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