ZhijuCen
d751b4af14
Two related improvements to the optimization-report parser:
1. Generic column typing — drop the hardcoded 'InpUseNewsFilter'
branch in parse_passes. Now the SpreadsheetML first body row's
<Data ss:Type='String'> marks a column as string (boolean Inp*
rendered as 'true'/'false' stays str); everything else is numeric
(Int64 when whole-numbered, float64 otherwise). No Inp* name is
referenced, so the parser handles any EA's parameter naming.
2. Generic param detection — _param_cols now returns every column
AFTER 'Trades' by position, not by Inp* prefix. Real exports don't
always use the Inp prefix; per user, 'Trades 以后的列数至少有一列,
但数量不定, 它们都是加入优化的输入参数'.
3. New 'outliers' subcommand — per-pass z-score scan over the 8
performance metrics (Result, Profit, Expected Payoff, Profit
Factor, Recovery Factor, Sharpe Ratio, Custom, Equity DD %).
Splits passes into two disjoint sets sorted by Result desc:
- Set A: at least one metric with |z|>=σ in the favourable
direction (higher-is-better metrics: z>=+σ; Equity DD %
uses z<=-σ because low DD is good).
- Set B: no performance-metric outlier.
Both sets EXCLUDE passes whose Trades count is itself a low-side
outlier (z<=-σ) — too few trades to trust. Excluded list shown
separately. Default σ=2, top 10 outliers / top 5 normal;
--sigma / --top-outliers / --top-normal / --json flags.
Output prints a per-metric mean/std/±σ reference table, then
each record split into Metrics group + Params group with the
outlier σ values annotated. Style mirrors the windows subcommand
in parse_tester_report.py.
Verified on jobs/246753 (432 passes, OneShotGold XAUUSD H4): 44 Set A
passes, 382 Set B, 6 excluded low-Trades passes, all 13 ad-hoc
verification assertions pass.
1083 lines
41 KiB
Python
1083 lines
41 KiB
Python
#!/usr/bin/env python3
|
||
"""
|
||
Parse MT5 Strategy Tester Optimization XML report (SpreadsheetML format).
|
||
|
||
Strategy Tester exports optimization results as an XML-tagged Excel workbook
|
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(also readable by LibreOffice Calc). The first worksheet "Tester Optimizator
|
||
Results" contains one row per parameter pass, plus a <DocumentProperties>
|
||
block with run metadata (EA, symbol, period, date range, deposit, leverage,
|
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broker, MT5 build).
|
||
|
||
This script extracts:
|
||
- <DocumentProperties>: strategy environment (title, deposit, leverage,
|
||
server, MT5 build, run timestamp)
|
||
- Worksheet: parameter columns + per-pass result metrics
|
||
(Pass, Result, Profit, Expected Payoff, Profit
|
||
Factor, Recovery Factor, Sharpe Ratio, Custom,
|
||
Equity DD %, Trades)
|
||
- Analysis (--analyze): parameter orthogonality, parameter effect (does a
|
||
parameter actually influence output?), best passes
|
||
by multiple criteria, trade-count distribution,
|
||
duplicates (passes with identical metric vectors
|
||
usually mean a parameter is dead), correlation
|
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between trade count and result.
|
||
|
||
Usage:
|
||
python skills/mql5/scripts/parse_optimizer_report.py <report.xml>
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python skills/mql5/scripts/parse_optimizer_report.py <report.xml> --json
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python skills/mql5/scripts/parse_optimizer_report.py <report.xml> --analyze
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python skills/mql5/scripts/parse_optimizer_report.py <report.xml> outliers [--top-outliers N] [--top-normal M] [--sigma K] [--json]
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"""
|
||
|
||
from __future__ import annotations
|
||
|
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import argparse
|
||
import json
|
||
import re
|
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import sys
|
||
import warnings
|
||
from dataclasses import dataclass, field, asdict
|
||
from datetime import datetime
|
||
from pathlib import Path
|
||
|
||
import pandas as pd
|
||
from bs4 import BeautifulSoup, XMLParsedAsHTMLWarning
|
||
|
||
# Silences "It looks like you're using an HTML parser to parse an XML document"
|
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# from the html.parser default. We intentionally use html.parser (no lxml
|
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# dependency).
|
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warnings.filterwarnings("ignore", category=XMLParsedAsHTMLWarning)
|
||
|
||
|
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# ── Data classes ─────────────────────────────────────────────────────
|
||
|
||
@dataclass
|
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class Env:
|
||
"""Strategy environment extracted from <DocumentProperties>."""
|
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title: str = ""
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author: str = ""
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||
revision: str = ""
|
||
created: str = ""
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||
company: str = ""
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||
mt5_version: str = ""
|
||
mt5_build: str = ""
|
||
server: str = ""
|
||
deposit: str = ""
|
||
leverage: str = ""
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||
condition: str = ""
|
||
# Derived (parsed from title)
|
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ea_name: str = ""
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||
symbol: str = ""
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||
period: str = ""
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||
date_from: str = ""
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||
date_to: str = ""
|
||
|
||
|
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# Column groups (stable, from MT5 export)
|
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METRIC_COLS = [
|
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"Result", "Profit", "Expected Payoff", "Profit Factor",
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"Recovery Factor", "Sharpe Ratio", "Custom", "Equity DD %", "Trades",
|
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]
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||
# Heuristic: every column that is not "Inp*" and not "Pass" is a metric.
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# InpUseNewsFilter is a boolean string, all other Inp* are numbers.
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||
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# ── Parsing ──────────────────────────────────────────────────────────
|
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|
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def parse_env(soup: BeautifulSoup) -> Env:
|
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"""Pull <DocumentProperties> into an Env dataclass."""
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env = Env()
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dp = soup.find("documentproperties")
|
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if not dp:
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return env
|
||
|
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# BeautifulSoup lower-cases tag names; keys are already lowercase.
|
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for child in dp.find_all():
|
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key = child.name
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val = child.get_text(strip=True)
|
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if key == "title":
|
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env.title = val
|
||
elif key == "author":
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env.author = val
|
||
elif key == "revision":
|
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env.revision = val
|
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elif key == "created":
|
||
env.created = val
|
||
elif key == "company":
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env.company = val
|
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elif key == "version":
|
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env.mt5_version = val
|
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elif key == "build":
|
||
env.mt5_build = val
|
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elif key == "server":
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env.server = val
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elif key == "deposit":
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env.deposit = val
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elif key == "leverage":
|
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env.leverage = val
|
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elif key == "condition":
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env.condition = val
|
||
|
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# Title pattern (observed): "<EA> <SYMBOL>,<PERIOD> <YYYY.MM.DD>-<YYYY.MM.DD>"
|
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m = re.match(
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r"(\S+)\s+(\w+),(\w+)\s+(\d{4}\.\d{2}\.\d{2})-(\d{4}\.\d{2}\.\d{2})",
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env.title,
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)
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if m:
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env.ea_name = m.group(1)
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env.symbol = m.group(2)
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env.period = m.group(3)
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env.date_from = m.group(4)
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env.date_to = m.group(5)
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return env
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|
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|
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def parse_passes(soup: BeautifulSoup) -> pd.DataFrame:
|
||
"""Pull the first worksheet into a typed DataFrame.
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||
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Column layout is determined from the SpreadsheetML header row: any
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||
<Data ss:Type="String"> cell in the header marks a string column
|
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(typically a boolean Inp* parameter rendered as "true"/"false");
|
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everything else is coerced to numeric. This is EA-agnostic — no
|
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hardcoded Inp* parameter names.
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The Pass column is always forced to nullable Int64 (it's a row id
|
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even when declared as Number in the SpreadsheetML). All other
|
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Number columns become float64 (or Int64 when every value is a whole
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number — useful for InpSLPips-style integer parameters).
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"""
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ws = soup.find("worksheet", attrs={"ss:name": "Tester Optimizator Results"})
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if not ws:
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# Fallback: first worksheet regardless of name
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ws = soup.find("worksheet")
|
||
if not ws:
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return pd.DataFrame()
|
||
|
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table = ws.find("table")
|
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if not table:
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return pd.DataFrame()
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rows = table.find_all("row")
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if len(rows) < 2:
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return pd.DataFrame()
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# Header row: just extract column names. Header cells are always
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# ss:Type="String" (they hold text labels), so we can't use them
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# to decide which body columns are strings.
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header_cells = rows[0].find_all("cell")
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headers: list[str] = [c.get_text(strip=True) for c in header_cells]
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# First body row: inspect each cell's <Data ss:Type="..."> to learn
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# which columns hold strings (e.g. boolean Inp* as "true"/"false").
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# Number-typed cells declare ss:Type="Number" (sometimes absent).
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first_body_cells = rows[1].find_all("cell")
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string_cols: set[int] = set()
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for i, c in enumerate(first_body_cells):
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data_tag = c.find("data")
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if data_tag is None:
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continue
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ss_type = (data_tag.get("ss:type") or "").lower()
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if ss_type == "string":
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string_cols.add(i)
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# Body rows: pull text. We don't trust per-row ss:Type for numeric
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# columns because MT5 sometimes leaves it off; pandas' to_numeric
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# is the authoritative check.
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body: list[list[str]] = []
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for r in rows[1:]:
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cells = r.find_all("cell")
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body.append([c.get_text(strip=True) for c in cells])
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df = pd.DataFrame(body, columns=headers)
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# Type inference driven by the first body row's ss:Type.
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for i, col in enumerate(df.columns):
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if col == "Pass":
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# Pass is always an integer row id
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df[col] = pd.to_numeric(df[col], errors="coerce").astype("Int64")
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continue
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if i in string_cols:
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# ss:Type="String" — keep as raw text (e.g. "true"/"false").
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df[col] = df[col].astype(str)
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continue
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# Numeric: try Int64 if every value is a whole number, else float64
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coerced = pd.to_numeric(df[col], errors="coerce")
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if pd.notna(coerced).all() and (coerced.dropna() % 1 == 0).all():
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df[col] = coerced.astype("Int64")
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else:
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df[col] = coerced.astype("float64")
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||
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return df
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||
|
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def parse_report(path: Path) -> tuple[Env, pd.DataFrame]:
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"""Convenience wrapper: file → (Env, DataFrame)."""
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with open(path, encoding="utf-8") as f:
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raw = f.read()
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soup = BeautifulSoup(raw, "html.parser")
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return parse_env(soup), parse_passes(soup)
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# ── Analysis ─────────────────────────────────────────────────────────
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def _param_cols(df: pd.DataFrame) -> list[str]:
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"""Input parameter columns the EA declared for optimization.
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The MT5 export puts Pass + the 9 standard metrics first, then
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every optimization parameter afterwards. We rely on column
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position (everything after ``Trades``), NOT on a name prefix —
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parameter names don't always start with ``Inp*`` in real exports.
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"""
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cols = list(df.columns)
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if "Trades" in cols:
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idx = cols.index("Trades")
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return cols[idx + 1:]
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# Fallback: no Trades column — assume nothing is a parameter
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return []
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|
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def _backtest_days(env: Env) -> int | None:
|
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if not (env.date_from and env.date_to):
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return None
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try:
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d0 = datetime.strptime(env.date_from, "%Y.%m.%d")
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d1 = datetime.strptime(env.date_to, "%Y.%m.%d")
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return (d1 - d0).days
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except ValueError:
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return None
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|
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|
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def analyze(df: pd.DataFrame, env: Env) -> dict:
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"""Run optimization analysis. Returns a dict with sections.
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Sections:
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- orthogonality: full pass count vs expected cartesian product
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- parameter_cardinality: how many distinct values each Inp* took
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- parameter_effect: is the parameter actually doing anything?
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(true = metric groups are statistically
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distinguishable; false = the param is dead)
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- duplicates: passes with identical metric vector ⇒ a
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parameter is not influencing output
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- best_passes: top-N by Profit / Profit Factor / Recovery
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Factor / Custom
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- trades: distribution, daily rate, correlations
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- param_cross: per-parameter mean Profit / Profit Factor
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"""
|
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out: dict = {"env_card": _env_card(env), "pass_count": int(len(df))}
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if df.empty:
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out["error"] = "No pass rows found"
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return out
|
||
|
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pcols = _param_cols(df)
|
||
|
||
# 1. Orthogonality — did the optimizer cover the full cartesian product?
|
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n = len(df)
|
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expected = 1
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cardinalities = {}
|
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for c in pcols:
|
||
u = df[c].nunique(dropna=True)
|
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cardinalities[c] = int(u)
|
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expected *= int(u) if u else 1
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out["orthogonality"] = {
|
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"actual": n,
|
||
"expected_cartesian": expected,
|
||
"complete": n == expected,
|
||
"missing": expected - n if expected > n else 0,
|
||
}
|
||
out["parameter_cardinality"] = cardinalities
|
||
|
||
# 2. Parameter effect — for each parameter, does changing its value
|
||
# produce statistically distinguishable Profit groups?
|
||
effects = {}
|
||
for c in pcols:
|
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groups = df.groupby(c, observed=True)["Profit"]
|
||
# Two signals: (a) range / std ratio; (b) min==max (suggests dead)
|
||
per_group_stats = groups.agg(["count", "mean", "median", "std", "min", "max"])
|
||
max_mean = per_group_stats["mean"].max()
|
||
min_mean = per_group_stats["mean"].min()
|
||
max_min = per_group_stats["max"].max()
|
||
min_min = per_group_stats["min"].min()
|
||
|
||
# Aggregate std across all passes (baseline noise)
|
||
global_std = df["Profit"].std() or 0.0
|
||
spread = max_mean - min_mean
|
||
|
||
# Effect = spread / global std. >1 = meaningful, <0.3 = negligible.
|
||
effect_ratio = round(spread / global_std, 2) if global_std > 0 else 0.0
|
||
|
||
# If max group min == min group max ⇒ groups don't overlap ⇒ strong
|
||
# effect. If they fully overlap ⇒ no separation.
|
||
no_overlap = max_min <= min_mean # best group's worst is worse than worst group's best
|
||
|
||
# Also check: the per-group means are within 1% of each other (truly dead)
|
||
if max_mean > 0:
|
||
relative_spread = spread / abs(max_mean)
|
||
else:
|
||
relative_spread = 0.0
|
||
|
||
effects[c] = {
|
||
"n_unique": int(df[c].nunique()),
|
||
"values": sorted(df[c].unique().tolist(), key=str),
|
||
"global_mean_profit": round(float(df["Profit"].mean()), 2),
|
||
"per_group_mean": {
|
||
str(k): round(float(v), 2)
|
||
for k, v in per_group_stats["mean"].items()
|
||
},
|
||
"spread_max_minus_min": round(float(spread), 2),
|
||
"effect_ratio_spread_over_std": effect_ratio,
|
||
"relative_spread_pct": round(relative_spread * 100, 1),
|
||
"groups_separated": bool(no_overlap),
|
||
"dead_param": bool(relative_spread < 0.01), # <1% spread = dead
|
||
}
|
||
out["parameter_effect"] = effects
|
||
|
||
# 3. Duplicates — passes with identical metric vector ⇒ that parameter
|
||
# combination is not actually influencing output.
|
||
metric_cols_present = [c for c in METRIC_COLS if c in df.columns]
|
||
dupes = (
|
||
df.groupby(metric_cols_present, dropna=False)
|
||
.size()
|
||
.reset_index(name="count")
|
||
)
|
||
dupes = dupes[dupes["count"] > 1].sort_values("count", ascending=False)
|
||
out["duplicates"] = {
|
||
"groups_with_dupes": int(len(dupes)),
|
||
"total_dup_rows": int(dupes["count"].sum() - len(dupes)) if len(dupes) else 0,
|
||
"examples": dupes.head(5).to_dict("records"),
|
||
}
|
||
|
||
# 4. Best passes by multiple criteria
|
||
out["best_passes"] = {
|
||
"by_profit": _top(df, "Profit", 5),
|
||
"by_profit_factor": _top(df, "Profit Factor", 5),
|
||
"by_recovery_factor": _top(df, "Recovery Factor", 5),
|
||
"by_custom": _top(df, "Custom", 5),
|
||
}
|
||
|
||
# 5. Trade-count distribution
|
||
if "Trades" in df.columns:
|
||
tr = df["Trades"]
|
||
out["trades"] = {
|
||
"min": int(tr.min()),
|
||
"max": int(tr.max()),
|
||
"mean": round(float(tr.mean()), 1),
|
||
"median": float(tr.median()),
|
||
"stdev": round(float(tr.std()), 2),
|
||
"deciles": [int(tr.quantile(q / 10)) for q in range(0, 11)],
|
||
}
|
||
# Trades/day: helps detect overtrading relative to backtest length
|
||
days = _backtest_days(env)
|
||
if days and days > 0:
|
||
median_trades = float(tr.median())
|
||
out["trades"]["backtest_days"] = days
|
||
out["trades"]["trades_per_day_median"] = round(median_trades / days, 3)
|
||
out["trades"]["trades_per_day_max"] = round(float(tr.max()) / days, 3)
|
||
# Correlations: overtrading usually correlates negatively with PF
|
||
if "Profit Factor" in df.columns:
|
||
out["trades"]["corr_trades_vs_profit_factor"] = round(
|
||
float(tr.corr(df["Profit Factor"])), 3
|
||
)
|
||
if "Profit" in df.columns:
|
||
out["trades"]["corr_trades_vs_profit"] = round(
|
||
float(tr.corr(df["Profit"])), 3
|
||
)
|
||
if "Equity DD %" in df.columns:
|
||
out["trades"]["corr_trades_vs_equity_dd"] = round(
|
||
float(tr.corr(df["Equity DD %"])), 3
|
||
)
|
||
|
||
# 6. Cross-analysis: each parameter's effect on key metrics
|
||
cross = {}
|
||
metric_targets = [c for c in ["Profit", "Profit Factor", "Trades"] if c in df.columns]
|
||
for p in pcols:
|
||
agg_dict = {m: ["mean", "median", "min", "max"] for m in metric_targets}
|
||
agg_dict["Pass"] = "count"
|
||
grp = df.groupby(p, observed=True).agg(agg_dict)
|
||
grp.columns = [f"{m}_{stat}" for m, stat in grp.columns]
|
||
cross[p] = (
|
||
grp.reset_index()
|
||
.rename(columns={"Pass_count": "n_passes"})
|
||
.to_dict("records")
|
||
)
|
||
out["param_cross"] = cross
|
||
|
||
# 7. Boolean parameter symmetry check
|
||
# For boolean Inp* (e.g. InpUseNewsFilter), a 1:1 identical outcome
|
||
# between true/false groups is the cleanest "dead parameter" signal.
|
||
bool_params = {}
|
||
for p in pcols:
|
||
if df[p].nunique() == 2 and set(df[p].unique()) <= {"true", "false"}:
|
||
for m in ["Profit", "Profit Factor", "Trades", "Recovery Factor"]:
|
||
if m not in df.columns:
|
||
continue
|
||
t = df.loc[df[p] == "true", m].mean()
|
||
f = df.loc[df[p] == "false", m].mean()
|
||
if abs(t - f) < 1e-6:
|
||
bool_params.setdefault(p, []).append(m)
|
||
if bool_params:
|
||
out["dead_boolean_params"] = {
|
||
p: sorted(set(metrics))
|
||
for p, metrics in bool_params.items()
|
||
}
|
||
|
||
return out
|
||
|
||
|
||
# ── Outlier analysis (per-pass z-score over optimization passes) ─────
|
||
|
||
# Per-metric direction: True = higher-is-better (we look at z >= +sigma),
|
||
# False = lower-is-better (we look at z <= -sigma). Equity DD % is the
|
||
# only lower-is-better metric — low DD means the strategy is safer.
|
||
_PERF_METRICS_HIGHER_IS_BETTER = {
|
||
"Result": True,
|
||
"Profit": True,
|
||
"Expected Payoff": True,
|
||
"Profit Factor": True,
|
||
"Recovery Factor": True,
|
||
"Sharpe Ratio": True,
|
||
"Custom": True,
|
||
"Equity DD %": False,
|
||
}
|
||
|
||
|
||
def _mean_std(values: list[float]) -> tuple[float, float]:
|
||
"""Return (mean, sample std N-1) of `values`. std=0 if N<2."""
|
||
n = len(values)
|
||
if n == 0:
|
||
return 0.0, 0.0
|
||
if n == 1:
|
||
return values[0], 0.0
|
||
mean = sum(values) / n
|
||
var = sum((x - mean) ** 2 for x in values) / (n - 1)
|
||
return mean, var ** 0.5
|
||
|
||
|
||
def _z_scores(values: list[float]) -> list[float]:
|
||
"""Sample-std z-scores for `values`. z=0 when std=0."""
|
||
mean, std = _mean_std(values)
|
||
if std == 0:
|
||
return [0.0] * len(values)
|
||
return [(v - mean) / std for v in values]
|
||
|
||
|
||
def _outlier_metrics_for_row(
|
||
z_by_metric: dict[str, float],
|
||
sigma: float,
|
||
) -> list[dict]:
|
||
"""Per-row outlier flags, in the "performance-strongly-strong" sense.
|
||
|
||
For each metric, the row is "strong" when its z is on the GOOD side
|
||
of the mean by at least `sigma` standard deviations:
|
||
|
||
- higher-is-better metric -> z >= sigma
|
||
- lower-is-better metric -> z <= -sigma (e.g. low DD is good)
|
||
|
||
Returns a list of {"metric": ..., "z": ...} entries, one per metric
|
||
that qualifies. Empty list means no performance metric is a
|
||
"strongly-strong" outlier.
|
||
"""
|
||
flags = []
|
||
for m, z in z_by_metric.items():
|
||
higher = _PERF_METRICS_HIGHER_IS_BETTER.get(m, True)
|
||
if higher and z >= sigma:
|
||
flags.append({"metric": m, "z": round(z, 2)})
|
||
elif (not higher) and z <= -sigma:
|
||
flags.append({"metric": m, "z": round(z, 2)})
|
||
return flags
|
||
|
||
|
||
def find_outliers(
|
||
df: pd.DataFrame,
|
||
sigma: float = 2.0,
|
||
top_outliers: int = 10,
|
||
top_normal: int = 5,
|
||
) -> dict:
|
||
"""Per-pass z-score outlier scan across the 8 performance metrics.
|
||
|
||
Two disjoint pass sets are returned:
|
||
|
||
set_outliers — passes with AT LEAST ONE performance metric whose
|
||
z-score crosses ±sigma in the "strongly-strong" direction
|
||
(higher-is-better: z >= +sigma; lower-is-better Equity DD %:
|
||
z <= -sigma). Sorted by Result desc; top `top_outliers` shown.
|
||
|
||
set_normal — passes with NO performance-metric outlier. Sorted by
|
||
Result desc; top `top_normal` shown.
|
||
|
||
Both sets EXCLUDE passes whose Trades count is itself a low-side
|
||
outlier (z <= -sigma). Such passes have too few trades to trust
|
||
their performance numbers — including them would inflate the
|
||
outlier set with cheap-pass artifacts.
|
||
|
||
Also returns the per-metric mean / std / sigma threshold table so
|
||
the user can judge whether the outlier counts are meaningful.
|
||
|
||
Output shape:
|
||
{
|
||
"per_metric": {"profit": {"mean":..., "std":..., "threshold_+sigma":...,
|
||
"threshold_-sigma":..., "direction": "higher"|"lower"},
|
||
...},
|
||
"trades": {"mean":..., "std":..., "low_outlier_z": ...},
|
||
"sigma": float,
|
||
"n_passes": int,
|
||
"excluded": [{"Pass":..., "Trades":..., "z": ...}, ...], # low-Trades outliers
|
||
"set_outliers":[{Pass, Result, profit, ..., outliers: [{metric, z}, ...],
|
||
params: {InpSLPips: ..., ...}}, ...],
|
||
"set_normal": [...],
|
||
"counts": {"outliers": int, "normal": int, "excluded": int},
|
||
}
|
||
"""
|
||
out: dict = {"sigma": sigma, "n_passes": int(len(df))}
|
||
|
||
if df.empty:
|
||
out["error"] = "No pass rows"
|
||
return out
|
||
|
||
# The 8 performance metrics (everything in METRIC_COLS except Trades)
|
||
perf_metrics = [c for c in METRIC_COLS if c in df.columns and c != "Trades"]
|
||
out["per_metric"] = {}
|
||
z_perf_by_row: list[dict[str, float]] = []
|
||
for m in perf_metrics:
|
||
vals = [float(v) for v in df[m].tolist()]
|
||
mean, std = _mean_std(vals)
|
||
zs = _z_scores(vals)
|
||
higher = _PERF_METRICS_HIGHER_IS_BETTER.get(m, True)
|
||
out["per_metric"][m] = {
|
||
"mean": round(mean, 4),
|
||
"std": round(std, 4),
|
||
"threshold_+sigma": round(mean + sigma * std, 4),
|
||
"threshold_-sigma": round(mean - sigma * std, 4),
|
||
"direction": "higher" if higher else "lower",
|
||
}
|
||
# Pad / align z rows with column index (defensive: pad if cols differ)
|
||
for i, z in enumerate(zs):
|
||
if i >= len(z_perf_by_row):
|
||
z_perf_by_row.append({})
|
||
z_perf_by_row[i][m] = z
|
||
|
||
# Trades: only used for the low-Trades exclusion filter
|
||
if "Trades" in df.columns:
|
||
trades_vals = [float(v) for v in df["Trades"].tolist()]
|
||
mean, std = _mean_std(trades_vals)
|
||
out["trades"] = {
|
||
"mean": round(mean, 2),
|
||
"std": round(std, 2),
|
||
"low_outlier_threshold": round(mean - sigma * std, 2),
|
||
}
|
||
trades_z = _z_scores(trades_vals)
|
||
else:
|
||
trades_z = [0.0] * len(df)
|
||
out["trades"] = None
|
||
|
||
pcols = _param_cols(df)
|
||
|
||
# Build per-row records; carry Pass / Result / metric values / z / params
|
||
rows = []
|
||
for idx, row in df.iterrows():
|
||
z_map = z_perf_by_row[idx] if idx < len(z_perf_by_row) else {}
|
||
rec = {
|
||
"Pass": int(row["Pass"]) if "Pass" in df.columns and pd.notna(row["Pass"]) else None,
|
||
"Result": float(row["Result"]) if "Result" in df.columns and pd.notna(row["Result"]) else None,
|
||
"perf_metrics": {m: float(row[m]) for m in perf_metrics if pd.notna(row[m])},
|
||
"z_metrics": {m: round(z_map.get(m, 0.0), 2) for m in perf_metrics},
|
||
"Trades": int(row["Trades"]) if "Trades" in df.columns and pd.notna(row["Trades"]) else None,
|
||
"Trades_z": round(trades_z[idx], 2) if idx < len(trades_z) else 0.0,
|
||
"params": {p: (row[p] if pd.notna(row[p]) else None) for p in pcols},
|
||
}
|
||
rec["outliers"] = _outlier_metrics_for_row(z_map, sigma)
|
||
rows.append(rec)
|
||
|
||
# Exclusion: passes with Trades z <= -sigma — too few trades to trust.
|
||
excluded = [r for r in rows if r["Trades_z"] <= -sigma]
|
||
eligible = [r for r in rows if r["Trades_z"] > -sigma]
|
||
out["excluded"] = [
|
||
{"Pass": r["Pass"], "Trades": r["Trades"], "Trades_z": r["Trades_z"]}
|
||
for r in excluded
|
||
]
|
||
|
||
# Sort eligible passes by Result desc
|
||
eligible.sort(key=lambda r: (r["Result"] is None, -(r["Result"] or 0.0)))
|
||
|
||
# Set A: at least one performance-metric outlier
|
||
set_a = [r for r in eligible if r["outliers"]]
|
||
# Set B: no performance-metric outlier
|
||
set_b = [r for r in eligible if not r["outliers"]]
|
||
|
||
out["set_outliers"] = set_a[:top_outliers]
|
||
out["set_normal"] = set_b[:top_normal]
|
||
out["counts"] = {
|
||
"outliers": len(set_a),
|
||
"normal": len(set_b),
|
||
"excluded_low_trades": len(excluded),
|
||
}
|
||
return out
|
||
|
||
|
||
def _fmt_outlier_row(rec: dict, pcols: list[str]) -> str:
|
||
"""One-pass outlier record as two compact lines (metrics, params).
|
||
|
||
Layout:
|
||
Pass 42 | Result=2.31 | Profit=800.10 EP=4.55 PF=1.61 RF=4.55 ... | z: Profit(+2.4σ), Sharpe(+2.1σ)
|
||
params: InpSLPips=30, InpTPPips=180, InpUseNewsFilter=true
|
||
"""
|
||
out_bits = []
|
||
pass_str = f"Pass {rec['Pass']}" if rec["Pass"] is not None else "Pass ?"
|
||
res_str = f"Result={rec['Result']:.2f}" if rec["Result"] is not None else "Result=?"
|
||
out_bits.append(f" {pass_str:<10} {res_str}")
|
||
|
||
# Metric values (in canonical METRIC_COLS order minus Trades)
|
||
metric_bits = []
|
||
for m in METRIC_COLS:
|
||
if m == "Trades":
|
||
continue
|
||
if m not in rec["perf_metrics"]:
|
||
continue
|
||
v = rec["perf_metrics"][m]
|
||
# Short labels for readability
|
||
short = {
|
||
"Result": "Res",
|
||
"Profit": "Prof",
|
||
"Expected Payoff": "EP",
|
||
"Profit Factor": "PF",
|
||
"Recovery Factor": "RF",
|
||
"Sharpe Ratio": "Sharpe",
|
||
"Custom": "Cust",
|
||
"Equity DD %": "EqDD%",
|
||
}.get(m, m)
|
||
metric_bits.append(f"{short}={v:.2f}")
|
||
trades_part = f" Trades={rec['Trades']}" if rec["Trades"] is not None else ""
|
||
out_bits.append(" Metrics: " + " ".join(metric_bits) + trades_part)
|
||
|
||
# Outlier annotations: only the metrics that crossed the threshold
|
||
if rec["outliers"]:
|
||
annot = ", ".join(
|
||
f"{o['metric']}({o['z']:+.2f}σ)" for o in rec["outliers"]
|
||
)
|
||
out_bits.append(f" Outliers: {annot}")
|
||
else:
|
||
out_bits.append(" Outliers: (none)")
|
||
|
||
# Input parameters — keep all of them, one per line if many
|
||
if pcols:
|
||
param_parts = []
|
||
for p in pcols:
|
||
val = rec["params"].get(p)
|
||
if isinstance(val, float) and val.is_integer():
|
||
val = int(val)
|
||
param_parts.append(f"{p}={val}")
|
||
# Wrap to <=4 params per line for readability
|
||
lines = []
|
||
cur = []
|
||
cur_len = 0
|
||
for part in param_parts:
|
||
if cur and cur_len + len(part) + 2 > 70:
|
||
lines.append(", ".join(cur))
|
||
cur = [part]
|
||
cur_len = len(part)
|
||
else:
|
||
cur.append(part)
|
||
cur_len += len(part) + 2
|
||
if cur:
|
||
lines.append(", ".join(cur))
|
||
out_bits.append(" Params: " + lines[0])
|
||
for extra in lines[1:]:
|
||
out_bits.append(" " + extra)
|
||
|
||
return "\n".join(out_bits)
|
||
|
||
|
||
def print_outliers(env: Env, df: pd.DataFrame, out: dict) -> None:
|
||
"""Pretty-print the outlier scan as a text report.
|
||
|
||
Layout follows the windows subcommand style: header card, then
|
||
a per-metric reference table (mean / std / sigma threshold),
|
||
then the two pass sets grouped by [Metrics] and [Params].
|
||
"""
|
||
print("=" * 78)
|
||
print(f" Optimization Outlier Scan (per-pass z-score across {out.get('n_passes', '?')} passes)")
|
||
print("=" * 78)
|
||
print(f" EA: {env.ea_name or '(unknown)'} Symbol: {env.symbol or '(unknown)'} "
|
||
f"Period: {env.period or '(unknown)'}")
|
||
print(f" Date range: {env.date_from} → {env.date_to}")
|
||
print(f" Sigma threshold: {out['sigma']:.1f} "
|
||
f"(direction: higher-is-better uses z>=+σ; Equity DD % uses z<=-σ)")
|
||
print()
|
||
|
||
if "error" in out:
|
||
print(f" ERROR: {out['error']}")
|
||
return
|
||
|
||
# Per-metric reference table
|
||
print(f" {'Metric':<22} {'Dir':>5} {'Mean':>12} {'Std':>10} "
|
||
f"{'+σ threshold':>14} {'-σ threshold':>14}")
|
||
print(" " + "─" * 80)
|
||
for m in METRIC_COLS:
|
||
if m == "Trades" or m not in out["per_metric"]:
|
||
continue
|
||
info = out["per_metric"][m]
|
||
print(f" {m:<22} {info['direction']:>5} "
|
||
f"{info['mean']:>12.4f} {info['std']:>10.4f} "
|
||
f"{info['threshold_+sigma']:>14.4f} {info['threshold_-sigma']:>14.4f}")
|
||
if out.get("trades"):
|
||
t = out["trades"]
|
||
print(f" {'Trades (excluded)':<22} {'low':>5} "
|
||
f"{t['mean']:>12.2f} {t['std']:>10.2f} "
|
||
f"{'--':>14} {t['low_outlier_threshold']:>14.2f}")
|
||
print()
|
||
|
||
# Counts
|
||
c = out["counts"]
|
||
print(f" Set A (>=1 perf outlier, sorted by Result desc): {c['outliers']} passes")
|
||
print(f" Set B (no perf outlier, sorted by Result desc): {c['normal']} passes")
|
||
if c["excluded_low_trades"]:
|
||
print(f" Excluded (Trades z<=-σ, too few trades to trust): {c['excluded_low_trades']} passes")
|
||
print()
|
||
|
||
pcols = _param_cols(df)
|
||
|
||
# Set A
|
||
if out["set_outliers"]:
|
||
print("=" * 78)
|
||
print(f" SET A — Passes with at least one performance-metric outlier")
|
||
print(f" (top {len(out['set_outliers'])} by Result; "
|
||
f"{c['outliers']} total eligible)")
|
||
print("=" * 78)
|
||
for rec in out["set_outliers"]:
|
||
print(_fmt_outlier_row(rec, pcols))
|
||
print()
|
||
else:
|
||
print(" SET A: empty (no eligible passes have a performance-metric outlier)")
|
||
print()
|
||
|
||
# Set B
|
||
if out["set_normal"]:
|
||
print("=" * 78)
|
||
print(f" SET B — Passes with no performance-metric outlier (top {len(out['set_normal'])} by Result)")
|
||
print("=" * 78)
|
||
for rec in out["set_normal"]:
|
||
print(_fmt_outlier_row(rec, pcols))
|
||
print()
|
||
else:
|
||
print(" SET B: empty (every eligible pass has at least one performance-metric outlier)")
|
||
print()
|
||
|
||
# Excluded (low-Trades)
|
||
if out["excluded"]:
|
||
print("=" * 78)
|
||
print(f" EXCLUDED — Passes with Trades z <= -{out['sigma']:.0f}σ (too few trades to trust)")
|
||
print("=" * 78)
|
||
for e in out["excluded"]:
|
||
print(f" Pass {e['Pass']:<6} Trades={e['Trades']:<5} Trades_z={e['Trades_z']:+.2f}σ")
|
||
print()
|
||
|
||
# Interpretation
|
||
print(" Interpretation:")
|
||
print(f" z = (this pass's value − mean across all passes) / std.")
|
||
print(f" For higher-is-better metrics, the outlier criterion is z >= +{out['sigma']:.0f}.")
|
||
print(f" For lower-is-better (Equity DD %), the criterion is z <= -{out['sigma']:.0f}.")
|
||
print(f" The Trades exclusion guards against over-fitting: a pass with")
|
||
print(f" very few trades can post an extreme metric value purely by")
|
||
print(f" luck, so we drop those before ranking the rest.")
|
||
|
||
|
||
def _env_card(env: Env) -> dict:
|
||
"""Compact strategy-environment summary for JSON / text output."""
|
||
days = _backtest_days(env)
|
||
return {
|
||
"ea_name": env.ea_name,
|
||
"symbol": env.symbol,
|
||
"period": env.period,
|
||
"date_from": env.date_from,
|
||
"date_to": env.date_to,
|
||
"backtest_days": days,
|
||
"deposit": env.deposit,
|
||
"leverage": env.leverage,
|
||
"server": env.server,
|
||
"mt5_version": env.mt5_version,
|
||
"mt5_build": env.mt5_build,
|
||
"run_created": env.created,
|
||
}
|
||
|
||
|
||
def _top(df: pd.DataFrame, col: str, n: int) -> list[dict]:
|
||
"""Top-N rows by `col`, with the criterion col + parameters preserved."""
|
||
if col not in df.columns or df.empty:
|
||
return []
|
||
pcols = _param_cols(df)
|
||
# Criterion col is the first metric; avoid duplicating it in the tail list
|
||
tail = ["Pass", "Profit", "Profit Factor", "Recovery Factor", "Trades"]
|
||
show = [c for c in [col] + tail if c in df.columns and c not in (col,)]
|
||
# Ensure col itself is first
|
||
show = [col] + [c for c in show if c != col]
|
||
# Pass is always useful
|
||
if "Pass" in df.columns and "Pass" not in show:
|
||
show = ["Pass"] + show
|
||
show = show + [c for c in pcols if c in df.columns and c not in show]
|
||
return df.nlargest(n, col)[show].to_dict("records")
|
||
|
||
|
||
# ── Text output ──────────────────────────────────────────────────────
|
||
|
||
def _fmt_param_row(row: dict, pcols: list[str]) -> str:
|
||
"""Format one best-pass row for the text report."""
|
||
bits = [f"Pass {row.get('Pass', '?'):>3}"]
|
||
for k in ("Profit", "Profit Factor", "Recovery Factor", "Trades"):
|
||
if k in row:
|
||
v = row[k]
|
||
if isinstance(v, float):
|
||
if k == "Trades":
|
||
bits.append(f"{k}={int(v)}")
|
||
else:
|
||
bits.append(f"{k}={v:.3f}")
|
||
else:
|
||
bits.append(f"{k}={v}")
|
||
for p in pcols:
|
||
if p in row:
|
||
bits.append(f"{p}={row[p]}")
|
||
return " ".join(bits)
|
||
|
||
|
||
def print_report(env: Env, df: pd.DataFrame) -> None:
|
||
print("=" * 70)
|
||
print("STRATEGY TESTER OPTIMIZATION REPORT")
|
||
print("=" * 70)
|
||
print(f" EA: {env.ea_name or '(unknown)'}")
|
||
print(f" Symbol: {env.symbol or '(unknown)'}")
|
||
print(f" Period: {env.period or '(unknown)'}")
|
||
print(f" Date range: {env.date_from} → {env.date_to}")
|
||
days = _backtest_days(env)
|
||
if days:
|
||
print(f" Backtest days: {days}")
|
||
print(f" Deposit: {env.deposit}")
|
||
print(f" Leverage: {env.leverage}")
|
||
print(f" Server: {env.server}")
|
||
print(f" MT5: {env.mt5_version} (build {env.mt5_build})")
|
||
print(f" Run created: {env.created}")
|
||
print()
|
||
if df.empty:
|
||
print(" (no pass rows found)")
|
||
return
|
||
|
||
pcols = _param_cols(df)
|
||
print(f" Passes: {len(df)}")
|
||
print(f" Parameters: {', '.join(pcols) or '(none)'}")
|
||
print()
|
||
print(" Parameter cardinalities:")
|
||
for p in pcols:
|
||
u = df[p].nunique()
|
||
print(f" {p}: {u} unique value(s)")
|
||
print()
|
||
print(" Use --analyze for full parameter-effect / duplicate / best-pass analysis.")
|
||
print(" Use --json for raw parsed data.")
|
||
|
||
|
||
def print_analyze(env: Env, df: pd.DataFrame, an: dict) -> None:
|
||
print_report(env, df)
|
||
if "error" in an:
|
||
print(f"\nERROR: {an['error']}")
|
||
return
|
||
pcols = _param_cols(df)
|
||
|
||
print()
|
||
print("=" * 70)
|
||
print("ORTHOGONALITY")
|
||
print("=" * 70)
|
||
ortho = an["orthogonality"]
|
||
print(f" Passes: {ortho['actual']}")
|
||
print(f" Expected (cart.): {ortho['expected_cartesian']}")
|
||
print(f" Complete: {ortho['complete']}")
|
||
if not ortho["complete"]:
|
||
print(f" Missing: {ortho['missing']}")
|
||
|
||
print()
|
||
print("=" * 70)
|
||
print("PARAMETER EFFECT (does the parameter change the result?)")
|
||
print("=" * 70)
|
||
for p, info in an["parameter_effect"].items():
|
||
flag = "DEAD" if info["dead_param"] else ("weak" if info["effect_ratio_spread_over_std"] < 0.5 else "active")
|
||
print(f"\n {p} [{flag}] "
|
||
f"spread={info['spread_max_minus_min']:.1f} "
|
||
f"rel_spread={info['relative_spread_pct']:.1f}% "
|
||
f"effect_ratio={info['effect_ratio_spread_over_std']}")
|
||
for val, mean in info["per_group_mean"].items():
|
||
print(f" {p}={val!s:>8} mean Profit = {mean}")
|
||
|
||
if an.get("dead_boolean_params"):
|
||
print()
|
||
print(" ⚠️ Boolean parameters with identical metric means on true/false:")
|
||
for p, metrics in an["dead_boolean_params"].items():
|
||
print(f" {p}: identical on {', '.join(metrics)} — this parameter did not influence the backtest")
|
||
|
||
print()
|
||
print("=" * 70)
|
||
print("DUPLICATES (passes with identical metric vectors → dead parameter)")
|
||
print("=" * 70)
|
||
dup = an["duplicates"]
|
||
print(f" Groups with duplicates: {dup['groups_with_dupes']}")
|
||
print(f" Total extra duplicate rows: {dup['total_dup_rows']}")
|
||
if dup["examples"]:
|
||
print(" Examples (top 5 by count):")
|
||
for ex in dup["examples"][:5]:
|
||
print(f" count={ex['count']} "
|
||
f"Profit={ex.get('Profit')} PF={ex.get('Profit Factor')} "
|
||
f"RF={ex.get('Recovery Factor')} Trades={ex.get('Trades')}")
|
||
|
||
print()
|
||
print("=" * 70)
|
||
print("BEST PASSES")
|
||
print("=" * 70)
|
||
for criterion, rows in an["best_passes"].items():
|
||
print(f"\n Top 5 by {criterion}:")
|
||
for r in rows:
|
||
print(f" {_fmt_param_row(r, pcols)}")
|
||
|
||
if "trades" in an:
|
||
tr = an["trades"]
|
||
print()
|
||
print("=" * 70)
|
||
print("TRADE COUNT DISTRIBUTION")
|
||
print("=" * 70)
|
||
print(f" Range: {tr['min']} – {tr['max']}")
|
||
print(f" Mean: {tr['mean']}")
|
||
print(f" Median: {tr['median']}")
|
||
print(f" Stdev: {tr['stdev']}")
|
||
print(f" Deciles: {tr['deciles']}")
|
||
if "backtest_days" in tr:
|
||
print(f" Trades/day (median over {tr['backtest_days']} days): {tr['trades_per_day_median']}")
|
||
print(f" Trades/day (max): {tr['trades_per_day_max']}")
|
||
for k in (
|
||
"corr_trades_vs_profit_factor",
|
||
"corr_trades_vs_profit",
|
||
"corr_trades_vs_equity_dd",
|
||
):
|
||
if k in tr:
|
||
hint = ""
|
||
v = tr[k]
|
||
if k == "corr_trades_vs_profit_factor" and v < -0.3:
|
||
hint = " ← more trades → worse PF (overtrading signal)"
|
||
elif k == "corr_trades_vs_equity_dd" and v > 0.3:
|
||
hint = " ← more trades → higher drawdown"
|
||
print(f" {k}: {v}{hint}")
|
||
|
||
|
||
# ── CLI ──────────────────────────────────────────────────────────────
|
||
|
||
def main() -> None:
|
||
parser = argparse.ArgumentParser(
|
||
description="Parse MT5 Strategy Tester Optimization XML report",
|
||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||
epilog="""
|
||
Examples:
|
||
# Text report (default)
|
||
python %(prog)s ReportOptimizer-*.xml
|
||
|
||
# JSON dump (raw parsed data)
|
||
python %(prog)s ReportOptimizer-*.xml --json
|
||
|
||
# Full optimization analysis (parameter effect, duplicates, best passes)
|
||
python %(prog)s ReportOptimizer-*.xml --analyze
|
||
|
||
# Outlier scan: per-pass z-score on the 8 perf metrics,
|
||
# split into "has at least one strong outlier" vs "no outlier"
|
||
python %(prog)s ReportOptimizer-*.xml outliers
|
||
python %(prog)s ReportOptimizer-*.xml outliers --top-outliers 5 --top-normal 5 --sigma 2
|
||
python %(prog)s ReportOptimizer-*.xml outliers --json
|
||
""",
|
||
)
|
||
parser.add_argument("report", help="Path to ReportOptimizer-*.xml file")
|
||
parser.add_argument("--json", action="store_true", help="Output raw parsed data as JSON")
|
||
parser.add_argument(
|
||
"--analyze",
|
||
action="store_true",
|
||
help="Run optimization analysis (parameter effect, duplicates, best passes, trade distribution)",
|
||
)
|
||
sub = parser.add_subparsers(dest="cmd")
|
||
|
||
p_out = sub.add_parser(
|
||
"outliers",
|
||
help="Per-pass z-score outlier scan on the 8 performance metrics. "
|
||
"Splits passes into a 'strongly-strong' set (at least one metric with |z|>=σ "
|
||
"in the favourable direction) and a 'no-outlier' set, both sorted by Result "
|
||
"desc and printed with the metrics group + the input-parameter group.",
|
||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||
epilog="""
|
||
Examples:
|
||
# Default: sigma=2, top 10 outlier passes, top 5 normal passes
|
||
python parse_optimizer_report.py ReportOptimizer-*.xml outliers
|
||
|
||
# Tighter sigma threshold and custom top-N
|
||
python parse_optimizer_report.py ReportOptimizer-*.xml outliers --sigma 3 --top-outliers 5
|
||
|
||
# JSON output for further processing
|
||
python parse_optimizer_report.py ReportOptimizer-*.xml outliers --json
|
||
|
||
For higher-is-better metrics (Profit, Profit Factor, Recovery Factor,
|
||
Sharpe Ratio, Expected Payoff, Custom, Result), an outlier is z >= +σ.
|
||
For lower-is-better (Equity DD %), an outlier is z <= -σ. Passes whose
|
||
Trades count is itself a low-side outlier (z <= -σ) are excluded
|
||
first — they have too few trades to trust.
|
||
""",
|
||
)
|
||
p_out.add_argument(
|
||
"--sigma", "-s", type=float, default=2.0,
|
||
help="z-score threshold for the outlier scan (default: 2.0)",
|
||
)
|
||
p_out.add_argument(
|
||
"--top-outliers", type=int, default=10,
|
||
help="Number of passes to show from the 'has at least one outlier' set (default: 10)",
|
||
)
|
||
p_out.add_argument(
|
||
"--top-normal", type=int, default=5,
|
||
help="Number of passes to show from the 'no outlier' set (default: 5)",
|
||
)
|
||
p_out.add_argument(
|
||
"--json", action="store_true", help="Output outliers data as JSON",
|
||
)
|
||
|
||
args = parser.parse_args()
|
||
|
||
path = Path(args.report)
|
||
if not path.exists():
|
||
print(f"Error: {path} not found", file=sys.stderr)
|
||
sys.exit(1)
|
||
|
||
env, df = parse_report(path)
|
||
analyze_data = analyze(df, env) if (args.analyze or args.json) else None
|
||
|
||
if args.cmd == "outliers":
|
||
out_data = find_outliers(
|
||
df,
|
||
sigma=args.sigma,
|
||
top_outliers=args.top_outliers,
|
||
top_normal=args.top_normal,
|
||
)
|
||
if getattr(args, "json", False):
|
||
payload = {
|
||
"env": asdict(env),
|
||
"pass_count": int(len(df)),
|
||
"columns": list(df.columns),
|
||
"outliers": out_data,
|
||
}
|
||
print(json.dumps(payload, indent=2, ensure_ascii=False, default=str))
|
||
else:
|
||
print_outliers(env, df, out_data)
|
||
return
|
||
|
||
if args.json or args.analyze:
|
||
payload = {
|
||
"env": asdict(env),
|
||
"pass_count": int(len(df)),
|
||
"columns": list(df.columns),
|
||
"passes": df.where(pd.notnull(df), None).to_dict("records"),
|
||
}
|
||
if analyze_data is not None:
|
||
payload["analyze"] = analyze_data
|
||
print(json.dumps(payload, indent=2, ensure_ascii=False, default=str))
|
||
else:
|
||
print_report(env, df)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|