tradermonty/claude-trading-skills

mt5-robot-tester

Select the best MetaTrader 5 trading robots (Expert Advisors) that have not been backtested yet, by running the MT5 Strategy Tester from the command line through a 3-round pipeline.

查看源码

使用方法

  1. 复制安装命令,在自己的智能体环境安装前先检查源码包。
  2. 从原始文档核对所需工具、凭据和支持的宿主。
  3. 说明输入材料与期望交付物,并对照来源检查输出。
npx skills add https://github.com/tradermonty/claude-trading-skills --skill mt5-robot-tester

开始前需了解

  • 安装不包含模型订阅、API 额度或访问文件的权限。
  • 仓库星数和安装量反映活跃情况,不等于独立质量或安全测试。
仓库原始内容

按源仓库内容呈现,保留标题、案例、代码、表格、链接以及原文引用的演示图片。

MT5 Robot Tester

Overview

Select the best MetaTrader 5 robots (Expert Advisors) from a candidates folder by driving the Strategy Tester from the command line through a 3-round pipeline, moving each bot between folders as it advances, and learning across runs to improve selection each loop. The whole run is checkpointed and resumable.

  • Round 1 — screening (all pairs): backtest the EA on each symbol in the

configured common.symbols list (one Optimization=0 backtest per symbol — MT5 build 6061 leaves the Optimization=3 XML empty, so per-symbol backtests are used). Gate: ≥5 symbols profitable AND best symbol ≥3× deposit.

  • Round 2 — best-pair backtest: single backtest on the best symbol; analyze

net profit %, worst drawdown %, % positive months, all-years-positive, LR Correlation, months-to-new-high.

  • Round 3 — sequential parameter optimization: optimize the 5–6 inputs after

MagicNumber, one at a time, range ±50% step 5%; then a final backtest.

  • Finalist: optimized result improves on Round 2 and profit **≥4×

deposit and worst drawdown ≤12%**.

Tested bots move to in-testing; finalists are also copied to finalists with their optimized .set.

When to Use

  • "Prueba robots / bots / EAs en MetaTrader 5."
  • Screen a folder of MT5 Expert Advisors and pick the best across all pairs.
  • Optimize EA parameters and decide finalists by profit/drawdown/consistency.
  • Resume an interrupted testing run.

Prerequisites

  • Windows + MetaTrader 5 installed (the tester runs terminal64.exe).
  • Broker tick data downloaded (default modeling is real ticks, Model=4).
  • The three folders under MQL5\Experts: candidates, in-testing, finalists.
  • `common.symbols` set in the config — the pairs Round 1 backtests (your

Market Watch symbols).

  • Optional per-bot .set files (config sets_dir) for the Round-2 baseline and

Round-3 parameter optimization. Every input is fixed during optimization except the one parameter currently being searched; without a .set, Round 3 is skipped and the verdict comes from Round 2.

  • Close MetaTrader 5 before running — the tester needs exclusive use of the

data folder.

  • Python 3.9+ (standard library only). No paid API.

Workflow

Step 1 — Configure

Copy assets/pipeline_config.template.json, fill in the three folder paths and set terminal_path to an explicit terminal64.exe path. For actual tester execution, a terminal path is required unless you opt into discovery when no path is supplied. Pass it via --terminal-path, config terminal_path, or $MT5_TERMINAL_PATH. --dry-run generates INIs without resolving or launching a terminal and needs no terminal path. Never commit real personal paths — pass the config at run time. Defaults already encode the agreed settings (2020.01.01→2026.06.30, H1, Model=4, 10000 USD, 1:100, gates and thresholds).

Terminal selection is opt-in. For safety, the pipeline never auto-discovers terminal64.exe under Program Files. On a machine that also trades live, the auto-detected terminal is often the broker's live terminal; the generated INI has no Login, so it wakes on whatever account was last used, and ShutdownTerminal=1 closes it at the end. To avoid silently borrowing (and closing) a live terminal, pass an explicit path to a portable, tester-only install (a /portable folder with its own data directory). If you explicitly accept the risk, --allow-auto-detect restores Program Files discovery only when no --terminal-path / $MT5_TERMINAL_PATH / config.terminal_path is set.
Migration from the old precedence/fallback behavior: selection now uses --terminal-path > config.terminal_path > MT5_TERMINAL_PATH; config outranks the environment variable. The highest-precedence supplied path must exist: a missing path is a hard error, with no fallback even with --allow-auto-detect. For example, a stale config.terminal_path blocks a valid MT5_TERMINAL_PATH. Fix or remove the stale config setting, or override it with a valid CLI path. Auto-detection is opt-in and applies only when no path is supplied.

Step 2 — Dry-run (optional)

Verify the generated Round-1 INIs without resolving or launching MT5. No terminal path or installed MT5 is required for this offline dry-run:

bash
python3 skills/mt5-robot-tester/scripts/mt5_batch_tester.py \
  --config my_config.json --output-dir reports/mt5_pipeline --dry-run

Step 3 — Run the pipeline

bash
python3 skills/mt5-robot-tester/scripts/mt5_batch_tester.py \
  --config my_config.json --output-dir reports/mt5_pipeline \
  --terminal-path "C:\Program Files\MetaTrader 5\tester\terminal64.exe"

Each bot flows R1 → R2 → R3 → finalist decision. Progress is written to state.json and run.log after every step.

Step 4 — Resume if interrupted

bash
python3 skills/mt5-robot-tester/scripts/mt5_batch_tester.py \
  --config my_config.json --output-dir reports/mt5_pipeline --resume \
  --terminal-path "C:\Program Files\MetaTrader 5\tester\terminal64.exe"

--resume skips completed bots and reuses finished rounds only while the execution config, EA binary, and input .set fingerprints still match. A changed period, symbol list, binary, or .set restarts that bot safely.

Optional — HTML control panel

Launch a local dashboard to see the bots in each folder, each bot's phase and verdict, and a Launch button — no CLI needed after starting it:

bash
python3 skills/mt5-robot-tester/scripts/dashboard.py \
  --config my_config.json --output-dir reports/mt5_pipeline

It serves http://127.0.0.1:8765/ (opens automatically, localhost only). The page auto-refreshes every 3 s: folder contents, per-bot phase (R1/R2/R3/done), pass/fail verdicts, summary counts, and the live run.log. Start/stop requests are limited to the exact local origin and require the per-server CSRF token.

Step 5 — Read the results

  • leaderboard_<ts>.md / .json — ranking with verdict and key metrics.
  • learnings.json / learnings.md — what the skill learned this loop

(parameter impact and symbol priors) under the configured output directory.

  • mt5_reports/ and mt5_ini/ — raw MT5 reports and configs per bot/round.

Round details

Round 1 gate (both required)

  1. count_positive_profit(passes) ≥ round1_min_positive (default 5).
  2. best_symbol_profit ≥ round1_min_profit_multiple × deposit (default 3×).

Fail → bot rejected (moved to in-testing).

Round 2 quality profile (reference thresholds)

Net profit ≥300%, worst DD <15% (larger of balance/equity %), positive months

70%, all years positive, LR Correlation ≥0.80, months-to-new-high ≤3.

Reported per bot; the hard finalist gate is Round 3.

Round 3 sequential optimization

For each of the 5–6 inputs after MagicNumber (learned order first), optimize that single parameter over [V×0.5, V×1.5] step V×0.05 (Optimization=1) while fixing every other .set input, fix its best value, then continue. Run a final backtest with the exact complete input set saved for a finalist.

Finalist

evaluate_finalist: improved on Round 2 and profit ≥4× deposit and worst DD ≤12%. → copied to finalists with <bot>.set.

Self-learning across loops

learnings.json accumulates, per run: parameter average profit improvement (reorders Round-3 optimization so the most impactful parameters are tried first), symbol priors (how often each is a best pair), and per-bot verdicts. This makes selection converge faster each loop. Deterministic — plain aggregate statistics.

Output Format

  • leaderboard_<ts>.json — list of `{name, verdict, bestsymbol, r2profit,

finalprofit, finaldd_pct, lr, reason}` sorted finalists-first by profit.

  • leaderboard_<ts>.md — same as a table.
  • state.json — resumable per-bot/per-round checkpoint.

Resources

  • scripts/mt5_batch_tester.py — pipeline orchestrator + INI builders (CLI).
  • scripts/parse_mt5_optimization.py — optimization report (XML/HTML) parser +

Round-1 gate.

  • scripts/parse_mt5_report.py — backtest report parser + balance-series metrics.
  • scripts/mt5_learnings.py — cross-run learning store.
  • scripts/mt5_common.py — shared parsing helpers (EN/ES headers, numbers).
  • references/mt5-cli-reference.md — MT5 [Tester]/[TesterInputs] keys, enums,

report formats and caveats.

  • assets/pipeline_config.template.json — config template with placeholders.

Key Principles

  1. Never commit personal paths — folders/terminal come from config/ENV/args.
  2. Relative `Report=` names because build 6061 ignores absolute report paths;

collect completed reports from the terminal data directory.

  1. Real ticks (`Model=4`) need broker tick data; it is slow — expect long runs.
  2. Resumable: every round checkpoints; --resume reuses only fingerprint-

matching work and retries execution errors.

  1. Fail closed: incomplete, timed-out, stale, or unparsable reports never

reject, promote, or move a candidate. Every unique Round-1 symbol must finish.

  1. Single MT5 owner: an OS lock is held for the process lifetime for each

shared MT5 data folder. If child termination cannot be confirmed, the whole run stops and writes a .blocked marker; verify the recorded PID/process tree has exited before removing that marker manually.

  1. Full-period metrics: months without deals at the start, end, or across a

full year remain part of the configured test period.

  1. Learn each loop: parameter/symbol statistics bias future runs toward wins.
  2. Verify against your build: report layout (esp. the deals table) and the

32 ms delay mapping can differ — see the reference's (verify) notes.

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