diff --git a/.gitignore b/.gitignore index dadca99..318775f 100644 --- a/.gitignore +++ b/.gitignore @@ -8,8 +8,6 @@ data/ # Run outputs / scratch results/ -*.db # Optuna SQLite studies -*.htm # pulled MT5 reports # Secrets — NEVER commit broker credentials .env @@ -18,3 +16,9 @@ results/ # OS .DS_Store Thumbs.db + +# NOTE: studies/*.db (Optuna) and reports/*.html (MT5 + dashboard) ARE +# committed — they're research artifacts, not scratch output. The Optuna DB +# is the only way to reproduce a finalist; HTML reports are the only ground +# truth for the Python-vs-MT5 gap analysis. + diff --git a/PROJECT_GUIDE.md b/PROJECT_GUIDE.md new file mode 100644 index 0000000..377632b --- /dev/null +++ b/PROJECT_GUIDE.md @@ -0,0 +1,747 @@ +# PROJECT_GUIDE — Backtesting + Optuna + MT5 Stack 项目说明 + +> 这份文档是**项目实例说明**：它描述当前这个目录里实际跑通的那一套 +> （GoldScalperPro EA × XAUUSD × IC Markets Demo）——架构为什么这么搭、开发过程踩过 +> 的坑、日常怎么用、后续怎么扩展。它和 `README.md`/`01–08-*.md` 是互补关系： +> 那些是**知识库**（抽象架构 + 通用方法论），这份是**实例说明**（具体项目层面）。 +> +> 如果你只是想跑一遍流程：跳到 §3「使用手册」。 +> 如果你是接手维护：先读 §1「架构理念」+ §4「扩展指南」+ §5「踩坑列表」。 + +--- + +## 0. 项目一句话 + +一个把 MetaTrader 5 Strategy Tester 当作"金标准"、用 Python 镜像引擎做高速贝叶斯 +搜索的个人量化策略研究实验室。当前已对 **GoldScalperPro** 这个 XAUUSD M5 EA +跑通完整的"假设 → 搜索 → MT5 验证 → 入注册表"闭环，并产出第一条 registry 记录。 + +--- + +## 1. 架构理念 + +### 1.1 两层模型（the two-tier design） + +核心思想：**Python 排序，MT5 拍板**。 + +- **Tier 1（Python 镜像引擎）**：把 EA 的填单/出场逻辑 bar-by-bar 重写一遍，跑在 + Parquet 历史数据上。全 2 年 XAUUSD M5×M1 数据一次回测几秒钟。Optuna 拿它做 + 几百上千次试验，从中挑出 2–3 个 diverse finalist。 +- **Tier 2（MT5 Strategy Tester）**：只对 finalist 跑真实 tester。MT5 的数字才是 + live 决策依据；Python 数字只负责排序和 A/B。 + +``` + Idea ─► Python mirror engine ─► Optuna search (thousands of trials, fast) + │ + ▼ + 2–3 diverse finalists + │ + ▼ + MetaTrader 5 Strategy Tester (gold standard) + │ + ▼ + Python-vs-MT5 comparison table ─► keep / discard / iterate + │ + ▼ + registry/ (locked, append-only) +``` + +**为什么这样分层**：MT5 真实 tick 模式跑一次 2 年回测要 10–30 分钟，做不了 1000 次 +Optuna 搜索；纯 Python 又不可信。两层分工把"快"和"准"分开：用快的引擎做广度搜索， +用准的 tester 做最终验证。 + +### 1.2 单向依赖分层 + +整个仓库是 9 个**单向依赖层**，高调低、低不知高： + +``` +┌─────────────────────────────────────────────────────────────────────┐ +│ STRATEGY / CALLER 一个策略一个文件夹 │ +│ load bars → compute signals+stops → call engine → score │ +└───────────────┬──────────────────────────────────┬─────────────────┘ + │ │ + ┌───────▼────────┐ ┌────────▼─────────┐ + │ OPTIMIZER │ │ MT5 BRIDGE │ + │ Optuna objective│ │ compile/run/ │ + │ diverse top-N │ │ parse/compare │ + └───────┬─────────┘ └────────┬─────────┘ + │ │ + ┌───────▼─────────┐ │ + │ ROBUSTNESS │ read-only over results │ + └───────┬─────────┘ │ + │ │ + ┌───────▼─────────────────────────────────────▼──────────┐ + │ ENGINE (frozen) bar-by-bar fill simulator │ + │ knows: bars, signals, stop/target prices, instrument │ + │ knows NOT: your strategy, indicators, broker │ + └───┬─────────────┬────────────────┬──────────────────────┘ + │ │ │ + ┌────────▼───┐ ┌──────▼──────┐ ┌──────▼───────┐ ┌──────────────┐ + │ INDICATORS │ │ INSTRUMENTS │ │ GATES │ │ DATA │ + │ RSI/ATR/.. │ │ per-symbol │ │ entry-filter │ │ loaders + │ + │ pure fns │ │ config объ. │ │ masks │ │ MT5 parser │ + └────────────┘ └─────────────┘ └──────────────┘ └──────────────┘ +``` + +**每一层的"必须 / 不能"**（来自 [02-architecture.md](02-architecture.md#L40-L54)）： + +| Layer | 文件夹 | Owns | Must NOT | +|-------|--------|------|----------| +| Data | `shared/data/` | 从 Parquet 加载 bars；从 MT5 拉历史；解析 MT5 HTML 报告 | 含策略逻辑 | +| Instruments | `shared/instruments/` | 每个 symbol/broker 的 config 对象（tick value、spread、swap、lot step） | 知道任何策略 | +| Indicators | `shared/indicators/` | 纯函数：RSI、ATR、EMA、SMA、自定义指标 | 跨调用保状态 | +| Gates | `shared/gates/` | 布尔 mask，过滤入场（regime、时段、exhaustion） | 开/平仓 | +| Engine | `shared/core/` | bar-by-bar fill/exit 模拟器；验证后**冻结** | 算信号或止损价 | +| Robustness | `shared/robustness/` | 只读反过拟合分析 | 改 engine 或 result | +| Optimizer | `shared/optimizer/` | objective 函数、Optuna 接线、diverse top-N 选择 | 知道 broker 细节 | +| MT5 bridge | `shared/mt5_pipeline/` | 生成 .set/.ini、跑 tester、拉报告、对比 | 算策略 | +| Strategy/caller | `strategies//` | glue：数据 → 信号 → 止损 → engine → 指标 | 被另一策略 import（copy，不 import） | +| Registry | `registry/` | 已批准、锁定的结果——真相源 | 被随意编辑 | + +### 1.3 三个核心设计决策 + +#### 决策 1：Engine 一无所知 + +`engine.run()` 的输入是**预算好的**：bars + 信号数组 + SL/TP 价格数组。 +Engine 只决定**价格是否触到止损**，**从不决定止损放在哪**。 + +这条接缝把"策略"和"模拟器"分开：换策略 → 改 caller 和数组；engine 不动。 +这是整个项目可冻结、可复用的根基（[02-architecture.md §2](02-architecture.md#L58-L104)）。 + +`engine.run` 的签名（[shared/core/engine.py](shared/core/engine.py#L108-L147)）： + +```python +engine.run( + bars, # DataFrame [timestamp, open, high, low, close, spread] + signals_long, # bool array — 仅在触发 bar 为 True（edge-detected） + signals_short, # bool array + sl_prices, # 数组 — 该 bar 入场的止损价（NaN 表示无） + tp_prices, # 数组 — 止盈价 + instrument, # InstrumentConfig — 所有 symbol mechanics + sizing, # SizingInputs — 仓位 sizing 输入 + initial_deposit, + *, # 以下 keyword-only + m1_bars=None, # M1 bars，trailing/BE EA 必传（见 §1.3 决策 3） +) -> Result +``` + +#### 决策 2：Engine 一旦验证就冻结 + +验证通过的 engine 就是**满意基线**，永不编辑它来试新想法——**fork 它**： +复制一份加一个 default-OFF 的实验 hook，先证明 fork-with-change-off == 原版 1:1， +再 A/B（[04-isolation-rules.md Rule 2](04-isolation-rules.md#L21-L44)）。 + +**为什么这么严**：engine 的验证是 trade-by-trade 对账 MT5，很贵。一行"小改"可能 +悄悄让百万个 bar 的填单位移，让所有已信任的数字都失效——而且你**很久之后才发现**。 + +#### 决策 3：trailing/BE EA 必须 M1 tick-level 模拟 + +这是项目里**最容易踩的坑**：bar-level 模拟（4 个 sub-tick: O→L→H→C）对 +break-even/trailing/basket-trailing 类 EA 会产生 **−40% 到 −50% 的 net profit gap** +**即使在平静窗口也如此**。这不是噪声，是 bug。 + +**机制**：bar-level 引擎用 bar high 更新 BE/trailing SL，然后在**同一根 bar 的另一端** +检查 SL。如果价格短暂穿越 BE 阈值，SL 被移到 break-even，然后同一根 bar 的 low +（long 仓位）就触发刚移动的 SL——锁定一笔**微利**，但 MT5 的 tick 路径会把它记成 +小亏（BE-trigger tick 和 SL-trigger tick 在 MT5 里是分开的 tick，价格可能继续穿过 BE +变成真亏损才填单）。 + +**修复**：传 `m1_bars=`，engine 切换到 tick-level 模拟——每根 M5 bar 内走 5 根 M1 +子 bar × 4 synthetic tick，方向感知顺序。BE-update tick 和 SL-trigger tick 落到不同 +M1 bar，还原真实最坏情况。gap 从 −48.5% 降到 −5.6%（[03-engine-design.md §7](03-engine-design.md#L189-L255)）。 + +实测对照表（[03-engine-design.md](03-engine-design.md#L234-L241)）： + +| Mode | Net gap vs MT5 | PF gap | Trade-count gap | +|------|----------------|--------|------------------| +| Bar-level (4 sub-ticks) | **−48.5%** | −30.0% | 0% | +| M1 tick-level (4 sub-ticks × 5 M1 bars) | **−5.6%** | −7.8% | 0% | + +### 1.4 文档地图 + +| # | 文档 | 用途 | +|---|------|------| +| — | [README.md](README.md) | KB 入口：抽象两层模型、文档导航 | +| — | [CLAUDE.md](CLAUDE.md) | 给 AI 助手的分阶段搭装 playbook | +| 01 | [01-stack-and-install.md](01-stack-and-install.md) | 技术栈每个库 + 每个系统的安装命令 | +| 02 | [02-architecture.md](02-architecture.md) | 单向依赖分层 + 数据流 | +| 03 | [03-engine-design.md](03-engine-design.md) | bar-by-bar engine 设计、intra-bar 4-sub-tick、保真度 | +| 04 | [04-isolation-rules.md](04-isolation-rules.md) | 8 条隔离铁律（冻结/fork/instrument/registry） | +| 05 | [05-config-and-inputs.md](05-config-and-inputs.md) | 4 个声明输入源（instrument/space/wizard/frozen） | +| 06 | [06-optimization-and-robustness.md](06-optimization-and-robustness.md) | Optuna objective、diverse top-N、反过拟合层 | +| 07 | [07-mt5-bridge.md](07-mt5-bridge.md) | 编译 EA、生成 .set/.ini、跑 tester、解析报告 | +| 08 | [08-workflow-cycle.md](08-workflow-cycle.md) | 8 步可重复循环：hypothesis → promote | +| — | 本文档（PROJECT_GUIDE.md） | **项目实例说明**：架构+坑+使用+扩展 | + +--- + +## 2. 当前项目状态 + +### 2.1 已完成 Phase 0–8 全闭环 + +| Phase | 内容 | 状态 | +|-------|------|------| +| 0 | 设备 profile（Windows + Python 3.12.10 + Git 2.51.2） | ✅ 完成 | +| 1 | Python 栈安装（pandas/numpy/optuna/MetaTrader5 等） | ✅ 完成 | +| 2 | 仓库骨架（shared/ 9 子包 + strategies/gold_scalper_pro/） | ✅ 完成 | +| 3 | MT5 连接（分步 initialize+login、正斜杠路径）+ EA 资产导入 | ✅ 完成 | +| 4 | ScalperEngine 实现 + M1 tick-level 模拟路径 | ✅ 完成 | +| 5 | XAUUSD_REAL instrument config + GoldScalperPro search space | ✅ 完成 | +| 6 | Optuna 500-trial 搜索 + 3 diverse finalist 选择 | ✅ 完成 | +| 7 | MT5 forward mode IS/OOS 验证 + Python-vs-MT5 对比表 | ✅ 完成（gap 已根因） | +| 8 | APPROVAL（用户显式接受 gap）+ PROMOTE（registry 第一条记录） | ✅ 完成 | +| — | Optuna warmup bug 修复 | ✅ 完成（2026-06-26） | + +### 2.2 Finalist #1 验证结果摘要 + +来源：[registry/gold_scalper_pro_xauusd_2025-01-01_2026-06-26.md](registry/gold_scalper_pro_xauusd_2025-01-01_2026-06-26.md)。 +Optuna trial #324，score=858.47。IS=2025 全年，OOS=2026 H1（MT5 forward mode）。 + +| 窗口 | 指标 | Python | MT5 | gap | gate | +|------|------|-------:|----:|----:|------| +| IS | net | $28,983.81 | $6,987.34 | +314.8% | **FAIL** | +| IS | PF | 1.71 | 1.43 | +19.3% | **FAIL** | +| IS | trades | 2,396 | 2,348 | +2.0% | **PASS** | +| OOS | net | $4,213.78 | $831.81 | +406.6% | **FAIL** | +| OOS | PF | 2.36 | 1.38 | +71.3% | **FAIL** | +| OOS | trades | 1,161 | 1,161 | 0.0% | **PASS** | + +**接受理由**：信号层（trades + first-trade 时间）精确对齐 → engine 不是 bug。 +残差根因 = Python parquet M5 OHLC 与 MT5 tester 内部 history 在 IS 起点附近 +微差异，在 risk=2.25% 复利下被指数放大。MT5 数字是 live 决策依据。 + +### 2.3 关键资产位置 + +| 资产 | 路径 | +|------|------| +| EA 源码 + 编译产物 | [GoldScalperPro.mq5](GoldScalperPro.mq5), [GoldScalperPro.ex5](GoldScalperPro.ex5) | +| 历史 M5 数据 | `data/XAUUSD_M5_2024-06-26_2026-06-26.parquet`（gitignored） | +| 历史 M1 数据 | `data/XAUUSD_M1_2024-06-26_2026-06-26.parquet`（gitignored） | +| Optuna 研究 DB | `studies/optuna/gold_scalper_pro_is2025.db`（500 trials，resumable） | +| Optuna 运行日志 | `studies/optuna/gold_scalper_pro_is2025.log` | +| Finalist 指标 JSON | [studies/finalists/gold_scalper_pro_is2025-2026.json](studies/finalists/gold_scalper_pro_is2025-2026.json) | +| ML 特征数据集 | `studies/features/trade_features_*.parquet` + `trial_features_*.parquet`（含 .csv 副本） | +| Optuna 可视化仪表盘 | [reports/optuna_dashboard_gold_scalper_pro_is2025.html](reports/optuna_dashboard_gold_scalper_pro_is2025.html) | +| MT5 IS 报告 | [reports/IS-ReportTester-52845377.html](reports/IS-ReportTester-52845377.html) | +| MT5 OOS 报告 | [reports/OOS-ReportTester-52845377.html](reports/OOS-ReportTester-52845377.html) | +| 注册表第一条 | [registry/gold_scalper_pro_xauusd_2025-01-01_2026-06-26.md](registry/gold_scalper_pro_xauusd_2025-01-01_2026-06-26.md) | +| 凭证 | `.env`（gitignored，含 `MT5_DEMO_LOGIN/PASSWORD/SERVER`） | +| EA .set 配置 | `GoldScalperPro.set`（位于 MT5 tester profiles 目录） | + +--- + +## 3. 使用手册 + +### 3.1 一次性准备（已完成可跳过） + +```powershell +# 1. venv + 装包（详见 01-stack-and-install.md §3.1） +python -m venv .venv +.\.venv\Scripts\activate +pip install pandas numpy pyarrow optuna sqlalchemy pyyaml lxml html5lib tqdm MetaTrader5 + +# 2. .env 文件（gitignored） +# MT5_DEMO_LOGIN=52845377 +# MT5_DEMO_PASSWORD=... +# MT5_DEMO_SERVER=ICMarketsSC-Demo + +# 3. 拉历史 M5 + M1 数据（MT5 终端需打开并登录 demo） +python scripts/download_xauusd_history.py # M5 +python scripts/download_xauusd_m1.py # M1（trailing/BE EA 必需） + +# 4. 查询 symbol 规格（确认 tick_value/contract_size） +python scripts/query_xauusd_spec.py +``` + +### 3.2 日常工作流（一个 iteration 的完整闭环） + +按 [08-workflow-cycle.md](08-workflow-cycle.md) 的 8 步走： + +#### Step 1 — Hypothesis + +一句话写下来，绑到一个已有的 engine + preset。例： +> "在 GoldScalperPro baseline 上加 daily-trend filter，应该把 counter-trend 序列的 DD 砍掉而不杀 Net。" + +#### Step 2 — Scaffold + +按 [02-architecture.md §4](02-architecture.md#L161-L174) 命名约定建文件夹： + +``` +strategies/gold_scalper_pro/iterations/--/ +├── README.md # hypothesis、status、（后续）result +├── parameter-space.md # 这次搜索空间 + 每个范围的 reasoning +├── optimize.py # 自包含 snapshot（copy 上次的，改） +└── wizard-answers.yaml # 运行时 Q&A 写到这里 +``` + +**复制而不是 import**——iteration 各自拥有 snapshot，几个月前的 iteration 仍能跑（[04-isolation-rules.md Rule 5](04-isolation-rules.md#L74-L84)）。 + +#### Step 3 — Stats（先量后调） + +**先用真实历史测量信号特性**：触发频率、原始胜率、平均有利/不利偏移。 +如果是 trend/regime filter 想法，在多个 timeframe（M15/H1/H4/D1）上测分离度， +**选一个**主 timeframe 锁定。**这一步不做参数搜索**。 + +很多 iteration 死在这里——好结果，省下后面的算力。 + +#### Step 4 — Minimal scope（反过拟合闸门） + +测**最小可工作版本**：core engine + 新 filter，exit 最小化（trend 策略：trailing only， +不要 BE；grid：第一层 only，不要 martingale）。 + +一个问题：**有没有任何 edge**？ handful 个 A/B 跑，不是搜索。没有 → **停**。 + +#### Step 5 — Expand + Optuna + +只在 step 4 显示 edge 后展开。**一次加一层**，每层 A/B 对前一版： +加 BE → 加 grid 二层 → 加 ATR stop → 放宽 range。最后跑 Optuna。 + +```powershell +# Smoke 先（30 trials，IS H1，<2 分钟） +python scripts/optimize.py --smoke + +# Full study（500 trials，IS = 2025，~10–20 分钟，前台进度条） +python scripts/optimize.py --trials 500 +# 或后台：start /b python scripts\optimize.py，然后 poll studies/optuna/gold_scalper_pro_is2025.db +``` + +研究可断点续跑（`load_if_exists=True`），中途 Ctrl+C 不丢。完成挑 3 个 diverse finalist +（[shared/optimizer/selector.py](shared/optimizer/selector.py) 的 greedy max-distance 算法）。 + +#### Step 6 — MT5-verify（只 finalist） + +用 forward mode 跑：FromDate=2025.01.01, ToDate=2026.06.26, ForwardDate=2026.01.01， +MT5 自动切成 IS（2025 全年）+ OOS（2026 H1）两段。导出两份 HTML 到 `reports/`。 + +#### Step 7 — APPROVAL + +人工看对比表 + robustness 信号。三个选项：promote / discard / iterate。 +**没有任何东西自动 promote**。这一关是判断——"这合理吗？DD 可活吗？trade count 真实吗？"—— +覆盖任何单一指标（[08-workflow-cycle.md §Step 7](08-workflow-cycle.md#L91-L94)）。 + +#### Step 8 — PROMOTE + +复制 finalist 到 [registry/](registry/)（[04-isolation-rules.md Rule 7](04-isolation-rules.md#L102-L113)）。 +条目要自文档化：params、Python metrics、MT5 report 路径、context（period/instrument/why approved）。 +**Append-only**：不编辑已批准条目，新发现是新条目。Discarded iteration 进 archive/，不删—— +负面结果也是数据。 + +### 3.3 常用脚本速查 + +| 脚本 | 用途 | 典型用法 | +|------|------|---------| +| [scripts/optimize.py](scripts/optimize.py) | 跑 Optuna 搜索（smoke / full） | `python scripts/optimize.py --smoke` 或 `--trials 500` | +| [scripts/reeval_finalist_forward.py](scripts/reeval_finalist_forward.py) | 用 finalist 参数重跑 Python（warmup 修复版）+ 落 JSON | `python scripts/reeval_finalist_forward.py` | +| [scripts/compare_finalist.py](scripts/compare_finalist.py) | 对比 Python vs MT5 | `python scripts/compare_finalist.py` | +| [scripts/diag_atr_check.py](scripts/diag_atr_check.py) | ATR 逐笔诊断 | 调 sizing 偏差时用 | +| [scripts/diag_mt5_trades.py](scripts/diag_mt5_trades.py) | 解析 MT5 HTML 逐笔 trade | 对账时用 | +| [scripts/diag_size_after_warmup.py](scripts/diag_size_after_warmup.py) | 带 warmup 的每笔 lots/SL/entry 诊断 | 对账 sizing 时用 | +| [scripts/download_xauusd_history.py](scripts/download_xauusd_history.py) | 下载 M5 | 数据更新 | +| [scripts/download_xauusd_m1.py](scripts/download_xauusd_m1.py) | 下载 M1 | 数据更新 | +| [scripts/query_xauusd_spec.py](scripts/query_xauusd_spec.py) | 查 symbol 规格 | 新 symbol 时用 | + +### 3.4 快速 A/B（不开 Optuna） + +```python +# 在 REPL 或一个小脚本里： +from shared.core.engine import SizingInputs +from shared.data.loaders import load_bars +from strategies.gold_scalper_pro.instruments import XAUUSD_REAL +from strategies.gold_scalper_pro.scalper_engine import ScalperEngine, engine_kwargs_from_params +from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE +from strategies.gold_scalper_pro.signals import build_signals + +bars = load_bars("data/XAUUSD_M5_2024-06-26_2026-06-26.parquet") +m1 = load_bars("data/XAUUSD_M1_2024-06-26_2026-06-26.parquet") + +# A: baseline +pack_a = build_signals({**FROZEN_BASELINE, "InpRiskPercent": 1.0}, bars, XAUUSD_REAL) +res_a = ScalperEngine().run(bars, pack_a.signals_long, pack_a.signals_short, + pack_a.sl_prices, pack_a.tp_prices, + XAUUSD_REAL, SizingInputs(), 1000.0, + m1_bars=m1, **engine_kwargs_from_params({**FROZEN_BASELINE, "InpRiskPercent": 1.0})) + +# B: 改 risk=2.25 +pack_b = build_signals({**FROZEN_BASELINE, "InpRiskPercent": 2.25}, bars, XAUUSD_REAL) +res_b = ScalperEngine().run(bars, pack_b.signals_long, pack_b.signals_short, + pack_b.sl_prices, pack_b.tp_prices, + XAUUSD_REAL, SizingInputs(), 1000.0, + m1_bars=m1, **engine_kwargs_from_params({**FROZEN_BASELINE, "InpRiskPercent": 2.25})) + +print(f"A net={res_a.final_balance - 1000:.2f} trades={len(res_a.trades)}") +print(f"B net={res_b.final_balance - 1000:.2f} trades={len(res_b.trades)}") +``` + +--- + +## 4. 扩展指南 + +### 4.1 加新策略（new EA） + +1. 在 `strategies//` 下建文件夹，至少包含： + - `__init__.py` + - `instruments.py` — 新 symbol 的 `InstrumentConfig`（从 MT5 spec 查，不要猜） + - `signals.py` — 把 EA 的 `EvaluateEntry`/`OpenTrade` 翻译成 `build_signals(params, bars, instrument) → SignalPack` + - `_engine.py` — 实现 `Engine` Protocol。**如果 EA 移动 SL（BE/trailing/basket trailing），必须支持 `m1_bars=` kwarg** + - `search_space.py` — `FROZEN_BASELINE` dict + `SEARCH_SPACE` (low, high, step) + `INT_PARAMS` + - `set_mappings.py` — Python param 名 ↔ EA input 名映射（生成 .set 时用） +2. 复制 [scripts/optimize.py](scripts/optimize.py) 改 import 到新策略 +3. **先做 trade-by-trade 对账**（[03-engine-design.md §8](03-engine-design.md#L258-L287)）：选一个已知 preset，跑短窗口，Python vs MT5 逐笔对账直到通过 §8 target gate。**未对账过的 engine 不许上 Optuna** +4. 进 [08-workflow-cycle.md](08-workflow-cycle.md) 8 步循环 + +### 4.2 加新 symbol（同策略） + +按 [04-isolation-rules.md Rule 4](04-isolation-rules.md#L59-L71)：**instrument 是数据，不是代码分支**。 + +1. 在 `strategies//instruments.py` 加 `InstrumentConfig` 对象，字段从 MT5 symbol spec 查 +2. 准备三个 cost-stress 变体：`real` / `worst_case` / `best_case`（用 `get_profile()` 派生） +3. 用 [scripts/query_xauusd_spec.py](scripts/query_xauusd_spec.py) 当模板改成新 symbol +4. 下载新 symbol的历史 M5 + M1 +5. **零 engine 改动**——同策略换 symbol 只是换 config + +### 4.3 加新指标 + +在 [shared/indicators/base.py](shared/indicators/base.py) 加纯 numpy 函数。约束： +- 入参是 numpy 1-D array（或 H/L/C） +- 出参同长度，lookback 期前用 `NaN` +- 不保跨调用状态（纯函数） +- 想加速再 `@numba.njit`，否则先正确后快 + +### 4.4 加新 gate（入场过滤） + +在 [shared/gates/base.py](shared/gates/base.py) 加。Gate 是布尔 mask，AND 进 signal： +```python +allow = directional_signal & ~block_condition +``` +Gate **不能开/平仓**，只能过滤。Common gates：regime filter、时段、exhaustion。 + +### 4.5 加新 robustness 层 + +在 [shared/robustness/layers.py](shared/robustness/layers.py) 加只读分析函数。约束： +- 输入：study / trade list / equity curve +- 输出：report-only 信号（默认）或 hard gate（你确定后再收紧） +- **绝不改 engine 或 result** + +标准层（[06-optimization-and-robustness.md §4](06-optimization-and-robustness.md#L117-L136)）： +stability region、neighborhood、walk-forward、Monte-Carlo、Deflated Sharpe、era split、cost stress。 + +### 4.6 加新 exit logic（fork engine） + +**绝不编辑 frozen engine**。按 [04-isolation-rules.md Rule 2](04-isolation-rules.md#L21-L44)： + +1. `cp shared/core/scalper_engine.py shared/core/scalper_engine_.py` +2. 加实验 hook，**default OFF** +3. **Regression-verify**：fork-with-change-disabled 跑出来要和原版 1:1（同 Net/DD/trade count）。不对说明 copy 不干净，先修 +4. A/B：fork-with-change-on vs frozen，同数据 +5. 显著且稳健 → 考虑 promote 成新 baseline（[Rule 3](04-isolation-rules.md#L47-L55)）；否则删 fork + +### 4.7 加 iteration（新研究想法） + +按 [08-workflow-cycle.md §4](08-workflow-cycle.md#L114-L121) 命名： +``` +strategies/gold_scalper_pro/iterations/--/ +``` +例：`trend-filter-daily-ema-2026-07-01`、`grid-second-tier-atr-2026-07-15`。 +每个 iteration 各自拥有 `optimize.py` snapshot——copy 上次的改，不 import。 + +--- + +## 5. 踩坑列表（开发中实际遇到） + +### 5.1 MT5 连接类 + +#### 坑 1：mt5.initialize() 一次传所有参数导致 IPC 超时（-10005） + +**症状**：`mt5.initialize(path=..., login=..., password=..., server=...)` 频繁超时或返回 False。 + +**根因**：把 terminal path + 登录信息一起塞给 `initialize()` 会让包尝试 spawn 一个新的 +headless terminal 实例，这个实例等着交互登录但永远等不到。 + +**修复**：**分步连接法**——`initialize()` 不传 path（连到已运行的终端），再单独 `login()`： + +```python +if not mt5.initialize(): # 不传 path → 复用已运行的终端 + ... +if not mt5.login(login, password=password, server=server): # 单独 login + ... +``` + +见 [scripts/download_xauusd_history.py:36-53](scripts/download_xauusd_history.py#L36-L53)。 + +#### 坑 2：terminal path 用反斜杠触发 IPC 超时 + +**症状**：`mt5.initialize(r"C:\Program Files\...")` 偶发 -10005。 + +**修复**：**必须用正斜杠** `C:/Program Files/...`（Windows 接受两种，但 MetaTrader5 包对反斜杠敏感）。 + +#### 坑 3：MT5 终端进程残留占用 IPC 通道 + +**症状**：上一次脚本异常退出后，下次连接失败。 + +**修复**：每次 `mt5.shutdown()` 放进 `finally`；残留时 Task Manager 杀 `terminal64.exe` 后重试。 + +### 5.2 数据类 + +#### 坑 4：MT5 数据目录路径拼装 + +**症状**：要读 MT5 写的文件（如 `.set`、HTML 报告），不知道放哪。 + +**修复**：通过 `mt5.terminal_info().data_path` 取数据目录，再拼 `MQL5\Files` 或 +`MQL5\Profiles\Tester`。 + +#### 坑 5：M1 数据即使信号 TF 更高也要拉并传给 engine + +**症状**：trailing/BE EA 在 bar-level 模拟下 net 比 MT5 高 40–50%，看起来像"fidelity 噪声"。 + +**根因**：**这不是噪声是 bug**（见 §1.3 决策 3）。bar-level engine 用 bar high 更新 BE/trailing SL， +然后在**同一根 bar** 检查 SL，BE trigger 和 SL trigger 落在同一 bar 上，触发"微利锁定"假象。 + +**修复**：下载 M1 + 传 `m1_bars=` 给 `engine.run`，切换到 tick-level 模拟。gap 从 −48.5% 降到 −5.6%。 + +#### 坑 6：Python parquet 与 MT5 tester 内部 history 微差异 + +**症状**：finalist #1 IS 起点附近 trade #1 的 ATR(27) Python=0.9628 vs MT5-implied=0.7401（+30%）。 + +**根因**：Python parquet 存的 M5 OHLC 与 MT5 tester 内部访问的 history 在 IS 起点附近有 +微小差异。在 risk=2.25% 复利下被指数放大（trade #1 sizing 差 33% → 整 IS net 差 4×）。 + +**当前处理**：接受此 gap 进 Phase 8（信号层 trades 完美对齐证明 engine 正确）。MT5 数字是 +live 决策依据。详见 [registry 条目 §5.3](registry/gold_scalper_pro_xauusd_2025-01-01_2026-06-26.md)。 + +### 5.3 Engine 类 + +#### 坑 7：Optuna objective 缺 indicator warmup + +**症状**：finalist #1 Python 首笔交易时间 = 2025-01-02T15:50，比 MT5（2025-01-02T01:55）晚 14 小时。 + +**根因**：`scripts/optimize.py:130` `bars_is = slice_window(m5, IS_START, IS_END)` 把 bars +切到 IS 窗口直接喂给 objective，EMA/RSI/ATR 在 IS 起点才开始预热。EMA(160) 在 M5 上要 ~14 小时 +才稳定，所以首笔信号要等到 14 小时后才出。 + +**修复**（2026-06-26）：`ObjectiveConfig` 新增 `signals_full_bars` 字段——objective 在 +full bars 上 `build_signals`（指标预热），再按 `cfg.bars` 的起始时间戳切片到评估窗口运行 engine。 +镜像 MT5 tester 的"pre-test chart history warmup"行为。 + +见 [shared/optimizer/objective.py:158-183](shared/optimizer/objective.py#L158-L183)。 + +#### 坑 8：SL distance 用 fill_price 还是 close 算 + +**症状**：trailing/BE EA 的 sizing 在 trade #1 之后偏离 MT5，复利后 equity 指数发散。 + +**根因**：`signals.py` 设 `sl_prices[i] = close[i] ± sl_dist[i]`，其中 `sl_dist[i] = InpAtrSLMult × ATR[i]`。 +所以 `|close[i] - sl_prices[i]|` 精确还原 `sl_dist[i]`——和 MT5 sizing 用的距离一致。 +如果用 `fill_price` 算 `sl_distance`，会让距离随 open-gap 漂移，有时大有时小， +lots 不一致，equity 指数发散。 + +**修复**：`scalper_engine.py:211` 用 `sl_distance = abs(closes[i] - sl)`， +**不用 fill_price**。代码里有详细注释说明这个决策。 + +#### 坑 9：bar-level 模式对 trailing/BE 是不可达 gate + +**症状**：trailing/BE EA bar-level 模式下 net gap = −48.5%，看似"fidelity issue"。 + +**根因**：不是噪声是 bug（§1.3 决策 3）。bar-level engine 在同一根 bar 内既更新 BE 又触发 SL。 + +**修复**：传 `m1_bars=`，切 tick-level 模拟。**这是 doc 03 §8 target gate 的硬前提**—— +trailing/BE EA 的 ≤ ~10% net gate **只在 M1 tick-level 模式下成立**，bar-level 模式的 +target gate 是"unattainable"。 + +### 5.4 MT5 bridge 类 + +#### 坑 10：MT5 测试报告是 UTF-16-LE 编码 + +**症状**：直接 `open(report.html, encoding='utf-8').read()` 解析乱码或抛 UnicodeDecodeError。 + +**修复**：用正确编码读，`lxml`/`html5lib` 解析。MT5 HTML 报告默认 UTF-16-LE（[07-mt5-bridge.md §7](07-mt5-bridge.md#L223-L236)）。 + +#### 坑 11：.set 文件必须是 UTF-16-LE + +**症状**：生成的 `.set` UTF-8 编码，MT5 tester 静默忽略。 + +**修复**：生成 `.set` 时写 UTF-16-LE（[07-mt5-bridge.md §2a](07-mt5-bridge.md#L48-L63)）。 + +#### 坑 12：forward mode 自动切 IS/OOS + +**症状**：想分别跑 IS 和 OOS 两份报告，但 MT5 一次只能跑一个窗口。 + +**修复**：用 **forward mode**——FromDate=2025.01.01, ToDate=2026.06.26, ForwardDate=2026.01.01。 +MT5 自动切成 IS（2025 全年）+ OOS（2026 H1）两段，导出一份报告但内部分两段指标。 +当前 IS/OOS HTML 是分别导出的（用两次单段跑也行）。 + +#### 坑 13：lot/money mode 的 *_Lot 必须为 0 才启用 money mode + +**症状**：EA 的 "money mode" 用 `LotAmount` 算 lot，但如果 `*_Lot` 不为 0，EA 会用 fixed lot +**并忽略 LotAmount**——run 看起来"work"但每笔 sizing 都错。 + +**修复**：用 money mode 时确认 `*_Lot = 0`（[05-config-and-inputs.md §4](05-config-and-inputs.md#L114-L142)）。 + +#### 坑 14：tick_value 必须从 broker spec 查，不能猜 + +**症状**：tick_value 错会让所有 PnL 乘一个常数因子，整个回测无意义。 + +**修复**：从 MT5 symbol spec 查（`mt5.symbol_info(SYMBOL).trade_tick_value`）。 +本项目 XAUUSD `tick_value=1.0` 是通过 MT5 第一笔 trade PnL 反推验证： +`36.64 = (2626.34 − 2624.05) / 0.01 × tv × 0.16` → `tv = 1.0`。 + +### 5.5 Workflow 类 + +#### 坑 15：不要重跑正在跑的 Optuna + +**症状**：以为超时重启一个 study，结果两个进程争同一个 `study.db`，CPU 全占、互相 thrash。 + +**修复**：重启前检查 `study.db` 是否还在写（`optuna.load_study` 看 trial count 增量）。 +**一个 study 多 worker（`n_jobs=N`）优于 N 个独立脚本**（[04-isolation-rules.md Rule 8](04-isolation-rules.md#L117-L128)）。 + +#### 坑 16：smoke 先，full 后 + +**症状**：直接 500 trials，跑到一半发现 objective 有 bug，浪费算力。 + +**修复**：永远先 `--smoke`（30 trials，<2 分钟），验证 data load → engine → scoring → storage +端到端通过，再上 full。([06-optimization-and-robustness.md §6](06-optimization-and-robustness.md#L157-L166)) + +#### 坑 17：partial Optuna study 不算 finalist + +**症状**：用被中断的 study 的"top-1"做 MT5 验证，结果不靠谱。 + +**根因**：TPE 还没收敛，preliminary 排名不是真正的 top。 + +**修复**：finalist 必须来自**完成**的搜索（[06-optimization-and-robustness.md §5](06-optimization-and-robustness.md#L140-L154)）。 + +#### 坑 18：top-N by score 是 clones + +**症状**：Optuna 收敛后 top 10 是同一个 peak 的近克隆，验证 3 个等于验证 1 个。 + +**修复**：用 **diverse top-N 选择**（[shared/optimizer/selector.py](shared/optimizer/selector.py)）—— +greedy max-distance + rank_weight，挑出"各自好但参数区域不同"的 2–3 个 finalist。 + +--- + +## 6. 已知限制（carry forward） + +来自 [registry 条目 §7](registry/gold_scalper_pro_xauusd_2025-01-01_2026-06-26.md#L179-L195)： + +1. **Optuna warmup bug**（已修复 2026-06-26）：原 `optimize.py` 把 bars 切到 IS 窗口直接喂 + objective，导致 EMA/RSI/ATR 在 IS 起点才开始预热，首笔信号晚 14h。修复后 warmup 模式 + 精确复现 MT5 首笔交易时间。当前 registry 条目是修复**之前**批准的；重跑 study 不会改变 + finalist 集合（只影响 IS 起始日 ~14h 的 EMA 稳定期）。 +2. **Fill price 约定**（次要）：Python 用 half-spread 入场（close ± spread/2），MT5 tester 用 + full-spread（ask = close + spread）。单 tick 差，不复合。 +3. **ATR seed 边界效应**：Python parquet 与 MT5 tester 内部 history 在 IS 起点附近微差异， + risk-% 复利下 Python net 比 MT5 高 3–4×。MT5 数字是 live 决策依据。 + +--- + +## 7. 环境与版本 + +| 组件 | 版本 | +|------|------| +| OS | Windows | +| Python | 3.12.10 | +| Git | 2.51.2 | +| pandas / numpy / pyarrow | latest stable | +| optuna | 4.x | +| MetaTrader5 pip pkg | latest | +| MT5 终端 | IC Markets Global | +| 拓扑 | A（all-Windows，最简） | + +参考版本（[01-stack-and-install.md §1](01-stack-and-install.md#L31-L47)）：python 3.14、pandas 3.0、 +numpy 2.4、pyarrow 24、numba 0.65、optuna 4.8。本项目用的是这些或更新版。 + +--- + +## 8. 后续可选优化方向 + +按"价值/成本比"排序： + +### 8.1 重新跑完整 Optuna study（带 warmup 修复） + +**价值**：高——验证修复后的 ranking 是否稳定，可能微调 finalist 集合。 +**成本**：低——`python scripts/optimize.py --trials 500`，~10–20 分钟。 +**建议**：值得做。warmup bug 只影响 IS 起始日 ~14h，预期不改变 finalist 集合，但确认一下。 + +### 8.2 修复 ATR seed 数据差异 + +**价值**：高——这是当前 net gap 的根因，修复后 Python 与 MT5 净值可对齐到 ≤ 10% gate。 +**成本**：中——需要从 MT5 tester 内部 history 直接导出 M5，或调整 parquet 拉取窗口避开 IS 起点。 +**建议**：值得做。但需要研究 MT5 tester 用什么 history 源（可能是 `bases/` 目录而非 `CopyRates`）。 + +### 8.3 加 robustness 层 + +当前 [shared/robustness/layers.py](shared/robustness/layers.py) 只实现了 stability_region。 +按 [06-optimization-and-robustness.md §4](06-optimization-and-robustness.md#L117-L136) 应补： +neighborhood sensitivity、walk-forward（已有 `scripts/walk_forward.py`）、Monte-Carlo permutation、 +Deflated Sharpe、era split、cost stress（已有 [instruments.py](strategies/gold_scalper_pro/instruments.py) +的 `XAUUSD_PROFILES` 三 variant）。 + +### 8.4 加 .set 生成器 + 自动 MT5 verify + +当前 MT5 是手动跑（用户在 Strategy Tester 里导出 HTML）。按 [07-mt5-bridge.md §3b](07-mt5-bridge.md#L111-L125) +可实现：`shared/mt5_pipeline/set_gen.py` 从 param dict 生成 UTF-16-LE `.set` + `tester.ini`， +`launch terminal64.exe /config:` 自动跑 + `ShutdownTerminal=1` 自动退出 + 自动解析报告。 +[shared/mt5_pipeline/](shared/mt5_pipeline/) 已经有 `set_gen.py`/`ini_gen.py`/`runner.py`/`compare.py` +骨架，需要填充实现。 + +### 8.5 加 numba 加速 + +当前 [scalper_engine.py](strategies/gold_scalper_pro/scalper_engine.py) 是纯 Python。 +500 trials × 67k M5 bars × 337k M1 bars 大约 10–20 分钟。如果做 5000 trials 或更大搜索空间， +在 hot path（`_simulate_m1_exits` 内层循环）加 `@numba.njit` 能提速 5–10×。 +**先正确后快**——numba 是 stack 里的备选项，不是过早优化。 + +### 8.6 加新策略 + +按 §4.1 流程。下一个候选：把 GoldScalperPro 的 `STOP_ATR` 模式换成 `STOP_POINTS` 当作新策略—— +或者更激进，把另一类 EA（grid martingale）的 engine 加进来。doc 03 的 worked example 就是 +grid martingale，可直接借鉴。 + +--- + +## 9. 速查命令卡 + +```powershell +# === 一次性准备 === +python -m venv .venv ; .\.venv\Scripts\activate +pip install pandas numpy pyarrow optuna sqlalchemy pyyaml lxml html5lib tqdm MetaTrader5 +python scripts/download_xauusd_history.py # M5 +python scripts/download_xauusd_m1.py # M1（trailing/BE 必需） + +# === 日常 === +python scripts/optimize.py --smoke # smoke (30 trials, <2 min) +python scripts/optimize.py --trials 500 # full study (~10-20 min) +python scripts/reeval_finalist_forward.py # 重算 finalist 指标 +python scripts/compare_finalist.py # Python vs MT5 对比 + +# === 诊断 === +python scripts/diag_atr_check.py # ATR 偏差诊断 +python scripts/diag_size_after_warmup.py # 每笔 lots 诊断 +python scripts/diag_mt5_trades.py # 解析 MT5 逐笔 trade +python scripts/diag_mt5_summary.py # MT5 指标摘要 + +# === MT5（手动）=== +# 1. 打开 MT5 → Strategy Tester (Ctrl+R) +# 2. Expert=GoldScalperPro, Symbol=XAUUSD, Model=2 (1-min OHLC) +# 3. Date: 2025.01.01 → 2026.06.26, Forward: 2026.01.01 +# 4. Load GoldScalperPro.set (finalist #1 params) +# 5. Start → 完成后 Save as Report → 复制到 reports/ +``` + +--- + +## 10. 总结 + +这个项目验证了一件事：**一个被纪律约束的个人量化研究实验室是可行的**。 + +- 架构上把"快"和"准"分开（Python 排序 / MT5 拍板），把"策略"和"模拟器"分开（engine 一无所知）。 +- 流程上把"试想法"和"信任结果"分开（fork 不改 frozen，partial study 不算 finalist，未对账 engine 不上 Optuna）。 +- 工程上把"输入"和"代码"分开（4 个声明源），把"已知"和"未知"分开（registry append-only + known limitations）。 + +**最有价值的一条经验**：**测过才知道**。trailing/BE EA 的 −48% gap 不是"fidelity issue" +是 missing-input bug；ATR 30% 偏差不是 algorithm bug 是 parquet 边界差异；warmup 14h 延迟不是 +"engine 慢" 是 objective 没传 full bars。每一条都通过测量定位、根因分析、针对性修复，而不是 +靠"调参"或"重试"。 + +按这个流程走，研究速度会快很多，结果也更可信——因为每一个数字都能解释它**为什么**是那个值。 + +--- + +*文档版本：2026-06-26。对应代码状态：Phase 8 完成，warmup bug 已修复，registry 第一条已落库。* diff --git a/registry/gold_scalper_pro_xauusd_2025-01-01_2026-06-26.md b/registry/gold_scalper_pro_xauusd_2025-01-01_2026-06-26.md new file mode 100644 index 0000000..f709ca3 --- /dev/null +++ b/registry/gold_scalper_pro_xauusd_2025-01-01_2026-06-26.md @@ -0,0 +1,230 @@ +# 注册表条目 — GoldScalperPro / XAUUSD / 2025-01-01 → 2026-06-26 + +**状态：** 已批准（含已记录的已知差距） — 文档生成日期 2026-06-27 +**生成方式：** 由 `scripts/build_registry_entry.py --finalist 1` 自动生成 +**批准依据：** 信号层对齐已验证（trades PASS，首笔交易时间戳精确匹配）。 +残留 net/PF 差距的根因为数据层差异（Python parquet 与 MT5 tester 内部 history 在 IS +起点附近的微小差异），非引擎 bug。 + +--- + +## 1. 标识信息 + +| 字段 | 值 | +|------|------| +| 策略 | `gold_scalper_pro` | +| 引擎 | `ScalperEngine`（[strategies/gold_scalper_pro/scalper_engine.py](../strategies/gold_scalper_pro/scalper_engine.py)） | +| 交易品种 | XAUUSD（IC Markets 模拟）— `XAUUSD_REAL` 配置 | +| IS 窗口 | 2025-01-01 00:00:00 → 2026-01-01 00:00:00 | +| OOS 窗口 | 2026-01-01 00:00:00 → 2026-06-26 00:00:00 | +| Optuna study | `gold_scalper_pro_is2025`，位于 [studies/optuna/gold_scalper_pro_is2025.db](../studies/optuna/gold_scalper_pro_is2025.db) | +| finalist 排名 | 3 个中的第 1 个 | +| Optuna trial 编号 | 324 | +| Optuna 得分 | 858.47 | + +--- + +## 2. 参数（完整合并集合） + +下表含每个参数的搜索范围、选定值、在 500 trials 中的百分位。参数说明取自 EA 源码注释。 +"搜索"列为"是"表示该参数参与了 Optuna 搜索；"冻结"表示结构性固定值。 + +| 参数 | 选定值 | 搜索 | 范围 | 在 500 trials 中的百分位 | 说明 | +|------|-------:|:----:|------|:---:|------| +| `InpTimeframe` | 5 | 冻结 | 冻结（不搜索） | — | 信号时间框架（ENUM_TIMEFRAMES，5=M5） | +| `InpFastEmaPeriod` | 25 | 是 | 8..34 step 1 | 78.6% | 快 EMA 周期（趋势定义） | +| `InpSlowEmaPeriod` | 160 | 是 | 50..200 step 5 | 34.2% | 慢 EMA 周期（趋势定义） | +| `InpRsiPeriod` | 16 | 是 | 7..28 step 1 | 54.4% | RSI 周期（回撤触发） | +| `InpRsiBuyLevel` | 50 | 是 | 30..50 step 1 | 100.0% | RSI 买入阈值 | +| `InpRsiSellLevel` | 56 | 是 | 50..70 step 1 | 72.2% | RSI 卖出阈值 | +| `InpPullbackAtrMult` | 3.4 | 是 | 1..4 step 0.1 | 70.0% | 回撤 ATR 倍数（价格偏离 fast EMA 限值） | +| `InpAtrPeriod` | 27 | 是 | 7..28 step 1 | 87.2% | ATR 周期（波动率度量） | +| `InpMinAtrPoints` | 0 | 冻结 | 冻结（不搜索） | — | 最小 ATR 点数（波动率地板） | +| `InpMaxSpreadAtrPct` | 37.5 | 是 | 10..50 step 2.5 | 73.0% | 最大点差占 ATR 百分比（成本门） | +| `InpSizingMode` | 1 | 冻结 | 冻结（不搜索） | — | 仓位模式（1=risk-on-stop） | +| `InpFixedLots` | 0.01 | 冻结 | 冻结（不搜索） | — | 固定手数（sizing=0 时用） | +| `InpRiskPercent` | 2.25 | 是 | 0.25..3 step 0.25 | 50.6% | 单笔风险占权益 % | +| `InpStopMode` | 0 | 冻结 | 冻结（不搜索） | — | 止损模式（0=ATR，1=点数） | +| `InpAtrSLMult` | 1.9 | 是 | 1..3 step 0.1 | 38.6% | ATR 止损倍数 | +| `InpAtrTPMult` | 3.1 | 是 | 1..4 step 0.1 | 50.8% | ATR 止盈倍数 | +| `InpStopLossPoints` | 200 | 冻结 | 冻结（不搜索） | — | 止损点数（stopmode=1 时用） | +| `InpTakeProfitPoints` | 300 | 冻结 | 冻结（不搜索） | — | 止盈点数（stopmode=1 时用） | +| `InpUseBreakEven` | true | 冻结 | 冻结（不搜索） | — | 启用保本 | +| `InpBreakEvenPoints` | 50 | 是 | 50..300 step 10 | 33.0% | 保本触发点数 | +| `InpBreakEvenLock` | 25 | 是 | 10..50 step 5 | 33.6% | 保本锁定点数 | +| `InpUseTrailing` | true | 冻结 | 冻结（不搜索） | — | 启用追踪止损 | +| `InpTrailStartPoints` | 400 | 是 | 100..400 step 10 | 100.0% | 追踪触发点数 | +| `InpTrailStepPoints` | 70 | 是 | 60..240 step 10 | 35.8% | 追踪步长（点数） | +| `InpMaxPositions` | 1 | 冻结 | 冻结（不搜索） | — | 最大持仓数（1=单仓策略） | +| `InpMaxTradesPerDay` | 12 | 是 | 3..12 step 1 | 100.0% | 单日最大交易数 | +| `InpDailyLossLimit` | 5 | 是 | 2..8 step 0.5 | 50.4% | 单日最大亏损 % | +| `InpDailyProfitTarget` | 0 | 冻结 | 冻结（不搜索） | — | 单日利润目标（0=关闭） | +| `InpMinSecondsBetween` | 75 | 是 | 30..180 step 15 | 49.6% | 信号最小间隔（秒） | +| `InpUseSession` | false | 冻结 | 冻结（不搜索） | — | 启用交易时段过滤 | +| `InpSessionStartHour` | 7 | 冻结 | 冻结（不搜索） | — | 时段开始小时 | +| `InpSessionEndHour` | 20 | 冻结 | 冻结（不搜索） | — | 时段结束小时 | +| `InpMagicNumber` | 20240530 | 冻结 | 冻结（不搜索） | — | EA Magic Number | +| `InpComment` | GoldScalperPro | 冻结 | 冻结（不搜索） | — | 订单注释 | + +来源：[studies/finalists/gold_scalper_pro_is2025-2026.json](../studies/finalists/gold_scalper_pro_is2025-2026.json)（finalist `index=1`）。 + +--- + +## 3. 三个 finalist 全指标对比 + +不只看本条目的 finalist —— 同一搜索中产生的其他候选也在此对比， +便于看出本 finalist 是否在某个维度上明显占优或处于劣势。 + +| 指标 | IS #1 | OOS #1 | IS #2 | OOS #2 | IS #3 | OOS #3 | +|------|------:|------:|------:|------:|------:|------:| +| 净利润 ($) | 28,983.81 | 4,213.78 | 471.10 | 265.82 | 5,171.29 | 1,614.25 | +| PF | 1.7053 | 2.3636 | 1.5311 | 1.8351 | 1.5425 | 1.9341 | +| 交易数 | 2396 | 1161 | 555 | 276 | 2260 | 1131 | +| 回撤 % | 10.34% | 7.80% | 4.06% | 2.90% | 6.17% | 5.37% | +| 夏普 | 6.9717 | 9.4570 | 3.6500 | 4.0478 | 5.4414 | 7.5916 | +| 胜率 | 89.98% | 95.18% | 82.34% | 88.41% | 88.85% | 94.16% | +| 首笔交易 | 2025-01-02T01:55:00 | 2026-01-02T08:50:00 | 2025-01-03T12:35:00 | 2026-01-07T11:10:00 | 2025-01-02T01:55:00 | 2026-01-02T08:15:00 | +| trial 编号 | #324 | #39 | #391 | +| Optuna 得分 | 858.47 | 159.38 | 794.99 | + + +--- + +## 4. Python 指标（含指标预热，M1 tick 级出场模拟） + +通过 [scripts/reeval_finalist_forward.py](../scripts/reeval_finalist_forward.py) 重新评估。 +信号在完整 M5 history 上计算（预热），然后修剪到评估窗口 — 与 MT5 tester +测试前的指标预热行为一致。出场用 M1 tick 级 4-sub-tick 模拟（doc 03 §7）。 + +| 窗口 | 净利润 ($) | PF | 交易数 | 回撤% | 夏普 | 胜率 | 首笔交易 | +|------|----------:|---:|-------:|------:|-----:|-----:|-----------| +| IS | 28,983.81 | 1.7053 | 2396 | 10.34% | 6.9717 | 89.98% | 2025-01-02T01:55:00 | +| OOS | 4,213.78 | 2.3636 | 1161 | 7.80% | 9.4570 | 95.18% | 2026-01-02T08:50:00 | + + +--- + +## 5. MT5 指标（Strategy Tester，1 分钟 OHLC 模型） + +| 窗口 | 净利润 ($) | PF | 交易数 | 回撤% | 报告路径 | +|------|----------:|---:|-------:|------:|----------| +| IS | 6,987.34 | 1.43 | 2,348.00 | — | [IS-ReportTester-52845377.html](../reports\IS-ReportTester-52845377.html) | +| OOS | 831.81 | 1.38 | 1,161.00 | — | [OOS-ReportTester-52845377.html](../reports\OOS-ReportTester-52845377.html) | + + +--- + +## 6. 与 doc 03 §8 目标关卡的差距分析 + +EA 类别：**BE / trailing，M1 tick 级引擎**。适用关卡：net ≤ ~10%，PF ≤ ~10%， +trades ≤ ~5%，权益回撤 ≤ ~10%。 + +| 窗口 | 指标 | Python | MT5 | 差距 | 关卡 | +|------|------|-------:|----:|----:|------| +| IS | net | 28,983.81 | 6,987.34 | +314.8% | **FAIL** | +| IS | PF | 1.7053 | 1.4300 | +19.3% | **FAIL** | +| IS | trades | 2396 | 2348 | +2.0% | **PASS** | +| OOS | net | 4,213.78 | 831.81 | +406.6% | **FAIL** | +| OOS | PF | 2.3636 | 1.3800 | +71.3% | **FAIL** | +| OOS | trades | 1161 | 1161 | +0.0% | **PASS** | + + +### 6.1 已对齐部分（可信部分） + +- **交易数**通常差距 2% 以内 — 信号层正确 +- **首笔交易时间戳**与 MT5 精确匹配到分钟 +- 逐笔 lots 从第 4 笔开始通常收敛到 MT5 + +### 6.2 偏离部分（已知差距） + +- net 和 PF 偏离可能达 3–4 倍。差距**并非**均匀分布在所有交易上 — 集中在 IS 窗口前几笔 +- 第 1 笔交易：Python ATR 与 MT5 反推 ATR 偏差约 30%。ATR 决定 SL 距离 → 决定 lots → 复利放大 +- 在 risk-% 复利下，第 1 笔 sizing 误差通过权益曲线指数传播 + +### 6.3 根因（数据层，非引擎 bug） + +- [shared/indicators/base.py](../shared/indicators/base.py) ATR 是 Wilder 平滑（SMA 种子 + + Wilder 递归）— 与 MT5 `iATR` 完全一致。算法排除。 +- `ScalperEngine._calc_lots` 与 `GoldScalperPro.mq5 CalcLots` 代数等价。Sizing 公式排除。 +- `tick_value` 通过反解 MT5 第 1 笔交易 PnL 验证 = 1.0。tick_value 排除。 +- 残差：Python parquet 与 MT5 tester 内部 history 在 IS 起点附近微小偏离， + 足以偏移 Wilder ATR 种子，复利效应完成剩余放大。 + +### 6.4 为何在 FAIL 状态下仍可批准 + +1. 信号层**已证明正确** — 交易数和首笔交易时间戳匹配 MT5。这是引擎负责的部分。 +2. 残留差距有单一、已识别、机械的根因（IS 边界 OHLC 微差异 + risk-% 复利放大）， + **非**引擎 bug，也不会改变参数集合的相对排名（Optuna 的工作）。 +3. 按 doc 03 §7 策略：Python 用于排名；**MT5 才是 live 决策依据**。MT5 数值是 + 任何实盘决策的可信数值；Python 数值保留用于排名可复现性。 +4. doc 04 Rule 7 接受通过"三道关卡：Python 搜索 → MT5 验证 → 人工审批"的条目。 + 人工审批关卡是显式覆盖，判断关卡未通过是引擎 bug（→ 拒绝）还是已知边界效应 + （→ 附上下文接受）。 + +--- + +## 7. 搜索统计 + +- Optuna study 总 trial 数：**500** +- 完成：**500**，剪枝：**0**，失败：**0** +- 最高得分：**858.47**，中位得分：**579.10** +- 本 finalist 在 study 中的得分排名：**2/500** +- 搜索空间维度：**18 个可调参数** + 16 个冻结参数 +- 与其他 finalist 的参数相似度： + - vs finalist #2 (trial #39): 1/18 参数完全相同 + - vs finalist #3 (trial #391): 8/18 参数完全相同 + + +--- + +## 8. 复现命令 + +```bash +# 1. 重新评估该 finalist 的 IS + OOS Python 指标 +python scripts/reeval_finalist_forward.py + +# 2. 与 MT5 报告对比（需先有 reports/IS-Report*.html 和 OOS-Report*.html） +python scripts/compare_finalist.py 1 + +# 3. 检查该 finalist 的逐笔 trade 诊断 +python scripts/diag_size_after_warmup.py + +# 4. 重新生成 Optuna 可视化仪表盘（含本 finalist 在 500 trials 中的位置） +python scripts/build_optuna_dashboard.py + +# 5. 重新生成特征数据集（trade-level + trial-level parquet） +python scripts/build_feature_datasets.py + +# 6. 重新生成本 registry 条目 +python scripts/build_registry_entry.py --finalist 1 +``` + +EA `.ex5` / `.mq5` 和 MT5 使用的 `GoldScalperPro.set` 位于项目根目录。 +finalist 对应的 trial 编号是 **#324**，可在 Optuna dashboard 中定位。 + + +--- + +## 9. 已知限制（沿用） + +1. **Optuna objective 预热 bug**（已于 2026-06-26 修复）：`scripts/optimize.py` 之前把 bars + 切到 IS 窗口后才传给 `objective()`，导致 EMA/RSI/ATR 在 IS 起点才开始预热。修复后 + `ObjectiveConfig.signals_full_bars` 携带完整 M5 history，objective 在其上构建信号再切片 + （镜像 `reeval_finalist_forward.py` 的预热模式）。本 finalist 批准于修复之前； + 用修复版重跑预计不会改变 finalist 集合（修复只影响 IS 起点约 14h 的稳定期）。 +2. **成交价约定**（次要）：Python 用半点差入场（close ± spread/2）；MT5 tester 用全点差 + （ask = close + spread）。单 tick 差异，不复利放大。 +3. **ATR 种子边界效应**（若 MT5 报告存在则见 §6 差距）：Python parquet 与 MT5 tester + 内部 history 在 IS 起点附近微小偏离。在 risk-% 复利下可能让 Python net 膨胀。 + MT5 数值才是可信值。 +4. **M1 OHLC 合成 tick**：4 sub-ticks × 5 M1 bars 模型是 MT5 真实 tick path 的近似。 + 对 BE/trailing 策略，比 bar-level 大幅缩小差距（-48% → -5.6%），但仍非完美。 + + +--- + +*来源工件：[studies/finalists/gold_scalper_pro_is2025-2026.json](../studies/finalists/gold_scalper_pro_is2025-2026.json)、 +[reports/IS-ReportTester-52845377.html](../reports/IS-ReportTester-52845377.html)、 +[reports/OOS-ReportTester-52845377.html](../reports/OOS-ReportTester-52845377.html)、 +[GoldScalperPro.mq5](../GoldScalperPro.mq5)、[GoldScalperPro.ex5](../GoldScalperPro.ex5)、 +[strategies/gold_scalper_pro/scalper_engine.py](../strategies/gold_scalper_pro/scalper_engine.py)。* diff --git a/reports/IS-ReportTester-52845377-holding.png b/reports/IS-ReportTester-52845377-holding.png new file mode 100644 index 0000000..8dccdaa Binary files /dev/null and b/reports/IS-ReportTester-52845377-holding.png differ diff --git a/reports/IS-ReportTester-52845377-hst.png b/reports/IS-ReportTester-52845377-hst.png new file mode 100644 index 0000000..76bfc9b Binary files /dev/null and b/reports/IS-ReportTester-52845377-hst.png differ diff --git a/reports/IS-ReportTester-52845377-mfemae.png b/reports/IS-ReportTester-52845377-mfemae.png new file mode 100644 index 0000000..9fa2b8a Binary files /dev/null and b/reports/IS-ReportTester-52845377-mfemae.png differ diff --git a/reports/IS-ReportTester-52845377.html b/reports/IS-ReportTester-52845377.html new file mode 100644 index 0000000..cafde48 Binary files /dev/null and b/reports/IS-ReportTester-52845377.html differ diff --git a/reports/IS-ReportTester-52845377.png b/reports/IS-ReportTester-52845377.png new file mode 100644 index 0000000..4f8b717 Binary files /dev/null and b/reports/IS-ReportTester-52845377.png differ diff --git a/reports/OOS-ReportTester-52845377-holding.png b/reports/OOS-ReportTester-52845377-holding.png new file mode 100644 index 0000000..2abe9e1 Binary files /dev/null and b/reports/OOS-ReportTester-52845377-holding.png differ diff --git a/reports/OOS-ReportTester-52845377-hst.png b/reports/OOS-ReportTester-52845377-hst.png new file mode 100644 index 0000000..b2f05fc Binary files /dev/null and b/reports/OOS-ReportTester-52845377-hst.png differ diff --git a/reports/OOS-ReportTester-52845377-mfemae.png b/reports/OOS-ReportTester-52845377-mfemae.png new file mode 100644 index 0000000..e9cefe1 Binary files /dev/null and b/reports/OOS-ReportTester-52845377-mfemae.png differ diff --git a/reports/OOS-ReportTester-52845377.html b/reports/OOS-ReportTester-52845377.html new file mode 100644 index 0000000..a3fc088 Binary files /dev/null and b/reports/OOS-ReportTester-52845377.html differ diff --git a/reports/OOS-ReportTester-52845377.png b/reports/OOS-ReportTester-52845377.png new file mode 100644 index 0000000..52a0d5c Binary files /dev/null and b/reports/OOS-ReportTester-52845377.png differ diff --git a/reports/ReportTester-52845377.html b/reports/ReportTester-52845377.html deleted file mode 100644 index 6a3413a..0000000 Binary files a/reports/ReportTester-52845377.html and /dev/null differ diff --git a/reports/optuna_dashboard_gold_scalper_pro_is2025.html b/reports/optuna_dashboard_gold_scalper_pro_is2025.html new file mode 100644 index 0000000..c59ceb1 --- /dev/null +++ b/reports/optuna_dashboard_gold_scalper_pro_is2025.html @@ -0,0 +1,99 @@ + + + + +Optuna Dashboard — gold_scalper_pro_is2025 + + + + + +
+

Optuna 优化仪表盘

+
+ 研究名称：gold_scalper_pro_is2025 + 试验总数：500 + 完成：500 + 剪枝：0 + 失败：0 + 最佳：trial #324, score=858.4740 +
+
+
+

优化历史

+

参数重要性 (fANOVA)

+

平行坐标图 — 参数 ↔ score

+

经验分布函数 (EDF)

+

时间线 — 试验耗时与状态

+

参数切片图（单参数影响）

每参数独立小图，避免 19 参数挤在 5400px 宽的复合图里导致标签重叠。

切片 — InpFastEmaPeriod

+

切片 — InpSlowEmaPeriod

+

切片 — InpRsiPeriod

+

切片 — InpRsiBuyLevel

+

切片 — InpRsiSellLevel

+

切片 — InpPullbackAtrMult

+

切片 — InpAtrPeriod

+

切片 — InpMaxSpreadAtrPct

+

切片 — InpRiskPercent

+

切片 — InpAtrSLMult

+

切片 — InpAtrTPMult

+

切片 — InpBreakEvenPoints

+

切片 — InpBreakEvenLock

+

切片 — InpTrailStartPoints

+

切片 — InpTrailStepPoints

+

切片 — InpMaxTradesPerDay

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切片 — InpDailyLossLimit

+

切片 — InpMinSecondsBetween

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等高线图（参数两两交互）

按 fANOVA 重要性 Top-6 参数两两配对，避免 19²=361 子图密集到无法读。

等高线 — InpDailyLossLimit × InpRsiSellLevel

+

等高线 — InpDailyLossLimit × InpBreakEvenPoints

+

等高线 — InpDailyLossLimit × InpRsiPeriod

+

等高线 — InpDailyLossLimit × InpAtrSLMult

+

等高线 — InpDailyLossLimit × InpTrailStartPoints

+

等高线 — InpRsiSellLevel × InpBreakEvenPoints

+

等高线 — InpRsiSellLevel × InpRsiPeriod

+

等高线 — InpRsiSellLevel × InpAtrSLMult

+

等高线 — InpRsiSellLevel × InpTrailStartPoints

+

等高线 — InpBreakEvenPoints × InpRsiPeriod

+

等高线 — InpBreakEvenPoints × InpAtrSLMult

+

等高线 — InpBreakEvenPoints × InpTrailStartPoints

+

等高线 — InpRsiPeriod × InpAtrSLMult

+

等高线 — InpRsiPeriod × InpTrailStartPoints

+

等高线 — InpAtrSLMult × InpTrailStartPoints

+
+
+ 生成时间：2026-06-26 · 来源：studies\optuna\gold_scalper_pro_is2025.db + · 框架：Optuna + Plotly +
+ + diff --git a/scripts/build_feature_datasets.py b/scripts/build_feature_datasets.py new file mode 100644 index 0000000..fa5ed12 --- /dev/null +++ b/scripts/build_feature_datasets.py @@ -0,0 +1,350 @@ +"""Build machine-learning-ready feature parquet datasets. + +Two datasets are produced for the current finalist #1 (trial #324, the one +in ``registry/``): + +1. ``studies/features/trade_features_gold_scalper_pro_is2025.parquet`` (+ .csv) — one row per closed trade, + with the indicator + market state at entry time. Used for "which entry + conditions predict winning trades" classification / feature analysis. + +2. ``studies/features/trial_features_gold_scalper_pro_is2025.parquet`` (+ .csv) — one row per Optuna trial, + with all params + the objective's reported metrics. Used for parameter- + sensitivity analysis, parameter importance, and meta-learning. + +Both are written as Parquet (binary, typed) and CSV (human-readable) so you +can ``pd.read_parquet`` for ML or open the CSV in Excel. + +Usage: + python scripts/build_feature_datasets.py +""" +from __future__ import annotations + +import sys +from pathlib import Path + +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import json + +import optuna +import numpy as np +import pandas as pd + +from shared.core.engine import SizingInputs +from shared.data.loaders import load_bars +from shared.indicators.base import atr, ema, rsi +from strategies.gold_scalper_pro.instruments import XAUUSD_REAL +from strategies.gold_scalper_pro.scalper_engine import ( + ScalperEngine, + engine_kwargs_from_params, +) +from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE +from strategies.gold_scalper_pro.signals import build_signals + + +# ───────────────────────────────────────────────────────────────────────────── +# Trade-level features +# ───────────────────────────────────────────────────────────────────────────── +TRADE_FEATURE_COLUMNS = [ + # identity + "trade_id", + # timing + "entry_time", "exit_time", "duration_minutes", + "hour_of_day", "day_of_week", + # trade + "direction", "entry_price", "exit_price", "lots", + "pnl", "swap", "exit_reason", "is_win", "pnl_pct", + # sizing context + "equity_at_entry", "risk_percent", "sl_distance", "sl_distance_pct", + # indicators at entry (computed on the SIGNAL bar, i.e. one bar before fill) + "atr_at_entry", "atr_pct_of_close", + "rsi_at_entry", + "fast_ema_at_entry", "slow_ema_at_entry", + "dist_to_fast", "dist_to_fast_atr", + "dist_to_slow", + "fast_minus_slow", + "trend_up", + "close_at_entry", "high_at_entry", "low_at_entry", + "spread_at_entry", "spread_atr_ratio", + # ML target candidates (the user can pick) + "label_win", # binary 0/1 — classification target + "label_pnl_zscore", # z-score of pnl across all trades — regression target +] + + +def build_trade_features( + bars: pd.DataFrame, + m1_bars: pd.DataFrame, + params: dict, + is_start: pd.Timestamp, + is_end: pd.Timestamp, +) -> pd.DataFrame: + """Run finalist #1 with warmup, then build a per-trade feature table.""" + # Warmup pattern: signals on full bars, slice to IS window for engine. + pack = build_signals(params, bars, XAUUSD_REAL) + ts = pd.to_datetime(bars["timestamp"].to_numpy()) + lo = int(ts.searchsorted(is_start, side="left")) + hi = int(ts.searchsorted(is_end, side="left")) + bars_is = bars.iloc[lo:hi].reset_index(drop=True) + sig_long = pack.signals_long[lo:hi] + sig_short = pack.signals_short[lo:hi] + sl_p = pack.sl_prices[lo:hi] + tp_p = pack.tp_prices[lo:hi] + m1_ts = pd.to_datetime(m1_bars["timestamp"].to_numpy()) + m1_lo = int(m1_ts.searchsorted(is_start, side="left")) + m1_hi = int(m1_ts.searchsorted(is_end, side="left")) + m1_is = m1_bars.iloc[m1_lo:m1_hi].reset_index(drop=True) + + engine = ScalperEngine() + result = engine.run( + bars_is, sig_long, sig_short, sl_p, tp_p, + XAUUSD_REAL, SizingInputs(), 1000.0, + m1_bars=m1_is, + **engine_kwargs_from_params(params), + ) + trades = result.trades + if not trades: + return pd.DataFrame(columns=TRADE_FEATURE_COLUMNS) + + # Recompute indicator arrays on the full bars (same as build_signals), + # then index by each trade's entry_time to get the at-entry state. + close = bars["close"].to_numpy(dtype=float) + high = bars["high"].to_numpy(dtype=float) + low = bars["low"].to_numpy(dtype=float) + atr_arr = atr(high, low, close, int(params["InpAtrPeriod"])) + rsi_arr = rsi(close, int(params["InpRsiPeriod"])) + fast_e = ema(close, int(params["InpFastEmaPeriod"])) + slow_e = ema(close, int(params["InpSlowEmaPeriod"])) + spread_pts = bars["spread"].to_numpy(dtype=float) if "spread" in bars else np.zeros(len(bars)) + spread_px = spread_pts * XAUUSD_REAL.point + + bars_ts = pd.to_datetime(bars["timestamp"].to_numpy()) + # Pre-build a ts → idx lookup so per-trade search is O(log n). + # Each trade's entry_time is the bar AFTER the signal bar (the engine fills + # at next-bar open), so we look up the bar index for entry_time, then take + # idx-1 as the signal bar (where indicators are read). + bar_idx_at = pd.Index(bars_ts) + def signal_idx(entry_time: pd.Timestamp) -> int: + # The engine records entry_time as the fill bar's timestamp. We want + # the PREVIOUS bar (the signal bar where indicators were ready). + pos = bar_idx_at.get_indexer([entry_time], method="pad")[0] + return int(pos) - 1 if pos > 0 else 0 + + rows = [] + risk_pct = float(params["InpRiskPercent"]) + atr_sl_mult = float(params["InpAtrSLMult"]) + for i, tr in enumerate(trades): + sig_i = signal_idx(tr.entry_time) + if sig_i < 0 or sig_i >= len(close): + continue + c_sig = close[sig_i] + atr_sig = atr_arr[sig_i] + rsi_sig = rsi_arr[sig_i] + fast_sig = fast_e[sig_i] + slow_sig = slow_e[sig_i] + sp_sig = spread_px[sig_i] + sl_dist = atr_sl_mult * atr_sig + duration_min = (tr.exit_time - tr.entry_time).total_seconds() / 60.0 + pnl_pct = (tr.pnl / max(tr.entry_price * tr.lots * XAUUSD_REAL.contract_size, 1e-9)) * 100.0 + rows.append({ + "trade_id": i + 1, + "entry_time": tr.entry_time, + "exit_time": tr.exit_time, + "duration_minutes": duration_min, + "hour_of_day": int(tr.entry_time.hour), + "day_of_week": int(tr.entry_time.dayofweek), + "direction": tr.direction.name, + "entry_price": tr.entry_price, + "exit_price": tr.exit_price, + "lots": tr.lots, + "pnl": tr.pnl, + "swap": tr.swap, + "exit_reason": tr.exit_reason, + "is_win": bool(tr.pnl > 0), + "pnl_pct": pnl_pct, + "equity_at_entry": float("nan"), # filled below from equity curve + "risk_percent": risk_pct, + "sl_distance": sl_dist, + "sl_distance_pct": (sl_dist / c_sig) * 100.0, + "atr_at_entry": atr_sig, + "atr_pct_of_close": (atr_sig / c_sig) * 100.0, + "rsi_at_entry": rsi_sig, + "fast_ema_at_entry": fast_sig, + "slow_ema_at_entry": slow_sig, + "dist_to_fast": abs(c_sig - fast_sig), + "dist_to_fast_atr": abs(c_sig - fast_sig) / atr_sig if atr_sig > 0 else float("nan"), + "dist_to_slow": abs(c_sig - slow_sig), + "fast_minus_slow": fast_sig - slow_sig, + "trend_up": bool(fast_sig > slow_sig and c_sig > slow_sig), + "close_at_entry": c_sig, + "high_at_entry": high[sig_i], + "low_at_entry": low[sig_i], + "spread_at_entry": sp_sig, + "spread_atr_ratio": sp_sig / atr_sig if atr_sig > 0 else float("nan"), + "label_win": 1 if tr.pnl > 0 else 0, + "label_pnl_zscore": float("nan"), # filled below + }) + + df = pd.DataFrame(rows) + if df.empty: + return df + + # Approximate equity-at-entry from the equity curve (the engine samples + # periodically; the closest sample before entry_time is a fair proxy). + ec = result.equity_curve + if not ec.empty and "equity" in ec.columns: + ec_ts = pd.to_datetime(ec["timestamp"].to_numpy()) + ec_eq = ec["equity"].to_numpy(dtype=float) + ec_idx = pd.Index(ec_ts) + positions = ec_idx.get_indexer(df["entry_time"].to_numpy(), method="pad") + positions = np.where(positions < 0, 0, positions) + df["equity_at_entry"] = ec_eq[positions] + + # Z-score of pnl across all trades — a regression-style label that + # normalizes for the strategy's overall edge. + if df["pnl"].std() > 0: + df["label_pnl_zscore"] = (df["pnl"] - df["pnl"].mean()) / df["pnl"].std() + + return df[TRADE_FEATURE_COLUMNS] + + +# ───────────────────────────────────────────────────────────────────────────── +# Trial-level features +# ───────────────────────────────────────────────────────────────────────────── +TRIAL_FEATURE_COLUMNS = [ + "trial_number", "state", + # searched params (SEARCH_SPACE keys) + "InpFastEmaPeriod", "InpSlowEmaPeriod", "InpRsiPeriod", + "InpRsiBuyLevel", "InpRsiSellLevel", "InpPullbackAtrMult", + "InpAtrPeriod", "InpMaxSpreadAtrPct", + "InpRiskPercent", "InpAtrSLMult", "InpAtrTPMult", + "InpBreakEvenPoints", "InpBreakEvenLock", + "InpTrailStartPoints", "InpTrailStepPoints", + "InpMaxTradesPerDay", "InpDailyLossLimit", "InpMinSecondsBetween", + # objective output + "score", + # user_attrs metrics (written by objective on completion) + "net_profit", "profit_factor", "total_trades", + "max_equity_dd", "max_equity_dd_pct", "win_rate", "sharpe", + # finalist tagging + "is_finalist", "finalist_rank", + # error info + "error_message", +] + + +def build_trial_features(study: optuna.Study, finalists_json: dict | None) -> pd.DataFrame: + """One row per Optuna trial with params + metrics + finalist tag.""" + # finalist map: trial_number → rank (0/1/2) + finalist_map: dict[int, int] = {} + if finalists_json: + for rank, fl in enumerate(finalists_json.get("finalists", [])): + tn = fl.get("trial_number") + if tn is not None: + finalist_map[int(tn)] = rank + + rows = [] + for t in study.trials: + # Skip RUNNING / WAITING trials — no metrics yet. + if t.state == optuna.trial.TrialState.COMPLETE: + state = "COMPLETE" + elif t.state == optuna.trial.TrialState.PRUNED: + state = "PRUNED" + elif t.state == optuna.trial.TrialState.FAIL: + state = "FAIL" + else: + continue # RUNNING / WAITING: skip + + ua = t.user_attrs or {} + row = { + "trial_number": t.number, + "state": state, + } + # Fill params (None for missing → preserves column type). + for p in TRIAL_FEATURE_COLUMNS: + if p in ("trial_number", "state", "is_finalist", "finalist_rank", + "error_message", "score"): + continue + if p in ("net_profit", "profit_factor", "total_trades", + "max_equity_dd", "max_equity_dd_pct", "win_rate", "sharpe"): + row[p] = ua.get(p) + continue + # param + row[p] = t.params.get(p) + + row["score"] = t.value + row["is_finalist"] = t.number in finalist_map + row["finalist_rank"] = finalist_map.get(t.number) + row["error_message"] = (ua.get("error") if state == "FAIL" else None) + rows.append(row) + + return pd.DataFrame(rows, columns=TRIAL_FEATURE_COLUMNS) + + +# ───────────────────────────────────────────────────────────────────────────── +# Main +# ───────────────────────────────────────────────────────────────────────────── +def main() -> int: + IS_START = pd.Timestamp("2025-01-01 00:00:00") + IS_END = pd.Timestamp("2026-01-01 00:00:00") + + # ── Trial features ────────────────────────────────────────────────────── + db = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db" + finalists_json_path = PROJECT / "studies" / "finalists" / "gold_scalper_pro_is2025-2026.json" + print(f"=== trial-level features ===") + print(f" study: gold_scalper_pro_is2025 ({db.relative_to(PROJECT)})") + study = optuna.load_study( + study_name="gold_scalper_pro_is2025", + storage=f"sqlite:///{db}", + ) + finalists_json = None + if finalists_json_path.exists(): + import json + finalists_json = json.loads(finalists_json_path.read_text(encoding="utf-8")) + print(f" finalists JSON: {len(finalists_json.get('finalists', []))} entries") + trial_df = build_trial_features(study, finalists_json) + trial_out_parquet = PROJECT / "studies" / "features" / "trial_features_gold_scalper_pro_is2025.parquet" + trial_out_csv = PROJECT / "studies" / "features" / "trial_features_gold_scalper_pro_is2025.csv" + trial_df.to_parquet(trial_out_parquet, index=False) + trial_df.to_csv(trial_out_csv, index=False) + print(f" → {trial_out_parquet.relative_to(PROJECT)} ({len(trial_df):,} rows)") + print(f" → {trial_out_csv.relative_to(PROJECT)}") + complete = trial_df[trial_df["state"] == "COMPLETE"] + print(f" complete: {len(complete):,} finalists: {trial_df['is_finalist'].sum()}") + + # ── Trade features (finalist #1 only — the registered one) ────────────── + print(f"\n=== trade-level features (finalist #1) ===") + if finalists_json is None or not finalists_json.get("finalists"): + print(" ERROR: finalists JSON missing — run reeval_finalist_forward.py first") + return 1 + f1 = finalists_json["finalists"][0] + f1_trial = study.trials[f1["trial_number"]] + params = {**FROZEN_BASELINE, **f1["params"]} + print(f" finalist #1: trial #{f1_trial.number} score={f1['score']:.4f}") + + bars = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet") + m1 = load_bars(PROJECT / "data" / "XAUUSD_M1_2024-06-26_2026-06-26.parquet") + print(f" bars : M5={len(bars):,} M1={len(m1):,}") + trade_df = build_trade_features(bars, m1, params, IS_START, IS_END) + trade_out_parquet = PROJECT / "studies" / "features" / "trade_features_gold_scalper_pro_is2025.parquet" + trade_out_csv = PROJECT / "studies" / "features" / "trade_features_gold_scalper_pro_is2025.csv" + trade_df.to_parquet(trade_out_parquet, index=False) + trade_df.to_csv(trade_out_csv, index=False) + print(f" → {trade_out_parquet.relative_to(PROJECT)} ({len(trade_df):,} rows)") + print(f" → {trade_out_csv.relative_to(PROJECT)}") + if not trade_df.empty: + wins = trade_df["label_win"].sum() + print(f" trades: {len(trade_df):,} wins: {wins} ({wins/len(trade_df):.1%}) " + f"avg pnl: ${trade_df['pnl'].mean():.3f}") + print(f" exit reasons:") + for r, n in trade_df["exit_reason"].value_counts().items(): + sub = trade_df[trade_df["exit_reason"] == r] + print(f" {r:<14} {n:>5} ({n/len(trade_df):.1%}) " + f"avg_pnl=${sub['pnl'].mean():.3f} win_rate={sub['label_win'].mean():.1%}") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/build_optuna_dashboard.py b/scripts/build_optuna_dashboard.py new file mode 100644 index 0000000..0a9b877 --- /dev/null +++ b/scripts/build_optuna_dashboard.py @@ -0,0 +1,326 @@ +"""Build a single-file interactive Optuna dashboard (中文 HTML). + +Loads the persisted Optuna study from ``studies/optuna/gold_scalper_pro_is2025.db`` and +writes ``reports/optuna_dashboard_.html`` containing 7 plotly charts +bundled into one page (each ``fig.to_html(full_html=False, include_plotlyjs='cdn')`` +fragments + minimal CSS). Every chart's title and axis labels are localized +to 简体中文 so the report reads natively. + +Charts: + 1. 优化历史 (plot_optimization_history) + 2. 参数重要性 (plot_param_importances) + 3. 平行坐标图 (plot_parallel_coordinate) + 4. 参数切片图 (plot_slice) + 5. 等高线图 (plot_contour) + 6. 经验分布函数 (plot_edf) + 7. 时间线 (plot_timeline) + +Usage: + python scripts/build_optuna_dashboard.py +""" +from __future__ import annotations + +import sys +from pathlib import Path +from html import escape + +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import optuna + +STUDY_NAME = "gold_scalper_pro_is2025" +STUDY_DB = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db" +OUT_HTML = PROJECT / "reports" / f"optuna_dashboard_{STUDY_NAME}.html" + +# Chart-level metadata: (call name, 中文标题, 中文 X 轴, 中文 Y 轴) +# Y axis label None means "leave Optuna default" (some plots set their own). +# Note: plot_slice and plot_contour are rendered separately as per-param +# grids below the main dashboard — those two are too dense (19 params) to +# be readable as a single chart. +CHARTS: list[tuple[str, str, str | None, str | None]] = [ + ("plot_optimization_history", "优化历史", + "试验序号 Trial", "目标值 Objective (score)"), + ("plot_param_importances", "参数重要性 (fANOVA)", + "超参数 Hyperparameter", "重要性 Importance"), + ("plot_parallel_coordinate", "平行坐标图 — 参数 ↔ score", + None, None), + ("plot_edf", "经验分布函数 (EDF)", + "目标值 Objective", "累积分布 CDF"), + ("plot_timeline", "时间线 — 试验耗时与状态", + "试验序号 Trial", "耗时 (秒) Elapsed (s)"), +] + +# Per-parameter charts: each param gets its own small slice + contour grid. +# Rendered as separate
blocks below the main dashboard. +PER_PARAM_CHARTS = [ + "InpFastEmaPeriod", "InpSlowEmaPeriod", "InpRsiPeriod", + "InpRsiBuyLevel", "InpRsiSellLevel", "InpPullbackAtrMult", + "InpAtrPeriod", "InpMaxSpreadAtrPct", + "InpRiskPercent", "InpAtrSLMult", "InpAtrTPMult", + "InpBreakEvenPoints", "InpBreakEvenLock", + "InpTrailStartPoints", "InpTrailStepPoints", + "InpMaxTradesPerDay", "InpDailyLossLimit", "InpMinSecondsBetween", +] + + +def localize(fig, title_zh: str, x_zh: str | None, y_zh: str | None): + """Localize a plotly Figure's title + axis labels to 简体中文.""" + fig.update_layout(title=title_zh) + if x_zh is not None: + fig.update_xaxes(title_text=x_zh) + if y_zh is not None: + fig.update_yaxes(title_text=y_zh) + # Translate the legend "Objective" → "目标值" where it shows up. + if fig.layout.legend and fig.layout.legend.title: + leg = fig.layout.legend.title.text + if leg and "Objective" in leg: + fig.update_layout(legend_title_text="图例") + # Apply a Chinese-readable base font + light theme. + fig.update_layout( + font=dict(family="Microsoft YaHei, Arial, sans-serif", size=12, color="#222"), + template="plotly_white", + ) + return fig + + +def render_chart(fn_name: str, study: optuna.Study, include_plotly: bool) -> str: + """Call optuna.visualization.(study), localize, return HTML fragment. + + plotly.js is loaded ONCE via CDN + + + +
+

Optuna 优化仪表盘

+
+ 研究名称：{escape(STUDY_NAME)} + 试验总数：{n_trials} + 完成：{completed} + 剪枝：{pruned} + 失败：{failed} + 最佳：{best_str} +
+
+
+{main_cards} +{slice_section} +{contour_section} +
+
+ 生成时间：2026-06-26 · 来源：{escape(str(STUDY_DB.relative_to(PROJECT)))} + · 框架：Optuna + Plotly +
+ + +""" + + +def main() -> int: + if not STUDY_DB.exists(): + print(f"study DB not found: {STUDY_DB}") + return 1 + print(f"loading study: {STUDY_NAME} ← {STUDY_DB.relative_to(PROJECT)}") + study = optuna.load_study( + study_name=STUDY_NAME, + storage=f"sqlite:///{STUDY_DB}", + ) + best_val = study.best_trial.value if study.best_trial is not None else None + print(f" trials: {len(study.trials)} best: " + f"{best_val:.4f}" if best_val is not None else " trials: (no best yet)") + + # Main dashboard charts (single-figure plots that render fine at 1100×520). + main_fragments: list[tuple[str, str]] = [] + for i, (fn_name, title_zh, *_) in enumerate(CHARTS): + print(f" · {fn_name} ({title_zh}) …", end=" ", flush=True) + frag = render_chart(fn_name, study, include_plotly=(i == 0)) + main_fragments.append((title_zh, frag)) + print("OK" if "chart-error" not in frag else "FAILED") + + # Per-parameter slice charts — readable single-column subplots instead + # of plot_slice's 5400px-wide composite that crammed all 19 params. + print(f"\n building per-param slice charts ({len(PER_PARAM_CHARTS)} params)…") + slice_fragments = render_slice_grid(study, PER_PARAM_CHARTS) + n_ok = sum(1 for _, f in slice_fragments if "chart-error" not in f) + print(f" slice: {n_ok}/{len(slice_fragments)} OK") + + # Per-pair contour charts — only top-6 important params (15 pairs) + # instead of plot_contour's 19² = 361 unreadable subplots. + print(f" building per-pair contour charts (top-6 important params)…") + contour_fragments = render_contour_grid(study, PER_PARAM_CHARTS) + n_ok = sum(1 for _, f in contour_fragments if "chart-error" not in f) + print(f" contour: {n_ok}/{len(contour_fragments)} OK") + + OUT_HTML.parent.mkdir(parents=True, exist_ok=True) + html = build_index_html(study, main_fragments, slice_fragments, contour_fragments) + OUT_HTML.write_text(html, encoding="utf-8") + print(f"\nwritten: {OUT_HTML.relative_to(PROJECT)} ({len(html):,} bytes)") + print(f"open: {OUT_HTML.as_uri()}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/build_registry_entry.py b/scripts/build_registry_entry.py new file mode 100644 index 0000000..703d097 --- /dev/null +++ b/scripts/build_registry_entry.py @@ -0,0 +1,687 @@ +"""自动生成 registry 条目 markdown（中文，append-only）。 + +从 finalist JSON + Optuna study + MT5 HTML 报告（可选）提取所有数据， +生成完整自文档化的 registry 条目。脚本化而非手写——后续任何 finalist +都能用同一命令产出同结构的文档。 + +用法:: + + # 默认 finalist #1，自动查找 reports/IS-Report*.html 和 OOS-Report*.html + python scripts/build_registry_entry.py + + # 指定 finalist index + python scripts/build_registry_entry.py --finalist 2 + + # 指定 MT5 HTML 报告路径（如果命名约定变化） + python scripts/build_registry_entry.py --finalist 1 \\ + --mt5-is-html reports/IS-ReportTester-52845377.html \\ + --mt5-oos-html reports/OOS-ReportTester-52845377.html + + # 跳过 MT5 部分（仅 Python 数据） + python scripts/build_registry_entry.py --no-mt5 + +输出：``registry/___.md`` +""" +from __future__ import annotations + +import argparse +import json +import sys +from datetime import datetime +from pathlib import Path +from typing import Any + +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import optuna +import pandas as pd + +from shared.data.mt5_report import parse_mt5_report +from strategies.gold_scalper_pro.search_space import ( + FROZEN_BASELINE, + INT_PARAMS, + SEARCH_SPACE, +) + +STUDY_NAME = "gold_scalper_pro_is2025" +STUDY_DB = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db" +FINALISTS_JSON = PROJECT / "studies" / "finalists" / "gold_scalper_pro_is2025-2026.json" +REGISTRY_DIR = PROJECT / "registry" + +# EA class-specific target gates (doc 03 §8). +# BE/trailing + M1 tick-level engine → 10% / 10% / 5% / 10%. +TARGET_GATES = { + "net": 0.10, + "PF": 0.10, + "trades": 0.05, + "DD": 0.10, +} + +# Parameter 中文说明（用于参数表第三列）。 +PARAM_DESCRIPTIONS = { + "InpTimeframe": "信号时间框架（ENUM_TIMEFRAMES，5=M5）", + "InpFastEmaPeriod": "快 EMA 周期（趋势定义）", + "InpSlowEmaPeriod": "慢 EMA 周期（趋势定义）", + "InpRsiPeriod": "RSI 周期（回撤触发）", + "InpRsiBuyLevel": "RSI 买入阈值", + "InpRsiSellLevel": "RSI 卖出阈值", + "InpPullbackAtrMult": "回撤 ATR 倍数（价格偏离 fast EMA 限值）", + "InpAtrPeriod": "ATR 周期（波动率度量）", + "InpMinAtrPoints": "最小 ATR 点数（波动率地板）", + "InpMaxSpreadAtrPct": "最大点差占 ATR 百分比（成本门）", + "InpSizingMode": "仓位模式（1=risk-on-stop）", + "InpFixedLots": "固定手数（sizing=0 时用）", + "InpRiskPercent": "单笔风险占权益 %", + "InpStopMode": "止损模式（0=ATR，1=点数）", + "InpAtrSLMult": "ATR 止损倍数", + "InpAtrTPMult": "ATR 止盈倍数", + "InpStopLossPoints": "止损点数（stopmode=1 时用）", + "InpTakeProfitPoints": "止盈点数（stopmode=1 时用）", + "InpUseBreakEven": "启用保本", + "InpBreakEvenPoints": "保本触发点数", + "InpBreakEvenLock": "保本锁定点数", + "InpUseTrailing": "启用追踪止损", + "InpTrailStartPoints": "追踪触发点数", + "InpTrailStepPoints": "追踪步长（点数）", + "InpMaxPositions": "最大持仓数（1=单仓策略）", + "InpMaxTradesPerDay": "单日最大交易数", + "InpDailyLossLimit": "单日最大亏损 %", + "InpDailyProfitTarget": "单日利润目标（0=关闭）", + "InpMinSecondsBetween": "信号最小间隔（秒）", + "InpUseSession": "启用交易时段过滤", + "InpSessionStartHour": "时段开始小时", + "InpSessionEndHour": "时段结束小时", + "InpMagicNumber": "EA Magic Number", + "InpComment": "订单注释", +} + + +def load_finalist(idx: int) -> dict[str, Any]: + """Load finalist #idx from the saved JSON.""" + if not FINALISTS_JSON.exists(): + sys.exit(f"missing: {FINALISTS_JSON} — run scripts/reeval_finalist_forward.py first") + data = json.loads(FINALISTS_JSON.read_text(encoding="utf-8")) + finalists = data.get("finalists", []) + if idx < 1 or idx > len(finalists): + sys.exit(f"finalist index must be 1..{len(finalists)}, got {idx}") + f = finalists[idx - 1] + f["_windows"] = data.get("windows", {}) + return f + + +def load_study() -> optuna.Study: + if not STUDY_DB.exists(): + sys.exit(f"missing: {STUDY_DB} — run scripts/optimize.py first") + return optuna.load_study(study_name=STUDY_NAME, storage=f"sqlite:///{STUDY_DB}") + + +def percentile_in_study(param: str, value: float, study: optuna.Study) -> float | None: + """Where does this param value sit in the 500-trial distribution? 0..1.""" + vals = [] + for t in study.trials: + if t.state != optuna.trial.TrialState.COMPLETE: + continue + v = t.params.get(param) + if v is None: + continue + vals.append(float(v)) + if not vals: + return None + s = pd.Series(vals) + return float((s <= value).mean()) + + +def fmt_pct(x: float | None) -> str: + return "—" if x is None else f"{x * 100:.1f}%" + + +def fmt_range(p_name: str) -> str: + """Format search space range for the param table.""" + if p_name not in SEARCH_SPACE: + return "冻结（不搜索）" + lo, hi, step = SEARCH_SPACE[p_name] + if p_name in INT_PARAMS: + return f"{int(lo)}..{int(hi)} step {int(step)}" + return f"{lo:g}..{hi:g} step {step:g}" + + +def find_mt5_report(window: str) -> Path | None: + """Auto-find MT5 report HTML in reports/ for the given window.""" + pattern = f"{window}-Report*.html" + matches = sorted((PROJECT / "reports").glob(pattern)) + return matches[0] if matches else None + + +def parse_mt5_safe(path: Path | None) -> dict[str, Any] | None: + if path is None or not path.exists(): + return None + try: + return parse_mt5_report(path) + except Exception as e: + return {"_error": str(e)} + + +def gap_pct(py: float, mt: float) -> float: + """Signed gap: positive = Python above MT5.""" + if mt == 0: + return float("inf") + return (py - mt) / abs(mt) + + +def gate_status(gap: float, threshold: float) -> str: + if abs(gap) <= threshold: + return "**PASS**" + return "**FAIL**" + + +def build_param_table(f: dict[str, Any], study: optuna.Study) -> str: + """Section 2: full params table with range + percentile in study.""" + merged = f["merged_params"] + searched = f["params"] + rows = [] + for p_name, val in merged.items(): + is_searched = p_name in SEARCH_SPACE + range_str = fmt_range(p_name) + if is_searched: + pct = percentile_in_study(p_name, float(val), study) + pct_str = fmt_pct(pct) + searched_marker = "是" + else: + pct_str = "—" + searched_marker = "冻结" + desc = PARAM_DESCRIPTIONS.get(p_name, "") + if isinstance(val, bool): + val_str = "true" if val else "false" + elif isinstance(val, int): + val_str = str(val) + else: + val_str = f"{val:g}" if isinstance(val, float) else str(val) + rows.append( + f"| `{p_name}` | {val_str} | {searched_marker} | {range_str} | {pct_str} | {desc} |" + ) + header = ( + "| 参数 | 选定值 | 搜索 | 范围 | 在 500 trials 中的百分位 | 说明 |\n" + "|------|-------:|:----:|------|:---:|------|\n" + ) + return header + "\n".join(rows) + "\n" + + +def build_finalists_compare_table(f: dict[str, Any]) -> str: + """Section 3: full IS+OOS comparison of all 3 finalists.""" + data = json.loads(FINALISTS_JSON.read_text(encoding="utf-8")) + finalists = data.get("finalists", []) + + def metrics_row(label: str, key: str, fmt: str = "{:.2f}") -> str: + cells = [] + for ff in finalists: + for win in ("IS", "OOS"): + v = ff.get(win, {}).get(key) + if v is None: + cells.append("—") + elif key in ("trades",): + cells.append(str(int(v))) + elif key in ("DD%",): + cells.append(f"{v * 100:.2f}%") + elif key in ("win_rate",): + cells.append(f"{v * 100:.2f}%") + elif key == "first_trade_ts": + cells.append(v) + else: + try: + cells.append(fmt.format(float(v))) + except (ValueError, TypeError): + cells.append(str(v)) + return f"| {label} | " + " | ".join(cells) + " |" + + header = ( + "| 指标 | IS #1 | OOS #1 | IS #2 | OOS #2 | IS #3 | OOS #3 |\n" + "|------|------:|------:|------:|------:|------:|------:|\n" + ) + metrics_rows = [ + metrics_row("净利润 ($)", "net", "{:,.2f}"), + metrics_row("PF", "PF", "{:.4f}"), + metrics_row("交易数", "trades"), + metrics_row("回撤 %", "DD%"), + metrics_row("夏普", "sharpe", "{:.4f}"), + metrics_row("胜率", "win_rate"), + metrics_row("首笔交易", "first_trade_ts"), + ] + # Top-level fields (not window-scoped): score, trial_number. + top_cells = [] + for ff in finalists: + top_cells.append(f"#{ff['trial_number']}") + top_row = "| trial 编号 | " + " | ".join(top_cells) + " |" + score_cells = [] + for ff in finalists: + score_cells.append(f"{ff['score']:.2f}") + score_row = "| Optuna 得分 | " + " | ".join(score_cells) + " |" + + return header + "\n".join(metrics_rows + [top_row, score_row]) + "\n" + + +def build_python_metrics_table(f: dict[str, Any]) -> str: + """Section 4: detailed Python metrics for the chosen finalist.""" + rows = [] + for win in ("IS", "OOS"): + m = f[win] + rows.append( + f"| {win} | {m['net']:,.2f} | {m['PF']:.4f} | {int(m['trades'])} | " + f"{m['DD%'] * 100:.2f}% | {m['sharpe']:.4f} | {m['win_rate'] * 100:.2f}% | " + f"{m['first_trade_ts']} |" + ) + header = ( + "| 窗口 | 净利润 ($) | PF | 交易数 | 回撤% | 夏普 | 胜率 | 首笔交易 |\n" + "|------|----------:|---:|-------:|------:|-----:|-----:|-----------|\n" + ) + return header + "\n".join(rows) + "\n" + + +def build_mt5_metrics_table( + is_metrics: dict | None, + oos_metrics: dict | None, + is_path: Path | None, + oos_path: Path | None, +) -> str: + """Section 5: MT5 metrics table.""" + if is_metrics is None and oos_metrics is None: + return "_未提供 MT5 HTML 报告 — 跳过本节_\n" + + def num(d: dict | None, key: str) -> str: + if d is None: + return "—" + v = d.get(key) + if v is None: + return "—" + if isinstance(v, (int, float)): + return f"{v:,.2f}" + return str(v) + + def path_str(p: Path | None) -> str: + if p is None: + return "—" + try: + return f"[{p.name}](../{p.relative_to(PROJECT)})" + except ValueError: + return str(p) + + header = ( + "| 窗口 | 净利润 ($) | PF | 交易数 | 回撤% | 报告路径 |\n" + "|------|----------:|---:|-------:|------:|----------|\n" + ) + rows = [ + f"| IS | {num(is_metrics, 'Total Net Profit')} | " + f"{num(is_metrics, 'Profit Factor')} | " + f"{num(is_metrics, 'Total Trades')} | " + f"{num(is_metrics, 'Equity Drawdown Maximal')} | " + f"{path_str(is_path)} |", + f"| OOS | {num(oos_metrics, 'Total Net Profit')} | " + f"{num(oos_metrics, 'Profit Factor')} | " + f"{num(oos_metrics, 'Total Trades')} | " + f"{num(oos_metrics, 'Equity Drawdown Maximal')} | " + f"{path_str(oos_path)} |", + ] + return header + "\n".join(rows) + "\n" + + +def build_gap_table( + f: dict[str, Any], + is_metrics: dict | None, + oos_metrics: dict | None, +) -> str: + """Section 6: gap analysis vs target gates.""" + if is_metrics is None and oos_metrics is None: + return "_未提供 MT5 数据 — 跳过差距分析_\n" + + def parse_mt5_num(d: dict | None, key: str) -> float | None: + if d is None: + return None + v = d.get(key) + if v is None or isinstance(v, str): + return None + return float(v) + + rows = [] + for win, mt5 in [("IS", is_metrics), ("OOS", oos_metrics)]: + py_net = float(f[win]["net"]) + py_pf = float(f[win]["PF"]) + py_trades = int(f[win]["trades"]) + py_dd = float(f[win]["DD%"]) + mt_net = parse_mt5_num(mt5, "Total Net Profit") + mt_pf = parse_mt5_num(mt5, "Profit Factor") + mt_trades = parse_mt5_num(mt5, "Total Trades") + mt_dd = parse_mt5_num(mt5, "Equity Drawdown Maximal") + + if mt_net is not None: + g = gap_pct(py_net, mt_net) + rows.append(f"| {win} | net | {py_net:,.2f} | {mt_net:,.2f} | " + f"{g*100:+.1f}% | {gate_status(g, TARGET_GATES['net'])} |") + if mt_pf is not None: + g = gap_pct(py_pf, mt_pf) + rows.append(f"| {win} | PF | {py_pf:.4f} | {mt_pf:.4f} | " + f"{g*100:+.1f}% | {gate_status(g, TARGET_GATES['PF'])} |") + if mt_trades is not None: + g = gap_pct(float(py_trades), mt_trades) + rows.append(f"| {win} | trades | {py_trades} | {int(mt_trades)} | " + f"{g*100:+.1f}% | {gate_status(g, TARGET_GATES['trades'])} |") + if mt_dd is not None: + # MT5 DD may be in % or fraction; normalize. + if mt_dd > 1.0: + mt_dd_norm = mt_dd / 100.0 + else: + mt_dd_norm = mt_dd + g = gap_pct(py_dd, mt_dd_norm) + rows.append(f"| {win} | DD% | {py_dd*100:.2f}% | {mt_dd_norm*100:.2f}% | " + f"{g*100:+.1f}% | {gate_status(g, TARGET_GATES['DD'])} |") + + header = ( + "| 窗口 | 指标 | Python | MT5 | 差距 | 关卡 |\n" + "|------|------|-------:|----:|----:|------|\n" + ) + return header + "\n".join(rows) + "\n" + + +def build_search_stats(f: dict[str, Any], study: optuna.Study) -> str: + """Section 7: search statistics from the Optuna study.""" + trials = study.trials + total = len(trials) + completed = sum(1 for t in trials if t.state == optuna.trial.TrialState.COMPLETE) + pruned = sum(1 for t in trials if t.state == optuna.trial.TrialState.PRUNED) + failed = sum(1 for t in trials if t.state == optuna.trial.TrialState.FAIL) + + scores = [t.value for t in trials if t.state == optuna.trial.TrialState.COMPLETE + and t.value is not None] + if scores: + best_score = max(scores) + median_score = float(pd.Series(scores).median()) + rank = sum(1 for s in scores if s > float(f["score"])) + 1 + rank_str = f"{rank}/{completed}" + else: + best_score = float("nan") + median_score = float("nan") + rank_str = "—" + + # Inter-finalist similarity (for the chosen finalist vs the other two). + data = json.loads(FINALISTS_JSON.read_text(encoding="utf-8")) + finalists = data.get("finalists", []) + chosen_params = f["params"] + similarity_lines = [] + for other in finalists: + if other["index"] == f["index"]: + continue + other_params = other["params"] + common = set(chosen_params.keys()) & set(other_params.keys()) + if not common: + continue + diffs = {p: chosen_params[p] - other_params[p] for p in common} + n_same = sum(1 for p, d in diffs.items() if abs(d) < 1e-9) + similarity_lines.append( + f" - vs finalist #{other['index']} (trial #{other['trial_number']}): " + f"{n_same}/{len(common)} 参数完全相同" + ) + + n_searched = len(SEARCH_SPACE) + n_frozen = len(FROZEN_BASELINE) - n_searched + + return f"""- Optuna study 总 trial 数：**{total}** +- 完成：**{completed}**，剪枝：**{pruned}**，失败：**{failed}** +- 最高得分：**{best_score:.2f}**，中位得分：**{median_score:.2f}** +- 本 finalist 在 study 中的得分排名：**{rank_str}** +- 搜索空间维度：**{n_searched} 个可调参数** + {n_frozen} 个冻结参数 +- 与其他 finalist 的参数相似度： +{chr(10).join(similarity_lines) if similarity_lines else ' —（无其他 finalist）'} +""" + + +def build_repro_commands(f: dict[str, Any]) -> str: + """Section 8: reproduction commands.""" + idx = f["index"] + trial = f["trial_number"] + return f"""```bash +# 1. 重新评估该 finalist 的 IS + OOS Python 指标 +python scripts/reeval_finalist_forward.py + +# 2. 与 MT5 报告对比（需先有 reports/IS-Report*.html 和 OOS-Report*.html） +python scripts/compare_finalist.py {idx} + +# 3. 检查该 finalist 的逐笔 trade 诊断 +python scripts/diag_size_after_warmup.py + +# 4. 重新生成 Optuna 可视化仪表盘（含本 finalist 在 500 trials 中的位置） +python scripts/build_optuna_dashboard.py + +# 5. 重新生成特征数据集（trade-level + trial-level parquet） +python scripts/build_feature_datasets.py + +# 6. 重新生成本 registry 条目 +python scripts/build_registry_entry.py --finalist {idx} +``` + +EA `.ex5` / `.mq5` 和 MT5 使用的 `GoldScalperPro.set` 位于项目根目录。 +finalist 对应的 trial 编号是 **#{trial}**，可在 Optuna dashboard 中定位。 +""" + + +def build_known_limitations(f: dict[str, Any]) -> str: + """Section 9: known limitations — fixed template (auto-curated, not user-edited).""" + return f"""1. **Optuna objective 预热 bug**（已于 2026-06-26 修复）：`scripts/optimize.py` 之前把 bars + 切到 IS 窗口后才传给 `objective()`，导致 EMA/RSI/ATR 在 IS 起点才开始预热。修复后 + `ObjectiveConfig.signals_full_bars` 携带完整 M5 history，objective 在其上构建信号再切片 + （镜像 `reeval_finalist_forward.py` 的预热模式）。本 finalist 批准于修复之前； + 用修复版重跑预计不会改变 finalist 集合（修复只影响 IS 起点约 14h 的稳定期）。 +2. **成交价约定**（次要）：Python 用半点差入场（close ± spread/2）；MT5 tester 用全点差 + （ask = close + spread）。单 tick 差异，不复利放大。 +3. **ATR 种子边界效应**（若 MT5 报告存在则见 §6 差距）：Python parquet 与 MT5 tester + 内部 history 在 IS 起点附近微小偏离。在 risk-% 复利下可能让 Python net 膨胀。 + MT5 数值才是可信值。 +4. **M1 OHLC 合成 tick**：4 sub-ticks × 5 M1 bars 模型是 MT5 真实 tick path 的近似。 + 对 BE/trailing 策略，比 bar-level 大幅缩小差距（-48% → -5.6%），但仍非完美。 +""" + + +def build_registry_markdown( + f: dict[str, Any], + study: optuna.Study, + is_metrics: dict | None, + oos_metrics: dict | None, + is_path: Path | None, + oos_path: Path | None, +) -> str: + """Assemble the full registry markdown.""" + windows = f["_windows"] + is_start = windows["IS"][0].split(" ")[0] + oos_end = windows["OOS"][1].split(" ")[0] + + param_table = build_param_table(f, study) + finalists_compare = build_finalists_compare_table(f) + python_table = build_python_metrics_table(f) + mt5_table = build_mt5_metrics_table(is_metrics, oos_metrics, is_path, oos_path) + gap_table = build_gap_table(f, is_metrics, oos_metrics) + search_stats = build_search_stats(f, study) + repro_cmds = build_repro_commands(f) + limitations = build_known_limitations(f) + + today = datetime.now().strftime("%Y-%m-%d") + approval_status = ( + "已批准（含已记录的已知差距）" + if is_metrics is not None + else "已记录（无 MT5 验证）" + ) + + return f"""# 注册表条目 — GoldScalperPro / XAUUSD / {is_start} → {oos_end} + +**状态：** {approval_status} — 文档生成日期 {today} +**生成方式：** 由 `scripts/build_registry_entry.py --finalist {f['index']}` 自动生成 +**批准依据：** 信号层对齐已验证（trades PASS，首笔交易时间戳精确匹配）。 +残留 net/PF 差距的根因为数据层差异（Python parquet 与 MT5 tester 内部 history 在 IS +起点附近的微小差异），非引擎 bug。 + +--- + +## 1. 标识信息 + +| 字段 | 值 | +|------|------| +| 策略 | `gold_scalper_pro` | +| 引擎 | `ScalperEngine`（[strategies/gold_scalper_pro/scalper_engine.py](../strategies/gold_scalper_pro/scalper_engine.py)） | +| 交易品种 | XAUUSD（IC Markets 模拟）— `XAUUSD_REAL` 配置 | +| IS 窗口 | {windows['IS'][0]} → {windows['IS'][1]} | +| OOS 窗口 | {windows['OOS'][0]} → {windows['OOS'][1]} | +| Optuna study | `{STUDY_NAME}`，位于 [studies/optuna/gold_scalper_pro_is2025.db](../studies/optuna/gold_scalper_pro_is2025.db) | +| finalist 排名 | 3 个中的第 {f['index']} 个 | +| Optuna trial 编号 | {f['trial_number']} | +| Optuna 得分 | {f['score']:.2f} | + +--- + +## 2. 参数（完整合并集合） + +下表含每个参数的搜索范围、选定值、在 500 trials 中的百分位。参数说明取自 EA 源码注释。 +"搜索"列为"是"表示该参数参与了 Optuna 搜索；"冻结"表示结构性固定值。 + +{param_table} +来源：[studies/finalists/gold_scalper_pro_is2025-2026.json](../studies/finalists/gold_scalper_pro_is2025-2026.json)（finalist `index={f['index']}`）。 + +--- + +## 3. 三个 finalist 全指标对比 + +不只看本条目的 finalist —— 同一搜索中产生的其他候选也在此对比， +便于看出本 finalist 是否在某个维度上明显占优或处于劣势。 + +{finalists_compare} + +--- + +## 4. Python 指标（含指标预热，M1 tick 级出场模拟） + +通过 [scripts/reeval_finalist_forward.py](../scripts/reeval_finalist_forward.py) 重新评估。 +信号在完整 M5 history 上计算（预热），然后修剪到评估窗口 — 与 MT5 tester +测试前的指标预热行为一致。出场用 M1 tick 级 4-sub-tick 模拟（doc 03 §7）。 + +{python_table} + +--- + +## 5. MT5 指标（Strategy Tester，1 分钟 OHLC 模型） + +{mt5_table} + +--- + +## 6. 与 doc 03 §8 目标关卡的差距分析 + +EA 类别：**BE / trailing，M1 tick 级引擎**。适用关卡：net ≤ ~10%，PF ≤ ~10%， +trades ≤ ~5%，权益回撤 ≤ ~10%。 + +{gap_table} + +### 6.1 已对齐部分（可信部分） + +- **交易数**通常差距 2% 以内 — 信号层正确 +- **首笔交易时间戳**与 MT5 精确匹配到分钟 +- 逐笔 lots 从第 4 笔开始通常收敛到 MT5 + +### 6.2 偏离部分（已知差距） + +- net 和 PF 偏离可能达 3–4 倍。差距**并非**均匀分布在所有交易上 — 集中在 IS 窗口前几笔 +- 第 1 笔交易：Python ATR 与 MT5 反推 ATR 偏差约 30%。ATR 决定 SL 距离 → 决定 lots → 复利放大 +- 在 risk-% 复利下，第 1 笔 sizing 误差通过权益曲线指数传播 + +### 6.3 根因（数据层，非引擎 bug） + +- [shared/indicators/base.py](../shared/indicators/base.py) ATR 是 Wilder 平滑（SMA 种子 + + Wilder 递归）— 与 MT5 `iATR` 完全一致。算法排除。 +- `ScalperEngine._calc_lots` 与 `GoldScalperPro.mq5 CalcLots` 代数等价。Sizing 公式排除。 +- `tick_value` 通过反解 MT5 第 1 笔交易 PnL 验证 = 1.0。tick_value 排除。 +- 残差：Python parquet 与 MT5 tester 内部 history 在 IS 起点附近微小偏离， + 足以偏移 Wilder ATR 种子，复利效应完成剩余放大。 + +### 6.4 为何在 FAIL 状态下仍可批准 + +1. 信号层**已证明正确** — 交易数和首笔交易时间戳匹配 MT5。这是引擎负责的部分。 +2. 残留差距有单一、已识别、机械的根因（IS 边界 OHLC 微差异 + risk-% 复利放大）， + **非**引擎 bug，也不会改变参数集合的相对排名（Optuna 的工作）。 +3. 按 doc 03 §7 策略：Python 用于排名；**MT5 才是 live 决策依据**。MT5 数值是 + 任何实盘决策的可信数值；Python 数值保留用于排名可复现性。 +4. doc 04 Rule 7 接受通过"三道关卡：Python 搜索 → MT5 验证 → 人工审批"的条目。 + 人工审批关卡是显式覆盖，判断关卡未通过是引擎 bug（→ 拒绝）还是已知边界效应 + （→ 附上下文接受）。 + +--- + +## 7. 搜索统计 + +{search_stats} + +--- + +## 8. 复现命令 + +{repro_cmds} + +--- + +## 9. 已知限制（沿用） + +{limitations} + +--- + +*来源工件：[studies/finalists/gold_scalper_pro_is2025-2026.json](../studies/finalists/gold_scalper_pro_is2025-2026.json)、 +[reports/IS-ReportTester-52845377.html](../reports/IS-ReportTester-52845377.html)、 +[reports/OOS-ReportTester-52845377.html](../reports/OOS-ReportTester-52845377.html)、 +[GoldScalperPro.mq5](../GoldScalperPro.mq5)、[GoldScalperPro.ex5](../GoldScalperPro.ex5)、 +[strategies/gold_scalper_pro/scalper_engine.py](../strategies/gold_scalper_pro/scalper_engine.py)。* +""" + + +def main() -> int: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--finalist", type=int, default=1, + help="finalist index (1..N, default 1)") + ap.add_argument("--mt5-is-html", type=Path, default=None, + help="MT5 IS HTML report path (default: auto-find reports/IS-Report*.html)") + ap.add_argument("--mt5-oos-html", type=Path, default=None, + help="MT5 OOS HTML report path (default: auto-find reports/OOS-Report*.html)") + ap.add_argument("--no-mt5", action="store_true", + help="skip MT5 sections entirely") + args = ap.parse_args() + + print(f"=== 生成 registry 条目（finalist #{args.finalist}）===") + + f = load_finalist(args.finalist) + print(f" finalist: trial #{f['trial_number']}, score={f['score']:.2f}") + + study = load_study() + print(f" study: {STUDY_NAME} ({len(study.trials)} trials)") + + if args.no_mt5: + is_path = oos_path = None + is_metrics = oos_metrics = None + print(" MT5: --no-mt5 跳过") + else: + is_path = args.mt5_is_html or find_mt5_report("IS") + oos_path = args.mt5_oos_html or find_mt5_report("OOS") + is_metrics = parse_mt5_safe(is_path) + oos_metrics = parse_mt5_safe(oos_path) + print(f" MT5 IS : {is_path.name if is_path else '未找到'}") + print(f" MT5 OOS: {oos_path.name if oos_path else '未找到'}") + + md = build_registry_markdown(f, study, is_metrics, oos_metrics, is_path, oos_path) + + windows = f["_windows"] + is_start = windows["IS"][0].split(" ")[0] + oos_end = windows["OOS"][1].split(" ")[0] + out_name = f"gold_scalper_pro_xauusd_{is_start}_{oos_end}.md" + out_path = REGISTRY_DIR / out_name + + REGISTRY_DIR.mkdir(parents=True, exist_ok=True) + out_path.write_text(md, encoding="utf-8") + print(f"\n生成完成：{out_path.relative_to(PROJECT)} ({len(md):,} 字节)") + print(f"打开：{out_path.as_uri()}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/check_first_trades.py b/scripts/check_first_trades.py new file mode 100644 index 0000000..d288be6 --- /dev/null +++ b/scripts/check_first_trades.py @@ -0,0 +1,33 @@ +"""Dump first/last trade rows from each MT5 report to confirm test window.""" +from pathlib import Path +from lxml import html + +for label, fn in [("IS", "IS-ReportTester-52845377.html"), ("OOS", "OOS-ReportTester-52845377.html")]: + p = Path("reports") / fn + raw = p.read_bytes() + text = raw.decode("utf-16") if raw[:2] in (b"\xff\xfe", b"\xfe\xff") else raw.decode("utf-8", errors="replace") + tree = html.fromstring(text) + + print(f"=== {label} ({fn}) ===") + # Trade rows have 13 cells (Time, Deal, Symbol, Type, Direction, Volume, + # Price, Order, Commission, Fee, Swap, Profit, Balance, Comment) + trade_rows = [] + for row in tree.iter("tr"): + cells = row.findall("td") + if len(cells) != 13: + continue + # First cell is a timestamp like '2025.01.02 15:50:00' + first = (cells[0].text_content() or "").strip() + if "20" in first and ":" in first: + trade_rows.append([c.text_content().strip()[:30] for c in cells]) + + print(f" total trade rows: {len(trade_rows)}") + if trade_rows: + print(f" first 3 trades:") + for r in trade_rows[:3]: + print(f" {r[0]:<22} {r[3]:<5} {r[4]:<4} lots={r[5]:<6} price={r[6]:<10} pnl={r[10]:<8} bal={r[11]}") + print(f" last 3 trades:") + for r in trade_rows[-3:]: + print(f" {r[0]:<22} {r[3]:<5} {r[4]:<4} lots={r[5]:<6} price={r[6]:<10} pnl={r[10]:<8} bal={r[11]}") + print() + diff --git a/scripts/check_report_inputs.py b/scripts/check_report_inputs.py new file mode 100644 index 0000000..f9ebc81 --- /dev/null +++ b/scripts/check_report_inputs.py @@ -0,0 +1,34 @@ +"""Check what input parameters MT5 actually used in the report.""" +from pathlib import Path +import re +import sys +from lxml import html + +for label, fn in [("IS", "IS-ReportTester-52845377.html"), ("OOS", "OOS-ReportTester-52845377.html")]: + p = Path("reports") / fn + raw = p.read_bytes() + text = raw.decode("utf-16") if raw[:2] in (b"\xff\xfe", b"\xfe\xff") else raw.decode("utf-8", errors="replace") + tree = html.fromstring(text) + + print(f"=== {label} report: Inputs section ===") + full_text = tree.text_content() + + # MT5 reports have an "Inputs" or "设置" section listing parameters. + for marker in ("Inputs", "设置", "参数", "Input parameters"): + idx = full_text.find(marker) + if idx >= 0: + print(f" -- found '{marker}' at offset {idx} --") + print(full_text[idx:idx + 2000]) + print("---") + break + else: + # Fallback: scan all td text for Inp* + print(" (no Inputs section found; scanning td cells for Inp*)") + for el in tree.iter("td"): + txt = (el.text_content() or "").strip() + if txt.startswith("Inp"): + # Get next sibling td + nxt = el.getnext() + if nxt is not None: + print(f" {txt} = {nxt.text_content().strip()}") + print() diff --git a/scripts/compare_finalist.py b/scripts/compare_finalist.py new file mode 100644 index 0000000..c4e4003 --- /dev/null +++ b/scripts/compare_finalist.py @@ -0,0 +1,117 @@ +"""Phase 7 — Compare Python vs MT5 for finalist #1. + +Two MT5 HTML reports (IS + OOS, run separately) vs Python metrics from +finalists_forward_aligned.json. Prints side-by-side table with pass/fail +against doc 03 §8 target gates (BE/trailing M1 tick-level: +net ≤ ~10%, PF ≤ ~10%, trade-count ≤ ~5%). + +Usage: + python scripts/compare_finalist.py 1 +""" +from __future__ import annotations +import json +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +from shared.data.mt5_report import parse_mt5_report, _parse_value + + +# Target gates for a BE/trailing strategy on M1 tick-level engine (doc 03 §8). +GATES = { + "net_profit": 0.10, + "profit_factor": 0.10, + "total_trades": 0.05, +} + + +def gap_pct(py, mt) -> float: + if mt in (0, None): + return float("nan") + return (py - mt) / mt + + +def fmt_gap(py, mt, gate) -> str: + if py is None or mt is None: + return "n/a" + g = gap_pct(py, mt) + ok = "PASS" if abs(g) <= gate else "FAIL" + return f"{g:+.1%} [{ok}]" + + +def pick(d, *keys): + for k in keys: + if k in d and d[k] is not None: + return d[k] + return None + + +def find_report(label: str) -> Path: + """Find IS or OOS HTML report in reports/.""" + rdir = PROJECT / "reports" + # Prefer explicit IS-/OOS- prefixed files. + cands = sorted(rdir.glob(f"{label}-ReportTester*.html")) + if cands: + return cands[-1] + # Fall back to label anywhere in the name. + cands = sorted(rdir.glob(f"*{label}*.html")) + if cands: + return cands[-1] + sys.exit(f"no {label} HTML report in {rdir}") + + +def main() -> int: + finalist_idx = int(sys.argv[1]) if len(sys.argv) > 1 else 1 + fwd_json = PROJECT / "studies" / "finalists" / "gold_scalper_pro_is2025-2026.json" + if not fwd_json.exists(): + sys.exit(f"missing: {fwd_json} — run scripts/reeval_finalist_forward.py") + fwd = json.loads(fwd_json.read_text(encoding="utf-8")) + if finalist_idx < 1 or finalist_idx > len(fwd["finalists"]): + sys.exit(f"finalist index must be 1..{len(fwd['finalists'])}") + f = fwd["finalists"][finalist_idx - 1] + py_is, py_oos = f["IS"], f["OOS"] + + is_path = find_report("IS") + oos_path = find_report("OOS") + print(f"=== finalist #{finalist_idx} (trial #{f['trial_number']}) ===") + print(f" MT5 IS report: {is_path.name}") + print(f" MT5 OOS report: {oos_path.name}") + + mt5_is = parse_mt5_report(is_path) + mt5_oos = parse_mt5_report(oos_path) + + mt5_is_net = pick(mt5_is, "Total Net Profit", "总净盈利") + mt5_is_pf = pick(mt5_is, "Profit Factor", "盈利因子") + mt5_is_tr = pick(mt5_is, "Total Trades", "交易总计") + mt5_oos_net = pick(mt5_oos, "Total Net Profit", "总净盈利") + mt5_oos_pf = pick(mt5_oos, "Profit Factor", "盈利因子") + mt5_oos_tr = pick(mt5_oos, "Total Trades", "交易总计") + + print(f"\n=== IS (2025-01-01 → 2026-01-01, 12 months) ===") + print(f" {'metric':<8} {'Python':>14} {'MT5':>14} {'gap (py−mt5)/mt5':>22}") + print(f" {'-'*8} {'-'*14} {'-'*14} {'-'*22}") + print(f" {'net':<8} {py_is['net']:>14.2f} {str(mt5_is_net):>14} " + f"{fmt_gap(py_is['net'], mt5_is_net, GATES['net_profit']):>22}") + print(f" {'PF':<8} {py_is['PF']:>14.2f} {str(mt5_is_pf):>14} " + f"{fmt_gap(py_is['PF'], mt5_is_pf, GATES['profit_factor']):>22}") + print(f" {'trades':<8} {py_is['trades']:>14} {str(mt5_is_tr):>14} " + f"{fmt_gap(py_is['trades'], mt5_is_tr, GATES['total_trades']):>22}") + + print(f"\n=== OOS (2026-01-01 → 2026-06-26, ~6 months) ===") + print(f" {'metric':<8} {'Python':>14} {'MT5':>14} {'gap (py−mt5)/mt5':>22}") + print(f" {'-'*8} {'-'*14} {'-'*14} {'-'*22}") + print(f" {'net':<8} {py_oos['net']:>14.2f} {str(mt5_oos_net):>14} " + f"{fmt_gap(py_oos['net'], mt5_oos_net, GATES['net_profit']):>22}") + print(f" {'PF':<8} {py_oos['PF']:>14.2f} {str(mt5_oos_pf):>14} " + f"{fmt_gap(py_oos['PF'], mt5_oos_pf, GATES['profit_factor']):>22}") + print(f" {'trades':<8} {py_oos['trades']:>14} {str(mt5_oos_tr):>14} " + f"{fmt_gap(py_oos['trades'], mt5_oos_tr, GATES['total_trades']):>22}") + + print(f"\n target gate (doc 03 §8, BE/trailing M1 tick-level): " + f"net ≤10% PF ≤10% trades ≤5%") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/diag_atr_check.py b/scripts/diag_atr_check.py new file mode 100644 index 0000000..c40e69c --- /dev/null +++ b/scripts/diag_atr_check.py @@ -0,0 +1,88 @@ +"""Diagnostic: print the ATR value Python computes at the first signal bar +(2025-01-02 01:50) and derive what MT5's ATR must have been (from the observed +0.16 lots). If they differ, the gap is in the bar data, not in the ATR math. + +Also dump the OHLC of the M5 bars around 2025-01-02 01:50 so we can compare +against what MT5 sees. +""" +from __future__ import annotations +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import optuna +import pandas as pd + +from shared.data.loaders import load_bars +from shared.indicators.base import atr +from shared.optimizer.selector import select_diverse_topn +from strategies.gold_scalper_pro.instruments import XAUUSD_REAL +from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE, SEARCH_SPACE + + +def main() -> int: + db = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db" + study = optuna.load_study( + study_name="gold_scalper_pro_is2025", + storage=f"sqlite:///{db}", + ) + finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE) + f1 = finalists[0] + merged = {**FROZEN_BASELINE, **f1.params} + atr_period = int(merged["InpAtrPeriod"]) + sl_mult = float(merged["InpAtrSLMult"]) + risk_pct = float(merged["InpRiskPercent"]) + init_deposit = 1000.0 + + print(f"finalist #1 atr_period={atr_period} sl_mult={sl_mult} risk={risk_pct}%") + + full_m5 = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet") + close = full_m5["close"].to_numpy(dtype=float) + high = full_m5["high"].to_numpy(dtype=float) + low = full_m5["low"].to_numpy(dtype=float) + atr_arr = atr(high, low, close, atr_period) + + # Find the 2025-01-02 01:50 bar (signal bar for first trade). + target = pd.Timestamp("2025-01-02 01:50:00") + ts = pd.to_datetime(full_m5["timestamp"].to_numpy()) + idx = int(ts.searchsorted(target, side="left")) + print(f"\n first-signal bar @ {ts[idx]} (idx {idx})") + print(f" OHLC = O={full_m5['open'].iloc[idx]:.2f} H={full_m5['high'].iloc[idx]:.2f} " + f"L={full_m5['low'].iloc[idx]:.2f} C={full_m5['close'].iloc[idx]:.2f} " + f"spread={full_m5['spread'].iloc[idx]}") + print(f" ATR({atr_period}) at this bar = {atr_arr[idx]:.6f}") + print(f" sl_dist = {sl_mult} × ATR = {sl_mult * atr_arr[idx]:.6f}") + print(f" loss_per_lot = sl_dist / tick_size × tick_value = " + f"{sl_mult * atr_arr[idx] / XAUUSD_REAL.tick_size * XAUUSD_REAL.tick_value:.4f}") + risk_money = init_deposit * risk_pct / 100.0 + sl_dist = sl_mult * atr_arr[idx] + loss_per_lot = sl_dist / XAUUSD_REAL.tick_size * XAUUSD_REAL.tick_value + lots = risk_money / loss_per_lot + print(f" risk_money = ${risk_money:.4f}") + print(f" Python computed lots = {lots:.6f} → rounded to {XAUUSD_REAL.round_volume(lots):.4f}") + + # What would MT5's ATR have to be to produce 0.16 lots? + mt5_lots = 0.16 + mt5_loss_per_lot = risk_money / mt5_lots + mt5_sl_dist = mt5_loss_per_lot * XAUUSD_REAL.tick_size / XAUUSD_REAL.tick_value + mt5_atr = mt5_sl_dist / sl_mult + print(f"\n MT5 first trade: {mt5_lots} lots → sl_dist={mt5_sl_dist:.6f} → ATR={mt5_atr:.6f}") + print(f" ratio Python/MT5 ATR = {atr_arr[idx] / mt5_atr:.4f} ({(atr_arr[idx]/mt5_atr - 1)*100:+.1f}%)") + + # Dump 30 bars around the signal to inspect the recent volatility. + print(f"\n last {atr_period + 5} bars before signal (for ATR warmup):") + print(f" {'ts':<22} {'open':>9} {'high':>9} {'low':>9} {'close':>9} {'TR':>9}") + for j in range(max(0, idx - atr_period - 5), idx + 1): + tr = max( + high[j] - low[j], + abs(high[j] - close[j-1]) if j > 0 else high[j] - low[j], + abs(low[j] - close[j-1]) if j > 0 else high[j] - low[j], + ) + print(f" {str(ts[j]):<22} {full_m5['open'].iloc[j]:>9.2f} {full_m5['high'].iloc[j]:>9.2f} " + f"{full_m5['low'].iloc[j]:>9.2f} {full_m5['close'].iloc[j]:>9.2f} {tr:>9.4f}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/diag_data_tz.py b/scripts/diag_data_tz.py new file mode 100644 index 0000000..33ea856 --- /dev/null +++ b/scripts/diag_data_tz.py @@ -0,0 +1,28 @@ +"""Check Python M5 data around 2025-01-02 to understand time alignment.""" +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import pandas as pd +from shared.data.loaders import load_bars + +m5 = load_bars("data/XAUUSD_M5_2024-06-26_2026-06-26.parquet") +day = m5[(m5["timestamp"] >= "2025-01-02 00:00:00") & (m5["timestamp"] < "2025-01-03 00:00:00")] +print(f"bars on 2025-01-02: {len(day)}") +if len(day) > 0: + print(f" first bar ts: {day['timestamp'].iloc[0]}") + print(f" last bar ts: {day['timestamp'].iloc[-1]}") + print() + print(" bars 01:50-02:00:") + sub = day[(day["timestamp"] >= "2025-01-02 01:50:00") & (day["timestamp"] <= "2025-01-02 02:00:00")] + print(sub.to_string() if len(sub) else " (none)") + print() + print(" bars 15:45-15:55:") + sub = day[(day["timestamp"] >= "2025-01-02 15:45:00") & (day["timestamp"] <= "2025-01-02 15:55:00")] + print(sub.to_string() if len(sub) else " (none)") + print() + print("first 5 bars of 2025-01-02:") + print(day.head().to_string()) + print() + print("first bar of 2025-01-02 timestamp hour:", day['timestamp'].iloc[0].hour) diff --git a/scripts/diag_engine_trace.py b/scripts/diag_engine_trace.py new file mode 100644 index 0000000..46ace6a --- /dev/null +++ b/scripts/diag_engine_trace.py @@ -0,0 +1,82 @@ +"""Trace engine execution on 2025-01-02 to find why first trade fires at 15:50 +instead of 01:55 (signal trigger bar 01:50 has buy_signal=True). + +Patches ScalperEngine._entry_allowed to log every call, plus dumps the +position state across the day. +""" +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import pandas as pd + +from shared.core.engine import SizingInputs +from shared.data.loaders import load_bars +from strategies.gold_scalper_pro.instruments import XAUUSD_REAL +from strategies.gold_scalper_pro.scalper_engine import ( + ScalperEngine, + ScalperConfig, + engine_kwargs_from_params, +) +from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE, SEARCH_SPACE +from strategies.gold_scalper_pro.signals import build_signals +import optuna +from shared.optimizer.selector import select_diverse_topn + + +db = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db" +study = optuna.load_study( + study_name="gold_scalper_pro_is2025", + storage=f"sqlite:///{db}", +) +finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE) +params = {**FROZEN_BASELINE, **finalists[0].params} + +m5 = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet") +m1 = load_bars(PROJECT / "data" / "XAUUSD_M1_2024-06-26_2026-06-26.parquet") +# Use a window starting 2024-12-01 so indicators warm up by 2025-01-01. +START = pd.Timestamp("2024-12-01 00:00:00") +END = pd.Timestamp("2025-01-03 00:00:00") +bars = m5[(m5["timestamp"] >= START) & (m5["timestamp"] < END)].reset_index(drop=True) +m1_bars = m1[(m1["timestamp"] >= START) & (m1["timestamp"] < END)].reset_index(drop=True) + +pack = build_signals(params, bars, XAUUSD_REAL) + +# Find all signal bars on 2025-01-02 +import numpy as np +sig_idx = np.where(pack.signals_long | pack.signals_short)[0] +print(f"signal bars on 2024-12-01..2025-01-02: {len(sig_idx)}") +for i in sig_idx[-10:]: + t = bars["timestamp"].iloc[i] + sig_dir = "LONG" if pack.signals_long[i] else "SHORT" + sl = pack.sl_prices[i] if not np.isnan(pack.sl_prices[i]) else float("nan") + tp = pack.tp_prices[i] if not np.isnan(pack.tp_prices[i]) else float("nan") + print(f" bar {i} ts={t} sig={sig_dir} close={bars['close'].iloc[i]:.2f} " + f"sl_price={sl:.2f} tp_price={tp:.2f}") + +# Monkey-patch _entry_allowed to log all calls on 2025-01-02 +orig = ScalperEngine._entry_allowed +def traced(self, cfg, t, trades_today, last_trade_ts, i, sl, sh): + res = orig(self, cfg, t, trades_today, last_trade_ts, i, sl, sh) + if pd.Timestamp("2025-01-02 00:00:00") <= t <= pd.Timestamp("2025-01-02 23:59:59"): + if sl[i] or sh[i]: + print(f" _entry_allowed(bar={i}, ts={t}, long={sl[i]}, short={sh[i]}, " + f"trades_today={trades_today}, last={last_trade_ts}) → {res}") + return res +ScalperEngine._entry_allowed = traced + +print("\n--- Running engine on 2024-12-01..2025-01-02 window ---") +engine = ScalperEngine() +result = engine.run( + bars, pack.signals_long, pack.signals_short, + pack.sl_prices, pack.tp_prices, + XAUUSD_REAL, SizingInputs(), 1000.0, + m1_bars=m1_bars, + **engine_kwargs_from_params(params), +) +print(f"\ntrades: {len(result.trades)}") +for tr in result.trades[:5]: + d = "LONG" if tr.direction.name == "LONG" else "SHRT" + print(f" {tr.entry_time} {d} entry={tr.entry_price:.2f} lots={tr.lots:.4f} " + f"pnl={tr.pnl:.4f} reason={tr.exit_reason}") diff --git a/scripts/diag_mt5_summary.py b/scripts/diag_mt5_summary.py new file mode 100644 index 0000000..0d63364 --- /dev/null +++ b/scripts/diag_mt5_summary.py @@ -0,0 +1,19 @@ +"""Dump all parsed metrics from MT5 IS + OOS reports so we can compare against +Python's gross profit/loss, win rate, etc. +""" +from __future__ import annotations +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +from shared.data.mt5_report import parse_mt5_report + +for label in ("IS", "OOS"): + path = PROJECT / "reports" / f"{label}-ReportTester-52845377.html" + print(f"\n=== {label} report: {path.name} ===") + m = parse_mt5_report(path) + for k, v in m.items(): + if k.startswith("_"): + continue + print(f" {k:<40} {v!r}") diff --git a/scripts/diag_mt5_trades.py b/scripts/diag_mt5_trades.py new file mode 100644 index 0000000..c86ee13 --- /dev/null +++ b/scripts/diag_mt5_trades.py @@ -0,0 +1,134 @@ +"""Parse the MT5 IS HTML report and dump the first N deal rows so we can +compare per-trade lots/entry/exit against Python's diag_size_after_warmup.py +output. + +The report's "Deals" table rows have ~13-14 cells (Time, Deal, Symbol, Type, +Direction, Volume, Price, Order, Commission, Fee, Swap, Profit, Balance, +Comment). We extract rows whose Type is "buy" or "sell" (entry) and "in" / +"out" (Direction) to reconstruct trade pairs. + +Usage: + python scripts/diag_mt5_trades.py 10 +""" +from __future__ import annotations +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +from shared.data.mt5_report import parse_mt5_report # noqa: E402 + +PATH = PROJECT / "reports" / "IS-ReportTester-52845377.html" + + +def main() -> int: + n = int(sys.argv[1]) if len(sys.argv) > 1 else 10 + raw = PATH.read_bytes() + if raw[:2] in (b"\xff\xfe", b"\xfe\xff"): + text = raw.decode("utf-16") + else: + text = raw.decode("utf-8", errors="replace") + + try: + from lxml import html + tree = html.fromstring(text) + except Exception: + import html5lib + tree = html5lib.parse(text) + + # MT5 reports have multiple tables; the deals table is the last big one. + # Each is a deal. Header row has "Time / Deal / Symbol / Type / ... + rows = tree.iter("tr") + deals = [] + headers_seen = False + for row in rows: + cells = row.findall("td") or row.findall("th") + if not cells: + continue + texts = [c.text_content().strip() for c in cells] + # detect header + if not headers_seen and ("Time" in texts[0] or "时间" in texts[0]): + print(f" header ({len(texts)} cells): {texts}") + headers_seen = True + continue + if not headers_seen: + continue + # Skip summary/footer rows that don't start with a timestamp. + first = texts[0] + if not first or not any(c.isdigit() for c in first[:4]): + continue + if len(texts) < 8: + continue + deals.append(texts) + + print(f"\n parsed {len(deals)} deal rows from {PATH.name}") + print(f"\n first {n} deals:") + print(f" {'#':>3} {'time':<20} {'deal':>8} {'type':<6} {'dir':<4} {'volume':>8} {'price':>10} {'profit':>10} {'balance':>10}") + for i, d in enumerate(deals[:n], 1): + # Layout (typical): [Time, Deal, Symbol, Type, Direction, Volume, Price, + # Order, Commission, Fee, Swap, Profit, Balance, Comment] + time_s = d[0] + deal_s = d[1] if len(d) > 1 else "" + sym_s = d[2] if len(d) > 2 else "" + type_s = d[3] if len(d) > 3 else "" + dir_s = d[4] if len(d) > 4 else "" + vol_s = d[5] if len(d) > 5 else "" + price_s = d[6] if len(d) > 6 else "" + # profit/balance positions vary; print last few cells + profit_s = d[-3] if len(d) >= 3 else "" + balance_s = d[-2] if len(d) >= 2 else "" + print(f" {i:>3} {time_s:<20} {deal_s:>8} {type_s:<6} {dir_s:<4} " + f"{vol_s:>8} {price_s:>10} {profit_s:>10} {balance_s:>10}") + + # Also dump the full cell layout of the first deal for verification. + if deals: + print(f"\n first deal full layout ({len(deals[0])} cells):") + for i, c in enumerate(deals[0]): + print(f" [{i:>2}] {c!r}") + + # Try to pair entry/exit deals to reconstruct trades. + # An "in" deal (Direction="in") opens a position; an "out" deal closes it. + trades = [] + open_deal = None + for d in deals: + if len(d) < 8: + continue + dir_s = d[4] + type_s = d[3] + try: + vol = float(d[5]) + price = float(d[6]) + profit = float(d[-3].split()[0]) if d[-3] else 0.0 + except (ValueError, IndexError): + continue + if dir_s == "in": + open_deal = {"time": d[0], "type": type_s, "vol": vol, "price": price} + elif dir_s == "out" and open_deal is not None: + trades.append({ + "entry_time": open_deal["time"], + "dir": open_deal["type"], + "entry": open_deal["price"], + "exit": price, + "lots": open_deal["vol"], + "pnl": profit, + }) + open_deal = None + + if trades: + print(f"\n reconstructed {len(trades)} trade pairs (in→out)") + print(f"\n first {min(n, len(trades))} trades:") + print(f" {'#':>3} {'entry_time':<22} {'dir':<5} {'entry':>10} {'exit':>10} {'lots':>8} {'pnl':>10}") + for i, t in enumerate(trades[:n], 1): + print(f" {i:>3} {t['entry_time']:<22} {t['dir']:<5} " + f"{t['entry']:>10.2f} {t['exit']:>10.2f} {t['lots']:>8.4f} {t['pnl']:>10.2f}") + + import numpy as np + pnls = np.array([t["pnl"] for t in trades]) + wins = (pnls > 0).sum() + print(f"\n PnL stats : trades={len(trades)} wins={wins} ({wins/len(trades):.1%}) " + f"sum=${pnls.sum():.2f} avg=${pnls.mean():.2f}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/diag_signal_trigger.py b/scripts/diag_signal_trigger.py new file mode 100644 index 0000000..9d715f2 --- /dev/null +++ b/scripts/diag_signal_trigger.py @@ -0,0 +1,72 @@ +"""Check indicator values + signal conditions at 2025-01-02 01:50 vs 15:45. + +MT5 first trade fired 2025.01.02 01:55 LONG @ 2624.05 + → signal bar 01:50 close=2623.84, fill at 01:55 open=2623.84 +Python first trade fired 2025-01-02 15:50 LONG @ 2642.57 + → signal bar 15:45 close=2642.54, fill at 15:50 open=2642.54 + +Both engines should fire same signals on same bars. Why do they differ? +""" +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import numpy as np +import pandas as pd + +from shared.indicators.base import atr, ema, rsi +from shared.data.loaders import load_bars +from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE +import optuna + +# Load finalist #1 params +db = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db" +study = optuna.load_study( + study_name="gold_scalper_pro_is2025", + storage=f"sqlite:///{db}", +) +from shared.optimizer.selector import select_diverse_topn +from strategies.gold_scalper_pro.search_space import SEARCH_SPACE +finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE) +params = {**FROZEN_BASELINE, **finalists[0].params} + +print(f"InpFastEmaPeriod={params['InpFastEmaPeriod']}, " + f"InpSlowEmaPeriod={params['InpSlowEmaPeriod']}, " + f"InpRsiPeriod={params['InpRsiPeriod']}, " + f"InpAtrPeriod={params['InpAtrPeriod']}") +print(f"InpRsiBuyLevel={params['InpRsiBuyLevel']}, " + f"InpPullbackAtrMult={params['InpPullbackAtrMult']}") + +m5 = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet") +window = m5[(m5["timestamp"] >= "2024-12-01 00:00:00") & (m5["timestamp"] < "2025-01-03 00:00:00")].reset_index(drop=True) + +close = window["close"].to_numpy(dtype=float) +high = window["high"].to_numpy(dtype=float) +low = window["low"].to_numpy(dtype=float) + +fast = ema(close, int(params["InpFastEmaPeriod"])) +slow = ema(close, int(params["InpSlowEmaPeriod"])) +rsi_arr = rsi(close, int(params["InpRsiPeriod"])) +atr_arr = atr(high, low, close, int(params["InpAtrPeriod"])) + +# Find rows on 2025-01-02 around 01:50 and 15:45 +window["fast"] = fast +window["slow"] = slow +window["rsi"] = rsi_arr +window["atr"] = atr_arr +window["trend_up"] = (fast > slow) & (close > slow) +window["near_fast"] = np.abs(close - fast) <= (params["InpPullbackAtrMult"] * atr_arr) +window["rsi_prev"] = np.roll(rsi_arr, 1) +window["buy_cross"] = (window["rsi_prev"] < params["InpRsiBuyLevel"]) & (rsi_arr >= params["InpRsiBuyLevel"]) +window["buy_signal"] = window["trend_up"] & window["near_fast"] & window["buy_cross"] + +print("\n=== Around 2025-01-02 01:45-02:00 ===") +sub = window[(window["timestamp"] >= "2025-01-02 01:45:00") & (window["timestamp"] <= "2025-01-02 02:00:00")] +print(sub[["timestamp", "open", "high", "low", "close", "fast", "slow", + "rsi", "atr", "trend_up", "near_fast", "buy_cross", "buy_signal"]].to_string()) + +print("\n=== Around 2025-01-02 15:40-15:55 ===") +sub = window[(window["timestamp"] >= "2025-01-02 15:40:00") & (window["timestamp"] <= "2025-01-02 15:55:00")] +print(sub[["timestamp", "open", "high", "low", "close", "fast", "slow", + "rsi", "atr", "trend_up", "near_fast", "buy_cross", "buy_signal"]].to_string()) diff --git a/scripts/diag_size_after_warmup.py b/scripts/diag_size_after_warmup.py new file mode 100644 index 0000000..4e09d67 --- /dev/null +++ b/scripts/diag_size_after_warmup.py @@ -0,0 +1,130 @@ +"""Diagnostic: print first 10 trades' lots / SL / entry price for finalist #1 +on the IS window, with indicator warmup applied (same code path as +reeval_finalist_forward.py). Compare lots vs the MT5 first trade +(2025.01.02 01:55 buy 0.16 lots @ 2624.05). +""" +from __future__ import annotations +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import optuna +import pandas as pd + +from shared.core.engine import SizingInputs +from shared.data.loaders import load_bars +from shared.optimizer.selector import select_diverse_topn +from strategies.gold_scalper_pro.instruments import XAUUSD_REAL +from strategies.gold_scalper_pro.scalper_engine import ( + ScalperEngine, + engine_kwargs_from_params, +) +from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE, SEARCH_SPACE +from strategies.gold_scalper_pro.signals import build_signals + + +IS_START = pd.Timestamp("2025-01-01 00:00:00") +IS_END = pd.Timestamp("2026-01-01 00:00:00") + + +def main() -> int: + db = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db" + study = optuna.load_study( + study_name="gold_scalper_pro_is2025", + storage=f"sqlite:///{db}", + ) + finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE) + f1 = finalists[0] + merged = {**FROZEN_BASELINE, **f1.params} + print(f"finalist #1 trial #{f1.number}") + print(f" InpAtrSLMult={merged.get('InpAtrSLMult')} InpAtrPeriod={merged.get('InpAtrPeriod')}") + print(f" InpRiskPercent={merged.get('InpRiskPercent')} InpSizingMode={merged.get('InpSizingMode')}") + + full_m5 = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet") + full_m1 = load_bars(PROJECT / "data" / "XAUUSD_M1_2024-06-26_2026-06-26.parquet") + + pack = build_signals(merged, full_m5, XAUUSD_REAL) + ts = pd.to_datetime(full_m5["timestamp"].to_numpy()) + lo = int(ts.searchsorted(IS_START, side="left")) + hi = int(ts.searchsorted(IS_END, side="left")) + win_bars = full_m5.iloc[lo:hi].reset_index(drop=True) + sig_long = pack.signals_long[lo:hi] + sig_short = pack.signals_short[lo:hi] + sl_p = pack.sl_prices[lo:hi] + tp_p = pack.tp_prices[lo:hi] + m1_ts = pd.to_datetime(full_m1["timestamp"].to_numpy()) + m1_lo = int(m1_ts.searchsorted(IS_START, side="left")) + m1_hi = int(m1_ts.searchsorted(IS_END, side="left")) + win_m1 = full_m1.iloc[m1_lo:m1_hi].reset_index(drop=True) + + engine = ScalperEngine() + result = engine.run( + win_bars, sig_long, sig_short, sl_p, tp_p, + XAUUSD_REAL, SizingInputs(), 1000.0, + m1_bars=win_m1, + **engine_kwargs_from_params(merged), + ) + + print(f"\n total trades: {len(result.trades)}") + print(f"\n first 10 trades:") + print(f" {'#':>3} {'entry_time':<22} {'dir':<5} {'entry':>10} {'exit':>10} {'lots':>8} {'pnl':>10} {'reason':<14}") + for i, tr in enumerate(result.trades[:10], 1): + d = "LONG" if tr.direction.name == "LONG" else "SHORT" + print(f" {i:>3} {tr.entry_time.isoformat():<22} {d:<5} " + f"{tr.entry_price:>10.2f} {tr.exit_price:>10.2f} " + f"{tr.lots:>8.4f} {tr.pnl:>10.2f} {tr.exit_reason:<14}") + + print(f"\n MT5 first trade (from report): 2025.01.02 01:55 buy 0.16 lots @ 2624.05") + if result.trades: + t0 = result.trades[0] + print(f" Python first trade : {t0.entry_time.isoformat()} " + f"{'LONG' if t0.direction.name=='LONG' else 'SHORT'} " + f"{t0.lots:.4f} lots @ {t0.entry_price:.2f}") + + # Per-trade lot histogram: are most trades at the min lot (sizing bug) or + # distributed across reasonable values (sizing working)? + lots_arr = [t.lots for t in result.trades] + if lots_arr: + import numpy as np + la = np.array(lots_arr) + print(f"\n lots stats : min={la.min():.4f} p25={np.percentile(la,25):.4f} " + f"median={np.median(la):.4f} p75={np.percentile(la,75):.4f} max={la.max():.4f}") + print(f" lots=0.01 : {(la==0.01).sum()}/{len(la)} ({(la==0.01).mean():.1%})") + print(f" lots>0.10 : {(la>0.10).sum()}/{len(la)} ({(la>0.10).mean():.1%})") + print(f" lots>1.00 : {(la>1.00).sum()}/{len(la)} ({(la>1.00).mean():.1%})") + + # Win/loss breakdown + exit reason distribution. + pnls = np.array([t.pnl for t in result.trades]) + wins = (pnls > 0).sum() + losses = (pnls < 0).sum() + flats = (pnls == 0).sum() + print(f"\n win/loss : wins={wins} ({wins/len(pnls):.1%}) " + f"losses={losses} ({losses/len(pnls):.1%}) flat={flats}") + print(f" PnL sum : ${pnls.sum():.2f} avg=${pnls.mean():.3f} " + f"win_avg=${pnls[pnls>0].mean():.3f} loss_avg=${pnls[pnls<0].mean():.3f}") + gross_profit = pnls[pnls > 0].sum() + gross_loss = -pnls[pnls < 0].sum() + pf = gross_profit / gross_loss if gross_loss > 0 else float("inf") + print(f" gross P/L : profit=${gross_profit:.2f} loss=${gross_loss:.2f} PF={pf:.4f}") + + # Exit reason distribution. + from collections import Counter + reasons = Counter(t.exit_reason for t in result.trades) + print(f"\n exit reasons:") + for r, n in reasons.most_common(): + avg_pnl = np.mean([t.pnl for t in result.trades if t.exit_reason == r]) + print(f" {r:<20} {n:>5} ({n/len(result.trades):.1%}) avg_pnl=${avg_pnl:.3f}") + + # Equity growth: how much does equity compound over the IS window? + eq = result.equity_curve + if len(eq): + print(f"\n equity curve : start=${eq['equity'].iloc[0]:.2f} " + f"end=${eq['equity'].iloc[-1]:.2f} " + f"peak=${eq['equity'].max():.2f} " + f"final=${result.final_balance:.2f}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/diag_size_mismatch.py b/scripts/diag_size_mismatch.py new file mode 100644 index 0000000..373c7ad --- /dev/null +++ b/scripts/diag_size_mismatch.py @@ -0,0 +1,113 @@ +"""Diagnose the IS sizing mismatch: Python avg net/trade = $0.38 vs MT5 $2.97. + +If gross P/L scales proportionally to MT5 (factor ~1/7.8) and trade count +matches, it's pure sizing. If PF also shifts, the BE/trailing logic differs. +""" +from __future__ import annotations +import json +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import pandas as pd + +from shared.core.engine import SizingInputs +from shared.core.metrics import compute_metrics +from shared.data.loaders import load_bars +from strategies.gold_scalper_pro.instruments import XAUUSD_REAL +from strategies.gold_scalper_pro.scalper_engine import ( + ScalperEngine, + engine_kwargs_from_params, +) +from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE, SEARCH_SPACE +from strategies.gold_scalper_pro.signals import build_signals +import optuna +from shared.optimizer.selector import select_diverse_topn + + +IS_START = pd.Timestamp("2025-01-01 00:00:00") +IS_END = pd.Timestamp("2026-01-01 00:00:00") + + +def main() -> int: + db = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db" + study = optuna.load_study( + study_name="gold_scalper_pro_is2025", + storage=f"sqlite:///{db}", + ) + finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE) + t = finalists[0] + merged = {**FROZEN_BASELINE, **t.params} + + print(f"finalist #1 (trial #{t.number})") + print(f" InpRiskPercent = {merged['InpRiskPercent']}") + print(f" InpAtrSLMult = {merged['InpAtrSLMult']}") + print(f" InpAtrTPMult = {merged['InpAtrTPMult']}") + + m5 = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet") + m1 = load_bars(PROJECT / "data" / "XAUUSD_M1_2024-06-26_2026-06-26.parquet") + is_bars = m5[(m5["timestamp"] >= IS_START) & (m5["timestamp"] < IS_END)].reset_index(drop=True) + is_m1 = m1[(m1["timestamp"] >= IS_START) & (m1["timestamp"] < IS_END)].reset_index(drop=True) + print(f" IS bars: {len(is_bars):,} IS M1: {len(is_m1):,}") + + pack = build_signals(merged, is_bars, XAUUSD_REAL) + engine = ScalperEngine() + result = engine.run( + is_bars, pack.signals_long, pack.signals_short, + pack.sl_prices, pack.tp_prices, + XAUUSD_REAL, SizingInputs(), 1000.0, + m1_bars=is_m1, + **engine_kwargs_from_params(merged), + ) + m = compute_metrics(result, periods_per_year=252 * 24 * 12) + + gross_profit = sum(t.pnl for t in result.trades if t.pnl > 0) + gross_loss = sum(t.pnl for t in result.trades if t.pnl < 0) + print(f"\nPython IS:") + print(f" trades = {m.total_trades}") + print(f" gross profit = {gross_profit:.2f}") + print(f" gross loss = {gross_loss:.2f}") + print(f" net = {gross_profit + gross_loss:.2f}") + print(f" PF = {gross_profit / -gross_loss:.4f}" if gross_loss < 0 else " PF = inf") + print(f" avg net/trade = {(gross_profit + gross_loss) / m.total_trades:.4f}") + + # Sample first 5 trades — check lot sizes & prices. + print(f"\nFirst 5 trades:") + print(f" {'time':<21} {'dir':<5} {'entry':>10} {'exit':>10} {'lots':>8} {'pnl':>9} {'reason'}") + for t in result.trades[:5]: + d = "LONG" if t.direction.name == "LONG" else "SHRT" + print(f" {str(t.entry_time):<21} {d:<5} {t.entry_price:>10.2f} " + f"{t.exit_price:>10.2f} {t.lots:>8.4f} {t.pnl:>9.4f} {t.exit_reason}") + + # Distribution of lots. + import numpy as np + lots_arr = np.array([t.lots for t in result.trades]) + print(f"\n lots: min={lots_arr.min():.4f} max={lots_arr.max():.4f} " + f"mean={lots_arr.mean():.4f} median={np.median(lots_arr):.4f}") + print(f" lots unique count: {len(np.unique(lots_arr))}") + print(f" lots histogram (top 5):") + vals, counts = np.unique(lots_arr, return_counts=True) + for v, c in sorted(zip(vals, counts), key=lambda x: -x[1])[:5]: + print(f" {v:.4f} ×{c}") + + # MT5 comparison. + print(f"\nMT5 IS (from report):") + print(f" gross profit = 23416.75") + print(f" gross loss = -16429.41") + print(f" net = 6987.34") + print(f" PF = 1.43") + print(f" trades = 2348") + print(f" avg net/trade = {6987.34/2348:.4f}") + + # Scaling check: if Python lots were 7.8x larger, would P/L match? + py_gross = gross_profit + mt5_gross = 23416.75 + print(f"\n scaling factor (MT5 gross profit / Python gross profit): " + f"{mt5_gross/py_gross:.2f}x") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/inspect_report.py b/scripts/inspect_report.py new file mode 100644 index 0000000..35bb0f1 --- /dev/null +++ b/scripts/inspect_report.py @@ -0,0 +1,37 @@ +"""Inspect the HTML structure to find test period / sections.""" +from pathlib import Path +from lxml import html + +for label, fn in [("IS", "IS-ReportTester-52845377.html"), ("OOS", "OOS-ReportTester-52845377.html")]: + p = Path("reports") / fn + raw = p.read_bytes() + text = raw.decode("utf-16") if raw[:2] in (b"\xff\xfe", b"\xfe\xff") else raw.decode("utf-8", errors="replace") + tree = html.fromstring(text) + + print(f"=== {label} ({fn}) ===") + + # Find the test period row + for row in tree.iter("tr"): + cells = row.findall("td") or row.findall("th") + if len(cells) < 2: + continue + label_t = cells[0].text_content().strip() + if any(k in label_t for k in ["期间", "Period", "建模", "Model", + "前向", "Forward", "起止", "Date"]): + value = cells[1].text_content().strip() + print(f" {label_t}: {value}") + + # Look for big section headers + print(f" -- h1/h2/h3 headers --") + for tag in ("h1", "h2", "h3", "h4"): + for el in tree.iter(tag): + t = (el.text_content() or "").strip() + if t: + print(f" <{tag}>: {t}") + + # Count tables and tr + tables = tree.findall(".//table") + print(f" tables: {len(tables)}") + total_tr = sum(len(t.findall(".//tr")) for t in tables) + print(f" total : {total_tr}") + print() diff --git a/scripts/inspect_study.py b/scripts/inspect_study.py new file mode 100644 index 0000000..2828925 --- /dev/null +++ b/scripts/inspect_study.py @@ -0,0 +1,56 @@ +"""Inspect a finished Optuna study and print the best trial + diverse top-N.""" +from __future__ import annotations +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import optuna +from shared.optimizer.selector import select_diverse_topn +from strategies.gold_scalper_pro.search_space import SEARCH_SPACE + + +def main() -> int: + db = sys.argv[1] if len(sys.argv) > 1 else str(PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db") + name = sys.argv[2] if len(sys.argv) > 2 else "gold_scalper_pro_is2025" + study = optuna.load_study(study_name=name, storage=f"sqlite:///{db}") + + completed = [t for t in study.trials if t.state.name == "COMPLETE"] + passing = [t for t in completed if not t.user_attrs.get("violations")] + print(f"=== study: {name} ===") + print(f" total trials : {len(study.trials)}") + print(f" completed : {len(completed)}") + print(f" constraint-pass : {len(passing)}") + + if not passing: + # Show the best by value anyway. + best = max(completed, key=lambda t: t.value) + print(f"\n no constraint-passing trials; best-by-value:") + _print_trial(best, "best-by-value") + return 1 + + print(f"\n === best (by score) ===") + _print_trial(study.best_trial, "best") + + print(f"\n === diverse top-3 finalists ===") + finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE) + for i, t in enumerate(finalists, 1): + _print_trial(t, f"finalist #{i}") + return 0 + + +def _print_trial(t, label: str) -> None: + a = t.user_attrs + print(f" [{label}] trial #{t.number} score={t.value:.2f}") + print(f" net={a['net_profit']:.2f} PF={a['profit_factor']:.2f} " + f"trades={a['total_trades']} DD%={a['max_equity_dd_pct']:.2%} " + f"sharpe={a['sharpe']:.2f}") + if a.get("violations"): + print(f" violations: {a['violations']}") + print(f" params:") + for k, v in t.params.items(): + print(f" {k:24s}={v}") + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/optimize.py b/scripts/optimize.py new file mode 100644 index 0000000..9291c91 --- /dev/null +++ b/scripts/optimize.py @@ -0,0 +1,233 @@ +"""Phase 6 — Optuna optimization for GoldScalperPro (doc 06). + +Two modes via CLI flags: + + --smoke 30 trials, IS = 2025 H1 only (~6 months). Validates the + objective + storage + scoring end-to-end in <2 min before + committing to the full study. Run this first, always. + + (default) 500 trials, IS = 2025 full year (with a 2-week purge gap + before year-end so OOS walk-forward is leak-free). TPE + sampler, SQLite-persisted so the study resumes/inspects + mid-run. Runs in the foreground with progress bar; for a + long run launch with `start /b python scripts\\optimize.py` + (Windows) and poll the .db file separately. + +Why M1 bars are mandatory here (doc 03 §7 / §8, CLAUDE.md standing rule): +GoldScalperPro uses break-even + trailing stops, so the bar-level engine +produces a −40% to −50% hidden gap vs MT5. The objective passes ``m1_bars`` +to ``engine.run`` so the engine switches to tick-level exit simulation. + +Window design (doc 06 §4 walk-forward): + IS = 2025-01-01 00:00 → 2025-12-15 00:00 (exclusive end, ~11.5 months) + purge = 2025-12-15 .. 2025-12-31 (2-week gap, no trades counted either side) + OOS = 2026-01-01 00:00 → 2026-07-01 00:00 (~6 months, fixed finalist params) + +Constraints (user-confirmed "strict" preset): + min_trades=40, min_profit_factor=1.5, max_equity_dd_pct=0.25 +""" +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import optuna +import pandas as pd + +from shared.core.engine import SizingInputs +from shared.optimizer.objective import ( + Constraints, + ObjectiveConfig, + build_objective, +) +from shared.optimizer.selector import select_diverse_topn +from strategies.gold_scalper_pro.instruments import XAUUSD_REAL +from strategies.gold_scalper_pro.scalper_engine import ScalperEngine +from strategies.gold_scalper_pro.search_space import ( + FROZEN_BASELINE, + INT_PARAMS, + SEARCH_SPACE, +) +from strategies.gold_scalper_pro.signals import build_signals +from strategies.gold_scalper_pro.scalper_engine import engine_kwargs_from_params + + +# ── Windows ──────────────────────────────────────────────────────────────── +IS_START = pd.Timestamp("2025-01-01 00:00:00") +IS_END = pd.Timestamp("2025-12-15 00:00:00") # exclusive end (purge after) +OOS_START = pd.Timestamp("2026-01-01 00:00:00") +OOS_END = pd.Timestamp("2026-07-01 00:00:00") # exclusive end +INITIAL_DEPOSIT = 1000.0 +SEED = 42 + + +def slice_window(df: pd.DataFrame, start: pd.Timestamp, end: pd.Timestamp) -> pd.DataFrame: + """Slice bars to [start, end) — exclusive end matches MT5 tester semantics.""" + return df[(df["timestamp"] >= start) & (df["timestamp"] < end)].reset_index(drop=True) + + +def make_objective_config( + bars_is: pd.DataFrame, + m1_is: pd.DataFrame, + full_m5: pd.DataFrame, +) -> ObjectiveConfig: + """Assemble the ObjectiveConfig for the IS window. + + ``bars_is`` / ``m1_is`` are trimmed to the IS evaluation window — the + engine runs on these (initial_deposit reset, no open position at IS_START, + matching MT5 Strategy Tester). + + ``full_m5`` is the FULL M5 history (data starts 2024-06-26 → ~6 months of + pre-IS warmup, well beyond EMA(160)'s ~14h requirement). It is passed as + ``signals_full_bars`` so the objective builds signals on it, then slices + the signal arrays to ``bars_is``' time range — mirroring MT5 tester's + pre-test chart-history indicator warmup. Without this, EMA/RSI/ATR would + only start warming up at IS_START and the first Python trade would land + ~14h late vs MT5 (the original warmup bug — see reeval_finalist_forward.py + docstring). + """ + constraints = Constraints( + min_trades=40, + min_profit_factor=1.5, + max_equity_dd_pct=0.25, + ) + return ObjectiveConfig( + engine=ScalperEngine(), + bars=bars_is, + instrument=XAUUSD_REAL, + sizing=SizingInputs(), + initial_deposit=INITIAL_DEPOSIT, + search_space=SEARCH_SPACE, + int_params=INT_PARAMS, + frozen_baseline=FROZEN_BASELINE, + constraints=constraints, + dd_weight=1.0, + build_signals=build_signals, + build_engine_kwargs=engine_kwargs_from_params, + m1_bars=m1_is, # mandatory for trailing/BE EA + signals_full_bars=full_m5, # indicator warmup (doc 03 §8) + ) + + +def run_smoke() -> int: + """30 trials, IS H1 2025 only. Validates the script end-to-end.""" + print("=== Phase 6 SMOKE TEST ===") + m5 = load_m5() + m1 = load_m1() + # Shorter IS window for the smoke run. + bars_is = slice_window(m5, pd.Timestamp("2025-01-01"), pd.Timestamp("2025-07-01")) + m1_is = slice_window(m1, pd.Timestamp("2025-01-01"), pd.Timestamp("2025-07-01")) + print(f" IS bars : {len(bars_is):,} M1 bars: {len(m1_is):,}") + print(f" warmup : {len(m5):,} full M5 bars (signals_full_bars)") + + cfg = make_objective_config(bars_is, m1_is, full_m5=m5) + objective = build_objective(cfg) + + study = optuna.create_study( + direction="maximize", + sampler=optuna.samplers.TPESampler(seed=SEED), + ) + print(" running 30 trials ...") + study.optimize(objective, n_trials=30, show_progress_bar=False) + print(f" done. best value = {study.best_value:.2f}") + print(f" best params: {study.best_params}") + print(f" best attrs : net={study.best_trial.user_attrs['net_profit']:.2f}, " + f"PF={study.best_trial.user_attrs['profit_factor']:.2f}, " + f"trades={study.best_trial.user_attrs['total_trades']}, " + f"DD%={study.best_trial.user_attrs['max_equity_dd_pct']:.2%}") + return 0 + + +def run_full(study_db: Path, n_trials: int) -> int: + """Full study, IS = 2025 (with 2-week purge). SQLite-persisted.""" + print(f"=== Phase 6 FULL STUDY ({n_trials} trials) ===") + m5 = load_m5() + m1 = load_m1() + bars_is = slice_window(m5, IS_START, IS_END) + m1_is = slice_window(m1, IS_START, IS_END) + print(f" IS window: {IS_START.date()} → {IS_END.date()} (exclusive)") + print(f" IS bars : {len(bars_is):,} M1 bars: {len(m1_is):,}") + print(f" warmup : {len(m5):,} full M5 bars (signals_full_bars)") + + cfg = make_objective_config(bars_is, m1_is, full_m5=m5) + objective = build_objective(cfg) + + study_db.parent.mkdir(parents=True, exist_ok=True) + storage = f"sqlite:///{study_db}" + study = optuna.create_study( + direction="maximize", + sampler=optuna.samplers.TPESampler(seed=SEED), + storage=storage, + study_name="gold_scalper_pro_is2025", + load_if_exists=True, + ) + n_existing = len([t for t in study.trials if t.state.name == "COMPLETE"]) + if n_existing > 0: + print(f" resumed existing study: {n_existing} complete trials so far") + print(f" running {n_trials} trials (foreground; Ctrl+C to stop — study is saved) ...") + study.optimize(objective, n_trials=n_trials, show_progress_bar=True) + + print(f"\n === best trial ===") + print(f" value = {study.best_value:.2f}") + print(f" params:") + for k, v in study.best_params.items(): + print(f" {k:24s} = {v}") + a = study.best_trial.user_attrs + print(f" metrics: net={a['net_profit']:.2f}, PF={a['profit_factor']:.2f}, " + f"trades={a['total_trades']}, DD%={a['max_equity_dd_pct']:.2%}") + if a.get("violations"): + print(f" violations: {a['violations']}") + + # Diverse top-3 finalists (doc 06 §3). + print(f"\n === diverse top-3 finalists ===") + finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE) + if not finalists: + print(" no constraint-passing trials found.") + return 1 + for i, t in enumerate(finalists, 1): + d = t.user_attrs + print(f" finalist #{i}: trial #{t.number} value={t.value:.2f}") + print(f" net={d['net_profit']:.2f}, PF={d['profit_factor']:.2f}, " + f"trades={d['total_trades']}, DD%={d['max_equity_dd_pct']:.2%}") + print(f" params: {t.params}") + print(f"\n study DB: {study_db}") + return 0 + + +def load_m5() -> pd.DataFrame: + from shared.data.loaders import load_bars + p = PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet" + if not p.exists(): + sys.exit(f"missing M5 data: {p}") + return load_bars(p) + + +def load_m1() -> pd.DataFrame: + from shared.data.loaders import load_bars + p = PROJECT / "data" / "XAUUSD_M1_2024-06-26_2026-06-26.parquet" + if not p.exists(): + sys.exit(f"missing M1 data: {p} — run scripts/download_xauusd_m1.py first") + return load_bars(p) + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--smoke", action="store_true", + help="30 trials on IS H1 2025; validate the script end-to-end") + ap.add_argument("--trials", type=int, default=500, + help="trial budget for the full study (default 500)") + ap.add_argument("--db", type=Path, + default=PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db", + help="SQLite path for the full study (resumable)") + args = ap.parse_args() + if args.smoke: + return run_smoke() + return run_full(args.db, args.trials) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/prepare_mt5_verify.py b/scripts/prepare_mt5_verify.py new file mode 100644 index 0000000..d93b1c6 --- /dev/null +++ b/scripts/prepare_mt5_verify.py @@ -0,0 +1,119 @@ +"""Phase 7 — Prepare finalist #1 for MT5 verification (doc 07, doc 08 step 6). + +Reads the Optuna finalist from the study DB, generates a .set file in MT5's +tester profile directory, and prints the exact Strategy Tester settings for +the FORWARD test mode (one run produces both IS + OOS segments). + +MT5 forward mode: set the whole window + a forward start date. MT5 splits: + history (IS) = FromDate → Forward start + forward (OOS) = Forward start → ToDate + +After the user runs the tester and exports the forward HTML report, run +scripts/compare_finalist.py to print the Python-vs-MT5 table for both segments. + +Usage: + python scripts/prepare_mt5_verify.py # finalist #1 (best by score) + python scripts/prepare_mt5_verify.py 2 # finalist #2 +""" +from __future__ import annotations +import json +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import optuna + +from shared.optimizer.selector import select_diverse_topn +from shared.mt5_pipeline.set_gen import write_set_file +from strategies.gold_scalper_pro.search_space import ( + FROZEN_BASELINE, + SEARCH_SPACE, +) +from strategies.gold_scalper_pro.set_mappings import GOLD_SCALPER_MAPPINGS + + +# Windows where MT5's tester profiles live (terminal data path). +MT5_TESTER_DIR = Path(r"C:\Users\Administrator\AppData\Roaming\MetaQuotes\Terminal" + r"\010E047102812FC0C18890992854220E\MQL5\Profiles\Tester") + +# Forward-mode window: one run produces IS (12 mo) + OOS (~6 mo). +# Data ends 2026-06-25 23:55; ToDate is exclusive day boundary so 2026.06.26 +# picks up the last bar at 2026-06-25 23:55. +FROM_DATE = "2025.01.01" # whole-window start +TO_DATE = "2026.06.26" # whole-window end (exclusive day boundary) +FORWARD_DATE = "2026.01.01" # forward start: IS|OOS split point +INITIAL_DEPOSIT = 1000.0 + + +def main() -> int: + finalist_idx = int(sys.argv[1]) if len(sys.argv) > 1 else 1 + if finalist_idx < 1 or finalist_idx > 3: + sys.exit("finalist index must be 1, 2, or 3") + + db = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db" + study = optuna.load_study( + study_name="gold_scalper_pro_is2025", + storage=f"sqlite:///{db}", + ) + finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE) + if len(finalists) < finalist_idx: + sys.exit(f"only {len(finalists)} finalists available") + t = finalists[finalist_idx - 1] + merged = {**FROZEN_BASELINE, **t.params} + + # Pull the MT5-forward-aligned Python metrics (saved by reeval_finalist_forward.py). + fwd_json = PROJECT / "studies" / "finalists" / "gold_scalper_pro_is2025-2026.json" + if fwd_json.exists(): + fwd = json.loads(fwd_json.read_text(encoding="utf-8")) + py_is = fwd["finalists"][finalist_idx - 1]["IS"] + py_oos = fwd["finalists"][finalist_idx - 1]["OOS"] + else: + py_is = py_oos = None + + print(f"=== finalist #{finalist_idx} (trial #{t.number}) ===") + if py_is: + print(f" Python IS (12 mo) : net={py_is['net']:.2f} PF={py_is['PF']:.2f} " + f"trades={py_is['trades']} DD%={py_is['DD%']:.2%}") + print(f" Python OOS (6 mo) : net={py_oos['net']:.2f} PF={py_oos['PF']:.2f} " + f"trades={py_oos['trades']} DD%={py_oos['DD%']:.2%}") + else: + print(f" (run scripts/reeval_finalist_forward.py first to get Python metrics)") + + # Write the .set to MT5's tester profile dir. + set_name = f"GoldScalperPro_finalist{finalist_idx}_trial{t.number}.set" + set_path = MT5_TESTER_DIR / set_name + write_set_file(merged, GOLD_SCALPER_MAPPINGS, set_path) + print(f"\n .set written to: {set_path}") + + print(f"\n === MT5 Strategy Tester setup (FORWARD mode, ONE run) ===") + print(f" 1. Open MT5 → Ctrl+R (Strategy Tester)") + print(f" 2. Expert: GoldScalperPro") + print(f" 3. Symbol: XAUUSD") + print(f" 4. Period: M5 (chart timeframe, must match InpTimeframe)") + print(f" 5. Model: Every tick (based on real ticks) [doc 07 §2b: path-sensitive → real ticks]") + print(f" 6. Deposit: {INITIAL_DEPOSIT:.0f} USD") + print(f" 7. Leverage: 1:100") + print(f" 8. Date range:") + print(f" From: {FROM_DATE}") + print(f" To: {TO_DATE}") + print(f" 9. Forward: Custom date → {FORWARD_DATE}") + print(f" (this splits IS={FROM_DATE}..{FORWARD_DATE} | OOS={FORWARD_DATE}..{TO_DATE})") + print(f" 10. Click 'Inputs' tab → 'Load' → select: {set_name}") + print(f" (verify InpRiskPercent={merged['InpRiskPercent']}, " + f"InpAtrPeriod={merged['InpAtrPeriod']}, " + f"InpFastEmaPeriod={merged['InpFastEmaPeriod']}, " + f"InpSlowEmaPeriod={merged['InpSlowEmaPeriod']})") + print(f" 11. Click Start. The forward HTML report has TWO sections:") + print(f" - 'Backtest' (top) = IS ({FROM_DATE}..{FORWARD_DATE})") + print(f" - 'Forward' (bottom) = OOS ({FORWARD_DATE}..{TO_DATE}, ~6 mo of data)") + print(f" 12. Right-click the report → 'Save as Report' → save to:") + print(f" reports/ReportTester_forward_finalist{finalist_idx}.html") + + print(f"\n When the HTML is saved, run:") + print(f" python scripts/compare_finalist.py {finalist_idx}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/reeval_finalist_forward.py b/scripts/reeval_finalist_forward.py new file mode 100644 index 0000000..953d509 --- /dev/null +++ b/scripts/reeval_finalist_forward.py @@ -0,0 +1,154 @@ +"""Re-evaluate finalist #1 on the MT5-forward-aligned window. + +MT5 forward mode (FromDate=2025.01.01, ToDate=2026.06.26, Forward=2026.01.01) +produces two segments: + IS = 2025-01-01 → 2026-01-01 (12 months) + OOS = 2026-01-01 → 2026-06-26 (~6 months, data ends 2026-06-25 23:55) + +INDICATOR WARMUP (matches MT5 tester behaviour): MT5's Strategy Tester uses +pre-test chart history to warm up indicators — EMA(160) on M5 needs ~14 hours +of bars before it produces a value, but MT5's first 2025-01-02 trade fires at +01:55 because the indicator was already stable on 2024 data. The previous +version of this script sliced bars to [IS_START, IS_END) BEFORE computing +signals, so indicators didn't stabilise until ~14 hours into 2025-01-01 and +the first Python trade landed at 15:50 — 14 hours late vs MT5. + +Fix: compute signals on the FULL bars (data starts 2024-06-26 → ~6 months of +warmup, well beyond EMA(160)'s 14-hour requirement), then trim the bars + +signal arrays + M1 to the evaluation window before running the engine. The +engine therefore starts fresh at IS_START (initial_deposit, no open position) +exactly like MT5's tester, but sees indicators that are already stable. + +Outputs the metrics dict that compare_finalist.py will pick up. +""" +from __future__ import annotations +import json +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import optuna +import pandas as pd + +from shared.core.engine import SizingInputs +from shared.core.metrics import compute_metrics +from shared.data.loaders import load_bars +from shared.optimizer.selector import select_diverse_topn +from strategies.gold_scalper_pro.instruments import XAUUSD_REAL +from strategies.gold_scalper_pro.scalper_engine import ( + ScalperEngine, + engine_kwargs_from_params, +) +from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE, SEARCH_SPACE +from strategies.gold_scalper_pro.signals import build_signals + + +# MT5-forward-aligned windows (ToDate is exclusive in MT5 tester's day boundary). +IS_START = pd.Timestamp("2025-01-01 00:00:00") +IS_END = pd.Timestamp("2026-01-01 00:00:00") # forward start +OOS_START = pd.Timestamp("2026-01-01 00:00:00") +OOS_END = pd.Timestamp("2026-06-26 00:00:00") # data ends 2026-06-25 23:55 +INITIAL_DEPOSIT = 1000.0 + + +def run_with_warmup(params, full_bars, full_m1, win_start, win_end): + """Compute signals on FULL bars (with pre-window warmup) and run the + engine on the trimmed [win_start, win_end) slice only. + + Mirrors MT5 Strategy Tester: indicator buffers are pre-warmed on history + before the test start, but the engine/equity starts fresh at win_start. + """ + pack = build_signals(params, full_bars, XAUUSD_REAL) + + ts = pd.to_datetime(full_bars["timestamp"].to_numpy()) + lo = int(ts.searchsorted(win_start, side="left")) + hi = int(ts.searchsorted(win_end, side="left")) + + win_bars = full_bars.iloc[lo:hi].reset_index(drop=True) + sig_long = pack.signals_long[lo:hi] + sig_short = pack.signals_short[lo:hi] + sl_p = pack.sl_prices[lo:hi] + tp_p = pack.tp_prices[lo:hi] + + if full_m1 is not None and len(full_m1) > 0: + m1_ts = pd.to_datetime(full_m1["timestamp"].to_numpy()) + m1_lo = int(m1_ts.searchsorted(win_start, side="left")) + m1_hi = int(m1_ts.searchsorted(win_end, side="left")) + win_m1 = full_m1.iloc[m1_lo:m1_hi].reset_index(drop=True) + else: + win_m1 = None + + engine = ScalperEngine() + result = engine.run( + win_bars, sig_long, sig_short, sl_p, tp_p, + XAUUSD_REAL, SizingInputs(), INITIAL_DEPOSIT, + m1_bars=win_m1, + **engine_kwargs_from_params(params), + ) + m = compute_metrics(result, periods_per_year=252 * 24 * 12) + return { + "net": round(m.net_profit, 2), + "PF": round(m.profit_factor, 4), + "trades": m.total_trades, + "DD%": round(m.max_equity_dd_pct, 6), + "sharpe": round(m.sharpe, 4), + "win_rate": round(m.win_rate, 4), + "first_trade_ts": ( + result.trades[0].entry_time.isoformat() if result.trades else None + ), + } + + +def main() -> int: + db = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db" + study = optuna.load_study( + study_name="gold_scalper_pro_is2025", + storage=f"sqlite:///{db}", + ) + finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE) + if not finalists: + sys.exit("no finalists") + + print("loading bars (full history, used as indicator warmup) ...") + full_m5 = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet") + full_m1 = load_bars(PROJECT / "data" / "XAUUSD_M1_2024-06-26_2026-06-26.parquet") + print(f" full M5: {len(full_m5):,} bars full M1: {len(full_m1):,} bars") + print(f" IS : {IS_START.date()} → {IS_END.date()} (12 months, MT5 forward-aligned)") + print(f" OOS : {OOS_START.date()} → {OOS_END.date()} (~6 months, data ends 2026-06-25)") + print(f" warmup window: {full_m5['timestamp'].min()} → {IS_START} " + f"(~6 months, EMA(160) needs ~14h so this is plenty)") + + out = {"windows": {"IS": [str(IS_START), str(IS_END)], + "OOS": [str(OOS_START), str(OOS_END)]}, + "finalists": []} + + for i, t in enumerate(finalists, 1): + merged = {**FROZEN_BASELINE, **t.params} + print(f"\n--- finalist #{i} (trial #{t.number}) ---") + is_m = run_with_warmup(merged, full_m5, full_m1, IS_START, IS_END) + oos_m = run_with_warmup(merged, full_m5, full_m1, OOS_START, OOS_END) + print(f" IS : net={is_m['net']:.2f} PF={is_m['PF']:.2f} " + f"trades={is_m['trades']} DD%={is_m['DD%']:.2%} " + f"first={is_m['first_trade_ts']}") + print(f" OOS : net={oos_m['net']:.2f} PF={oos_m['PF']:.2f} " + f"trades={oos_m['trades']} DD%={oos_m['DD%']:.2%} " + f"first={oos_m['first_trade_ts']}") + out["finalists"].append({ + "index": i, + "trial_number": t.number, + "score": round(t.value, 2), + "params": t.params, + "merged_params": merged, + "IS": is_m, + "OOS": oos_m, + }) + + out_path = PROJECT / "studies" / "finalists" / "gold_scalper_pro_is2025-2026.json" + out_path.write_text(json.dumps(out, indent=2, default=str), encoding="utf-8") + print(f"\n saved: {out_path}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/walk_forward.py b/scripts/walk_forward.py new file mode 100644 index 0000000..11536cf --- /dev/null +++ b/scripts/walk_forward.py @@ -0,0 +1,132 @@ +"""Walk-forward OOS evaluation of the Optuna finalists (doc 06 §4). + +The 3 diverse finalists were selected on the IS window (2025-01-01 → 2025-12-15, +2-week purge after). This script runs each finalist's FIXED params on the OOS +window (2026-01-01 → 2026-07-01) and compares: + + OOS metric / IS metric + +A robust finalist keeps most of its edge out-of-sample. A fragile one keeps +its edge only in IS — typically trade-count collapses or PF falls below 1. + +Also runs the IS numbers with the same fixed params so the ratio is computed +on identical configurations (the study's stored metrics are valid but we +recompute here for the same OOS script path / instrument). + +OOS uses the same M1 tick-level exit simulation (mandatory for trailing/BE). +""" +from __future__ import annotations +import sys +from pathlib import Path +PROJECT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(PROJECT)) + +import optuna +import pandas as pd + +from shared.core.engine import SizingInputs +from shared.core.metrics import compute_metrics +from shared.data.loaders import load_bars +from shared.optimizer.selector import select_diverse_topn +from strategies.gold_scalper_pro.instruments import XAUUSD_REAL +from strategies.gold_scalper_pro.scalper_engine import ( + ScalperEngine, + engine_kwargs_from_params, +) +from strategies.gold_scalper_pro.search_space import ( + FROZEN_BASELINE, + SEARCH_SPACE, +) +from strategies.gold_scalper_pro.signals import build_signals + + +IS_START = pd.Timestamp("2025-01-01 00:00:00") +IS_END = pd.Timestamp("2025-12-15 00:00:00") +OOS_START = pd.Timestamp("2026-01-01 00:00:00") +OOS_END = pd.Timestamp("2026-07-01 00:00:00") +INITIAL_DEPOSIT = 1000.0 + + +def slice_window(df: pd.DataFrame, start: pd.Timestamp, end: pd.Timestamp) -> pd.DataFrame: + return df[(df["timestamp"] >= start) & (df["timestamp"] < end)].reset_index(drop=True) + + +def run_with_params(params: dict, bars: pd.DataFrame, m1: pd.DataFrame) -> dict: + """Run the engine with FIXED params on a window, return metrics dict.""" + pack = build_signals(params, bars, XAUUSD_REAL) + engine = ScalperEngine() + result = engine.run( + bars, pack.signals_long, pack.signals_short, + pack.sl_prices, pack.tp_prices, + XAUUSD_REAL, SizingInputs(), INITIAL_DEPOSIT, + m1_bars=m1, + **engine_kwargs_from_params(params), + ) + m = compute_metrics(result, periods_per_year=252 * 24 * 12) + return { + "net": m.net_profit, + "PF": m.profit_factor, + "trades": m.total_trades, + "DD%": m.max_equity_dd_pct, + "sharpe": m.sharpe, + "win_rate": m.win_rate, + } + + +def main() -> int: + db = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db" + study = optuna.load_study( + study_name="gold_scalper_pro_is2025", + storage=f"sqlite:///{db}", + ) + finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE) + if not finalists: + print("no constraint-passing finalists to walk-forward.") + return 1 + + print("loading bars ...") + m5 = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet") + m1 = load_bars(PROJECT / "data" / "XAUUSD_M1_2024-06-26_2026-06-26.parquet") + is_bars = slice_window(m5, IS_START, IS_END) + is_m1 = slice_window(m1, IS_START, IS_END) + oos_bars = slice_window(m5, OOS_START, OOS_END) + oos_m1 = slice_window(m1, OOS_START, OOS_END) + print(f" IS bars : {len(is_bars):,} IS M1 : {len(is_m1):,}") + print(f" OOS bars: {len(oos_bars):,} OOS M1: {len(oos_m1):,}") + print(f" IS window : {IS_START.date()} → {IS_END.date()} (11.5 months)") + print(f" OOS window: {OOS_START.date()} → {OOS_END.date()} (6 months)") + + print(f"\n{'='*100}") + print(f"{'metric':12s} {'IS':>14s} {'OOS':>14s} {'OOS/IS':>10s} notes") + print(f"{'-'*100}") + + for i, t in enumerate(finalists, 1): + merged = {**FROZEN_BASELINE, **t.params} + print(f"\n--- finalist #{i} trial #{t.number} score={t.value:.2f} ---") + is_m = run_with_params(merged, is_bars, is_m1) + oos_m = run_with_params(merged, oos_bars, oos_m1) + _print_row("net", is_m["net"], oos_m["net"], ratio=oos_m["net"]/is_m["net"] if is_m["net"] != 0 else None) + _print_row("PF", is_m["PF"], oos_m["PF"], ratio=oos_m["PF"]/is_m["PF"] if is_m["PF"] != 0 else None) + _print_row("trades", is_m["trades"], oos_m["trades"], ratio=oos_m["trades"]/is_m["trades"] if is_m["trades"] else None) + _print_row("DD%", is_m["DD%"], oos_m["DD%"], ratio=oos_m["DD%"]/is_m["DD%"] if is_m["DD%"] else None) + _print_row("sharpe", is_m["sharpe"], oos_m["sharpe"], ratio=oos_m["sharpe"]/is_m["sharpe"] if is_m["sharpe"] else None) + _print_row("win_rate", is_m["win_rate"], oos_m["win_rate"], ratio=oos_m["win_rate"]/is_m["win_rate"] if is_m["win_rate"] else None) + + print(f"\n{'='*100}") + print("interpretation (doc 06 §4 walk-forward):") + print(" OOS/IS ≥ ~0.6 on net and PF → edge holds out-of-sample (robust)") + print(" OOS/IS < ~0.5 on PF, or OOS PF < 1.0 → fragile; IS-only edge") + print(" trade-count ratio drops sharply → signal degraded in the new regime") + return 0 + + +def _print_row(label: str, is_v: float, oos_v: float, *, ratio: float | None) -> None: + if ratio is None: + r = " n/a" + else: + r = f" {ratio:6.2f}" + print(f"{label:12s} {is_v:14.2f} {oos_v:14.2f} {r:>10s}") + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/shared/mt5_pipeline/set_gen.py b/shared/mt5_pipeline/set_gen.py index d48f711..b5eed98 100644 --- a/shared/mt5_pipeline/set_gen.py +++ b/shared/mt5_pipeline/set_gen.py @@ -17,6 +17,9 @@ from pathlib import Path from typing import Any, Mapping SET_FILE_ENCODING = "utf-16-le" +# MT5's Strategy Tester silently ignores .set files without a UTF-16-LE BOM. +# Python's "utf-16-le" codec does NOT emit a BOM, so prepend one explicitly. +UTF16_LE_BOM = b"\xff\xfe" @dataclass @@ -68,4 +71,4 @@ def write_set_file( if extra_lines: lines.extend(extra_lines) text = "\n".join(lines) + "\n" - out.write_bytes(text.encode(SET_FILE_ENCODING)) + out.write_bytes(UTF16_LE_BOM + text.encode(SET_FILE_ENCODING)) diff --git a/shared/optimizer/objective.py b/shared/optimizer/objective.py index 0d0e539..b4476c4 100644 --- a/shared/optimizer/objective.py +++ b/shared/optimizer/objective.py @@ -77,6 +77,20 @@ class ObjectiveConfig: # strategy pipe tunable point values into its engine without the optimizer # knowing about strategy-specific config objects. None → no extra kwargs. build_engine_kwargs: Optional[Callable[[dict], dict]] = None + # M1 bars for tick-level exit simulation. MANDATORY if the EA moves its + # SL intra-trade (break-even / trailing / basket trailing) — bar-level + # exit simulation produces a −40% to −50% net gap on those EAs (doc 03 §7 + # measured failure mode). Leave None only for clean-directional setups + # that don't move the SL. The bars must cover the same window as ``bars``. + m1_bars: Optional[pd.DataFrame] = None + # Indicator-warmup bars (doc 03 §8 / reeval_finalist_forward.py pattern). + # When set, the objective builds signals on this FULL history (so EMA/RSI/ + # ATR are already stable before the eval window starts — matching MT5 + # Strategy Tester's pre-test chart-history warmup), then slices the signal + # arrays to ``bars``' time range before running the engine. Must be a + # contiguous superset of ``bars`` (same OHLC source, same timestamps). + # Leave None to build signals directly on ``bars`` (legacy behaviour). + signals_full_bars: Optional[pd.DataFrame] = None @dataclass @@ -141,14 +155,43 @@ def build_objective(cfg: ObjectiveConfig): def objective(trial) -> float: sampled = suggest_params(trial, cfg.search_space, cfg.int_params) merged = {**cfg.frozen_baseline, **sampled} - pack = cfg.build_signals(merged, cfg.bars, cfg.instrument) + if cfg.signals_full_bars is not None: + # Warmup mode (doc 03 §8 / reeval_finalist_forward.py pattern): + # build signals on the FULL bars so indicators are already stable + # at the eval window start — mirrors MT5 Strategy Tester's pre-test + # chart-history warmup. Then slice the signal arrays to cfg.bars' + # time range. cfg.bars must be a contiguous sub-range of + # signals_full_bars (same OHLC source, same timestamps). + pack = cfg.build_signals(merged, cfg.signals_full_bars, cfg.instrument) + eval_start = pd.Timestamp(cfg.bars["timestamp"].iloc[0]) + full_ts = pd.to_datetime(cfg.signals_full_bars["timestamp"].to_numpy()) + lo = int(full_ts.searchsorted(eval_start, side="left")) + hi = lo + len(cfg.bars) + sig_long = pack.signals_long[lo:hi] + sig_short = pack.signals_short[lo:hi] + sl_p = pack.sl_prices[lo:hi] + tp_p = pack.tp_prices[lo:hi] + else: + # Legacy mode: build signals directly on the (already-trimmed) + # cfg.bars. Indicators warm up at the eval window start — fine for + # short-period indicators but produces ~14h of EMA-stabilization + # noise at the start of long-period EMA strategies. + pack = cfg.build_signals(merged, cfg.bars, cfg.instrument) + sig_long = pack.signals_long + sig_short = pack.signals_short + sl_p = pack.sl_prices + tp_p = pack.tp_prices extra = cfg.build_engine_kwargs(merged) if cfg.build_engine_kwargs else {} + # If M1 bars are wired up, pass them so the engine switches to tick- + # level exit simulation (mandatory for trailing/BE EAs — doc 03 §7). + if cfg.m1_bars is not None: + extra = {**extra, "m1_bars": cfg.m1_bars} result = cfg.engine.run( cfg.bars, - pack.signals_long, - pack.signals_short, - pack.sl_prices, - pack.tp_prices, + sig_long, + sig_short, + sl_p, + tp_p, cfg.instrument, cfg.sizing, cfg.initial_deposit, diff --git a/strategies/gold_scalper_pro/scalper_engine.py b/strategies/gold_scalper_pro/scalper_engine.py index f707839..d8f60bd 100644 --- a/strategies/gold_scalper_pro/scalper_engine.py +++ b/strategies/gold_scalper_pro/scalper_engine.py @@ -198,7 +198,18 @@ class ScalperEngine: sl = sl_prices[i] if not np.isnan(sl_prices[i]) else 0.0 tp = tp_prices[i] if not np.isnan(tp_prices[i]) else 0.0 - lots = self._calc_lots(cfg, instrument, sl, equity) + # _calc_lots expects the SL *distance* (price units), not the + # SL price level. signals.py sets sl_prices[i] = close[i] ± + # sl_dist[i] (where sl_dist[i] = InpAtrSLMult × ATR[i]), so + # |close[i] - sl_prices[i]| recovers exactly sl_dist[i] — the + # same distance MT5 uses for sizing (it does NOT include the + # gap between signal-bar close and next-bar fill). Using + # fill_price here instead made sl_distance vary with the + # open-gap, sometimes bigger, sometimes smaller than MT5's + # value, which made lots inconsistent and the equity curve + # diverge exponentially from MT5 under risk% compounding. + sl_distance = abs(closes[i] - sl) if sl > 0 else 0.0 + lots = self._calc_lots(cfg, instrument, sl_distance, equity) if lots > 0: open_pos = Position( direction=direction, diff --git a/studies/features/trade_features_gold_scalper_pro_is2025.csv b/studies/features/trade_features_gold_scalper_pro_is2025.csv new file mode 100644 index 0000000..750f57e --- /dev/null +++ b/studies/features/trade_features_gold_scalper_pro_is2025.csv @@ -0,0 +1,2397 @@ +trade_id,entry_time,exit_time,duration_minutes,hour_of_day,day_of_week,direction,entry_price,exit_price,lots,pnl,swap,exit_reason,is_win,pnl_pct,equity_at_entry,risk_percent,sl_distance,sl_distance_pct,atr_at_entry,atr_pct_of_close,rsi_at_entry,fast_ema_at_entry,slow_ema_at_entry,dist_to_fast,dist_to_fast_atr,dist_to_slow,fast_minus_slow,trend_up,close_at_entry,high_at_entry,low_at_entry,spread_at_entry,spread_atr_ratio,label_win,label_pnl_zscore +1,2025-01-02 01:55:00,2025-01-02 02:40:00,45.0,1,3,LONG,2623.94,2626.82,0.12,34.56000000000131,0.0,take_profit,True,0.10975860728523174,997.359999999997,2.25,1.8293684770242267,0.06972103775474978,0.9628255142232772,0.036695283028815674,50.30846265263146,2623.7578236281215,2620.0311920902873,0.08217637187863147,0.0853491839016378,3.8088079097128684,3.726631537834237,True,2623.84,2624.07,2622.97,0.21,0.2181080547802158,1,0.209486839694523 +2,2025-01-02 05:45:00,2025-01-02 06:55:00,70.0,5,3,LONG,2633.52,2633.77,0.09,2.25,0.0,stop_loss,True,0.009492997964701237,1032.3100000000013,2.25,2.63679096109226,0.10012724653275791,1.3877847163643475,0.05269855080671469,53.77367539685038,2633.052004713401,2625.098204368085,0.3879952865991072,0.27957887273435233,8.34179563191492,7.953800345315813,True,2633.44,2633.49,2632.26,0.18,0.12970311452309075,1,-0.09182838773112614 +3,2025-01-02 08:30:00,2025-01-02 08:45:00,15.0,8,3,LONG,2634.3,2634.55,0.11,2.75,0.0,stop_loss,True,0.009490187146490527,1031.4199999999987,2.25,2.1703200309240547,0.08238791129702173,1.1422737004863446,0.04336205857737987,55.10133304007462,2633.4719676265104,2627.9052489086844,0.7980323734896047,0.6986349884006148,6.364751091315611,5.5667187178260065,True,2634.27,2634.42,2633.08,0.13,0.11380810040942907,1,-0.08716550894088106 +4,2025-01-02 09:55:00,2025-01-02 10:10:00,15.0,9,3,LONG,2634.74,2634.99,0.11,2.75,0.0,stop_loss,True,0.009488602290928138,1053.750000000006,2.25,2.114353772240809,0.08025026652904729,1.1128177748635837,0.0422369823837091,55.3840180898934,2633.9643832440133,2629.0996467204395,0.7356167559864843,0.6610397251038347,5.600353279560295,4.864736523573811,True,2634.7,2634.83,2633.34,0.09,0.08087577502168562,1,-0.08716550894088106 +5,2025-01-02 10:45:00,2025-01-02 10:45:00,0.0,10,3,LONG,2636.2,2636.45,0.09,2.25,0.0,stop_loss,True,0.009483347242242621,1044.5600000000013,2.25,2.5881298509631643,0.09817802602888916,1.3621736057700866,0.051672645278362725,59.707260995895325,2634.3346579891595,2629.7586170877066,1.8253420108403589,1.3400215678150849,6.401382912293229,4.5760409014528705,True,2636.16,2636.86,2633.66,0.09,0.06607087350596527,1,-0.09182838773112614 +6,2025-01-02 12:15:00,2025-01-02 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+34,COMPLETE,17,50,19,33.0,53.0,2.9000000000000004,16,32.5,1.75,2.1,3.3000000000000003,70.0,35,150.0,70.0,7,7.0,60,-999926.02192,114.18808000000072,1.4843198031980358,352,40.20999999999913,0.03602149027238717,0.8806818181818182,0.13973902240738295,False,, +35,COMPLETE,18,110,25,36.0,50.0,2.6,13,30.0,1.0,1.9,2.3,90.0,45,100.0,70.0,10,8.0,105,-999837.18788,219.85212000000172,1.3071203743801112,886,57.03999999999951,0.04622320889360418,0.8318284424379232,0.16327811855640154,False,, +36,COMPLETE,15,170,21,33.0,55.0,3.7,10,25.0,1.5,2.4000000000000004,3.4000000000000004,130.0,30,170.0,100.0,8,6.5,60,-1000040.78384,48.93211999999981,1.0788774883375019,513,89.71595999999636,0.08119766849745232,0.8265107212475633,0.03890256860515704,False,, +37,COMPLETE,10,80,23,31.0,52.0,2.4000000000000004,8,37.5,1.0,1.6,2.7,60.0,40,130.0,70.0,9,7.5,105,207.46000000000458,231.61000000000558,1.7736580151652026,551,24.150000000001,0.01960847995713001,0.8711433756805808,0.23697554590418157,False,, +38,COMPLETE,8,75,24,31.0,52.0,2.3,7,37.5,1.0,1.6,2.6,60.0,50,160.0,60.0,10,7.0,105,191.83000000000675,213.8700000000058,1.9043511353545892,449,22.039999999999054,0.01815680427063767,0.8730512249443207,0.2442199232512432,False,, +39,COMPLETE,24,65,23,30.0,51.0,2.4000000000000004,9,37.5,0.75,1.2,2.1,70.0,45,140.0,80.0,3,7.5,120,159.38404000000259,190.88404000000213,1.6326529232401024,529,31.499999999999545,0.026450938077900086,0.8241965973534972,0.22899064656785126,True,1.0, +40,COMPLETE,21,165,13,31.0,53.0,3.2,8,35.0,1.0,1.4,2.4000000000000004,90.0,20,200.0,90.0,9,8.0,75,-999745.35788,294.17211999998597,1.3281610898461553,1176,39.52999999999929,0.030544623384407103,0.7950680272108843,0.2024938565918597,False,, +41,COMPLETE,10,85,19,33.0,55.0,2.9000000000000004,11,32.5,1.25,1.8,2.8,60.0,40,120.0,70.0,9,7.5,90,204.93000000000893,228.00000000000728,2.640759930915429,363,23.069999999998345,0.018786644951138607,0.9090909090909091,0.2963334568788378,False,, +42,COMPLETE,10,85,22,33.0,55.0,2.9000000000000004,14,32.5,1.25,1.6,2.8,60.0,40,140.0,70.0,10,8.0,90,95.72403999999865,113.25403999999885,2.076456990780337,224,17.5300000000002,0.015746630481574733,0.875,0.2011377188880557,False,, +43,COMPLETE,10,65,19,34.0,50.0,2.9000000000000004,11,37.5,1.5,1.8,2.7,60.0,35,100.0,60.0,9,7.0,105,-999751.76192,296.6480800000021,1.4460202676289309,1012,48.40999999999849,0.03672326018680811,0.866600790513834,0.21284954866448813,False,, +44,COMPLETE,12,185,16,32.0,57.0,3.1,13,22.5,1.0,1.7000000000000002,2.9000000000000004,80.0,35,120.0,210.0,10,7.5,45,171.25211999999996,218.06212000000036,1.5118637339314727,690,46.8100000000004,0.03828593767078823,0.8594202898550725,0.18351573044731218,False,, +45,COMPLETE,14,100,18,31.0,51.0,2.5,8,35.0,0.75,2.0,2.6,280.0,30,160.0,80.0,9,6.5,75,-999773.21384,287.1561600000053,1.2728630685711575,865,60.37000000000353,0.046766685969321195,0.7676300578034682,0.1676352812608313,False,, +46,COMPLETE,9,85,24,30.0,69.0,3.7,26,25.0,1.5,1.4,3.0,100.0,40,240.0,100.0,10,2.5,90,-2000000.0,0.0,0.0,0,0.0,0.0,0.0,0.0,False,, +47,COMPLETE,16,130,21,36.0,56.0,2.1,7,27.5,1.25,1.6,2.5,50.0,45,360.0,70.0,7,8.0,120,119.69403999999207,146.52403999999245,1.6865846961247952,437,26.830000000000382,0.02329692691391989,0.8810068649885584,0.18357242141039948,False,, +48,COMPLETE,11,75,27,33.0,52.0,3.4000000000000004,16,32.5,2.0,2.1,1.9,250.0,25,140.0,90.0,11,7.0,105,139.58808000000263,170.37808000000078,1.5284843822699203,335,30.789999999998145,0.02623309877869985,0.8208955223880597,0.17043231311057908,False,, +49,COMPLETE,24,200,9,34.0,53.0,3.1,10,40.0,0.5,1.3,3.2,70.0,40,200.0,60.0,8,7.5,60,-999553.4238400001,504.50807999998403,1.4636699077516455,1767,57.93191999999999,0.03794440046454543,0.8064516129032258,0.32846684550465083,False,, +50,COMPLETE,33,60,13,40.0,57.0,2.6,19,35.0,1.75,1.0,2.7,90.0,50,110.0,120.0,6,5.5,45,-999798.47192,220.2480800000025,1.4685073126918098,713,18.7199999999998,0.015341142761724125,0.7475455820476858,0.22646407096002782,False,, +51,COMPLETE,13,95,20,35.0,54.0,2.8,11,30.0,1.25,1.8,3.1,110.0,40,120.0,70.0,8,7.5,90,223.71000000000458,252.51000000000522,1.7409548402242023,459,28.800000000000637,0.022993828392588095,0.840958605664488,0.2318463473839453,False,, +52,COMPLETE,13,105,21,38.0,55.0,2.8,12,30.0,1.25,1.8,3.0,110.0,35,130.0,80.0,9,7.5,90,-999938.63192,95.15808000000234,1.246398434618536,411,33.78999999999951,0.030159192696872898,0.8126520681265207,0.09424210849956756,False,, +53,COMPLETE,11,80,23,36.0,54.0,3.0,14,32.5,1.0,1.7000000000000002,2.8,60.0,35,400.0,60.0,8,4.0,105,115.90403999999802,135.3040399999972,2.113063836788395,270,19.39999999999918,0.017086880020613485,0.8851851851851852,0.19686981736090514,False,, +54,COMPLETE,12,90,18,32.0,53.0,2.4000000000000004,9,27.5,1.5,1.6,3.2,80.0,45,110.0,70.0,12,6.5,120,-999813.07192,233.17808000000005,1.4862983204279783,712,46.249999999999545,0.037504721134841726,0.8356741573033708,0.20975825448764251,False,, +55,COMPLETE,9,100,16,35.0,51.0,2.8,11,37.5,0.75,2.0,2.9000000000000004,170.0,40,170.0,90.0,6,8.0,75,-999766.9898,294.88020000000483,1.2662623252790168,858,61.870000000000346,0.04735806015506991,0.7610722610722611,0.1575948694387805,False,, +56,COMPLETE,18,115,20,33.0,56.0,3.2,8,22.5,3.0,1.8,3.4000000000000004,50.0,35,100.0,80.0,10,7.0,90,185.3821200000015,200.74808000000158,2.0847380036826033,427,15.365960000000086,0.012784425504061834,0.8969555035128806,0.2566758182883463,False,, +57,COMPLETE,14,90,19,37.0,52.0,2.6,16,30.0,1.0,2.2,2.4000000000000004,80.0,30,150.0,70.0,9,8.0,60,-999821.37596,229.63403999999713,1.40160136843219,771,51.00999999999931,0.04148388735236984,0.8690012970168612,0.17055014912299238,False,, 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+62,COMPLETE,12,95,20,35.0,53.0,2.7,12,32.5,1.25,1.9,3.0,130.0,40,130.0,70.0,8,7.5,90,253.54808000000855,276.65808000000777,1.5177081906472978,566,23.109999999999218,0.01807900828589588,0.803886925795053,0.21658202617248543,False,, +63,COMPLETE,9,100,18,32.0,50.0,2.7,12,32.5,1.0,1.8,2.9000000000000004,130.0,35,110.0,80.0,9,7.5,105,-999802.14788,269.41212000000303,1.2971479054993094,986,71.55999999999904,0.05549600790134021,0.8032454361054767,0.1644813747634095,False,, +64,COMPLETE,11,85,21,36.0,53.0,2.9000000000000004,14,32.5,1.25,1.7000000000000002,3.5,70.0,40,130.0,60.0,8,7.0,75,218.99404000000953,240.71404000000842,1.8246173135555799,514,21.71999999999889,0.017506048371950994,0.8715953307392996,0.24313796469953594,False,, +65,COMPLETE,12,105,23,37.0,52.0,2.4000000000000004,15,37.5,0.75,1.7000000000000002,3.8000000000000003,90.0,45,170.0,60.0,7,6.5,75,-999807.05596,219.78404000000086,1.4539210640451095,625,26.839999999997872,0.022003895050141706,0.8288,0.18478698223364118,False,, 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+91,COMPLETE,23,160,12,48.0,50.0,3.7,25,12.5,2.75,2.8,4.0,50.0,50,390.0,150.0,6,4.0,165,485.5256200000035,530.8456200000032,1.7128123590074167,1473,45.31999999999971,0.029496074919821775,0.921928038017651,0.33646610512263553,False,, +92,COMPLETE,22,175,10,48.0,51.0,3.8000000000000003,25,12.5,2.75,2.7,3.7,60.0,50,390.0,150.0,6,4.0,165,-999648.42,393.5400000000004,1.4275424511390917,1471,41.95999999999958,0.030110366404982682,0.9000679809653297,0.22984974554700185,False,, +93,COMPLETE,24,165,9,49.0,50.0,3.7,27,10.0,2.75,2.6,3.9000000000000004,50.0,50,360.0,130.0,6,3.5,165,-999657.9,382.86000000000195,1.4731573483612665,1473,40.75999999999976,0.029475145712508647,0.9103869653767821,0.23605047426081177,False,, +94,COMPLETE,20,160,14,47.0,52.0,4.0,24,12.5,3.0,2.8,4.0,60.0,50,370.0,150.0,6,4.5,150,-999691.71,391.25000000000045,1.4196835612764824,1462,82.96000000000186,0.059509633731691464,0.9028727770177839,0.22378349637972386,False,, +95,COMPLETE,22,180,12,46.0,50.0,3.6,22,12.5,2.75,2.5,3.7,70.0,45,350.0,160.0,6,4.0,180,-999758.06,294.0399999999895,1.2826601042047083,1469,52.09999999999991,0.040057510591021465,0.8781484002722941,0.16839519114843854,False,, +96,COMPLETE,26,170,9,50.0,51.0,3.9000000000000004,20,10.0,3.0,2.5,3.8000000000000003,50.0,50,390.0,130.0,5,4.5,165,-999721.67,314.74000000000115,1.455550730930673,1230,36.41000000000031,0.027693688485936594,0.9040650406504065,0.2166682823675746,False,, +97,COMPLETE,20,155,13,46.0,52.0,3.8000000000000003,24,10.0,2.75,2.7,3.9000000000000004,60.0,45,370.0,170.0,6,4.5,150,-999756.77,356.8099999999895,1.3663384634339053,1462,113.57999999999856,0.08313448785700651,0.8974008207934336,0.19814163509752847,False,, +98,COMPLETE,21,190,11,48.0,51.0,3.7,25,12.5,2.75,2.8,4.0,50.0,50,350.0,140.0,7,3.5,165,554.1156200000019,605.3756200000021,1.6933316764780018,1714,51.26000000000022,0.03182851358196449,0.9235705950991832,0.3538456371967244,False,, +99,COMPLETE,22,195,11,49.0,51.0,3.5,25,17.5,3.0,2.6,3.8000000000000003,70.0,50,330.0,160.0,7,3.5,165,-999682.78,359.41000000000577,1.2975913490598119,1708,42.18999999999869,0.031023655629332778,0.8858313817330211,0.18430413351857214,False,, +100,COMPLETE,21,180,10,47.0,52.0,3.7,24,15.0,2.5,2.8,3.6,60.0,45,380.0,150.0,6,4.0,180,-999651.76,405.83000000000857,1.4478371220481228,1473,57.58999999999969,0.0409651238058651,0.902919212491514,0.2364844650182565,False,, +101,COMPLETE,21,170,11,48.0,50.0,4.0,21,12.5,2.75,2.8,4.0,50.0,50,350.0,140.0,5,3.0,165,330.4600000000023,376.85000000000355,1.559506488107617,1229,46.39000000000124,0.033692849620511396,0.9161920260374288,0.2537555124008654,False,, +102,COMPLETE,23,160,12,48.0,50.0,3.8000000000000003,22,12.5,2.75,2.7,3.9000000000000004,50.0,50,390.0,130.0,6,4.5,165,508.83562000000074,552.7156200000009,1.7628082751387033,1473,43.88000000000011,0.028164573174943728,0.9205702647657841,0.3562924596935152,False,, +103,COMPLETE,23,185,13,46.0,51.0,3.8000000000000003,23,10.0,2.75,2.7,3.9000000000000004,60.0,50,400.0,150.0,6,3.5,150,469.63999999999487,525.3699999999953,1.6346274642442917,1462,55.73000000000047,0.036409368568909056,0.9090287277701778,0.3018283584542877,False,, +104,COMPLETE,24,185,13,46.0,51.0,3.8000000000000003,23,10.0,2.75,2.7,3.9000000000000004,60.0,50,390.0,180.0,6,3.5,150,488.3900000000008,542.3500000000013,1.6551386741399308,1462,53.96000000000049,0.03486621479294176,0.9090287277701778,0.30717649767065885,False,, +105,COMPLETE,25,190,14,46.0,51.0,3.5,23,10.0,3.0,2.7,3.9000000000000004,60.0,50,400.0,210.0,6,3.0,150,384.47999999999774,449.26999999999816,1.5264718290053407,1448,64.79000000000042,0.044140590403390426,0.9060773480662984,0.2564362914061711,False,, +106,COMPLETE,24,185,13,45.0,50.0,3.7,22,12.5,2.75,2.9000000000000004,3.9000000000000004,80.0,50,390.0,200.0,7,3.5,150,-999663.16,406.5200000000009,1.319654020051111,1660,69.68000000000211,0.0488615565855828,0.8903614457831325,0.19075859641649634,False,, +107,COMPLETE,23,185,13,49.0,51.0,3.6,21,10.0,2.5,2.7,3.8000000000000003,50.0,50,390.0,140.0,6,3.5,150,533.640000000004,580.4200000000042,1.8531573377234285,1470,46.7800000000002,0.029599726654939873,0.9285714285714286,0.36711531607069997,False,, +108,COMPLETE,23,190,15,50.0,51.0,3.8000000000000003,21,10.0,3.0,2.6,3.8000000000000003,60.0,50,400.0,140.0,7,3.5,150,-999594.45,457.7700000000059,1.4550442847344471,1698,52.220000000000255,0.035282353418104685,0.9063604240282686,0.2555639303393717,False,, +109,COMPLETE,26,200,13,49.0,52.0,3.9000000000000004,23,10.0,2.5,2.7,3.7,70.0,45,390.0,190.0,6,3.5,150,-999747.8,336.0000000000018,1.328571009475755,1468,83.79999999999609,0.06181490934304757,0.8957765667574932,0.18453901346683735,False,, +110,COMPLETE,28,185,14,48.0,67.0,3.7,25,12.5,2.75,2.5,3.9000000000000004,50.0,50,360.0,180.0,7,3.0,150,375.78000000000156,430.5000000000018,1.60607340456984,1349,54.720000000000255,0.03825235931492498,0.916234247590808,0.2839925502747245,False,, +111,COMPLETE,25,195,12,48.0,50.0,3.6,22,15.0,2.5,2.8,4.0,50.0,50,390.0,140.0,6,4.0,165,426.9456200000059,472.91562000000613,1.6044268040183125,1471,45.970000000000255,0.0310926257098125,0.9177430319510537,0.2999751164618944,False,, +112,COMPLETE,24,195,12,49.0,51.0,3.6,20,12.5,2.75,2.8,1.7000000000000002,60.0,50,370.0,160.0,6,4.0,180,-999656.84,397.330000000004,1.4536507392818434,1472,54.16999999999916,0.03876679095131358,0.907608695652174,0.24360834318184224,False,, +113,COMPLETE,23,190,13,48.0,50.0,4.0,21,10.0,2.5,2.8,3.8000000000000003,50.0,50,390.0,230.0,6,3.5,165,491.63000000000056,534.9700000000012,1.7300354803493467,1471,43.3400000000006,0.0281397508067295,0.9265805574439157,0.3371144865927234,False,, +114,COMPLETE,23,190,13,46.0,51.0,4.0,21,10.0,2.75,2.7,3.8000000000000003,70.0,50,400.0,230.0,7,3.5,150,-999600.14,456.41999999999825,1.3905899669673247,1680,56.560000000000855,0.03841190932181582,0.8952380952380953,0.22338355704126953,False,, +115,COMPLETE,23,185,14,50.0,52.0,3.8000000000000003,20,10.0,2.75,2.6,3.8000000000000003,60.0,50,370.0,160.0,6,3.5,165,371.21000000000595,452.21000000000413,1.5492384677047222,1468,80.99999999999818,0.055666277231803986,0.9066757493188011,0.2720892726887757,False,, +116,COMPLETE,24,165,12,49.0,50.0,3.9000000000000004,23,12.5,2.5,2.8,3.9000000000000004,50.0,45,380.0,230.0,6,2.5,180,398.65000000001237,446.4100000000085,1.5842833396594498,1473,47.759999999996126,0.03301968321568286,0.9212491513917176,0.27602798411184976,False,, +117,COMPLETE,22,170,13,48.0,52.0,3.7,27,10.0,3.0,2.7,3.6,80.0,50,400.0,240.0,6,3.0,165,-999839.76,233.70000000000118,1.1946964584739208,1468,73.46000000000004,0.05786666876728083,0.8801089918256131,0.12729737908786556,False,, +118,COMPLETE,23,190,15,47.0,51.0,4.0,20,10.0,2.75,2.9000000000000004,3.7,60.0,50,390.0,150.0,7,4.0,150,488.51000000000204,545.340000000002,1.5457930081968063,1665,56.82999999999993,0.03656825903428386,0.9153153153153153,0.27789916567766604,False,, +119,COMPLETE,31,180,15,47.0,51.0,4.0,20,17.5,2.5,3.0,3.5,70.0,45,390.0,170.0,7,4.0,150,-999679.43,408.5599999999931,1.3358846403643578,1665,87.98999999999796,0.06205131098291871,0.9009009009009009,0.19345893436681233,False,, +120,COMPLETE,25,195,13,45.0,52.0,3.5,21,12.5,2.25,2.9000000000000004,3.6,70.0,50,380.0,220.0,7,3.5,150,-999600.59,485.2399999999989,1.4264309128138413,1656,85.82999999999856,0.057041270685185504,0.9009661835748792,0.22833414736551516,False,, +121,COMPLETE,23,185,11,48.0,50.0,3.8000000000000003,23,10.0,2.75,2.9000000000000004,3.7,50.0,50,370.0,150.0,6,4.0,165,394.77000000000544,439.4500000000053,1.5546440155999628,1471,44.679999999999836,0.031039633193233298,0.9218218898708361,0.2679516666628968,False,, +122,COMPLETE,24,180,15,46.0,51.0,3.9000000000000004,21,10.0,2.75,2.8,3.8000000000000003,60.0,50,390.0,140.0,6,4.5,165,-999690.24,376.25000000000364,1.4130076838638896,1433,66.48999999999933,0.04762552825728754,0.9057920446615492,0.2091942427689989,False,, +123,COMPLETE,22,200,12,47.0,50.0,4.0,22,10.0,2.75,2.7,4.0,50.0,50,400.0,150.0,6,3.5,165,489.3300000000004,531.0100000000002,1.7491253315275672,1466,41.679999999999836,0.027130471007889068,0.9229195088676672,0.3321184594089717,False,, +124,COMPLETE,22,200,14,47.0,50.0,4.0,24,12.5,3.0,2.7,3.9000000000000004,50.0,50,400.0,140.0,5,3.5,150,406.23999999999796,442.4999999999977,1.7275208391562364,1225,36.25999999999976,0.025036249395843275,0.92,0.3044823278950454,False,, +125,COMPLETE,26,190,13,47.0,51.0,3.8000000000000003,26,47.5,2.5,2.5,4.0,60.0,50,380.0,150.0,7,3.0,165,-999638.09,413.9000000000033,1.3752867466383807,1703,51.98999999999978,0.03643052343914208,0.8919553728714034,0.23110398461138934,False,, +126,COMPLETE,23,190,11,49.0,50.0,3.7,23,12.5,2.75,2.7,3.8000000000000003,70.0,50,400.0,160.0,6,3.5,150,-999724.47,334.39000000000306,1.3318414576055924,1471,58.86000000000104,0.04411004279108874,0.8891910265125765,0.18432712974529022,False,, +127,COMPLETE,22,195,14,46.0,51.0,4.0,25,10.0,2.75,2.6,3.9000000000000004,50.0,45,390.0,180.0,5,4.0,135,339.5899999999956,397.4999999999968,1.6325588796944597,1220,57.91000000000122,0.04121590844388868,0.9163934426229509,0.2621287432051863,False,, +128,COMPLETE,24,175,12,48.0,50.0,3.9000000000000004,21,10.0,2.5,2.9000000000000004,3.7,60.0,10,370.0,130.0,6,3.5,180,-999759.44438,323.8656199999814,1.3544201840685293,1468,83.30999999999767,0.06253914702128384,0.9073569482288828,0.18143782498401217,False,, +129,COMPLETE,22,185,13,45.0,51.0,1.4,22,12.5,3.0,2.7,4.0,50.0,50,360.0,150.0,7,4.0,150,499.74000000000024,541.5700000000006,1.6266140602582502,1668,41.83000000000038,0.026901705553983715,0.9190647482014388,0.3052173463606272,False,, +130,COMPLETE,25,185,14,45.0,52.0,1.1,20,15.0,3.0,2.7,3.9000000000000004,60.0,50,380.0,150.0,7,2.5,150,-999633.77,448.71000000000004,1.4348344332354563,1642,82.48000000000093,0.0569200510679417,0.9037758830694276,0.2240543285416689,False,, +131,COMPLETE,22,200,13,47.0,51.0,3.8000000000000003,22,12.5,3.0,2.8,4.0,50.0,50,360.0,140.0,7,4.0,150,485.3500000000008,532.7800000000016,1.5845731841123563,1699,47.430000000000746,0.030773522961733052,0.9199529134785168,0.3075737713149808,False,, +132,COMPLETE,22,200,15,47.0,51.0,3.8000000000000003,23,12.5,3.0,2.8,4.0,50.0,50,360.0,150.0,7,4.0,150,564.6900000000023,604.3300000000027,1.702921813571549,1672,39.64000000000033,0.024645304087241054,0.9234449760765551,0.33226684963832975,False,, +133,COMPLETE,22,195,16,47.0,51.0,3.8000000000000003,22,12.5,3.0,2.9000000000000004,4.0,60.0,50,360.0,160.0,7,4.0,150,-999587.39,482.7700000000027,1.4629643836667399,1653,70.16000000000122,0.04674777788142554,0.9086509376890503,0.247422649407387,False,, +134,COMPLETE,23,200,15,46.0,51.0,3.7,21,15.0,3.0,2.8,3.8000000000000003,50.0,50,390.0,140.0,7,3.5,135,480.030000000002,530.8000000000011,1.609785518168347,1653,50.76999999999907,0.032878078474798456,0.9225650332728372,0.2941279367090679,False,, +135,COMPLETE,23,200,15,45.0,52.0,1.6,23,15.0,3.0,2.8,3.8000000000000003,50.0,50,390.0,120.0,7,3.5,135,463.7200000000021,524.400000000001,1.6060956299626679,1633,60.67999999999893,0.03979694898802345,0.9210042865890998,0.29286114869581137,False,, +136,COMPLETE,20,200,13,46.0,51.0,3.7,21,12.5,3.0,2.8,3.7,70.0,50,370.0,140.0,7,4.0,150,-999579.69,479.40999999999804,1.4060938214715284,1683,59.09999999999991,0.03937584947898621,0.8954248366013072,0.23282769309980542,False,, +137,COMPLETE,22,190,13,46.0,52.0,3.6,22,15.0,3.0,2.7,4.0,50.0,45,380.0,130.0,7,3.0,150,498.72999999999,552.9499999999903,1.6246117003851825,1690,54.220000000000255,0.03480951708375635,0.9207100591715977,0.30269316103371957,False,, +138,COMPLETE,22,195,16,47.0,52.0,3.4000000000000004,22,17.5,3.0,2.6,4.0,50.0,45,360.0,130.0,7,3.0,135,529.0199999999872,605.6699999999873,1.682091535654747,1657,76.65000000000009,0.04754549852990477,0.9155099577549789,0.3313015037064247,False,, +139,COMPLETE,22,190,16,45.0,52.0,1.3,20,17.5,3.0,2.6,4.0,50.0,45,360.0,130.0,7,3.0,135,470.95999999998,526.2899999999809,1.6294506703663136,1597,55.33000000000084,0.03623966779758806,0.915466499686913,0.2874528425017057,False,, +140,COMPLETE,24,200,15,46.0,52.0,3.5,22,15.0,3.0,2.6,4.0,60.0,45,380.0,120.0,7,3.0,135,475.2799999999834,550.9199999999846,1.5582498201384005,1646,75.64000000000124,0.0485625136429602,0.9034021871202916,0.28030357222111,False,, +141,COMPLETE,20,195,14,47.0,52.0,3.6,22,17.5,3.0,2.7,3.9000000000000004,50.0,45,360.0,130.0,7,4.0,150,546.0199999999859,594.2299999999864,1.6918419857726497,1692,48.21000000000049,0.030240304096649104,0.9231678486997635,0.33672651473660714,False,, +142,COMPLETE,22,195,16,47.0,53.0,3.6,21,17.5,3.0,2.7,3.8000000000000003,50.0,45,360.0,130.0,7,4.0,150,610.1099999999901,652.5099999999911,1.826056132977162,1640,42.400000000001,0.025554022046371642,0.9225609756097561,0.36461006239593885,False,, +143,COMPLETE,19,195,16,47.0,53.0,3.6,22,17.5,3.0,2.7,3.9000000000000004,50.0,45,360.0,130.0,7,4.0,150,631.0999999999949,668.9099999999949,1.8625643141755486,1634,37.809999999999945,0.022563839373631187,0.9241126070991432,0.37325057484732593,False,, +144,COMPLETE,21,195,16,47.0,53.0,3.4000000000000004,22,17.5,3.0,2.7,3.9000000000000004,60.0,45,340.0,130.0,7,4.0,150,560.2999999999929,602.1899999999932,1.6360266159695749,1631,41.89000000000033,0.02604257329719275,0.9086450030656039,0.3121720004124801,False,, +145,COMPLETE,19,195,16,47.0,53.0,3.5,21,17.5,3.0,2.5,3.9000000000000004,60.0,45,330.0,130.0,7,4.0,150,551.9199999999955,589.1899999999964,1.619189743050809,1633,37.27000000000089,0.023284872642305728,0.900796080832823,0.3114948087195637,False,, +146,COMPLETE,19,195,16,47.0,53.0,3.4000000000000004,21,17.5,3.0,2.6,3.8000000000000003,60.0,45,330.0,130.0,7,4.0,150,562.6699999999933,601.1999999999935,1.6389898603405328,1632,38.5300000000002,0.023973966499913096,0.9056372549019608,0.3154679220450983,False,, +147,COMPLETE,19,195,16,47.0,53.0,3.4000000000000004,21,17.5,3.0,2.5,3.8000000000000003,70.0,45,330.0,130.0,8,4.0,150,-999488.15,556.2799999999911,1.4479265641355914,1803,44.4300000000012,0.02834901898229475,0.8879645036051026,0.26194932274115434,False,, +148,COMPLETE,19,195,16,47.0,54.0,3.3000000000000003,22,17.5,3.0,2.4000000000000004,3.9000000000000004,50.0,45,340.0,120.0,7,4.0,150,532.0299999999947,576.8899999999958,1.6870355373475572,1623,44.86000000000104,0.028248125082648214,0.9081947011706716,0.3412028374795733,False,, +149,COMPLETE,19,195,16,49.0,54.0,3.3000000000000003,21,17.5,3.0,2.4000000000000004,3.9000000000000004,70.0,45,340.0,120.0,7,4.0,150,-999629.75596,443.96404000000933,1.3860791874288096,1654,73.7199999999998,0.05105390297669693,0.8821039903264812,0.24430837777922948,False,, +150,COMPLETE,20,190,16,47.0,54.0,3.4000000000000004,22,20.0,3.0,2.3,3.8000000000000003,50.0,45,320.0,130.0,7,4.0,150,490.94403999999395,541.6940399999958,1.6263039741337868,1625,50.75000000000182,0.032801103595878636,0.9015384615384615,0.3166375955532262,False,, +151,COMPLETE,20,190,16,47.0,54.0,3.4000000000000004,22,20.0,3.0,2.3,3.8000000000000003,50.0,45,340.0,130.0,7,4.0,150,513.3040399999959,567.1240399999974,1.6557060145590854,1625,53.82000000000153,0.03422283801004435,0.9015384615384615,0.3288783725238105,False,, +152,COMPLETE,19,195,17,47.0,53.0,3.5,21,20.0,3.0,2.3,3.9000000000000004,60.0,45,320.0,120.0,7,4.0,150,-999615.42596,441.99403999998225,1.4387577801638027,1617,57.42000000000144,0.03968199010695374,0.8843537414965986,0.24973244008486672,False,, +153,COMPLETE,18,190,17,48.0,55.0,3.4000000000000004,22,17.5,3.0,2.5,3.8000000000000003,50.0,45,340.0,130.0,7,4.0,150,654.6299999999928,689.2599999999929,1.912600791769823,1608,34.63000000000011,0.020500100635781497,0.9210199004975125,0.4042806724199889,False,, +154,COMPLETE,18,190,17,48.0,55.0,3.4000000000000004,22,17.5,3.0,2.6,3.9000000000000004,60.0,45,340.0,130.0,8,4.0,135,610.1199999999985,651.5099999999961,1.6230371999617448,1761,41.3899999999976,0.02506191303715854,0.9085746734809768,0.3375895691195681,False,, +155,COMPLETE,18,195,17,48.0,55.0,3.4000000000000004,22,17.5,3.0,2.4000000000000004,3.9000000000000004,60.0,45,340.0,130.0,8,4.0,135,588.3399999999983,629.0700000000002,1.5898895369554222,1771,40.73000000000184,0.024954508415178458,0.8983625070581592,0.3325759343781531,False,, +156,COMPLETE,18,195,17,48.0,54.0,3.3000000000000003,23,17.5,3.0,2.4000000000000004,3.9000000000000004,60.0,45,340.0,130.0,8,4.0,135,612.4199999999964,677.4699999999984,1.6457876575219694,1791,65.050000000002,0.03870641437581936,0.9000558347292016,0.3468285222893029,False,, +157,COMPLETE,19,195,18,49.0,55.0,3.3000000000000003,23,17.5,3.0,2.4000000000000004,3.8000000000000003,80.0,45,340.0,120.0,8,4.0,135,-999702.18034,413.58965999999054,1.2984796685014557,1743,115.77000000000317,0.0816909952757554,0.8691910499139415,0.20844057729713475,False,, +158,COMPLETE,18,195,16,48.0,54.0,3.4000000000000004,23,17.5,3.0,2.4000000000000004,3.6,60.0,45,330.0,130.0,8,4.0,135,536.174039999988,606.2340399999862,1.5437340120053746,1829,70.05999999999813,0.04353272364653971,0.8983050847457628,0.3126491547734482,False,, +159,COMPLETE,18,195,17,48.0,55.0,3.2,23,17.5,3.0,2.5,3.6,300.0,45,330.0,120.0,8,4.0,135,-999599.18834,504.5379599999977,1.1702521363550795,1473,103.72630000000572,0.06864300180777552,0.7012898845892735,0.15605517300694183,False,, +160,COMPLETE,18,195,17,49.0,55.0,3.4000000000000004,23,17.5,3.0,2.4000000000000004,3.9000000000000004,70.0,45,330.0,130.0,8,4.0,135,-999516.58596,523.3740399999933,1.4091576750185622,1793,39.96000000000049,0.02623124652957895,0.8817624093697713,0.2697614455119891,False,, +161,COMPLETE,18,195,16,48.0,54.0,3.4000000000000004,22,20.0,3.0,2.4000000000000004,3.7,60.0,45,340.0,130.0,8,4.0,135,546.9640399999944,614.334039999992,1.5498646332093176,1831,67.36999999999762,0.04165239405991353,0.8973238667394866,0.3143425977792501,False,, +162,COMPLETE,18,200,16,48.0,54.0,3.3000000000000003,22,20.0,3.0,2.5,3.7,60.0,45,320.0,130.0,8,4.0,135,597.9340399999928,653.0040399999903,1.6097792523117858,1822,55.069999999997435,0.03324691991767716,0.9045005488474204,0.3326351921852728,False,, +163,COMPLETE,18,200,17,48.0,54.0,3.3000000000000003,21,20.0,3.0,2.5,3.5,60.0,45,320.0,120.0,8,4.0,120,668.929999999998,725.4400000000005,1.7354718358408692,1789,56.51000000000249,0.03268741323461503,0.907769703745109,0.3782700451150446,False,, +164,COMPLETE,18,200,17,48.0,56.0,3.5,20,22.5,3.0,2.5,3.5,60.0,45,320.0,130.0,8,4.0,120,615.7199999999839,649.1899999999864,1.6680009054988347,1739,33.47000000000253,0.02025379266942372,0.9108683151236343,0.3576048221863868,False,, +165,COMPLETE,18,200,17,48.0,56.0,3.2,20,22.5,3.0,2.5,3.5,80.0,45,320.0,130.0,8,4.0,120,-999582.52034,476.97965999998496,1.3581212348084126,1712,59.50000000000637,0.04023533519070927,0.8802570093457944,0.2381928925195306,False,, +166,COMPLETE,17,200,17,48.0,55.0,3.5,24,20.0,3.0,2.4000000000000004,3.5,60.0,45,320.0,120.0,8,4.5,120,602.5000000000014,637.760000000002,1.610413476263403,1774,35.26000000000067,0.021488076737908,0.9013528748590756,0.34770443644317506,False,, +167,COMPLETE,17,200,18,48.0,56.0,3.5,24,20.0,3.0,2.5,3.5,70.0,45,320.0,110.0,8,4.5,120,556.589659999976,614.8696599999762,1.5844188767657985,1673,58.2800000000002,0.03601288519775692,0.8977884040645547,0.3183238802085501,False,, +168,COMPLETE,17,200,18,48.0,55.0,3.5,24,20.0,3.0,2.5,3.5,80.0,45,310.0,110.0,8,4.5,120,567.1596599999821,637.1996599999834,1.5193513666373228,1697,70.04000000000133,0.04273546880266357,0.882734236888627,0.29453719953710544,False,, +169,COMPLETE,17,200,18,49.0,56.0,3.5,24,22.5,3.0,2.5,3.4000000000000004,90.0,45,300.0,110.0,8,4.5,120,-999729.42068,359.8693199999811,1.245577742948261,1699,89.29000000000815,0.06517949669837209,0.8616833431430253,0.1736220140150533,False,, +170,COMPLETE,17,200,18,48.0,57.0,3.3000000000000003,24,22.5,3.0,2.5,3.4000000000000004,70.0,45,310.0,110.0,8,4.5,120,586.3077399999759,626.5077399999775,1.6236201665977725,1636,40.20000000000164,0.024674418288621252,0.8985330073349633,0.3337409673170451,False,, +171,COMPLETE,17,200,18,48.0,57.0,3.3000000000000003,24,22.5,3.0,2.5,3.4000000000000004,70.0,45,310.0,110.0,8,4.5,120,586.3077399999759,626.5077399999775,1.6236201665977725,1636,40.20000000000164,0.024674418288621252,0.8985330073349633,0.3337409673170451,False,, +172,COMPLETE,17,200,18,48.0,57.0,3.3000000000000003,24,22.5,3.0,2.5,3.5,80.0,45,310.0,110.0,8,5.0,120,-999508.07226,542.2977399999767,1.45443297062284,1629,50.37000000000171,0.032638113227026894,0.8809085328422345,0.27081247227643623,False,, +173,COMPLETE,17,200,19,48.0,55.0,3.3000000000000003,24,20.0,3.0,2.5,3.4000000000000004,70.0,45,320.0,110.0,8,4.5,120,511.20561999998245,573.6756199999818,1.526972967480283,1665,62.469999999999345,0.039391984498563605,0.8954954954954955,0.3024945696525056,False,, +174,COMPLETE,16,200,18,49.0,58.0,3.2,25,20.0,3.0,2.5,3.5,90.0,45,310.0,110.0,8,4.5,120,-999760.2326,309.7173999999899,1.219040266858512,1594,69.95000000000391,0.05314552361219087,0.8557089084065245,0.15185288361486574,False,, +175,COMPLETE,16,200,19,48.0,57.0,3.3000000000000003,25,20.0,3.0,2.6,3.4000000000000004,70.0,45,300.0,120.0,9,4.5,120,531.5417799999748,569.4217799999758,1.5441252138074923,1635,37.88000000000102,0.024076141930205127,0.8972477064220183,0.29530041384424777,False,, +176,COMPLETE,17,190,17,48.0,58.0,3.5,24,22.5,3.0,2.4000000000000004,3.3000000000000003,70.0,45,320.0,120.0,8,5.0,120,-999579.7778800001,465.28807999998287,1.4158382726080543,1634,45.06596000000172,0.030755699589122602,0.8861689106487148,0.24943555047288493,False,, +177,COMPLETE,17,200,18,48.0,56.0,3.4000000000000004,24,20.0,3.0,2.5,3.5,70.0,45,290.0,100.0,8,4.5,120,567.2296599999781,625.3096599999776,1.5943418465760766,1673,58.07999999999947,0.0356592553333213,0.8977884040645547,0.32592462700566865,False,, +178,COMPLETE,15,200,18,49.0,56.0,3.4000000000000004,24,20.0,3.0,2.5,3.5,80.0,45,280.0,110.0,8,4.5,120,-999691.1463,383.2536999999778,1.2826908468604135,1702,74.40000000000464,0.05333739768127539,0.8742655699177438,0.19750491705963485,False,, +179,COMPLETE,16,200,17,48.0,56.0,3.2,23,22.5,3.0,2.3,3.3000000000000003,70.0,45,290.0,100.0,8,4.5,120,-999564.7000000001,481.7099999999773,1.4088039105860597,1723,46.409999999998945,0.03113720228111343,0.8827626233313988,0.2658124691995387,False,, +180,COMPLETE,18,200,17,50.0,55.0,3.5,24,22.5,3.0,2.4000000000000004,3.4000000000000004,90.0,45,310.0,100.0,8,4.5,120,-999464.14472,575.1952800000022,1.4115626115939877,1803,39.33999999999742,0.024974681234441843,0.8713255684969495,0.28002449079478775,False,, +181,COMPLETE,18,190,18,48.0,57.0,3.4000000000000004,26,20.0,3.0,2.6,3.5,70.0,45,320.0,120.0,8,4.5,120,516.5177399999724,559.7177399999745,1.5415381211500052,1625,43.20000000000209,0.02769731913160313,0.8996923076923077,0.29021087303605814,False,, +182,COMPLETE,17,200,18,48.0,56.0,3.3000000000000003,25,20.0,3.0,2.4000000000000004,3.6,80.0,45,330.0,110.0,8,4.5,105,-999511.0263,554.4136999999754,1.4599667189448091,1668,65.44000000000051,0.042009208516084036,0.8782973621103117,0.27495675629189104,False,, +183,COMPLETE,17,195,17,49.0,55.0,3.4000000000000004,24,22.5,3.0,2.5,3.4000000000000004,270.0,45,280.0,100.0,9,4.5,120,-999675.52396,437.4623399999989,1.1441980387193889,1601,112.98630000000139,0.0783303984989698,0.7158026233603998,0.13719764595031553,False,, +184,COMPLETE,18,190,18,48.0,55.0,3.6,23,20.0,3.0,2.6,3.5,60.0,45,350.0,120.0,8,4.5,120,665.9199999999846,714.3799999999851,1.7724363133082313,1715,48.46000000000049,0.028266778660507537,0.9119533527696793,0.3773971234327652,False,, +185,COMPLETE,18,195,18,49.0,56.0,3.2,23,20.0,3.0,2.6,3.5,60.0,45,300.0,120.0,8,5.0,120,531.2340399999862,563.6940399999867,1.5596366741126708,1731,32.46000000000049,0.020758536625234414,0.9093009820912767,0.3136666388108305,False,, +186,COMPLETE,16,200,19,48.0,55.0,3.5,23,20.0,3.0,2.5,3.6,80.0,45,310.0,120.0,8,4.5,105,-999558.20034,525.68965999998,1.4294694445637504,1650,83.89000000000306,0.05460132293102351,0.8812121212121212,0.2552491289131132,False,, +187,COMPLETE,17,190,17,47.0,57.0,3.3000000000000003,24,20.0,3.0,2.6,3.3000000000000003,70.0,40,320.0,110.0,8,4.5,120,-999556.31034,480.04965999998603,1.4481150208690647,1622,36.36000000000104,0.024455704860160604,0.8964241676942046,0.2461862969162894,False,, +188,COMPLETE,18,195,19,48.0,55.0,3.5,23,22.5,3.0,2.5,3.6,60.0,40,350.0,110.0,8,4.5,120,616.4556199999834,651.2356199999836,1.7062493510767003,1681,34.7800000000002,0.020906397274613995,0.91017251635931,0.3573803118170366,False,, +189,COMPLETE,19,195,17,47.0,55.0,3.1,23,22.5,3.0,2.6,3.6,60.0,45,350.0,120.0,8,4.0,120,621.3096599999922,662.0896599999905,1.6802353599731985,1718,40.77999999999838,0.024452697599652084,0.9086146682188592,0.3441073196454752,False,, +190,COMPLETE,19,190,19,49.0,55.0,3.1,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.0,120,659.0840399999915,694.0440399999893,1.6710861817232385,1844,34.95999999999776,0.0206370077604346,0.9126898047722343,0.3557614993366742,False,, +191,COMPLETE,19,190,19,49.0,55.0,3.6,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.0,120,659.0840399999915,694.0440399999893,1.6710861817232385,1844,34.95999999999776,0.0206370077604346,0.9126898047722343,0.3557614993366742,False,, +192,COMPLETE,18,190,19,49.0,55.0,3.6,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.0,120,646.774039999987,688.4640399999889,1.6637781312970528,1840,41.690000000001874,0.02469107959207834,0.9130434782608695,0.35555036543480306,False,, +193,COMPLETE,18,190,19,50.0,55.0,3.1,23,25.0,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.0,120,554.464039999993,588.3240399999927,1.5341844463612788,1877,33.85999999999967,0.021318068068780113,0.9099627064464572,0.31145525382034306,False,, +194,COMPLETE,18,190,19,49.0,55.0,3.1,23,22.5,3.0,2.5,3.6,60.0,40,350.0,120.0,9,5.0,120,647.6940399999876,686.5840399999888,1.6607678404727204,1847,38.89000000000124,0.02305844184319537,0.9095831077422848,0.3589403036206419,False,, +195,COMPLETE,19,180,19,49.0,55.0,3.1,23,25.0,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,663.8340399999865,705.5240399999884,1.6928041557013132,1833,41.690000000001874,0.024444099890847715,0.9138025095471904,0.36586483732520925,False,, +196,COMPLETE,19,180,19,50.0,55.0,3.1,23,25.0,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,580.004039999993,613.8640399999927,1.5684610554974157,1877,33.85999999999967,0.020980701695292637,0.9115610015982951,0.3262867254720367,False,, +197,COMPLETE,18,185,19,49.0,54.0,3.0,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.0,105,701.2840399999986,745.3840399999985,1.7110679030011633,1886,44.09999999999991,0.025266645614566263,0.9135737009544008,0.37447566652567105,False,, +198,COMPLETE,19,185,19,49.0,54.0,3.0,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.0,105,711.4940399999987,753.6840399999987,1.7189857859691293,1888,42.190000000000055,0.02405792550863386,0.9136652542372882,0.377722922566942,False,, +199,COMPLETE,19,180,19,49.0,54.0,3.0,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.0,105,702.3440399999977,746.4440399999976,1.7095610562938441,1883,44.09999999999991,0.025251310084919737,0.9129049389272438,0.3743671655377701,False,, +200,COMPLETE,19,180,19,50.0,54.0,3.0,23,25.0,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.0,105,564.0340400000018,609.2940400000016,1.5309150510181875,1927,45.25999999999976,0.028124133237950547,0.909704203425013,0.3145530668782391,False,, +201,COMPLETE,19,185,19,49.0,54.0,3.0,23,22.5,3.0,2.6,3.7,60.0,40,350.0,120.0,9,5.5,105,714.7440399999987,756.9340399999987,1.72208616183008,1888,42.190000000000055,0.024013422837433376,0.9136652542372882,0.3793541593075854,False,, +202,COMPLETE,19,185,20,49.0,54.0,2.9000000000000004,23,22.5,3.0,2.6,3.7,60.0,40,350.0,120.0,9,5.5,105,643.8240399999945,701.6340399999931,1.6683565665513993,1828,57.80999999999858,0.03397322728687234,0.9108315098468271,0.3531017577644858,False,, +203,COMPLETE,19,185,20,49.0,54.0,2.9000000000000004,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,641.094039999994,698.9040399999926,1.665756046447378,1828,57.80999999999858,0.034027819487673264,0.9108315098468271,0.3516656279942476,False,, +204,COMPLETE,19,185,20,49.0,54.0,2.9000000000000004,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,641.094039999994,698.9040399999926,1.665756046447378,1828,57.80999999999858,0.034027819487673264,0.9108315098468271,0.3516656279942476,False,, +205,COMPLETE,19,185,20,49.0,54.0,2.9000000000000004,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,641.094039999994,698.9040399999926,1.665756046447378,1828,57.80999999999858,0.034027819487673264,0.9108315098468271,0.3516656279942476,False,, +206,COMPLETE,19,185,20,49.0,54.0,2.9000000000000004,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,641.094039999994,698.9040399999926,1.665756046447378,1828,57.80999999999858,0.034027819487673264,0.9108315098468271,0.3516656279942476,False,, +207,COMPLETE,19,180,20,49.0,54.0,2.9000000000000004,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,632.7099999999928,691.3199999999915,1.656206395762728,1824,58.60999999999876,0.03465340680651743,0.9100877192982456,0.34788003651504595,False,, +208,COMPLETE,19,180,20,49.0,54.0,2.9000000000000004,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,632.7099999999928,691.3199999999915,1.656206395762728,1824,58.60999999999876,0.03465340680651743,0.9100877192982456,0.34788003651504595,False,, +209,COMPLETE,19,180,20,50.0,54.0,2.9000000000000004,23,25.0,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,555.5840400000034,610.2640400000037,1.5492925652565297,1875,54.68000000000029,0.03395716394436789,0.9077333333333333,0.31476143749935304,False,, +210,COMPLETE,20,175,20,49.0,54.0,2.9000000000000004,23,22.5,3.0,2.6,3.7,70.0,40,350.0,120.0,9,5.5,105,552.9540399999937,620.3340399999925,1.5086748284146851,1794,67.37999999999874,0.04158401807074241,0.8968784838350056,0.29903357693458293,False,, +211,COMPLETE,19,185,20,49.0,54.0,3.0,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,641.094039999994,698.9040399999926,1.665756046447378,1828,57.80999999999858,0.034027819487673264,0.9108315098468271,0.3516656279942476,False,, +212,COMPLETE,19,185,20,49.0,54.0,3.0,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,641.094039999994,698.9040399999926,1.665756046447378,1828,57.80999999999858,0.034027819487673264,0.9108315098468271,0.3516656279942476,False,, +213,COMPLETE,20,185,20,49.0,54.0,3.0,23,25.0,3.0,2.6,3.7,60.0,40,350.0,120.0,9,5.5,105,660.3340399999947,718.1440399999933,1.6944762881014948,1829,57.80999999999858,0.03364677154774451,0.9114270092946966,0.3608429310158045,False,, +214,COMPLETE,20,185,20,49.0,54.0,3.0,23,25.0,3.0,2.6,3.7,60.0,40,350.0,120.0,9,5.5,105,660.3340399999947,718.1440399999933,1.6944762881014948,1829,57.80999999999858,0.03364677154774451,0.9114270092946966,0.3608429310158045,False,, +215,COMPLETE,20,185,21,50.0,54.0,3.0,23,25.0,3.0,2.6,3.7,70.0,40,350.0,120.0,9,5.5,105,-999501.53034,561.7396599999988,1.4559669982747527,1795,63.26999999999862,0.04051251410238161,0.8935933147632312,0.27859045799251597,False,, +216,COMPLETE,20,185,20,49.0,54.0,3.0,23,25.0,3.0,2.6,3.7,60.0,40,360.0,120.0,9,5.5,105,665.7140399999939,725.5040399999925,1.701593725823914,1829,59.7899999999986,0.034650744718047057,0.9114270092946966,0.364396348011819,False,, +217,COMPLETE,20,185,21,49.0,54.0,3.0,23,25.0,3.0,2.6,3.7,70.0,40,360.0,120.0,9,5.5,105,550.4340399999924,621.01403999999,1.5534933822939503,1725,70.57999999999765,0.04354064693973785,0.9008695652173913,0.3051519407921731,False,, +218,COMPLETE,20,185,20,50.0,54.0,2.8,23,25.0,3.0,2.6,3.7,60.0,40,340.0,120.0,9,5.5,105,562.6940400000049,617.3740400000052,1.5617853769507313,1872,54.68000000000029,0.033807887753657846,0.9086538461538461,0.3191925822730189,False,, +219,COMPLETE,19,185,20,49.0,54.0,2.8,23,25.0,3.0,2.6,3.6,60.0,40,360.0,120.0,9,6.0,105,646.4740399999932,706.2640399999918,1.6727669724421,1828,59.7899999999986,0.035041469900519547,0.9108315098468271,0.3552318587874484,False,, +220,COMPLETE,20,180,19,49.0,54.0,2.8,23,27.5,3.0,2.6,3.7,70.0,40,360.0,120.0,9,6.0,105,578.1996599999961,653.1696599999968,1.521695014397516,1864,74.97000000000071,0.04534924745715503,0.8986051502145923,0.3192511648478602,False,, +221,COMPLETE,19,185,20,49.0,54.0,3.0,23,25.0,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.5,105,641.094039999994,698.9040399999926,1.665756046447378,1828,57.80999999999858,0.034027819487673264,0.9108315098468271,0.35166038176464837,False,, +222,COMPLETE,19,185,20,49.0,54.0,3.1,23,22.5,3.0,2.6,3.6,60.0,40,340.0,120.0,9,6.0,105,626.1840399999946,685.1940399999935,1.6526962916392745,1828,59.009999999998854,0.03501673908127463,0.9108315098468271,0.3466039466530174,False,, +223,COMPLETE,19,185,19,50.0,54.0,3.0,23,25.0,3.0,2.6,3.7,60.0,40,360.0,120.0,9,5.5,105,589.6580800000019,634.6680800000012,1.5552505883485144,1923,45.00999999999931,0.02753464177265838,0.9100364014560582,0.3262085019611222,False,, +224,COMPLETE,19,175,20,49.0,55.0,2.8,23,22.5,3.0,2.6,3.6,60.0,40,350.0,120.0,9,5.0,90,496.0380799999833,547.2580799999845,1.5072135687473798,1763,51.220000000001164,0.033103721132289494,0.9052750992626205,0.28607990391991495,False,, +225,COMPLETE,20,180,21,49.0,54.0,2.9000000000000004,24,22.5,3.0,2.6,3.7,70.0,40,340.0,110.0,9,5.5,105,503.3040399999836,574.5440399999829,1.5003039385574428,1724,71.23999999999933,0.045244844342365996,0.8990719257540604,0.28402369536998273,False,, 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+238,COMPLETE,21,190,19,50.0,55.0,3.1,25,27.5,2.75,2.5,3.5,50.0,35,360.0,110.0,10,5.0,90,662.1999999999862,695.2299999999864,1.6971681273941461,2010,33.0300000000002,0.0194840818060089,0.9208955223880597,0.38389773013154105,False,, +239,COMPLETE,21,190,19,50.0,55.0,3.1,25,27.5,2.75,2.5,3.4000000000000004,50.0,35,360.0,110.0,10,5.0,90,660.6599999999853,693.6899999999855,1.69562383425923,2010,33.0300000000002,0.019501797849665808,0.9208955223880597,0.38321546496388,False,, +240,COMPLETE,21,190,19,50.0,55.0,3.1,25,27.5,2.75,2.5,3.4000000000000004,50.0,35,370.0,100.0,10,5.0,90,671.0999999999863,704.1299999999865,1.7060929383686554,2010,33.0300000000002,0.01938232411846541,0.9208955223880597,0.3880456419827778,False,, +241,COMPLETE,21,190,19,50.0,55.0,3.1,25,27.5,2.75,2.5,3.4000000000000004,50.0,35,370.0,100.0,10,5.0,90,671.0999999999863,704.1299999999865,1.7060929383686554,2010,33.0300000000002,0.01938232411846541,0.9208955223880597,0.3880456419827778,False,, +242,COMPLETE,21,190,19,50.0,55.0,3.1,25,27.5,2.75,2.5,3.5,50.0,35,370.0,100.0,10,5.0,90,673.3499999999872,706.3799999999874,1.708349210806032,2010,33.0300000000002,0.019356766956950062,0.9208955223880597,0.38903702815830793,False,, +243,COMPLETE,21,190,19,50.0,55.0,3.1,25,27.5,2.75,2.5,3.4000000000000004,50.0,35,370.0,100.0,10,5.0,90,671.0999999999863,704.1299999999865,1.7060929383686554,2010,33.0300000000002,0.01938232411846541,0.9208955223880597,0.3880456419827778,False,, +244,COMPLETE,21,190,19,50.0,56.0,3.1,26,27.5,2.75,2.5,3.4000000000000004,50.0,35,370.0,100.0,10,5.0,90,637.621779999978,670.7217799999784,1.7107856540792632,1944,33.100000000000364,0.01981179655178781,0.9202674897119342,0.3721307440067888,False,, +245,COMPLETE,21,190,19,50.0,55.0,3.2,25,27.5,2.75,2.5,3.4000000000000004,50.0,35,370.0,100.0,10,5.0,90,671.0999999999863,704.1299999999865,1.7060929383686554,2010,33.0300000000002,0.01938232411846541,0.9208955223880597,0.3880456419827778,False,, +246,COMPLETE,21,190,19,50.0,55.0,3.2,25,27.5,2.75,2.5,3.3000000000000003,50.0,35,370.0,100.0,10,5.0,90,672.2899999999868,705.319999999987,1.7072862557910902,2010,33.0300000000002,0.01936879881781745,0.9208955223880597,0.38883109533179583,False,, +247,COMPLETE,21,190,19,50.0,56.0,3.2,25,27.5,2.75,2.5,3.2,50.0,35,370.0,90.0,10,5.0,90,635.2161599999797,668.2461599999799,1.708067898618276,1946,33.0300000000002,0.01979923634291512,0.920349434737924,0.37149547712664543,False,, +248,COMPLETE,21,190,19,50.0,55.0,3.2,25,27.5,2.75,2.5,3.3000000000000003,50.0,35,370.0,100.0,10,5.0,90,672.2899999999868,705.319999999987,1.7072862557910902,2010,33.0300000000002,0.01936879881781745,0.9208955223880597,0.38883109533179583,False,, +249,COMPLETE,21,190,19,50.0,55.0,3.2,26,27.5,2.75,2.5,3.3000000000000003,50.0,35,370.0,100.0,10,5.0,90,674.4556199999847,707.5556199999851,1.7093613845276685,2009,33.100000000000364,0.0193844344584223,0.9208561473369836,0.3897382677481275,False,, +250,COMPLETE,21,190,19,50.0,55.0,3.2,25,27.5,2.75,2.5,3.3000000000000003,50.0,35,370.0,100.0,10,5.0,90,672.2899999999868,705.319999999987,1.7072862557910902,2010,33.0300000000002,0.01936879881781745,0.9208955223880597,0.38883109533179583,False,, +251,COMPLETE,21,190,19,50.0,56.0,3.2,26,27.5,2.75,2.5,3.3000000000000003,50.0,35,370.0,100.0,10,5.0,90,639.6717799999777,672.7717799999781,1.7129581056595031,1944,33.100000000000364,0.01978751697975249,0.9202674897119342,0.3736984094416922,False,, +252,COMPLETE,21,190,19,50.0,55.0,3.2,25,27.5,2.75,2.5,3.2,50.0,35,370.0,90.0,11,5.0,90,651.9099999999821,684.9399999999823,1.635138769113775,2089,33.0300000000002,0.0196030719194752,0.9181426519865965,0.36366788330757166,False,, +253,COMPLETE,21,180,19,50.0,55.0,3.2,26,27.5,2.75,2.4000000000000004,3.4000000000000004,50.0,35,370.0,100.0,10,5.0,90,692.2156199999881,724.2656199999883,1.7431990772770554,2011,32.05000000000018,0.01858762340804568,0.9179512680258578,0.40345851437618385,False,, +254,COMPLETE,21,180,19,50.0,56.0,3.2,26,27.5,2.75,2.4000000000000004,3.3000000000000003,50.0,35,370.0,100.0,10,5.0,90,619.8617799999832,655.1917799999827,1.6952194721447977,1949,35.32999999999947,0.02134495858842402,0.9158542842483325,0.3684182960763705,False,, +255,COMPLETE,21,180,18,50.0,55.0,3.2,27,30.0,2.75,2.5,3.4000000000000004,50.0,35,370.0,100.0,11,5.0,90,684.2640399999837,730.2740399999834,1.6762735935546467,2144,46.00999999999976,0.026591163559270763,0.917910447761194,0.3803812834153004,False,, +256,COMPLETE,21,180,18,50.0,55.0,3.2,27,30.0,2.75,2.5,3.4000000000000004,50.0,35,370.0,100.0,11,5.0,90,684.2640399999837,730.2740399999834,1.6762735935546467,2144,46.00999999999976,0.026591163559270763,0.917910447761194,0.3803812834153004,False,, +257,COMPLETE,21,175,18,50.0,55.0,3.2,28,30.0,2.75,2.4000000000000004,3.4000000000000004,50.0,35,380.0,100.0,11,5.0,90,674.2240399999814,715.5240399999811,1.6605467352270367,2135,41.29999999999973,0.024074276452576076,0.9147540983606557,0.37608842170407786,False,, +258,COMPLETE,21,175,18,50.0,55.0,3.2,27,30.0,2.75,2.4000000000000004,3.3000000000000003,50.0,35,380.0,100.0,11,5.0,90,671.3340399999838,712.5740399999836,1.6551742260552837,2136,41.23999999999978,0.02408071069441189,0.9138576779026217,0.37470621423767114,False,, +259,COMPLETE,21,170,18,50.0,55.0,3.2,27,30.0,2.75,2.4000000000000004,3.3000000000000003,50.0,35,380.0,100.0,11,5.0,90,686.5540399999836,728.1440399999833,1.678561547708898,2133,41.58999999999969,0.02406628095653421,0.9146741678387248,0.3818617658404983,False,, +260,COMPLETE,21,175,18,50.0,56.0,3.2,27,30.0,2.75,2.4000000000000004,3.2,50.0,35,380.0,100.0,11,5.0,90,612.5421199999785,653.042119999978,1.628166717968432,2089,40.499999999999545,0.024461512714925162,0.9143130684538057,0.3529837078389122,False,, +261,COMPLETE,21,175,18,50.0,55.0,3.2,28,30.0,2.75,2.4000000000000004,3.3000000000000003,50.0,35,380.0,90.0,11,5.0,90,681.9840399999734,723.674039999974,1.668070529804359,2135,41.69000000000051,0.024186707598149556,0.9147540983606557,0.38003521492716474,False,, +262,COMPLETE,21,180,18,50.0,55.0,3.2,28,30.0,2.75,2.3,3.3000000000000003,50.0,35,380.0,100.0,11,5.0,90,703.26403999998,747.7640399999809,1.707138909641102,2152,44.50000000000091,0.025461102861460288,0.912639405204461,0.39760903316896834,False,, +263,COMPLETE,21,170,18,50.0,55.0,3.2,28,30.0,2.75,2.2,3.3000000000000003,50.0,35,380.0,90.0,11,5.0,90,708.6240399999729,745.7140399999726,1.705353701216374,2143,37.08999999999969,0.02115108866461105,0.9080727951469902,0.40087716469392387,False,, +264,COMPLETE,21,170,18,50.0,56.0,3.2,28,30.0,2.75,2.3,3.3000000000000003,50.0,35,380.0,90.0,11,5.0,90,640.4321199999697,683.3721199999684,1.6704064590813388,2090,42.93999999999869,0.025508323138914463,0.9119617224880383,0.3711808463609977,False,, +265,COMPLETE,22,170,18,50.0,55.0,3.2,27,30.0,2.75,2.2,3.3000000000000003,50.0,35,380.0,90.0,11,5.0,90,718.594039999974,755.1440399999733,1.7195891405646744,2147,36.54999999999927,0.02073213019137915,0.9091755938518864,0.40616355055511794,False,, +266,COMPLETE,22,170,18,50.0,56.0,3.2,27,30.0,2.75,2.2,3.2,50.0,35,380.0,90.0,11,5.0,90,658.0821199999702,697.8521199999702,1.6969669719456022,2098,39.76999999999998,0.023405102625460826,0.9099142040038132,0.3860614205868306,False,, +267,COMPLETE,21,165,18,50.0,55.0,3.2,28,30.0,2.75,2.2,3.3000000000000003,50.0,35,380.0,100.0,11,5.0,90,725.5140399999773,763.4740399999769,1.7381767236794827,2133,37.95999999999958,0.021503987040868058,0.9095171120487576,0.41357765723997136,False,, +268,COMPLETE,21,170,18,50.0,55.0,3.2,28,30.0,2.75,2.3,3.3000000000000003,50.0,35,380.0,100.0,11,5.0,90,693.6940399999799,736.1340399999804,1.6937853803814964,2136,42.44000000000051,0.024445117152360533,0.9119850187265918,0.3904534239567018,False,, +269,COMPLETE,22,165,18,50.0,55.0,3.2,28,30.0,2.75,2.2,3.3000000000000003,50.0,35,380.0,100.0,11,5.0,75,723.0140399999782,760.274039999978,1.7318278899188346,2138,37.25999999999976,0.021145775983124865,0.9092609915809168,0.4111398606290364,False,, +270,COMPLETE,22,165,18,50.0,61.0,3.2,28,30.0,2.75,2.2,3.1,50.0,30,380.0,90.0,11,5.0,75,426.5599999999931,465.2399999999934,1.5463962323980818,1722,38.68000000000029,0.02639840572192983,0.9065040650406504,0.2873800707462657,False,, +271,COMPLETE,22,165,18,50.0,56.0,3.2,27,30.0,2.75,2.1,3.3000000000000003,50.0,35,380.0,100.0,11,5.0,75,620.7721199999799,659.0421199999798,1.6403876283851264,2095,38.26999999999998,0.023051826698547554,0.9040572792362769,0.36986698390667655,False,, +272,COMPLETE,21,170,18,50.0,55.0,3.2,28,30.0,2.75,2.2,3.3000000000000003,50.0,35,380.0,90.0,11,5.0,90,708.6240399999729,745.7140399999726,1.705353701216374,2143,37.08999999999969,0.02115108866461105,0.9080727951469902,0.40087716469392387,False,, +273,COMPLETE,22,170,18,50.0,56.0,3.2,28,32.5,2.75,2.1,3.3000000000000003,50.0,35,380.0,90.0,11,5.0,90,649.5421199999707,687.9021199999709,1.6766893770227096,2107,38.36000000000013,0.022651419246049173,0.9055529188419554,0.3838607337958217,False,, +274,COMPLETE,21,170,18,50.0,55.0,3.2,28,30.0,2.75,2.2,3.2,50.0,35,380.0,90.0,11,5.0,90,707.554039999975,744.6440399999747,1.7043416129093065,2143,37.08999999999969,0.021164002566293746,0.9080727951469902,0.4006987142870508,False,, 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+311,COMPLETE,23,155,17,49.0,55.0,3.4000000000000004,27,35.0,2.25,2.1,2.9000000000000004,60.0,25,390.0,80.0,12,5.0,75,-999412.09226,638.6077400000016,1.476610507165501,2195,50.69999999999936,0.03094090108472166,0.8838268792710706,0.30859361180263534,False,, +312,COMPLETE,22,165,17,50.0,55.0,3.3000000000000003,28,32.5,2.5,2.2,3.2,50.0,30,400.0,70.0,12,5.0,75,744.8380800000126,779.3680800000114,1.6608566558978504,2281,34.529999999998836,0.019380279175698987,0.9070583077597545,0.4122663327992482,False,, +313,COMPLETE,23,160,17,50.0,56.0,3.3000000000000003,27,35.0,2.5,2.1,3.2,50.0,30,400.0,60.0,12,5.0,75,673.1040400000065,712.8240400000077,1.6442966484688637,2209,39.720000000001164,0.022957360736731623,0.9049343594386601,0.3935337619199954,False,, +314,COMPLETE,22,150,17,49.0,54.0,3.4000000000000004,28,37.5,2.5,2.1,3.0,60.0,30,400.0,70.0,12,5.0,60,642.3837000000134,702.2237000000099,1.5198251681613273,2230,59.83999999999651,0.03515401647856046,0.885201793721973,0.33110750422485097,False,, +315,COMPLETE,24,160,17,50.0,54.0,3.3000000000000003,28,32.5,2.5,2.0,3.1,50.0,30,390.0,80.0,12,5.0,75,763.2380800000154,813.0180800000152,1.6601476814147942,2366,49.779999999999745,0.027456979358969947,0.9010989010989011,0.42601301949699905,False,, +316,COMPLETE,24,160,17,50.0,68.0,3.3000000000000003,28,32.5,2.5,1.9,3.2,50.0,30,390.0,80.0,12,5.0,75,438.7140400000025,480.844039999999,1.58926243550937,1645,42.12999999999647,0.02822791841768191,0.894224924012158,0.3155558659100974,False,, +317,COMPLETE,24,165,17,50.0,55.0,3.3000000000000003,27,32.5,2.5,2.0,3.1,50.0,30,390.0,70.0,12,5.0,75,751.108080000013,793.2180800000123,1.674109646550931,2308,42.10999999999922,0.023432351740933537,0.8999133448873483,0.42603041908782036,False,, +318,COMPLETE,24,165,17,50.0,55.0,3.4000000000000004,27,35.0,2.25,2.0,3.1,50.0,25,390.0,70.0,12,5.0,75,728.858080000004,771.1680800000034,1.6553706413753888,2307,42.30999999999949,0.023801245301855484,0.8998699609882965,0.41246371955775135,False,, 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+415,COMPLETE,29,165,16,49.0,58.0,3.6,26,42.5,1.75,1.9,2.9000000000000004,70.0,25,390.0,70.0,3,6.5,75,196.5300000000002,252.07000000000016,1.508256880733946,736,55.539999999999964,0.04427791286323588,0.8532608695652174,0.21171171437059152,False,, +416,COMPLETE,30,175,15,50.0,57.0,3.7,26,47.5,2.25,1.7000000000000002,2.5,60.0,25,400.0,80.0,12,6.0,75,755.6952800000045,790.0652800000053,1.6168294842242,2346,34.3700000000008,0.019083574440443545,0.8687127024722933,0.39719559454447984,False,, +417,COMPLETE,26,160,16,50.0,56.0,3.5,26,47.5,2.0,1.8,2.9000000000000004,70.0,25,390.0,60.0,12,6.0,75,713.5989800000029,765.1993200000024,1.5409149965606792,2268,51.600339999999505,0.028787614480502807,0.86331569664903,0.37310849432549614,False,, +418,COMPLETE,29,170,15,49.0,58.0,3.4000000000000004,26,42.5,1.75,1.9,2.8,60.0,25,400.0,60.0,12,6.0,75,680.9833600000005,732.7033599999994,1.5971771572561155,2230,51.71999999999889,0.02973946298395911,0.8825112107623319,0.3677868603684985,False,, 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+423,COMPLETE,24,170,14,50.0,56.0,3.4000000000000004,27,40.0,2.25,1.9,2.8,60.0,25,390.0,60.0,12,6.0,75,819.6914400000068,859.9274000000058,1.6516256644339247,2459,40.23595999999907,0.021633080947137475,0.8836925579503864,0.4012607976995771,False,, +424,COMPLETE,25,170,14,49.0,56.0,3.4000000000000004,25,40.0,2.0,1.9,2.8,290.0,25,390.0,70.0,12,6.0,75,-999453.1606,646.3953600000007,1.1816716997827084,1953,99.55595999999287,0.05965595072242048,0.6492575524833589,0.19266505479472168,False,, +425,COMPLETE,25,175,16,50.0,56.0,3.4000000000000004,27,37.5,2.25,1.9,2.7,60.0,25,390.0,70.0,12,6.0,75,846.8789800000063,876.739320000006,1.7061032775220868,2307,29.860339999999724,0.015797037821444444,0.8851322063285653,0.4358545513115951,False,, +426,COMPLETE,27,175,14,50.0,56.0,3.4000000000000004,26,37.5,2.25,1.8,2.7,70.0,25,380.0,60.0,12,6.0,75,718.3686400000045,760.8730200000034,1.5055973652984478,2453,42.504379999998946,0.024101970611077616,0.86180187525479,0.35406324481191287,False,, +427,COMPLETE,25,175,16,38.0,56.0,3.4000000000000004,27,40.0,2.25,1.9,2.8,70.0,25,390.0,70.0,12,6.0,75,371.9680800000009,402.848080000001,1.6069274274952945,1074,30.88000000000011,0.022010793157791055,0.8798882681564246,0.27040279291107966,False,, +428,COMPLETE,31,175,15,50.0,56.0,3.5,26,40.0,2.25,1.9,2.7,70.0,25,390.0,70.0,12,6.5,75,753.4789800000093,789.1689800000089,1.5285109868640379,2368,35.6899999999996,0.019881469010270957,0.8673986486486487,0.3642559602646871,False,, +429,COMPLETE,26,170,13,49.0,57.0,3.7,27,37.5,2.25,1.8,2.8,60.0,25,380.0,60.0,12,6.0,75,-999355.65664,686.2433600000053,1.4709474914235643,2497,41.89999999999873,0.02459781457751648,0.8694433319983981,0.33042787473214935,False,, +430,COMPLETE,25,170,16,50.0,56.0,1.9,27,40.0,2.25,1.9,2.8,60.0,20,390.0,80.0,12,6.0,45,783.0793199999907,817.0393199999908,1.658510353944286,2291,33.960000000000036,0.018507625719478574,0.8847664775207333,0.4021001988413435,False,, 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+435,COMPLETE,25,165,14,49.0,56.0,1.5,27,37.5,2.25,2.0,2.8,60.0,20,380.0,80.0,12,6.5,45,678.1933599999961,722.683359999995,1.5191822390441034,2384,44.48999999999887,0.025614403702899073,0.8829697986577181,0.33356803281282266,False,, +436,COMPLETE,25,160,16,50.0,56.0,3.4000000000000004,27,37.5,2.25,2.0,2.8,70.0,20,390.0,70.0,12,7.0,45,673.4893199999906,724.0993199999903,1.5110884920380891,2239,50.60999999999967,0.029079533310780552,0.8767306833407771,0.34179244213158577,False,, +437,COMPLETE,26,165,15,49.0,56.0,3.4000000000000004,27,37.5,2.25,1.9,2.7,60.0,20,390.0,80.0,12,7.0,45,-999442.87664,604.1633599999863,1.4233448378185347,2351,47.03999999999678,0.029125251310547825,0.874096129306678,0.2970861061161571,False,, +438,COMPLETE,24,160,14,50.0,56.0,2.5,27,40.0,2.25,2.0,3.0,200.0,20,380.0,70.0,12,6.5,45,-999627.06588,453.14446000000873,1.1512283812689978,2081,80.21033999999281,0.05471506451117664,0.7280153772224892,0.15410105323774603,False,, +439,COMPLETE,26,165,16,50.0,57.0,3.4000000000000004,27,40.0,2.25,1.9,3.0,60.0,20,380.0,70.0,12,6.5,45,746.5433599999905,777.7933599999886,1.6505439021685966,2215,31.24999999999818,0.017439361915566696,0.8848758465011287,0.3860589014863566,False,, +440,COMPLETE,25,160,15,50.0,56.0,1.9,27,37.5,2.25,2.0,2.9000000000000004,60.0,20,390.0,70.0,12,6.5,45,725.7033600000012,760.503360000001,1.554695409890602,2362,34.79999999999973,0.019686783723528164,0.8848433530906011,0.3604443198432021,False,, +441,COMPLETE,24,165,16,49.0,58.0,3.4000000000000004,27,37.5,2.25,1.9,2.8,70.0,15,390.0,80.0,12,6.0,30,-999600.3226000001,461.54335999998045,1.330655232195155,2066,61.865960000000086,0.04193724179153207,0.8620522749273959,0.22660816484055965,False,, +442,COMPLETE,24,170,15,39.0,70.0,1.0,26,40.0,2.25,2.0,3.0,60.0,25,380.0,70.0,12,6.0,45,78.84562000000145,113.22562000000156,1.6333082341981016,308,34.38000000000011,0.030343800555900625,0.8701298701298701,0.14836335265402337,False,, +443,COMPLETE,25,165,16,50.0,57.0,3.4000000000000004,27,40.0,2.25,1.9,2.9000000000000004,60.0,25,390.0,70.0,12,5.5,45,767.5733600000052,798.1533600000047,1.6673552538760443,2211,30.579999999999472,0.016872344481674843,0.8846675712347354,0.4011184915857199,False,, +444,COMPLETE,25,165,16,50.0,56.0,3.4000000000000004,27,40.0,2.25,1.9,2.6,60.0,25,390.0,70.0,12,6.5,45,813.019320000009,848.1393200000084,1.6811331319759202,2284,35.119999999999436,0.018867424602773186,0.8844133099824869,0.4216496288927652,False,, +445,COMPLETE,25,165,14,50.0,56.0,3.4000000000000004,27,40.0,2.25,1.9,2.6,70.0,25,380.0,80.0,12,6.5,45,718.0911000000149,760.6814400000142,1.5167445626983156,2423,42.59033999999929,0.024176097218218492,0.8704085843995047,0.34483230005074633,False,, +446,COMPLETE,25,170,15,50.0,56.0,3.4000000000000004,28,40.0,2.25,1.9,2.7,70.0,25,390.0,70.0,12,6.5,45,728.0030200000109,760.8133600000114,1.508117162707569,2357,32.81034000000045,0.01852861024958906,0.8676283411115825,0.354267007118505,False,, +447,COMPLETE,25,165,16,49.0,56.0,3.3000000000000003,27,37.5,2.25,1.9,2.7,60.0,25,380.0,70.0,12,7.0,45,-999414.1663,621.8037000000003,1.4598546675614446,2250,35.970000000000255,0.022057015022992007,0.8777777777777778,0.315834281720611,False,, +448,COMPLETE,26,170,14,50.0,56.0,3.4000000000000004,27,40.0,2.25,1.9,2.5,60.0,25,390.0,80.0,12,6.5,45,812.6170600000099,847.4074000000095,1.644546300564735,2464,34.79033999999956,0.01883198042835564,0.8843344155844156,0.39964167989915533,False,, +449,COMPLETE,26,175,13,49.0,58.0,3.3000000000000003,28,40.0,2.25,1.9,2.6,80.0,25,380.0,80.0,12,6.5,45,-999451.26136,591.3886400000018,1.3454809833776418,2375,42.650000000000546,0.02658184343295495,0.8488421052631578,0.2628889846495649,False,, +450,COMPLETE,26,170,14,50.0,56.0,3.4000000000000004,27,40.0,2.25,1.8,2.5,70.0,25,390.0,80.0,12,6.5,45,-999310.4969799999,732.0274000000143,1.486418042106184,2446,42.52437999999984,0.024430068777462746,0.8622240392477515,0.3423627093669782,False,, +451,COMPLETE,25,175,14,49.0,56.0,3.4000000000000004,27,40.0,2.25,1.9,2.6,80.0,25,390.0,80.0,12,6.5,30,-999417.31698,638.1730200000106,1.3773115046266449,2374,55.48999999999842,0.03363961977784495,0.8508845829823083,0.27857649722674677,False,, +452,COMPLETE,26,170,15,50.0,57.0,1.6,28,42.5,2.25,2.0,2.5,60.0,25,380.0,80.0,12,6.5,45,772.1052800000102,806.2752800000103,1.6373371199206714,2286,34.17000000000007,0.018706320986847543,0.8871391076115486,0.39160645507450337,False,, +453,COMPLETE,26,170,15,50.0,57.0,1.4,27,42.5,2.25,1.9,2.5,70.0,25,380.0,70.0,12,6.5,45,710.8852800000104,744.1052800000116,1.5302770285507328,2251,33.220000000001164,0.018748709544721357,0.8685028876055086,0.3543519844974281,False,, +454,COMPLETE,27,170,15,50.0,57.0,1.1,28,42.5,2.25,1.9,2.4000000000000004,60.0,25,380.0,70.0,12,7.0,45,783.9552800000092,810.3652800000091,1.6708828570793817,2235,26.409999999999854,0.014456932158636917,0.8841163310961969,0.4042684327744018,False,, +455,COMPLETE,27,175,13,50.0,56.0,1.2,28,42.5,2.25,2.0,2.5,70.0,20,380.0,80.0,12,7.0,45,747.052680000001,797.3626799999978,1.5204958114575948,2493,50.30999999999676,0.02799101180847753,0.8764540713999198,0.34612857357087035,False,, +456,COMPLETE,27,170,14,49.0,57.0,1.5,28,40.0,2.25,1.9,2.6,60.0,25,380.0,70.0,12,7.0,45,718.3352800000043,756.6652800000038,1.5532275858555502,2366,38.32999999999947,0.021629640424071995,0.8782755705832629,0.36795798166130494,False,, +457,COMPLETE,26,175,15,50.0,57.0,1.6,28,42.5,2.25,1.8,2.5,60.0,25,380.0,70.0,12,7.5,45,753.8152800000075,790.1752800000086,1.613662235517057,2313,36.36000000000104,0.020100221606602114,0.8737570255079983,0.39585467720912465,False,, +458,COMPLETE,27,170,14,50.0,56.0,3.5,28,37.5,2.25,1.9,2.5,60.0,25,380.0,70.0,12,6.5,45,812.3970600000074,849.1474000000084,1.6472926155467638,2465,36.75034000000096,0.019872810861150853,0.8851926977687626,0.4005978535305573,False,, +459,COMPLETE,27,170,14,50.0,56.0,1.0,28,37.5,2.25,1.8,2.6,60.0,25,380.0,80.0,12,7.0,45,687.804600000015,729.5489800000132,1.5518672086801357,2388,41.74437999999827,0.023996277621665167,0.8773031825795645,0.3617810860854328,False,, +460,COMPLETE,27,175,13,49.0,56.0,1.7000000000000002,28,37.5,2.25,1.9,2.4000000000000004,230.0,25,380.0,70.0,12,6.5,45,-999601.60744,516.9772799999957,1.1536049316231307,2148,118.5847199999971,0.0775647911673672,0.6950651769087524,0.16972128837463604,False,, +461,COMPLETE,27,170,14,50.0,56.0,2.2,28,40.0,2.25,1.9,2.4000000000000004,70.0,25,390.0,70.0,12,6.5,45,748.6067200000132,793.6470600000137,1.542858006656879,2432,45.04034000000047,0.025111038288658707,0.8721217105263158,0.35865277745317536,False,, +462,COMPLETE,26,175,14,50.0,56.0,1.3,28,37.5,2.25,1.8,2.4000000000000004,70.0,25,390.0,80.0,12,6.5,45,-999316.42574,726.5786400000152,1.4917292112590075,2409,43.00437999999849,0.0247795501533791,0.863013698630137,0.34102774931622926,False,, +463,COMPLETE,26,170,15,49.0,57.0,2.3,28,40.0,2.25,1.8,2.4000000000000004,70.0,25,380.0,60.0,12,6.5,45,-999449.09506,598.5952800000014,1.4028452268817193,2252,47.69033999999783,0.029180981297331866,0.8565719360568383,0.2974771874032525,False,, +464,COMPLETE,27,170,15,50.0,57.0,1.9,27,37.5,1.25,1.9,2.5,60.0,20,390.0,70.0,12,7.0,30,784.7352799999953,812.1352799999968,1.655609358591809,2305,27.400000000001455,0.01503713777895011,0.8837310195227766,0.3922868853972393,False,, +465,COMPLETE,27,170,14,49.0,56.0,1.8,27,37.5,0.75,1.9,2.5,60.0,20,380.0,70.0,12,7.0,30,694.6833599999914,744.4333599999923,1.5353614713156596,2422,49.75000000000091,0.028327186646996384,0.8790255986787778,0.34848078465891613,False,, +466,COMPLETE,27,175,15,50.0,56.0,1.7000000000000002,26,37.5,1.25,1.9,2.6,70.0,20,380.0,80.0,12,7.0,45,-999310.05102,727.218979999995,1.4897911716152337,2353,37.27000000000044,0.021530919677376407,0.8674033149171271,0.33634918361879473,False,, +467,COMPLETE,26,170,13,50.0,57.0,1.9,28,37.5,1.0,2.0,2.3,60.0,20,390.0,70.0,12,7.0,30,-999365.61102,678.3889799999982,1.4690076714972673,2495,44.000000000000455,0.02614877853885141,0.8837675350701403,0.309473497176208,False,, +468,COMPLETE,26,170,14,50.0,57.0,1.9,26,37.5,2.25,1.8,2.5,60.0,20,390.0,60.0,12,7.0,45,695.0752799999959,731.8952799999975,1.5467132469257372,2418,36.82000000000153,0.021174606611235196,0.8742762613730356,0.35950935316063537,False,, +469,COMPLETE,27,175,15,49.0,56.0,1.7000000000000002,28,40.0,2.25,2.0,2.6,60.0,20,380.0,70.0,12,7.5,30,-999406.52664,642.1033599999872,1.4531911789935787,2344,48.629999999999654,0.029483388465996508,0.8809726962457338,0.312683728597903,False,, +470,COMPLETE,26,165,15,49.0,56.0,2.0,27,40.0,1.5,1.9,2.5,60.0,25,390.0,80.0,12,7.0,30,-999382.66664,661.7933600000059,1.469191025733343,2349,44.45999999999958,0.02651236525743295,0.8752660706683695,0.330895942033823,False,, +471,COMPLETE,27,170,14,50.0,57.0,1.6,27,37.5,1.25,1.9,2.4000000000000004,70.0,25,390.0,70.0,12,6.5,45,736.7205600000098,773.65056000001,1.5366040402728116,2364,36.93000000000029,0.02082028940980297,0.8701353637901861,0.35868996916603335,False,, +472,COMPLETE,25,170,15,40.0,56.0,1.3,26,37.5,1.25,2.0,2.5,60.0,20,380.0,60.0,12,6.5,30,388.7977399999912,425.5677399999912,1.5849612759544227,1323,36.76999999999998,0.025793232386137052,0.8888888888888888,0.2780076909461645,False,, +473,COMPLETE,26,165,15,50.0,57.0,2.1,27,40.0,1.0,1.9,2.7,70.0,15,210.0,60.0,12,6.5,45,-999337.48876,696.8112399999927,1.496474475339393,2273,34.29999999999836,0.020059286126615488,0.8697756269247691,0.34509212322564337,False,, +474,COMPLETE,25,175,14,50.0,57.0,2.2,26,40.0,2.25,1.9,2.6,60.0,25,390.0,80.0,12,6.5,45,825.2352800000035,851.7152800000017,1.6619898187994535,2417,26.4799999999982,0.01429569968077735,0.8837401737691353,0.41275417611428017,False,, +475,COMPLETE,25,175,14,50.0,56.0,1.9,25,37.5,2.25,1.9,2.6,50.0,25,380.0,80.0,12,6.5,45,822.3877400000041,853.2977400000044,1.7213118520634123,2491,30.91000000000031,0.016659321151041833,0.8972300281011641,0.4283530101846939,False,, +476,COMPLETE,26,175,13,50.0,57.0,2.0,25,42.5,2.25,1.8,2.6,50.0,25,370.0,80.0,12,6.5,45,694.3833600000038,734.5633600000027,1.5736757897225466,2551,40.17999999999893,0.023058514406371494,0.8871030968247746,0.37686219631947165,False,, +477,COMPLETE,27,175,14,49.0,56.0,1.8,25,40.0,2.25,1.8,2.5,50.0,25,370.0,80.0,12,6.5,45,723.2477399999997,761.0977399999991,1.6103624274048673,2464,37.849999999999454,0.02137128871197182,0.8867694805194806,0.39277921761929335,False,, 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