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Documentation
SKILL.md
npx skills add tradermonty/claude-trading-skills --skill 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.
npx skills add tradermonty/claude-trading-skills --skill mt5-robot-tester
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.
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.MagicNumber, one at a time, range ±50% step 5%; then a final backtest.Tested bots move to in-testing; finalists are also copied to finalists with
their optimized .set.
terminal64.exe).Model=4).MQL5\Experts: candidates, in-testing, finalists.common.symbols set in the config — the pairs Round 1 backtests (your
Market Watch symbols)..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.Copy assets/pipeline_config.template.json, fill in the three folder paths and
(optionally) 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).
Verify the generated Round-1 INIs without launching MT5:
Each bot flows R1 → R2 → R3 → finalist decision. Progress is written to
state.json and run.log after every step.
--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.
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:
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.
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.count_positive_profit(passes) ≥ round1_min_positive (default 5).best_symbol_profit ≥ round1_min_profit_multiple × deposit (default 3×).Fail → bot rejected (moved to in-testing).
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.
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.
evaluate_finalist: improved on Round 2 and profit ≥4× deposit and worst
DD ≤12%. → copied to finalists with <bot>.set.
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.
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.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.Report= names because build 6061 ignores absolute report paths;
collect completed reports from the terminal data directory.Model=4) need broker tick data; it is slow — expect long runs.--resume reuses only fingerprint-
matching work and retries execution errors..blocked marker; verify the recorded PID/process tree
has exited before removing that marker manually.