Mt5 Robot Tester

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 (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).

Step 2 — Dry-run (optional)

Verify the generated Round-1 INIs without launching MT5:

bashCopy code
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

bashCopy code
python3 skills/mt5-robot-tester/scripts/mt5_batch_tester.py \
  --config my_config.json --output-dir reports/mt5_pipeline

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

bashCopy code
python3 skills/mt5-robot-tester/scripts/mt5_batch_tester.py \
  --config my_config.json --output-dir reports/mt5_pipeline --resume

--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:

bashCopy code
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.