添加本地数据源，以后就可以直接用本地数据了

ChanHeng.py

@@ -0,0 +1,414 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+"""
+使用 ccxt 获取币安交易所所有 `*/USDT` 交易对最新 100 根 1 小时 K 线数据，并筛选出长期横盘的币种。
+
+横盘判定基于以下三项指标（均可通过命令行参数调整）：
+1. 价格振幅占均价的比例（默认 ≤ 5%）
+2. 收盘价线性回归斜率占均价的比例（默认 ≤ 0.05%）
+3. 收盘价标准差占均价的比例（默认 ≤ 1.5%）
+
+满足以上全部条件的交易对会被视为长期横盘。
+"""
+
+import argparse
+import csv
+import logging
+import math
+import statistics
+import sys
+import time
+from dataclasses import dataclass
+from typing import Iterable, List, Optional, Sequence
+
+import ccxt
+
+# python ChanHeng.py --range-threshold 5 --slope-threshold 5 --std-threshold 0.015
+
+DEFAULT_LIMIT = 100
+DEFAULT_TIMEFRAME = "1h"
+STABLECOINS = {
+    "USDT",
+    "USDC",
+    "BUSD",
+    "TUSD",
+    "USDP",
+    "DAI",
+    "FDUSD",
+    "SUSD",
+    "UST",
+    "USTC",
+    "EUR",
+    "TRY",
+    "BFUSD",
+    "USDE",
+    "XUSD",
+    "USD1",
+    "XUSD"
+}
+
+
+@dataclass
+class SidewaysMetrics:
+    symbol: str
+    price_range_pct: float
+    slope_pct: float
+    std_pct: float
+    mean_close: float
+    last_close: float
+    data_points: int
+
+
+def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="筛选币安长期横盘币种（默认 500 根 1 小时 K 线）"
+    )
+    parser.add_argument(
+        "--timeframe",
+        default=DEFAULT_TIMEFRAME,
+        help="K 线周期（默认：1h）",
+    )
+    parser.add_argument(
+        "--limit",
+        type=int,
+        default=DEFAULT_LIMIT,
+        help="每个交易对获取的 K 线数量（默认：500）",
+    )
+    parser.add_argument(
+        "--range-threshold",
+        type=float,
+        default=0.05,
+        help="最大价格振幅占均价比例阈值（默认：0.05，表示 5%%）",
+    )
+    parser.add_argument(
+        "--slope-threshold",
+        type=float,
+        default=0.0005,
+        help="线性回归斜率占均价比例阈值（默认：0.0005，约 0.05%%）",
+    )
+    parser.add_argument(
+        "--std-threshold",
+        type=float,
+        default=0.015,
+        help="标准差占均价比例阈值（默认：0.015，表示 1.5%%）",
+    )
+    parser.add_argument(
+        "--quote",
+        action="append",
+        default=[],
+        help="只保留指定计价货币的交易对，可重复指定（示例：--quote USDT --quote FDUSD）",
+    )
+    parser.add_argument(
+        "--symbol",
+        action="append",
+        default=[],
+        help="仅检测指定交易对，可重复（不指定则遍历所有符合条件的现货交易对）",
+    )
+    parser.add_argument(
+        "--max-symbols",
+        type=int,
+        default=None,
+        help="限制最多检测的交易对数量（用于调试）",
+    )
+    parser.add_argument(
+        "--sleep",
+        type=float,
+        default=0.35,
+        help="请求失败后的基础重试等待秒数（默认：0.35）",
+    )
+    parser.add_argument(
+        "--retries",
+        type=int,
+        default=3,
+        help="单个交易对请求失败后的最大重试次数（默认：3）",
+    )
+    parser.add_argument(
+        "--include-inactive",
+        action="store_true",
+        help="包含已下架/不可交易的交易对（默认不包含）",
+    )
+    parser.add_argument(
+        "--export",
+        type=str,
+        default=None,
+        help="将筛选结果导出为 CSV 文件的路径",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="输出更详细的日志信息",
+    )
+    return parser.parse_args(argv)
+
+
+def setup_logging(verbose: bool) -> None:
+    level = logging.DEBUG if verbose else logging.INFO
+    logging.basicConfig(
+        level=level,
+        format="%(asctime)s [%(levelname)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+
+
+def create_exchange() -> ccxt.binance:
+    exchange = ccxt.binance({"enableRateLimit": True})
+    exchange.options["defaultType"] = "spot"
+    return exchange
+
+
+def iter_target_symbols(
+    exchange: ccxt.binance,
+    quotes: Sequence[str],
+    includes: Sequence[str],
+    include_inactive: bool,
+) -> List[str]:
+    markets = exchange.load_markets()
+    filtered = []
+
+    quote_set = {quote.upper() for quote in quotes}
+    include_set = {sym.upper() for sym in includes}
+
+    for symbol, meta in markets.items():
+        if not meta.get("spot", False):
+            continue
+        if not include_inactive and meta.get("active") is False:
+            continue
+
+        normalized_symbol = symbol.upper()
+
+        if include_set and normalized_symbol not in include_set:
+            continue
+
+        parts = symbol.split("/")
+        if len(parts) != 2:
+            continue
+
+        base_asset, quote_asset = parts[0].upper(), parts[1].upper()
+
+        target_quote = quote_set or {"USDT"}
+        if quote_asset not in target_quote:
+            continue
+
+        if base_asset in STABLECOINS:
+            continue
+
+        filtered.append(symbol)
+
+    filtered.sort()
+    logging.info(
+        "已筛选 %s 个目标交易对（quote 过滤：%s，专门列表：%s）",
+        len(filtered),
+        ",".join(sorted(quote_set or {"USDT"})),
+        ",".join(sorted(include_set)) or "无",
+    )
+    return filtered
+
+
+def fetch_ohlcv_with_retry(
+    exchange: ccxt.binance,
+    symbol: str,
+    timeframe: str,
+    limit: int,
+    retries: int,
+    base_sleep: float,
+) -> List[List[float]]:
+    attempt = 0
+    while True:
+        try:
+            return exchange.fetch_ohlcv(symbol, timeframe=timeframe, limit=limit)
+        except ccxt.RateLimitExceeded as exc:
+            wait_time = max(exchange.rateLimit / 1000.0 if exchange.rateLimit else 0, base_sleep)
+            logging.debug("触发限频，等待 %.2f 秒后重试 %s：%s", wait_time, symbol, exc)
+            time.sleep(wait_time)
+        except (ccxt.NetworkError, ccxt.ExchangeError) as exc:
+            attempt += 1
+            if attempt > retries:
+                logging.warning("多次获取失败，跳过 %s：%s", symbol, exc)
+                return []
+            wait_time = base_sleep * attempt
+            logging.debug("请求失败，等待 %.2f 秒后重试 %s（第 %d 次）：%s", wait_time, symbol, attempt, exc)
+            time.sleep(wait_time)
+
+
+def linear_regression_slope(values: Sequence[float]) -> float:
+    n = len(values)
+    if n < 2:
+        return 0.0
+    mean_x = (n - 1) / 2.0
+    mean_y = sum(values) / n
+    numerator = 0.0
+    denominator = 0.0
+    for idx, value in enumerate(values):
+        dx = idx - mean_x
+        numerator += dx * (value - mean_y)
+        denominator += dx * dx
+
+    if denominator == 0:
+        return 0.0
+    return numerator / denominator
+
+
+def compute_sideways_metrics(closes: Sequence[float], symbol: str) -> Optional[SidewaysMetrics]:
+    if not closes:
+        return None
+
+    mean_close = sum(closes) / len(closes)
+    if math.isclose(mean_close, 0.0):
+        return None
+
+    max_close = max(closes)
+    min_close = min(closes)
+    price_range_pct = (max_close - min_close) / mean_close
+
+    slope = linear_regression_slope(closes)
+    slope_pct = slope / mean_close
+
+    std_dev = statistics.pstdev(closes) if len(closes) > 1 else 0.0
+    std_pct = std_dev / mean_close
+
+    return SidewaysMetrics(
+        symbol=symbol,
+        price_range_pct=price_range_pct,
+        slope_pct=slope_pct,
+        std_pct=std_pct,
+        mean_close=mean_close,
+        last_close=closes[-1],
+        data_points=len(closes),
+    )
+
+
+def is_sideways(metrics: SidewaysMetrics, range_threshold: float, slope_threshold: float, std_threshold: float) -> bool:
+    return (
+        metrics.price_range_pct <= range_threshold
+        and abs(metrics.slope_pct) <= slope_threshold
+        and metrics.std_pct <= std_threshold
+    )
+
+
+def export_results(path: str, results: Sequence[SidewaysMetrics]) -> None:
+    fieldnames = [
+        "symbol",
+        "price_range_pct",
+        "slope_pct",
+        "std_pct",
+        "mean_close",
+        "last_close",
+        "data_points",
+    ]
+    with open(path, "w", newline="", encoding="utf-8") as fp:
+        writer = csv.DictWriter(fp, fieldnames=fieldnames)
+        writer.writeheader()
+        for item in results:
+            writer.writerow(
+                {
+                    "symbol": item.symbol,
+                    "price_range_pct": f"{item.price_range_pct:.6f}",
+                    "slope_pct": f"{item.slope_pct:.6f}",
+                    "std_pct": f"{item.std_pct:.6f}",
+                    "mean_close": f"{item.mean_close:.8f}",
+                    "last_close": f"{item.last_close:.8f}",
+                    "data_points": item.data_points,
+                }
+            )
+    logging.info("结果已导出至 %s", path)
+
+
+def run(argv: Optional[Sequence[str]] = None) -> int:
+    args = parse_args(argv)
+    if not args.quote:
+        args.quote = ["USDT"]
+    setup_logging(args.verbose)
+
+    exchange = create_exchange()
+    symbols = iter_target_symbols(
+        exchange=exchange,
+        quotes=args.quote,
+        includes=args.symbol,
+        include_inactive=args.include_inactive,
+    )
+
+    if args.max_symbols is not None:
+        symbols = symbols[: args.max_symbols]
+        logging.info("出于调试目的，仅检测前 %d 个交易对。", len(symbols))
+
+    if not symbols:
+        logging.error("未找到任何满足条件的交易对，请检查过滤条件。")
+        return 1
+
+    sideways_results: List[SidewaysMetrics] = []
+    total = len(symbols)
+
+    for idx, symbol in enumerate(symbols, start=1):
+        logging.info("(%d/%d) 正在获取 %s 的 %s K 线（limit=%d）", idx, total, symbol, args.timeframe, args.limit)
+        ohlcv = fetch_ohlcv_with_retry(
+            exchange=exchange,
+            symbol=symbol,
+            timeframe=args.timeframe,
+            limit=args.limit,
+            retries=args.retries,
+            base_sleep=args.sleep,
+        )
+
+        if len(ohlcv) < max(100, args.limit // 2):
+            logging.debug("交易对 %s 返回数据不足（%d 根），跳过。", symbol, len(ohlcv))
+            continue
+
+        closes = [entry[4] for entry in ohlcv if entry[4] is not None]
+        metrics = compute_sideways_metrics(closes, symbol)
+        if not metrics:
+            continue
+
+        if is_sideways(metrics, args.range_threshold, args.slope_threshold, args.std_threshold):
+            sideways_results.append(metrics)
+            logging.info(
+                "识别为横盘：%s | 振幅 %.2f%% | 斜率 %.4f%% | 标准差 %.2f%%",
+                symbol,
+                metrics.price_range_pct * 100,
+                metrics.slope_pct * 100,
+                metrics.std_pct * 100,
+            )
+        else:
+            logging.debug(
+                "未满足条件：%s | 振幅 %.2f%% | 斜率 %.4f%% | 标准差 %.2f%%",
+                symbol,
+                metrics.price_range_pct * 100,
+                metrics.slope_pct * 100,
+                metrics.std_pct * 100,
+            )
+
+    if not sideways_results:
+        logging.warning("未检测到满足定义的长期横盘交易对。")
+        return 0
+
+    sideways_results.sort(key=lambda item: (item.price_range_pct, abs(item.slope_pct), item.std_pct))
+    print("=" * 88)
+    print(
+        f"共识别 {len(sideways_results)} 个长期横盘交易对（阈值：振幅≤{args.range_threshold:.2%}，"
+        f"斜率≤{args.slope_threshold:.2%}，标准差≤{args.std_threshold:.2%}）"
+    )
+    print("=" * 88)
+    header = f"{'Symbol':15s} {'Range%':>10s} {'Slope%':>10s} {'STD%':>10s} {'Mean':>14s} {'Last':>14s} {'Count':>6s}"
+    print(header)
+    print("-" * len(header))
+    for item in sideways_results:
+        print(
+            f"{item.symbol:15s}"
+            f" {item.price_range_pct * 100:10.4f}"
+            f" {item.slope_pct * 100:10.4f}"
+            f" {item.std_pct * 100:10.4f}"
+            f" {item.mean_close:14.8f}"
+            f" {item.last_close:14.8f}"
+            f" {item.data_points:6d}"
+        )
+
+    if args.export:
+        export_results(args.export, sideways_results)
+
+    logging.info("任务完成。")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(run())
+