mt5-robot-tester

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MT5 Robot Tester

MT5 机器人测试工具

Overview

概述

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

通过命令行驱动策略测试器执行三轮流程，从候选者文件夹中筛选出最佳MetaTrader 5机器人（Expert Advisors），测试过程中会将机器人在不同文件夹间转移，且跨运行周期学习以优化每一轮的筛选结果。整个运行过程支持断点续跑。

第一轮——全品种筛选：在配置的
```
common.symbols
```
列表中的每个品种上对EA进行回测（每个品种执行一次
```
Optimization=0
```
回测——MT5版本6061会使
```
Optimization=3
```
的XML为空，因此采用单品种回测）。准入条件：至少5个品种盈利，且表现最佳的品种收益≥初始资金的3倍。
第二轮——最优品种回测：在表现最佳的品种上执行单次回测；分析净利润率、最大回撤率、盈利月份占比、全年盈利占比、LR相关性、创新高所需月份数。
第三轮——顺序参数优化：对
```
MagicNumber
```
之后的5-6个输入参数逐一进行优化，参数范围为原值的±50%，步长5%；随后执行最终回测。
最终入选者：优化结果优于第二轮，且收益≥初始资金的4倍，同时最大回撤≤12%。

已测试的机器人会被转移至测试中文件夹；最终入选者会连同其优化后的

.set

文件一同复制到最终入选者文件夹。

When to Use

适用场景

"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.

在MetaTrader 5中测试机器人/EA。
筛选文件夹中的MT5 Expert Advisors，选出全品种中的最佳者。
优化EA参数，并根据收益/回撤/稳定性确定最终入选者。
恢复中断的测试运行。

Prerequisites

前置条件

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.

已安装Windows系统 + MetaTrader 5（测试器运行
```
terminal64.exe
```
）。
已下载经纪商tick数据（默认采用真实tick数据建模，
```
Model=4
```
）。
在
```
MQL5\Experts
```
下创建三个文件夹：candidates、in-testing、finalists。
在配置文件中设置**
```
common.symbols
```
**——第一轮回测涉及的品种（即您的市场观察列表中的品种）。
可选：为第二轮基准测试和第三轮参数优化准备每个机器人对应的
```
.set
```
文件（配置
```
sets_dir
```
）。优化过程中，除当前正在搜索的参数外，所有其他输入参数均固定；若无
```
.set
```
文件，则跳过第三轮，根据第二轮结果判定。
运行前关闭MetaTrader 5——测试器需要独占数据文件夹的使用权。
Python 3.9+（仅需标准库）。无需付费API。

Workflow

工作流程

Step 1 — Configure

步骤1——配置

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).

复制

assets/pipeline_config.template.json

，填写三个文件夹路径，（可选）填写

terminal_path

。切勿提交真实个人路径——运行时传入配置文件即可。默认设置已包含约定参数（测试周期2020.01.01→2026.06.30，H1周期，

Model=4

，初始资金10000美元，杠杆1:100，准入条件及阈值）。

Step 2 — Dry-run (optional)

步骤2——试运行（可选）

Verify the generated Round-1 INIs without launching MT5:

bash

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

无需启动MT5，验证生成的第一轮INI文件：

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

步骤3——运行流程

bash

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.

bash

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

每个机器人会依次经过第一轮→第二轮→第三轮→最终入选判定。每一步完成后，进度会写入

state.json

和

run.log

。

Step 4 — Resume if interrupted

步骤4——恢复中断的运行

bash

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.

bash

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

--resume

参数会跳过已完成测试的机器人，仅在执行配置、EA二进制文件和输入

.set

文件的指纹匹配时，复用已完成的测试轮次。若测试周期、品种列表、二进制文件或

.set

文件发生变化，会安全地重新启动该机器人的测试。

Optional — HTML control panel

可选——HTML控制面板

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.

启动本地仪表盘，查看各文件夹中的机器人、每个机器人的测试阶段及判定结果，还有启动按钮——启动后无需再使用CLI：

bash

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

仪表盘会在

http://127.0.0.1:8765/

提供服务（自动打开，仅本地可访问）。页面每3秒自动刷新：显示文件夹内容、每个机器人的测试阶段（第一轮/第二轮/第三轮/完成）、通过/未通过判定、汇总统计数，以及实时的

run.log

。启动/停止请求仅限本地源，并需要服务器专属的CSRF令牌。

Step 5 — Read the results

步骤5——查看结果

```
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.

```
leaderboard_<ts>.md
```
/
```
.json
```
——按收益排序的排名列表，包含判定结果及关键指标，最终入选者排在前面。
```
learnings.json
```
/
```
learnings.md
```
——本轮运行中工具学到的内容（参数影响及品种优先级），位于配置的输出目录下。
```
mt5_reports/
```
和
```
mt5_ini/
```
——每个机器人/测试轮次的原始MT5报告和配置文件。

Round details

轮次细节

Round 1 gate (both required)

第一轮准入条件（需同时满足）

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).

count_positive_profit(passes) ≥ round1_min_positive

（默认值为5）。

best_symbol_profit ≥ round1_min_profit_multiple × deposit

（默认值为3倍初始资金）。

未通过→机器人被淘汰（转移至in-testing文件夹）。

Round 2 quality profile (reference thresholds)

第二轮质量指标（参考阈值）

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.

净利润≥300%，最大回撤<15%（余额/权益回撤率的较大值），盈利月份占比>70%，全年盈利，LR相关性≥0.80，创新高所需月份数≤3。每个机器人都会报告这些指标；最终入选者的硬性准入条件由第三轮决定。

Round 3 sequential optimization

第三轮顺序参数优化

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.

针对

MagicNumber

之后的5-6个输入参数（按学习到的优先级排序），逐一优化单个参数，范围为

[原值×0.5, 原值×1.5]

，步长

原值×0.05

（

Optimization=1

），同时固定

.set

文件中的所有其他输入参数，确定该参数的最优值后继续下一个参数。最后使用完整的最优输入集执行一次最终回测，并保存结果供最终入选者使用。

Finalist

最终入选者判定

evaluate_finalist

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

<bot>.set

evaluate_finalist

：优化结果优于第二轮且收益≥初始资金的4倍且最大回撤≤12%。→ 复制到finalists文件夹，同时保存

<bot>.set

文件。

Self-learning across loops

跨运行周期自学习

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.

learnings.json

会在每次运行时累积数据：参数平均收益提升（重新排序第三轮优化顺序，优先测试影响最大的参数）、品种优先级（每个品种成为最优品种的频率），以及每个机器人的判定结果。这使得每一轮筛选的收敛速度更快。整个过程是确定性的——仅使用聚合统计数据。

Output Format

输出格式

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.

leaderboard_<ts>.json

——包含

{name, verdict, best_symbol, r2_profit, final_profit, final_dd_pct, lr, reason}

的列表，按收益从高到低排序，最终入选者在前。

```
leaderboard_<ts>.md
```
——以表格形式呈现相同内容。
```
state.json
```
——支持断点续跑的每个机器人/每一轮测试的 checkpoint。

Resources

资源

```
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.

```
scripts/mt5_batch_tester.py
```
——流程编排器 + INI文件生成器（CLI工具）。
```
scripts/parse_mt5_optimization.py
```
——优化报告（XML/HTML）解析器 + 第一轮准入条件判定。
```
scripts/parse_mt5_report.py
```
——回测报告解析器 + 资金曲线指标计算。
```
scripts/mt5_learnings.py
```
——跨运行周期学习数据存储工具。
```
scripts/mt5_common.py
```
——共享解析工具（支持英/西文表头、数字解析）。
```
references/mt5-cli-reference.md
```
——MT5的
```
[Tester]
```
/
```
[TesterInputs]
```
参数、枚举值、报告格式及注意事项。
```
assets/pipeline_config.template.json
```
——带占位符的配置模板。

Key Principles

核心原则

Never commit personal paths — folders/terminal come from config/ENV/args.
Relative
Report=
names because build 6061 ignores absolute report paths; collect completed reports from the terminal data directory.
Real ticks (
Model=4
) need broker tick data; it is slow — expect long runs.
Resumable: every round checkpoints;
```
--resume
```
reuses only fingerprint- matching work and retries execution errors.
Fail closed: incomplete, timed-out, stale, or unparsable reports never reject, promote, or move a candidate. Every unique Round-1 symbol must finish.
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.
Full-period metrics: months without deals at the start, end, or across a full year remain part of the configured test period.
Learn each loop: parameter/symbol statistics bias future runs toward wins.
Verify against your build: report layout (esp. the deals table) and the 32 ms delay mapping can differ — see the reference's (verify) notes.

切勿提交个人路径——文件夹/终端路径来自配置文件/环境变量/命令行参数。
使用相对路径
Report=
——因为版本6061会忽略绝对报告路径；需从终端数据目录收集已完成的报告。
**真实tick数据（
```
Model=4
```
）**需要经纪商tick数据；测试速度较慢——预计运行时间较长。
支持断点续跑：每一轮测试都会生成checkpoint；
```
--resume
```
仅复用指纹匹配的已完成工作，并重试执行错误。
默认拒绝：不完整、超时、过期或无法解析的报告不会淘汰、晋级或移动候选机器人。第一轮的每个品种测试必须全部完成。
MT5独占使用：每个共享MT5数据文件夹在进程运行期间会持有系统锁。若无法确认子进程已终止，整个运行会停止并写入
```
.blocked
```
标记；手动移除该标记前，请确认记录的PID/进程树已退出。
全周期指标：测试周期内开头、结尾或全年无交易的月份仍会纳入统计。
每轮学习：参数/品种统计数据会使后续运行更倾向于选出优秀结果。
适配您的MT5版本：报告布局（尤其是交易表格）和32毫秒延迟映射可能因版本而异——请参考文档中的*(verify)*说明。