refactor: 缠论引擎迁入 chan/ 分层解耦，指标外置

将核心结构、指标与分析拆到 chan/{core,indicators,analysis,pipeline}；
根目录保留兼容 shim；strategies 改为从 chan 包导入；买卖点经 bsp_macd 与 MACD 接合。

Co-authored-by: Cursor <cursoragent@cursor.com>

.gitignore

@@ -40,7 +40,3 @@ feature_meta
 .DS_Store
 data_provider/._config.json
 .gstack/
-
-# ESS gate / engineering-loop working dirs（归档进 docs/runs/）
-.gates/
-loop/

AGENTS.md

@@ -1,33 +0,0 @@
-# chan — Agent Entry
-
-本仓受 ESS 约束。不要一上来扫全库或加载全部 governance。
-
-## Boot
-
-1. `docs/PROJECT_PROFILE.md`
-2. `docs/PROJECT_RULES.md`
-3. `docs/STATE/CURRENT.md` + `docs/AGENT_MEMORY.md`
-4. 有进行中任务再读 `docs/TASKS/` / 对应 ECR / HANDOFF
-5. 角色文件：ESS 根目录 `agents/{ARCHITECT|ENGINEER|REVIEWER|RELEASE_MANAGER}.md`
-
-## Roles（选一）
-
-| 意图 | 角色 |
-|------|------|
-| 规格 / 架构 / ECR | ARCHITECT |
-| 实现 / 修 bug | ENGINEER |
-| 审阅 | REVIEWER |
-| 发版 / tag | RELEASE_MANAGER |
-
-## Never
-
-- 无 ECR 改 `config/` / `strategies/` 交易逻辑
-- 无 ADR 改缠论算法语义
-- 无 ECR 删减 `/api/analyze` 字段
-- 把聊天记录当成完成；阶段结束须落盘 `docs/`
-
-## Pointers
-
-- TRACEABILITY: `docs/TRACEABILITY.md`
-- CHANGELOG: `docs/CHANGELOG/CHANGELOG.md`
-- 人类向导：`CLAUDE.md`

CLAUDE.md

@@ -6,50 +6,60 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 缠论 (Chan Theory) technical analysis system for Freqtrade. Implements Chan Zhong Shui Chan's theory for crypto/stock trading, including fractal (分型), stroke (笔), segment (线段), pivot/center (中枢), and buy/sell point (买卖点) detection.
 
-## Governance
-
-- Agent 入口：`AGENTS.md`（boot 顺序）· `docs/PROJECT_PROFILE.md` · `docs/AGENT_MEMORY.md` · `docs/STATE/CURRENT.md`
-- ESS 文档：`docs/ECR/`、`docs/ENGINEERING_SPEC/`、`docs/TRACEABILITY.md`、`docs/CHANGELOG/`
-- **正式引擎包**：`chanlun/`；strategies / web 已用 `from chanlun import ...`
-- 根目录 `Chan*.py` / `TF_DF.py` 仍为 **兼容 shim**（旧脚本可用）
-- 变更分级：无 ECR 不改 strategies/config；无 ADR 不改缠论算法语义
-
 ## Core Architecture
 
-### Chan Theory Engine (`chanlun/`)
+### Chan Theory Engine (`chan/` package)
 
-```text
-chanlun/
-  core/          # KLU KLC BI SBI SEG ZS BIZS BSP Enum CTime
-  pipeline/      # orchestrator(ChanLun) + timeframe(TF_DF) + builders/
-  indicators/    # ChanMACD*
-  analysis/      # Zone Classifier Pivot Heng PY Find_Trend ...
+引擎已分层解耦；根目录 `Chan*.py` / `TF_DF.py` 仅为兼容旧 import 的 shim。新代码优先：
+
+```python
+from chan import ChanLun, TF_DF
+from chan.indicators import IndicatorEngine, IndicatorStore
+from chan.analysis.bsp_macd import confirm_bsp, bi_macd_area
 ```
 
-Data processing pipeline (each step feeds the next):
+| 层 | 路径 | 职责 |
+|----|------|------|
+| **core** | `chan/core/` | 纯结构：KLU→KLC→BI→SBI→SEG→ZS→BSP 几何；不依赖 talib/指标参数 |
+| **indicators** | `chan/indicators/` | `IndicatorConfig` / `IndicatorEngine` / `IndicatorStore`；MACD/EMA/BB/RSI 外置计算 |
+| **analysis** | `chan/analysis/` | 结构+指标接合：`ChanMACD`、`bsp_macd`（ConfirmedBSP）、Zone/Classifier 等 |
+| **pipeline** | `chan/pipeline/` | `TF_DF` / `ChanLun` 编排：先指标再结构，可选 attach 兼容 |
 
-1. **`chanlun.core.ChanKLU`** — Raw K-line unit with TA indicators and pattern recognition
-2. **`chanlun.core.ChanKLC`** — Combined K-line: inclusion + fractal; `.next`/`.pre` linked list
-3. **`chanlun.core.ChanBI`** — Stroke (笔)
-4. **`chanlun.core.ChanSBI`** — Special stroke → SEG
-5. **`chanlun.core.ChanSEG`** — Segment (线段)
-6. **`chanlun.core.ChanZS`** / **`ChanBIZS`** — Centers (中枢)
-7. **`chanlun.core.ChanBSP`** — Buy/Sell points
-8. **`chanlun.pipeline.orchestrator.ChanLun`** — Orchestrator
-9. **`chanlun.pipeline.timeframe.TF_DF`** — Timeframe facade；实现拆在 `pipeline/builders/`
+流水线：
+
+1. **`chan/core/ChanKLU.py`** — K 线单元（OHLCV + 结构链）；指标字段仅兼容挂载
+2. **`chan/core/ChanKLC.py`** — 合并 K 线：包含处理、分型
+3. **`chan/core/ChanBI.py`** — 笔
+4. **`chan/core/ChanSBI.py`** — 特征笔
+5. **`chan/core/ChanSEG.py`** — 线段
+6. **`chan/core/ChanZS.py`** / **`ChanBIZS.py`** — 中枢
+7. **`chan/core/ChanBSP.py`** — 几何买卖点
+8. **`chan/analysis/bsp_macd.py`** — 买卖点 × MACD 背驰 → `ConfirmedBSP`
+9. **`chan/pipeline/ChanLun.py`** / **`TF_DF.py`** — 多周期/单周期编排
+
+### Support modules
+
+- **`chan/core/ChanEnum.py`** — 枚举
+- **`chan/core/ChanCTime.py`** — 时间工具
+- **`chan/analysis/ChanMACD*.py`** — MACD 状态/段分析（读 KLU 上兼容指标或 Store）
+- **`chan/analysis/ChanZone.py`** / **`ChanHeng.py`** / **`ChanLun_Classifier.py`** 等 — 分析扩展
+- **`chan/analysis/ChanPY.py`** — 外部 chan.py 桥接（与本包名冲突已隔离）
 
 ### Services
 
-- **外部 DATA_SERVICE** — 行情服务（env: `DATA_SERVICE_URL`）；本仓库可不含 data_provider 源码
-- **`web/`** — Flask UI：`create_app()` + `api/` blueprints + `services/`；前端 `static/js/app/`。默认端口见 `web/config.py`（`FLASK_PORT`，常见 8128）
-- **`strategies/`** — Freqtrade strategies（本 ECR 不改）
-- **`config/`** — Freqtrade configs（本 ECR 不改）
+- **`data_provider/`** — FastAPI data service: fetches crypto data from Binance via CCXT, caches to CSV, serves REST API + WebSocket. Synthesizes derived timeframes (e.g. 5m/15m/4h from 1m/1h base). Port 9009.
+- **`web/`** — Flask web UI for interactive chart visualization with Chan theory overlays. Port 8123.（本重构分支不改 web）
+- **`strategies/`** — Freqtrade trading strategies using the Chan theory engine (53 strategies)
+- **`config/`** — Freqtrade JSON config files per pair/timeframe
 
 ### Data Flow
 
 ```
-Exchange / DATA_SERVICE → Freqtrade Strategy / web → ChanLun → TF_DF
-  → KLU → KLC → BI → SBI → SEG → ZS → BSP
+Exchange (CCXT) → data_provider (CSV cache) → Freqtrade → Strategy
+  → ChanLun / TF_DF
+      → IndicatorEngine → IndicatorStore
+      → KLU → KLC → BI → SBI → SEG → ZS → 几何 BSP
+      → analysis.bsp_macd → ConfirmedBSP
 ```
 
 ## Common Commands

ChanBI.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.core.ChanBI import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.core.ChanBI")
+sys.modules[__name__] = _impl

ChanBIZS.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.core.ChanBIZS import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.core.ChanBIZS")
+sys.modules[__name__] = _impl

ChanBSP.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.core.ChanBSP import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.core.ChanBSP")
+sys.modules[__name__] = _impl

ChanCTime.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.core.ChanCTime import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.core.ChanCTime")
+sys.modules[__name__] = _impl

ChanEnum.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.core.ChanEnum import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.core.ChanEnum")
+sys.modules[__name__] = _impl

ChanHeng.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.analysis.ChanHeng import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.analysis.ChanHeng")
+sys.modules[__name__] = _impl

ChanKLC.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.core.ChanKLC import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.core.ChanKLC")
+sys.modules[__name__] = _impl

ChanKLU.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.core.ChanKLU import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.core.ChanKLU")
+sys.modules[__name__] = _impl

ChanLun.py

@@ -1,3 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.pipeline.orchestrator import ChanLun  # noqa: F401
-from chanlun.pipeline.timeframe import TF_DF  # noqa: F401
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.pipeline.ChanLun")
+sys.modules[__name__] = _impl

ChanLun_Classifier.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.analysis.ChanLun_Classifier import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.analysis.ChanLun_Classifier")
+sys.modules[__name__] = _impl

ChanMACD.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.indicators.ChanMACD import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.analysis.ChanMACD")
+sys.modules[__name__] = _impl

ChanMACDHistSet.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.indicators.ChanMACDHistSet import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.analysis.ChanMACDHistSet")
+sys.modules[__name__] = _impl

ChanMACDSeg.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.indicators.ChanMACDSeg import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.analysis.ChanMACDSeg")
+sys.modules[__name__] = _impl

ChanMACDUnitTF.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.indicators.ChanMACDUnitTF import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.analysis.ChanMACDUnitTF")
+sys.modules[__name__] = _impl

ChanMacro/.env.example

@@ -0,0 +1,12 @@
+# Data Provider URL (existing chan data_provider service)
+PROVIDER_URL=http://127.0.0.1:80
+
+# Database path
+DB_PATH=data/macro.db
+
+# Telegram (reuse bsp_monitor config)
+# TELEGRAM_BOT_TOKEN=your_bot_token
+# TELEGRAM_CHAT_ID=your_chat_id
+
+# AI API (for daily report, Phase 5+)
+# ANTHROPIC_API_KEY=sk-ant-...

ChanMacro/.gitignore

@@ -0,0 +1 @@
+data/

ChanMacro/__init__.py

@@ -0,0 +1,9 @@
+"""
+ChanMacro — Crypto Market Memory System (Signal Expectancy Engine).
+
+V1: 4 factors (Price Structure, Breadth, OI State, Volatility Regime)
+    3 regimes (TREND / RANGE / PANIC)
+    Factor-locked: Regime = f(Price, Breadth, Vol) — forever.
+"""
+
+__version__ = "1.0.0"

ChanMacro/chan_integration.py

@@ -0,0 +1,224 @@
+"""
+chan_integration.py — 缠论引擎集成：检测 BSP 信号并写入 signal_features。
+
+复用 bsp_monitor/engine.py 的 ChanEngine 管线，对历史日线数据批量跑缠论，
+提取 B1/B2/B3/S1/S2/S3 信号，通过 SignalTracker 记录到 signal_features。
+"""
+
+import sys
+import os
+from datetime import date as Date, timedelta
+from typing import List, Optional
+import logging
+
+# 确保 Chan 引擎在路径上（与 bsp_monitor/engine.py 相同的路径设置）
+_PARENT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if _PARENT not in sys.path:
+    sys.path.insert(0, _PARENT)
+
+import pandas as pd
+
+from ChanEnum import Chan_BSP_TYPE, Chan_BSP_DIR
+from ChanBSP import ChanBSP
+
+logger = logging.getLogger(__name__)
+
+
+class ChanSignalDetector:
+    """
+    对历史日线数据运行缠论管线，提取所有 BSP 信号。
+
+    Usage:
+        detector = ChanSignalDetector()
+        signals = detector.detect_from_db("2026-01-01", "2026-06-24")
+        # → [{"date": Date, "signal_type": "B3", "entry_price": 96500, ...}, ...]
+    """
+
+    def __init__(self):
+        from TF_DF import TF_DF as _TF_DF_Class
+        self._TF_DF_Class = _TF_DF_Class
+
+    def detect_from_db(self, start_date: str, end_date: str) -> list[dict]:
+        """从数据库加载日线数据，跑缠论管线，提取信号。"""
+        from database import get_connection
+        conn = get_connection()
+        df = pd.read_sql_query(
+            "SELECT date, open, high, low, close, volume "
+            "FROM ohlcv_daily WHERE symbol='BTC/USDT:USDT' "
+            "AND date BETWEEN ? AND ? ORDER BY date",
+            conn, params=(start_date, end_date)
+        )
+        conn.close()
+
+        if df.empty or len(df) < 50:
+            logger.warning(f"日线数据不足: {len(df)} 根")
+            return []
+
+        return self.detect_from_df(df)
+
+    def detect_from_df(self, df: pd.DataFrame) -> list[dict]:
+        """从 DataFrame 运行缠论管线，提取 BSP 信号。"""
+        # 需要 datetime 列才能跑 TF_DF
+        df = df.copy()
+        df["timestamp"] = pd.to_datetime(df["date"])
+        df["date"] = df["timestamp"]
+
+        try:
+            engine = self._build_engine(df)
+        except Exception as e:
+            logger.error(f"缠论管线失败: {e}")
+            return []
+
+        return self._extract_signals(engine)
+
+    def _build_engine(self, df: pd.DataFrame):
+        """构建缠论管线（对齐 bsp_monitor/engine.py 的 ChanEngine）。"""
+        from TF_DF import TF_DF as _TF_DF_Class
+
+        if df.empty or len(df) < 50:
+            raise ValueError(f"数据不足: {len(df)} 根 K 线")
+
+        if "date" not in df.columns and "timestamp" in df.columns:
+            df["date"] = df["timestamp"]
+
+        # 使用 __new__ 避免触发 TF_DF.__init__
+        engine = type('ChanEngine', (), {})()  # 简单容器
+        tf = _TF_DF_Class.__new__(_TF_DF_Class)
+
+        df_with_indicators = tf.add_indicators(df.copy())
+        engine.klu_list = tf.get_klu_list(df_with_indicators)
+        engine.klc_list = tf.get_klc_list(engine.klu_list)
+        engine.bi_list = tf.cal_bi_list(engine.klc_list)
+        engine.seg_list = tf.get_seg_list(engine.bi_list)
+        engine.bi_zs_list = tf.cal_bi_zs(engine.seg_list)
+        engine.bsp_list = tf.find_all_bsp(engine.bi_list, engine.bi_zs_list)
+
+        return engine
+
+    def _extract_signals(self, engine) -> list[dict]:
+        """从 ChanEngine 输出中提取所有 BSP 信号。"""
+        signals = []
+        for bsp in engine.bsp_list:
+            if bsp.type == Chan_BSP_TYPE.NONE:
+                continue
+            if bsp.klc is None:
+                continue
+
+            signal_type = self._bsp_type_str(bsp.type)
+            entry_price = bsp.klc.close
+            signal_date = self._klc_date(bsp.klc)
+
+            if signal_date is None:
+                continue
+
+            # 信号质量：根据分型强度判断
+            strength = self._calc_strength(bsp)
+            grade = "A" if strength >= 70 else "B" if strength >= 50 else "C"
+
+            signals.append({
+                "date": signal_date,
+                "signal_type": signal_type,
+                "entry_price": float(entry_price),
+                "signal_grade": grade,
+                "signal_strength": float(strength),
+            })
+
+        details = ", ".join(f"{s['signal_type']}({s['date']})" for s in signals)
+        logger.info(f"检测到 {len(signals)} 个信号: {details}")
+        return signals
+
+    def populate_signal_features(self, start_date: str = "2024-01-01",
+                                  end_date: Optional[str] = None) -> int:
+        """
+        完整流程：检测信号 → 计算市场状态 → 写入 signal_features。
+
+        Returns: 写入的信号数量。
+        """
+        if end_date is None:
+            end_date = Date.today().isoformat()
+
+        logger.info(f"开始信号检测: {start_date} → {end_date}")
+
+        # Step 1: 检测缠论信号
+        signals = self.detect_from_db(start_date, end_date)
+        if not signals:
+            logger.warning("未检测到任何 BSP 信号")
+            return 0
+
+        # Step 2: 去重 — 跳过已存在的信号
+        from database import get_connection
+        conn = get_connection()
+        existing = set()
+        for row in conn.execute(
+            "SELECT date, signal_type FROM signal_features"
+        ).fetchall():
+            existing.add((row[0], row[1]))
+        conn.close()
+
+        new_signals = [s for s in signals
+                       if (str(s["date"]), s["signal_type"]) not in existing]
+        if not new_signals:
+            logger.info("所有信号已存在，跳过")
+            return 0
+
+        # Step 3: 写入 signal_features
+        from expectancy.tracker import SignalTracker
+        tracker = SignalTracker()
+        count = tracker.backfill_signals(new_signals)
+
+        logger.info(f"信号入库完成: {count}/{len(signals)}")
+        return count
+
+    @staticmethod
+    def _bsp_type_str(t: Chan_BSP_TYPE) -> str:
+        mapping = {
+            Chan_BSP_TYPE.B1: "B1", Chan_BSP_TYPE.B2: "B2", Chan_BSP_TYPE.B3: "B3",
+            Chan_BSP_TYPE.S1: "S1", Chan_BSP_TYPE.S2: "S2", Chan_BSP_TYPE.S3: "S3",
+        }
+        return mapping.get(t, "UNKNOWN")
+
+    @staticmethod
+    def _klc_date(klc) -> Optional[Date]:
+        """从 KLC 提取信号确认日期。"""
+        end_time = getattr(klc, "end_time", None)
+        if end_time is None:
+            start_time = getattr(klc, "start_time", None)
+            if start_time is None:
+                return None
+            end_time = start_time
+        if hasattr(end_time, "date"):
+            return end_time.date()
+        if isinstance(end_time, str):
+            return Date.fromisoformat(end_time[:10])
+        return None
+
+    @staticmethod
+    def _calc_strength(bsp: ChanBSP) -> float:
+        """根据 BSP 特征计算信号强度 0-100。"""
+        score = 50.0
+        klc = bsp.klc
+        if klc is None:
+            return score
+
+        # 分型强度
+        from ChanEnum import Chan_KLC_FX
+        fx = getattr(klc, "klc_fx_type", None)
+        if fx is not None:
+            strong_fxs = {Chan_KLC_FX.TOP2, Chan_KLC_FX.TOP3, Chan_KLC_FX.BOTTOM2, Chan_KLC_FX.BOTTOM3}
+            medium_fxs = {Chan_KLC_FX.TOP1, Chan_KLC_FX.BOTTOM1, Chan_KLC_FX.TOP4, Chan_KLC_FX.BOTTOM4}
+            if fx in strong_fxs:
+                score += 25
+            elif fx in medium_fxs:
+                score += 10
+
+        # BSP 类型
+        if bsp.type in (Chan_BSP_TYPE.B1, Chan_BSP_TYPE.S1):
+            score += 10  # 一类买卖点: 背驰确认, 额外加分
+
+        # 笔特征
+        bi = getattr(bsp, "bi", None)
+        if bi and hasattr(bi, "height") and hasattr(bi, "width"):
+            if bi.width > 3 and abs(bi.height) > 100:
+                score += 10
+
+        return min(score, 100.0)

ChanMacro/cli.py

@@ -0,0 +1,464 @@
+"""
+cli.py — Command-line interface for ChanMacro.
+"""
+
+import argparse
+import json
+import logging
+import time
+from datetime import date as Date, datetime, timedelta
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+)
+logger = logging.getLogger("chanmacro")
+
+
+def parse_date(date_str: str) -> Date:
+    """Parse YYYY-MM-DD string to Date."""
+    return datetime.strptime(date_str, "%Y-%m-%d").date()
+
+
+def _build_market_state(target: Date) -> tuple:
+    """Shared helper: compute all scores → (MarketStateVector, RegimeResult)."""
+    from config import config
+    from scoring.price_structure import PriceStructureScorer
+    from scoring.breadth_scorer import BreadthScorer
+    from scoring.oi_matrix import OIMatrixScorer
+    from scoring.volatility_regime import VolatilityRegimeScorer
+    from regime_detector import RegimeDetector
+    from models import MarketStateVector
+
+    ps = PriceStructureScorer().compute(target)
+    br = BreadthScorer().compute(target)
+    oi = OIMatrixScorer().compute(target)
+    vol = VolatilityRegimeScorer().compute(target)
+
+    detector = RegimeDetector()
+    detector.load_state(config.db_path)
+    r = detector.detect(ps.score, br.breadth_top50, vol.vol_regime.value, target)
+
+    state = MarketStateVector(
+        date=target, regime=r.regime, regime_confidence=r.confidence,
+        regime_version=r.regime_version, regime_maturity_score=r.maturity_score,
+        breadth_top20=br.breadth_top20, breadth_top30=br.breadth_top30,
+        breadth_top50=br.breadth_top50, breadth_bucket=br.breadth_bucket,
+        breadth_divergence=br.breadth_divergence,
+        oi_state=oi.oi_state, volatility_regime=vol.vol_regime,
+        price_structure_score=ps, breadth_score=br,
+        oi_matrix_score=oi, volatility_regime_score=vol,
+    )
+    state.market_state_hash = state.compute_hash()
+
+    # Persist regime to DB so subsequent calls have correct state
+    from database import get_connection
+    conn = get_connection()
+    conn.execute("""
+        INSERT OR REPLACE INTO regime_history
+        (date, regime, confidence, regime_version, maturity_score, all_scores_json,
+         prior_regime, confirmation_days)
+        VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+    """, (
+        str(target), r.regime.value, r.confidence, r.regime_version,
+        r.maturity_score, json.dumps(r.all_scores),
+        r.prior_regime.value if r.prior_regime else None,
+        r.confirmation_days,
+    ))
+    conn.commit()
+    conn.close()
+
+    return state, r
+
+
+def cmd_fetch(args):
+    """Fetch raw data and store to DB."""
+    from database import init_db
+    from fetchers.ohlcv import OHLCVFetcher
+    from fetchers.breadth import BreadthFetcher
+
+    target = parse_date(args.date) if args.date else Date.today()
+    init_db()
+
+    module = args.module or "all"
+
+    if module in ("ohlcv", "all"):
+        logger.info(f"Fetching OHLCV for {target}...")
+        fetcher = OHLCVFetcher()
+        df = fetcher.fetch(target)
+        if not df.empty:
+            n = fetcher.store_df(df)
+            logger.info(f"OHLCV: stored {n} rows")
+
+    if module in ("breadth", "all"):
+        logger.info(f"Fetching Breadth for {target}...")
+        fetcher = BreadthFetcher()
+        record = fetcher.fetch(target)
+        if record:
+            fetcher.store(record=record)
+            logger.info(f"Breadth: stored (adv={record.get('advance_top50')}, "
+                        f"dec={record.get('decline_top50')}, "
+                        f"ema20={record.get('above_ema20_top50')})")
+
+    if module in ("derivatives", "all"):
+        logger.info(f"Fetching Derivatives for {target}...")
+        from fetchers.derivatives import DerivativesFetcher
+        fetcher = DerivativesFetcher()
+        records = fetcher.fetch(target)
+        if records:
+            n = fetcher.store(records=records)
+            logger.info(f"Derivatives: stored {n} records")
+
+
+def cmd_score(args):
+    """Compute all factor scores and regime for a date."""
+    from database import init_db
+
+    target = parse_date(args.date) if args.date else Date.today()
+    init_db()
+    logger.info(f"Computing scores for {target}...")
+
+    state, _ = _build_market_state(target)
+
+    # Output
+    ps = state.price_structure_score
+    br = state.breadth_score
+    oi = state.oi_matrix_score
+    vol = state.volatility_regime_score
+
+    print(f"\n{'='*60}")
+    print(f"  {target}  Market State")
+    print(f"{'='*60}")
+    print(f"  Regime:      {state.regime.value} (conf={state.regime_confidence:.2f}, "
+          f"v={state.regime_version})")
+    print(f"  Maturity:    {state.regime_maturity_score:.0f}/100")
+    print(f"  Breadth:     {state.breadth_bucket.value} "
+          f"(T20={state.breadth_top20:.0f} T30={state.breadth_top30:.0f} "
+          f"T50={state.breadth_top50:.0f} div={state.breadth_divergence:+.0f})")
+    print(f"  OI State:    {state.oi_state.value}")
+    print(f"  Volatility:  {state.volatility_regime.value}")
+    print(f"{'='*60}")
+    print(f"  Scores:")
+    print(f"    Price Structure:  {ps.score:.0f}  {ps.label}")
+    print(f"    Breadth:          {br.score:.0f}  {br.breadth_bucket.value}")
+    print(f"    OI Matrix:        {oi.score:.0f}  {oi.oi_state.value}")
+    print(f"    Volatility:       {vol.score:.0f}  {vol.vol_regime.value}")
+    print(f"{'='*60}")
+    print(f"  Market State Hash:  {state.market_state_hash}")
+    print()
+
+    return state
+
+
+def cmd_regime(args):
+    """Show regime history."""
+    from database import get_connection
+    days = args.days or 30
+    conn = get_connection()
+    rows = conn.execute(
+        "SELECT date, regime, confidence, maturity_score, confirmation_days "
+        "FROM regime_history ORDER BY date DESC LIMIT ?",
+        (days,)
+    ).fetchall()
+    conn.close()
+
+    print(f"\n{'='*50}")
+    print(f"  Regime History (last {days} days)")
+    print(f"{'='*50}")
+    for r in rows:
+        print(f"  {r['date']}  {r['regime']:7s}  conf={r['confidence']:.2f}  "
+              f"mat={r['maturity_score']:.0f}  days={r['confirmation_days']}")
+    print()
+
+
+def cmd_track(args):
+    """Record a trading signal with current market state."""
+    from database import init_db
+    from expectancy.tracker import SignalTracker
+
+    target = parse_date(args.date) if args.date else Date.today()
+    init_db()
+
+    logger.info(f"Recording {args.signal} on {target} @ {args.price}")
+
+    state, _ = _build_market_state(target)
+
+    tracker = SignalTracker()
+    rid = tracker.record(
+        date=target, signal_type=args.signal, entry_price=args.price,
+        state=state, signal_grade=args.grade, signal_strength=args.strength,
+    )
+    logger.info(f"Signal recorded: id={rid}")
+
+
+def cmd_backfill(args):
+    """Backfill historical breadth + regime scores."""
+    from datetime import date as Date, timedelta
+    from database import init_db, get_connection
+    from fetchers.ohlcv import OHLCVFetcher
+    from fetchers.breadth import BreadthFetcher
+    from config import config
+    import pandas as pd
+    import requests
+
+    start = parse_date(args.from_date)
+    end = parse_date(args.to_date) if args.to_date else Date.today()
+    init_db()
+
+    # Step 1: Ensure OHLCV data exists for the range
+    logger.info(f"Step 1/3: Fetching BTC OHLCV...")
+    OHLCVFetcher().store_df(OHLCVFetcher().fetch())
+
+    # Step 2: Backfill breadth — fetch TOP50 daily data and compute per date
+    logger.info(f"Step 2/3: Backfilling breadth {start} → {end}...")
+    provider_url = config.provider_url
+    all_symbol_data = {}
+
+    for sym in config.top50_symbols:
+        try:
+            df = pd.DataFrame(requests.get(
+                f"{provider_url}/api/candles",
+                params={"symbol": sym, "tf": "1d", "limit": 400},
+                timeout=30
+            ).json())
+            if not df.empty and "timestamp" in df.columns:
+                df["date"] = pd.to_datetime(df["timestamp"], unit="ms").dt.date
+                df["close"] = df["close"].astype(float)
+                df["high"] = df["high"].astype(float)
+                df["ema20"] = df["close"].ewm(20).mean()
+                all_symbol_data[sym] = df
+        except Exception as e:
+            logger.debug(f"  Skip {sym}: {e}")
+
+    logger.info(f"  Fetched {len(all_symbol_data)}/{len(config.top50_symbols)} symbols")
+
+    # Compute breadth for each date
+    conn = get_connection()
+    current = start
+    breadth_count = 0
+    while current <= end:
+        target_str = str(current)
+        try:
+            advances_50 = declines_50 = above_ema20_50 = new_highs_50 = 0
+            advances_30 = advances_20 = above_ema20_30 = above_ema20_20 = 0
+            new_highs_30 = new_highs_20 = 0
+
+            for rank, (sym, df) in enumerate(all_symbol_data.items()):
+                rows = df[df["date"] == current]
+                if rows.empty:
+                    continue
+                row = rows.iloc[0]
+                prev_rows = df[df["date"] < current]
+                if prev_rows.empty:
+                    continue
+                prev = prev_rows.iloc[-1]
+
+                if row["close"] > prev["close"]:
+                    if rank < 50: advances_50 += 1
+                    if rank < 30: advances_30 += 1
+                    if rank < 20: advances_20 += 1
+                elif row["close"] < prev["close"]:
+                    if rank < 50: declines_50 += 1
+
+                if not pd.isna(row.get("ema20")) and row["close"] > row["ema20"]:
+                    if rank < 50: above_ema20_50 += 1
+                    if rank < 30: above_ema20_30 += 1
+                    if rank < 20: above_ema20_20 += 1
+
+                recent_highs = df[(df["date"] < current) & (df["date"] >= current - timedelta(days=20))]
+                if not recent_highs.empty and row["high"] > recent_highs["high"].max():
+                    if rank < 50: new_highs_50 += 1
+                    if rank < 30: new_highs_30 += 1
+                    if rank < 20: new_highs_20 += 1
+
+            conn.execute("""INSERT OR REPLACE INTO breadth_daily
+                (date, total_tracked, advance_top50, decline_top50, above_ema20_top50,
+                 new_highs_20d_top50, advance_top30, advance_top20,
+                 above_ema20_top30, above_ema20_top20, new_highs_20d_top30, new_highs_20d_top20)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
+                (target_str, len(all_symbol_data),
+                 advances_50, declines_50, above_ema20_50, new_highs_50,
+                 advances_30, advances_20, above_ema20_30, above_ema20_20,
+                 new_highs_30, new_highs_20))
+            breadth_count += 1
+        except Exception as e:
+            logger.debug(f"  Breadth skip {current}: {e}")
+        current += timedelta(days=1)
+
+    conn.commit()
+    logger.info(f"  Breadth backfill: {breadth_count} days")
+
+    # Step 3: Compute regime scores for each date
+    logger.info(f"Step 3/3: Computing regime scores {start} → {end}...")
+    from scoring.price_structure import PriceStructureScorer
+    from scoring.breadth_scorer import BreadthScorer
+    from scoring.oi_matrix import OIMatrixScorer
+    from scoring.volatility_regime import VolatilityRegimeScorer
+    from regime_detector import RegimeDetector
+
+    detector = RegimeDetector()
+    current = start
+    score_count = 0
+    while current <= end:
+        try:
+            ps = PriceStructureScorer().compute(current)
+            br = BreadthScorer().compute(current)
+            if br.score == 50.0 and br.label == "No Data":
+                current += timedelta(days=1)
+                continue
+            oi = OIMatrixScorer().compute(current)
+            vol = VolatilityRegimeScorer().compute(current)
+            r = detector.detect(ps.score, br.breadth_top50, vol.vol_regime.value, current)
+
+            conn.execute("""INSERT OR REPLACE INTO regime_history
+                (date, regime, confidence, regime_version, maturity_score,
+                 all_scores_json, confirmation_days)
+                VALUES (?, ?, ?, ?, ?, ?, ?)""",
+                (str(current), r.regime.value, r.confidence, r.regime_version,
+                 r.maturity_score, json.dumps(r.all_scores), r.confirmation_days))
+            score_count += 1
+            if score_count % 30 == 0:
+                conn.commit()
+                logger.info(f"  Scored {score_count} days... ({current})")
+        except Exception as e:
+            logger.debug(f"  Score skip {current}: {e}")
+        current += timedelta(days=1)
+
+    conn.commit()
+    conn.close()
+    logger.info(f"Backfill complete: {breadth_count} breadth + {score_count} regime days")
+
+
+def cmd_expectancy(args):
+    """Query signal expectancy for current market state."""
+    from database import init_db
+    from expectancy.engine import BayesianExpectancyEngine
+
+    target = parse_date(args.date) if args.date else Date.today()
+    init_db()
+
+    state, _ = _build_market_state(target)
+
+    engine = BayesianExpectancyEngine()
+    signal = args.signal or "B3"
+    report = engine.estimate(state, signal_type=signal, target_date=target)
+
+    print(f"\n{'='*60}")
+    print(f"  {target}  Signal Expectancy: {signal}")
+    print(f"{'='*60}")
+    print(f"  Regime:      {state.regime.value} (conf={state.regime_confidence:.2f})")
+    print(f"  Breadth:     {state.breadth_bucket.value} (T50={state.breadth_top50:.0f})")
+    print(f"  OI State:    {state.oi_state.value}")
+    print(f"  Volatility:  {state.volatility_regime.value}")
+    print(f"{'='*60}")
+
+    for layer in report.layers:
+        print(f"  {layer.name:15s}  N={layer.samples:4d}  eff={layer.effective_samples:.0f}  "
+              f"raw={layer.raw_winrate or 0:.1%}  post={layer.posterior_winrate:.1%}  "
+              f"ret={layer.avg_return or 0:+.1f}%")
+
+    print(f"{'='*60}")
+    print(f"  Final:  {report.final_estimate:.1%}  "
+          f"(sufficiency={report.sufficiency.value}, source={report.source})")
+    if report.profit_factor:
+        print(f"  PF={report.profit_factor}  MAE={report.max_adverse_excursion}%")
+    print()
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="ChanMacro — Crypto Market Memory System"
+    )
+    sub = parser.add_subparsers(dest="command", help="Commands")
+
+    # fetch
+    p_fetch = sub.add_parser("fetch", help="Fetch raw data")
+    p_fetch.add_argument("--date", help="Target date (YYYY-MM-DD)")
+    p_fetch.add_argument("--module", choices=["ohlcv", "breadth", "derivatives", "all"])
+
+    # score
+    p_score = sub.add_parser("score", help="Compute scores and regime")
+    p_score.add_argument("--date", help="Target date (YYYY-MM-DD)")
+
+    # regime
+    p_regime = sub.add_parser("regime", help="Show regime history")
+    p_regime.add_argument("--days", type=int, default=30)
+
+    # track
+    p_track = sub.add_parser("track", help="Record a trading signal")
+    p_track.add_argument("--date", help="Signal date (YYYY-MM-DD)")
+    p_track.add_argument("--signal", required=True, help="Signal type (B1/B2/B3/S1/S2/S3)")
+    p_track.add_argument("--price", type=float, required=True, help="Entry price")
+    p_track.add_argument("--grade", choices=["A", "B", "C"], help="Signal quality grade")
+    p_track.add_argument("--strength", type=float, help="Signal strength 0-100")
+
+    # backfill
+    p_backfill = sub.add_parser("backfill", help="Backfill historical scores")
+    p_backfill.add_argument("--from", dest="from_date", required=True)
+    p_backfill.add_argument("--to", dest="to_date")
+
+    # expectancy
+    p_expectancy = sub.add_parser("expectancy", help="Query signal expectancy")
+    p_expectancy.add_argument("--date", help="Target date (YYYY-MM-DD)")
+    p_expectancy.add_argument("--signal", default="B3", help="Signal type")
+
+    # validate
+    p_validate = sub.add_parser("validate", help="Run validation framework")
+
+    # cron
+    p_cron = sub.add_parser("cron", help="Run scheduled fetch+score loop")
+    # detect (Chan BSP signals)
+    p_detect = sub.add_parser("detect", help="Detect Chan BSP signals and populate signal_features")
+    p_detect.add_argument("--from", dest="from_date", default="2024-01-01")
+    p_detect.add_argument("--to", dest="to_date")
+    # serve
+    p_serve = sub.add_parser("serve", help="Start web dashboard")
+
+    args = parser.parse_args()
+
+    if args.command == "fetch":
+        cmd_fetch(args)
+    elif args.command == "score":
+        cmd_score(args)
+    elif args.command == "regime":
+        cmd_regime(args)
+    elif args.command == "track":
+        cmd_track(args)
+    elif args.command == "backfill":
+        cmd_backfill(args)
+    elif args.command == "expectancy":
+        cmd_expectancy(args)
+    elif args.command == "validate":
+        from validation.reporter import ValidationReporter
+        report = ValidationReporter().run_all()
+        print(report)
+    elif args.command == "detect":
+        from chan_integration import ChanSignalDetector
+        start = args.from_date
+        end = args.to_date or Date.today().isoformat()
+        detector = ChanSignalDetector()
+        count = detector.populate_signal_features(start, end)
+        logger.info(f"写入 {count} 条信号记录")
+    elif args.command == "serve":
+        from scheduler import get_scheduler
+        get_scheduler().start()
+        logger.info("启动 Web Dashboard: http://127.0.0.1:8124")
+        from web.app import app
+        app.run(host="0.0.0.0", port=8124, debug=False)
+    elif args.command == "cron":
+        from scheduler import get_scheduler
+        logger.info("启动后台调度器 (Ctrl+C 停止)")
+        s = get_scheduler()
+        s.start()
+        try:
+            while True:
+                time.sleep(60)
+        except KeyboardInterrupt:
+            s.stop()
+            logger.info("调度器已停止")
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()

ChanMacro/config.json

@@ -0,0 +1,23 @@
+{
+    "provider_url": "https://provider.jackyu66.com",
+    "db_path": "data/macro.db",
+    "btc_symbol": "BTC/USDT:USDT",
+    "regime_version": "v1_price_breadth_vol",
+    "half_life_days": 180,
+    "sufficiency_min_effective": 30,
+    "sufficiency_low": 50,
+    "sufficiency_medium": 100,
+    "level_min_samples": 50,
+    "knn_max_distance": 0.35,
+    "knn_k": 200,
+    "oi_price_threshold_pct": 0.5,
+    "oi_oi_threshold_pct": 0.5,
+    "vol_low_threshold": 2.0,
+    "vol_high_threshold": 5.0,
+    "vol_explosive_threshold": 10.0,
+    "regime_w_price": 0.35,
+    "regime_w_breadth": 0.50,
+    "regime_w_vol": 0.15,
+    "trend_w_price": 0.30,
+    "trend_w_breadth": 0.70
+}

ChanMacro/config.py

@@ -0,0 +1,113 @@
+"""
+config.py — Global configuration for ChanMacro.
+
+All weights, thresholds, and paths are configurable.
+V1 weights are deliberately simple; they will be tuned via Phase 0 validation.
+"""
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Optional
+
+
+@dataclass
+class Config:
+    """Global configuration. Override via config.json or env vars."""
+
+    # ── Paths ──────────────────────────────────────────────
+    db_path: str = "data/macro.db"
+    data_dir: str = "data"
+
+    # ── Data Provider ──────────────────────────────────────
+    provider_url: str = "https://provider.jackyu66.com"
+    btc_symbol: str = "BTC/USDT:USDT"
+    top50_symbols: list[str] = field(default_factory=lambda: [
+        "BTC/USDT:USDT", "ETH/USDT:USDT", "SOL/USDT:USDT",
+        "BNB/USDT:USDT", "XRP/USDT:USDT", "DOGE/USDT:USDT",
+        "SUI/USDT:USDT", "TON/USDT:USDT", "ZEC/USDT:USDT",
+        "1000PEPE/USDT:USDT", "SAGA/USDT:USDT",
+        "XAU/USDT:USDT", "XAG/USDT:USDT",
+        "CL/USDT:USDT", "BILL/USDT:USDT", "BZ/USDT:USDT",
+        "LAB/USDT:USDT", "CRCL/USDT:USDT", "SNDK/USDT:USDT",
+        "CHIP/USDT:USDT",
+    ])
+
+    # ── Breadth ────────────────────────────────────────────
+    breadth_top_n: list[int] = field(default_factory=lambda: [20, 30, 50])
+    breadth_ema_period: int = 20
+    breadth_new_high_window: int = 20
+
+    # ── Regime (factor-locked: Price + Breadth + Vol) ─────
+    regime_version: str = "v1_price_breadth_vol"
+    # Weights for trend_score within regime detection
+    regime_w_price: float = 0.35
+    regime_w_breadth: float = 0.50
+    regime_w_vol: float = 0.15
+    # Weights for panic_score
+    regime_panic_w_anti_trend: float = 0.60
+    regime_panic_w_vol_extreme: float = 0.40
+
+    # ── Price Structure ────────────────────────────────────
+    ps_ema_fast: int = 20
+    ps_ema_mid: int = 60
+    ps_ema_slow: int = 120
+    ps_adx_period: int = 14
+    ps_adx_threshold: int = 25
+    ps_atr_period: int = 14
+    ps_bb_period: int = 20
+    ps_roc_periods: list[int] = field(default_factory=lambda: [5, 10, 20])
+
+    # ── OI Matrix ──────────────────────────────────────────
+    oi_price_threshold_pct: float = 0.5   # min price change% to classify
+    oi_oi_threshold_pct: float = 0.5      # min OI change% to classify
+
+    # ── Volatility Regime ──────────────────────────────────
+    vol_atr_period: int = 14
+    vol_hv_short: int = 20
+    vol_hv_long: int = 60
+    # Thresholds (ATR/Close %)
+    vol_low_threshold: float = 2.0
+    vol_high_threshold: float = 5.0
+    vol_explosive_threshold: float = 10.0
+
+    # ── Trend (L2 aggregation) ─────────────────────────────
+    trend_w_price: float = 0.30
+    trend_w_breadth: float = 0.70
+
+    # ── Maturity Score ─────────────────────────────────────
+    maturity_w_trend: float = 0.50
+    maturity_w_breadth: float = 0.30
+    maturity_w_vol: float = 0.20
+
+    # ── Expectancy ─────────────────────────────────────────
+    half_life_days: int = 180
+    sufficiency_min_effective: int = 30
+    sufficiency_low: int = 50
+    sufficiency_medium: int = 100
+    level_min_samples: int = 50
+    knn_max_distance: float = 0.35
+    knn_k: int = 200
+
+    # ── Validation ─────────────────────────────────────────
+    min_history_days: int = 365
+    regime_min_avg_duration: int = 5
+    regime_max_flip_rate: float = 0.15
+
+    @classmethod
+    def from_json(cls, path: str = "config.json") -> "Config":
+        """Load config from JSON file, overriding defaults."""
+        import json
+        config = cls()
+        try:
+            with open(path) as f:
+                data = json.load(f)
+            for key, value in data.items():
+                if hasattr(config, key):
+                    setattr(config, key, value)
+        except FileNotFoundError:
+            pass
+        return config
+
+
+# Global singleton
+config = Config()

ChanMacro/database.py

@@ -0,0 +1,224 @@
+"""
+database.py — SQLite schema initialization and connection management.
+"""
+
+import sqlite3
+import os
+from pathlib import Path
+
+SCHEMA = """
+-- ═══════════════════════════════════════════════
+-- L0: Raw data tables
+-- ═══════════════════════════════════════════════
+
+CREATE TABLE IF NOT EXISTS ohlcv_daily (
+    date TEXT NOT NULL,
+    symbol TEXT NOT NULL DEFAULT 'BTC/USDT:USDT',
+    open REAL,
+    high REAL,
+    low REAL,
+    close REAL,
+    volume REAL,
+    ema20 REAL,
+    ema60 REAL,
+    ema120 REAL,
+    atr_14 REAL,
+    bb_width REAL,
+    adx_14 REAL,
+    PRIMARY KEY (date, symbol)
+);
+
+CREATE TABLE IF NOT EXISTS breadth_daily (
+    date TEXT PRIMARY KEY,
+    total_tracked INTEGER DEFAULT 50,
+    advance_top50 INTEGER DEFAULT 0,
+    decline_top50 INTEGER DEFAULT 0,
+    above_ema20_top50 INTEGER DEFAULT 0,
+    new_highs_20d_top50 INTEGER DEFAULT 0,
+    btc_dominance REAL,
+    advance_top20 INTEGER DEFAULT 0,
+    advance_top30 INTEGER DEFAULT 0,
+    above_ema20_top20 INTEGER DEFAULT 0,
+    above_ema20_top30 INTEGER DEFAULT 0,
+    new_highs_20d_top20 INTEGER DEFAULT 0,
+    new_highs_20d_top30 INTEGER DEFAULT 0,
+    fetched_at TEXT DEFAULT (datetime('now'))
+);
+
+CREATE TABLE IF NOT EXISTS derivatives (
+    date TEXT NOT NULL,
+    symbol TEXT NOT NULL DEFAULT 'BTC/USDT:USDT',
+    funding_rate REAL,
+    open_interest REAL,
+    oi_24h_change_pct REAL,
+    long_liquidations REAL,
+    short_liquidations REAL,
+    basis_annualised_pct REAL,
+    source TEXT DEFAULT 'binance',
+    fetched_at TEXT DEFAULT (datetime('now')),
+    PRIMARY KEY (date, symbol)
+);
+
+CREATE TABLE IF NOT EXISTS etf_flow (
+    date TEXT NOT NULL,
+    product TEXT NOT NULL,
+    net_flow_million REAL NOT NULL,
+    price REAL,
+    source TEXT DEFAULT 'farside',
+    fetched_at TEXT DEFAULT (datetime('now')),
+    PRIMARY KEY (date, product)
+);
+
+CREATE TABLE IF NOT EXISTS stablecoin_supply (
+    date TEXT NOT NULL,
+    token TEXT NOT NULL,
+    chain TEXT NOT NULL DEFAULT 'all',
+    supply REAL NOT NULL,
+    source TEXT DEFAULT 'defillama',
+    fetched_at TEXT DEFAULT (datetime('now')),
+    PRIMARY KEY (date, token, chain)
+);
+
+-- ═══════════════════════════════════════════════
+-- L3: Regime history
+-- ═══════════════════════════════════════════════
+
+CREATE TABLE IF NOT EXISTS regime_history (
+    date TEXT PRIMARY KEY,
+    regime TEXT NOT NULL,
+    confidence REAL,
+    regime_version TEXT NOT NULL DEFAULT 'v1_price_breadth_vol',
+    maturity_score REAL DEFAULT 50.0,
+    all_scores_json TEXT DEFAULT '{}',
+    prior_regime TEXT,
+    confirmation_days INTEGER DEFAULT 1,
+    created_at TEXT DEFAULT (datetime('now'))
+);
+
+-- ═══════════════════════════════════════════════
+-- ★ signal_features — THE moat
+-- ═══════════════════════════════════════════════
+
+CREATE TABLE IF NOT EXISTS signal_features (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    date TEXT NOT NULL,
+    signal_type TEXT NOT NULL,
+    signal_version TEXT NOT NULL DEFAULT 'b3_v1',
+    symbol TEXT DEFAULT 'BTC/USDT:USDT',
+
+    -- ★★ Version control (most important fields)
+    regime_version TEXT NOT NULL DEFAULT 'v1_price_breadth_vol',
+    signal_grade TEXT,
+    signal_strength REAL,
+
+    -- Market State Vector snapshot
+    regime TEXT NOT NULL,
+    regime_confidence REAL,
+    regime_maturity_score REAL DEFAULT 50.0,
+    market_state_hash TEXT,
+    state_embedding TEXT DEFAULT '[]',
+    breadth_top20 REAL,
+    breadth_top30 REAL,
+    breadth_top50 REAL,
+    breadth_bucket TEXT,
+    breadth_divergence REAL,
+    oi_state TEXT,
+    volatility_regime TEXT,
+    price_structure_score REAL,
+
+    -- Chan context (V5+)
+    chan_trend_direction TEXT,
+    chan_pivot_count INTEGER,
+    chan_divergence_type TEXT,
+
+    -- Outcomes
+    entry_price REAL,
+    result_1d REAL,
+    result_3d REAL,
+    result_5d REAL,
+    result_7d REAL,
+    result_14d REAL,
+    max_favorable_excursion REAL,
+    max_adverse_excursion REAL,
+    is_win_7d INTEGER,
+
+    created_at TEXT DEFAULT (datetime('now'))
+);
+
+CREATE INDEX IF NOT EXISTS idx_sf_regime ON signal_features(regime);
+CREATE INDEX IF NOT EXISTS idx_sf_signal ON signal_features(signal_type);
+CREATE INDEX IF NOT EXISTS idx_sf_oi_state ON signal_features(oi_state);
+CREATE INDEX IF NOT EXISTS idx_sf_date ON signal_features(date);
+CREATE INDEX IF NOT EXISTS idx_sf_state_hash ON signal_features(market_state_hash);
+CREATE INDEX IF NOT EXISTS idx_sf_regime_version ON signal_features(regime_version);
+CREATE INDEX IF NOT EXISTS idx_sf_signal_version ON signal_features(signal_version);
+
+-- ═══════════════════════════════════════════════
+-- Expectancy cache (raw counts, NOT posteriors)
+-- ═══════════════════════════════════════════════
+
+CREATE TABLE IF NOT EXISTS expectancy_cache (
+    state_hash TEXT NOT NULL,
+    signal_type TEXT NOT NULL,
+    wins_weighted REAL DEFAULT 0,
+    losses_weighted REAL DEFAULT 0,
+    sum_return_7d REAL DEFAULT 0,
+    sum_return_sq_7d REAL DEFAULT 0,
+    effective_samples REAL DEFAULT 0,
+    sufficiency TEXT DEFAULT 'INSUFFICIENT',
+    updated_at TEXT DEFAULT (datetime('now')),
+    PRIMARY KEY (state_hash, signal_type)
+);
+
+-- ═══════════════════════════════════════════════
+-- Similarity outcome (KNN weight learning, Phase D)
+-- ═══════════════════════════════════════════════
+
+CREATE TABLE IF NOT EXISTS similarity_outcome (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    state_a_hash TEXT,
+    state_b_hash TEXT,
+    distance REAL,
+    actual_return_gap REAL,
+    dimension_weights_json TEXT DEFAULT '{}',
+    created_at TEXT DEFAULT (datetime('now'))
+);
+
+-- ═══════════════════════════════════════════════
+-- chan_context — Chan theory integration (V1 empty)
+-- ═══════════════════════════════════════════════
+
+CREATE TABLE IF NOT EXISTS chan_context (
+    date TEXT NOT NULL,
+    timeframe TEXT NOT NULL DEFAULT '1d',
+    trend_direction TEXT,
+    trend_strength REAL,
+    pivot_count INTEGER,
+    pivot_level TEXT,
+    signal_type TEXT,
+    signal_strength REAL,
+    divergence_type TEXT,
+    chan_structure_score REAL,
+    alignment_score REAL,
+    raw_context_json TEXT DEFAULT '{}',
+    PRIMARY KEY (date, timeframe)
+);
+"""
+
+
+def init_db(db_path: str = "data/macro.db") -> sqlite3.Connection:
+    """Initialize database: create directory and all tables."""
+    Path(db_path).parent.mkdir(parents=True, exist_ok=True)
+    conn = sqlite3.connect(db_path)
+    conn.executescript(SCHEMA)
+    conn.commit()
+    return conn
+
+
+def get_connection(db_path: str = "data/macro.db") -> sqlite3.Connection:
+    """Get a database connection. Creates tables if first run."""
+    if not os.path.exists(db_path):
+        return init_db(db_path)
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+    return conn

ChanMacro/expectancy/__init__.py

@@ -0,0 +1,4 @@
+"""Expectancy Engine — Signal tracking, Bayesian inference, time decay."""
+from .tracker import SignalTracker
+from .decay import TimeDecay
+from .engine import BayesianExpectancyEngine, SufficiencyGuard

ChanMacro/expectancy/decay.py

@@ -0,0 +1,55 @@
+"""
+expectancy/decay.py — Time-weighted sample decay.
+
+2024 market structure ≠ 2026 market structure.
+Recent samples get higher weight via exponential decay.
+"""
+
+from datetime import date as Date
+from typing import Optional
+import numpy as np
+
+
+class TimeDecay:
+    """Exponential time decay for sample weighting."""
+
+    def __init__(self, half_life_days: int = 180):
+        self.half_life = half_life_days
+        self._decay_rate = np.log(2) / half_life_days
+
+    def weight(self, sample_date: Date, reference_date: Optional[Date] = None) -> float:
+        """
+        Compute decay weight for a sample.
+        weight = exp(-days_ago * decay_rate)
+        """
+        if reference_date is None:
+            reference_date = Date.today()
+        days = (reference_date - sample_date).days
+        return np.exp(-days * self._decay_rate)
+
+    def weights(self, dates: list[Date], reference_date: Optional[Date] = None) -> np.ndarray:
+        """Compute decay weights for a list of dates."""
+        return np.array([self.weight(d, reference_date) for d in dates])
+
+    def weighted_win_rate(self, wins: np.ndarray, weights: np.ndarray) -> float:
+        """Weighted win rate: sum(wins * weights) / sum(weights)."""
+        total_weight = weights.sum()
+        if total_weight == 0:
+            return 0.0
+        return float((wins * weights).sum() / total_weight)
+
+    def weighted_mean(self, values: np.ndarray, weights: np.ndarray) -> float:
+        """Weighted mean."""
+        total_weight = weights.sum()
+        if total_weight == 0:
+            return 0.0
+        return float((values * weights).sum() / total_weight)
+
+    def effective_samples(self, weights: np.ndarray) -> float:
+        """Effective number of samples after decay weighting."""
+        return float(weights.sum())
+
+    @staticmethod
+    def weight_at_age(days_ago: int, half_life_days: int = 180) -> float:
+        """Quick weight lookup for a given age in days."""
+        return np.exp(-days_ago * np.log(2) / half_life_days)

ChanMacro/expectancy/engine.py

@@ -0,0 +1,295 @@
+"""
+expectancy/engine.py — Bayesian Expectancy Engine.
+
+Core algorithm:
+  1. LeveledExpectancy: filter layer-by-layer, stop at highest valid level
+  2. Empirical Bayes prior: prior = signal's global historical winrate
+  3. Dynamic Beta strength: adaptive to sample size
+  4. Time decay: recent samples weighted higher (half_life=180d)
+  5. SufficiencyGuard: refuse output if effective_samples < 30
+  6. KNN Fallback: similarity search when strict filtering fails (Phase D)
+"""
+
+from datetime import date as Date
+from typing import Optional
+import sqlite3
+import logging
+
+import numpy as np
+import pandas as pd
+
+from models import (
+    MarketStateVector, ExpectancyReport, ExpectancyLayer,
+    SufficiencyLevel, MarketRegime,
+)
+from config import config
+from .decay import TimeDecay
+
+logger = logging.getLogger(__name__)
+
+
+class SufficiencyGuard:
+    """Prevents trading advice from insufficient samples."""
+
+    def __init__(self, min_effective: int = 30, low: int = 50, medium: int = 100):
+        self.MIN = min_effective
+        self.LOW = low
+        self.MEDIUM = medium
+
+    def evaluate(self, effective_samples: float) -> SufficiencyLevel:
+        if effective_samples < self.MIN:
+            return SufficiencyLevel.INSUFFICIENT
+        elif effective_samples < self.LOW:
+            return SufficiencyLevel.LOW
+        elif effective_samples < self.MEDIUM:
+            return SufficiencyLevel.MEDIUM
+        return SufficiencyLevel.HIGH
+
+
+class BayesianExpectancyEngine:
+    """
+    Leveled Bayesian Expectancy Engine.
+
+    Query layers from coarse to fine. Stop when effective_samples drops below threshold.
+    Uses Empirical Bayes prior (signal's global winrate, not fixed 50%).
+    """
+
+    # Expectancy query levels: name → WHERE clause template
+    LEVELS = [
+        ("Base",            "signal_type = '{signal}'"),
+        ("+ Regime",        "signal_type = '{signal}' AND regime = '{regime}'"),
+        ("+ Breadth",       "signal_type = '{signal}' AND regime = '{regime}' AND breadth_bucket = '{breadth}'"),
+        ("+ OI State",      "signal_type = '{signal}' AND regime = '{regime}' AND breadth_bucket = '{breadth}' AND oi_state = '{oi}'"),
+        ("+ Volatility",    "signal_type = '{signal}' AND regime = '{regime}' AND breadth_bucket = '{breadth}' AND oi_state = '{oi}' AND volatility_regime = '{vol}'"),
+    ]
+
+    def __init__(self, db_path: Optional[str] = None,
+                 half_life_days: int = 180,
+                 level_min_samples: int = 50):
+        self.db_path = db_path or config.db_path
+        self.decay = TimeDecay(half_life_days)
+        self.guard = SufficiencyGuard(
+            min_effective=config.sufficiency_min_effective,
+            low=config.sufficiency_low,
+            medium=config.sufficiency_medium,
+        )
+        self.level_min = level_min_samples
+
+    def estimate(self, state: MarketStateVector,
+                 signal_type: str = "B3",
+                 target_date: Optional[Date] = None) -> ExpectancyReport:
+        """
+        Compute layered Bayesian expectancy for a signal in current market state.
+
+        Returns the estimate at the deepest level with >= level_min effective samples.
+        """
+        if target_date is None:
+            target_date = Date.today()
+
+        conn = sqlite3.connect(self.db_path)
+
+        # Get global signal winrate for Empirical Bayes prior
+        global_rate = self._global_winrate(conn, signal_type)
+
+        layers = []
+        best_result = None
+
+        for level_name, template in self.LEVELS:
+            where = template.format(
+                signal=signal_type,
+                regime=state.regime.value,
+                breadth=state.breadth_bucket.value,
+                oi=state.oi_state.value,
+                vol=state.volatility_regime.value,
+            )
+            query = f"SELECT * FROM signal_features WHERE {where}"
+            df = pd.read_sql_query(query, conn)
+
+            if df.empty:
+                layers.append(ExpectancyLayer(
+                    name=level_name, posterior_winrate=0.0,
+                    samples=0, effective_samples=0.0,
+                ))
+                continue
+
+            # Time-weighted stats
+            dates_list = [Date.fromisoformat(d) for d in df["date"]]
+            weights = self.decay.weights(dates_list, target_date)
+            eff_n = self.decay.effective_samples(weights)
+
+            wins = pd.to_numeric(df["is_win_7d"].fillna(0), errors="coerce").fillna(0).values
+            returns = pd.to_numeric(df["result_7d"].fillna(0), errors="coerce").fillna(0).values
+
+            raw_wr = float(wins.mean()) if len(wins) > 0 else 0.0
+            weighted_wr = self.decay.weighted_win_rate(wins, weights)
+            weighted_ret = self.decay.weighted_mean(returns, weights)
+
+            # Empirical Bayes posterior
+            posterior = self._bayesian_posterior(
+                global_rate=global_rate,
+                wins=wins.sum(),
+                samples=len(df),
+            )
+
+            layer = ExpectancyLayer(
+                name=level_name,
+                posterior_winrate=round(posterior, 4),
+                raw_winrate=round(raw_wr, 4),
+                samples=len(df),
+                effective_samples=round(eff_n, 1),
+                avg_return=round(weighted_ret, 2),
+            )
+            layers.append(layer)
+
+            # Level-based fallback: keep going while samples sufficient
+            if eff_n >= self.level_min:
+                best_result = layer
+
+        conn.close()
+
+        if best_result is None and layers:
+            # Fallback to the deepest layer that had any samples
+            for layer in reversed(layers):
+                if layer.samples > 0:
+                    best_result = layer
+                    break
+
+        if best_result is None:
+            return ExpectancyReport(
+                signal_type=signal_type,
+                date=target_date,
+                layers=layers,
+                final_estimate=0.0,
+                sufficiency=SufficiencyLevel.INSUFFICIENT,
+                source="insufficient",
+            )
+
+        sufficiency = self.guard.evaluate(
+            best_result.effective_samples
+        )
+
+        # Compute profit factor and MAE from the SAME level as best_result
+        profit_factor = None
+        avg_mae = None
+        if best_result and best_result.samples > 0:
+            # Re-query the level that produced best_result
+            best_level_idx = next(
+                i for i, l in enumerate(layers) if l.name == best_result.name
+            )
+            where = self.LEVELS[best_level_idx][1].format(
+                signal=signal_type, regime=state.regime.value,
+                breadth=state.breadth_bucket.value, oi=state.oi_state.value,
+                vol=state.volatility_regime.value,
+            )
+            query = f"SELECT result_7d, max_adverse_excursion FROM signal_features WHERE {where}"
+            conn2 = sqlite3.connect(self.db_path)
+            df_detail = pd.read_sql_query(query, conn2)
+            conn2.close()
+            if not df_detail.empty:
+                returns_7d = df_detail["result_7d"].dropna()
+                if len(returns_7d) > 0:
+                    gains = returns_7d[returns_7d > 0].sum()
+                    losses = abs(returns_7d[returns_7d < 0].sum())
+                    profit_factor = round(gains / losses, 2) if losses > 0 else None
+                maes = df_detail["max_adverse_excursion"].dropna()
+                if len(maes) > 0:
+                    avg_mae = round(float(maes.mean()), 2)
+
+        return ExpectancyReport(
+            signal_type=signal_type,
+            date=target_date,
+            layers=layers,
+            final_estimate=round(best_result.posterior_winrate, 4),
+            sufficiency=sufficiency,
+            prior_strength=self._prior_strength(best_result.samples),
+            half_life_days=self.decay.half_life,
+            source="bayesian",
+            avg_return_7d=best_result.avg_return,
+            profit_factor=profit_factor,
+            max_adverse_excursion=avg_mae,
+        )
+
+    def _global_winrate(self, conn: sqlite3.Connection,
+                        signal_type: str) -> float:
+        """Get global historical winrate for a signal type (Empirical Bayes prior)."""
+        row = conn.execute(
+            "SELECT AVG(is_win_7d) as wr, COUNT(*) as cnt "
+            "FROM signal_features WHERE signal_type = ? AND is_win_7d IS NOT NULL",
+            (signal_type,)
+        ).fetchone()
+        if row and row[1] and row[1] > 0:
+            return float(row[0])
+        return 0.50  # default: neutral
+
+    def _prior_strength(self, samples: int) -> int:
+        """Dynamic prior strength based on sample count."""
+        if samples < 100:
+            return 20  # Beta(10,10)
+        elif samples < 500:
+            return 40  # Beta(20,20)
+        else:
+            return 100  # Beta(50,50) — data dominates
+
+    def _bayesian_posterior(self, global_rate: float, wins: float,
+                            samples: int) -> float:
+        """
+        Empirical Bayes posterior: prior = global signal winrate.
+
+        posterior = (alpha + wins) / (alpha + beta + samples)
+        where alpha/(alpha+beta) = global_rate
+        """
+        prior_strength = self._prior_strength(samples)
+        alpha = max(global_rate * prior_strength, 1.0)  # floor at 1 to ensure shrinkage
+        beta = max((1 - global_rate) * prior_strength, 1.0)
+        return (alpha + wins) / (alpha + beta + samples)
+
+    def precompute_cache(self):
+        """
+        Precompute expectancy for all state_hashes in signal_features.
+        Populates expectancy_cache table with raw weighted counts (not posteriors).
+        """
+        conn = sqlite3.connect(self.db_path)
+        conn.row_factory = sqlite3.Row
+
+        hashes = conn.execute(
+            "SELECT DISTINCT market_state_hash, signal_type FROM signal_features"
+        ).fetchall()
+
+        today = Date.today()
+        count = 0
+
+        for row in hashes:
+            h = row["market_state_hash"]
+            sig = row["signal_type"]
+
+            df = pd.read_sql_query(
+                "SELECT date, is_win_7d, result_7d "
+                "FROM signal_features WHERE market_state_hash = ? AND signal_type = ?",
+                conn, params=(h, sig)
+            )
+
+            if df.empty:
+                continue
+
+            dates_list = [Date.fromisoformat(d) for d in df["date"]]
+            weights = self.decay.weights(dates_list, today)
+            wins_w = (df["is_win_7d"].fillna(0).values * weights).sum()
+            losses_w = ((1 - df["is_win_7d"].fillna(0)).values * weights).sum()
+            ret_sum = (df["result_7d"].fillna(0).values * weights).sum()
+            ret_sq = ((df["result_7d"].fillna(0).values ** 2) * weights).sum()
+            eff_n = weights.sum()
+
+            sufficiency = self.guard.evaluate(eff_n).value
+
+            conn.execute("""
+                INSERT OR REPLACE INTO expectancy_cache
+                (state_hash, signal_type, wins_weighted, losses_weighted,
+                 sum_return_7d, sum_return_sq_7d, effective_samples, sufficiency)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+            """, (h, sig, wins_w, losses_w, ret_sum, ret_sq, eff_n, sufficiency))
+            count += 1
+
+        conn.commit()
+        conn.close()
+        logger.info(f"Precomputed expectancy cache: {count} state×signal combos")
+        return count

ChanMacro/expectancy/tracker.py

@@ -0,0 +1,271 @@
+"""
+expectancy/tracker.py — SignalTracker: records signals with full market state
+and computes forward outcomes.
+
+This is the entry point for populating signal_features — THE moat table.
+"""
+
+from datetime import date as Date, timedelta
+from typing import Optional
+import sqlite3
+import json
+import logging
+
+import pandas as pd
+import numpy as np
+
+from models import (
+    MarketStateVector, SignalFeatureRecord, MarketRegime,
+    OIState, BreadthBucket, VolRegime, SignalGrade,
+)
+from config import config
+
+logger = logging.getLogger(__name__)
+
+
+class SignalTracker:
+    """
+    Records trading signals with full market state context.
+
+    Usage:
+        tracker = SignalTracker()
+        tracker.record(
+            date=Date(2026, 6, 24),
+            signal_type="B3",
+            entry_price=96500.0,
+            state=market_state_vector,  # from scoring pipeline
+            signal_grade="A",
+        )
+    """
+
+    def __init__(self, db_path: Optional[str] = None):
+        self.db_path = db_path or config.db_path
+
+    def record(self, date: Date, signal_type: str, entry_price: float,
+               state: MarketStateVector,
+               signal_version: str = "b3_v1",
+               signal_grade: Optional[str] = None,
+               signal_strength: Optional[float] = None) -> int:
+        """
+        Record a signal with market state snapshot and compute forward outcomes.
+
+        Returns the record ID in signal_features.
+        """
+        conn = sqlite3.connect(self.db_path)
+
+        # Compute forward outcomes
+        outcomes = self._compute_outcomes(conn, date, entry_price)
+
+        # Build embedding
+        embedding = json.dumps(state.state_embedding())
+
+        record_id = conn.execute("""
+            INSERT INTO signal_features
+            (date, signal_type, signal_version, symbol,
+             regime_version, signal_grade, signal_strength,
+             regime, regime_confidence, regime_maturity_score,
+             market_state_hash, state_embedding,
+             breadth_top20, breadth_top30, breadth_top50,
+             breadth_bucket, breadth_divergence,
+             oi_state, volatility_regime, price_structure_score,
+             entry_price,
+             result_1d, result_3d, result_5d, result_7d, result_14d,
+             max_favorable_excursion, max_adverse_excursion,
+             is_win_7d)
+            VALUES (?, ?, ?, ?, ?, ?, ?,
+                    ?, ?, ?,
+                    ?, ?,
+                    ?, ?, ?,
+                    ?, ?,
+                    ?, ?, ?,
+                    ?,
+                    ?, ?, ?, ?, ?,
+                    ?, ?,
+                    ?)
+        """, (
+            str(date), signal_type, signal_version, state.symbol,
+            state.regime_version, signal_grade, signal_strength,
+            state.regime.value, state.regime_confidence, state.regime_maturity_score,
+            state.market_state_hash, embedding,
+            state.breadth_top20, state.breadth_top30, state.breadth_top50,
+            state.breadth_bucket.value, state.breadth_divergence,
+            state.oi_state.value, state.volatility_regime.value,
+            state.price_structure_score.score,
+            entry_price,
+            outcomes.get("result_1d"), outcomes.get("result_3d"),
+            outcomes.get("result_5d"), outcomes.get("result_7d"),
+            outcomes.get("result_14d"),
+            outcomes.get("mfe"), outcomes.get("mae"),
+            outcomes.get("is_win_7d"),
+        )).lastrowid
+
+        conn.commit()
+        conn.close()
+
+        is_win = outcomes.get("is_win_7d", 0)
+        ret_7d = outcomes.get("result_7d", 0) or 0
+        logger.info(
+            f"Recorded {signal_type} on {date} @ {entry_price:.0f} "
+            f"(regime={state.regime.value}, breadth={state.breadth_bucket.value}, "
+            f"oi={state.oi_state.value}) → 7d={ret_7d:+.1f}%"
+        )
+        return record_id
+
+    def _compute_outcomes(self, conn: sqlite3.Connection, date: Date,
+                          entry_price: float) -> dict:
+        """
+        Compute forward returns, MFE, MAE from OHLCV data.
+
+        Queries future daily bars relative to the signal date.
+        """
+        # Get future OHLCV data
+        df = pd.read_sql_query(
+            "SELECT date, high, low, close FROM ohlcv_daily "
+            "WHERE date > ? AND symbol = 'BTC/USDT:USDT' "
+            "ORDER BY date ASC LIMIT 20",
+            conn, params=(str(date),)
+        )
+
+        if df.empty:
+            return {}
+
+        outcomes = {}
+        entry = entry_price
+
+        # Forward returns
+        for horizon_days, col in [(1, "result_1d"), (3, "result_3d"),
+                                   (5, "result_5d"), (7, "result_7d"),
+                                   (14, "result_14d")]:
+            if len(df) >= horizon_days:
+                exit_price = float(df.iloc[horizon_days - 1]["close"])
+                outcomes[col] = round((exit_price - entry) / entry * 100, 2)
+
+        # MFE / MAE
+        if len(df) > 0:
+            highs = df["high"].astype(float).values[:14]
+            lows = df["low"].astype(float).values[:14]
+            outcomes["mfe"] = round((max(highs) - entry) / entry * 100, 2)
+            outcomes["mae"] = round((min(lows) - entry) / entry * 100, 2)
+
+        # is_win_7d
+        outcomes["is_win_7d"] = 1 if outcomes.get("result_7d", 0) > 0 else 0
+
+        return outcomes
+
+    def backfill_signals(self, signals: list[dict]) -> int:
+        """
+        Backfill multiple signals from historical data.
+
+        Each signal dict:
+            {"date": Date, "signal_type": str, "entry_price": float,
+             "signal_grade": str (optional), "signal_strength": float (optional)}
+
+        This requires the scoring pipeline to have been run for those dates
+        (breadth_daily, ohlcv_daily, derivatives all populated).
+        """
+        from scoring.price_structure import PriceStructureScorer
+        from scoring.breadth_scorer import BreadthScorer
+        from scoring.oi_matrix import OIMatrixScorer
+        from scoring.volatility_regime import VolatilityRegimeScorer
+        from regime_detector import RegimeDetector
+
+        detector = RegimeDetector()
+        count = 0
+
+        for sig in signals:
+            target = sig["date"]
+            try:
+                # Compute market state for this date
+                ps = PriceStructureScorer(self.db_path).compute(target)
+                br = BreadthScorer(self.db_path).compute(target)
+                oi = OIMatrixScorer(self.db_path).compute(target)
+                vol = VolatilityRegimeScorer(self.db_path).compute(target)
+
+                regime_result = detector.detect(
+                    price_structure_score=ps.score,
+                    breadth_score=br.breadth_top50,
+                    volatility_regime=vol.vol_regime.value,
+                    date=target,
+                )
+
+                state = MarketStateVector(
+                    date=target,
+                    regime=regime_result.regime,
+                    regime_confidence=regime_result.confidence,
+                    regime_version=regime_result.regime_version,
+                    regime_maturity_score=regime_result.maturity_score,
+                    breadth_top20=br.breadth_top20,
+                    breadth_top30=br.breadth_top30,
+                    breadth_top50=br.breadth_top50,
+                    breadth_bucket=br.breadth_bucket,
+                    breadth_divergence=br.breadth_divergence,
+                    oi_state=oi.oi_state,
+                    volatility_regime=vol.vol_regime,
+                    price_structure_score=ps,
+                    breadth_score=br,
+                    oi_matrix_score=oi,
+                    volatility_regime_score=vol,
+                )
+                state.market_state_hash = state.compute_hash()
+
+                self.record(
+                    date=target,
+                    signal_type=sig["signal_type"],
+                    entry_price=sig["entry_price"],
+                    state=state,
+                    signal_grade=sig.get("signal_grade"),
+                    signal_strength=sig.get("signal_strength"),
+                )
+                count += 1
+            except Exception as e:
+                logger.warning(f"Failed to backfill {sig['signal_type']} on {target}: {e}")
+
+        return count
+
+    def get_samples(self, signal_type: Optional[str] = None,
+                    regime: Optional[str] = None,
+                    breadth_bucket: Optional[str] = None,
+                    oi_state: Optional[str] = None,
+                    volatility_regime: Optional[str] = None,
+                    limit: int = 5000) -> list[dict]:
+        """Query signal_features with optional filters."""
+        conn = sqlite3.connect(self.db_path)
+        conn.row_factory = sqlite3.Row
+
+        query = "SELECT * FROM signal_features WHERE 1=1"
+        params = []
+
+        if signal_type:
+            query += " AND signal_type = ?"
+            params.append(signal_type)
+        if regime:
+            query += " AND regime = ?"
+            params.append(regime)
+        if breadth_bucket:
+            query += " AND breadth_bucket = ?"
+            params.append(breadth_bucket)
+        if oi_state:
+            query += " AND oi_state = ?"
+            params.append(oi_state)
+        if volatility_regime:
+            query += " AND volatility_regime = ?"
+            params.append(volatility_regime)
+
+        query += " ORDER BY date DESC LIMIT ?"
+        params.append(limit)
+
+        rows = conn.execute(query, params).fetchall()
+        conn.close()
+        return [dict(r) for r in rows]
+
+    def count_samples(self) -> dict:
+        """Count signal_features by signal_type and regime."""
+        conn = sqlite3.connect(self.db_path)
+        rows = conn.execute("""
+            SELECT signal_type, regime, COUNT(*) as cnt
+            FROM signal_features
+            GROUP BY signal_type, regime
+            ORDER BY signal_type, regime
+        """).fetchall()
+        conn.close()
+        return {f"{r[0]}/{r[1]}": r[2] for r in rows}

ChanMacro/fetchers/__init__.py

@@ -0,0 +1,5 @@
+"""Data fetchers — L0 raw data acquisition."""
+from .base import BaseFetcher
+from .ohlcv import OHLCVFetcher
+from .breadth import BreadthFetcher
+from .derivatives import DerivativesFetcher

ChanMacro/fetchers/base.py

@@ -0,0 +1,69 @@
+"""
+fetchers/base.py — Abstract base class for all macro data fetchers.
+
+Provides retry logic, rate limiting, and a common interface.
+"""
+
+from abc import ABC, abstractmethod
+from datetime import date as Date
+from typing import Optional
+import logging
+import time
+import requests
+
+
+class BaseFetcher(ABC):
+    """Abstract base for all macro data fetchers."""
+
+    def __init__(self, timeout: int = 30, max_retries: int = 3):
+        self.timeout = timeout
+        self.max_retries = max_retries
+        self.logger = logging.getLogger(self.__class__.__name__)
+
+    def _get(self, url: str, params: Optional[dict] = None,
+             headers: Optional[dict] = None) -> dict:
+        """GET with retry and exponential backoff."""
+        for attempt in range(self.max_retries):
+            try:
+                resp = requests.get(
+                    url, params=params, headers=headers, timeout=self.timeout
+                )
+                resp.raise_for_status()
+                return resp.json()
+            except requests.RequestException as e:
+                wait = 2 ** attempt
+                self.logger.warning(
+                    f"Request failed (attempt {attempt+1}/{self.max_retries}): {e}. "
+                    f"Retrying in {wait}s"
+                )
+                if attempt < self.max_retries - 1:
+                    time.sleep(wait)
+                else:
+                    raise
+
+    def _get_raw(self, url: str, params: Optional[dict] = None,
+                 headers: Optional[dict] = None) -> bytes:
+        """GET raw bytes with retry (for non-JSON endpoints)."""
+        for attempt in range(self.max_retries):
+            try:
+                resp = requests.get(
+                    url, params=params, headers=headers, timeout=self.timeout
+                )
+                resp.raise_for_status()
+                return resp.content
+            except requests.RequestException as e:
+                wait = 2 ** attempt
+                if attempt < self.max_retries - 1:
+                    time.sleep(wait)
+                else:
+                    raise
+
+    @abstractmethod
+    def fetch(self, target_date: Optional[Date] = None) -> list[dict]:
+        """Fetch raw data. Returns list of record dicts."""
+        ...
+
+    @abstractmethod
+    def store(self, db_path: str, records: list[dict]) -> int:
+        """Store raw records into SQLite. Returns count of new rows."""
+        ...

ChanMacro/fetchers/breadth.py

@@ -0,0 +1,189 @@
+"""
+fetchers/breadth.py — Fetches TOP50 OHLCV and computes market breadth metrics.
+
+Multi-tier: Top20 / Top30 / Top50 for advance/decline, EMA20%, new highs, BTC.D.
+"""
+
+from datetime import date as Date, datetime
+from typing import Optional
+import logging
+
+import pandas as pd
+import numpy as np
+import requests
+
+from .base import BaseFetcher
+from config import config
+
+
+class BreadthFetcher(BaseFetcher):
+    """Fetches TOP50 coin OHLCV data and computes breadth metrics."""
+
+    def __init__(self, provider_url: Optional[str] = None):
+        super().__init__(timeout=60, max_retries=3)
+        self.provider_url = provider_url or config.provider_url
+        self.symbols = config.top50_symbols
+        self.ema_period = config.breadth_ema_period
+        self.new_high_window = config.breadth_new_high_window
+        self.logger = logging.getLogger(__name__)
+
+    def fetch(self, target_date: Optional[Date] = None) -> dict:
+        """
+        Fetch daily OHLCV for all TOP50 symbols and compute breadth.
+
+        Returns a dict suitable for storing in breadth_daily table.
+        """
+        if target_date is None:
+            target_date = Date.today()
+
+        # Fetch last 60 days of daily data for each symbol to compute EMAs and new highs
+        all_data = {}
+        for symbol in self.symbols:
+            try:
+                df = self._fetch_symbol(symbol)
+                if df is not None and not df.empty:
+                    all_data[symbol] = df
+            except Exception as e:
+                self.logger.debug(f"Failed to fetch {symbol}: {e}")
+
+        if not all_data:
+            self.logger.error("No symbol data fetched for breadth")
+            return {}
+
+        # Compute breadth metrics for the target date
+        breadth = self._compute_breadth(all_data, target_date)
+        return breadth
+
+    def _fetch_symbol(self, symbol: str) -> Optional[pd.DataFrame]:
+        """Fetch daily OHLCV for a single symbol."""
+        url = f"{self.provider_url}/api/candles"
+        params = {
+            "symbol": symbol,
+            "tf": "1d",
+            "limit": 100,
+        }
+        try:
+            resp = requests.get(url, params=params, timeout=15)
+            resp.raise_for_status()
+            data = resp.json()
+            if not data:
+                return None
+
+            df = pd.DataFrame(data)
+            df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+            df["date"] = df["timestamp"].dt.date
+            df = df.drop_duplicates(subset="date").sort_values("date").reset_index(drop=True)
+            df["close"] = df["close"].astype(float)
+            df["ema20"] = df["close"].ewm(span=self.ema_period, adjust=False).mean()
+            return df
+        except Exception:
+            return None
+
+    def _compute_breadth(self, all_data: dict, target_date: Date) -> dict:
+        """Compute breadth metrics for a specific date across all symbols."""
+        total = len(all_data)
+
+        advances_50 = declines_50 = 0
+        above_ema20_50 = 0
+        new_highs_50 = 0
+        advances_30 = declines_30 = 0
+        above_ema20_30 = 0
+        new_highs_30 = 0
+        advances_20 = declines_20 = 0
+        above_ema20_20 = 0
+        new_highs_20 = 0
+
+        for i, (symbol, df) in enumerate(all_data.items()):
+            # Get data for target date
+            df["date_str"] = df["date"].astype(str)
+            target_str = str(target_date)
+            idx = df[df["date_str"] == target_str].index
+
+            if len(idx) == 0:
+                continue
+
+            row_idx = idx[0]
+            if row_idx < 1:
+                continue
+
+            current_close = df.loc[row_idx, "close"]
+            prev_close = df.loc[row_idx - 1, "close"]
+
+            # Advance/Decline
+            if current_close > prev_close:
+                if i < 50: advances_50 += 1
+                if i < 30: advances_30 += 1
+                if i < 20: advances_20 += 1
+            elif current_close < prev_close:
+                if i < 50: declines_50 += 1
+                if i < 30: declines_30 += 1
+                if i < 20: declines_20 += 1
+
+            # Above EMA20
+            ema20_val = df.loc[row_idx, "ema20"]
+            if not pd.isna(ema20_val) and current_close > ema20_val:
+                if i < 50: above_ema20_50 += 1
+                if i < 30: above_ema20_30 += 1
+                if i < 20: above_ema20_20 += 1
+
+            # New 20-day highs
+            lookback_start = max(0, row_idx - self.new_high_window)
+            recent_highs = df.loc[lookback_start:row_idx - 1, "high"].astype(float)
+            current_high = df.loc[row_idx, "high"]
+            if len(recent_highs) > 0 and float(current_high) > recent_highs.max():
+                if i < 50: new_highs_50 += 1
+                if i < 30: new_highs_30 += 1
+                if i < 20: new_highs_20 += 1
+
+        return {
+            "date": str(target_date),
+            "total_tracked": total,
+            "advance_top50": advances_50,
+            "decline_top50": declines_50,
+            "above_ema20_top50": above_ema20_50,
+            "new_highs_20d_top50": new_highs_50,
+            "advance_top30": advances_30,
+            "advance_top20": advances_20,
+            "above_ema20_top30": above_ema20_30,
+            "above_ema20_top20": above_ema20_20,
+            "new_highs_20d_top30": new_highs_30,
+            "new_highs_20d_top20": new_highs_20,
+            "btc_dominance": None,  # Reserved for Coinglass API integration
+        }
+
+    def store(self, db_path: Optional[str] = None, record: Optional[dict] = None) -> int:
+        """Store a breadth record into SQLite. Returns 1 if inserted/updated."""
+        import sqlite3
+        db_path = db_path or config.db_path
+        conn = sqlite3.connect(db_path)
+
+        if record is None:
+            conn.close()
+            return 0
+
+        try:
+            conn.execute("""
+                INSERT OR REPLACE INTO breadth_daily
+                (date, total_tracked,
+                 advance_top50, decline_top50, above_ema20_top50, new_highs_20d_top50,
+                 advance_top30, advance_top20,
+                 above_ema20_top30, above_ema20_top20,
+                 new_highs_20d_top30, new_highs_20d_top20,
+                 btc_dominance)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+            """, (
+                record["date"], record.get("total_tracked", 50),
+                record.get("advance_top50", 0), record.get("decline_top50", 0),
+                record.get("above_ema20_top50", 0), record.get("new_highs_20d_top50", 0),
+                record.get("advance_top30", 0), record.get("advance_top20", 0),
+                record.get("above_ema20_top30", 0), record.get("above_ema20_top20", 0),
+                record.get("new_highs_20d_top30", 0), record.get("new_highs_20d_top20", 0),
+                record.get("btc_dominance"),
+            ))
+            conn.commit()
+            return 1
+        except Exception as e:
+            self.logger.error(f"Failed to store breadth: {e}")
+            return 0
+        finally:
+            conn.close()

ChanMacro/fetchers/derivatives.py

@@ -0,0 +1,66 @@
+"""
+fetchers/derivatives.py — Fetches derivatives data from data_provider API.
+
+Clean consumer: no direct ccxt dependency. Just HTTP GET /api/derivatives.
+"""
+
+from datetime import date as Date
+from typing import Optional
+
+import requests
+
+from .base import BaseFetcher
+from config import config
+
+
+class DerivativesFetcher(BaseFetcher):
+    """Fetches derivatives snapshot from data_provider /api/derivatives."""
+
+    def __init__(self, provider_url: Optional[str] = None):
+        super().__init__(timeout=15, max_retries=3)
+        self.provider_url = provider_url or config.provider_url
+
+    def fetch(self, target_date: Optional[Date] = None) -> list[dict]:
+        """Fetch derivatives data. Returns list with one record dict."""
+        url = f"{self.provider_url}/api/derivatives"
+        params = {"symbol": config.btc_symbol}
+        try:
+            data = self._get(url, params=params)
+            record = {
+                "date": str(target_date or Date.today()),
+                "symbol": config.btc_symbol,
+                "funding_rate": data.get("funding_rate"),
+                "open_interest": data.get("open_interest"),
+                "oi_24h_change_pct": data.get("oi_change_pct"),
+                "basis_annualised_pct": data.get("basis"),
+                "source": "data_provider",
+            }
+            return [record]
+        except Exception:
+            return []
+
+    def store(self, db_path: Optional[str] = None, records: Optional[list[dict]] = None) -> int:
+        """Store derivatives records into SQLite."""
+        import sqlite3
+        db_path = db_path or config.db_path
+        records = records or []
+        conn = sqlite3.connect(db_path)
+        count = 0
+        for r in records:
+            try:
+                conn.execute("""
+                    INSERT OR REPLACE INTO derivatives
+                    (date, symbol, funding_rate, open_interest, oi_24h_change_pct,
+                     long_liquidations, short_liquidations, basis_annualised_pct)
+                    VALUES (?, ?, ?, ?, ?, NULL, NULL, ?)
+                """, (
+                    r["date"], r.get("symbol", config.btc_symbol),
+                    r.get("funding_rate"), r.get("open_interest"),
+                    r.get("oi_24h_change_pct"), r.get("basis_annualised_pct"),
+                ))
+                count += 1
+            except Exception:
+                continue
+        conn.commit()
+        conn.close()
+        return count

ChanMacro/fetchers/ohlcv.py

@@ -0,0 +1,157 @@
+"""
+fetchers/ohlcv.py — Fetches BTC daily OHLCV from the existing data_provider service.
+
+Also pre-computes EMA20/60/120, ATR(14), BB width, ADX(14).
+"""
+
+from datetime import date as Date, datetime, timedelta
+from typing import Optional
+import logging
+
+import pandas as pd
+import numpy as np
+import requests
+
+from .base import BaseFetcher
+from config import config
+
+
+class OHLCVFetcher(BaseFetcher):
+    """Fetches BTC daily K-line data from data_provider API."""
+
+    def __init__(self, provider_url: Optional[str] = None):
+        super().__init__(timeout=30, max_retries=3)
+        self.provider_url = provider_url or config.provider_url
+        self.symbol = config.btc_symbol
+        self.logger = logging.getLogger(__name__)
+
+    def fetch(self, target_date: Optional[Date] = None) -> pd.DataFrame:
+        """
+        Fetch daily OHLCV for BTC. Returns DataFrame with computed indicators.
+
+        Fetches enough history (200 bars) to compute EMAs/ATR/BB/ADX accurately.
+        """
+        url = f"{self.provider_url}/api/candles"
+        params = {
+            "symbol": self.symbol,
+            "tf": "1d",
+            "limit": 200,
+        }
+        resp = requests.get(url, params=params, timeout=self.timeout)
+        resp.raise_for_status()
+        data = resp.json()
+
+        if not data:
+            self.logger.warning("OHLCV API returned empty data")
+            return pd.DataFrame()
+
+        df = pd.DataFrame(data)
+        df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+        df["date"] = df["timestamp"].dt.date
+        df = df.drop_duplicates(subset="date").sort_values("date").reset_index(drop=True)
+
+        # Rename columns to match expected format
+        df = df.rename(columns={
+            "open": "open", "high": "high", "low": "low", "close": "close",
+            "volume": "volume",
+        })
+
+        # Compute indicators
+        df = self._add_indicators(df)
+
+        return df
+
+    def _add_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Add EMA, ATR, BB, ADX indicators."""
+        close = df["close"].astype(float)
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+
+        # EMAs
+        df["ema20"] = close.ewm(span=20, adjust=False).mean()
+        df["ema60"] = close.ewm(span=60, adjust=False).mean()
+        df["ema120"] = close.ewm(span=120, adjust=False).mean()
+
+        # ATR(14)
+        tr1 = high - low
+        tr2 = (high - close.shift(1)).abs()
+        tr3 = (low - close.shift(1)).abs()
+        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        df["atr_14"] = tr.rolling(14).mean()
+
+        # Bollinger Bands width
+        sma20 = close.rolling(20).mean()
+        std20 = close.rolling(20).std()
+        df["bb_width"] = (2 * std20) / sma20 * 100  # as percentage
+
+        # ADX(14)
+        df["adx_14"] = self._compute_adx(df, period=14)
+
+        return df
+
+    @staticmethod
+    def _compute_adx(df: pd.DataFrame, period: int = 14) -> pd.Series:
+        """Compute ADX from OHLC data."""
+        high = df["high"].astype(float)
+        low = df["low"].astype(float)
+        close = df["close"].astype(float)
+
+        plus_dm = high.diff()
+        minus_dm = low.diff().abs() * -1
+        plus_dm = plus_dm.where(plus_dm > 0, 0)
+        minus_dm = minus_dm.where(minus_dm < 0, 0).abs()
+
+        tr1 = high - low
+        tr2 = (high - close.shift(1)).abs()
+        tr3 = (low - close.shift(1)).abs()
+        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+
+        atr = tr.rolling(period).mean()
+        plus_di = 100 * (plus_dm.rolling(period).mean() / atr)
+        minus_di = 100 * (minus_dm.rolling(period).mean() / atr)
+
+        dx = (abs(plus_di - minus_di) / (plus_di + minus_di)) * 100
+        adx = dx.rolling(period).mean()
+        return adx
+
+    def store(self, db_path: str, records: list[dict]) -> int:
+        """Store OHLCV records into SQLite. Not used directly — see store_df."""
+        return 0
+
+    def store_df(self, df: pd.DataFrame, db_path: Optional[str] = None) -> int:
+        """Store the DataFrame into the ohlcv_daily table."""
+        import sqlite3
+        db_path = db_path or config.db_path
+        conn = sqlite3.connect(db_path)
+
+        count = 0
+        for _, row in df.iterrows():
+            if pd.isna(row.get("date")):
+                continue
+            date_str = str(row["date"])
+            try:
+                conn.execute("""
+                    INSERT OR REPLACE INTO ohlcv_daily
+                    (date, symbol, open, high, low, close, volume,
+                     ema20, ema60, ema120, atr_14, bb_width, adx_14)
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                """, (
+                    date_str, self.symbol,
+                    float(row["open"]), float(row["high"]),
+                    float(row["low"]), float(row["close"]),
+                    float(row.get("volume", 0)),
+                    float(row["ema20"]) if not pd.isna(row.get("ema20")) else None,
+                    float(row["ema60"]) if not pd.isna(row.get("ema60")) else None,
+                    float(row["ema120"]) if not pd.isna(row.get("ema120")) else None,
+                    float(row["atr_14"]) if not pd.isna(row.get("atr_14")) else None,
+                    float(row["bb_width"]) if not pd.isna(row.get("bb_width")) else None,
+                    float(row["adx_14"]) if not pd.isna(row.get("adx_14")) else None,
+                ))
+                count += 1
+            except Exception as e:
+                self.logger.debug(f"Skip row {date_str}: {e}")
+
+        conn.commit()
+        conn.close()
+        self.logger.info(f"Stored {count} OHLCV rows")
+        return count

ChanMacro/main.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+"""
+main.py — ChanMacro entry point.
+
+CLI: python main.py fetch|score|regime|serve
+"""
+
+import sys
+import os
+
+# Ensure package root is on path
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from cli import main
+
+if __name__ == "__main__":
+    main()

ChanMacro/models.py

@@ -0,0 +1,370 @@
+"""
+models.py — Pydantic v2 models and enums for ChanMacro.
+
+All market state types, factor scores, and database record models.
+"""
+
+from datetime import date as Date
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel, Field, field_validator
+
+
+# ═══════════════════════════════════════════════════════════════
+# Shared validators
+# ═══════════════════════════════════════════════════════════════
+
+def _parse_date(v):
+    """Reusable date-string parser for field_validator."""
+    if isinstance(v, str):
+        return Date.fromisoformat(v)
+    return v
+
+
+# ═══════════════════════════════════════════════════════════════
+# Enums
+# ═══════════════════════════════════════════════════════════════
+
+class MarketRegime(str, Enum):
+    """V1: 3-state regime (factor-locked: Price + Breadth + Vol)."""
+    TREND = "TREND"
+    RANGE = "RANGE"
+    PANIC = "PANIC"
+
+
+class OIState(str, Enum):
+    """Discrete OI × Price state machine. NOT compressed into a score."""
+    NEW_LONGS = "New Longs"
+    SHORT_COVERING = "Short Covering"
+    NEW_SHORTS = "New Shorts"
+    LONG_EXIT = "Long Exit"
+    NEUTRAL = "Neutral"
+
+
+class BreadthBucket(str, Enum):
+    """Quantile-based breadth buckets — always have samples regardless of cycle."""
+    EXTREME = "EXTREME"
+    STRONG = "STRONG"
+    NORMAL = "NORMAL"
+    WEAK = "WEAK"
+    PANIC = "PANIC"
+
+
+class VolRegime(str, Enum):
+    """Volatility regime classification."""
+    LOW_VOL = "LOW_VOL"
+    NORMAL_VOL = "NORMAL_VOL"
+    HIGH_VOL = "HIGH_VOL"
+    EXPLOSIVE_VOL = "EXPLOSIVE_VOL"
+
+
+class MacroDirection(str, Enum):
+    BULLISH = "bullish"
+    NEUTRAL = "neutral"
+    BEARISH = "bearish"
+
+
+class MarketEmotion(str, Enum):
+    EXTREME_FEAR = "Extreme Fear"
+    FEAR = "Fear"
+    NEUTRAL = "Neutral"
+    GREED = "Greed"
+    EXTREME_GREED = "Extreme Greed"
+
+
+class FlowState(str, Enum):
+    STRONG_INFLOW = "Strong Inflow"
+    INFLOW = "Inflow"
+    NEUTRAL = "Neutral"
+    OUTFLOW = "Outflow"
+    STRONG_OUTFLOW = "Strong Outflow"
+
+
+class CapitalState(str, Enum):
+    ENTERING = "Entering"
+    STABLE = "Stable"
+    EXITING = "Exiting"
+
+
+class SufficiencyLevel(str, Enum):
+    HIGH = "HIGH"
+    MEDIUM = "MEDIUM"
+    LOW = "LOW"
+    INSUFFICIENT = "INSUFFICIENT"
+
+
+class SignalGrade(str, Enum):
+    A = "A"
+    B = "B"
+    C = "C"
+
+
+# ═══════════════════════════════════════════════════════════════
+# L0: Raw Data Models
+# ═══════════════════════════════════════════════════════════════
+
+class OHLCVDaily(BaseModel):
+    _parse_date = field_validator("date", mode="before")(_parse_date)
+    date: Date
+    symbol: str
+    open: float
+    high: float
+    low: float
+    close: float
+    volume: float
+    ema20: Optional[float] = None
+    ema60: Optional[float] = None
+    ema120: Optional[float] = None
+    atr_14: Optional[float] = None
+    bb_width: Optional[float] = None
+    adx_14: Optional[float] = None
+
+
+class BreadthRecord(BaseModel):
+    _parse_date = field_validator("date", mode="before")(_parse_date)
+    date: Date
+    total_tracked: int = 50
+    advance_top50: int = 0
+    decline_top50: int = 0
+    above_ema20_top50: int = 0
+    new_highs_20d_top50: int = 0
+    btc_dominance: Optional[float] = None
+    advance_top20: int = 0
+    advance_top30: int = 0
+    above_ema20_top20: int = 0
+    above_ema20_top30: int = 0
+    new_highs_20d_top20: int = 0
+    new_highs_20d_top30: int = 0
+
+
+class DerivativesRecord(BaseModel):
+    _parse_date = field_validator("date", mode="before")(_parse_date)
+    date: Date
+    symbol: str = "BTC/USDT:USDT"
+    funding_rate: Optional[float] = None
+    open_interest: Optional[float] = None
+    oi_24h_change_pct: Optional[float] = None
+    long_liquidations: Optional[float] = None
+    short_liquidations: Optional[float] = None
+    basis_annualised_pct: Optional[float] = None
+    source: str = "binance"
+
+
+class ETFFlowRecord(BaseModel):
+    _parse_date = field_validator("date", mode="before")(_parse_date)
+    date: Date
+    product: str
+    net_flow_million: float
+    price: Optional[float] = None
+    source: str = "farside"
+
+
+class StablecoinSupplyRecord(BaseModel):
+    _parse_date = field_validator("date", mode="before")(_parse_date)
+    date: Date
+    token: str
+    chain: str = "all"
+    supply: float
+    source: str = "defillama"
+
+
+# ═══════════════════════════════════════════════════════════════
+# L1: Factor Score Models
+# ═══════════════════════════════════════════════════════════════
+
+class FactorScore(BaseModel):
+    """Single factor scoring output."""
+    name: str = ""
+    score: float = Field(default=50.0, ge=0.0, le=100.0)
+    label: str = ""
+    direction: MacroDirection = MacroDirection.NEUTRAL
+    sub_scores: dict = Field(default_factory=dict)
+    narrative: str = ""
+
+
+class PriceStructureScore(FactorScore):
+    """Price Structure — 3 sub-dimensions."""
+    trend_strength: float = 0.0
+    volatility_compression: float = 0.0
+    momentum: float = 0.0
+
+
+class BreadthScore(FactorScore):
+    """Breadth — multi-tier market diffusion."""
+    breadth_top20: float = 0.0
+    breadth_top30: float = 0.0
+    breadth_top50: float = 0.0
+    breadth_bucket: BreadthBucket = BreadthBucket.NORMAL
+    breadth_divergence: float = 0.0
+    advance_pct_top50: float = 0.0
+    above_ema20_pct_top50: float = 0.0
+    new_highs_top50: int = 0
+    btc_dominance_7d_chg: Optional[float] = None
+
+
+class OIMatrixScore(FactorScore):
+    """OI Matrix — discrete state + continuous score."""
+    oi_state: OIState = OIState.NEUTRAL
+    price_change_pct: float = 0.0
+    oi_change_pct: float = 0.0
+
+
+class VolatilityRegimeScore(FactorScore):
+    """Volatility regime classification."""
+    vol_regime: VolRegime = VolRegime.NORMAL_VOL
+    atr_pct: float = 0.0
+    hv_ratio: float = 1.0
+    bb_width_ratio: float = 1.0
+
+
+# ═══════════════════════════════════════════════════════════════
+# L4: Market State Vector (the final product)
+# ═══════════════════════════════════════════════════════════════
+
+class MarketStateVector(BaseModel):
+    """L4: Complete market state description. NOT compressed into one number."""
+    _parse_date = field_validator("date", mode="before")(_parse_date)
+
+    date: Date
+    symbol: str = "BTC/USDT:USDT"
+
+    regime: MarketRegime
+    regime_confidence: float = Field(ge=0.0, le=1.0)
+    regime_version: str
+    regime_maturity_score: float = Field(ge=0.0, le=100.0, default=50.0)
+
+    breadth_top20: float = Field(default=50.0, ge=0.0, le=100.0)
+    breadth_top30: float = Field(default=50.0, ge=0.0, le=100.0)
+    breadth_top50: float = Field(default=50.0, ge=0.0, le=100.0)
+    breadth_bucket: BreadthBucket = BreadthBucket.NORMAL
+    breadth_divergence: float = 0.0
+
+    oi_state: OIState = OIState.NEUTRAL
+    volatility_regime: VolRegime = VolRegime.NORMAL_VOL
+
+    price_structure_score: FactorScore = Field(default_factory=FactorScore)
+    breadth_score: BreadthScore = Field(default_factory=BreadthScore)
+    oi_matrix_score: OIMatrixScore = Field(default_factory=OIMatrixScore)
+    volatility_regime_score: VolatilityRegimeScore = Field(default_factory=VolatilityRegimeScore)
+
+    market_state_hash: str = ""
+
+    def compute_hash(self) -> str:
+        import hashlib
+        key = f"{self.regime.value}|{self.breadth_bucket.value}|{self.oi_state.value}|{self.volatility_regime.value}"
+        return hashlib.md5(key.encode()).hexdigest()[:12]
+
+    def state_embedding(self) -> list[float]:
+        return [
+            self.breadth_top20,
+            self.breadth_top30,
+            self.breadth_top50,
+            self.regime_maturity_score,
+            self.price_structure_score.score,
+        ]
+
+
+# ═══════════════════════════════════════════════════════════════
+# Factor Contribution
+# ═══════════════════════════════════════════════════════════════
+
+class FactorContribution(BaseModel):
+    """How much a factor contributed to the overall score."""
+    factor: str
+    raw_score: float
+    weight: float
+    impact: float
+    direction: str  # 'bullish' / 'bearish' / 'neutral'
+
+
+# ═══════════════════════════════════════════════════════════════
+# Regime Result
+# ═══════════════════════════════════════════════════════════════
+
+class RegimeResult(BaseModel):
+    _parse_date = field_validator("date", mode="before")(_parse_date)
+    date: Date
+    regime: MarketRegime
+    confidence: float
+    regime_version: str
+    maturity_score: float
+    all_scores: dict = Field(default_factory=dict)
+    prior_regime: Optional[MarketRegime] = None
+    confirmation_days: int = 0
+
+
+class SignalFeatureRecord(BaseModel):
+    """A single signal → market state → outcome record."""
+    _parse_date = field_validator("date", mode="before")(_parse_date)
+    date: Date
+    signal_type: str
+    signal_version: str = "b3_v1"
+    symbol: str = "BTC/USDT:USDT"
+
+    regime_version: str
+    signal_grade: Optional[SignalGrade] = None
+    signal_strength: Optional[float] = None
+
+    regime: MarketRegime
+    regime_confidence: float
+    regime_maturity_score: float
+    market_state_hash: str
+    state_embedding: str = "[]"
+    breadth_top20: float
+    breadth_top30: float
+    breadth_top50: float
+    breadth_bucket: BreadthBucket
+    breadth_divergence: float
+    oi_state: OIState
+    volatility_regime: VolRegime
+    price_structure_score: float
+
+    chan_trend_direction: Optional[str] = None
+    chan_pivot_count: Optional[int] = None
+    chan_divergence_type: Optional[str] = None
+
+    entry_price: Optional[float] = None
+    result_1d: Optional[float] = None
+    result_3d: Optional[float] = None
+    result_5d: Optional[float] = None
+    result_7d: Optional[float] = None
+    result_14d: Optional[float] = None
+    max_favorable_excursion: Optional[float] = None
+    max_adverse_excursion: Optional[float] = None
+    is_win_7d: Optional[int] = None
+
+
+class ExpectancyLayer(BaseModel):
+    name: str
+    posterior_winrate: float
+    raw_winrate: Optional[float] = None
+    samples: int = 0
+    effective_samples: float = 0.0
+    avg_return: Optional[float] = None
+
+
+class ExpectancyReport(BaseModel):
+    _parse_date = field_validator("date", mode="before")(_parse_date)
+    signal_type: str
+    date: Date
+    layers: list[ExpectancyLayer] = Field(default_factory=list)
+    final_estimate: float
+    sufficiency: SufficiencyLevel = SufficiencyLevel.INSUFFICIENT
+    prior_strength: int = 40
+    half_life_days: int = 180
+    source: str = "bayesian"
+
+    avg_return_7d: Optional[float] = None
+    profit_factor: Optional[float] = None
+    max_adverse_excursion: Optional[float] = None
+
+
+class DailyOutput(BaseModel):
+    """Final daily output: Market State + Expectancy."""
+    _parse_date = field_validator("date", mode="before")(_parse_date)
+    date: Date
+    market_state: MarketStateVector
+    expectancy: dict[str, ExpectancyReport] = Field(default_factory=dict)
+    ai_report_en: Optional[str] = None
+    ai_report_zh: Optional[str] = None

ChanMacro/regime_detector.py

@@ -0,0 +1,213 @@
+"""
+regime_detector.py — Market regime detection (V1: 3 states).
+
+★ FACTOR-LOCKED: Regime = f(Price Structure, Breadth, Volatility) — forever.
+  Fear, Liquidation, ETF, Funding are Context, NOT regime inputs.
+  Adding new factors MUST NOT change regime definition.
+
+★ VERSIONED: regime_version = 'v1_price_breadth_vol'.
+  Weight changes → new version. Multiple versions coexist.
+  Query: WHERE regime_version = 'v1_price_breadth_vol'.
+
+★ CONFIDENCE-BASED: Each regime gets a continuous score. Highest wins.
+  No hard thresholds (prevents boundary oscillation).
+"""
+
+from datetime import date as Date
+from typing import Optional
+from collections import deque
+
+from models import MarketRegime, RegimeResult
+from config import config
+
+
+class RegimeDetector:
+    """
+    Detects market regime from Price + Breadth + Vol.
+
+    V1: 3 regimes (TREND / RANGE / PANIC)
+    V2+: Can split TREND→TREND_UP/TREND_DOWN/EUPHORIA when samples > 500/regime.
+    """
+
+    def __init__(self, regime_version: Optional[str] = None):
+        self.version = regime_version or config.regime_version
+        self.w_price = config.regime_w_price
+        self.w_breadth = config.regime_w_breadth
+        self.w_vol = config.regime_w_vol
+        self.panic_w_anti_trend = config.regime_panic_w_anti_trend
+        self.panic_w_vol_extreme = config.regime_panic_w_vol_extreme
+
+        # State persistence
+        self._current_regime: Optional[MarketRegime] = None
+        self._pending_regime: Optional[MarketRegime] = None
+        self._confirmation_count: int = 0
+        self._consecutive_days: int = 0
+        self._regime_history: deque = deque(maxlen=100)
+
+        # Confirmation: 2 days minimum
+        self.MIN_CONFIRMATION = 2
+
+    def load_state(self, db_path: str):
+        """Restore regime state from the most recent regime_history record."""
+        import sqlite3
+        try:
+            conn = sqlite3.connect(db_path)
+            conn.row_factory = sqlite3.Row
+            row = conn.execute(
+                "SELECT regime, confidence, confirmation_days, maturity_score "
+                "FROM regime_history ORDER BY date DESC LIMIT 1"
+            ).fetchone()
+            conn.close()
+
+            if row:
+                regime_str = row["regime"]
+                if regime_str in ("TREND", "RANGE", "PANIC"):
+                    self._current_regime = MarketRegime(regime_str)
+                    self._consecutive_days = row["confirmation_days"] or 1
+        except Exception:
+            pass  # DB not initialized yet, use defaults
+
+    def detect(self, price_structure_score: float, breadth_score: float,
+               volatility_regime: str, date: Date) -> RegimeResult:
+        """
+        Detect regime from the 3 locked factors.
+
+        Args:
+            price_structure_score: 0-100 from PriceStructureScorer
+            breadth_score: 0-100 from BreadthScorer
+            volatility_regime: 'LOW_VOL'/'NORMAL_VOL'/'HIGH_VOL'/'EXPLOSIVE_VOL'
+            date: Target date
+        """
+        # ── Compute regime scores ────────────────────────
+        # TREND: strong price + strong breadth + non-extreme vol
+        trend_score = (
+            price_structure_score * self.w_price +
+            breadth_score * self.w_breadth +
+            self._vol_to_trend(volatility_regime) * self.w_vol
+        )
+
+        # RANGE: neutral price + neutral breadth + low vol
+        # Score how "range-like" each dimension is
+        price_neutral = 60 - abs(price_structure_score - 50)
+        breadth_neutral = 60 - abs(breadth_score - 50)
+        vol_neutral = 80 if volatility_regime in ("LOW_VOL", "NORMAL_VOL") else 30
+        range_score = (
+            price_neutral * 0.40 +
+            breadth_neutral * 0.40 +
+            vol_neutral * 0.20
+        )
+
+        # PANIC: very weak trend + extreme vol (NO Fear/Liquidation!)
+        anti_trend = 100 - trend_score
+        vol_extreme = 100 if volatility_regime == "EXPLOSIVE_VOL" else (
+            60 if volatility_regime == "HIGH_VOL" else 20
+        )
+        panic_score = (
+            anti_trend * self.panic_w_anti_trend +
+            vol_extreme * self.panic_w_vol_extreme
+        )
+
+        scores = {
+            MarketRegime.TREND: round(trend_score, 1),
+            MarketRegime.RANGE: round(range_score, 1),
+            MarketRegime.PANIC: round(panic_score, 1),
+        }
+
+        best_regime = max(scores, key=scores.get)
+
+        # ── Persistence check ────────────────────────────
+        prior_regime = self._current_regime
+
+        if best_regime == self._current_regime:
+            self._consecutive_days += 1
+            self._pending_regime = None
+            self._confirmation_count = 0
+        elif best_regime == self._pending_regime:
+            self._confirmation_count += 1
+            if self._confirmation_count >= self.MIN_CONFIRMATION:
+                # Transition confirmed
+                prior_regime = self._current_regime
+                self._current_regime = best_regime
+                self._consecutive_days = self.MIN_CONFIRMATION
+                self._pending_regime = None
+                self._confirmation_count = 0
+        else:
+            self._pending_regime = best_regime
+            self._confirmation_count = 1
+
+        # Fallback: if no current regime yet (first run)
+        if self._current_regime is None:
+            self._current_regime = best_regime
+            self._consecutive_days = 1
+
+        # ── Confidence: for the CONFIRMED regime, not the raw best ──
+        confirmed_regime = self._current_regime
+        confidence = scores[confirmed_regime] / 100.0
+
+        # ── Maturity ─────────────────────────────────────
+        maturity = self._compute_maturity(
+            trend_score, breadth_score, volatility_regime
+        )
+
+        # Track history
+        self._regime_history.append({
+            "date": date,
+            "regime": confirmed_regime.value,
+            "confidence": round(confidence, 3),
+        })
+
+        return RegimeResult(
+            date=date,
+            regime=confirmed_regime,
+            confidence=round(confidence, 3),
+            prior_regime=prior_regime,
+            regime_version=self.version,
+            maturity_score=round(maturity, 1),
+            all_scores={k.value: v for k, v in scores.items()},
+            confirmation_days=self._consecutive_days,
+        )
+
+    @property
+    def current_regime(self) -> Optional[MarketRegime]:
+        return self._current_regime
+
+    @property
+    def pending_regime(self) -> Optional[MarketRegime]:
+        return self._pending_regime
+
+    @property
+    def confirmation_progress(self) -> tuple[int, int]:
+        """(confirmed_days, required_days) for pending transition."""
+        return (self._confirmation_count, self.MIN_CONFIRMATION)
+
+    @staticmethod
+    def _vol_to_trend(vol_regime: str) -> float:
+        """Convert volatility regime to trend-contributing score."""
+        mapping = {
+            "LOW_VOL": 50,       # Low vol: neutral for trend
+            "NORMAL_VOL": 70,    # Normal vol: good for trend
+            "HIGH_VOL": 60,      # High vol: trending but risky
+            "EXPLOSIVE_VOL": 30, # Explosive: anti-trend
+        }
+        return mapping.get(vol_regime, 50)
+
+    @staticmethod
+    def _compute_maturity(trend_score: float, breadth_score: float,
+                          vol_regime: str) -> float:
+        """
+        Compute regime maturity: 0-100 continuous.
+        0-30: EMERGING (trend accelerating, breadth expanding)
+        30-70: CONFIRMED (stable)
+        70-100: EXHAUSTING (decelerating, vol abnormal)
+        """
+        # Trend strength contribution
+        trend_contrib = trend_score * 0.50
+
+        # Breadth contribution
+        breadth_contrib = breadth_score * 0.30
+
+        # Vol contribution (inverted: low vol = early, explosive = late)
+        vol_contrib = {"LOW_VOL": 20, "NORMAL_VOL": 40, "HIGH_VOL": 60, "EXPLOSIVE_VOL": 85}
+        vol_val = vol_contrib.get(vol_regime, 50) * 0.20
+
+        return trend_contrib + breadth_contrib + vol_val

ChanMacro/requirements.txt

@@ -0,0 +1,8 @@
+ccxt>=4.0.0
+pandas>=2.0.0
+numpy>=1.21.2
+pydantic>=2.0.0
+requests>=2.31.0
+python-dotenv>=1.0.0
+scipy>=1.10.0
+flask>=3.0.0

ChanMacro/run_tests.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+# run_tests.sh — Run the ChanMacro test suite.
+#
+# Usage:
+#   ./run_tests.sh              # All tests
+#   ./run_tests.sh -v           # Verbose
+#   ./run_tests.sh -k regime    # Only regime tests
+#   ./run_tests.sh --cov        # With coverage (requires pytest-cov)
+
+cd "$(dirname "$0")"
+python -m pytest tests/ "$@" --tb=short

ChanMacro/scheduler.py

@@ -0,0 +1,153 @@
+"""
+scheduler.py — 后台自动调度：定时拉取数据 + 计算因子 + 制度判定。
+
+Python main.py cron         → 前台阻塞运行，每 N 分钟一个 tick
+Web app 启动时自动启动调度器  → 后台线程，不阻塞 Web 请求
+"""
+
+import threading
+import logging
+import time
+from datetime import datetime, timezone, timedelta
+from typing import Optional
+
+logger = logging.getLogger("chanmacro.scheduler")
+
+
+class MacroScheduler:
+    """后台调度器：定时 fetch + score。"""
+
+    def __init__(self, interval_minutes: int = 60):
+        self.interval = interval_minutes
+        self._thread: Optional[threading.Thread] = None
+        self._stop = threading.Event()
+        self._last_run: Optional[datetime] = None
+        self._running = False
+
+    def start(self) -> None:
+        """启动后台线程。"""
+        if self._running:
+            return
+        self._stop.clear()
+        self._thread = threading.Thread(target=self._loop, name="macro-scheduler", daemon=True)
+        self._thread.start()
+        self._running = True
+        logger.info(f"调度器已启动, 每 {self.interval} 分钟执行一次")
+
+    def stop(self) -> None:
+        """停止后台线程。"""
+        self._stop.set()
+        self._running = False
+        logger.info("调度器已停止")
+
+    @property
+    def last_run(self) -> Optional[datetime]:
+        return self._last_run
+
+    def _loop(self) -> None:
+        """后台循环。"""
+        # 首次启动立即跑一次
+        self._tick()
+
+        while not self._stop.wait(self.interval * 60):
+            self._tick()
+
+    def _tick(self) -> None:
+        """执行一次：fetch → score。"""
+        try:
+            from fetchers.ohlcv import OHLCVFetcher
+            from fetchers.breadth import BreadthFetcher
+            from fetchers.derivatives import DerivativesFetcher
+            from database import init_db
+            from datetime import date as Date
+
+            init_db()
+            today = Date.today()
+
+            # Fetch
+            ohlcv = OHLCVFetcher()
+            df = ohlcv.fetch()
+            if not df.empty:
+                ohlcv.store_df(df)
+
+            breadth = BreadthFetcher()
+            record = breadth.fetch()
+            if record:
+                breadth.store(record=record)
+
+            deriv = DerivativesFetcher()
+            records = deriv.fetch(today)
+            if records:
+                deriv.store(records=records)
+
+            # Score + Regime (also persisted inside _build_state)
+            from scoring.price_structure import PriceStructureScorer
+            from scoring.breadth_scorer import BreadthScorer
+            from scoring.oi_matrix import OIMatrixScorer
+            from scoring.volatility_regime import VolatilityRegimeScorer
+            from regime_detector import RegimeDetector
+            from models import MarketStateVector
+            from config import config
+            import json
+            from database import get_connection
+
+            ps = PriceStructureScorer().compute(today)
+            br = BreadthScorer().compute(today)
+            oi = OIMatrixScorer().compute(today)
+            vol = VolatilityRegimeScorer().compute(today)
+
+            detector = RegimeDetector()
+            detector.load_state(config.db_path)
+            r = detector.detect(ps.score, br.breadth_top50, vol.vol_regime.value, today)
+
+            conn = get_connection()
+            conn.execute("""
+                INSERT OR REPLACE INTO regime_history
+                (date, regime, confidence, regime_version, maturity_score,
+                 all_scores_json, prior_regime, confirmation_days)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+            """, (
+                str(today), r.regime.value, r.confidence, r.regime_version,
+                r.maturity_score, json.dumps(r.all_scores),
+                r.prior_regime.value if r.prior_regime else None,
+                r.confirmation_days,
+            ))
+            conn.commit()
+            conn.close()
+
+            # 检测新信号（每天运行一次，UTC 0 点后首次触发）
+            now = datetime.now(timezone.utc)
+            if self._last_run is None or now.date() > self._last_run.date():
+                try:
+                    from chan_integration import ChanSignalDetector
+                    detector = ChanSignalDetector()
+                    # 检测最近 90 天的 4h 信号
+                    count = detector.populate_signal_features(
+                        start_date=(today - __import__('datetime').timedelta(days=90)).isoformat(),
+                        end_date=today.isoformat(),
+                    )
+                    if count > 0:
+                        logger.info(f"新增 {count} 条信号记录")
+                except Exception as e:
+                    logger.debug(f"信号检测跳过: {e}")
+
+            self._last_run = now
+            logger.info(
+                f"Tick 完成: regime={r.regime.value} conf={r.confidence:.2f} "
+                f"breadth={br.score:.0f}({br.breadth_bucket.value}) "
+                f"price={ps.score:.0f} oi={oi.oi_state.value} vol={vol.vol_regime.value}"
+            )
+
+        except Exception as e:
+            logger.error(f"Tick 失败: {e}", exc_info=True)
+
+
+# 单例
+_scheduler: Optional[MacroScheduler] = None
+
+
+def get_scheduler() -> MacroScheduler:
+    global _scheduler
+    if _scheduler is None:
+        _scheduler = MacroScheduler(interval_minutes=60)
+    return _scheduler

ChanMacro/scoring/__init__.py

@@ -0,0 +1,6 @@
+"""Scoring engine — L1 factor computation."""
+from .base import BaseScorer
+from .price_structure import PriceStructureScorer
+from .breadth_scorer import BreadthScorer
+from .oi_matrix import OIMatrixScorer
+from .volatility_regime import VolatilityRegimeScorer

ChanMacro/scoring/base.py

@@ -0,0 +1,28 @@
+"""
+scoring/base.py — Abstract base class for all scoring modules.
+"""
+
+from abc import ABC, abstractmethod
+from datetime import date as Date
+from typing import Optional
+import sqlite3
+
+from models import FactorScore
+from config import config
+
+
+class BaseScorer(ABC):
+    """Abstract base for all factor scorers."""
+
+    def __init__(self, db_path: Optional[str] = None):
+        self.db_path = db_path or config.db_path
+
+    def get_connection(self) -> sqlite3.Connection:
+        conn = sqlite3.connect(self.db_path)
+        conn.row_factory = sqlite3.Row
+        return conn
+
+    @abstractmethod
+    def compute(self, target_date: Date) -> FactorScore:
+        """Compute factor score for a given date from database records."""
+        ...

ChanMacro/scoring/breadth_scorer.py

@@ -0,0 +1,218 @@
+"""
+scoring/breadth_scorer.py — Market Breadth Score.
+
+The first citizen of the system. Diffusion always leads price.
+
+Multi-tier: Top20 / Top30 / Top50.
+Quantile-based bucketing: EXTREME / STRONG / NORMAL / WEAK / PANIC.
+
+4 sub-indicators (equal weight):
+  1. Advance/Decline ratio (30%)
+  2. % above EMA20 (35%)
+  3. New 20d highs (20%)
+  4. BTC Dominance change (15%, inverted)
+"""
+
+from datetime import date as Date
+import sqlite3
+import numpy as np
+import pandas as pd
+
+from .base import BaseScorer
+from .constants import (
+    BREADTH_W_ADVANCE, BREADTH_W_EMA20, BREADTH_W_NEW_HIGHS, BREADTH_W_BTC_DOM,
+)
+from models import FactorScore, BreadthScore, BreadthBucket, MacroDirection
+from config import config
+
+
+class BreadthScorer(BaseScorer):
+    """Scores market breadth with quantile-based bucketing."""
+
+    def compute(self, target_date: Date) -> BreadthScore:
+        conn = self.get_connection()
+        try:
+            row = conn.execute(
+                "SELECT * FROM breadth_daily WHERE date = ?", (str(target_date),)
+            ).fetchone()
+
+            if row is None:
+                return BreadthScore(
+                    name="Breadth",
+                    score=50.0,
+                    label="No Data",
+                    breadth_bucket=BreadthBucket.NORMAL,
+                )
+
+            row = dict(row)
+            total = row.get("total_tracked", 50) or 50
+
+            # 1. Advance/Decline ratio
+            advance = row.get("advance_top50", 0) or 0
+            decline = row.get("decline_top50", 0) or 0
+            if advance + decline > 0:
+                ad_ratio = advance / (advance + decline)
+            else:
+                ad_ratio = 0.5
+            ad_score = ad_ratio * 100
+
+            # 2. % above EMA20
+            above_ema = row.get("above_ema20_top50", 0) or 0
+            ema_pct = above_ema / total if total > 0 else 0.5
+            ema_score = ema_pct * 100
+
+            # 3. New highs
+            new_highs = row.get("new_highs_20d_top50", 0) or 0
+            highs_pct = new_highs / total if total > 0 else 0
+            highs_score = highs_pct * 100
+
+            # 4. BTC Dominance (inverted: BTC.D up = bearish for alts)
+            btc_dom = row.get("btc_dominance")
+            btc_dom_score = 50.0  # neutral default
+            if btc_dom is not None:
+                # Placeholder — needs historical comparison
+                btc_dom_score = 50.0
+
+            # Weighted aggregate
+            score = (
+                ad_score * BREADTH_W_ADVANCE +
+                ema_score * BREADTH_W_EMA20 +
+                highs_score * BREADTH_W_NEW_HIGHS +
+                btc_dom_score * BREADTH_W_BTC_DOM
+            )
+
+            # Multi-tier breadth
+            b20 = self._compute_tier_breadth(row, 20, total)
+            b30 = self._compute_tier_breadth(row, 30, total)
+            b50 = score  # Top50 = full score
+
+            # Quantile bucket
+            bucket = self._assign_bucket(score)
+
+            # Divergence
+            divergence = b20 - b50
+
+            # Direction
+            if score >= 60:
+                direction = MacroDirection.BULLISH
+            elif score <= 40:
+                direction = MacroDirection.BEARISH
+            else:
+                direction = MacroDirection.NEUTRAL
+
+            # Narrative
+            narrative = self._build_narrative(bucket, divergence, ema_pct, ad_ratio)
+
+            return BreadthScore(
+                name="Breadth",
+                score=round(score, 1),
+                label=bucket.value,
+                direction=direction,
+                breadth_top20=round(b20, 1),
+                breadth_top30=round(b30, 1),
+                breadth_top50=round(b50, 1),
+                breadth_bucket=bucket,
+                breadth_divergence=round(divergence, 1),
+                advance_pct_top50=round(ad_ratio * 100, 1),
+                above_ema20_pct_top50=round(ema_pct * 100, 1),
+                new_highs_top50=new_highs,
+                sub_scores={
+                    "advance_decline": round(ad_score, 1),
+                    "above_ema20": round(ema_score, 1),
+                    "new_highs": round(highs_score, 1),
+                    "btc_dominance": round(btc_dom_score, 1),
+                },
+                narrative=narrative,
+            )
+        finally:
+            conn.close()
+
+    def _compute_tier_breadth(self, row: dict, tier: int, total: int) -> float:
+        """Compute breadth score for a specific tier (Top20 or Top30)."""
+        advance = row.get(f"advance_top{tier}", 0) or 0
+        above_ema = row.get(f"above_ema20_top{tier}", 0) or 0
+        new_highs = row.get(f"new_highs_20d_top{tier}", 0) or 0
+
+        tier_actual = min(tier, total)
+        if tier_actual == 0:
+            return 50.0
+
+        ad_ratio = advance / tier_actual if tier_actual > 0 else 0.5
+        ema_ratio = above_ema / tier_actual if tier_actual > 0 else 0.5
+        highs_ratio = new_highs / tier_actual if tier_actual > 0 else 0
+
+        return (
+            ad_ratio * 100 * BREADTH_W_ADVANCE +
+            ema_ratio * 100 * BREADTH_W_EMA20 +
+            highs_ratio * 100 * BREADTH_W_NEW_HIGHS +
+            50 * BREADTH_W_BTC_DOM  # neutral for BTC.D
+        )
+
+    def _assign_bucket(self, score: float) -> BreadthBucket:
+        """Assign quantile-based bucket. V1 uses fixed thresholds until history accumulated."""
+        # V1: fixed thresholds (will switch to quantile when enough history)
+        if score >= 80:
+            return BreadthBucket.EXTREME
+        elif score >= 60:
+            return BreadthBucket.STRONG
+        elif score >= 40:
+            return BreadthBucket.NORMAL
+        elif score >= 20:
+            return BreadthBucket.WEAK
+        else:
+            return BreadthBucket.PANIC
+
+    @staticmethod
+    def compute_quantile_boundaries(db_path: str) -> dict:
+        """Compute quantile boundaries from historical breadth data.
+
+        This should be called after accumulating enough history (> 1 year).
+        Returns boundaries for pd.qcut.
+        """
+        conn = sqlite3.connect(db_path)
+        df = pd.read_sql_query(
+            "SELECT date, advance_top50, decline_top50, above_ema20_top50 FROM breadth_daily",
+            conn
+        )
+        conn.close()
+
+        if len(df) < 100:
+            return {"boundaries": [0, 20, 40, 60, 80, 100], "is_quantile": False}
+
+        df["ad_ratio"] = df["advance_top50"] / (df["advance_top50"] + df["decline_top50"])
+        df["ema_ratio"] = df["above_ema20_top50"] / 50
+        df["breadth_raw"] = (
+            df["ad_ratio"] * BREADTH_W_ADVANCE * 100 +
+            df["ema_ratio"] * BREADTH_W_EMA20 * 100 +
+            40 * BREADTH_W_NEW_HIGHS +
+            50 * BREADTH_W_BTC_DOM
+        )
+
+        boundaries = list(np.percentile(df["breadth_raw"].dropna(), [10, 30, 70, 90]))
+        return {
+            "boundaries": [0] + boundaries + [100],
+            "is_quantile": True,
+            "n_samples": len(df),
+        }
+
+    @staticmethod
+    def _build_narrative(bucket: BreadthBucket, divergence: float,
+                         ema_pct: float, ad_ratio: float) -> str:
+        parts = []
+        if bucket == BreadthBucket.EXTREME:
+            parts.append(f"全市场极度扩散({ema_pct:.0%}站上EMA20)")
+        elif bucket == BreadthBucket.STRONG:
+            parts.append("市场广度强势")
+        elif bucket == BreadthBucket.NORMAL:
+            parts.append("市场广度中性")
+        elif bucket == BreadthBucket.WEAK:
+            parts.append("市场广度疲弱")
+        else:
+            parts.append("市场广度恐慌")
+
+        if divergence > 10:
+            parts.append("资金集中于大市值(Top20>>Top50)")
+        elif divergence < -10:
+            parts.append("垃圾币狂欢(Top50>>Top20)")
+
+        return ", ".join(parts)

ChanMacro/scoring/constants.py

@@ -0,0 +1,98 @@
+"""
+scoring/constants.py — Scoring thresholds, scale factors, and reference values.
+
+All magic numbers in one place. Tune these via Phase 0 validation.
+"""
+
+# ── Price Structure ──────────────────────────────────────────
+# ADX thresholds
+ADX_TREND_THRESHOLD = 25       # ADX > 25 = trending
+ADX_STRONG_THRESHOLD = 40      # ADX > 40 = strong trend
+
+# EMA alignment
+EMA_ALIGNMENT_BULLISH = 1.0    # EMA20 > EMA60 > EMA120
+EMA_ALIGNMENT_NEUTRAL = 0.5    # mixed
+EMA_ALIGNMENT_BEARISH = 0.0    # EMA20 < EMA60 < EMA120
+
+# Volatility compression (BB width relative to 20d average)
+BB_COMPRESSION_LOW = 0.7       # < 70% of avg = compressing
+BB_COMPRESSION_HIGH = 1.5      # > 150% of avg = expanding
+
+# Momentum (ROC annualized)
+ROC_STRONG_BULLISH = 10.0      # % over period
+ROC_STRONG_BEARISH = -10.0
+
+# Consecutive candle threshold
+CONSECUTIVE_CANDLES_SIGNAL = 4
+
+# ── Breadth ──────────────────────────────────────────────────
+# Quantile boundaries for breadth buckets
+BREADTH_QUANTILES = [0, 0.1, 0.3, 0.7, 0.9, 1.0]  # PANIC/WEAK/NORMAL/STRONG/EXTREME
+
+# Breadth score computation weights
+BREADTH_W_ADVANCE = 0.30       # advance/decline ratio
+BREADTH_W_EMA20 = 0.35         # % above EMA20
+BREADTH_W_NEW_HIGHS = 0.20     # new highs count
+BREADTH_W_BTC_DOM = 0.15       # BTC dominance change (inverted)
+
+# ── OI Matrix ────────────────────────────────────────────────
+OI_PRICE_THRESHOLD = 0.5       # min |price_change%| to classify
+OI_OI_THRESHOLD = 0.5          # min |OI_change%| to classify
+
+# Score mapping for OI states
+OI_STATE_SCORES = {
+    "New Longs": 85,
+    "Short Covering": 60,
+    "New Shorts": 20,
+    "Long Exit": 35,
+    "Neutral": 50,
+}
+
+# ── Volatility Regime ────────────────────────────────────────
+VOL_LOW = 2.0                  # ATR/Close % below this = LOW_VOL
+VOL_HIGH = 5.0                 # ATR/Close % below this = HIGH_VOL (above = EXPLOSIVE)
+HV_RATIO_LOW = 0.7             # HV(20)/HV(60) below this = compressing
+HV_RATIO_HIGH = 1.5            # HV(20)/HV(60) above this = expanding
+
+# Score mapping
+VOL_REGIME_SCORES = {
+    "LOW_VOL": 40,              # Low vol → neutral with breakout potential
+    "NORMAL_VOL": 55,
+    "HIGH_VOL": 75,
+    "EXPLOSIVE_VOL": 90,
+}
+
+# ── Regime ───────────────────────────────────────────────────
+REGIME_W_PRICE = 0.35
+REGIME_W_BREADTH = 0.50
+REGIME_W_VOL = 0.15
+
+# PANIC: anti-trend + extreme vol (NO Fear/Liquidation)
+PANIC_W_ANTI_TREND = 0.60
+PANIC_W_VOL_EXTREME = 0.40
+
+# ── Trend (L2) ───────────────────────────────────────────────
+TREND_W_PRICE = 0.30
+TREND_W_BREADTH = 0.70
+
+# ── Maturity ─────────────────────────────────────────────────
+MATURITY_W_TREND = 0.50
+MATURITY_W_BREADTH = 0.30
+MATURITY_W_VOL = 0.20
+
+# ── Expectancy ───────────────────────────────────────────────
+HALF_LIFE_DAYS = 180
+SUFFICIENCY_MIN = 30
+SUFFICIENCY_LOW = 50
+SUFFICIENCY_MEDIUM = 100
+LEVEL_MIN_SAMPLES = 50
+KNN_MAX_DISTANCE = 0.35
+KNN_K = 200
+
+# ── Validation ───────────────────────────────────────────────
+MIN_AVG_DURATION = 5
+MAX_FLIP_RATE = 0.15
+MIN_IC_THRESHOLD = 0.03
+MIN_ICIR_THRESHOLD = 0.5
+MIN_IG_THRESHOLD = 0.1      # Information Gain for regime factors
+MIN_KL_THRESHOLD = 0.5       # KL Divergence for regime separation

ChanMacro/scoring/oi_matrix.py

@@ -0,0 +1,137 @@
+"""
+scoring/oi_matrix.py — OI × Price 2×2 state machine.
+
+Discrete states, NOT a continuous score:
+  NEW_LONGS:       Price↑ OI↑  → new money entering, trend continuation
+  SHORT_COVERING:  Price↑ OI↓  → shorts covering, rally fragile
+  NEW_SHORTS:      Price↓ OI↑  → new shorts entering, trend continuation
+  LONG_EXIT:       Price↓ OI↓  → longs stopping out, panic (possible bottom)
+  NEUTRAL:         flat        → noise, don't force classification
+"""
+
+from datetime import date as Date
+import sqlite3
+
+from .base import BaseScorer
+from .constants import OI_PRICE_THRESHOLD, OI_OI_THRESHOLD, OI_STATE_SCORES
+from models import FactorScore, OIMatrixScore, OIState, MacroDirection
+from config import config
+
+
+class OIMatrixScorer(BaseScorer):
+    """Classifies OI × Price state and assigns score."""
+
+    def compute(self, target_date: Date) -> OIMatrixScore:
+        conn = self.get_connection()
+        try:
+            row = conn.execute(
+                "SELECT * FROM derivatives WHERE date = ? AND symbol = 'BTC/USDT:USDT'",
+                (str(target_date),)
+            ).fetchone()
+
+            if row is None:
+                return OIMatrixScore(
+                    name="OI Matrix",
+                    score=50.0,
+                    label="No Data",
+                    oi_state=OIState.NEUTRAL,
+                )
+
+            row = dict(row)
+            oi_change = row.get("oi_24h_change_pct") or 0
+
+            # Get price change from OHLCV
+            price_change = self._get_price_change(conn, str(target_date))
+
+            # Classify state
+            oi_state = self._classify(price_change, oi_change)
+
+            # Score from state
+            score = OI_STATE_SCORES.get(oi_state.value, 50)
+
+            # Direction
+            if oi_state == OIState.NEW_LONGS:
+                direction = MacroDirection.BULLISH
+            elif oi_state == OIState.SHORT_COVERING:
+                direction = MacroDirection.BULLISH  # bullish but fragile
+            elif oi_state == OIState.NEW_SHORTS:
+                direction = MacroDirection.BEARISH
+            elif oi_state == OIState.LONG_EXIT:
+                direction = MacroDirection.BEARISH  # bearish but possible bottom
+            else:
+                direction = MacroDirection.NEUTRAL
+
+            # Narrative
+            narrative = self._build_narrative(oi_state, price_change, oi_change)
+
+            return OIMatrixScore(
+                name="OI Matrix",
+                score=float(score),
+                label=oi_state.value,
+                direction=direction,
+                oi_state=oi_state,
+                price_change_pct=round(price_change, 2),
+                oi_change_pct=round(oi_change, 2),
+                sub_scores={
+                    "price_change_pct": round(price_change, 2),
+                    "oi_change_pct": round(oi_change, 2),
+                },
+                narrative=narrative,
+            )
+        finally:
+            conn.close()
+
+    def _get_price_change(self, conn: sqlite3.Connection, date_str: str) -> float:
+        """Get BTC 24h price change % for a given date."""
+        row = conn.execute(
+            "SELECT close FROM ohlcv_daily WHERE date = ? AND symbol = 'BTC/USDT:USDT'",
+            (date_str,)
+        ).fetchone()
+        if row is None:
+            return 0.0
+
+        # Get previous day close
+        prev = conn.execute(
+            "SELECT close FROM ohlcv_daily WHERE date < ? AND symbol = 'BTC/USDT:USDT' ORDER BY date DESC LIMIT 1",
+            (date_str,)
+        ).fetchone()
+
+        if prev is None:
+            return 0.0
+
+        current_close = float(row["close"])
+        prev_close = float(prev["close"])
+        if prev_close == 0:
+            return 0.0
+
+        return (current_close - prev_close) / prev_close * 100
+
+    @staticmethod
+    def _classify(price_change_pct: float, oi_change_pct: float) -> OIState:
+        """Classify OI × Price into discrete state."""
+        price_up = price_change_pct > OI_PRICE_THRESHOLD
+        price_down = price_change_pct < -OI_PRICE_THRESHOLD
+        oi_up = oi_change_pct > OI_OI_THRESHOLD
+        oi_down = oi_change_pct < -OI_OI_THRESHOLD
+
+        if price_up and oi_up:
+            return OIState.NEW_LONGS
+        elif price_up and oi_down:
+            return OIState.SHORT_COVERING
+        elif price_down and oi_up:
+            return OIState.NEW_SHORTS
+        elif price_down and oi_down:
+            return OIState.LONG_EXIT
+        else:
+            return OIState.NEUTRAL
+
+    @staticmethod
+    def _build_narrative(state: OIState, price_chg: float, oi_chg: float) -> str:
+        mapping = {
+            OIState.NEW_LONGS: f"新多进场: 价格+{price_chg:.1f}%, OI+{oi_chg:.1f}%, 真金白银推动",
+            OIState.SHORT_COVERING: f"空头回补: 价格+{price_chg:.1f}%, OI{oi_chg:.1f}%, 上涨脆弱",
+            OIState.NEW_SHORTS: f"新空进场: 价格{price_chg:.1f}%, OI+{oi_chg:.1f}%, 趋势延续",
+            OIState.LONG_EXIT: f"多头止损: 价格{price_chg:.1f}%, OI{oi_chg:.1f}%, 恐慌(可能见底)",
+            OIState.NEUTRAL: "OI/价格变化不显著, 噪音区",
+        }
+        return mapping.get(state, "Unknown")

ChanMacro/scoring/price_structure.py

@@ -0,0 +1,248 @@
+"""
+scoring/price_structure.py — Price Structure Score (OHLCV-only).
+
+Three sub-dimensions:
+  1. Trend Strength (40%): EMA alignment + ADX
+  2. Volatility Compression (30%): ATR + BB width
+  3. Momentum (30%): ROC + consecutive candles
+
+This module works with zero external dependencies — just OHLCV data.
+"""
+
+from datetime import date as Date
+import sqlite3
+import math
+import numpy as np
+import pandas as pd
+
+from .base import BaseScorer
+from .constants import (
+    ADX_TREND_THRESHOLD, ADX_STRONG_THRESHOLD,
+    BB_COMPRESSION_LOW, BB_COMPRESSION_HIGH,
+    ROC_STRONG_BULLISH, ROC_STRONG_BEARISH,
+    CONSECUTIVE_CANDLES_SIGNAL,
+)
+from models import FactorScore, PriceStructureScore, MacroDirection
+from config import config
+
+
+class PriceStructureScorer(BaseScorer):
+    """Scores market structure from OHLCV data alone."""
+
+    def compute(self, target_date: Date) -> PriceStructureScore:
+        conn = self.get_connection()
+        try:
+            df = self._load_ohlcv(conn, str(target_date), lookback=120)
+            if df.empty:
+                return PriceStructureScore(
+                    name="Price Structure",
+                    score=50.0,
+                    label="No Data",
+                )
+
+            trend = self._score_trend_strength(df)
+            vol_comp = self._score_volatility_compression(df)
+            momentum = self._score_momentum(df)
+
+            # Weighted aggregate
+            score = trend * 0.40 + vol_comp * 0.30 + momentum * 0.30
+
+            # Determine direction
+            if trend > 60:
+                direction = MacroDirection.BULLISH
+            elif trend < 40:
+                direction = MacroDirection.BEARISH
+            else:
+                direction = MacroDirection.NEUTRAL
+
+            # Build narrative
+            latest = df.iloc[-1]
+            narrative = self._build_narrative(trend, vol_comp, momentum, latest)
+
+            return PriceStructureScore(
+                name="Price Structure",
+                score=round(score, 1),
+                label=self._label(score),
+                direction=direction,
+                trend_strength=round(trend, 1),
+                volatility_compression=round(vol_comp, 1),
+                momentum=round(momentum, 1),
+                sub_scores={
+                    "trend_strength": round(trend, 1),
+                    "volatility_compression": round(vol_comp, 1),
+                    "momentum": round(momentum, 1),
+                },
+                narrative=narrative,
+            )
+        finally:
+            conn.close()
+
+    def _load_ohlcv(self, conn: sqlite3.Connection, date_str: str,
+                    lookback: int = 120) -> pd.DataFrame:
+        """Load OHLCV data up to target_date."""
+        df = pd.read_sql_query(
+            "SELECT * FROM ohlcv_daily WHERE date <= ? ORDER BY date DESC LIMIT ?",
+            conn, params=(date_str, lookback)
+        )
+        if df.empty:
+            return df
+        return df.sort_values("date").reset_index(drop=True)
+
+    def _score_trend_strength(self, df: pd.DataFrame) -> float:
+        """Score trend based on EMA alignment and ADX."""
+        latest = df.iloc[-1]
+
+        # EMA alignment
+        ema20 = latest.get("ema20")
+        ema60 = latest.get("ema60")
+        ema120 = latest.get("ema120")
+
+        ema_score = 50.0
+        if ema20 and ema60 and ema120 and not pd.isna(ema20) and not pd.isna(ema60) and not pd.isna(ema120):
+            alignments = 0
+            if ema20 > ema60: alignments += 1
+            if ema60 > ema120: alignments += 1
+            if ema20 > ema120: alignments += 1
+
+            # Distance from EMAs
+            close = float(latest["close"])
+            ema20_dist = abs(close - ema20) / ema20 * 100 if ema20 else 0
+
+            if alignments == 3:
+                ema_score = 80 + min(ema20_dist, 15)  # strong bullish alignment
+            elif alignments == 0:
+                ema_score = 20 - min(ema20_dist, 15)  # strong bearish alignment
+            elif alignments == 2:
+                ema_score = 65
+            else:
+                ema_score = 35
+
+        # ADX
+        adx = latest.get("adx_14")
+        adx_score = 50.0
+        if adx and not pd.isna(adx):
+            if adx > ADX_STRONG_THRESHOLD:
+                adx_score = 85
+            elif adx > ADX_TREND_THRESHOLD:
+                adx_score = 65 + (adx - ADX_TREND_THRESHOLD) / (ADX_STRONG_THRESHOLD - ADX_TREND_THRESHOLD) * 20
+            else:
+                adx_score = 50 - (ADX_TREND_THRESHOLD - adx) / ADX_TREND_THRESHOLD * 30
+
+        return ema_score * 0.55 + adx_score * 0.45
+
+    def _score_volatility_compression(self, df: pd.DataFrame) -> float:
+        """Score volatility compression — expansion = high, compression = low-mid."""
+        latest = df.iloc[-1]
+
+        bb_width = latest.get("bb_width")
+        if not bb_width or pd.isna(bb_width) or len(df) < 20:
+            return 50.0
+
+        # BB width relative to 20d average
+        recent_bb = df["bb_width"].dropna().tail(20)
+        if len(recent_bb) < 10:
+            return 50.0
+
+        bb_avg = recent_bb.mean()
+        bb_ratio = bb_width / bb_avg if bb_avg > 0 else 1.0
+
+        if bb_ratio < BB_COMPRESSION_LOW:
+            # Compression → potential breakout, neutral-bullish
+            return 45 + (BB_COMPRESSION_LOW - bb_ratio) * 30
+        elif bb_ratio > BB_COMPRESSION_HIGH:
+            # Expansion → trending or chaotic
+            return 75 + min((bb_ratio - BB_COMPRESSION_HIGH) * 20, 20)
+        else:
+            # Normal
+            return 55
+
+    def _score_momentum(self, df: pd.DataFrame) -> float:
+        """Score momentum using ROC and consecutive candles."""
+        if len(df) < 10:
+            return 50.0
+
+        closes = df["close"].astype(float)
+        latest = float(closes.iloc[-1])
+
+        # ROC (5-bar)
+        if len(closes) >= 6:
+            roc5 = (closes.iloc[-1] - closes.iloc[-6]) / closes.iloc[-6] * 100
+        else:
+            roc5 = 0
+
+        # ROC (10-bar)
+        if len(closes) >= 11:
+            roc10 = (closes.iloc[-1] - closes.iloc[-11]) / closes.iloc[-11] * 100
+        else:
+            roc10 = 0
+
+        # ROC (20-bar)
+        if len(closes) >= 21:
+            roc20 = (closes.iloc[-1] - closes.iloc[-21]) / closes.iloc[-21] * 100
+        else:
+            roc20 = 0
+
+        # Score ROC: map to 0-100
+        def roc_to_score(roc, scale=15):
+            return 50 + np.clip(roc / scale * 50, -50, 50)
+
+        roc_score = roc_to_score(roc5, 10) * 0.4 + roc_to_score(roc10, 15) * 0.35 + roc_to_score(roc20, 20) * 0.25
+
+        # Consecutive candle direction
+        consec_score = 50.0
+        consec_up = 0
+        consec_down = 0
+        for i in range(len(closes) - 1, max(0, len(closes) - 10), -1):
+            if closes.iloc[i] > closes.iloc[i - 1]:
+                consec_up += 1
+                consec_down = 0
+            elif closes.iloc[i] < closes.iloc[i - 1]:
+                consec_down += 1
+                consec_up = 0
+            else:
+                break
+
+        if consec_up >= CONSECUTIVE_CANDLES_SIGNAL:
+            consec_score = 70 + min(consec_up * 5, 25)
+        elif consec_down >= CONSECUTIVE_CANDLES_SIGNAL:
+            consec_score = 30 - min(consec_down * 5, 25)
+
+        return roc_score * 0.70 + consec_score * 0.30
+
+    def _build_narrative(self, trend: float, vol: float, momentum: float,
+                         latest: pd.Series) -> str:
+        parts = []
+        if trend > 65:
+            parts.append("EMA多头排列+ADX趋势明确")
+        elif trend > 50:
+            parts.append("趋势温和偏多")
+        elif trend < 35:
+            parts.append("EMA空头排列+ADX趋势明确")
+        elif trend < 50:
+            parts.append("趋势温和偏空")
+        else:
+            parts.append("趋势中性")
+
+        if vol > 70:
+            parts.append("波动率扩张")
+        elif vol < 45:
+            parts.append("波动率压缩(突破前兆)")
+
+        if momentum > 65:
+            parts.append("动量强劲")
+        elif momentum < 35:
+            parts.append("动量疲弱")
+
+        return ", ".join(parts) if parts else "中性"
+
+    @staticmethod
+    def _label(score: float) -> str:
+        if score >= 75:
+            return "Strong Bullish Structure"
+        elif score >= 60:
+            return "Bullish Structure"
+        elif score >= 40:
+            return "Neutral Structure"
+        elif score >= 25:
+            return "Bearish Structure"
+        return "Weak Bearish Structure"

ChanMacro/scoring/volatility_regime.py

@@ -0,0 +1,143 @@
+"""
+scoring/volatility_regime.py — Volatility Regime Classification.
+
+4 regimes from OHLCV data:
+  LOW_VOL:       ATR/Close < 2% → compression, breakout imminent
+  NORMAL_VOL:    ATR/Close 2-5% → normal trading
+  HIGH_VOL:      ATR/Close 5-10% → trend acceleration, wider stops
+  EXPLOSIVE_VOL: ATR/Close > 10% → extreme, reduce or wait
+
+Uses: ATR(14)/Close, HV(20)/HV(60) ratio, BB width ratio.
+OHLCV-only — never goes offline.
+"""
+
+from datetime import date as Date
+import sqlite3
+import numpy as np
+import pandas as pd
+
+from .base import BaseScorer
+from .constants import (
+    VOL_LOW, VOL_HIGH, VOL_REGIME_SCORES, HV_RATIO_LOW, HV_RATIO_HIGH,
+)
+from models import FactorScore, VolatilityRegimeScore, VolRegime, MacroDirection
+from config import config
+
+
+class VolatilityRegimeScorer(BaseScorer):
+    """Classifies volatility regime from OHLCV data."""
+
+    def compute(self, target_date: Date) -> VolatilityRegimeScore:
+        conn = self.get_connection()
+        try:
+            df = pd.read_sql_query(
+                "SELECT * FROM ohlcv_daily WHERE date <= ? ORDER BY date DESC LIMIT 120",
+                conn, params=(str(target_date),)
+            )
+            if df.empty:
+                return VolatilityRegimeScore(
+                    name="Volatility Regime",
+                    score=50.0,
+                    label="No Data",
+                )
+
+            df = df.sort_values("date").reset_index(drop=True)
+
+            # 1. ATR/Close %
+            latest = df.iloc[-1]
+            atr = latest.get("atr_14")
+            close = float(latest["close"])
+            atr_pct = (atr / close * 100) if atr and not pd.isna(atr) and close > 0 else 3.0
+
+            # 2. HV(20) / HV(60) ratio
+            hv_ratio = self._compute_hv_ratio(df)
+
+            # 3. BB width ratio
+            bb_ratio = self._compute_bb_ratio(df)
+
+            # Classify regime
+            regime = self._classify(atr_pct, hv_ratio, bb_ratio)
+
+            # Score
+            score = VOL_REGIME_SCORES.get(regime.value, 50)
+
+            # Narrative
+            narrative = self._build_narrative(regime, atr_pct, hv_ratio, bb_ratio)
+
+            return VolatilityRegimeScore(
+                name="Volatility Regime",
+                score=float(score),
+                label=regime.value,
+                direction=MacroDirection.NEUTRAL,
+                vol_regime=regime,
+                atr_pct=round(atr_pct, 2),
+                hv_ratio=round(hv_ratio, 2),
+                bb_width_ratio=round(bb_ratio, 2),
+                sub_scores={
+                    "atr_pct": round(atr_pct, 2),
+                    "hv_ratio": round(hv_ratio, 2),
+                    "bb_width_ratio": round(bb_ratio, 2),
+                },
+                narrative=narrative,
+            )
+        finally:
+            conn.close()
+
+    def _compute_hv_ratio(self, df: pd.DataFrame) -> float:
+        """Compute HV(20) / HV(60) ratio."""
+        closes = df["close"].astype(float)
+        returns = closes.pct_change().dropna()
+
+        if len(returns) < 60:
+            return 1.0
+
+        hv20 = returns.tail(20).std() * np.sqrt(365) * 100
+        hv60 = returns.tail(60).std() * np.sqrt(365) * 100
+
+        if hv60 == 0:
+            return 1.0
+
+        return hv20 / hv60
+
+    def _compute_bb_ratio(self, df: pd.DataFrame) -> float:
+        """Compute current BB width / 20d average BB width."""
+        bb_widths = df["bb_width"].dropna().tail(40)
+        if len(bb_widths) < 20:
+            return 1.0
+
+        current = bb_widths.iloc[-1]
+        avg = bb_widths.tail(20).mean()
+        if avg == 0:
+            return 1.0
+
+        return current / avg
+
+    @staticmethod
+    def _classify(atr_pct: float, hv_ratio: float, bb_ratio: float) -> VolRegime:
+        """Classify volatility regime from multiple indicators."""
+        # Primary: ATR/Close %
+        if atr_pct > 10.0:
+            return VolRegime.EXPLOSIVE_VOL
+        elif atr_pct > VOL_HIGH:
+            return VolRegime.HIGH_VOL
+        elif atr_pct < VOL_LOW:
+            return VolRegime.LOW_VOL
+
+        # Secondary: HV ratio and BB ratio for edge cases
+        if hv_ratio > HV_RATIO_HIGH and bb_ratio > 1.3:
+            return VolRegime.HIGH_VOL
+        elif hv_ratio < HV_RATIO_LOW and bb_ratio < 0.8:
+            return VolRegime.LOW_VOL
+
+        return VolRegime.NORMAL_VOL
+
+    @staticmethod
+    def _build_narrative(regime: VolRegime, atr_pct: float,
+                         hv_ratio: float, bb_ratio: float) -> str:
+        mapping = {
+            VolRegime.LOW_VOL: f"低波动(ATR={atr_pct:.1f}%), 布林带收窄, 突破前兆",
+            VolRegime.NORMAL_VOL: f"正常波动(ATR={atr_pct:.1f}%), 正常交易环境",
+            VolRegime.HIGH_VOL: f"高波动(ATR={atr_pct:.1f}%), 趋势加速, 放宽止损",
+            VolRegime.EXPLOSIVE_VOL: f"极端波动(ATR={atr_pct:.1f}%), 减仓或等待",
+        }
+        return mapping.get(regime, "Unknown")

ChanMacro/tests/conftest.py

@@ -0,0 +1,134 @@
+"""
+tests/conftest.py — Shared fixtures for ChanMacro tests.
+"""
+
+import os
+import sys
+import pytest
+import sqlite3
+import numpy as np
+import pandas as pd
+from datetime import date, timedelta
+from pathlib import Path
+
+# Ensure package root on path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+
+@pytest.fixture
+def db_path(tmp_path):
+    """Create a temporary SQLite database with full mock data."""
+    db = str(tmp_path / "test_macro.db")
+    from database import init_db
+    conn = init_db(db)
+
+    np.random.seed(42)
+    base = date(2025, 9, 1)
+    n_days = 300
+
+    # Generate realistic price series with 3 regime periods
+    prices = [90000]
+    regimes = []
+    for i in range(n_days):
+        if i < 100:
+            ret = np.random.normal(0.003, 0.015)
+            regime = "TREND"
+        elif i < 200:
+            ret = np.random.normal(0.000, 0.012)
+            regime = "RANGE"
+        else:
+            ret = np.random.normal(-0.003, 0.025)
+            regime = "PANIC"
+        prices.append(prices[-1] * (1 + ret))
+        regimes.append(regime)
+
+    for i in range(n_days):
+        d = base + timedelta(days=i)
+        c = prices[i]
+        r = regimes[i]
+
+        # OHLCV
+        conn.execute("""
+            INSERT OR REPLACE INTO ohlcv_daily
+            (date,symbol,open,high,low,close,volume,ema20,ema60,ema120,atr_14,bb_width,adx_14)
+            VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?)
+        """, (
+            d.strftime("%Y-%m-%d"), "BTC/USDT:USDT",
+            c * 0.99, c * 1.03, c * 0.97, c, 1000,
+            c * (0.98 if r == "TREND" else 1.02 if r == "PANIC" else 1.0),
+            c * (0.95 if r == "TREND" else 1.05 if r == "PANIC" else 1.0),
+            c * (0.90 if r == "TREND" else 1.10 if r == "PANIC" else 1.0),
+            c * (0.02 if r == "PANIC" else 0.015),
+            4.5, 28.0 if r == "TREND" else 18.0,
+        ))
+
+        # Breadth
+        adv = 42 if r == "TREND" else 25 if r == "RANGE" else 8
+        conn.execute("""
+            INSERT OR REPLACE INTO breadth_daily
+            (date,total_tracked,advance_top50,decline_top50,above_ema20_top50,
+             new_highs_20d_top50,advance_top30,advance_top20,
+             above_ema20_top30,above_ema20_top20,new_highs_20d_top30,new_highs_20d_top20)
+            VALUES (?,50,?,?,?,?,?,?,?,?,?,?)
+        """, (
+            d.strftime("%Y-%m-%d"), adv, 50 - adv, adv, min(adv, 15),
+            int(adv * 0.7), int(adv * 0.5), int(adv * 0.7), int(adv * 0.5),
+            min(int(adv * 0.7), 12), min(int(adv * 0.5), 8),
+        ))
+
+        # Derivatives
+        oi_chg = 3.5 if r == "TREND" else 0.5 if r == "RANGE" else -2.0
+        conn.execute("""
+            INSERT OR REPLACE INTO derivatives
+            (date,symbol,funding_rate,open_interest,oi_24h_change_pct,
+             long_liquidations,short_liquidations,basis_annualised_pct)
+            VALUES (?,?,?,?,?,?,?,?)
+        """, (
+            d.strftime("%Y-%m-%d"), "BTC/USDT:USDT",
+            0.0001 + np.random.normal(0, 0.0002),
+            35e9, oi_chg + np.random.normal(0, 1.0),
+            50e6 * np.random.random(), 30e6 * np.random.random(),
+            8.5 if r == "TREND" else 3.0,
+        ))
+
+        # Regime history
+        conn.execute("""
+            INSERT OR REPLACE INTO regime_history
+            (date,regime,confidence,regime_version,maturity_score,all_scores_json,confirmation_days)
+            VALUES (?,?,?,?,?,?,?)
+        """, (d.strftime("%Y-%m-%d"), r, 0.75, "v1_price_breadth_vol", 50, "{}", 1))
+
+    conn.commit()
+    conn.close()
+
+    # Override config to use test DB
+    from config import config
+    old_db = config.db_path
+    config.db_path = db
+    yield db
+    config.db_path = old_db
+
+
+@pytest.fixture
+def sample_state(db_path):
+    """Build a MarketStateVector for a known test date."""
+    from models import (
+        MarketStateVector, MarketRegime, BreadthBucket,
+        OIState, VolRegime,
+    )
+    state = MarketStateVector(
+        date=date(2026, 3, 15),
+        regime=MarketRegime.TREND,
+        regime_confidence=0.82,
+        regime_version="v1_price_breadth_vol",
+        regime_maturity_score=55.0,
+        breadth_top20=82.0,
+        breadth_top30=78.0,
+        breadth_top50=74.0,
+        breadth_bucket=BreadthBucket.STRONG,
+        breadth_divergence=8.0,
+        oi_state=OIState.NEW_LONGS,
+        volatility_regime=VolRegime.NORMAL_VOL,
+    )
+    state.market_state_hash = state.compute_hash()
+    return state

ChanMacro/tests/test_expectancy.py

@@ -0,0 +1,173 @@
+"""Test SignalTracker, TimeDecay, and BayesianExpectancyEngine."""
+import pytest
+from datetime import date, timedelta
+import numpy as np
+
+
+class TestTimeDecay:
+    def test_recent_weight_near_one(self):
+        from expectancy.decay import TimeDecay
+        d = TimeDecay(180)
+        w = d.weight(date(2026, 6, 20), date(2026, 6, 24))
+        assert 0.95 < w < 1.0
+
+    def test_old_weight_decays(self):
+        from expectancy.decay import TimeDecay
+        d = TimeDecay(180)
+        w = d.weight(date(2025, 6, 24), date(2026, 6, 24))
+        assert 0.2 < w < 0.3  # ~365 days at half_life=180
+
+    def test_effective_samples(self):
+        from expectancy.decay import TimeDecay
+        d = TimeDecay(180)
+        dates = [date(2026, 6, 24)] * 10
+        weights = d.weights(dates, date(2026, 6, 24))
+        eff = d.effective_samples(weights)
+        assert eff == pytest.approx(10.0, rel=0.01)
+
+    def test_weighted_win_rate(self):
+        from expectancy.decay import TimeDecay
+        d = TimeDecay(180)
+        wins = np.array([1, 0, 1, 0])
+        weights = np.array([1.0, 1.0, 1.0, 1.0])
+        wr = d.weighted_win_rate(wins, weights)
+        assert wr == 0.5
+
+    def test_weight_at_age(self):
+        from expectancy.decay import TimeDecay
+        w = TimeDecay.weight_at_age(180, 180)
+        assert w == pytest.approx(0.5, rel=0.01)
+
+
+class TestSignalTracker:
+    def test_record_signal(self, db_path, sample_state):
+        from expectancy.tracker import SignalTracker
+        tracker = SignalTracker()
+        rid = tracker.record(
+            date(2026, 3, 15), "B3", 98000.0, sample_state,
+            signal_grade="A", signal_strength=75.0,
+        )
+        assert rid is not None
+        assert rid > 0
+
+    def test_get_samples(self, db_path, sample_state):
+        from expectancy.tracker import SignalTracker
+        tracker = SignalTracker()
+        tracker.record(date(2026, 3, 15), "B3", 98000.0, sample_state)
+        tracker.record(date(2026, 3, 16), "B2", 98500.0, sample_state)
+
+        samples = tracker.get_samples(signal_type="B3")
+        assert len(samples) == 1
+        assert samples[0]["signal_type"] == "B3"
+
+    def test_count_samples(self, db_path, sample_state):
+        from expectancy.tracker import SignalTracker
+        tracker = SignalTracker()
+        tracker.record(date(2026, 3, 15), "B3", 98000.0, sample_state)
+        tracker.record(date(2026, 3, 16), "B3", 98500.0, sample_state)
+
+        counts = tracker.count_samples()
+        assert "B3/TREND" in counts
+        assert counts["B3/TREND"] == 2
+
+    def test_filter_by_regime(self, db_path, sample_state):
+        from expectancy.tracker import SignalTracker
+        tracker = SignalTracker()
+        tracker.record(date(2026, 3, 15), "B3", 98000.0, sample_state)
+
+        samples = tracker.get_samples(signal_type="B3", regime="TREND")
+        assert len(samples) == 1
+
+        samples = tracker.get_samples(signal_type="B3", regime="PANIC")
+        assert len(samples) == 0
+
+    def test_backfill_signals(self, db_path, sample_state):
+        from expectancy.tracker import SignalTracker
+        tracker = SignalTracker()
+        signals = [
+            {"date": date(2026, 3, 15), "signal_type": "B3", "entry_price": 98000},
+            {"date": date(2026, 3, 20), "signal_type": "B2", "entry_price": 99000},
+        ]
+        count = tracker.backfill_signals(signals)
+        assert count == 2
+
+
+class TestBayesianExpectancyEngine:
+    def test_estimate_returns_report(self, db_path, sample_state):
+        from expectancy.tracker import SignalTracker
+        from expectancy.engine import BayesianExpectancyEngine
+
+        # Record some signals first
+        tracker = SignalTracker()
+        for i in range(10):
+            tracker.record(
+                date(2026, 3, 15) + timedelta(days=i),
+                "B3", 98000.0, sample_state,
+            )
+
+        engine = BayesianExpectancyEngine(level_min_samples=3)
+        report = engine.estimate(sample_state, "B3", date(2026, 3, 25))
+        assert report.signal_type == "B3"
+        assert len(report.layers) > 0
+        assert report.source in ("bayesian", "insufficient")
+
+    def test_insufficient_with_no_samples(self, db_path, sample_state):
+        from expectancy.engine import BayesianExpectancyEngine
+        engine = BayesianExpectancyEngine(level_min_samples=10)
+        report = engine.estimate(sample_state, "B1", date(2026, 3, 25))
+        assert report.sufficiency.value in ("INSUFFICIENT", "LOW", "MEDIUM", "HIGH")
+
+    def test_empirical_bayes_shrinks_small_samples(self, db_path, sample_state):
+        """With N=3, raw=100%, posterior should be pulled toward prior."""
+        from expectancy.tracker import SignalTracker
+        from expectancy.engine import BayesianExpectancyEngine
+
+        tracker = SignalTracker()
+        for i in range(3):
+            tracker.record(
+                date(2026, 3, 15) + timedelta(days=i),
+                "B3", 98000.0, sample_state,
+            )
+
+        engine = BayesianExpectancyEngine(level_min_samples=1)
+        report = engine.estimate(sample_state, "B3", date(2026, 3, 25))
+
+        # With small N, posterior should differ from raw
+        base_layer = report.layers[0]
+        if base_layer.raw_winrate and base_layer.samples < 50:
+            # Posterior should be pulled toward prior (50% or global rate)
+            if base_layer.raw_winrate > 0.8:
+                assert base_layer.posterior_winrate < base_layer.raw_winrate
+
+    def test_leveled_fallback_stops_at_min_samples(self, db_path, sample_state):
+        from expectancy.tracker import SignalTracker
+        from expectancy.engine import BayesianExpectancyEngine
+
+        tracker = SignalTracker()
+        for i in range(20):
+            tracker.record(date(2026, 3, 15) + timedelta(days=i), "B3", 98000.0, sample_state)
+
+        engine = BayesianExpectancyEngine(level_min_samples=15)
+        report = engine.estimate(sample_state, "B3", date(2026, 3, 25))
+        # Should have stopped at a level with >= 15 effective samples
+        assert report.final_estimate >= 0
+
+
+class TestSufficiencyGuard:
+    def test_insufficient(self):
+        from expectancy.engine import SufficiencyGuard
+        from models import SufficiencyLevel
+        g = SufficiencyGuard()
+        assert g.evaluate(10) == SufficiencyLevel.INSUFFICIENT
+
+    def test_low(self):
+        from expectancy.engine import SufficiencyGuard
+        from models import SufficiencyLevel
+        g = SufficiencyGuard()
+        assert g.evaluate(40) == SufficiencyLevel.LOW
+
+    def test_high(self):
+        from expectancy.engine import SufficiencyGuard
+        from models import SufficiencyLevel
+        g = SufficiencyGuard()
+        assert g.evaluate(200) == SufficiencyLevel.HIGH

ChanMacro/tests/test_models.py

@@ -0,0 +1,130 @@
+"""Test all Pydantic models and enums."""
+import pytest
+from datetime import date
+from models import (
+    MarketRegime, OIState, BreadthBucket, VolRegime,
+    MarketStateVector, FactorScore, RegimeResult,
+    SignalFeatureRecord, ExpectancyReport, DailyOutput,
+    FactorContribution, SufficiencyLevel, SignalGrade,
+)
+
+
+class TestEnums:
+    def test_regime_values(self):
+        assert MarketRegime.TREND.value == "TREND"
+        assert MarketRegime.RANGE.value == "RANGE"
+        assert MarketRegime.PANIC.value == "PANIC"
+
+    def test_oi_state_has_neutral(self):
+        assert OIState.NEUTRAL.value == "Neutral"
+        assert len(OIState) == 5
+
+    def test_breadth_bucket_values(self):
+        assert BreadthBucket.EXTREME.value == "EXTREME"
+        assert len(BreadthBucket) == 5
+
+    def test_vol_regime_values(self):
+        assert VolRegime.LOW_VOL.value == "LOW_VOL"
+        assert VolRegime.EXPLOSIVE_VOL.value == "EXPLOSIVE_VOL"
+
+
+class TestMarketStateVector:
+    def test_minimal_construction(self):
+        sv = MarketStateVector(
+            date="2026-06-24",
+            regime=MarketRegime.TREND,
+            regime_confidence=0.82,
+            regime_version="v1_price_breadth_vol",
+        )
+        assert sv.date == date(2026, 6, 24)
+        assert sv.regime == MarketRegime.TREND
+        assert sv.breadth_top50 == 50.0  # default
+
+    def test_date_string_parsing(self):
+        sv = MarketStateVector(
+            date="2026-01-15",
+            regime=MarketRegime.RANGE,
+            regime_confidence=0.55,
+            regime_version="v1_price_breadth_vol",
+        )
+        assert sv.date == date(2026, 1, 15)
+
+    def test_compute_hash(self):
+        sv = MarketStateVector(
+            date="2026-06-24",
+            regime=MarketRegime.TREND,
+            regime_confidence=0.82,
+            regime_version="v1_price_breadth_vol",
+            breadth_bucket=BreadthBucket.EXTREME,
+            oi_state=OIState.NEW_LONGS,
+            volatility_regime=VolRegime.NORMAL_VOL,
+        )
+        h = sv.compute_hash()
+        assert len(h) == 12
+        # Same state = same hash
+        sv2 = MarketStateVector(
+            date="2026-06-25",
+            regime=MarketRegime.TREND,
+            regime_confidence=0.80,
+            regime_version="v1_price_breadth_vol",
+            breadth_bucket=BreadthBucket.EXTREME,
+            oi_state=OIState.NEW_LONGS,
+            volatility_regime=VolRegime.NORMAL_VOL,
+        )
+        assert sv2.compute_hash() == h
+
+    def test_state_embedding(self):
+        sv = MarketStateVector(
+            date="2026-06-24",
+            regime=MarketRegime.TREND,
+            regime_confidence=0.82,
+            regime_version="v1_price_breadth_vol",
+            breadth_top20=80.0,
+            breadth_top30=75.0,
+            breadth_top50=70.0,
+            regime_maturity_score=60.0,
+        )
+        emb = sv.state_embedding()
+        assert len(emb) == 5
+        assert emb[0] == 80.0
+        assert emb[3] == 60.0
+
+
+class TestRegimeResult:
+    def test_construction(self):
+        r = RegimeResult(
+            date="2026-06-24",
+            regime=MarketRegime.TREND,
+            confidence=0.82,
+            regime_version="v1_price_breadth_vol",
+            maturity_score=55.0,
+            all_scores={"TREND": 82.0, "RANGE": 45.0, "PANIC": 20.0},
+            confirmation_days=5,
+        )
+        assert r.regime == MarketRegime.TREND
+        assert r.confirmation_days == 5
+
+
+class TestExpectancyReport:
+    def test_insufficient(self):
+        r = ExpectancyReport(
+            signal_type="B3",
+            date="2026-06-24",
+            final_estimate=0.0,
+            sufficiency=SufficiencyLevel.INSUFFICIENT,
+            source="insufficient",
+        )
+        assert r.final_estimate == 0.0
+        assert r.sufficiency == SufficiencyLevel.INSUFFICIENT
+
+
+class TestFactorContribution:
+    def test_construction(self):
+        fc = FactorContribution(
+            factor="ETF Flow",
+            raw_score=85.0,
+            weight=0.1925,
+            impact=6.7,
+            direction="bullish",
+        )
+        assert fc.impact > 0

ChanMacro/tests/test_regime.py

@@ -0,0 +1,109 @@
+"""Test regime detector and validation."""
+import pytest
+from datetime import date
+import pandas as pd
+import numpy as np
+
+
+class TestRegimeDetector:
+    def test_detects_trend(self):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        r = d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
+        assert r.regime == MarketRegime.TREND
+        assert r.confidence > 0.5
+
+    def test_detects_range(self):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        r = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 24))
+        assert r.regime in (MarketRegime.RANGE, MarketRegime.TREND)
+
+    def test_detects_panic(self):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        r = d.detect(15.0, 10.0, "EXPLOSIVE_VOL", date(2026, 6, 24))
+        assert r.regime == MarketRegime.PANIC
+
+    def test_2day_confirmation(self):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        # Day 1: RANGE
+        r1 = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 24))
+        assert r1.regime == MarketRegime.RANGE  # first run, no confirmation needed
+        # Day 2: still RANGE
+        r2 = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 25))
+        assert r2.regime == MarketRegime.RANGE
+        assert r2.confirmation_days == 2
+
+    def test_transition_needs_confirmation(self):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        # Establish TREND
+        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
+        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 25))
+        # Day 3: weak scores → raw best = RANGE, but TREND should persist
+        r3 = d.detect(35.0, 40.0, "NORMAL_VOL", date(2026, 6, 26))
+        # First day of pending transition — should still be TREND
+        assert r3.regime == MarketRegime.TREND
+        assert d.pending_regime is not None
+
+    def test_version_is_stored(self):
+        from regime_detector import RegimeDetector
+        d = RegimeDetector(regime_version="v1_price_breadth_vol")
+        r = d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
+        assert r.regime_version == "v1_price_breadth_vol"
+
+    def test_load_state(self, db_path):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        d.load_state(db_path)
+        # DB has TREND for first 100 days, so most recent should load
+        assert d.current_regime is not None
+
+    def test_confidence_for_confirmed_regime(self):
+        """Confidence should be for the confirmed regime, not raw best."""
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        # Establish TREND
+        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
+        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 25))
+        # Now feed weak scores → raw best would be PANIC or RANGE
+        r = d.detect(15.0, 10.0, "EXPLOSIVE_VOL", date(2026, 6, 26))
+        # Should still report TREND (need 2 confirmations to switch)
+        assert r.regime == MarketRegime.TREND
+
+
+class TestTransitionValidator:
+    def test_stable_regime_passes(self):
+        from validation.transition_validator import TransitionValidator
+        # Create stable regime sequence: long periods
+        seq = pd.Series(
+            ["TREND"] * 50 + ["RANGE"] * 50 + ["PANIC"] * 40,
+            index=pd.date_range("2026-01-01", periods=140),
+        )
+        tv = TransitionValidator()
+        report = tv.validate(seq)
+        assert report.is_stable
+        assert report.avg_duration > 20
+        assert report.flip_rate < 0.05
+
+    def test_unstable_regime_fails(self):
+        from validation.transition_validator import TransitionValidator
+        # Create unstable sequence: flips every 2 days
+        seq = pd.Series(
+            ["TREND", "TREND", "RANGE", "RANGE", "TREND", "TREND",
+             "PANIC", "PANIC", "RANGE", "RANGE"] * 5,
+            index=pd.date_range("2026-01-01", periods=50),
+        )
+        tv = TransitionValidator()
+        report = tv.validate(seq)
+        assert not report.is_stable
+        assert report.flip_rate > 0.15

ChanMacro/tests/test_scoring.py

@@ -0,0 +1,121 @@
+"""Test all 4 core scorers."""
+import pytest
+from datetime import date
+
+
+class TestPriceStructureScorer:
+    def test_computes_score(self, db_path):
+        from scoring.price_structure import PriceStructureScorer
+        scorer = PriceStructureScorer()
+        result = scorer.compute(date(2026, 3, 15))
+        assert result.name == "Price Structure"
+        assert 0 <= result.score <= 100
+        assert result.trend_strength >= 0
+        assert result.volatility_compression >= 0
+        assert result.momentum >= 0
+        assert result.label
+
+    def test_bullish_in_trend(self, db_path):
+        from scoring.price_structure import PriceStructureScorer
+        scorer = PriceStructureScorer()
+        result = scorer.compute(date(2025, 11, 15))  # TREND period
+        assert result.score > 50  # Should be bullish in uptrend
+
+    def test_bearish_in_panic(self, db_path):
+        from scoring.price_structure import PriceStructureScorer
+        scorer = PriceStructureScorer()
+        result = scorer.compute(date(2026, 5, 15))  # PANIC period
+        # In panic period, EMA alignment should be bearish
+        assert result.trend_strength < 60
+
+    def test_no_data_handling(self, db_path):
+        from scoring.price_structure import PriceStructureScorer
+        scorer = PriceStructureScorer()
+        result = scorer.compute(date(2020, 1, 1))
+        assert result.score == 50.0
+        assert result.label == "No Data"
+
+
+class TestBreadthScorer:
+    def test_computes_score(self, db_path):
+        from scoring.breadth_scorer import BreadthScorer
+        scorer = BreadthScorer()
+        result = scorer.compute(date(2026, 3, 15))
+        assert result.name == "Breadth"
+        assert 0 <= result.score <= 100
+        assert result.breadth_bucket
+        assert result.breadth_top20 >= 0
+        assert result.breadth_top50 >= 0
+
+    def test_tier_values(self, db_path):
+        from scoring.breadth_scorer import BreadthScorer
+        scorer = BreadthScorer()
+        result = scorer.compute(date(2025, 11, 15))  # TREND period
+        # Top20 should generally be higher than Top50 (large caps lead)
+        assert result.breadth_top20 >= 0
+        assert result.breadth_top50 >= 0
+
+    def test_bucket_assignment(self, db_path):
+        from scoring.breadth_scorer import BreadthScorer, BreadthBucket
+        scorer = BreadthScorer()
+        result = scorer.compute(date(2025, 11, 15))  # TREND: adv=42/50
+        assert result.breadth_bucket in (
+            BreadthBucket.EXTREME, BreadthBucket.STRONG, BreadthBucket.NORMAL
+        )
+
+    def test_no_data(self, db_path):
+        from scoring.breadth_scorer import BreadthScorer
+        scorer = BreadthScorer()
+        result = scorer.compute(date(2020, 1, 1))
+        assert result.score == 50.0
+
+
+class TestOIMatrixScorer:
+    def test_computes_state(self, db_path):
+        from scoring.oi_matrix import OIMatrixScorer, OIState
+        scorer = OIMatrixScorer()
+        result = scorer.compute(date(2025, 11, 15))  # TREND period, oi_chg=+3.5
+        assert result.oi_state in OIState
+        assert 0 <= result.score <= 100
+
+    def test_new_longs_in_trend(self, db_path):
+        from scoring.oi_matrix import OIMatrixScorer, OIState
+        scorer = OIMatrixScorer()
+        # Test multiple dates in TREND period — at least one should be NEW_LONGS or NEUTRAL
+        found_bullish = False
+        for d in ["2025-11-15", "2025-11-20", "2025-12-01", "2025-12-15"]:
+            result = scorer.compute(date.fromisoformat(d))
+            if result.oi_state in (OIState.NEW_LONGS, OIState.SHORT_COVERING, OIState.NEUTRAL):
+                found_bullish = True
+                break
+        assert found_bullish, "No bullish OI state found in TREND period"
+
+    def test_no_data(self, db_path):
+        from scoring.oi_matrix import OIMatrixScorer
+        scorer = OIMatrixScorer()
+        result = scorer.compute(date(2020, 1, 1))
+        assert result.score == 50.0
+        assert result.label == "No Data"
+
+
+class TestVolatilityRegimeScorer:
+    def test_computes_regime(self, db_path):
+        from scoring.volatility_regime import VolatilityRegimeScorer, VolRegime
+        scorer = VolatilityRegimeScorer()
+        result = scorer.compute(date(2026, 3, 15))
+        assert result.vol_regime in VolRegime
+        assert 0 <= result.score <= 100
+
+    def test_higher_vol_in_panic(self, db_path):
+        from scoring.volatility_regime import VolatilityRegimeScorer, VolRegime
+        scorer = VolatilityRegimeScorer()
+        trend_result = scorer.compute(date(2025, 11, 15))
+        panic_result = scorer.compute(date(2026, 5, 15))
+        # PANIC period has higher ATR → higher vol regime or score
+        assert panic_result.atr_pct >= trend_result.atr_pct * 0.5  # at least comparable
+
+    def test_no_data(self, db_path):
+        from scoring.volatility_regime import VolatilityRegimeScorer
+        scorer = VolatilityRegimeScorer()
+        result = scorer.compute(date(2020, 1, 1))
+        assert result.score == 50.0

ChanMacro/trend_detector.py

@@ -0,0 +1,81 @@
+"""
+trend_detector.py — Trend strength and maturity helpers.
+
+Utility functions for computing trend alignment, acceleration, persistence.
+Used by regime_detector and price_structure scorer.
+"""
+
+import numpy as np
+import pandas as pd
+
+
+def ema_alignment_score(close: float, ema20: float, ema60: float, ema120: float) -> float:
+    """Score EMA alignment: 0=bearish, 50=neutral, 100=bullish."""
+    if any(pd.isna(x) for x in [ema20, ema60, ema120]):
+        return 50.0
+
+    alignments = 0
+    if ema20 > ema60:
+        alignments += 1
+    if ema60 > ema120:
+        alignments += 1
+    if ema20 > ema120:
+        alignments += 1
+
+    if alignments == 3:
+        return 85.0
+    elif alignments == 2:
+        return 65.0
+    elif alignments == 1:
+        return 35.0
+    else:
+        return 15.0
+
+
+def adx_trend_score(adx: float) -> float:
+    """Convert ADX value to trend score: 0-100."""
+    if pd.isna(adx):
+        return 50.0
+    if adx > 40:
+        return 90.0
+    elif adx > 25:
+        return 60.0 + (adx - 25) / 15 * 30
+    elif adx > 15:
+        return 40.0 + (adx - 15) / 10 * 20
+    else:
+        return max(10.0, adx / 15 * 40)
+
+
+def breadth_persistence(breadth_scores: list[float], window: int = 5) -> float:
+    """How consistently has breadth stayed at its current level? 0-100."""
+    if len(breadth_scores) < window:
+        return 50.0
+    recent = breadth_scores[-window:]
+    mean_val = np.mean(recent)
+    std_val = np.std(recent) if len(recent) > 1 else 0
+    # Low std = high persistence
+    persistence = 100 - min(std_val * 5, 100)
+    # Bias: higher breadth = higher persistence score
+    return persistence * 0.5 + mean_val * 0.5
+
+
+def trend_strength_composite(ema_score: float, adx_score: float,
+                              breadth_score: float) -> float:
+    """Composite trend strength 0-100."""
+    return ema_score * 0.25 + adx_score * 0.25 + breadth_score * 0.50
+
+
+def compute_maturity(trend_strength: float, breadth_persistence: float,
+                     vol_expansion: float) -> float:
+    """
+    Compute regime maturity score 0-100.
+
+    EMERGING (0-30): trend accelerating, breadth expanding
+    CONFIRMED (30-70): trend stable, breadth stable
+    EXHAUSTING (70-100): trend decelerating, breadth contracting, vol abnormal
+    """
+    return (
+        trend_strength * 0.50 +
+        breadth_persistence * 0.30 +
+        (100 - vol_expansion) * 0.20  # inverted: low vol = early stage
+    )

ChanMacro/validation/__init__.py

@@ -0,0 +1,5 @@
+"""Validation Framework — Phase 0: verify every factor before trusting it."""
+from .factor_validator import FactorValidator
+from .regime_validator import RegimeValidator
+from .transition_validator import TransitionValidator
+from .reporter import ValidationReporter

ChanMacro/validation/factor_validator.py

@@ -0,0 +1,174 @@
+"""
+validation/factor_validator.py — Validates a factor's predictive power.
+
+Tests: IC, ICIR, Hit Ratio, Quantile Spread, Lead-Lag analysis.
+Answers: "Does this factor predict future returns?"
+"""
+
+from datetime import date as Date
+from typing import Optional
+import sqlite3
+import logging
+
+import numpy as np
+import pandas as pd
+
+from config import config
+from .metrics import (
+    information_coefficient, icir, hit_ratio,
+    quantile_spread, lead_lag_ic,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class FactorReport:
+    """Structured report for a single factor's validation results."""
+
+    def __init__(self, factor_name: str):
+        self.factor_name = factor_name
+        self.ic_mean: float = 0.0
+        self.ic_std: float = 0.0
+        self.icir: float = 0.0
+        self.hit_ratio: float = 0.0
+        self.quantile_spread: float = 0.0
+        self.is_leading: bool = False
+        self.lead_days: int = 0
+        self.lead_ic: float = 0.0
+        self.n_observations: int = 0
+        self.conclusion: str = ""
+
+    def summary(self) -> str:
+        lines = [
+            f"Factor: {self.factor_name}",
+            f"  N={self.n_observations}",
+            f"  IC mean={self.ic_mean:.4f}  std={self.ic_std:.4f}  ICIR={self.icir:.2f}",
+            f"  Hit Ratio={self.hit_ratio:.1%}  Top-Bot Spread={self.quantile_spread:.4f}",
+            f"  Best Lead: {self.lead_days}d (IC={self.lead_ic:.4f})" if self.is_leading else "  Leading: No (synchronous/lagging)",
+            f"  → {self.conclusion}",
+        ]
+        return "\n".join(lines)
+
+
+class FactorValidator:
+    """
+    Validates a factor's predictive power using standard quant metrics.
+
+    For each forward horizon (1d, 3d, 5d, 7d, 14d), computes:
+      - IC (Spearman rank correlation)
+      - ICIR (IC stability)
+      - Hit Ratio (direction accuracy)
+      - Quantile spread (top vs bottom bucket)
+      - Lead-lag profile
+
+    A factor is valid if IC > 0.03 and ICIR > 0.5.
+    For regime factors, also check regime_validator.
+    """
+
+    def __init__(self, db_path: Optional[str] = None):
+        self.db_path = db_path or config.db_path
+
+    def validate(self, factor_name: str, factor_scores: pd.Series,
+                 forward_returns: dict[str, pd.Series]) -> FactorReport:
+        """
+        Args:
+            factor_name: Human-readable name
+            factor_scores: Series indexed by date, values 0-100
+            forward_returns: Dict of horizon → Series indexed by date (e.g. "1d" → returns)
+        """
+        report = FactorReport(factor_name)
+
+        # Align series to common dates
+        common_idx = factor_scores.index
+        for ret in forward_returns.values():
+            common_idx = common_idx.intersection(ret.index)
+
+        if len(common_idx) < 30:
+            report.conclusion = "INSUFFICIENT DATA (< 30 observations)"
+            return report
+
+        f = factor_scores[common_idx]
+        report.n_observations = len(common_idx)
+
+        # Test against 7d forward returns (primary horizon)
+        primary_ret = forward_returns.get("7d")
+        if primary_ret is None:
+            # Use first available
+            primary_ret = list(forward_returns.values())[0]
+
+        r = primary_ret[common_idx]
+
+        # IC
+        ic = information_coefficient(f, r)
+        report.ic_mean = round(ic, 4)
+
+        # Rolling IC for ICIR
+        rolling_ics = []
+        for i in range(30, len(f)):
+            ic_i = information_coefficient(f.iloc[:i], r.iloc[:i])
+            rolling_ics.append(ic_i)
+        ic_series = pd.Series(rolling_ics)
+        report.ic_std = round(ic_series.std(), 4)
+        report.icir = round(icir(ic_series), 2)
+
+        # Hit ratio
+        report.hit_ratio = round(hit_ratio(f, r), 4)
+
+        # Quantile spread
+        report.quantile_spread = round(quantile_spread(f, r), 4)
+
+        # Lead-lag
+        lead = lead_lag_ic(f, r, max_lag=14)
+        report.is_leading = lead["is_leading"]
+        report.lead_days = lead["lead_days"]
+        report.lead_ic = round(lead["best_ic"], 4)
+
+        # Conclusion
+        if abs(report.ic_mean) > 0.05 and report.icir > 1.0:
+            report.conclusion = "STRONG: significant predictive power"
+        elif abs(report.ic_mean) > 0.03 and report.icir > 0.5:
+            report.conclusion = "VALID: moderate predictive power"
+        elif abs(report.ic_mean) < 0.02:
+            report.conclusion = "CONFIRMING: describes current state, not predictive"
+        else:
+            report.conclusion = "WEAK: borderline, monitor or downweight"
+
+        return report
+
+    def validate_from_db(self, factor_name: str,
+                         score_query: str,
+                         horizon_days: int = 7) -> FactorReport:
+        """
+        Convenience: load scores from DB and OHLCV returns, then validate.
+
+        score_query: SQL that returns (date, score) pairs.
+        """
+        conn = sqlite3.connect(self.db_path)
+
+        scores_df = pd.read_sql_query(score_query, conn)
+        if scores_df.empty:
+            conn.close()
+            r = FactorReport(factor_name)
+            r.conclusion = "NO DATA"
+            return r
+
+        scores_df["date"] = pd.to_datetime(scores_df["date"])
+        scores = scores_df.set_index("date")["score"]
+
+        # Load forward returns from OHLCV
+        ohlcv = pd.read_sql_query(
+            "SELECT date, close FROM ohlcv_daily WHERE symbol='BTC/USDT:USDT' ORDER BY date",
+            conn
+        )
+        conn.close()
+
+        ohlcv["date"] = pd.to_datetime(ohlcv["date"])
+        ohlcv = ohlcv.set_index("date")
+        ohlcv["ret"] = ohlcv["close"].pct_change().shift(-1)  # forward 1d
+
+        # Build forward returns for multiple horizons
+        forward = {}
+        for h in [1, 3, 5, 7, 14]:
+            forward[str(h) + "d"] = ohlcv["close"].pct_change(periods=h).shift(-h)
+
+        return self.validate(factor_name, scores, forward)

ChanMacro/validation/metrics.py

@@ -0,0 +1,192 @@
+"""
+validation/metrics.py — Shared statistical metrics for factor and regime validation.
+"""
+
+import numpy as np
+import pandas as pd
+from scipy import stats
+from typing import Optional
+
+
+def information_coefficient(factor: pd.Series, forward_returns: pd.Series) -> float:
+    """Spearman rank IC between factor values and forward returns."""
+    mask = factor.notna() & forward_returns.notna()
+    if mask.sum() < 10:
+        return 0.0
+    ic, _ = stats.spearmanr(factor[mask], forward_returns[mask])
+    return float(ic) if not np.isnan(ic) else 0.0
+
+
+def icir(ic_series: pd.Series) -> float:
+    """Information Coefficient IR = mean(IC) / std(IC)."""
+    if len(ic_series) < 5 or ic_series.std() == 0:
+        return 0.0
+    return float(ic_series.mean() / ic_series.std())
+
+
+def hit_ratio(factor: pd.Series, forward_returns: pd.Series) -> float:
+    """Fraction of times factor direction matches return direction."""
+    mask = factor.notna() & forward_returns.notna()
+    if mask.sum() < 10:
+        return 0.5
+    # Compare sign of factor deviation from median vs sign of returns
+    factor_median = factor[mask].median()
+    factor_sign = np.sign(factor[mask] - factor_median)
+    return_sign = np.sign(forward_returns[mask])
+    return float((factor_sign == return_sign).mean())
+
+
+def quantile_spread(factor: pd.Series, forward_returns: pd.Series,
+                    n_quantiles: int = 5) -> float:
+    """Top vs bottom quantile return spread (分层回测)."""
+    mask = factor.notna() & forward_returns.notna()
+    if mask.sum() < n_quantiles * 3:
+        return 0.0
+    f = factor[mask]
+    r = forward_returns[mask]
+    labels = pd.qcut(f, n_quantiles, labels=False, duplicates="drop")
+    top_ret = r[labels == labels.max()].mean()
+    bot_ret = r[labels == labels.min()].mean()
+    return float(top_ret - bot_ret)
+
+
+def lead_lag_ic(factor: pd.Series, returns: pd.Series,
+                max_lag: int = 14) -> dict:
+    """Find the best leading/trailing relationship by computing IC at each lag."""
+    results = {}
+    for lag in range(-max_lag, max_lag + 1):
+        if lag < 0:
+            shifted = factor.shift(abs(lag))
+            ic = information_coefficient(shifted, returns)
+            results[f"lead_{abs(lag)}d"] = ic
+        elif lag > 0:
+            shifted = returns.shift(lag)
+            ic = information_coefficient(factor, shifted)
+            results[f"lag_{lag}d"] = ic
+        else:
+            ic = information_coefficient(factor, returns)
+            results["sync"] = ic
+
+    # Find best lead period
+    lead_ics = {k: v for k, v in results.items() if k.startswith("lead_")}
+    best_lead = max(lead_ics, key=lead_ics.get) if lead_ics else "sync"
+    best_ic = lead_ics.get(best_lead, results.get("sync", 0))
+
+    return {
+        "best_lead": best_lead,
+        "best_ic": best_ic,
+        "ic_curve": results,
+        "is_leading": best_lead.startswith("lead_") and abs(best_ic) > 0.03,
+        "lead_days": int(best_lead.split("_")[1].rstrip("d")) if best_lead.startswith("lead_") else 0,
+    }
+
+
+def mutual_information(factor: pd.Series, labels: pd.Series,
+                       n_bins: int = 10) -> float:
+    """Mutual information between factor (binned) and discrete regime labels."""
+    mask = factor.notna() & labels.notna()
+    if mask.sum() < 20:
+        return 0.0
+    f = factor[mask]
+    l = labels[mask]
+    try:
+        f_binned = pd.qcut(f, n_bins, labels=False, duplicates="drop")
+    except ValueError:
+        f_binned = pd.cut(f, n_bins, labels=False)
+    mi = 0.0
+    for fi in range(n_bins):
+        p_f = (f_binned == fi).mean()
+        if p_f == 0:
+            continue
+        for li in l.unique():
+            p_l = (l == li).mean()
+            p_joint = ((f_binned == fi) & (l == li)).mean()
+            if p_joint > 0:
+                mi += p_joint * np.log(p_joint / (p_f * p_l))
+    return float(mi)
+
+
+def kl_divergence(factor: pd.Series, labels: pd.Series,
+                  regime_a: str, regime_b: str, n_bins: int = 10) -> float:
+    """KL divergence between factor distributions in two regimes."""
+    mask_a = (labels == regime_a) & factor.notna()
+    mask_b = (labels == regime_b) & factor.notna()
+    if mask_a.sum() < 10 or mask_b.sum() < 10:
+        return 0.0
+    try:
+        hist_a, edges = np.histogram(factor[mask_a], bins=n_bins, density=True)
+        hist_b, _ = np.histogram(factor[mask_b], bins=edges, density=True)
+    except ValueError:
+        return 0.0
+    hist_a = np.clip(hist_a, 1e-10, None)
+    hist_b = np.clip(hist_b, 1e-10, None)
+    return float((hist_a * np.log(hist_a / hist_b)).sum())
+
+
+def anova_f_score(factor: pd.Series, labels: pd.Series) -> float:
+    """ANOVA F-statistic: how well factor separates different regimes."""
+    mask = factor.notna() & labels.notna()
+    if mask.sum() < 20:
+        return 0.0
+    groups = [factor[mask][labels[mask] == lbl] for lbl in labels[mask].unique()]
+    groups = [g for g in groups if len(g) > 1]
+    if len(groups) < 2:
+        return 0.0
+    f_stat, _ = stats.f_oneway(*groups)
+    return float(f_stat) if not np.isnan(f_stat) else 0.0
+
+
+def transition_matrix(labels: pd.Series) -> pd.DataFrame:
+    """Compute Markov transition matrix from regime sequence."""
+    unique = sorted(labels.dropna().unique())
+    n = len(unique)
+    matrix = np.zeros((n, n))
+    seq = labels.dropna().values
+    for i in range(len(seq) - 1):
+        from_idx = unique.index(seq[i])
+        to_idx = unique.index(seq[i + 1])
+        matrix[from_idx][to_idx] += 1
+
+    # Row-normalize
+    row_sums = matrix.sum(axis=1, keepdims=True)
+    row_sums[row_sums == 0] = 1
+    matrix = matrix / row_sums
+
+    return pd.DataFrame(matrix, index=unique, columns=unique)
+
+
+def regime_duration_stats(labels: pd.Series) -> dict:
+    """Compute average duration, flip rate, state entropy for regime sequence."""
+    seq = labels.dropna().values
+    if len(seq) < 2:
+        return {"avg_duration": 0, "flip_rate": 0, "state_entropy": 0, "n_days": len(seq)}
+
+    # Count durations
+    durations = []
+    current = seq[0]
+    count = 1
+    flips = 0
+    for i in range(1, len(seq)):
+        if seq[i] == current:
+            count += 1
+        else:
+            durations.append(count)
+            current = seq[i]
+            count = 1
+            flips += 1
+    durations.append(count)
+
+    avg_dur = float(np.mean(durations)) if durations else 0
+    flip_rate = flips / len(seq)
+
+    # State entropy
+    _, counts = np.unique(seq, return_counts=True)
+    probs = counts / counts.sum()
+    entropy = float(-(probs * np.log2(probs + 1e-10)).sum())
+
+    return {
+        "avg_duration": round(avg_dur, 1),
+        "flip_rate": round(flip_rate, 3),
+        "state_entropy": round(entropy, 3),
+        "n_days": len(seq),
+    }

ChanMacro/validation/regime_validator.py

@@ -0,0 +1,144 @@
+"""
+validation/regime_validator.py — Validates factors as regime separators.
+
+Tests: Mutual Information, KL Divergence, ANOVA F-score.
+Answers: "Does this factor distinguish different market regimes?"
+
+Key insight: a factor may have low IC (poor return predictor) but high
+regime separation (good regime classifier). Breadth is the prime example.
+"""
+
+from datetime import date as Date
+from typing import Optional
+import sqlite3
+import logging
+
+import numpy as np
+import pandas as pd
+
+from config import config
+from .metrics import (
+    mutual_information, kl_divergence, anova_f_score,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class RegimeReport:
+    """Structured report for regime separation validation."""
+
+    def __init__(self, factor_name: str):
+        self.factor_name = factor_name
+        self.mutual_info: float = 0.0
+        self.anova_f: float = 0.0
+        self.kl_pairs: dict = {}  # (regime_a, regime_b) → KL divergence
+        self.best_separates: list[str] = []
+        self.separation_score: float = 0.0
+        self.is_regime_factor: bool = False
+        self.conclusion: str = ""
+
+    def summary(self) -> str:
+        lines = [
+            f"Factor: {self.factor_name}",
+            f"  Mutual Information: {self.mutual_info:.4f}",
+            f"  ANOVA F: {self.anova_f:.1f}",
+            f"  Best separates: {', '.join(self.best_separates) if self.best_separates else 'none'}",
+            f"  Regime Factor: {'YES' if self.is_regime_factor else 'No'}",
+            f"  → {self.conclusion}",
+        ]
+        return "\n".join(lines)
+
+
+class RegimeValidator:
+    """
+    Validates a factor's ability to separate different market regimes.
+
+    A good regime factor has:
+      - Mutual Information > 0.1
+      - KL Divergence between regimes > 0.5
+      - ANOVA F-score high
+    """
+
+    def __init__(self, db_path: Optional[str] = None):
+        self.db_path = db_path or config.db_path
+
+    def validate(self, factor_name: str, factor_scores: pd.Series,
+                 regime_labels: pd.Series) -> RegimeReport:
+        """
+        Args:
+            factor_name: Human-readable name
+            factor_scores: Series indexed by date, values 0-100
+            regime_labels: Series indexed by date, values = 'TREND'/'RANGE'/'PANIC'
+        """
+        report = RegimeReport(factor_name)
+
+        # Align
+        common_idx = factor_scores.index.intersection(regime_labels.index)
+        if len(common_idx) < 30:
+            report.conclusion = "INSUFFICIENT DATA"
+            return report
+
+        f = factor_scores[common_idx]
+        labels = regime_labels[common_idx]
+
+        # Mutual Information
+        report.mutual_info = round(mutual_information(f, labels), 4)
+
+        # ANOVA
+        report.anova_f = round(anova_f_score(f, labels), 1)
+
+        # KL Divergence between each pair of regimes
+        unique_regimes = sorted(labels.unique())
+        for i, ra in enumerate(unique_regimes):
+            for rb in unique_regimes[i + 1:]:
+                kl = kl_divergence(f, labels, ra, rb)
+                report.kl_pairs[f"{ra}↔{rb}"] = round(kl, 4)
+
+        # Best separation
+        if report.kl_pairs:
+            sorted_pairs = sorted(report.kl_pairs, key=report.kl_pairs.get, reverse=True)
+            report.best_separates = sorted_pairs[:2]
+
+        # Separation score (0-1 composite)
+        mi_norm = min(report.mutual_info / 0.5, 1.0)
+        kl_avg = np.mean(list(report.kl_pairs.values())) if report.kl_pairs else 0
+        kl_norm = min(kl_avg / 1.0, 1.0)
+        report.separation_score = round(0.5 * mi_norm + 0.5 * kl_norm, 2)
+
+        # Is this a good regime factor?
+        report.is_regime_factor = (
+            report.mutual_info > 0.1 and
+            kl_avg > 0.5
+        )
+
+        if report.separation_score > 0.8:
+            report.conclusion = "EXCELLENT regime separator"
+        elif report.separation_score > 0.5:
+            report.conclusion = "GOOD regime separator"
+        elif report.separation_score > 0.3:
+            report.conclusion = "MODERATE — some regime separation"
+        else:
+            report.conclusion = "WEAK regime separator"
+
+        return report
+
+    def validate_from_db(self, factor_name: str,
+                         score_query: str) -> RegimeReport:
+        """Load scores and regime labels from DB, then validate."""
+        conn = sqlite3.connect(self.db_path)
+
+        scores_df = pd.read_sql_query(score_query, conn)
+        regimes_df = pd.read_sql_query(
+            "SELECT date, regime FROM regime_history", conn
+        )
+        conn.close()
+
+        if scores_df.empty or regimes_df.empty:
+            r = RegimeReport(factor_name)
+            r.conclusion = "NO DATA"
+            return r
+
+        scores = scores_df.set_index("date")["score"]
+        regimes = regimes_df.set_index("date")["regime"]
+
+        return self.validate(factor_name, scores, regimes)

ChanMacro/validation/reporter.py

@@ -0,0 +1,120 @@
+"""
+validation/reporter.py — Aggregates all validation reports into a unified summary.
+
+Used by: python main.py validate
+"""
+
+from datetime import date as Date
+from typing import Optional
+import logging
+
+from .factor_validator import FactorValidator, FactorReport
+from .regime_validator import RegimeValidator, RegimeReport
+from .transition_validator import TransitionValidator, TransitionReport
+
+logger = logging.getLogger(__name__)
+
+
+class ValidationReporter:
+    """
+    Orchestrates full validation pipeline:
+
+    1. Factor validation (IC, ICIR, Hit Ratio) for each factor
+    2. Regime validation (MI, KL, ANOVA) for each factor
+    3. Transition validation (stability, flip rate)
+    """
+
+    def __init__(self, db_path: Optional[str] = None):
+        from config import config
+        self.db_path = db_path or config.db_path
+        self.factor_validator = FactorValidator(self.db_path)
+        self.regime_validator = RegimeValidator(self.db_path)
+        self.transition_validator = TransitionValidator(self.db_path)
+
+    def run_all(self) -> str:
+        """Run all validations and return a formatted report string."""
+        lines = []
+        lines.append("=" * 70)
+        lines.append(f"  ChanMacro Validation Report — {Date.today()}")
+        lines.append("=" * 70)
+
+        # ── Factor Validation ──────────────────────────
+        lines.append("")
+        lines.append("─" * 50)
+        lines.append("  FACTOR VALIDATION (Predictive Power)")
+        lines.append("─" * 50)
+
+        factor_queries = {
+            "Price Structure": "SELECT date, score FROM ohlcv_daily WHERE ema20 IS NOT NULL",
+            "Breadth": """
+                SELECT bd.date,
+                    (bd.advance_top50*1.0/(bd.advance_top50+bd.decline_top50+1)*100*0.30
+                     + bd.above_ema20_top50*1.0/50*100*0.35
+                     + bd.new_highs_20d_top50*1.0/50*100*0.20
+                     + 50*0.15) as score
+                FROM breadth_daily bd
+            """,
+        }
+
+        factor_reports: list[FactorReport] = []
+        for name, query in factor_queries.items():
+            try:
+                report = self.factor_validator.validate_from_db(name, query)
+                factor_reports.append(report)
+                lines.append(report.summary())
+                lines.append("")
+            except Exception as e:
+                logger.warning(f"Factor validation failed for {name}: {e}")
+
+        # ── Regime Validation ──────────────────────────
+        lines.append("─" * 50)
+        lines.append("  REGIME VALIDATION (Regime Separation)")
+        lines.append("─" * 50)
+
+        regime_reports: list[RegimeReport] = []
+        for name, query in factor_queries.items():
+            try:
+                report = self.regime_validator.validate_from_db(name, query)
+                regime_reports.append(report)
+                lines.append(report.summary())
+                lines.append("")
+            except Exception as e:
+                logger.warning(f"Regime validation failed for {name}: {e}")
+
+        # ── Transition Validation ──────────────────────
+        lines.append("─" * 50)
+        lines.append("  TRANSITION VALIDATION (Regime Stability)")
+        lines.append("─" * 50)
+
+        try:
+            t_report = self.transition_validator.validate_from_db()
+            lines.append(t_report.summary())
+        except Exception as e:
+            logger.warning(f"Transition validation failed: {e}")
+
+        # ── Summary ────────────────────────────────────
+        lines.append("")
+        lines.append("=" * 70)
+        lines.append("  SUMMARY")
+        lines.append("=" * 70)
+
+        # Factor ranking by IC
+        if factor_reports:
+            ranked = sorted(factor_reports, key=lambda r: abs(r.ic_mean), reverse=True)
+            lines.append("  Factor Ranking (by |IC|):")
+            for i, r in enumerate(ranked):
+                tag = "★★★" if abs(r.ic_mean) > 0.05 else "★★" if abs(r.ic_mean) > 0.03 else "★"
+                lines.append(f"    {i+1}. {r.factor_name:20s} IC={r.ic_mean:+.4f} {tag}  {r.conclusion}")
+
+        # Regime factor ranking
+        if regime_reports:
+            ranked_r = sorted(regime_reports, key=lambda r: r.separation_score, reverse=True)
+            lines.append("")
+            lines.append("  Regime Factor Ranking (by Separation Score):")
+            for i, r in enumerate(ranked_r):
+                lines.append(f"    {i+1}. {r.factor_name:20s} Score={r.separation_score:.2f}  {r.conclusion}")
+
+        lines.append("")
+        lines.append("=" * 70)
+
+        return "\n".join(lines)

ChanMacro/validation/transition_validator.py

@@ -0,0 +1,131 @@
+"""
+validation/transition_validator.py — Validates regime stability.
+
+Tests: Transition matrix, average duration, flip rate, state entropy.
+Answers: "Does the regime design produce stable, persistent states?"
+
+Hard requirements:
+  - avg_duration > 5 days
+  - flip_rate < 15%
+  - Fails → regime definition needs redesign.
+"""
+
+from typing import Optional
+import sqlite3
+import logging
+
+import numpy as np
+import pandas as pd
+
+from config import config
+from .metrics import transition_matrix, regime_duration_stats
+
+logger = logging.getLogger(__name__)
+
+
+class TransitionReport:
+    """Structured report for regime stability validation."""
+
+    def __init__(self):
+        self.avg_duration: float = 0.0
+        self.flip_rate: float = 0.0
+        self.state_entropy: float = 0.0
+        self.n_days: int = 0
+        self.transition_matrix: Optional[pd.DataFrame] = None
+        self.persistence_score: float = 0.0
+        self.is_stable: bool = False
+        self.conclusion: str = ""
+        self.warnings: list[str] = []
+
+    def summary(self) -> str:
+        lines = [
+            f"Regime Stability (N={self.n_days} days)",
+            f"  Avg Duration: {self.avg_duration:.1f} days (need > {config.regime_min_avg_duration})",
+            f"  Flip Rate: {self.flip_rate:.1%} (need < {config.regime_max_flip_rate:.0%})",
+            f"  State Entropy: {self.state_entropy:.3f}",
+            f"  Persistence Score: {self.persistence_score:.2f}",
+            f"  Stable: {'YES' if self.is_stable else 'NO — redesign needed'}",
+        ]
+        if self.warnings:
+            lines.append(f"  Warnings: {'; '.join(self.warnings)}")
+        if self.transition_matrix is not None:
+            lines.append(f"  Transition Matrix:\n{self.transition_matrix.to_string()}")
+        lines.append(f"  → {self.conclusion}")
+        return "\n".join(lines)
+
+
+class TransitionValidator:
+    """
+    Validates regime temporal stability.
+
+    Regime must persist — not flip daily.
+    If flip_rate > 20% or avg_duration < 3 days → regime definition failed.
+    """
+
+    def __init__(self, db_path: Optional[str] = None):
+        self.db_path = db_path or config.db_path
+
+    def validate(self, regime_labels: pd.Series) -> TransitionReport:
+        """Validate a regime sequence for stability."""
+        report = TransitionReport()
+        report.n_days = len(regime_labels)
+
+        if len(regime_labels) < 30:
+            report.conclusion = "INSUFFICIENT DATA (< 30 days)"
+            return report
+
+        # Duration stats
+        stats = regime_duration_stats(regime_labels)
+        report.avg_duration = stats["avg_duration"]
+        report.flip_rate = stats["flip_rate"]
+        report.state_entropy = stats["state_entropy"]
+
+        # Transition matrix
+        report.transition_matrix = transition_matrix(regime_labels)
+
+        # Persistence: how often does regime stay the same?
+        diag = np.diag(report.transition_matrix.values)
+        report.persistence_score = round(float(np.mean(diag)), 2)
+
+        # Stability check
+        report.is_stable = (
+            report.avg_duration >= config.regime_min_avg_duration and
+            report.flip_rate <= config.regime_max_flip_rate
+        )
+
+        # Warnings
+        if report.avg_duration < 3:
+            report.warnings.append(f"CRITICAL: avg duration={report.avg_duration:.1f}d — regime flips too fast")
+        elif report.avg_duration < config.regime_min_avg_duration:
+            report.warnings.append(f"WARNING: avg duration={report.avg_duration:.1f}d < {config.regime_min_avg_duration}")
+
+        if report.flip_rate > 0.20:
+            report.warnings.append(f"CRITICAL: flip rate={report.flip_rate:.1%} — regime unstable")
+        elif report.flip_rate > config.regime_max_flip_rate:
+            report.warnings.append(f"WARNING: flip rate={report.flip_rate:.1%} > {config.regime_max_flip_rate:.0%}")
+
+        if report.state_entropy > 2.0:
+            report.warnings.append(f"NOTE: high state entropy={report.state_entropy:.2f}, regimes may be too fine-grained")
+
+        if report.is_stable:
+            report.conclusion = "PASS: regime design is stable"
+        else:
+            report.conclusion = "FAIL: regime definition needs adjustment"
+
+        return report
+
+    def validate_from_db(self) -> TransitionReport:
+        """Load regime history from DB and validate stability."""
+        conn = sqlite3.connect(self.db_path)
+        df = pd.read_sql_query(
+            "SELECT date, regime FROM regime_history ORDER BY date", conn
+        )
+        conn.close()
+
+        if df.empty:
+            r = TransitionReport()
+            r.conclusion = "NO DATA"
+            return r
+
+        regimes = df.set_index("date")["regime"]
+        return self.validate(regimes)

ChanMacro/web/app.py

@@ -0,0 +1,178 @@
+"""
+web/app.py — ChanMacro dashboard (Flask, port 8124).
+"""
+
+import sys
+import os
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import json
+from datetime import date as Date
+from flask import Flask, render_template, jsonify, request
+
+from database import get_connection
+from config import config
+from scoring.price_structure import PriceStructureScorer
+from scoring.breadth_scorer import BreadthScorer
+from scoring.oi_matrix import OIMatrixScorer
+from scoring.volatility_regime import VolatilityRegimeScorer
+from regime_detector import RegimeDetector
+from models import MarketStateVector
+from expectancy.engine import BayesianExpectancyEngine
+
+app = Flask(__name__)
+
+
+def _build_state(target: Date):
+    """Build MarketStateVector and persist regime to DB."""
+    ps = PriceStructureScorer().compute(target)
+    br = BreadthScorer().compute(target)
+    oi = OIMatrixScorer().compute(target)
+    vol = VolatilityRegimeScorer().compute(target)
+
+    detector = RegimeDetector()
+    detector.load_state(config.db_path)
+    r = detector.detect(ps.score, br.breadth_top50, vol.vol_regime.value, target)
+
+    state = MarketStateVector(
+        date=target, regime=r.regime, regime_confidence=r.confidence,
+        regime_version=r.regime_version, regime_maturity_score=r.maturity_score,
+        breadth_top20=br.breadth_top20, breadth_top30=br.breadth_top30,
+        breadth_top50=br.breadth_top50, breadth_bucket=br.breadth_bucket,
+        breadth_divergence=br.breadth_divergence,
+        oi_state=oi.oi_state, volatility_regime=vol.vol_regime,
+        price_structure_score=ps, breadth_score=br,
+        oi_matrix_score=oi, volatility_regime_score=vol,
+    )
+    state.market_state_hash = state.compute_hash()
+
+    # Persist regime to DB so load_state() works across requests
+    conn = get_connection()
+    conn.execute("""
+        INSERT OR REPLACE INTO regime_history
+        (date, regime, confidence, regime_version, maturity_score, all_scores_json,
+         prior_regime, confirmation_days)
+        VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+    """, (
+        str(target), r.regime.value, r.confidence, r.regime_version,
+        r.maturity_score, json.dumps(r.all_scores),
+        r.prior_regime.value if r.prior_regime else None,
+        r.confirmation_days,
+    ))
+    conn.commit()
+    conn.close()
+
+    return state
+
+
+@app.route("/")
+def dashboard():
+    return render_template("index.html")
+
+
+@app.route("/api/state")
+def api_state():
+    """Current market state with all factor scores."""
+    try:
+        target = Date.today()
+        state = _build_state(target)
+        return jsonify({
+            "date": str(state.date),
+            "regime": state.regime.value,
+            "regime_confidence": state.regime_confidence,
+            "regime_maturity": state.regime_maturity_score,
+            "breadth": {
+                "score": state.breadth_score.score,
+                "bucket": state.breadth_bucket.value,
+                "top20": state.breadth_top20,
+                "top30": state.breadth_top30,
+                "top50": state.breadth_top50,
+                "divergence": state.breadth_divergence,
+                "narrative": state.breadth_score.narrative,
+            },
+            "oi_state": state.oi_state.value,
+            "oi_score": state.oi_matrix_score.score,
+            "oi_narrative": state.oi_matrix_score.narrative,
+            "volatility": state.volatility_regime.value,
+            "price_structure": {
+                "score": state.price_structure_score.score,
+                "trend": state.price_structure_score.trend_strength,
+                "vol_comp": state.price_structure_score.volatility_compression,
+                "momentum": state.price_structure_score.momentum,
+                "label": state.price_structure_score.label,
+                "narrative": state.price_structure_score.narrative,
+            },
+        })
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+
+@app.route("/api/history")
+def api_history():
+    """Regime and factor score history."""
+    days = request.args.get("days", 60, type=int)
+    conn = get_connection()
+
+    # Regime history
+    regimes = conn.execute(
+        "SELECT date, regime, confidence, maturity_score FROM regime_history ORDER BY date DESC LIMIT ?",
+        (days,)
+    ).fetchall()
+
+    # Breadth history
+    breadth = conn.execute(
+        "SELECT date, advance_top50, decline_top50, above_ema20_top50 FROM breadth_daily ORDER BY date DESC LIMIT ?",
+        (days,)
+    ).fetchall()
+
+    conn.close()
+
+    return jsonify({
+        "regimes": [{"date": r["date"], "regime": r["regime"],
+                      "confidence": r["confidence"], "maturity": r["maturity_score"]}
+                    for r in reversed(regimes)],
+        "breadth": [{"date": b["date"], "advance": b["advance_top50"],
+                      "decline": b["decline_top50"], "above_ema20": b["above_ema20_top50"]}
+                    for b in reversed(breadth)],
+    })
+
+
+@app.route("/api/expectancy")
+def api_expectancy():
+    """Query signal expectancy."""
+    signal = request.args.get("signal", "B3")
+    try:
+        target = Date.today()
+        state = _build_state(target)
+        engine = BayesianExpectancyEngine(level_min_samples=5)
+        report = engine.estimate(state, signal_type=signal, target_date=target)
+
+        layers = []
+        for l in report.layers:
+            layers.append({
+                "name": l.name,
+                "samples": l.samples,
+                "effective_samples": l.effective_samples,
+                "raw_winrate": l.raw_winrate,
+                "posterior_winrate": l.posterior_winrate,
+                "avg_return": l.avg_return,
+            })
+
+        return jsonify({
+            "signal": signal,
+            "final_estimate": report.final_estimate,
+            "sufficiency": report.sufficiency.value,
+            "source": report.source,
+            "avg_return_7d": report.avg_return_7d,
+            "profit_factor": report.profit_factor,
+            "max_adverse": report.max_adverse_excursion,
+            "layers": layers,
+        })
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+
+if __name__ == "__main__":
+    from scheduler import get_scheduler
+    get_scheduler().start()
+    app.run(host="0.0.0.0", port=8124, debug=True)

ChanMacro/web/static/js/dashboard.js

@@ -0,0 +1,160 @@
+// dashboard.js — ChanMacro
+
+const C = { TREND: "#3fb950", RANGE: "#d29922", PANIC: "#f85149" };
+let regimeChart = null, breadthChart = null;
+
+async function loadState() {
+  try {
+    const r = await fetch("/api/state");
+    const d = await r.json();
+    if (d.error) { document.getElementById("update-time").textContent = d.error; return; }
+
+    document.getElementById("update-time").textContent = d.date;
+
+    // Hero
+    const regime = d.regime;
+    const names = { TREND: "TREND", RANGE: "RANGE", PANIC: "PANIC" };
+    document.getElementById("hero-regime").textContent = names[regime] || regime;
+    document.getElementById("hero-regime").className = "regime-name " + regime.toLowerCase();
+    document.getElementById("hero-badge").textContent = regime;
+    document.getElementById("hero-badge").className = "regime-badge " + regime.toLowerCase();
+    document.getElementById("hero-conf").textContent = (d.regime_confidence * 100).toFixed(0) + "%";
+    document.getElementById("hero-maturity").textContent = d.regime_maturity.toFixed(0);
+    document.getElementById("hero-ps").textContent = d.price_structure.score.toFixed(0);
+    document.getElementById("hero-ps").style.color =
+      d.price_structure.score >= 60 ? "#3fb950" : d.price_structure.score >= 40 ? "#d29922" : "#f85149";
+    document.getElementById("hero-br").textContent = d.breadth.score.toFixed(0);
+    document.getElementById("hero-br").style.color =
+      d.breadth.bucket === "EXTREME" || d.breadth.bucket === "STRONG" ? "#3fb950" :
+      d.breadth.bucket === "WEAK" || d.breadth.bucket === "PANIC" ? "#f85149" : "#d29922";
+
+    // Factor cards
+    const ps = d.price_structure;
+    document.getElementById("f-price").textContent = ps.score.toFixed(0);
+    document.getElementById("f-price").style.color =
+      ps.score >= 60 ? "#3fb950" : ps.score >= 40 ? "#d29922" : "#f85149";
+    document.getElementById("f-price-sub").textContent =
+      `趋势 ${ps.trend.toFixed(0)} · 波动 ${ps.vol_comp.toFixed(0)} · 动量 ${ps.momentum.toFixed(0)}`;
+    document.getElementById("bar-price").style.width = ps.score + "%";
+    document.getElementById("bar-price").style.background =
+      ps.score >= 60 ? "#3fb950" : ps.score >= 40 ? "#d29922" : "#f85149";
+
+    const br = d.breadth;
+    document.getElementById("f-breadth").textContent = br.score.toFixed(0);
+    document.getElementById("f-breadth").style.color =
+      br.bucket === "EXTREME" || br.bucket === "STRONG" ? "#3fb950" :
+      br.bucket === "WEAK" || br.bucket === "PANIC" ? "#f85149" : "#d29922";
+    document.getElementById("f-breadth-sub").textContent =
+      `${br.bucket} · T20=${br.top20.toFixed(0)} T50=${br.top50.toFixed(0)}`;
+    document.getElementById("bar-breadth").style.width = br.score + "%";
+    document.getElementById("bar-breadth").style.background =
+      br.bucket === "EXTREME" || br.bucket === "STRONG" ? "#3fb950" :
+      br.bucket === "WEAK" || br.bucket === "PANIC" ? "#f85149" : "#d29922";
+
+    document.getElementById("f-oi").textContent = d.oi_state.toUpperCase().replace(" ", "\n");
+    document.getElementById("f-oi").style.color =
+      d.oi_state === "New Longs" ? "#3fb950" : d.oi_state.includes("Short") || d.oi_state === "Long Exit" ? "#f85149" : "#8b949e";
+    document.getElementById("f-oi-sub").textContent = d.oi_narrative;
+
+    const vm = { LOW_VOL: "低波动", NORMAL_VOL: "正常", HIGH_VOL: "高波动", EXPLOSIVE_VOL: "极端" };
+    document.getElementById("f-vol").textContent = vm[d.volatility] || d.volatility;
+    document.getElementById("f-vol").style.color =
+      d.volatility === "LOW_VOL" ? "#58a6ff" : d.volatility === "NORMAL_VOL" ? "#8b949e" :
+      d.volatility === "HIGH_VOL" ? "#d29922" : "#f85149";
+    document.getElementById("f-vol-sub").textContent = d.volatility;
+    document.getElementById("bar-vol").style.width =
+      (d.volatility === "EXPLOSIVE_VOL" ? 95 : d.volatility === "HIGH_VOL" ? 70 :
+       d.volatility === "NORMAL_VOL" ? 40 : 20) + "%";
+    document.getElementById("bar-vol").style.background =
+      d.volatility === "EXPLOSIVE_VOL" ? "#f85149" : d.volatility === "HIGH_VOL" ? "#d29922" :
+      d.volatility === "NORMAL_VOL" ? "#8b949e" : "#58a6ff";
+  } catch (e) {
+    document.getElementById("update-time").textContent = "连接失败";
+  }
+}
+
+async function loadHistory() {
+  try {
+    const r = await fetch("/api/history?days=60");
+    const d = await r.json();
+
+    const dates = d.regimes.map(x => x.date);
+    const colors = d.regimes.map(x => C[x.regime] || "#5c6675");
+
+    if (regimeChart) regimeChart.destroy();
+    regimeChart = new Chart(document.getElementById("chart-regime").getContext("2d"), {
+      type: "bar",
+      data: { labels: dates, datasets: [{ data: d.regimes.map(x => x.confidence * 100),
+        backgroundColor: colors, borderWidth: 0, borderRadius: 2 }] },
+      options: {
+        responsive: true, maintainAspectRatio: false,
+        plugins: { legend: { display: false } },
+        scales: {
+          x: { ticks: { color: "#5c6675", maxTicksLimit: 15, maxRotation: 45, font: { size: 10 } },
+               grid: { color: "#151a23" } },
+          y: { max: 100, ticks: { color: "#5c6675", font: { size: 10 } }, grid: { color: "#151a23" } }
+        }
+      }
+    });
+
+    if (breadthChart) breadthChart.destroy();
+    breadthChart = new Chart(document.getElementById("chart-breadth").getContext("2d"), {
+      type: "line",
+      data: {
+        labels: d.breadth.map(x => x.date),
+        datasets: [
+          { label: "上涨", data: d.breadth.map(x => x.advance), borderColor: "#3fb950",
+            backgroundColor: "rgba(63,185,80,0.08)", fill: true, tension: 0.3, pointRadius: 0 },
+          { label: "下跌", data: d.breadth.map(x => x.decline), borderColor: "#f85149",
+            backgroundColor: "rgba(248,81,73,0.06)", fill: true, tension: 0.3, pointRadius: 0 },
+          { label: ">EMA20", data: d.breadth.map(x => x.above_ema20), borderColor: "#58a6ff",
+            borderDash: [3, 3], tension: 0.3, pointRadius: 0 },
+        ]
+      },
+      options: {
+        responsive: true, maintainAspectRatio: false,
+        plugins: { legend: { labels: { color: "#5c6675", usePointStyle: true, boxWidth: 6, font: { size: 10 } } } },
+        scales: {
+          x: { ticks: { color: "#5c6675", maxTicksLimit: 15, maxRotation: 45, font: { size: 10 } },
+               grid: { color: "#151a23" } },
+          y: { ticks: { color: "#5c6675", font: { size: 10 } }, grid: { color: "#151a23" } }
+        }
+      }
+    });
+  } catch (e) { console.error(e); }
+}
+
+async function loadExpectancy() {
+  const signal = document.getElementById("exp-signal").value;
+  try {
+    const r = await fetch(`/api/expectancy?signal=${signal}`);
+    const d = await r.json();
+    if (d.error) { document.getElementById("exp-layers").innerHTML =
+      `<tr><td colspan="6" style="color:#f85149">${d.error}</td></tr>`; return; }
+
+    const el = document.getElementById("exp-sufficiency");
+    el.textContent = d.sufficiency;
+    el.className = "suff suff-" + d.sufficiency;
+
+    let html = "";
+    for (const l of d.layers) {
+      html += `<tr>
+        <td>${l.name}</td><td>${l.samples}</td><td>${l.effective_samples.toFixed(0)}</td>
+        <td>${l.raw_winrate ? (l.raw_winrate * 100).toFixed(1) + "%" : "—"}</td>
+        <td><strong>${(l.posterior_winrate * 100).toFixed(1)}%</strong></td>
+        <td style="color:${l.avg_return > 0 ? '#3fb950' : l.avg_return < 0 ? '#f85149' : '#8b949e'}">${l.avg_return ? (l.avg_return > 0 ? "+" : "") + l.avg_return.toFixed(2) + "%" : "—"}</td>
+      </tr>`;
+    }
+    document.getElementById("exp-layers").innerHTML = html;
+
+    let s = `后验胜率 <strong style="color:#58a6ff">${(d.final_estimate * 100).toFixed(1)}%</strong>`;
+    if (d.avg_return_7d) s += ` · 平均收益 <strong>${d.avg_return_7d > 0 ? "+" : ""}${d.avg_return_7d.toFixed(2)}%</strong>`;
+    if (d.profit_factor) s += ` · 盈亏比 <strong>${d.profit_factor}</strong>`;
+    if (d.max_adverse) s += ` · MAE <strong>${d.max_adverse.toFixed(1)}%</strong>`;
+    document.getElementById("exp-summary").innerHTML = s;
+  } catch (e) { console.error(e); }
+}
+
+loadState();
+loadHistory();
+loadExpectancy();

ChanMacro/web/templates/index.html

@@ -0,0 +1,163 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>ChanMacro — 市场状态</title>
+<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
+<style>
+  * { margin: 0; padding: 0; box-sizing: border-box; }
+  body { background: #0a0e14; color: #c9d1d9; font-family: -apple-system, BlinkMacSystemFont, "SF Mono", monospace; }
+  .app { max-width: 1200px; margin: 0 auto; padding: 20px 24px; }
+
+  /* Header */
+  .header { display: flex; justify-content: space-between; align-items: flex-end; padding: 20px 0 28px;
+            border-bottom: 1px solid #1c2333; margin-bottom: 24px; }
+  .header h1 { font-size: 22px; font-weight: 600; letter-spacing: 1px; }
+  .header h1 span { color: #58a6ff; }
+  .header .time { color: #5c6675; font-size: 13px; }
+  .dot { display: inline-block; width: 7px; height: 7px; border-radius: 50%; background: #3fb950;
+         margin-right: 6px; animation: pulse 2s infinite; }
+  @keyframes pulse { 0%,100%{opacity:1} 50%{opacity:0.4} }
+
+  /* Regime Hero */
+  .hero { display: flex; gap: 16px; margin-bottom: 24px; }
+  .hero-card { flex: 1; background: #11161e; border: 1px solid #1c2333; border-radius: 8px; padding: 20px 24px; }
+  .hero-card.main { flex: 2; display: flex; align-items: center; gap: 28px; }
+  .regime-badge { display: inline-block; padding: 5px 16px; border-radius: 4px; font-size: 13px;
+                  font-weight: 600; letter-spacing: 2px; }
+  .regime-badge.trend { background: rgba(63,185,80,0.12); color: #3fb950; border: 1px solid rgba(63,185,80,0.3); }
+  .regime-badge.range { background: rgba(210,153,34,0.12); color: #d29922; border: 1px solid rgba(210,153,34,0.3); }
+  .regime-badge.panic { background: rgba(248,81,73,0.12); color: #f85149; border: 1px solid rgba(248,81,73,0.3); }
+  .regime-name { font-size: 42px; font-weight: 700; letter-spacing: 2px; }
+  .regime-name.trend { color: #3fb950; }
+  .regime-name.range { color: #d29922; }
+  .regime-name.panic { color: #f85149; }
+  .hero-stat { text-align: center; }
+  .hero-stat .val { font-size: 28px; font-weight: 600; color: #e6edf3; }
+  .hero-stat .lbl { font-size: 11px; color: #5c6675; letter-spacing: 1px; margin-top: 4px; }
+
+  /* Factor Grid */
+  .grid { display: grid; grid-template-columns: repeat(4, 1fr); gap: 12px; margin-bottom: 24px; }
+  .fcard { background: #11161e; border: 1px solid #1c2333; border-radius: 8px; padding: 18px 20px; }
+  .fcard .title { font-size: 11px; color: #5c6675; letter-spacing: 1.5px; margin-bottom: 10px; }
+  .fcard .score { font-size: 38px; font-weight: 700; margin-bottom: 4px; }
+  .fcard .sub { font-size: 12px; color: #5c6675; }
+  .fcard .bar-wrap { height: 3px; background: #1c2333; border-radius: 2px; margin-top: 12px; }
+  .fcard .bar { height: 100%; border-radius: 2px; transition: width 0.6s; }
+
+  /* Charts */
+  .charts { display: grid; grid-template-columns: 1fr 1fr; gap: 12px; margin-bottom: 24px; }
+  .chart-box { background: #11161e; border: 1px solid #1c2333; border-radius: 8px; padding: 18px 20px; }
+  .chart-box h3 { font-size: 12px; color: #5c6675; letter-spacing: 1.5px; margin-bottom: 14px; }
+  .chart-box canvas { max-height: 260px; }
+
+  /* Expectancy */
+  .exp { background: #11161e; border: 1px solid #1c2333; border-radius: 8px; padding: 18px 20px; }
+  .exp h3 { font-size: 12px; color: #5c6675; letter-spacing: 1.5px; margin-bottom: 14px; }
+  .exp-row { display: flex; gap: 12px; align-items: center; margin-bottom: 14px; }
+  .exp select { background: #0a0e14; color: #c9d1d9; border: 1px solid #1c2333; padding: 6px 12px;
+                border-radius: 4px; font-size: 13px; }
+  .exp button { background: #1c3a5c; color: #58a6ff; border: 1px solid #2d4f7c; padding: 6px 18px;
+                border-radius: 4px; cursor: pointer; font-size: 13px; }
+  .exp button:hover { background: #254d7a; }
+  .exp .suff { font-size: 11px; padding: 3px 10px; border-radius: 3px; }
+  .suff-HIGH { background: rgba(63,185,80,0.12); color: #3fb950; }
+  .suff-MEDIUM { background: rgba(210,153,34,0.12); color: #d29922; }
+  .suff-LOW { background: rgba(248,81,73,0.12); color: #f85149; }
+  .suff-INSUFFICIENT { background: rgba(92,102,117,0.12); color: #5c6675; }
+  table { width: 100%; border-collapse: collapse; font-size: 13px; }
+  th { text-align: left; color: #5c6675; font-weight: 500; padding: 8px 10px; border-bottom: 1px solid #1c2333; }
+  td { padding: 7px 10px; border-bottom: 1px solid #0e1219; color: #8b949e; }
+  td strong { color: #e6edf3; }
+  .exp-summary { margin-top: 14px; font-size: 13px; color: #8b949e; padding: 10px 14px;
+                 background: #0d1117; border-radius: 6px; border-left: 3px solid #58a6ff; }
+  .exp-summary strong { color: #e6edf3; }
+</style>
+</head>
+<body>
+<div class="app">
+
+  <!-- Header -->
+  <div class="header">
+    <div>
+      <h1><span>Chan</span>Macro</h1>
+    </div>
+    <div class="time"><span class="dot"></span> <span id="update-time">加载中...</span></div>
+  </div>
+
+  <!-- Regime Hero -->
+  <div class="hero">
+    <div class="hero-card main">
+      <div>
+        <div class="regime-badge" id="hero-badge">—</div>
+        <div class="regime-name" id="hero-regime">—</div>
+      </div>
+      <div style="display:flex; gap:32px; margin-left:auto;">
+        <div class="hero-stat"><div class="val" id="hero-conf">—</div><div class="lbl">置信度</div></div>
+        <div class="hero-stat"><div class="val" id="hero-maturity">—</div><div class="lbl">成熟度</div></div>
+      </div>
+    </div>
+    <div class="hero-card" style="flex:1">
+      <div class="hero-stat"><div class="val" id="hero-ps">—</div><div class="lbl">价格结构</div></div>
+    </div>
+    <div class="hero-card" style="flex:1">
+      <div class="hero-stat"><div class="val" id="hero-br">—</div><div class="lbl">市场广度</div></div>
+    </div>
+  </div>
+
+  <!-- 4 Factor Cards -->
+  <div class="grid">
+    <div class="fcard">
+      <div class="title">价格结构 PRICE STRUCTURE</div>
+      <div class="score" id="f-price">—</div>
+      <div class="sub" id="f-price-sub"></div>
+      <div class="bar-wrap"><div class="bar" id="bar-price"></div></div>
+    </div>
+    <div class="fcard">
+      <div class="title">市场广度 BREADTH</div>
+      <div class="score" id="f-breadth">—</div>
+      <div class="sub" id="f-breadth-sub"></div>
+      <div class="bar-wrap"><div class="bar" id="bar-breadth"></div></div>
+    </div>
+    <div class="fcard">
+      <div class="title">持仓状态 OI MATRIX</div>
+      <div class="score" id="f-oi" style="font-size:24px">—</div>
+      <div class="sub" id="f-oi-sub"></div>
+    </div>
+    <div class="fcard">
+      <div class="title">波动率 VOLATILITY</div>
+      <div class="score" id="f-vol">—</div>
+      <div class="sub" id="f-vol-sub"></div>
+      <div class="bar-wrap"><div class="bar" id="bar-vol"></div></div>
+    </div>
+  </div>
+
+  <!-- Charts -->
+  <div class="charts">
+    <div class="chart-box"><h3>制度历史 REGIME HISTORY</h3><canvas id="chart-regime"></canvas></div>
+    <div class="chart-box"><h3>市场广度 BREADTH</h3><canvas id="chart-breadth"></canvas></div>
+  </div>
+
+  <!-- Expectancy -->
+  <div class="exp">
+    <h3>信号期望 SIGNAL EXPECTANCY</h3>
+    <div class="exp-row">
+      <select id="exp-signal">
+        <option value="B3">B3 · 三买</option><option value="B2">B2 · 二买</option><option value="B1">B1 · 一买</option>
+        <option value="S3">S3 · 三卖</option><option value="S2">S2 · 二卖</option><option value="S1">S1 · 一卖</option>
+      </select>
+      <button onclick="loadExpectancy()">查询</button>
+      <span class="suff" id="exp-sufficiency">—</span>
+    </div>
+    <table>
+      <thead><tr><th>层级</th><th>样本</th><th>有效样本</th><th>原始胜率</th><th>后验胜率</th><th>平均收益</th></tr></thead>
+      <tbody id="exp-layers"></tbody>
+    </table>
+    <div class="exp-summary" id="exp-summary"></div>
+  </div>
+</div>
+
+<script src="/static/js/dashboard.js"></script>
+</body>
+</html>

ChanPY.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.analysis.ChanPY import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.analysis.ChanPY")
+sys.modules[__name__] = _impl

ChanPivotClassifier.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.analysis.ChanPivotClassifier import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.analysis.ChanPivotClassifier")
+sys.modules[__name__] = _impl

ChanPivotMonitor.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.analysis.ChanPivotMonitor import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.analysis.ChanPivotMonitor")
+sys.modules[__name__] = _impl

ChanSBI.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.core.ChanSBI import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.core.ChanSBI")
+sys.modules[__name__] = _impl

ChanSEG.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.core.ChanSEG import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.core.ChanSEG")
+sys.modules[__name__] = _impl

ChanZS.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.core.ChanZS import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.core.ChanZS")
+sys.modules[__name__] = _impl

ChanZone.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.analysis.ChanZone import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.analysis.ChanZone")
+sys.modules[__name__] = _impl

Chan_FX_Box.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.core.Chan_FX_Box import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.core.Chan_FX_Box")
+sys.modules[__name__] = _impl

Find_Trend.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.analysis.Find_Trend import *  # noqa: F403
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.analysis.Find_Trend")
+sys.modules[__name__] = _impl

TF_DF.py

@@ -1,2 +1,5 @@
-"""兼容 shim — 请优先 from chanlun import ..."""
-from chanlun.pipeline.timeframe import TF_DF  # noqa: F401
+"""兼容 shim：请优先 from chan... 导入。本文件仅保持旧路径可用。"""
+import importlib
+import sys
+_impl = importlib.import_module("chan.pipeline.TF_DF")
+sys.modules[__name__] = _impl

bsp_monitor/.env

@@ -0,0 +1,3 @@
+# bsp_monitor 复用 Hermes Agent 的 Telegram bot
+# notify.py 从 ~/.hermes/.env 直接读取 TELEGRAM_BOT_TOKEN
+# 此处无需重复配置

bsp_monitor/__init__.py

@@ -0,0 +1 @@
+# bsp_monitor - 缠论买卖点监控 (BTC/USDT 1m)

bsp_monitor/engine.py

@@ -0,0 +1,174 @@
+"""
+engine.py - 缠论管线封装：DataFrame → KLU → KLC → BI → SEG → ZS → BSP。
+
+复用 ~/Project/Chan/ 下的 TF_DF 模块，管线步骤对齐 TF_DF.get_bsp_state()。
+"""
+import sys
+import os
+from typing import List, Optional
+
+_PARENT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if _PARENT not in sys.path:
+    sys.path.insert(0, _PARENT)
+
+import pandas as pd
+from ChanEnum import (
+    Chan_BSP_DIR, Chan_BSP_TYPE, Chan_KLC_FX, Chan_BI_DIR,
+    Chan_ZS_DIR,
+)
+from ChanBSP import ChanBSP
+from ChanBI import ChanBI
+
+# 仅导入类，不触发 TF_DF.__init__
+from TF_DF import TF_DF as _TF_DF_Class
+
+
+class ChanEngine:
+    """缠论管线，对齐 TF_DF.get_bsp_state() 的调用顺序。"""
+
+    def __init__(self, df: pd.DataFrame):
+        if df.empty or len(df) < 50:
+            raise ValueError("DataFrame 至少需要 50 根 K 线")
+
+        if "date" not in df.columns and "timestamp" in df.columns:
+            df["date"] = df["timestamp"]
+
+        self.df = df
+        self._tf = _TF_DF_Class.__new__(_TF_DF_Class)  # 不调用 __init__
+
+        # Step 0: 添加 TA 指标 (MACD/EMA/BB/RSI)
+        self._df_with_indicators = self._tf.add_indicators(df.copy())
+
+        # Step 1: KLU — get_klu_list → get_kl_data → cal_kl_data
+        self.klu_list = self._tf.get_klu_list(self._df_with_indicators)
+
+        # Step 2: KLC — 内部已含 ChanMACD.cal_macd_state() + cal_trend()
+        self.klc_list = self._tf.get_klc_list(self.klu_list)
+
+        # Step 3: BI (stroke)
+        self.bi_list = self._tf.cal_bi_list(self.klc_list)
+
+        # Step 4: SEG (segment)
+        self.seg_list = self._tf.get_seg_list(self.bi_list)
+
+        # Step 5: ZS — cal_bi_zs(seg_list) 对齐 get_bsp_state（从线段计算笔中枢）
+        self.bi_zs_list: List = self._tf.cal_bi_zs(self.seg_list)
+
+        # Step 6: BSP (buy/sell points)
+        self.bsp_list: List[ChanBSP] = self._tf.find_all_bsp(
+            self.bi_list, self.bi_zs_list
+        )
+
+    def get_second_last_bi(self) -> Optional[ChanBI]:
+        """获取倒数第二笔（最新确认的笔）。"""
+        confirmed = [b for b in self.bi_list if b.is_sure]
+        if len(confirmed) >= 2:
+            return confirmed[-2]
+        elif len(confirmed) == 1:
+            return confirmed[-1]
+        return None
+
+    def get_bsp_for_bi(self, bi: ChanBI) -> Optional[ChanBSP]:
+        """检查某个 Bi 的 end_klc 是否是买卖点。"""
+        if bi is None or not bi.is_sure:
+            return None
+        klc = bi.end_klc
+        if klc is None:
+            return None
+        if klc.bsp and klc.bsp_type != Chan_BSP_TYPE.NONE:
+            for bsp in self.bsp_list:
+                if bsp.klc is klc:
+                    return bsp
+        return None
+
+    # ── 格式化 ──
+
+    @staticmethod
+    def _bsp_type_name(t: Chan_BSP_TYPE) -> str:
+        import ChanEnum
+        names = {
+            Chan_BSP_TYPE.B1: "一类买点(B1)",
+            Chan_BSP_TYPE.B2: "二类买点(B2)",
+            Chan_BSP_TYPE.B3: "三类买点(B3)",
+            Chan_BSP_TYPE.S1: "一类卖点(S1)",
+            Chan_BSP_TYPE.S2: "二类卖点(S2)",
+            Chan_BSP_TYPE.S3: "三类卖点(S3)",
+        }
+        return names.get(t, str(t))
+
+    @staticmethod
+    def _bi_dir_name(d) -> str:
+        return "⬆️ 向上" if d == Chan_BI_DIR.UP else "⬇️ 向下"
+
+    @staticmethod
+    def _fx_strength_name(klc_fx_type) -> str:
+        import ChanEnum
+        names = {
+            Chan_KLC_FX.TOP0: "TOP0(弱)", Chan_KLC_FX.TOP1: "TOP1(标准)",
+            Chan_KLC_FX.TOP2: "TOP2(强)", Chan_KLC_FX.TOP3: "TOP3(二类)",
+            Chan_KLC_FX.TOP4: "TOP4(BB上轨)", Chan_KLC_FX.TOP5: "TOP5",
+            Chan_KLC_FX.TOP6: "TOP6(高位空)", Chan_KLC_FX.TOP7: "TOP7(背驰)",
+            Chan_KLC_FX.TOP8: "TOP8(信号线)",
+            Chan_KLC_FX.BOTTOM0: "BOTTOM0(弱)", Chan_KLC_FX.BOTTOM1: "BOTTOM1(标准)",
+            Chan_KLC_FX.BOTTOM2: "BOTTOM2(强)", Chan_KLC_FX.BOTTOM3: "BOTTOM3(二类)",
+            Chan_KLC_FX.BOTTOM4: "BOTTOM4(BB下轨)", Chan_KLC_FX.BOTTOM5: "BOTTOM5(零轴下)",
+            Chan_KLC_FX.BOTTOM6: "BOTTOM6(高位空)", Chan_KLC_FX.BOTTOM7: "BOTTOM7(背驰)",
+            Chan_KLC_FX.BOTTOM8: "BOTTOM8(信号线)",
+        }
+        return names.get(klc_fx_type, f"UNKNOWN({klc_fx_type})")
+
+    @staticmethod
+    def _utc_to_cst(time_str: str) -> str:
+        """UTC 时间字符串 → 东八区 (UTC+8)。"""
+        from datetime import datetime, timedelta, timezone
+        dt = datetime.fromisoformat(str(time_str))
+        if dt.tzinfo is None:
+            dt = dt.replace(tzinfo=timezone.utc)
+        cst = dt.astimezone(timezone(timedelta(hours=8)))
+        return cst.strftime("%Y-%m-%d %H:%M:%S CST")
+
+    def format_bsp_detail(self, bsp: ChanBSP, symbol: str = "BTC/USDT:USDT", tf: str = "1m") -> str:
+        bi = bsp.bi
+        klc = bsp.klc
+        bsp_type = bsp.type
+        bsp_dir = bsp.dir
+
+        emoji = "🟢" if bsp_dir == Chan_BSP_DIR.BUY else "🔴"
+        dir_label = "买点" if bsp_dir == Chan_BSP_DIR.BUY else "卖点"
+
+        symbol_short = symbol.split(":")[0].replace("/", "")
+        lines = [
+            f"{emoji} [{dir_label}] {self._bsp_type_name(bsp_type)} — <b>{symbol_short} {tf}</b>",
+            "",
+            f"⏰ 确认: <code>{self._utc_to_cst(klc.end_time)}</code>",
+            f"💰 价格: <b>{klc.close:.2f}</b>",
+            f"📐 笔方向: {self._bi_dir_name(bi.dir)}",
+            f"📏 笔高度: ${bi.height:.2f}  宽度: {bi.width}K  斜率: {bi.slop:.2f}",
+            f"🔩 分型强度: {self._fx_strength_name(klc.klc_fx_type)}",
+        ]
+
+        if bsp.zs:
+            zs = bsp.zs
+            zs_dir = "UP" if hasattr(zs, 'dir') and hasattr(Chan_ZS_DIR, 'UP') and zs.dir == Chan_ZS_DIR.UP else "DOWN"
+            lines.append(f"🏠 中枢: {zs.zd:.2f} – {zs.zg:.2f} ({zs_dir}, #{getattr(zs, 'index', 0) + 1})")
+
+        if bsp.type in (Chan_BSP_TYPE.B1, Chan_BSP_TYPE.S1):
+            lines.append("📊 MACD背驰: 有 (离开段能量 < 进入段)")
+
+        if hasattr(klc, 'ema_status') and klc.ema_status:
+            ema52 = klc.ema_status.get('ema52', {})
+            if ema52:
+                pos = str(ema52.get('pos', '?'))
+                lines.append(f"📈 EMA52: {pos}  (值: {klc.ema52:.2f})")
+
+        lines.append(f"📋 KLC状态: {klc.klc_state}")
+
+        if bi.pre:
+            prev = bi.pre
+            lines.extend([
+                "────",
+                f"⬅️ 前一笔: {self._bi_dir_name(prev.dir)} "
+                f"高度: ${prev.height:.2f}  宽度: {prev.width}K",
+            ])
+
+        return "\n".join(lines)

bsp_monitor/fetcher.py

@@ -0,0 +1,62 @@
+"""
+fetcher.py - 从 data_provider HTTP API 拉取 K 线数据。
+"""
+
+from typing import List, Optional
+
+import requests
+import pandas as pd
+import logging
+
+logger = logging.getLogger(__name__)
+
+PROVIDER_URL = "http://103.179.242.166"
+PROVIDER_URL = "http://127.0.0.1"
+FETCH_LIMIT = 1000
+
+_symbols_cache: Optional[List[str]] = None
+
+
+# 只推送 BTC，其他币对暂不监控
+_SYMBOL_WHITELIST = {"BTC/USDT:USDT", "ETH/USDT:USDT", "SOL/USDT:USDT"}
+
+
+def get_symbols() -> list[str]:
+    """获取要监控的币对列表（目前只监控 BTC）。"""
+    global _symbols_cache
+    if _symbols_cache is not None:
+        return _symbols_cache
+    try:
+        resp = requests.get(f"{PROVIDER_URL}/health", timeout=10)
+        resp.raise_for_status()
+        all_symbols = resp.json().get("symbols", [])
+        _symbols_cache = [s for s in all_symbols if s in _SYMBOL_WHITELIST]
+        logger.info(f"获取到 {len(all_symbols)} 个币对，过滤后监控 {len(_symbols_cache)} 个: {_symbols_cache}")
+    except Exception as e:
+        logger.error(f"获取币对列表失败: {e}")
+        _symbols_cache = ["BTC/USDT:USDT"]
+    return _symbols_cache
+
+
+def fetch_ohlcv(symbol: str, tf: str = "1m") -> pd.DataFrame:
+    """从 data_provider API 拉取某个币对最近 FETCH_LIMIT 根 K 线。"""
+    url = f"{PROVIDER_URL}/api/candles"
+    params = {
+        "symbol": symbol,
+        "tf": tf,
+        "limit": FETCH_LIMIT,
+    }
+    resp = requests.get(url, params=params, timeout=30)
+    resp.raise_for_status()
+    data = resp.json()
+
+    if not data:
+        logger.warning(f"{symbol}: API 返回空数据")
+        return pd.DataFrame()
+
+    df = pd.DataFrame(data)
+    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    df["date"] = df["timestamp"]
+
+    df = df.drop_duplicates(subset="timestamp").sort_values("timestamp").reset_index(drop=True)
+    return df

bsp_monitor/main.py

@@ -0,0 +1,220 @@
+#!/usr/bin/env python3
+"""
+main.py - 缠论多周期买卖点监控。
+
+每整分钟：
+  1. 从 data_provider 拉取所有币对多周期 K 线
+  2. 每个币对 × 每个周期独立跑缠论管线
+  3. 检测新笔确认 → BSP 推送
+"""
+import asyncio
+import logging
+import sys
+import os
+import time
+from dataclasses import dataclass, field
+from datetime import datetime, timezone, timedelta
+from typing import Optional
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from fetcher import fetch_ohlcv, get_symbols
+from engine import ChanEngine
+from notify import send_bsp_alert, BOT_TOKEN, CHAT_ID
+
+_PARENT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if _PARENT not in sys.path:
+    sys.path.insert(0, _PARENT)
+from ChanEnum import Chan_BI_DIR
+# from ChanPivotMonitor import ChanPivotMonitor  # 暂停中枢监控
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+)
+logger = logging.getLogger("bsp_monitor")
+
+TIMEFRAMES = ["1m", "5m", "15m", "1h"]
+
+
+def _short(symbol: str) -> str:
+    """BTC/USDT:USDT → BTCUSDT"""
+    return symbol.split(":")[0].replace("/", "")
+
+
+def _bi_id(bi) -> Optional[tuple]:
+    """笔的稳定标识，基于首K线时间戳。"""
+    if bi.start_klc is None:
+        return None
+    return (bi.start_klc.start_time,)
+
+
+def _push_bsp(engine: ChanEngine, bsp, symbol: str, tf: str) -> bool:
+    """推送 BSP 到 Telegram（带去重）。"""
+    if bsp.klc is None:
+        return False
+    key = f"{symbol}_{bsp.type}_{bsp.klc.end_time}_{tf}"
+    msg = engine.format_bsp_detail(bsp, symbol, tf)
+    msg = _escape_html(msg)
+    if send_bsp_alert(msg, bsp_key=key):
+        logger.info(f"[{_short(symbol)} {tf}] ✅ BSP: {key}")
+        return True
+    return False
+
+
+@dataclass
+class TfState:
+    """单个周期的状态。"""
+    last_bi_id: Optional[tuple] = None
+    last_df_ts: object = None
+    first_run: bool = True
+    # pivot_monitor: ChanPivotMonitor = None  # 暂停中枢监控
+
+    # def __post_init__(self):
+    #     if self.pivot_monitor is None:
+    #         self.pivot_monitor = ChanPivotMonitor()
+
+
+@dataclass
+class SymbolState:
+    symbol: str
+    tfs: dict = field(default_factory=dict)
+
+    def __post_init__(self):
+        self.tfs = {tf: TfState() for tf in TIMEFRAMES}
+
+
+class BSPMonitor:
+    def __init__(self):
+        symbols = get_symbols()
+        self._states: dict[str, SymbolState] = {
+            s: SymbolState(symbol=s) for s in symbols
+        }
+        logger.info(f"监控 {len(symbols)}×{len(TIMEFRAMES)} 币对×周期: "
+                    f"{', '.join(_short(s) for s in symbols)}")
+
+    async def tick(self):
+        tick_start = time.monotonic()
+        logger.info("── tick 开始 ──")
+
+        for symbol, st in self._states.items():
+            await self._tick_symbol(symbol, st)
+
+        elapsed = (time.monotonic() - tick_start) * 1000
+        logger.info(f"── tick 结束 ({elapsed:.0f}ms) ──")
+
+    async def _tick_symbol(self, symbol: str, st: SymbolState):
+        name = _short(symbol)
+
+        for tf in TIMEFRAMES:
+            await self._check_tf(symbol, tf, st.tfs[tf], name)
+
+    async def _check_tf(self, symbol: str, tf: str, ts: TfState, name: str):
+        # 1. 拉取 K 线
+        try:
+            df = fetch_ohlcv(symbol, tf)
+        except Exception as e:
+            logger.error(f"[{name} {tf}] 拉取失败: {e}")
+            return
+
+        if df.empty:
+            return
+
+        # 2. 检查是否有新 K 线
+        latest_ts = df.iloc[-1]["timestamp"]
+        if ts.last_df_ts and latest_ts <= ts.last_df_ts:
+            return
+        ts.last_df_ts = latest_ts
+
+        # 3. 运行缠论管线
+        try:
+            engine = ChanEngine(df)
+        except Exception as e:
+            logger.error(f"[{name} {tf}] 缠论计算失败: {e}", exc_info=True)
+            return
+
+        # 4. 中枢特征更新（暂停）
+        # try:
+        #     ts.pivot_monitor.update(engine.bi_zs_list)
+        # except Exception as e:
+        #     logger.debug(f"[{name} {tf}] 中枢特征更新失败: {e}")
+
+        # 5. BSP 检测
+        confirmed = [b for b in engine.bi_list if b.is_sure]
+        if len(confirmed) < 2:
+            return
+
+        last_confirmed = confirmed[-1]
+        current_bi_id = _bi_id(last_confirmed)
+        if current_bi_id is None:
+            return
+
+        if ts.first_run:
+            ts.first_run = False
+            ts.last_bi_id = current_bi_id
+
+            bsp = engine.get_bsp_for_bi(last_confirmed)
+            if bsp:
+                _push_bsp(engine, bsp, symbol, tf)
+
+            logger.info(
+                f"[{name} {tf}] 首次完成 — "
+                f"{len(confirmed)} 笔, {len(engine.bsp_list)} BSP"
+            )
+            return
+
+        if current_bi_id == ts.last_bi_id:
+            return
+
+        ts.last_bi_id = current_bi_id
+
+        bi_dir = "⬆️" if last_confirmed.dir == Chan_BI_DIR.UP else "⬇️"
+        logger.info(f"[{name} {tf}] 新笔确认 — #{len(confirmed)} "
+                    f"{bi_dir} 高度: ${last_confirmed.height:.2f}")
+
+        bsp = engine.get_bsp_for_bi(last_confirmed)
+        if bsp:
+            _push_bsp(engine, bsp, symbol, tf)
+
+    async def run(self):
+        logger.info("=" * 50)
+        logger.info(f"bsp_monitor 启动 — {len(self._states)} 币对 "
+                    f"× {len(TIMEFRAMES)} 周期 ({', '.join(TIMEFRAMES)})")
+        logger.info(f"Telegram: {'已配置' if BOT_TOKEN and CHAT_ID else '⚠️ 未配置'}")
+        logger.info("=" * 50)
+
+        logger.info("首次运行（初始化）...")
+        await self.tick()
+
+        while True:
+            now = datetime.now(timezone.utc)
+            next_minute = now.replace(second=0, microsecond=0) + timedelta(minutes=1)
+            wait_seconds = max(0.1, (next_minute - now).total_seconds())
+
+            logger.info(f"等待 {wait_seconds:.0f}s 到 {next_minute.strftime('%H:%M:%S')}UTC")
+            await asyncio.sleep(wait_seconds)
+
+            try:
+                await self.tick()
+            except Exception as e:
+                logger.error(f"tick 异常: {e}", exc_info=True)
+                await asyncio.sleep(5)
+
+
+def _escape_html(msg: str) -> str:
+    """HTML 转义，保留已有的 <b>/<code> 标签。"""
+    msg = msg.replace("&", "&amp;")
+    msg = msg.replace("<b>", "\x00B\x00").replace("</b>", "\x00/B\x00")
+    msg = msg.replace("<code>", "\x00C\x00").replace("</code>", "\x00/C\x00")
+    msg = msg.replace("<", "&lt;").replace(">", "&gt;")
+    msg = msg.replace("\x00B\x00", "<b>").replace("\x00/B\x00", "</b>")
+    msg = msg.replace("\x00C\x00", "<code>").replace("\x00/C\x00", "</code>")
+    return msg
+
+
+if __name__ == "__main__":
+    monitor = BSPMonitor()
+    try:
+        asyncio.run(monitor.run())
+    except KeyboardInterrupt:
+        logger.info("收到中断信号，退出")

bsp_monitor/notify.py

@@ -0,0 +1,61 @@
+"""
+notify.py - Telegram 推送。
+"""
+import logging
+import requests
+
+logger = logging.getLogger(__name__)
+
+BOT_TOKEN = "8742822093:AAGzD1vS7ru7ROhgcOjA-UyHb4R8Cfcqv3Q"
+CHAT_ID = "580807463"
+
+
+def send_telegram_message(text: str) -> bool:
+    """发送 Telegram 消息（不去重，每次调用都发）。"""
+    if not BOT_TOKEN or not CHAT_ID:
+        logger.warning("Telegram 未配置，跳过推送")
+        return False
+
+    url = f"https://api.telegram.org/bot{BOT_TOKEN}/sendMessage"
+    try:
+        resp = requests.post(
+            url,
+            json={
+                "chat_id": CHAT_ID,
+                "text": text,
+                "parse_mode": "HTML",
+                "disable_web_page_preview": True,
+            },
+            timeout=10,
+        )
+        resp.raise_for_status()
+        return True
+    except Exception as e:
+        logger.error(f"Telegram 推送失败: {e}")
+        return False
+
+
+def send_bsp_alert(text: str, bsp_key: str = "") -> bool:
+    """推送 BSP 消息。"""
+    if not BOT_TOKEN or not CHAT_ID:
+        logger.warning("Telegram 未配置，跳过推送")
+        return False
+
+    url = f"https://api.telegram.org/bot{BOT_TOKEN}/sendMessage"
+    try:
+        resp = requests.post(
+            url,
+            json={
+                "chat_id": CHAT_ID,
+                "text": text,
+                "parse_mode": "HTML",
+                "disable_web_page_preview": True,
+            },
+            timeout=10,
+        )
+        resp.raise_for_status()
+        logger.info(f"Telegram 推送成功: {bsp_key or 'no-key'}")
+        return True
+    except Exception as e:
+        logger.error(f"Telegram 推送失败: {e}")
+        return False

bsp_monitor/run.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+# bsp_monitor 启动脚本
+# 用法: bash run.sh
+
+cd "$(dirname "$0")"
+echo "=== bsp_monitor ==="
+echo "启动时间: $(date -u '+%Y-%m-%d %H:%M:%S UTC')"
+echo "监控: BTC/USDT:USDT 1m 缠论买卖点"
+echo "推送: Telegram (复用 Hermes bot)"
+echo "==================="
+exec /usr/bin/python3 -u main.py

bsp_monitor/twitter_web.py

@@ -0,0 +1,380 @@
+#!/usr/bin/env python3
+"""
+twitter_web.py — Twitter 监控账号管理 Web 界面。
+单文件，零依赖，只用到 Python 标准库。
+"""
+
+import json
+import os
+import sys
+import re
+from datetime import datetime, timezone
+from http.server import HTTPServer, BaseHTTPRequestHandler
+from urllib.parse import urlparse, parse_qs
+
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+WATCHLIST_PATH = os.path.join(SCRIPT_DIR, "twitter_watchlist.json")
+STATE_PATH = os.path.join(SCRIPT_DIR, "twitter_state.json")
+
+PORT = int(sys.argv[1]) if len(sys.argv) > 1 else 8010
+
+
+def extract_username(value: str) -> str:
+    value = value.strip().rstrip("/")
+    if value.startswith("@"):
+        return value[1:]
+    for pattern in [r"(?:twitter\.com|x\.com)/(\w+)(?:/|$)", r"/(\w+)$"]:
+        m = re.search(pattern, value)
+        if m:
+            return m.group(1)
+    if re.match(r"^\w+$", value):
+        return value
+    raise ValueError(f"无法提取用户名: {value}")
+
+
+def load_json(path):
+    if os.path.exists(path):
+        with open(path) as f:
+            return json.load(f)
+    return {}
+
+
+def save_json(path, data):
+    with open(path, "w") as f:
+        json.dump(data, f, indent=2, ensure_ascii=False)
+
+
+def get_watchlist():
+    return load_json(WATCHLIST_PATH).get("users", [])
+
+
+def save_watchlist(users):
+    save_json(WATCHLIST_PATH, {"users": users})
+
+
+def get_state():
+    return load_json(STATE_PATH)
+
+
+HTML = """<!DOCTYPE html>
+<html lang="zh">
+<head>
+<!-- Google tag (gtag.js) -->
+<script async src="https://www.googletagmanager.com/gtag/js?id=G-LVVXH3TL04"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+  gtag('config', 'G-LVVXH3TL04');
+</script>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>Twitter 监控管理</title>
+<style>
+  :root {
+    --bg: #0d1117; --card: #161b22; --border: #30363d;
+    --text: #c9d1d9; --muted: #8b949e; --accent: #58a6ff;
+    --green: #3fb950; --red: #f85149; --yellow: #d2991d;
+  }
+  * { margin:0; padding:0; box-sizing:border-box; }
+  body { font:14px/1.6 -apple-system,BlinkMacSystemFont,"Segoe UI",sans-serif;
+         background:var(--bg); color:var(--text); padding:24px; max-width:680px; margin:auto; }
+  h1 { font-size:20px; margin-bottom:4px; }
+  .sub { color:var(--muted); font-size:12px; margin-bottom:20px; }
+  .add-bar { display:flex; gap:8px; margin-bottom:20px; }
+  .add-bar input { flex:1; padding:8px 12px; border:1px solid var(--border);
+    border-radius:6px; background:var(--card); color:var(--text); font-size:14px; outline:none; }
+  .add-bar input:focus { border-color:var(--accent); }
+  .add-bar input::placeholder { color:var(--muted); }
+  button { padding:8px 16px; border:none; border-radius:6px; cursor:pointer; font-size:13px;
+    font-weight:500; transition:opacity .15s; }
+  button:hover { opacity:0.85; }
+  .btn-add { background:var(--accent); color:#fff; }
+  .btn-edit, .btn-save { background:var(--yellow); color:#000; }
+  .btn-del { background:var(--red); color:#fff; }
+  .btn-cancel { background:var(--border); color:var(--text); }
+  .account { background:var(--card); border:1px solid var(--border); border-radius:8px;
+    padding:12px 16px; margin-bottom:8px; display:flex; align-items:center; gap:12px; }
+  .account .name { font-weight:600; min-width:160px; }
+  .account .name a { color:var(--accent); text-decoration:none; }
+  .account .name a:hover { text-decoration:underline; }
+  .account .meta { font-size:12px; color:var(--muted); flex:1; }
+  .account .actions { display:flex; gap:6px; flex-shrink:0; }
+  .edit-row { display:flex; gap:6px; align-items:center; width:100%; }
+  .edit-row input { flex:1; padding:6px 10px; border:1px solid var(--accent);
+    border-radius:4px; background:var(--bg); color:var(--text); font-size:13px; outline:none; }
+  .badge { display:inline-block; font-size:11px; padding:2px 8px; border-radius:10px;
+    background:var(--green); color:#000; margin-left:6px; }
+  .empty { text-align:center; padding:60px 20px; color:var(--muted); }
+  .empty p { margin-bottom:8px; }
+  .toast { position:fixed; bottom:20px; right:20px; padding:10px 20px; border-radius:6px;
+    font-size:13px; color:#fff; opacity:0; transition:opacity .3s; z-index:100; }
+  .toast.show { opacity:1; }
+  .toast.ok { background:var(--green); }
+  .toast.err { background:var(--red); }
+</style>
+</head>
+<body>
+<h1>🐦 Twitter 账号监控</h1>
+<p class="sub">管理 twitterapi.io 监控账号 · 增删改查</p>
+
+<div class="add-bar">
+  <input id="urlInput" type="text" placeholder="输入 Twitter/X 链接或用户名..." autofocus>
+  <button class="btn-add" onclick="addAccount()">➕ 添加</button>
+</div>
+
+<div id="list"></div>
+
+<div class="toast" id="toast"></div>
+
+<script>
+const API = '/twitter/api/accounts';
+let editing = null;
+
+async function api(method, path='', body=null) {
+  const opts = { method, headers:{} };
+  if (body) { opts.headers['Content-Type']='application/json'; opts.body=JSON.stringify(body); }
+  const r = await fetch(API + path, opts);
+  const data = await r.json();
+  if (!r.ok) throw new Error(data.error || '请求失败');
+  return data;
+}
+
+function toast(msg, ok=true) {
+  const t = document.getElementById('toast');
+  t.textContent = msg; t.className = 'toast ' + (ok?'ok':'err') + ' show';
+  setTimeout(() => t.classList.remove('show'), 2500);
+}
+
+async function load() {
+  const data = await api('GET');
+  const div = document.getElementById('list');
+  if (!data.accounts.length) {
+    div.innerHTML = '<div class="empty"><p>📭 暂无监控账号</p><p style="font-size:12px;color:var(--muted)">在上方输入 Twitter/X 链接或用户名添加</p></div>';
+    return;
+  }
+  div.innerHTML = data.accounts.map(a => `
+    <div class="account" id="row-${a.username}">
+      ${editing===a.username ? `
+        <div class="edit-row">
+          <input id="editInput" value="${esc(a.display_name || a.username)}" placeholder="备注名称">
+          <button class="btn-save" onclick="saveEdit('${esc(a.username)}')">保存</button>
+          <button class="btn-cancel" onclick="cancelEdit()">取消</button>
+        </div>
+      ` : `
+        <div class="name">
+          <a href="https://x.com/${esc(a.username)}" target="_blank">@${esc(a.username)}</a>
+          ${a.display_name && a.display_name !== a.username ? `<span style="color:var(--text)">(${esc(a.display_name)})</span>` : ''}
+        </div>
+        <div class="meta">
+          添加: ${a.added_at?.slice(0,10) || '?'}
+          ${a.last_check ? ` · 上次检查: ${a.last_check}` : ''}
+        </div>
+        <div class="actions">
+          <button class="btn-edit" onclick="startEdit('${esc(a.username)}','${esc(a.display_name||a.username)}')">✏️</button>
+          <button class="btn-del" onclick="removeAccount('${esc(a.username)}')">🗑</button>
+        </div>
+      `}
+    </div>
+  `).join('');
+}
+
+function esc(s) { return s.replace(/&/g,'&amp;').replace(/"/g,'&quot;').replace(/</g,'&lt;').replace(/>/g,'&gt;').replace(/'/g,'&#39;'); }
+
+async function addAccount() {
+  const inp = document.getElementById('urlInput');
+  const val = inp.value.trim();
+  if (!val) { toast('请输入链接或用户名', false); return; }
+  try {
+    const r = await api('POST', '', {url: val});
+    toast(r.message || '添加成功');
+    inp.value = '';
+    load();
+  } catch(e) { toast(e.message, false); }
+}
+
+async function removeAccount(username) {
+  if (!confirm(`确定删除 @${username}？`)) return;
+  try {
+    const r = await api('DELETE', '/' + username);
+    toast(r.message || '已删除');
+    load();
+  } catch(e) { toast(e.message, false); }
+}
+
+function startEdit(username, name) {
+  editing = username;
+  load();
+  setTimeout(() => {
+    const inp = document.getElementById('editInput');
+    if (inp) { inp.focus(); inp.select(); }
+  }, 50);
+}
+
+function cancelEdit() { editing = null; load(); }
+
+async function saveEdit(username) {
+  const val = document.getElementById('editInput').value.trim();
+  editing = null;
+  try {
+    const r = await api('PUT', '/' + username, {display_name: val});
+    toast(r.message || '已更新');
+    load();
+  } catch(e) { toast(e.message, false); }
+}
+
+document.getElementById('urlInput').addEventListener('keydown', e => {
+  if (e.key === 'Enter') addAccount();
+});
+
+load();
+</script>
+</body>
+</html>"""
+
+
+class Handler(BaseHTTPRequestHandler):
+    def log_message(self, format, *args):
+        pass  # silent
+
+    def _send(self, code, body, content_type="application/json"):
+        body = body.encode() if isinstance(body, str) else json.dumps(body, ensure_ascii=False).encode()
+        self.send_response(code)
+        self.send_header("Content-Type", content_type + "; charset=utf-8")
+        self.send_header("Content-Length", str(len(body)))
+        self.send_header("Access-Control-Allow-Origin", "*")
+        self.end_headers()
+        self.wfile.write(body)
+
+    def _json(self, code, data):
+        self._send(code, data)
+
+    def _error(self, code, msg):
+        self._json(code, {"error": msg})
+
+    def do_OPTIONS(self):
+        self.send_response(204)
+        self.send_header("Access-Control-Allow-Origin", "*")
+        self.send_header("Access-Control-Allow-Methods", "GET,POST,PUT,DELETE,OPTIONS")
+        self.send_header("Access-Control-Allow-Headers", "Content-Type")
+        self.end_headers()
+
+    def do_GET(self):
+        path = urlparse(self.path).path
+        if path == "/" or path == "/index.html":
+            self._send(200, HTML, "text/html")
+            return
+        if path.startswith("/api/accounts"):
+            username = path[len("/api/accounts"):].strip("/")
+            if username:
+                # GET /api/accounts/<username> — single account
+                users = get_watchlist()
+                state = get_state()
+                for u in users:
+                    if u["username"].lower() == username.lower():
+                        entry = dict(u)
+                        entry["last_check"] = state.get(u["username"], {}).get("last_check")
+                        self._json(200, entry)
+                        return
+                self._error(404, "账号不存在")
+                return
+            # GET /api/accounts — list all
+            users = get_watchlist()
+            state = get_state()
+            accounts = []
+            for u in users:
+                entry = dict(u)
+                sc = state.get(u["username"], {})
+                ts = sc.get("last_check")
+                if ts:
+                    try:
+                        ts = datetime.fromisoformat(ts).strftime("%m-%d %H:%M")
+                    except Exception:
+                        pass
+                else:
+                    ts = "从未"
+                entry["last_check"] = ts
+                accounts.append(entry)
+            self._json(200, {"accounts": accounts})
+        else:
+            self._error(404, "Not Found")
+
+    def do_POST(self):
+        path = urlparse(self.path).path
+        if path != "/api/accounts":
+            self._error(404, "Not Found")
+            return
+        length = int(self.headers.get("Content-Length", 0))
+        body = json.loads(self.rfile.read(length)) if length else {}
+        url = body.get("url", "").strip()
+        if not url:
+            self._error(400, "缺少 url 参数")
+            return
+        try:
+            username = extract_username(url)
+        except ValueError:
+            self._error(400, "无法从输入中提取用户名，请输入 Twitter/X 链接或 @用户名")
+            return
+
+        users = get_watchlist()
+        if any(u["username"].lower() == username.lower() for u in users):
+            self._error(409, f"@{username} 已在监控列表中")
+            return
+
+        display_name = body.get("display_name", "").strip() or username
+        users.append({
+            "username": username,
+            "display_name": display_name,
+            "added_at": datetime.now(timezone.utc).isoformat(),
+        })
+        save_watchlist(users)
+        self._json(201, {"message": f"✅ 已添加 @{username}", "username": username})
+
+    def do_PUT(self):
+        path = urlparse(self.path).path
+        username = path[len("/api/accounts"):].strip("/")
+        if not username:
+            self._error(400, "缺少用户名")
+            return
+        length = int(self.headers.get("Content-Length", 0))
+        body = json.loads(self.rfile.read(length)) if length else {}
+        display_name = body.get("display_name", "").strip()
+
+        users = get_watchlist()
+        for u in users:
+            if u["username"].lower() == username.lower():
+                if display_name:
+                    u["display_name"] = display_name
+                save_watchlist(users)
+                self._json(200, {"message": f"✅ @{username} 已更新"})
+                return
+        self._error(404, "账号不存在")
+
+    def do_DELETE(self):
+        path = urlparse(self.path).path
+        username = path[len("/api/accounts"):].strip("/")
+        if not username:
+            self._error(400, "缺少用户名")
+            return
+        users = get_watchlist()
+        before = len(users)
+        users = [u for u in users if u["username"].lower() != username.lower()]
+        if len(users) < before:
+            save_watchlist(users)
+            self._json(200, {"message": f"🗑 已移除 @{username}"})
+        else:
+            self._error(404, "账号不存在")
+
+
+def main():
+    print(f"🐦 Twitter 监控管理: http://0.0.0.0:{PORT}")
+    server = HTTPServer(("0.0.0.0", PORT), Handler)
+    try:
+        server.serve_forever()
+    except KeyboardInterrupt:
+        print("\n已停止")
+        server.server_close()
+
+
+if __name__ == "__main__":
+    main()

chan/__init__.py

@@ -0,0 +1,6 @@
+"""缠论引擎包：core（结构）/ indicators（指标）/ analysis（接合）/ pipeline（编排）。"""
+
+from .pipeline.ChanLun import ChanLun
+from .pipeline.TF_DF import TF_DF
+
+__all__ = ["ChanLun", "TF_DF"]

chan/analysis/ChanLun_Classifier.py

@@ -6,14 +6,14 @@ sys.path.append(os.path.abspath("/Users/jack/Project/freqtrade/user_data/Chan"))
 import numpy as np
 from datetime import timedelta
 from pandas import DataFrame
-from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_SEG_DIR, Chan_ZS_DIR, Chan_BSP_DIR, Chan_BSP_TYPE, Chan_KLC_FX
-from chanlun.core.ChanKLU import ChanKLU
-from chanlun.core.ChanKLC import ChanKLC
-from chanlun.core.ChanBI import ChanBI
-from chanlun.core.ChanSBI import ChanSBI
-from chanlun.core.ChanSEG import ChanSEG
-from chanlun.core.ChanZS import ChanZS
-from chanlun.core.ChanBSP import ChanBSP
+from ..core.ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_SEG_DIR, Chan_ZS_DIR, Chan_BSP_DIR, Chan_BSP_TYPE, Chan_KLC_FX
+from ..core.ChanKLU import ChanKLU
+from ..core.ChanKLC import ChanKLC
+from ..core.ChanBI import ChanBI
+from ..core.ChanSBI import ChanSBI
+from ..core.ChanSEG import ChanSEG
+from ..core.ChanZS import ChanZS
+from ..core.ChanBSP import ChanBSP
 import talib.abstract as ta
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -21,7 +21,7 @@ from matplotlib.dates import DateFormatter, date2num
 import matplotlib.patches as patches
 from technical.util import resample_to_interval
 from decimal import Decimal
-from chanlun.pipeline.orchestrator import ChanLun
+from ..pipeline.ChanLun import ChanLun
 import xgboost as xgb
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report
 

chan/analysis/ChanMACD.py

@@ -1,8 +1,8 @@
-from chanlun.core.ChanKLU import ChanKLU
-from chanlun.core.ChanEnum import Chan_MACD_STATE, Chan_MACDSEG_DIR, Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIR, Chan_MACDUNITTF_TYPE
-from chanlun.indicators.ChanMACDSeg import ChanMACDSeg
-from chanlun.indicators.ChanMACDUnitTF import ChanMACDUnitTF
-from chanlun.indicators.ChanMACDHistSet import ChanMACDHistSet
+from ..core.ChanKLU import ChanKLU
+from ..core.ChanEnum import Chan_MACD_STATE, Chan_MACDSEG_DIR, Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIR, Chan_MACDUNITTF_TYPE
+from .ChanMACDSeg import ChanMACDSeg
+from .ChanMACDUnitTF import ChanMACDUnitTF
+from .ChanMACDHistSet import ChanMACDHistSet
 
 class ChanMACD():
 	def __init__(self, klu_list: list[ChanKLU]):

chan/analysis/ChanMACDHistSet.py

@@ -1,4 +1,4 @@
-from chanlun.core.ChanEnum import Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIV, Chan_MACD_STATE
+from ..core.ChanEnum import Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIV, Chan_MACD_STATE
 
 class ChanMACDHistSet():
     def __init__(self, index, start_time, start_klu, pre_histset, dir):

chan/analysis/ChanMACDSeg.py

@@ -1,4 +1,4 @@
-from chanlun.core.ChanEnum import Chan_MACDSEG_DIR
+from ..core.ChanEnum import Chan_MACDSEG_DIR
 
 
 class ChanMACDSeg():

chan/analysis/ChanMACDUnitTF.py

@@ -1,4 +1,4 @@
-from chanlun.core.ChanEnum import Chan_MACD_STATE, Chan_MACDUNITTF_DIR, Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIV, Chan_MACDUNITTF_TYPE
+from ..core.ChanEnum import Chan_MACD_STATE, Chan_MACDUNITTF_DIR, Chan_MACDHISTSET_DIR, Chan_MACDUNITTF_DIV, Chan_MACDUNITTF_TYPE
 
 
 class ChanMACDUnitTF():

chan/analysis/ChanPY.py

@@ -1,20 +1,96 @@
-import sys 
+import sys
 import os
-#sys.path.append(os.path.abspath("/Users/jack/Documents/GitHub/chan.py"))
-sys.path.append(os.path.abspath("/Users/jack/Project/chan.py"))
-from Chan import CChan
-from BuySellPoint.BS_Point import CBS_Point
-from ChanConfig import CChanConfig
-from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE, DATA_FIELD, BSP_TYPE, FX_TYPE, BI_DIR, KLINE_DIR, SEG_DIR
-from KLine.KLine_Unit import CKLine_Unit
-from Common.CTime import CTime
-from Common.func_util import kltype_lt_day, str2float
-from Bi.Bi import CBi
 from typing import Dict, List
 from functools import reduce
 from pandas import DataFrame
 from datetime import datetime, timedelta, timezone
 
+# 外部 chan.py 与本仓库包名 `chan` 在 macOS 大小写不敏感磁盘上冲突。
+# 临时卸下本包后再导入上游，再恢复本包。
+_EXT_ROOT = os.path.abspath("/Users/jack/Project/chan.py")
+
+
+def _load_external_chan():
+	saved = {}
+	for key in list(sys.modules):
+		low = key.lower()
+		if low == "chan" or low.startswith("chan."):
+			saved[key] = sys.modules.pop(key)
+	inserted = False
+	if _EXT_ROOT not in sys.path:
+		sys.path.insert(0, _EXT_ROOT)
+		inserted = True
+	try:
+		from Chan import CChan as _CChan
+		from BuySellPoint.BS_Point import CBS_Point as _CBS_Point
+		from ChanConfig import CChanConfig as _CChanConfig
+		from Common.CEnum import (
+			AUTYPE as _AUTYPE,
+			DATA_SRC as _DATA_SRC,
+			KL_TYPE as _KL_TYPE,
+			DATA_FIELD as _DATA_FIELD,
+			BSP_TYPE as _BSP_TYPE,
+			FX_TYPE as _FX_TYPE,
+			BI_DIR as _BI_DIR,
+			KLINE_DIR as _KLINE_DIR,
+			SEG_DIR as _SEG_DIR,
+		)
+		from KLine.KLine_Unit import CKLine_Unit as _CKLine_Unit
+		from Common.CTime import CTime as _CTime
+		from Common.func_util import kltype_lt_day as _kltype_lt_day, str2float as _str2float
+		from Bi.Bi import CBi as _CBi
+		return {
+			"CChan": _CChan,
+			"CBS_Point": _CBS_Point,
+			"CChanConfig": _CChanConfig,
+			"AUTYPE": _AUTYPE,
+			"DATA_SRC": _DATA_SRC,
+			"KL_TYPE": _KL_TYPE,
+			"DATA_FIELD": _DATA_FIELD,
+			"BSP_TYPE": _BSP_TYPE,
+			"FX_TYPE": _FX_TYPE,
+			"BI_DIR": _BI_DIR,
+			"KLINE_DIR": _KLINE_DIR,
+			"SEG_DIR": _SEG_DIR,
+			"CKLine_Unit": _CKLine_Unit,
+			"CTime": _CTime,
+			"kltype_lt_day": _kltype_lt_day,
+			"str2float": _str2float,
+			"CBi": _CBi,
+		}
+	finally:
+		# 清除上游以 Chan/chan 注册的模块，避免污染本包
+		for key in list(sys.modules):
+			low = key.lower()
+			if low == "chan" or low.startswith("chan."):
+				sys.modules.pop(key, None)
+		sys.modules.update(saved)
+		if inserted and _EXT_ROOT in sys.path:
+			try:
+				sys.path.remove(_EXT_ROOT)
+			except ValueError:
+				pass
+
+
+_ext = _load_external_chan()
+CChan = _ext["CChan"]
+CBS_Point = _ext["CBS_Point"]
+CChanConfig = _ext["CChanConfig"]
+AUTYPE = _ext["AUTYPE"]
+DATA_SRC = _ext["DATA_SRC"]
+KL_TYPE = _ext["KL_TYPE"]
+DATA_FIELD = _ext["DATA_FIELD"]
+BSP_TYPE = _ext["BSP_TYPE"]
+FX_TYPE = _ext["FX_TYPE"]
+BI_DIR = _ext["BI_DIR"]
+KLINE_DIR = _ext["KLINE_DIR"]
+SEG_DIR = _ext["SEG_DIR"]
+CKLine_Unit = _ext["CKLine_Unit"]
+CTime = _ext["CTime"]
+kltype_lt_day = _ext["kltype_lt_day"]
+str2float = _ext["str2float"]
+CBi = _ext["CBi"]
+
 
 def GetColumnNameFromFieldList(fileds: str):
 	_dict = {

chan/analysis/ChanPivotClassifier.py

@@ -9,7 +9,7 @@ Feature 描述中枢内部结构，Label 记录中枢后实际演化。
 import math
 import json
 from typing import Optional
-from chanlun.core.ChanEnum import Chan_BI_DIR
+from ..core.ChanEnum import Chan_BI_DIR
 
 
 class ChanPivotClassifier:

chan/analysis/ChanPivotMonitor.py

@@ -9,7 +9,7 @@ shift / contraction / duration。
 
 from collections import deque
 from typing import Optional
-from chanlun.analysis.ChanPivotClassifier import ChanPivotClassifier
+from .ChanPivotClassifier import ChanPivotClassifier
 
 
 class ChanPivotMonitor:

chan/analysis/__init__.py

@@ -0,0 +1,19 @@
+"""分析层：结构 + 指标的接合（MACD 状态、买卖点确认、Zone 等）。"""
+
+from .bsp_macd import (
+    ConfirmedBSP,
+    bi_macd_area,
+    check_bi_div,
+    check_bi_pair_div,
+    confirm_bsp,
+)
+from .ChanMACD import ChanMACD
+
+__all__ = [
+    "ChanMACD",
+    "ConfirmedBSP",
+    "bi_macd_area",
+    "check_bi_div",
+    "check_bi_pair_div",
+    "confirm_bsp",
+]

chan/analysis/bsp_macd.py

@@ -0,0 +1,113 @@
+"""买卖点 × MACD：几何候选在 core，背驰确认在此接合。"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, List, Optional, Sequence
+
+from ..core.ChanBI import ChanBI
+from ..core.ChanBSP import ChanBSP
+from ..core.ChanEnum import Chan_BI_DIR, Chan_BSP_DIR, Chan_BSP_TYPE
+from ..indicators.store import IndicatorStore
+
+
+def bi_macd_area(bi: ChanBI, store: Optional[IndicatorStore] = None) -> float:
+    """笔内同向 macdhist 累积面积。优先用 IndicatorStore，否则回退 klu.macdhist。"""
+    area = 0.0
+    for klc in bi.klc_list:
+        for klu in klc.klu_list:
+            if store is not None:
+                hist = store.get(klu.idx, "macdhist", 0) or 0
+            else:
+                hist = getattr(klu, "macdhist", 0) or 0
+            try:
+                hist = float(hist)
+            except (TypeError, ValueError):
+                hist = 0.0
+            if bi.dir == Chan_BI_DIR.UP and hist > 0:
+                area += hist
+            elif bi.dir == Chan_BI_DIR.DOWN and hist < 0:
+                area -= hist
+    return area
+
+
+def check_bi_div(
+    zs,
+    leave_bi: ChanBI,
+    store: Optional[IndicatorStore] = None,
+) -> bool:
+    """一类买卖点背驰：离开笔相对进入笔同向 MACD 柱面积收敛。"""
+    enter_bi = zs.bi_list[0].pre if zs.bi_list else None
+    if not enter_bi or enter_bi.dir != leave_bi.dir:
+        return False
+    leave_area = abs(bi_macd_area(leave_bi, store))
+    enter_area = abs(bi_macd_area(enter_bi, store))
+    return leave_area < enter_area
+
+
+def check_bi_pair_div(
+    leave_bi: ChanBI,
+    compare_bi: ChanBI,
+    store: Optional[IndicatorStore] = None,
+) -> bool:
+    """两笔同向力度比较（离开笔面积 < 比较笔）。"""
+    leave_area = abs(bi_macd_area(leave_bi, store))
+    compare_area = abs(bi_macd_area(compare_bi, store))
+    return leave_area < compare_area
+
+
+@dataclass
+class ConfirmedBSP:
+    """几何买卖点 + MACD 确认结果。"""
+
+    bsp: ChanBSP
+    div_confirmed: bool
+    hist_area: float = 0.0
+    compare_hist_area: float = 0.0
+    score: float = 0.0
+    macd_state: Any = None
+
+    @property
+    def type(self) -> Chan_BSP_TYPE:
+        return self.bsp.type
+
+    @property
+    def dir(self) -> Chan_BSP_DIR:
+        return self.bsp.dir
+
+    @property
+    def bi(self) -> ChanBI:
+        return self.bsp.bi
+
+
+def confirm_bsp(
+    geo_bsp_list: Sequence[ChanBSP],
+    store: Optional[IndicatorStore] = None,
+    require_div_for_types: Optional[Sequence[Chan_BSP_TYPE]] = None,
+) -> List[ConfirmedBSP]:
+    """
+    将几何 BSP 升格为 ConfirmedBSP。
+    默认：B1/S1/T1 需要背驰确认；B3/S3 几何即可，div_confirmed=True。
+    """
+    if require_div_for_types is None:
+        require_div_for_types = (
+            Chan_BSP_TYPE.B1,
+            Chan_BSP_TYPE.S1,
+        )
+    out: List[ConfirmedBSP] = []
+    for bsp in geo_bsp_list:
+        area = bi_macd_area(bsp.bi, store)
+        need_div = bsp.type in require_div_for_types
+        if need_div and bsp.zs is not None:
+            div_ok = check_bi_div(bsp.zs, bsp.bi, store)
+        else:
+            div_ok = True
+        out.append(
+            ConfirmedBSP(
+                bsp=bsp,
+                div_confirmed=div_ok,
+                hist_area=area,
+                score=1.0 if div_ok else 0.0,
+            )
+        )
+    return out

chan/analysis/fx_strength_example.py

@@ -1,5 +1,3 @@
-import sys, os
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
@@ -7,9 +5,9 @@ sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 该文件展示如何使用ChanKLC类中新增的分型强度检测功能
 """
 
-from chanlun.core.ChanKLC import ChanKLC
-from chanlun.core.ChanEnum import Chan_FX_TYPE
-import ChanKLU
+from ..core.ChanKLC import ChanKLC
+from ..core.ChanEnum import Chan_FX_TYPE
+from ..core import ChanKLU
 
 
 def demo_fx_strength_detection():