ChanPivotMonitor: 实时中枢特征跟踪 + Telegram推送

- ChanPivotClassifier: 提取 calc_duration/contraction/shift 为 @staticmethod，新增 compute_features()
- ChanPivotMonitor: 实时追踪当前中枢，bi_count 增长时重新计算 shift/contraction/duration
- bsp_monitor/fetcher: 改用 data_provider HTTP API 替代直连 CCXT
- bsp_monitor/notify: 新增 send_telegram_message() 通用推送
- bsp_monitor/main: 集成 ChanPivotMonitor，有新笔或 BSP 时推送到 Telegram

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

bsp_monitor/fetcher.py

@@ -1,45 +1,39 @@
 """
-fetcher.py - CCXT REST 拉取 Binance 永续合约 1m K 线，从固定起点累积。
+fetcher.py - 从 data_provider HTTP API 拉取 K 线数据。
 """
-import ccxt
+
+import requests
 import pandas as pd
 import logging
-from datetime import datetime, timezone
 
 logger = logging.getLogger(__name__)
 
 SYMBOL = "BTC/USDT:USDT"
 TIMEFRAME = "1m"
-# 每次拉取最近 LIMIT 根 K 线（Binance 上限 1500，足够缠论管线用 ~25h 数据）
-_FETCH_LIMIT = 1000
-
-_exchange = None
-
-
-def _get_exchange():
-    global _exchange
-    if _exchange is None:
-        _exchange = ccxt.binance({
-            "enableRateLimit": True,
-            "options": {"defaultType": "future"},
-        })
-        _exchange.load_markets()
-        logger.info("ccxt binance 已初始化")
-    return _exchange
+PROVIDER_URL = "http://103.179.242.166"
+FETCH_LIMIT = 1000
 
 
 def fetch_ohlcv() -> pd.DataFrame:
-    """拉取最近 _FETCH_LIMIT 根 1m K 线。
+    """从 data_provider API 拉取最近 FETCH_LIMIT 根 1m K 线。"""
+    url = f"{PROVIDER_URL}/api/candles"
+    params = {
+        "symbol": SYMBOL,
+        "tf": TIMEFRAME,
+        "limit": FETCH_LIMIT,
+    }
+    resp = requests.get(url, params=params, timeout=30)
+    resp.raise_for_status()
+    data = resp.json()
 
-    管线每次重跑最新的 K 线窗口。
-    用 limit 而非 since 避免 API 500 根限制截断新数据。
-    """
-    exchange = _get_exchange()
-    raw = exchange.fetch_ohlcv(SYMBOL, TIMEFRAME, limit=_FETCH_LIMIT)
+    if not data:
+        logger.warning("API 返回空数据")
+        return pd.DataFrame()
 
-    df = pd.DataFrame(raw, columns=["timestamp", "open", "high", "low", "close", "volume"])
+    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)
+    logger.info(f"拉取 {len(df)} 根 {TIMEFRAME} K 线 from {PROVIDER_URL}")
     return df