本文へ移動
cccskills
無料GitHub で公開

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. Use when the user wants to batch-test MT5 bots/EAs, screen robots across all symbols, optimize EA parameters, or move candidate bots to finalists based on profit, drawdown, positive months/years and equity-curve criteria. Runs terminal64.exe headless; Windows + MetaTrader 5 required at run time.

インストール方法を見る

含まれるファイル(19)

  • SKILL.md10.2 KB
  • assets/dashboard.html11.4 KB
  • assets/pipeline_config.template.json2.3 KB
  • references/mt5-cli-reference.md6.4 KB
  • requirements.txt70 B
  • scripts/dashboard.py15.5 KB
  • scripts/mt5_batch_tester.py56.5 KB
  • scripts/mt5_common.py7.4 KB
  • scripts/mt5_learnings.py7.5 KB
  • scripts/parse_mt5_optimization.py6.5 KB
  • scripts/parse_mt5_report.py9.7 KB
  • scripts/tests/fixtures/sample_opt.xml12.0 KB
  • scripts/tests/fixtures/sample_report.htm2.8 KB
  • scripts/tests/test_batch_tester.py40.1 KB
  • scripts/tests/test_dashboard_gates.py3.6 KB
  • scripts/tests/test_dashboard.py7.8 KB
  • scripts/tests/test_learnings.py1.9 KB
  • scripts/tests/test_parse_optimization.py1.9 KB
  • scripts/tests/test_parse_report.py2.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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:

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

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

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:

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, best_symbol, r2_profit, final_profit, final_dd_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.
  3. Real ticks (Model=4) need broker tick data; it is slow — expect long runs.
  4. Resumable: every round checkpoints; --resume reuses only fingerprint- matching work and retries execution errors.
  5. Fail closed: incomplete, timed-out, stale, or unparsable reports never reject, promote, or move a candidate. Every unique Round-1 symbol must finish.
  6. 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.
  7. Full-period metrics: months without deals at the start, end, or across a full year remain part of the configured test period.
  8. Learn each loop: parameter/symbol statistics bias future runs toward wins.
  9. Verify against your build: report layout (esp. the deals table) and the 32 ms delay mapping can differ — see the reference's (verify) notes.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.

日本語の概要は準備中です。原文の説明を表示しています。

tradermonty/claude-trading-skills2,9952026年10月13日 更新

This skill should be used when analyzing market breadth charts, specifically the S&P 500 Breadth Index (200-Day MA based) and the US Stock Market Uptrend Stock Ratio charts. Use this skill when the user provides breadth chart images for analysis, requests market breadth assessment, positioning strategy recommendations, or wants to understand medium-term strategic and short-term tactical market outlook based on breadth indicators. All analysis and output are conducted in English.

日本語の概要は準備中です。原文の説明を表示しています。

tradermonty/claude-trading-skills2,9952026年10月13日 更新

This skill should be used when analyzing market breadth charts, specifically the S&P 500 Breadth Index (200-Day MA based) and the US Stock Market Uptrend Stock Ratio charts. Use this skill when the user provides breadth chart images for analysis, requests market breadth assessment, positioning strategy recommendations, or wants to understand medium-term strategic and short-term tactical market outlook based on breadth indicators. Also works WITHOUT chart images by fetching CSV data directly from public sources. All analysis and output are conducted in English.

日本語の概要は準備中です。原文の説明を表示しています。

tradermonty/claude-trading-skills2,9952026年10月13日 更新

Generate Minervini-style breakout trade plans from VCP screener output with worst-case risk calculation, portfolio heat management, and Alpaca-compatible order templates (stop-limit bracket for pre-placement, limit bracket for post-confirmation). Use when user has VCP screener results and wants actionable trade plans with entry/stop/target levels and position sizing.

日本語の概要は準備中です。原文の説明を表示しています。

tradermonty/claude-trading-skills2,9952026年10月13日 更新

Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.

日本語の概要は準備中です。原文の説明を表示しています。

tradermonty/claude-trading-skills2,9952026年10月13日 更新

Synthesize the three Jason Shapiro contrarian-pipeline verdicts (COT crowding, news-reaction failure, weekly price-action confirmation) into one actionable setup_status via a fail-closed precedence state machine. Pure, offline synthesis -- no network, no API keys, no computation beyond validation and precedence.

日本語の概要は準備中です。原文の説明を表示しています。

tradermonty/claude-trading-skills2,9952026年10月13日 更新

tradermonty のスキルをすべて見る

このスキルの問題を報告する