refactor: 精简仓库为 chanlun 核心与 web 分析，移除威科夫与遗留模块

删除根目录旧 Chan 模块、策略、配置、文档及 wyckoff 相关代码；更新缠论 pipeline 与笔中枢计算；补充 research 研究与 web 测试。

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

research/lib/data.py

@@ -0,0 +1,108 @@
+"""研究用数据层：本地历史数据优先，回落到远端数据服务。
+
+远端服务的小周期只保留 90 天（派生自 1m），做区间套时样本严重不足。
+本项目 data/ 下是完整下载的 BTC/ETH/SOL 全周期数据（1m~1w，2172~2543 天），
+是主力数据源；旧的 freqtrade 目录作为备用。
+"""
+from __future__ import annotations
+
+from pathlib import Path
+
+import pandas as pd
+import requests
+
+DATA_SERVICE_URL = "https://provider.jackyu66.com"
+CACHE_DIR = Path(__file__).resolve().parents[1] / ".cache"
+NUMERIC_COLS = ("open", "high", "low", "close", "volume")
+
+# 按优先级查找，第一个命中的目录生效
+LOCAL_DATA_DIRS = (
+    Path(__file__).resolve().parents[2] / "data",
+    Path("/Users/jack/Project/freqtrade/user_data/data"),
+)
+
+
+def _cache_path(symbol: str, tf: str, limit: int) -> Path:
+    safe = symbol.replace("/", "_").replace(":", "-")
+    return CACHE_DIR / f"{safe}__{tf}__{limit}.parquet"
+
+
+def _normalize(raw: list[dict]) -> pd.DataFrame:
+    df = pd.DataFrame(raw)
+    df["timestamp"] = pd.to_numeric(df["timestamp"], errors="coerce")
+    for col in NUMERIC_COLS:
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+    df = df.dropna(subset=["timestamp", *NUMERIC_COLS])
+    df = df.drop_duplicates(subset=["timestamp"]).sort_values("timestamp").reset_index(drop=True)
+    df["timestamp"] = df["timestamp"].astype("int64")
+    df["date"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True).dt.tz_convert("Asia/Shanghai")
+    return df[["timestamp", "date", *NUMERIC_COLS]]
+
+
+def _freqtrade_path(symbol: str, tf: str, exchange: str = "binance") -> Path | None:
+    """定位 feather 文件。BTC/USDT:USDT -> BTC_USDT_USDT-1h-futures.feather"""
+    for root in LOCAL_DATA_DIRS:
+        base = root / exchange
+        if ":" in symbol:  # 永续合约
+            name = symbol.replace("/", "_").replace(":", "_")
+            cand = base / "futures" / f"{name}-{tf}-futures.feather"
+        else:
+            name = symbol.replace("/", "_")
+            cand = base / f"{name}-{tf}.feather"
+        if cand.exists():
+            return cand
+    return None
+
+
+def load_local(symbol: str, tf: str, exchange: str = "binance") -> pd.DataFrame | None:
+    """读取 freqtrade 本地历史数据，转成与远端一致的列结构。"""
+    path = _freqtrade_path(symbol, tf, exchange)
+    if path is None:
+        return None
+    df = pd.read_feather(path)
+    df = df.rename(columns={c: c.lower() for c in df.columns})
+    if "date" not in df.columns:
+        return None
+    date = pd.to_datetime(df["date"], utc=True)
+    # 不能用 date.astype("int64")//10**6：该值的单位取决于列的精度
+    # （datetime64[ns] 给纳秒、[ms] 给毫秒），对毫秒精度的文件会把时间戳砸平，
+    # 进而被 drop_duplicates 删掉九成数据。下面的写法与底层精度无关。
+    epoch_ms = (date - pd.Timestamp("1970-01-01", tz="UTC")) // pd.Timedelta("1ms")
+    out = pd.DataFrame({
+        "timestamp": epoch_ms.astype("int64"),
+        "date": date.dt.tz_convert("Asia/Shanghai"),
+    })
+    for col in NUMERIC_COLS:
+        out[col] = pd.to_numeric(df[col], errors="coerce") if col in df else 0.0
+    out = out.dropna(subset=list(NUMERIC_COLS))
+    return out.drop_duplicates(subset=["timestamp"]).sort_values("timestamp").reset_index(drop=True)
+
+
+def fetch_ohlcv(
+    symbol: str,
+    tf: str,
+    limit: int = 5000,
+    refresh: bool = False,
+    prefer_local: bool = True,
+) -> pd.DataFrame:
+    """获取K线。优先本地 freqtrade 数据，其次本地缓存，最后回源数据服务。"""
+    if prefer_local and not refresh:
+        local = load_local(symbol, tf)
+        if local is not None and len(local) > 0:
+            return local.tail(limit).reset_index(drop=True) if limit and len(local) > limit else local
+
+    path = _cache_path(symbol, tf, limit)
+    if path.exists() and not refresh:
+        return pd.read_parquet(path)
+
+    resp = requests.get(
+        f"{DATA_SERVICE_URL}/api/candles",
+        params={"symbol": symbol, "tf": tf, "limit": limit},
+        timeout=60,
+    )
+    resp.raise_for_status()
+    df = _normalize(resp.json())
+
+    CACHE_DIR.mkdir(parents=True, exist_ok=True)
+    df.to_parquet(path, index=False)
+    return df