total Trades: {res.total_trades:>8} Total Deals: {res.total_deals}")
|
|
print(f" Short (won%): {res.short_trades:>8} ({res.short_won_pct:.2f}%)")
|
|
print(f" Long (won%): {res.long_trades:>8} ({res.long_won_pct:.2f}%)")
|
|
print(f" Profit Trades: {res.profit_trades:>8} ({res.profit_trades_pct:.2f}%)")
|
|
print(f" Loss Trades: {res.loss_trades:>8} ({res.loss_trades_pct:.2f}%)")
|
|
print(f" Largest Win: {res.largest_profit_trade:>12,.2f}")
|
|
print(f" Largest Loss: {res.largest_loss_trade:>12,.2f}")
|
|
print(f" Avg Win: {res.avg_profit_trade:>12,.2f}")
|
|
print(f" Avg Loss: {res.avg_loss_trade:>12,.2f}")
|
|
print(f" Max Consec Wins: {res.max_consec_wins:>4} (${res.max_consec_wins_amt:,.2f})")
|
|
print(f" Max Consec Loss: {res.max_consec_losses:>4} (${res.max_consec_losses_amt:,.2f})")
|
|
|
|
print(f"\n{'─' * 72}")
|
|
print(" Holding Times")
|
|
print(f"{'─' * 72}")
|
|
print(f" Min: {res.min_hold_time} Max: {res.max_hold_time} Avg: {res.avg_hold_time}")
|
|
if analyze_data:
|
|
print(f" Idle (no position): {analyze_data.get('idle_time', '')}")
|
|
|
|
print(f"\n{'─' * 72}")
|
|
print(f" Orders: {len(r.orders)} Deals: {len(r.deals)}")
|
|
print(f"{'─' * 72}")
|
|
|
|
if r.orders:
|
|
print(f"\n {'Open Time':<20} {'Ord':>5} {'Type':<5} {'Vol':>6} {'Price':>10} {'SL':>10} {'TP':>10} {'State':<8} {'Comment'}")
|
|
for o in r.orders[:10]:
|
|
print(f" {o.open_time:<20} {o.order:>5} {o.type:<5} {o.volume:>6} {o.price:>10.2f} {o.sl:>10.2f} {o.tp:>10.2f} {o.state:<8} {o.comment}")
|
|
if len(r.orders) > 10:
|
|
print(f" ... ({len(r.orders) - 10} more)")
|
|
|
|
if r.deals:
|
|
print(f"\n {'Time':<20} {'Deal':>5} {'Type':<5} {'Dir':<4} {'Vol':>6} {'Price':>10} {'Comm':>8} {'Swap':>8} {'Profit':>10} {'Balance':>10}")
|
|
for d in r.deals[:10]:
|
|
print(f" {d.time:<20} {d.deal:>5} {d.type:<5} {d.direction:<4} {d.volume:>6.2f} {d.price:>10.2f} {d.commission:>8.2f} {d.swap:>8.2f} {d.profit:>10.2f} {d.balance:>10.2f}")
|
|
if len(r.deals) > 10:
|
|
print(f" ... ({len(r.deals) - 10} more)")
|
|
|
|
|
|
|
|
# ── Trade Analysis ───────────────────────────────────────────────────
|
|
|
|
def pair_trades(deals: list) -> list:
|
|
"""Pair entry/exit deals into complete trades."""
|
|
trading = [d for d in deals if d.type != "balance"]
|
|
|
|
trades = []
|
|
i = 0
|
|
while i < len(trading):
|
|
if trading[i].direction == "in":
|
|
entry = trading[i]
|
|
if i + 1 < len(trading) and trading[i + 1].direction == "out":
|
|
exit_d = trading[i + 1]
|
|
net = (exit_d.profit + entry.commission + exit_d.commission
|
|
+ entry.swap + exit_d.swap)
|
|
sl_dist = 0.0
|
|
if "sl" in exit_d.comment:
|
|
sl_dist = abs(entry.price - exit_d.price)
|
|
trades.append({
|
|
"open_time": entry.time,
|
|
"close_time": exit_d.time,
|
|
"type": entry.type,
|
|
"volume": entry.volume,
|
|
"entry": entry.price,
|
|
"exit": exit_d.price,
|
|
"profit": exit_d.profit,
|
|
"commission": entry.commission + exit_d.commission,
|
|
"swap": entry.swap + exit_d.swap,
|
|
"net": net,
|
|
"comment": exit_d.comment,
|
|
"sl_distance": sl_dist,
|
|
})
|
|
i += 2
|
|
else:
|
|
i += 1
|
|
else:
|
|
i += 1
|
|
return trades
|
|
|
|
|
|
def format_duration(td) -> str:
|
|
"""Format timedelta as HH:MM:SS."""
|
|
total = int(td.total_seconds())
|
|
sign = "-" if total < 0 else ""
|
|
total = abs(total)
|
|
h, rem = divmod(total, 3600)
|
|
m, s = divmod(rem, 60)
|
|
return f"{sign}{h:02d}:{m:02d}:{s:02d}"
|
|
|
|
|
|
def analyze_report(report: Report) -> dict:
|
|
"""Run full trade analysis on parsed report."""
|
|
from datetime import datetime, timedelta
|
|
|
|
deposit = report.settings.initial_deposit
|
|
trades = pair_trades(report.deals)
|
|
|
|
if not trades:
|
|
return {"error": "No trades found", "trades": []}
|
|
|
|
# Parse backtest start/end dates from period string
|
|
# e.g. "H4 (2024.01.01 - 2025.06.22)"
|
|
bt_start = None
|
|
bt_end = None
|
|
period = report.settings.period
|
|
m_dates = re.search(
|
|
r"(\d{4}\.\d{2}\.\d{2})\s*-\s*(\d{4}\.\d{2}\.\d{2})\s*\)\s*$", period
|
|
)
|
|
if m_dates:
|
|
try:
|
|
bt_start = datetime.strptime(m_dates.group(1), "%Y.%m.%d")
|
|
bt_end = datetime.strptime(m_dates.group(2), "%Y.%m.%d")
|
|
except ValueError:
|
|
pass
|
|
|
|
# Per-trade risk check
|
|
for t in trades:
|
|
t["risk_pct"] = abs(t["net"]) / deposit * 100 if deposit > 0 else 0
|
|
|
|
# SL hit vs TP hit
|
|
sl_trades = [t for t in trades if "sl " in t["comment"]]
|
|
tp_trades = [t for t in trades if "tp " in t["comment"]]
|
|
other = [t for t in trades if t not in sl_trades and t not in tp_trades]
|
|
|
|
avg_win = (sum(t["net"] for t in tp_trades) / len(tp_trades)) if tp_trades else 0
|
|
avg_loss = (sum(t["net"] for t in sl_trades) / len(sl_trades)) if sl_trades else 0
|
|
win_loss_ratio = abs(avg_win / avg_loss) if avg_loss != 0 else 0
|
|
breakeven_wr = (abs(avg_loss) / (avg_win + abs(avg_loss))
|
|
if (avg_win + abs(avg_loss)) > 0 else 0)
|
|
|
|
# Consecutive loss analysis
|
|
streaks = []
|
|
streak = 0
|
|
for t in trades:
|
|
if t["net"] <= 0:
|
|
streak += 1
|
|
else:
|
|
if streak > 0:
|
|
streaks.append(streak)
|
|
streak = 0
|
|
if streak > 0:
|
|
streaks.append(streak)
|
|
|
|
# Re-entry detection: SL hit followed by same direction with larger lot
|
|
reentries = []
|
|
for i in range(len(trades) - 1):
|
|
t1, t2 = trades[i], trades[i + 1]
|
|
if "sl " in t1["comment"] and t1["type"] == t2["type"]:
|
|
if t2["volume"] > t1["volume"]:
|
|
reentries.append({
|
|
"after_trade": i + 1,
|
|
"time": t2["open_time"],
|
|
"type": t2["type"],
|
|
"prev_lot": t1["volume"],
|
|
"new_lot": t2["volume"],
|
|
"multiplier": round(t2["volume"] / t1["volume"], 1),
|
|
})
|
|
|
|
# Monthly breakdown
|
|
monthly = {}
|
|
for t in trades:
|
|
month = t["open_time"][:7]
|
|
if month not in monthly:
|
|
monthly[month] = {"count": 0, "net": 0.0, "wins": 0, "losses": 0}
|
|
monthly[month]["count"] += 1
|
|
monthly[month]["net"] += t["net"]
|
|
if t["net"] > 0:
|
|
monthly[month]["wins"] += 1
|
|
else:
|
|
monthly[month]["losses"] += 1
|
|
|
|
for m in monthly:
|
|
d = monthly[m]
|
|
d["net"] = round(d["net"], 2)
|
|
d["win_rate"] = round(d["wins"] / d["count"] * 100, 1) if d["count"] else 0
|
|
|
|
# Volume pattern
|
|
lots = [t["volume"] for t in trades]
|
|
unique_lots = sorted(set(lots))
|
|
|
|
# Idle time: total backtest duration minus time in positions
|
|
idle_str = ""
|
|
if bt_start and bt_end:
|
|
total_duration = bt_end - bt_start
|
|
position_time = timedelta()
|
|
for t in trades:
|
|
close_dt = datetime.strptime(t["close_time"], "%Y.%m.%d %H:%M:%S")
|
|
open_dt = datetime.strptime(t["open_time"], "%Y.%m.%d %H:%M:%S")
|
|
position_time += close_dt - open_dt
|
|
idle_td = total_duration - position_time
|
|
idle_str = format_duration(idle_td)
|
|
|
|
return {
|
|
"sl_hits": len(sl_trades),
|
|
"tp_hits": len(tp_trades),
|
|
"other_exits": len(other),
|
|
"win_loss_ratio": round(win_loss_ratio, 2),
|
|
"breakeven_win_rate": round(breakeven_wr * 100, 1),
|
|
"win_rate_gap_pct": round((len(tp_trades) / len(trades) - breakeven_wr) * 100, 1),
|
|
"consec_loss_streaks": streaks,
|
|
"reentries": reentries,
|
|
"monthly": monthly,
|
|
"lot_pattern": {
|
|
"unique_lots": unique_lots,
|
|
"uniform": len(unique_lots) == 1,
|
|
},
|
|
"idle_time": idle_str,
|
|
"trades": trades,
|
|
}
|
|
|
|
|
|
# ── CLI ──────────────────────────────────────────────────────────────
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(description="Parse MT5 Strategy Tester HTML report")
|
|
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)")
|
|
args = parser.parse_args()
|
|
|
|
path = Path(args.report)
|
|
if not path.exists():
|
|
print(f"Error: {path} not found", file=sys.stderr)
|
|
sys.exit(1)
|
|
|
|
report = parse_report(path)
|
|
|
|
# Always compute analyze data (needed for idle_time in text report)
|
|
analyze_data = analyze_report(report)
|
|
|
|
if args.analyze:
|
|
report_dict = asdict(report)
|
|
report_dict["analyze"] = analyze_data
|
|
print(json.dumps(report_dict, indent=2, ensure_ascii=False))
|
|
elif args.json:
|
|
print(json.dumps(asdict(report), indent=2, ensure_ascii=False))
|
|
else:
|
|
print_report(report, analyze_data)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|