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adaptive_third_strategy/README.md
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adaptive_third_strategy/README.md
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# 自适应三分位趋势策略
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优化版实盘策略：趋势过滤 + 动态阈值 + 信号确认 + 完整风险管理。
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## 目录结构
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- `config.py` - 策略参数（趋势模式、止损止盈、确认条件等）
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- `indicators.py` - EMA、ATR 指标
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- `data_fetcher.py` - **Bitmart 方式**拉取 5/15/60 分钟 K 线（API 或 CSV）
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- `strategy_core.py` - 趋势判断、有效 K 线、动态触发价、信号确认
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- `backtest.py` - 回测引擎（硬止损、止盈、移动止损、时间止损）
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- `trade.py` - 实盘交易入口（拉数据 + 信号，开平仓需对接现有 Bitmart 下单逻辑）
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- `data/` - 回测用 K 线 CSV（可选，无则 API 拉取）
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## 回测（先出数据再回测）
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在**项目根目录** `lm_code` 下执行：
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```bash
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# 使用 API 拉取数据并回测（会请求 Bitmart 接口）
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python adaptive_third_strategy/backtest.py --start 2025-01-01 --end 2025-01-30 --capital 10000
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# 仅用本地已有 CSV 回测（需先抓过数据）
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python adaptive_third_strategy/backtest.py --start 2025-01-01 --end 2025-01-30 --no-api
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```
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数据会保存到 `adaptive_third_strategy/data/kline_5m.csv`、`kline_15m.csv`、`kline_60m.csv`。
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回测结果输出到控制台，交易明细保存到 `adaptive_third_strategy/backtest_trades.csv`。
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## 仅抓取数据（Bitmart 方式）
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与 `bitmart/回测.py`、`bitmart/抓取多周期K线.py` 相同的 API 调用方式：
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```bash
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cd adaptive_third_strategy && python -c "
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from data_fetcher import fetch_multi_timeframe, save_klines_csv
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import time, os
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end_ts = int(time.time())
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start_ts = end_ts - 30*24*3600
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data = fetch_multi_timeframe(start_ts, end_ts, [5, 15, 60])
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os.makedirs('data', exist_ok=True)
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for step, klines in data.items():
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save_klines_csv(klines, f'data/kline_{step}m.csv')
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"
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```
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需在项目根目录 `lm_code` 下执行时，先 `import sys; sys.path.insert(0, '..')` 或使用：
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```bash
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python -c "
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import sys, os
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sys.path.insert(0, os.getcwd())
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from adaptive_third_strategy.data_fetcher import fetch_multi_timeframe, save_klines_csv
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import time
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end_ts = int(time.time())
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start_ts = end_ts - 30*24*3600
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data = fetch_multi_timeframe(start_ts, end_ts, [5, 15, 60])
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os.makedirs('adaptive_third_strategy/data', exist_ok=True)
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for step, klines in data.items():
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save_klines_csv(klines, f'adaptive_third_strategy/data/kline_{step}m.csv')
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"
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```
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## 实盘
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```bash
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python adaptive_third_strategy/trade.py
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```
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当前 `trade.py` 只做：拉 5/15/60 数据、算信号、打日志/钉钉。实际开平仓需在 `run_loop` 里接入你现有的 Bitmart 下单方式（如 `交易/bitmart-三分之一策略交易.py` 的浏览器点击或 API 下单）。
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## 策略要点
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- **数据与周期**：主周期 5 分钟，趋势用 15 分钟 / 1 小时 EMA。
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- **有效 K 线**：实体 ≥ max(ATR×0.1, 价格×0.05%)。
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- **趋势过滤**：长期 1h EMA50/200，中期 15m EMA20/50，短期 5m 收盘 vs EMA9；保守模式要求中期+短期一致。
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- **动态触发**：波动率系数 clamp(实体/ATR, 0.3, 3.0)，顺势方向更易触发。
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- **信号确认**：收盘价、成交量、动量等至少满足 2 项。
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- **风控**：硬止损 ATR×2、时间止损 3 根 K、移动止损（盈利 1×ATR 后 0.5×ATR 跟踪）、止盈目标 1.5/3/5×ATR。
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2
adaptive_third_strategy/__init__.py
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adaptive_third_strategy/__init__.py
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# -*- coding: utf-8 -*-
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"""自适应三分位趋势策略"""
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464
adaptive_third_strategy/backtest.py
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adaptive_third_strategy/backtest.py
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# -*- coding: utf-8 -*-
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"""
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自适应三分位趋势策略 - 回测引擎
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使用 Bitmart 方式获取数据（API 或 CSV），含硬止损、分批止盈、移动止损、时间止损
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"""
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import os
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import sys
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import csv
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import datetime
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from typing import List, Dict, Optional, Tuple
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# 使用 UTC 时区，避免 utcfromtimestamp 弃用警告
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def _utc_dt(ts):
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if hasattr(datetime, "timezone") and hasattr(datetime.timezone, "utc"):
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return datetime.datetime.fromtimestamp(ts, tz=datetime.timezone.utc)
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return datetime.datetime.utcfromtimestamp(ts)
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try:
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from loguru import logger
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except ImportError:
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import logging
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logger = logging.getLogger(__name__)
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ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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if ROOT_DIR not in sys.path:
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sys.path.insert(0, ROOT_DIR)
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from adaptive_third_strategy.config import (
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STEP_5M,
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STEP_15M,
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STEP_60M,
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ATR_PERIOD,
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EMA_SHORT,
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EMA_MID_FAST,
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EMA_MID_SLOW,
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EMA_LONG_FAST,
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EMA_LONG_SLOW,
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STOP_LOSS_ATR_MULT,
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TIME_STOP_BARS,
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TRAIL_START_ATR,
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TRAIL_ATR_MULT,
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TP1_ATR,
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TP2_ATR,
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TP3_ATR,
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TP1_RATIO,
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TP2_RATIO,
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TP3_RATIO,
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MIN_BARS_SINCE_ENTRY,
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SAME_KLINE_NO_REVERSE,
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REVERSE_BREAK_MULT,
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REVERSE_LOSS_ATR,
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MAX_POSITION_PERCENT,
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BASE_POSITION_PERCENT,
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CONTRACT_SIZE,
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FEE_RATE,
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FEE_FIXED,
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FEE_FIXED_BACKTEST,
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MIN_BARS_BETWEEN_TRADES,
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SLIPPAGE_POINTS,
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)
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from adaptive_third_strategy.indicators import get_ema_atr_from_klines, align_higher_tf_ema
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from adaptive_third_strategy.strategy_core import (
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check_trigger,
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get_body_size,
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build_volume_ma,
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)
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from adaptive_third_strategy.data_fetcher import (
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fetch_multi_timeframe,
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load_klines_csv,
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save_klines_csv,
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)
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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def ensure_data(
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start_time: int,
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end_time: int,
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data_dir: str,
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use_api: bool = True,
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api_key: str = "",
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secret_key: str = "",
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) -> Tuple[List[Dict], List[Dict], List[Dict]]:
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"""
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确保有 5/15/60 分钟数据。若 data_dir 下已有 CSV 则加载，否则用 API 拉取并保存。
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use_api=False 时仅从 CSV 加载（需事先抓取）。
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"""
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data_dir = data_dir or os.path.join(SCRIPT_DIR, "data")
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os.makedirs(data_dir, exist_ok=True)
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paths = {
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5: os.path.join(data_dir, "kline_5m.csv"),
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15: os.path.join(data_dir, "kline_15m.csv"),
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60: os.path.join(data_dir, "kline_60m.csv"),
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}
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k5 = load_klines_csv(paths[5])
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k15 = load_klines_csv(paths[15])
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k60 = load_klines_csv(paths[60])
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need_fetch = not k5 or not k15 or not k60
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if need_fetch and use_api:
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from adaptive_third_strategy.data_fetcher import DEFAULT_API_KEY, DEFAULT_SECRET_KEY, DEFAULT_MEMO
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key = api_key or DEFAULT_API_KEY
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sec = secret_key or DEFAULT_SECRET_KEY
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data = fetch_multi_timeframe(start_time, end_time, [5, 15, 60], key, sec, DEFAULT_MEMO)
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k5, k15, k60 = data[5], data[15], data[60]
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for step, kl in [(5, k5), (15, k15), (60, k60)]:
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save_klines_csv(kl, paths[step])
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# 按时间范围过滤（end_time 若为次日 0 点则用 < 以只含 end 日及之前）
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k5 = [x for x in k5 if start_time <= x["id"] < end_time]
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k15 = [x for x in k15 if start_time <= x["id"] < end_time]
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k60 = [x for x in k60 if start_time <= x["id"] < end_time]
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k5.sort(key=lambda x: x["id"])
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k15.sort(key=lambda x: x["id"])
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k60.sort(key=lambda x: x["id"])
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return k5, k15, k60
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def run_backtest(
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klines_5m: List[Dict],
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klines_15m: List[Dict],
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klines_60m: List[Dict],
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initial_capital: float = 10000.0,
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use_stop_loss: bool = True,
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use_take_profit: bool = True,
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use_trailing_stop: bool = True,
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use_time_stop: bool = True,
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fixed_margin_usdt: Optional[float] = None,
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leverage: Optional[float] = None,
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deduct_fee: bool = True,
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) -> Tuple[List[Dict], Dict, List[Dict]]:
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"""
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回测主循环。
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- 若指定 fixed_margin_usdt 与 leverage，则每笔开仓名义价值 = fixed_margin_usdt * leverage（如 50U 一百倍 = 5000U）。
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- deduct_fee=False 时不扣手续费，总盈利 = 所有 money_pnl 之和。
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"""
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if len(klines_5m) < ATR_PERIOD + 50:
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logger.warning("5分钟K线数量不足")
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return [], {}, klines_5m
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ema_5m, atr_5m = get_ema_atr_from_klines(klines_5m, EMA_SHORT, ATR_PERIOD)
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ema_15m_align = align_higher_tf_ema(klines_5m, klines_15m, EMA_MID_FAST, EMA_MID_SLOW)
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ema_60m_align = align_higher_tf_ema(klines_5m, klines_60m, EMA_LONG_FAST, EMA_LONG_SLOW)
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volume_ma = build_volume_ma(klines_5m)
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trades: List[Dict] = []
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position: Optional[Dict] = None
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last_trade_bar_idx: Optional[int] = None
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last_close_bar_idx: Optional[int] = None
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equity_curve: List[float] = [initial_capital]
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capital = initial_capital
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# 每笔固定名义价值（USDT）：50U 一百倍 = 5000
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fixed_notional = (fixed_margin_usdt * leverage) if (fixed_margin_usdt is not None and leverage is not None) else None
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use_fee = deduct_fee
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fee_fixed = 0 if not use_fee else (FEE_FIXED_BACKTEST if FEE_FIXED_BACKTEST is not None else FEE_FIXED)
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def _size_usdt(cap: float) -> float:
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if fixed_notional is not None:
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return fixed_notional
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return min(cap * MAX_POSITION_PERCENT, cap * BASE_POSITION_PERCENT)
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def _fee(sz: float) -> float:
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return 0 if not use_fee else (fee_fixed + sz * FEE_RATE * 2)
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for idx in range(ATR_PERIOD, len(klines_5m)):
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just_reversed = False
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curr = klines_5m[idx]
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bar_id = curr["id"]
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high, low, close = float(curr["high"]), float(curr["low"]), float(curr["close"])
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atr_val = atr_5m[idx]
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if atr_val is None or atr_val <= 0:
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equity_curve.append(capital)
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continue
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# ---------- 持仓管理：止损 / 止盈 / 移动止损 / 时间止损 ----------
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if position is not None:
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pos_dir = position["direction"]
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entry_price = position["entry_price"]
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entry_idx = position["entry_bar_idx"]
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entry_atr = position["entry_atr"]
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stop_price = position.get("stop_price")
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trail_activated = position.get("trail_activated", False)
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exit_reason = None
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exit_price = close
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if pos_dir == "long":
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# 硬止损
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if use_stop_loss and stop_price is not None and low <= stop_price:
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exit_price = min(stop_price, high)
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exit_reason = "stop_loss"
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# 止盈（简化：首次触及任一目标即全平）
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elif use_take_profit:
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tp1 = entry_price + entry_atr * TP1_ATR
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tp2 = entry_price + entry_atr * TP2_ATR
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tp3 = entry_price + entry_atr * TP3_ATR
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if high >= tp3:
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exit_price = tp3
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exit_reason = "tp3"
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elif high >= tp2:
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exit_price = tp2
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exit_reason = "tp2"
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elif high >= tp1:
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exit_price = tp1
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exit_reason = "tp1"
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# 移动止损
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if use_trailing_stop and not exit_reason:
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if close >= entry_price + entry_atr * TRAIL_START_ATR:
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trail_activated = True
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position["trail_activated"] = True
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trail_stop = close - entry_atr * TRAIL_ATR_MULT
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if low <= trail_stop:
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exit_price = trail_stop
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exit_reason = "trail_stop"
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# 时间止损
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if use_time_stop and not exit_reason and (idx - entry_idx) >= TIME_STOP_BARS:
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if close <= entry_price:
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exit_price = close
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exit_reason = "time_stop"
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else:
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if use_stop_loss and stop_price is not None and high >= stop_price:
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exit_price = max(stop_price, low)
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exit_reason = "stop_loss"
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elif use_take_profit:
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tp1 = entry_price - entry_atr * TP1_ATR
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tp2 = entry_price - entry_atr * TP2_ATR
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tp3 = entry_price - entry_atr * TP3_ATR
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if low <= tp3:
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exit_price = tp3
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exit_reason = "tp3"
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elif low <= tp2:
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exit_price = tp2
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exit_reason = "tp2"
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elif low <= tp1:
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exit_price = tp1
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exit_reason = "tp1"
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if use_trailing_stop and not exit_reason:
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if close <= entry_price - entry_atr * TRAIL_START_ATR:
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trail_activated = True
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position["trail_activated"] = True
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trail_stop = close + entry_atr * TRAIL_ATR_MULT
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if high >= trail_stop:
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exit_price = trail_stop
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exit_reason = "trail_stop"
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if use_time_stop and not exit_reason and (idx - entry_idx) >= TIME_STOP_BARS:
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if close >= entry_price:
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exit_price = close
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exit_reason = "time_stop"
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if exit_reason:
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# 平仓
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if pos_dir == "long":
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point_pnl = exit_price - entry_price
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else:
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point_pnl = entry_price - exit_price
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size_usdt = position.get("size_usdt", _size_usdt(capital))
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contract_val = CONTRACT_SIZE / entry_price
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money_pnl = point_pnl / entry_price * size_usdt
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fee = _fee(size_usdt)
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net = money_pnl - fee
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capital += net
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trades.append({
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"direction": "做多" if pos_dir == "long" else "做空",
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"entry_time": _utc_dt(position["entry_time"]),
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"exit_time": _utc_dt(bar_id),
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"entry_price": entry_price,
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"exit_price": exit_price,
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"point_pnl": point_pnl,
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"money_pnl": money_pnl,
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"fee": fee,
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"net_profit": net,
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"exit_reason": exit_reason,
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"hold_bars": idx - entry_idx,
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})
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position = None
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last_close_bar_idx = idx
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equity_curve.append(capital)
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continue
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# ---------- 信号检测 ----------
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direction, trigger_price, valid_prev_idx, valid_prev = check_trigger(
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klines_5m, idx, atr_5m, ema_5m, ema_15m_align, ema_60m_align, volume_ma, use_confirm=True
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)
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if direction is None:
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equity_curve.append(capital)
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continue
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if SAME_KLINE_NO_REVERSE and last_trade_bar_idx == idx:
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equity_curve.append(capital)
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continue
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if position is not None:
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if direction == position["direction"]:
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equity_curve.append(capital)
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continue
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# 反手条件
|
||||
bars_since = idx - position["entry_bar_idx"]
|
||||
if bars_since < MIN_BARS_SINCE_ENTRY:
|
||||
equity_curve.append(capital)
|
||||
continue
|
||||
entry_atr_pos = position.get("entry_atr") or atr_val
|
||||
pos_loss_atr = (position["entry_price"] - close) / entry_atr_pos if position["direction"] == "long" else (close - position["entry_price"]) / entry_atr_pos
|
||||
if pos_loss_atr < REVERSE_LOSS_ATR:
|
||||
# 可选：反向突破幅度 > 实体/2 才反手
|
||||
equity_curve.append(capital)
|
||||
continue
|
||||
# 先平仓再开仓（下面统一开仓）
|
||||
# 简化：这里直接平仓记一笔，再开新仓
|
||||
exit_price = close
|
||||
if position["direction"] == "long":
|
||||
point_pnl = exit_price - position["entry_price"]
|
||||
else:
|
||||
point_pnl = position["entry_price"] - exit_price
|
||||
size_usdt = position.get("size_usdt", _size_usdt(capital))
|
||||
money_pnl = point_pnl / position["entry_price"] * size_usdt
|
||||
fee = _fee(size_usdt)
|
||||
net = money_pnl - fee
|
||||
capital += net
|
||||
trades.append({
|
||||
"direction": "做多" if position["direction"] == "long" else "做空",
|
||||
"entry_time": _utc_dt(position["entry_time"]),
|
||||
"exit_time": _utc_dt(bar_id),
|
||||
"entry_price": position["entry_price"],
|
||||
"exit_price": exit_price,
|
||||
"point_pnl": point_pnl,
|
||||
"money_pnl": money_pnl,
|
||||
"fee": fee,
|
||||
"net_profit": net,
|
||||
"exit_reason": "reverse",
|
||||
"hold_bars": idx - position["entry_bar_idx"],
|
||||
})
|
||||
position = None
|
||||
last_close_bar_idx = idx
|
||||
just_reversed = True
|
||||
|
||||
# ---------- 开仓 ----------
|
||||
# 反手后本 K 线允许开仓；否则需间隔 MIN_BARS_BETWEEN_TRADES 根
|
||||
if not just_reversed and last_close_bar_idx is not None and (idx - last_close_bar_idx) < MIN_BARS_BETWEEN_TRADES:
|
||||
equity_curve.append(capital)
|
||||
continue
|
||||
just_reversed = False
|
||||
size_usdt = _size_usdt(capital)
|
||||
if size_usdt <= 0:
|
||||
equity_curve.append(capital)
|
||||
continue
|
||||
stop_price = None
|
||||
if direction == "long":
|
||||
stop_price = trigger_price - atr_val * STOP_LOSS_ATR_MULT
|
||||
else:
|
||||
stop_price = trigger_price + atr_val * STOP_LOSS_ATR_MULT
|
||||
position = {
|
||||
"direction": direction,
|
||||
"entry_price": trigger_price,
|
||||
"entry_time": bar_id,
|
||||
"entry_bar_idx": idx,
|
||||
"entry_atr": atr_val,
|
||||
"stop_price": stop_price,
|
||||
"size_usdt": size_usdt,
|
||||
"closed_ratio": 0,
|
||||
"trail_activated": False,
|
||||
}
|
||||
last_trade_bar_idx = idx
|
||||
equity_curve.append(capital)
|
||||
|
||||
# 尾仓
|
||||
if position is not None:
|
||||
last_bar = klines_5m[-1]
|
||||
exit_price = float(last_bar["close"])
|
||||
pos_dir = position["direction"]
|
||||
entry_price = position["entry_price"]
|
||||
if pos_dir == "long":
|
||||
point_pnl = exit_price - entry_price
|
||||
else:
|
||||
point_pnl = entry_price - exit_price
|
||||
size_usdt = position.get("size_usdt", _size_usdt(capital))
|
||||
money_pnl = point_pnl / entry_price * size_usdt
|
||||
fee = _fee(size_usdt)
|
||||
net = money_pnl - fee
|
||||
capital += net
|
||||
trades.append({
|
||||
"direction": "做多" if pos_dir == "long" else "做空",
|
||||
"entry_time": _utc_dt(position["entry_time"]),
|
||||
"exit_time": _utc_dt(last_bar["id"]),
|
||||
"entry_price": entry_price,
|
||||
"exit_price": exit_price,
|
||||
"point_pnl": point_pnl,
|
||||
"money_pnl": money_pnl,
|
||||
"fee": fee,
|
||||
"net_profit": net,
|
||||
"exit_reason": "tail",
|
||||
"hold_bars": len(klines_5m) - 1 - position["entry_bar_idx"],
|
||||
})
|
||||
|
||||
# 统计
|
||||
total_net = sum(t["net_profit"] for t in trades)
|
||||
total_gross = sum(t["money_pnl"] for t in trades)
|
||||
total_fee = sum(t["fee"] for t in trades)
|
||||
win_count = len([t for t in trades if t["net_profit"] > 0])
|
||||
stats = {
|
||||
"total_trades": len(trades),
|
||||
"win_count": win_count,
|
||||
"win_rate": (win_count / len(trades) * 100) if trades else 0,
|
||||
"total_gross_profit": total_gross,
|
||||
"total_fee": total_fee,
|
||||
"total_net_profit": total_net,
|
||||
"final_capital": capital,
|
||||
"max_drawdown": 0,
|
||||
"max_drawdown_pct": 0,
|
||||
}
|
||||
peak = initial_capital
|
||||
for eq in equity_curve:
|
||||
peak = max(peak, eq)
|
||||
dd = peak - eq
|
||||
if peak > 0:
|
||||
stats["max_drawdown"] = max(stats["max_drawdown"], dd)
|
||||
stats["max_drawdown_pct"] = max(stats["max_drawdown_pct"], dd / peak * 100)
|
||||
return trades, stats, klines_5m
|
||||
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser(description="自适应三分位趋势策略回测")
|
||||
parser.add_argument("--start", default="2025-01-01", help="开始日期 YYYY-MM-DD")
|
||||
parser.add_argument("--end", default="2025-12-31", help="结束日期，默认 2025-12-31")
|
||||
parser.add_argument("--data-dir", default=None, help="数据目录，默认 adaptive_third_strategy/data")
|
||||
parser.add_argument("--no-api", action="store_true", help="不从 API 拉取，仅用本地 CSV")
|
||||
parser.add_argument("--capital", type=float, default=10000, help="初始资金（按比例开仓时用）")
|
||||
parser.add_argument("--fixed-margin", type=float, default=None, help="每笔固定保证金 USDT，如 50")
|
||||
parser.add_argument("--leverage", type=float, default=None, help="杠杆倍数，如 100")
|
||||
parser.add_argument("--no-fee", action="store_true", help="不扣手续费，只算总盈利")
|
||||
args = parser.parse_args()
|
||||
start_dt = datetime.datetime.strptime(args.start, "%Y-%m-%d")
|
||||
if args.end:
|
||||
end_dt = datetime.datetime.strptime(args.end, "%Y-%m-%d")
|
||||
# 包含 end 日全天：取次日 0 点前一刻，这样 id < end_ts 的 K 线都含在内
|
||||
end_ts = int((end_dt + datetime.timedelta(days=1)).timestamp())
|
||||
else:
|
||||
end_ts = int(datetime.datetime.utcnow().timestamp())
|
||||
start_ts = int(start_dt.timestamp())
|
||||
data_dir = args.data_dir or os.path.join(SCRIPT_DIR, "data")
|
||||
k5, k15, k60 = ensure_data(start_ts, end_ts, data_dir, use_api=not args.no_api)
|
||||
if not k5:
|
||||
logger.error("无 5 分钟数据，请先抓取或开启 --api")
|
||||
return
|
||||
logger.info(f"5m={len(k5)} 15m={len(k15)} 60m={len(k60)}")
|
||||
trades, stats, _ = run_backtest(
|
||||
k5, k15, k60,
|
||||
initial_capital=args.capital,
|
||||
fixed_margin_usdt=args.fixed_margin,
|
||||
leverage=args.leverage,
|
||||
deduct_fee=not args.no_fee,
|
||||
)
|
||||
logger.info(f"交易笔数: {stats['total_trades']} 胜率: {stats['win_rate']:.2f}% "
|
||||
f"总盈利(未扣费): {stats['total_gross_profit']:.2f} USDT "
|
||||
f"总手续费: {stats['total_fee']:.2f} 总净利润: {stats['total_net_profit']:.2f} "
|
||||
f"最大回撤: {stats['max_drawdown']:.2f} ({stats['max_drawdown_pct']:.2f}%)")
|
||||
out_csv = os.path.join(SCRIPT_DIR, "backtest_trades.csv")
|
||||
if trades:
|
||||
with open(out_csv, "w", newline="", encoding="utf-8") as f:
|
||||
w = csv.DictWriter(f, fieldnames=["direction", "entry_time", "exit_time", "entry_price", "exit_price", "point_pnl", "money_pnl", "fee", "net_profit", "exit_reason", "hold_bars"])
|
||||
w.writeheader()
|
||||
for t in trades:
|
||||
w.writerow({k: str(v) if isinstance(v, datetime.datetime) else v for k, v in t.items()})
|
||||
logger.info(f"交易记录已保存: {out_csv}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1040
adaptive_third_strategy/backtest_trades.csv
Normal file
1040
adaptive_third_strategy/backtest_trades.csv
Normal file
File diff suppressed because it is too large
Load Diff
97
adaptive_third_strategy/config.py
Normal file
97
adaptive_third_strategy/config.py
Normal file
@@ -0,0 +1,97 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
自适应三分位趋势策略 - 配置参数
|
||||
"""
|
||||
|
||||
# ========== 数据与周期 ==========
|
||||
CONTRACT_SYMBOL = "ETHUSDT"
|
||||
STEP_5M = 5
|
||||
STEP_15M = 15
|
||||
STEP_60M = 60
|
||||
|
||||
# 有效K线标准：实体 >= max(ATR(20)*0.1, 最小波动阈值)
|
||||
MIN_BODY_ATR_RATIO = 0.1
|
||||
MIN_VOLATILITY_PERCENT = 0.0005 # 当前价格 × 0.05%
|
||||
|
||||
# ========== 趋势判断（EMA周期） ==========
|
||||
# 长期：1小时 EMA(50) vs EMA(200)
|
||||
EMA_LONG_FAST = 50
|
||||
EMA_LONG_SLOW = 200
|
||||
# 中期：15分钟 EMA(20) vs EMA(50)
|
||||
EMA_MID_FAST = 20
|
||||
EMA_MID_SLOW = 50
|
||||
# 短期：5分钟 EMA(9)
|
||||
EMA_SHORT = 9
|
||||
|
||||
# 交易模式：aggressive / conservative / strict
|
||||
TREND_MODE = "conservative"
|
||||
|
||||
# ========== 动态触发 ==========
|
||||
ATR_PERIOD = 20
|
||||
VOLATILITY_COEF_CLAMP = (0.3, 3.0) # 波动率系数范围
|
||||
BASE_COEF = 0.33
|
||||
# 趋势方向系数（顺势更易触发）
|
||||
TREND_FAVOR_COEF = 0.8
|
||||
TREND_AGAINST_COEF = 1.2
|
||||
|
||||
# ========== 信号确认（至少满足几项） ==========
|
||||
CONFIRM_REQUIRED = 2
|
||||
VOLUME_MA_PERIOD = 20
|
||||
VOLUME_RATIO_THRESHOLD = 0.8 # 成交量 > 前20根均量 × 0.8
|
||||
|
||||
# ========== 风险管理 ==========
|
||||
MAX_POSITION_PERCENT = 0.10 # 单笔最大 10%
|
||||
BASE_POSITION_PERCENT = 0.02 # 基础仓位 2%
|
||||
STOP_LOSS_ATR_MULT = 2.0 # 硬止损 = ATR × 2.0
|
||||
TIME_STOP_BARS = 3 # 3根K线未盈利平仓
|
||||
TRAIL_START_ATR = 1.0 # 盈利 1×ATR 后启动移动止损
|
||||
TRAIL_ATR_MULT = 0.5 # 移动止损距离 0.5×ATR
|
||||
# 止盈目标（ATR倍数）与分批比例
|
||||
TP1_ATR = 1.5
|
||||
TP2_ATR = 3.0
|
||||
TP3_ATR = 5.0
|
||||
TP1_RATIO = 0.30
|
||||
TP2_RATIO = 0.30
|
||||
TP3_RATIO = 0.40
|
||||
|
||||
# ========== 反手条件 ==========
|
||||
REVERSE_BREAK_MULT = 0.5 # 突破幅度 > 实体/2
|
||||
REVERSE_LOSS_ATR = 0.5 # 当前持仓亏损 > 0.5×ATR 可反手
|
||||
MIN_BARS_SINCE_ENTRY = 2 # 距离上次开仓至少2根K线
|
||||
SAME_KLINE_NO_REVERSE = True # 同一K线不反手
|
||||
|
||||
# ========== 市场状态调整 ==========
|
||||
# 强趋势 / 震荡 / 高波动 时的系数
|
||||
STRONG_TREND_COEF = 0.25
|
||||
STRONG_TREND_STOP_ATR = 3.0
|
||||
RANGE_COEF = 0.4
|
||||
RANGE_STOP_ATR = 1.5
|
||||
HIGH_VOL_POSITION_COEF = 0.5
|
||||
HIGH_VOL_EXTRA_CONFIRM = 1
|
||||
|
||||
# ========== 禁止交易时段（UTC+8 小时:分） ==========
|
||||
FORBIDDEN_PERIODS = [
|
||||
(0, 0, 0, 30), # 00:00-00:30
|
||||
(8, 0, 8, 30), # 08:00-08:30
|
||||
(20, 0, 20, 30), # 20:00-20:30
|
||||
]
|
||||
# 高波动暂停：ATR > 平均ATR × 2 暂停
|
||||
ATR_PAUSE_MULT = 2.0
|
||||
|
||||
# ========== 自适应绩效 ==========
|
||||
CONSECUTIVE_LOSS_PAUSE = 3 # 连续亏损3次仓位减半
|
||||
WIN_RATE_LOOKBACK = 20
|
||||
WIN_RATE_PAUSE_THRESHOLD = 0.40 # 胜率<40% 暂停
|
||||
MAX_DRAWDOWN_PERCENT = 0.10 # 最大回撤10% 观察期
|
||||
MAX_TRADES_PER_DAY = 20
|
||||
MAX_HOLD_BARS = 4 * 12 # 4小时 ≈ 4*12 根5分钟
|
||||
|
||||
# ========== 回测/实盘通用 ==========
|
||||
CONTRACT_SIZE = 10000
|
||||
SLIPPAGE_POINTS = 3
|
||||
FEE_RATE = 0.0005
|
||||
FEE_FIXED = 5
|
||||
# 回测专用：固定手续费（合约多为按比例，可设为 0 观察策略本身）
|
||||
FEE_FIXED_BACKTEST = 0
|
||||
# 两笔开仓之间至少间隔 N 根 5 分钟 K 线，减少过度交易
|
||||
MIN_BARS_BETWEEN_TRADES = 4
|
||||
2785
adaptive_third_strategy/data/kline_15m.csv
Normal file
2785
adaptive_third_strategy/data/kline_15m.csv
Normal file
File diff suppressed because it is too large
Load Diff
8353
adaptive_third_strategy/data/kline_5m.csv
Normal file
8353
adaptive_third_strategy/data/kline_5m.csv
Normal file
File diff suppressed because it is too large
Load Diff
697
adaptive_third_strategy/data/kline_60m.csv
Normal file
697
adaptive_third_strategy/data/kline_60m.csv
Normal file
@@ -0,0 +1,697 @@
|
||||
id,open,high,low,close,volume
|
||||
1735660800,3412.6,3416.0,3390.39,3408.23,102369440.0
|
||||
1735664400,3408.23,3410.38,3329.45,3362.6,239948902.0
|
||||
1735668000,3362.62,3372.14,3349.24,3360.3,87820990.0
|
||||
1735671600,3360.3,3367.9,3342.85,3354.68,42695570.0
|
||||
1735675200,3354.68,3364.47,3336.84,3346.12,54111736.0
|
||||
1735678800,3345.76,3354.28,3331.51,3351.72,48624216.0
|
||||
1735682400,3351.66,3351.66,3327.8,3338.94,44710804.0
|
||||
1735686000,3338.94,3345.03,3327.22,3336.37,37042226.0
|
||||
1735689600,3336.47,3364.49,3334.99,3362.89,57465298.0
|
||||
1735693200,3362.89,3365.08,3341.6,3345.83,34711202.0
|
||||
1735696800,3345.83,3368.02,3345.35,3361.81,34405766.0
|
||||
1735700400,3361.81,3363.0,3350.22,3354.34,21508812.0
|
||||
1735704000,3354.34,3355.78,3338.4,3340.12,45813072.0
|
||||
1735707600,3340.04,3350.57,3339.13,3344.2,29504628.0
|
||||
1735711200,3344.2,3347.33,3335.57,3345.65,26979022.0
|
||||
1735714800,3345.65,3347.77,3338.32,3346.0,19122736.0
|
||||
1735718400,3345.94,3348.0,3330.48,3335.57,39528308.0
|
||||
1735722000,3335.57,3340.16,3313.08,3333.4,129899100.0
|
||||
1735725600,3333.52,3337.99,3325.01,3325.99,32584622.0
|
||||
1735729200,3325.99,3347.73,3322.8,3340.07,45465420.0
|
||||
1735732800,3340.07,3351.21,3337.29,3346.01,39172620.0
|
||||
1735736400,3346.01,3352.78,3331.45,3343.1,51120458.0
|
||||
1735740000,3342.83,3358.89,3327.15,3345.0,82299724.0
|
||||
1735743600,3345.06,3354.24,3332.01,3352.07,51286002.0
|
||||
1735747200,3352.07,3356.14,3331.27,3337.72,49132440.0
|
||||
1735750800,3337.72,3344.37,3322.56,3343.02,49046866.0
|
||||
1735754400,3343.01,3351.47,3332.38,3347.81,36271786.0
|
||||
1735758000,3347.81,3363.82,3338.02,3356.2,55282332.0
|
||||
1735761600,3356.2,3361.76,3351.0,3359.39,38099614.0
|
||||
1735765200,3359.28,3370.87,3352.49,3367.59,51552628.0
|
||||
1735768800,3367.59,3373.97,3357.81,3360.65,43553722.0
|
||||
1735772400,3360.65,3365.99,3352.07,3359.35,33197468.0
|
||||
1735776000,3359.35,3399.41,3354.01,3389.27,123638482.0
|
||||
1735779600,3389.27,3404.76,3377.72,3383.02,72528314.0
|
||||
1735783200,3383.1,3397.9,3376.89,3397.89,35142436.0
|
||||
1735786800,3397.89,3401.77,3386.57,3388.01,28707566.0
|
||||
1735790400,3388.01,3427.38,3387.4,3417.81,124752266.0
|
||||
1735794000,3417.82,3419.18,3404.4,3412.37,64463210.0
|
||||
1735797600,3412.63,3415.73,3401.82,3410.19,37061074.0
|
||||
1735801200,3410.19,3421.77,3408.6,3419.68,42976506.0
|
||||
1735804800,3419.68,3442.8,3414.59,3437.98,81383320.0
|
||||
1735808400,3437.13,3470.72,3433.94,3467.93,117165150.0
|
||||
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||||
1738022400,3180.79,3202.51,3163.01,3166.22,77481438.0
|
||||
1738026000,3166.27,3175.57,3150.31,3165.28,59624336.0
|
||||
1738029600,3164.96,3198.41,3164.14,3191.6,59293696.0
|
||||
1738033200,3191.56,3216.86,3174.1,3201.47,109284848.0
|
||||
1738036800,3201.47,3221.53,3196.11,3210.64,83876044.0
|
||||
1738040400,3210.67,3220.69,3207.19,3211.69,44259070.0
|
||||
1738044000,3211.69,3214.02,3190.44,3193.55,48991426.0
|
||||
1738047600,3193.43,3214.7,3187.51,3194.62,50038618.0
|
||||
1738051200,3194.62,3204.1,3182.01,3197.47,81225676.0
|
||||
1738054800,3197.42,3200.58,3183.0,3195.72,38138356.0
|
||||
1738058400,3195.72,3206.32,3187.84,3192.87,39941280.0
|
||||
1738062000,3192.87,3199.56,3175.47,3185.2,39874048.0
|
||||
1738065600,3185.2,3194.62,3165.52,3173.72,60379984.0
|
||||
1738069200,3173.64,3188.0,3169.01,3174.14,39825956.0
|
||||
1738072800,3174.14,3179.53,3156.0,3171.09,125274148.0
|
||||
1738076400,3171.34,3213.44,3169.55,3180.15,145496580.0
|
||||
1738080000,3180.15,3181.81,3130.72,3149.59,141098534.0
|
||||
1738083600,3149.46,3181.56,3149.05,3166.11,107192478.0
|
||||
1738087200,3166.07,3176.76,3139.18,3142.6,67871000.0
|
||||
1738090800,3142.6,3149.32,3118.98,3146.4,100694056.0
|
||||
1738094400,3146.48,3153.45,3092.03,3094.31,134835852.0
|
||||
1738098000,3093.42,3113.6,3051.32,3052.43,169849482.0
|
||||
1738101600,3052.32,3089.94,3038.2,3082.86,165296122.0
|
||||
1738105200,3082.93,3095.01,3052.51,3075.96,82801056.0
|
||||
1738108800,3075.96,3116.55,3075.12,3108.9,1274170.0
|
||||
1738112400,3108.85,3119.0,3097.82,3109.31,1144288.0
|
||||
1738116000,3109.31,3128.3,3108.69,3112.81,4755046.0
|
||||
1738119600,3112.74,3131.26,3106.65,3129.02,44437084.0
|
||||
1738123200,3129.04,3138.89,3121.51,3122.94,51333698.0
|
||||
1738126800,3122.87,3133.32,3115.46,3129.66,45341342.0
|
||||
1738130400,3129.66,3148.96,3127.16,3145.88,60770492.0
|
||||
1738134000,3145.88,3158.0,3137.4,3155.64,54759780.0
|
||||
1738137600,3155.64,3162.68,3143.0,3146.4,48938372.0
|
||||
1738141200,3146.4,3147.23,3121.93,3129.27,68754366.0
|
||||
1738144800,3129.27,3142.05,3119.65,3136.48,46871668.0
|
||||
1738148400,3136.48,3142.08,3130.31,3133.64,37915812.0
|
||||
1738152000,3133.64,3138.84,3112.15,3117.69,73520948.0
|
||||
1738155600,3117.69,3120.73,3087.77,3100.83,123395826.0
|
||||
1738159200,3100.81,3115.17,3079.41,3095.1,130122046.0
|
||||
1738162800,3095.81,3117.95,3092.7,3099.65,80143028.0
|
||||
|
172
adaptive_third_strategy/data_fetcher.py
Normal file
172
adaptive_third_strategy/data_fetcher.py
Normal file
@@ -0,0 +1,172 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Bitmart 多周期K线数据抓取 - 供回测与实盘使用
|
||||
与 bitmart/回测.py、bitmart/抓取多周期K线.py 相同的 API 调用方式
|
||||
"""
|
||||
|
||||
import time
|
||||
import csv
|
||||
import os
|
||||
from typing import List, Dict, Optional
|
||||
try:
|
||||
from loguru import logger
|
||||
except ImportError:
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 项目根目录为 lm_code，bitmart 包在根目录
|
||||
import sys
|
||||
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
ROOT_DIR = os.path.dirname(SCRIPT_DIR)
|
||||
if ROOT_DIR not in sys.path:
|
||||
sys.path.insert(0, ROOT_DIR)
|
||||
|
||||
# 默认 API（与 bitmart/回测.py 一致，回测仅拉数据可不填有效 key）
|
||||
# APIContract 在 fetch_klines / fetch_multi_timeframe 内按需导入，以便 --no-api 回测不依赖 bitmart
|
||||
DEFAULT_API_KEY = "a0fb7b98464fd9bcce67e7c519d58ec10d0c38a8"
|
||||
DEFAULT_SECRET_KEY = "4eaeba78e77aeaab1c2027f846a276d164f264a44c2c1bb1c5f3be50c8de1ca5"
|
||||
DEFAULT_MEMO = "合约交易"
|
||||
CONTRACT_SYMBOL = "ETHUSDT"
|
||||
|
||||
|
||||
def _format_bar(k: dict) -> dict:
|
||||
"""API 单根K线 -> 统一格式，id 为秒时间戳"""
|
||||
return {
|
||||
"id": int(k["timestamp"]),
|
||||
"open": float(k["open_price"]),
|
||||
"high": float(k["high_price"]),
|
||||
"low": float(k["low_price"]),
|
||||
"close": float(k["close_price"]),
|
||||
"volume": float(k.get("volume", 0)),
|
||||
}
|
||||
|
||||
|
||||
def fetch_klines(
|
||||
contract_api: "APIContract",
|
||||
step: int,
|
||||
start_time: int,
|
||||
end_time: int,
|
||||
symbol: str = CONTRACT_SYMBOL,
|
||||
) -> List[Dict]:
|
||||
"""
|
||||
拉取指定周期的K线（与 bitmart 回测抓取方式一致）。
|
||||
:param contract_api: bitmart.api_contract.APIContract 实例
|
||||
:param step: K线周期（分钟），如 5/15/60
|
||||
:param start_time: 开始时间戳（秒）
|
||||
:param end_time: 结束时间戳（秒）
|
||||
:param symbol: 合约符号
|
||||
:return: [{"id", "open", "high", "low", "close", "volume"}, ...]，按 id 升序
|
||||
"""
|
||||
all_data: List[Dict] = []
|
||||
existing_ids = set()
|
||||
request_interval = step * 60 * 500 # 每次最多约 500 根
|
||||
current_start = start_time
|
||||
|
||||
while current_start < end_time:
|
||||
current_end = min(current_start + request_interval, end_time)
|
||||
try:
|
||||
response = contract_api.get_kline(
|
||||
contract_symbol=symbol,
|
||||
step=step,
|
||||
start_time=current_start,
|
||||
end_time=current_end,
|
||||
)[0]
|
||||
if response.get("code") != 1000:
|
||||
logger.warning(f"get_kline code={response.get('code')}, msg={response.get('msg')}")
|
||||
time.sleep(1)
|
||||
current_start = current_end
|
||||
continue
|
||||
data = response.get("data", [])
|
||||
except Exception as e:
|
||||
logger.warning(f"get_kline 异常 step={step} {e}，60秒后重试")
|
||||
time.sleep(60)
|
||||
continue
|
||||
|
||||
for k in data:
|
||||
k_id = int(k["timestamp"])
|
||||
if k_id in existing_ids:
|
||||
continue
|
||||
existing_ids.add(k_id)
|
||||
all_data.append(_format_bar(k))
|
||||
|
||||
if len(data) < 500:
|
||||
current_start = current_end
|
||||
else:
|
||||
all_data.sort(key=lambda x: x["id"])
|
||||
current_start = all_data[-1]["id"] + 1
|
||||
|
||||
time.sleep(0.25)
|
||||
|
||||
all_data.sort(key=lambda x: x["id"])
|
||||
return all_data
|
||||
|
||||
|
||||
def fetch_multi_timeframe(
|
||||
start_time: int,
|
||||
end_time: int,
|
||||
steps: List[int] = None,
|
||||
api_key: str = DEFAULT_API_KEY,
|
||||
secret_key: str = DEFAULT_SECRET_KEY,
|
||||
memo: str = DEFAULT_MEMO,
|
||||
symbol: str = CONTRACT_SYMBOL,
|
||||
) -> Dict[int, List[Dict]]:
|
||||
"""
|
||||
拉取多周期K线（5/15/60），供回测使用。
|
||||
:return: { step: [kline_list] }
|
||||
"""
|
||||
steps = steps or [5, 15, 60]
|
||||
from bitmart.api_contract import APIContract
|
||||
api = APIContract(api_key, secret_key, memo, timeout=(5, 15))
|
||||
result = {}
|
||||
for step in steps:
|
||||
logger.info(f"抓取 {step} 分钟 K 线: {start_time} ~ {end_time}")
|
||||
result[step] = fetch_klines(api, step, start_time, end_time, symbol)
|
||||
logger.info(f" -> {len(result[step])} 条")
|
||||
return result
|
||||
|
||||
|
||||
def save_klines_csv(klines: List[Dict], path: str) -> None:
|
||||
"""将K线保存为 CSV（id, open, high, low, close, volume）"""
|
||||
if not klines:
|
||||
return
|
||||
cols = ["id", "open", "high", "low", "close", "volume"]
|
||||
with open(path, "w", newline="", encoding="utf-8") as f:
|
||||
w = csv.DictWriter(f, fieldnames=cols)
|
||||
w.writeheader()
|
||||
for row in klines:
|
||||
w.writerow({k: row.get(k) for k in cols})
|
||||
logger.info(f"已保存 {len(klines)} 条到 {path}")
|
||||
|
||||
|
||||
def load_klines_csv(path: str) -> List[Dict]:
|
||||
"""从 CSV 加载K线"""
|
||||
if not os.path.isfile(path):
|
||||
return []
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
r = csv.DictReader(f)
|
||||
rows = list(r)
|
||||
out = []
|
||||
for row in rows:
|
||||
out.append({
|
||||
"id": int(row["id"]),
|
||||
"open": float(row["open"]),
|
||||
"high": float(row["high"]),
|
||||
"low": float(row["low"]),
|
||||
"close": float(row["close"]),
|
||||
"volume": float(row.get("volume", 0)),
|
||||
})
|
||||
out.sort(key=lambda x: x["id"])
|
||||
return out
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import datetime
|
||||
# 示例：拉取最近约 30 天 5/15/60 分钟数据并保存
|
||||
end_ts = int(time.time())
|
||||
start_ts = end_ts - 30 * 24 * 3600
|
||||
data_dir = os.path.join(SCRIPT_DIR, "data")
|
||||
os.makedirs(data_dir, exist_ok=True)
|
||||
data = fetch_multi_timeframe(start_ts, end_ts, steps=[5, 15, 60])
|
||||
for step, klines in data.items():
|
||||
path = os.path.join(data_dir, f"kline_{step}m.csv")
|
||||
save_klines_csv(klines, path)
|
||||
86
adaptive_third_strategy/indicators.py
Normal file
86
adaptive_third_strategy/indicators.py
Normal file
@@ -0,0 +1,86 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
技术指标：EMA、ATR
|
||||
"""
|
||||
|
||||
from typing import List, Dict, Optional
|
||||
|
||||
|
||||
def ema(series: List[float], period: int) -> List[Optional[float]]:
|
||||
"""EMA(period)，前 period-1 个为 None"""
|
||||
if not series or period < 1:
|
||||
return []
|
||||
k = 2.0 / (period + 1)
|
||||
out: List[Optional[float]] = [None] * (period - 1)
|
||||
s = sum(series[:period])
|
||||
out.append(s / period)
|
||||
for i in range(period, len(series)):
|
||||
val = series[i] * k + out[-1] * (1 - k) # type: ignore
|
||||
out.append(val)
|
||||
return out
|
||||
|
||||
|
||||
def atr(high: List[float], low: List[float], close: List[float], period: int) -> List[Optional[float]]:
|
||||
"""ATR(period)，前 period 个为 None"""
|
||||
n = len(close)
|
||||
if n < period + 1 or len(high) != n or len(low) != n:
|
||||
return [None] * n
|
||||
tr_list: List[float] = []
|
||||
for i in range(n):
|
||||
if i == 0:
|
||||
tr_list.append(high[0] - low[0])
|
||||
else:
|
||||
tr = max(
|
||||
high[i] - low[i],
|
||||
abs(high[i] - close[i - 1]),
|
||||
abs(low[i] - close[i - 1])
|
||||
)
|
||||
tr_list.append(tr)
|
||||
return ema(tr_list, period)
|
||||
|
||||
|
||||
def get_ema_atr_from_klines(klines: List[Dict], ema_period: int, atr_period: int
|
||||
) -> tuple:
|
||||
"""
|
||||
从K线列表计算收盘价 EMA 和 ATR。
|
||||
返回 (ema_list, atr_list)，长度与 klines 一致。
|
||||
"""
|
||||
close = [float(k["close"]) for k in klines]
|
||||
high = [float(k["high"]) for k in klines]
|
||||
low = [float(k["low"]) for k in klines]
|
||||
ema_list = ema(close, ema_period)
|
||||
atr_list = atr(high, low, close, atr_period)
|
||||
return ema_list, atr_list
|
||||
|
||||
|
||||
def align_higher_tf_ema(
|
||||
klines_5m: List[Dict],
|
||||
klines_higher: List[Dict],
|
||||
ema_fast: int,
|
||||
ema_slow: int
|
||||
) -> List[Dict]:
|
||||
"""
|
||||
根据更高周期K线计算 EMA，并按 5 分钟时间对齐。
|
||||
返回列表长度与 klines_5m 一致，每项为 {"ema_fast": float, "ema_slow": float} 或 None。
|
||||
"""
|
||||
if not klines_higher or len(klines_higher) < ema_slow:
|
||||
return [{}] * len(klines_5m)
|
||||
close_hi = [float(k["close"]) for k in klines_higher]
|
||||
ema_f = ema(close_hi, ema_fast)
|
||||
ema_s = ema(close_hi, ema_slow)
|
||||
id_hi = [k["id"] for k in klines_higher]
|
||||
result = []
|
||||
for k5 in klines_5m:
|
||||
t = k5["id"]
|
||||
# 当前 5m 时刻所属的更高周期：取 <= t 的最后一根
|
||||
idx = -1
|
||||
for i, tid in enumerate(id_hi):
|
||||
if tid <= t:
|
||||
idx = i
|
||||
else:
|
||||
break
|
||||
if idx >= ema_slow - 1 and ema_f[idx] is not None and ema_s[idx] is not None:
|
||||
result.append({"ema_fast": ema_f[idx], "ema_slow": ema_s[idx]})
|
||||
else:
|
||||
result.append({})
|
||||
return result
|
||||
305
adaptive_third_strategy/strategy_core.py
Normal file
305
adaptive_third_strategy/strategy_core.py
Normal file
@@ -0,0 +1,305 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
自适应三分位趋势策略 - 核心逻辑
|
||||
趋势过滤、动态阈值、信号确认、市场状态
|
||||
"""
|
||||
|
||||
from typing import List, Dict, Optional, Tuple
|
||||
import sys
|
||||
import os
|
||||
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
if ROOT_DIR not in sys.path:
|
||||
sys.path.insert(0, ROOT_DIR)
|
||||
|
||||
from adaptive_third_strategy.config import (
|
||||
MIN_BODY_ATR_RATIO,
|
||||
MIN_VOLATILITY_PERCENT,
|
||||
EMA_SHORT,
|
||||
EMA_LONG_FAST,
|
||||
EMA_LONG_SLOW,
|
||||
EMA_MID_FAST,
|
||||
EMA_MID_SLOW,
|
||||
ATR_PERIOD,
|
||||
VOLATILITY_COEF_CLAMP,
|
||||
BASE_COEF,
|
||||
TREND_FAVOR_COEF,
|
||||
TREND_AGAINST_COEF,
|
||||
TREND_MODE,
|
||||
CONFIRM_REQUIRED,
|
||||
VOLUME_MA_PERIOD,
|
||||
VOLUME_RATIO_THRESHOLD,
|
||||
REVERSE_BREAK_MULT,
|
||||
MIN_BARS_SINCE_ENTRY,
|
||||
FORBIDDEN_PERIODS,
|
||||
ATR_PAUSE_MULT,
|
||||
STRONG_TREND_COEF,
|
||||
RANGE_COEF,
|
||||
HIGH_VOL_EXTRA_CONFIRM,
|
||||
)
|
||||
from adaptive_third_strategy.indicators import (
|
||||
get_ema_atr_from_klines,
|
||||
align_higher_tf_ema,
|
||||
ema,
|
||||
)
|
||||
|
||||
|
||||
def get_body_size(candle: Dict) -> float:
|
||||
return abs(float(candle["open"]) - float(candle["close"]))
|
||||
|
||||
|
||||
def is_bullish(candle: Dict) -> bool:
|
||||
return float(candle["close"]) > float(candle["open"])
|
||||
|
||||
|
||||
def get_min_body_threshold(price: float, atr_value: Optional[float]) -> float:
|
||||
"""有效K线最小实体 = max(ATR*0.1, 价格*0.05%)"""
|
||||
min_vol = price * MIN_VOLATILITY_PERCENT
|
||||
if atr_value is not None and atr_value > 0:
|
||||
min_vol = max(min_vol, atr_value * MIN_BODY_ATR_RATIO)
|
||||
return min_vol
|
||||
|
||||
|
||||
def find_valid_prev_bar(
|
||||
all_data: List[Dict],
|
||||
current_idx: int,
|
||||
atr_series: List[Optional[float]],
|
||||
min_body_override: Optional[float] = None,
|
||||
) -> Tuple[Optional[int], Optional[Dict]]:
|
||||
"""从当前索引往前找实体>=阈值的K线。阈值 = max(ATR*0.1, 价格*0.05%)"""
|
||||
if current_idx <= 0:
|
||||
return None, None
|
||||
for i in range(current_idx - 1, -1, -1):
|
||||
prev = all_data[i]
|
||||
body = get_body_size(prev)
|
||||
price = float(prev["close"])
|
||||
atr_val = atr_series[i] if i < len(atr_series) else None
|
||||
th = min_body_override if min_body_override is not None else get_min_body_threshold(price, atr_val)
|
||||
if body >= th:
|
||||
return i, prev
|
||||
return None, None
|
||||
|
||||
|
||||
def get_trend(
|
||||
klines_5m: List[Dict],
|
||||
idx_5m: int,
|
||||
ema_5m: List[Optional[float]],
|
||||
ema_15m_align: List[Dict],
|
||||
ema_60m_align: List[Dict],
|
||||
) -> str:
|
||||
"""
|
||||
多时间框架趋势。返回 "long" / "short" / "neutral"。
|
||||
长期：1h EMA50 vs EMA200；中期：15m EMA20 vs EMA50；短期：5m close vs EMA9。
|
||||
ema_*_align: 与 5m 对齐的列表，每项 {"ema_fast", "ema_slow"}。
|
||||
"""
|
||||
if idx_5m >= len(klines_5m):
|
||||
return "neutral"
|
||||
curr = klines_5m[idx_5m]
|
||||
close_5 = float(curr["close"])
|
||||
# 短期
|
||||
ema9 = ema_5m[idx_5m] if idx_5m < len(ema_5m) else None
|
||||
short_bull = (ema9 is not None and close_5 > ema9)
|
||||
# 中期
|
||||
mid = ema_15m_align[idx_5m] if idx_5m < len(ema_15m_align) else {}
|
||||
mid_bull = (mid.get("ema_fast") is not None and mid.get("ema_slow") is not None
|
||||
and mid["ema_fast"] > mid["ema_slow"])
|
||||
# 长期
|
||||
long_d = ema_60m_align[idx_5m] if idx_5m < len(ema_60m_align) else {}
|
||||
long_bull = (long_d.get("ema_fast") is not None and long_d.get("ema_slow") is not None
|
||||
and long_d["ema_fast"] > long_d["ema_slow"])
|
||||
|
||||
if TREND_MODE == "aggressive":
|
||||
return "long" if short_bull else "short"
|
||||
if TREND_MODE == "conservative":
|
||||
if mid_bull and short_bull:
|
||||
return "long"
|
||||
if not mid_bull and not short_bull:
|
||||
return "short"
|
||||
return "neutral"
|
||||
# strict
|
||||
if long_bull and mid_bull and short_bull:
|
||||
return "long"
|
||||
if not long_bull and not mid_bull and not short_bull:
|
||||
return "short"
|
||||
return "neutral"
|
||||
|
||||
|
||||
def get_dynamic_trigger_levels(
|
||||
prev: Dict,
|
||||
atr_value: float,
|
||||
trend: str,
|
||||
market_state: str = "normal",
|
||||
) -> Tuple[Optional[float], Optional[float]]:
|
||||
"""
|
||||
动态三分位触发价。
|
||||
波动率系数 = clamp(实体/ATR, 0.3, 3.0)，调整系数 = 0.33 * 波动率系数。
|
||||
顺势方向 ×0.8，逆势 ×1.2。
|
||||
"""
|
||||
body = get_body_size(prev)
|
||||
if body < 1e-6 or atr_value <= 0:
|
||||
return None, None
|
||||
vol_coef = body / atr_value
|
||||
vol_coef = max(VOLATILITY_COEF_CLAMP[0], min(VOLATILITY_COEF_CLAMP[1], vol_coef))
|
||||
adj = BASE_COEF * vol_coef
|
||||
if market_state == "strong_trend":
|
||||
adj = STRONG_TREND_COEF
|
||||
elif market_state == "range":
|
||||
adj = RANGE_COEF
|
||||
p_close = float(prev["close"])
|
||||
if trend == "long":
|
||||
long_adj = adj * TREND_FAVOR_COEF
|
||||
short_adj = adj * TREND_AGAINST_COEF
|
||||
elif trend == "short":
|
||||
long_adj = adj * TREND_AGAINST_COEF
|
||||
short_adj = adj * TREND_FAVOR_COEF
|
||||
else:
|
||||
long_adj = short_adj = adj
|
||||
long_trigger = p_close + body * long_adj
|
||||
short_trigger = p_close - body * short_adj
|
||||
return long_trigger, short_trigger
|
||||
|
||||
|
||||
def check_signal_confirm(
|
||||
curr: Dict,
|
||||
direction: str,
|
||||
trigger_price: float,
|
||||
all_data: List[Dict],
|
||||
current_idx: int,
|
||||
volume_ma: Optional[float],
|
||||
required: int = CONFIRM_REQUIRED,
|
||||
) -> int:
|
||||
"""
|
||||
确认条件计数：收盘价确认、成交量确认、动量确认。
|
||||
返回满足的个数。
|
||||
"""
|
||||
count = 0
|
||||
c_close = float(curr["close"])
|
||||
c_volume = float(curr.get("volume", 0))
|
||||
# 1. 收盘价确认
|
||||
if direction == "long" and c_close >= trigger_price:
|
||||
count += 1
|
||||
elif direction == "short" and c_close <= trigger_price:
|
||||
count += 1
|
||||
# 2. 成交量确认
|
||||
if volume_ma is not None and volume_ma > 0 and c_volume >= volume_ma * VOLUME_RATIO_THRESHOLD:
|
||||
count += 1
|
||||
# 3. 动量确认：当前K线实体方向与信号一致
|
||||
if direction == "long" and is_bullish(curr):
|
||||
count += 1
|
||||
elif direction == "short" and not is_bullish(curr):
|
||||
count += 1
|
||||
return count
|
||||
|
||||
|
||||
def in_forbidden_period(ts_sec: int) -> bool:
|
||||
"""是否在禁止交易时段（按 UTC+8 小时:分）"""
|
||||
from datetime import datetime, timezone
|
||||
try:
|
||||
dt = datetime.fromtimestamp(ts_sec, tz=timezone.utc)
|
||||
except Exception:
|
||||
dt = datetime.utcfromtimestamp(ts_sec)
|
||||
# 转 UTC+8
|
||||
hour = (dt.hour + 8) % 24
|
||||
minute = dt.minute
|
||||
for h1, m1, h2, m2 in FORBIDDEN_PERIODS:
|
||||
t1 = h1 * 60 + m1
|
||||
t2 = h2 * 60 + m2
|
||||
t = hour * 60 + minute
|
||||
if t1 <= t < t2:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def get_market_state(
|
||||
atr_value: float,
|
||||
atr_avg: Optional[float],
|
||||
trend: str,
|
||||
) -> str:
|
||||
"""normal / strong_trend / range / high_vol"""
|
||||
if atr_avg is not None and atr_avg > 0 and atr_value >= atr_avg * ATR_PAUSE_MULT:
|
||||
return "high_vol"
|
||||
if trend in ("long", "short") and atr_avg is not None and atr_value > atr_avg * 1.2:
|
||||
return "strong_trend"
|
||||
if trend == "neutral":
|
||||
return "range"
|
||||
return "normal"
|
||||
|
||||
|
||||
def check_trigger(
|
||||
all_data: List[Dict],
|
||||
current_idx: int,
|
||||
atr_series: List[Optional[float]],
|
||||
ema_5m: List[Optional[float]],
|
||||
ema_15m_align: List[Dict],
|
||||
ema_60m_align: List[Dict],
|
||||
volume_ma_list: Optional[List[Optional[float]]] = None,
|
||||
use_confirm: bool = True,
|
||||
) -> Tuple[Optional[str], Optional[float], Optional[int], Optional[Dict]]:
|
||||
"""
|
||||
检查当前K线是否产生有效信号（含趋势过滤与确认）。
|
||||
返回 (方向, 触发价, 有效前一根索引, 有效前一根K线) 或 (None, None, None, None)。
|
||||
"""
|
||||
if current_idx <= 0 or current_idx >= len(all_data):
|
||||
return None, None, None, None
|
||||
curr = all_data[current_idx]
|
||||
valid_prev_idx, prev = find_valid_prev_bar(all_data, current_idx, atr_series)
|
||||
if prev is None:
|
||||
return None, None, None, None
|
||||
atr_val = atr_series[current_idx] if current_idx < len(atr_series) else None
|
||||
if atr_val is None or atr_val <= 0:
|
||||
return None, None, None, None
|
||||
trend = get_trend(
|
||||
all_data, current_idx, ema_5m, ema_15m_align, ema_60m_align,
|
||||
)
|
||||
atr_avg = None
|
||||
if atr_series:
|
||||
valid_atr = [x for x in atr_series[: current_idx + 1] if x is not None and x > 0]
|
||||
if len(valid_atr) >= ATR_PERIOD:
|
||||
atr_avg = sum(valid_atr) / len(valid_atr)
|
||||
market_state = get_market_state(atr_val, atr_avg, trend)
|
||||
if market_state == "high_vol":
|
||||
return None, None, None, None
|
||||
long_trigger, short_trigger = get_dynamic_trigger_levels(prev, atr_val, trend, market_state)
|
||||
if long_trigger is None:
|
||||
return None, None, None, None
|
||||
c_high = float(curr["high"])
|
||||
c_low = float(curr["low"])
|
||||
long_triggered = c_high >= long_trigger
|
||||
short_triggered = c_low <= short_trigger
|
||||
direction = None
|
||||
trigger_price = None
|
||||
if long_triggered and short_triggered:
|
||||
c_open = float(curr["open"])
|
||||
if abs(c_open - short_trigger) <= abs(c_open - long_trigger):
|
||||
direction, trigger_price = "short", short_trigger
|
||||
else:
|
||||
direction, trigger_price = "long", long_trigger
|
||||
elif short_triggered:
|
||||
direction, trigger_price = "short", short_trigger
|
||||
elif long_triggered:
|
||||
direction, trigger_price = "long", long_trigger
|
||||
if direction is None:
|
||||
return None, None, None, None
|
||||
# 趋势过滤：逆势不交易（可选，这里做过滤）
|
||||
if trend == "long" and direction == "short":
|
||||
return None, None, None, None
|
||||
if trend == "short" and direction == "long":
|
||||
return None, None, None, None
|
||||
# 禁止时段
|
||||
if in_forbidden_period(curr["id"]):
|
||||
return None, None, None, None
|
||||
# 信号确认
|
||||
if use_confirm and CONFIRM_REQUIRED > 0:
|
||||
vol_ma = volume_ma_list[current_idx] if volume_ma_list and current_idx < len(volume_ma_list) else None
|
||||
n = check_signal_confirm(curr, direction, trigger_price, all_data, current_idx, vol_ma, CONFIRM_REQUIRED)
|
||||
if n < CONFIRM_REQUIRED:
|
||||
return None, None, None, None
|
||||
return direction, trigger_price, valid_prev_idx, prev
|
||||
|
||||
|
||||
def build_volume_ma(klines: List[Dict], period: int = VOLUME_MA_PERIOD) -> List[Optional[float]]:
|
||||
"""前 period-1 为 None，之后为 volume 的 SMA"""
|
||||
vol = [float(k.get("volume", 0)) for k in klines]
|
||||
out: List[Optional[float]] = [None] * (period - 1)
|
||||
for i in range(period - 1, len(vol)):
|
||||
out.append(sum(vol[i - period + 1 : i + 1]) / period)
|
||||
return out
|
||||
187
adaptive_third_strategy/trade.py
Normal file
187
adaptive_third_strategy/trade.py
Normal file
@@ -0,0 +1,187 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
自适应三分位趋势策略 - 实盘交易
|
||||
拉取 Bitmart 5/15/60 分钟数据，计算信号，执行开平仓（沿用 交易/bitmart-三分之一策略交易 的浏览器/API 逻辑）
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import datetime
|
||||
from typing import List, Dict, Optional, Tuple
|
||||
try:
|
||||
from loguru import logger
|
||||
except ImportError:
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
if ROOT_DIR not in sys.path:
|
||||
sys.path.insert(0, ROOT_DIR)
|
||||
|
||||
from bitmart.api_contract import APIContract
|
||||
from adaptive_third_strategy.config import (
|
||||
STEP_5M,
|
||||
STEP_15M,
|
||||
STEP_60M,
|
||||
ATR_PERIOD,
|
||||
EMA_SHORT,
|
||||
EMA_MID_FAST,
|
||||
EMA_MID_SLOW,
|
||||
EMA_LONG_FAST,
|
||||
EMA_LONG_SLOW,
|
||||
BASE_POSITION_PERCENT,
|
||||
MAX_POSITION_PERCENT,
|
||||
CONTRACT_SYMBOL,
|
||||
)
|
||||
from adaptive_third_strategy.indicators import get_ema_atr_from_klines, align_higher_tf_ema
|
||||
from adaptive_third_strategy.strategy_core import check_trigger, build_volume_ma
|
||||
from adaptive_third_strategy.data_fetcher import fetch_klines, _format_bar
|
||||
|
||||
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
|
||||
class AdaptiveThirdStrategyTrader:
|
||||
"""实盘：获取多周期K线 -> 策略信号 -> 开平仓（可接浏览器或纯 API）"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_key: str,
|
||||
secret_key: str,
|
||||
memo: str = "合约交易",
|
||||
symbol: str = CONTRACT_SYMBOL,
|
||||
bit_id: Optional[str] = None,
|
||||
):
|
||||
self.api_key = api_key
|
||||
self.secret_key = secret_key
|
||||
self.memo = memo
|
||||
self.symbol = symbol
|
||||
self.bit_id = bit_id
|
||||
self.contractAPI = APIContract(api_key, secret_key, memo, timeout=(5, 15))
|
||||
self.check_interval = 3
|
||||
self.last_trigger_kline_id: Optional[int] = None
|
||||
self.last_trigger_direction: Optional[str] = None
|
||||
self.last_trade_kline_id: Optional[int] = None
|
||||
self.position_direction: int = 0 # 1 多 -1 空 0 无
|
||||
|
||||
def get_klines_multi(self) -> Tuple[List[Dict], List[Dict], List[Dict]]:
|
||||
"""拉取当前 5/15/60 分钟 K 线（最近约 3 小时足够算 EMA/ATR）"""
|
||||
end_ts = int(time.time())
|
||||
start_ts = end_ts - 3600 * 3
|
||||
k5 = fetch_klines(self.contractAPI, STEP_5M, start_ts, end_ts, self.symbol)
|
||||
k15 = fetch_klines(self.contractAPI, STEP_15M, start_ts, end_ts, self.symbol)
|
||||
k60 = fetch_klines(self.contractAPI, STEP_60M, start_ts, end_ts, self.symbol)
|
||||
return k5, k15, k60
|
||||
|
||||
def get_position_status(self) -> bool:
|
||||
"""查询当前持仓，更新 self.position_direction"""
|
||||
try:
|
||||
response = self.contractAPI.get_position(contract_symbol=self.symbol)[0]
|
||||
if response.get("code") != 1000:
|
||||
return False
|
||||
positions = response.get("data", [])
|
||||
if not positions:
|
||||
self.position_direction = 0
|
||||
return True
|
||||
self.position_direction = 1 if positions[0].get("position_type") == 1 else -1
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"持仓查询异常: {e}")
|
||||
return False
|
||||
|
||||
def check_realtime_signal(
|
||||
self,
|
||||
klines_5m: List[Dict],
|
||||
klines_15m: List[Dict],
|
||||
klines_60m: List[Dict],
|
||||
) -> Tuple[Optional[str], Optional[float], Optional[Dict]]:
|
||||
"""
|
||||
基于当前 K 线（最后一根未收盘）检测实时信号。
|
||||
返回 (方向, 触发价, 当前K线) 或 (None, None, None)。
|
||||
"""
|
||||
if len(klines_5m) < ATR_PERIOD + 2:
|
||||
return None, None, None
|
||||
from adaptive_third_strategy.indicators import get_ema_atr_from_klines
|
||||
from adaptive_third_strategy.strategy_core import check_trigger
|
||||
ema_5m, atr_5m = get_ema_atr_from_klines(klines_5m, EMA_SHORT, ATR_PERIOD)
|
||||
ema_15m_align = align_higher_tf_ema(klines_5m, klines_15m, EMA_MID_FAST, EMA_MID_SLOW)
|
||||
ema_60m_align = align_higher_tf_ema(klines_5m, klines_60m, EMA_LONG_FAST, EMA_LONG_SLOW)
|
||||
volume_ma = build_volume_ma(klines_5m)
|
||||
curr_idx = len(klines_5m) - 1
|
||||
direction, trigger_price, _, _ = check_trigger(
|
||||
klines_5m, curr_idx, atr_5m, ema_5m, ema_15m_align, ema_60m_align, volume_ma, use_confirm=True
|
||||
)
|
||||
curr = klines_5m[curr_idx]
|
||||
curr_id = curr["id"]
|
||||
if direction is None:
|
||||
return None, None, None
|
||||
if self.last_trigger_kline_id == curr_id and self.last_trigger_direction == direction:
|
||||
return None, None, None
|
||||
if self.last_trade_kline_id == curr_id:
|
||||
return None, None, None
|
||||
if (direction == "long" and self.position_direction == 1) or (direction == "short" and self.position_direction == -1):
|
||||
return None, None, None
|
||||
return direction, trigger_price, curr
|
||||
|
||||
def run_loop(self, ding_callback=None):
|
||||
"""
|
||||
主循环：拉数据 -> 检测信号 -> 若需交易则调用开平仓（需外部实现或注入）。
|
||||
ding_callback(msg, error=False) 可选，用于钉钉通知。
|
||||
"""
|
||||
def ding(msg: str, error: bool = False):
|
||||
if ding_callback:
|
||||
ding_callback(msg, error)
|
||||
else:
|
||||
if error:
|
||||
logger.error(msg)
|
||||
else:
|
||||
logger.info(msg)
|
||||
|
||||
logger.info("自适应三分位趋势策略实盘启动，按 Bitmart 方式拉取 5/15/60 分钟数据")
|
||||
while True:
|
||||
try:
|
||||
k5, k15, k60 = self.get_klines_multi()
|
||||
if len(k5) < ATR_PERIOD + 2:
|
||||
time.sleep(self.check_interval)
|
||||
continue
|
||||
if not self.get_position_status():
|
||||
time.sleep(self.check_interval)
|
||||
continue
|
||||
direction, trigger_price, curr = self.check_realtime_signal(k5, k15, k60)
|
||||
if direction and trigger_price is not None and curr is not None:
|
||||
curr_id = curr["id"]
|
||||
ding(f"信号: {direction} @ {trigger_price:.2f} 当前K线 id={curr_id}")
|
||||
# 这里接入你的开平仓实现：平仓 + 开单（可调用 交易/bitmart-三分之一策略交易 的 平仓/开单 或 API 下单）
|
||||
# 示例：仅记录，不实际下单
|
||||
self.last_trigger_kline_id = curr_id
|
||||
self.last_trigger_direction = direction
|
||||
self.last_trade_kline_id = curr_id
|
||||
time.sleep(self.check_interval)
|
||||
except Exception as e:
|
||||
logger.exception(e)
|
||||
time.sleep(60)
|
||||
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser(description="自适应三分位趋势策略实盘")
|
||||
parser.add_argument("--api-key", default="", help="Bitmart API Key")
|
||||
parser.add_argument("--secret-key", default="", help="Bitmart Secret Key")
|
||||
parser.add_argument("--bit-id", default="", help="比特浏览器 ID（若用浏览器下单）")
|
||||
args = parser.parse_args()
|
||||
api_key = args.api_key or os.environ.get("BITMART_API_KEY", "a0fb7b98464fd9bcce67e7c519d58ec10d0c38a8")
|
||||
secret_key = args.secret_key or os.environ.get("BITMART_SECRET_KEY", "4eaeba78e77aeaab1c2027f846a276d164f264a44c2c1bb1c5f3be50c8de1ca5")
|
||||
trader = AdaptiveThirdStrategyTrader(api_key, secret_key, bit_id=args.bit_id or None)
|
||||
try:
|
||||
from 交易.tools import send_dingtalk_message
|
||||
def ding(msg, error=False):
|
||||
prefix = "❌自适应三分位：" if error else "🔔自适应三分位："
|
||||
send_dingtalk_message(f"{prefix}{msg}")
|
||||
except Exception:
|
||||
def ding(msg, error=False):
|
||||
logger.info(msg)
|
||||
trader.run_loop(ding_callback=ding)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user