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卡尔曼策略新增md文件

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2025-11-07 16:37:16 +08:00
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futures_trading_strategies/MA/ReversalVolatilityStrategy/ReversalVolatilityStrategy.py Normal file
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# src/strategies/ReversalVolatilityStrategy.py
import numpy as np
import talib
from typing import Optional, Dict, Any, List, Tuple
from src.core_data import Bar, Order
from src.indicators.indicators import StochasticOscillator, RSI
from src.strategies.base_strategy import Strategy
# def calculate_atr(bars: List[Bar], period: int) -> float:
# """
# 从Bar对象列表中计算平均真实波幅(ATR)。
# """
# if len(bars) < period + 1:
# return 0.0
# true_ranges = []
# for i in range(1, len(bars)):
# high_low = bars[i].high - bars[i].low
# high_prev_close = abs(bars[i].high - bars[i - 1].close)
# low_prev_close = abs(bars[i].low - bars[i - 1].close)
# tr = max(high_low, high_prev_close, low_prev_close)
# true_ranges.append(tr)
# return np.mean(true_ranges[-(period):])
def find_fvg(bars: List[Bar]) -> Optional[Tuple[str, float, float]]:
"""
使用最近的三根K线识别公允价值缺口(FVG)。
返回:
一个元组 (方向, 上沿价格, 下沿价格) 或 None (如果没有找到FVG)。
'bullish': K线1的最高价和K线3的最低价之间的缺口。
'bearish': K线1的最低价和K线3的最高价之间的缺口。
"""
if len(bars) < 3:
return None
bar1, bar2, bar3 = bars[-3], bars[-2], bars[-1]
# 检查看涨FVG (向上的失衡)
if bar1.high < bar3.low:
# 缺口本身就意味着中间的K线是强劲的
return ('bullish', bar3.low, bar1.high)
# 检查看跌FVG (向下的失衡)
if bar1.low > bar3.high:
return ('bearish', bar1.low, bar3.high)
return None
class ReversalVolatilityStrategy(Strategy):
"""
一个反转波动率策略，该策略在价格回调至公允价值缺口(FVG)时入场，
并使用基于ATR的两阶段止盈系统，其中第二阶段为ATR回撤止盈。
"""
def __init__(
self,
context: Any,
main_symbol: str,
enable_log: bool,
trade_volume: int,
atr_period: int = 14,
stop_loss_atr_multiplier: float = 1.0,
partial_profit_atr_multiplier: float | List[float] = 2.0,
# full_profit_atr_multiplier: float = 4.0, # 移除固定止盈乘数
trailing_stop_atr_multiplier: float = 1.0, # 新增：回撤止盈ATR乘数
open_atr_multiplier: float | List[float] = 1.0,
order_direction=None,
indicators=[None, None],
):
"""
Args:
context: 回测上下文。
symbol (str): 主要交易的合约代码。
trade_volume (int): 每笔交易的总手数。必须是偶数。
atr_period (int): 计算ATR的回看周期。
stop_loss_atr_multiplier (float): ATR的乘数，用于设置止损。
partial_profit_atr_multiplier (float): ATR的乘数，用于设置第一止盈目标。
trailing_stop_atr_multiplier (float): ATR的乘数，用于设置最大回撤止盈。
"""
super().__init__(context, main_symbol, enable_log)
if order_direction is None:
order_direction = ['BUY', 'SELL', ]
# if trade_volume % 2 != 0:
# raise ValueError("为了实现部分止盈, trade_volume必须是偶数。")
self.trade_volume = trade_volume
self.atr_period = atr_period
self.stop_loss_atr_multiplier = stop_loss_atr_multiplier
if isinstance(partial_profit_atr_multiplier, float) or isinstance(partial_profit_atr_multiplier, int):
self.partial_profit_atr_multiplier = [partial_profit_atr_multiplier, partial_profit_atr_multiplier]
elif isinstance(partial_profit_atr_multiplier, list):
self.partial_profit_atr_multiplier = partial_profit_atr_multiplier
self.trailing_stop_atr_multiplier = trailing_stop_atr_multiplier
if isinstance(open_atr_multiplier, float) or isinstance(open_atr_multiplier, int):
self.open_atr_multiplier = [open_atr_multiplier, open_atr_multiplier]
elif isinstance(open_atr_multiplier, list):
self.open_atr_multiplier = open_atr_multiplier # 初始化新增参数
self.order_direction = order_direction
self.indicator_long = indicators[0]
self.indicator_short = indicators[1]
self.main_symbol = main_symbol
self.order_id_counter = 0
self._last_order_id: Optional[str] = None
# 字典，用于存储交易的元数据，如入场价、入场时的ATR和最高/最低价
self.position_meta: Dict[str, Any] = {}
self.log(
"ReversalVolatilityStrategy 初始化参数: \n"
f"交易量={self.trade_volume}, ATR周期={self.atr_period}, \n"
f"止损ATR乘数={self.stop_loss_atr_multiplier}, 部分止盈ATR乘数={self.partial_profit_atr_multiplier}, \n"
f"回撤止盈ATR乘数={self.trailing_stop_atr_multiplier}" # 更新日志信息
)
def on_init(self):
super().on_init()
self.cancel_all_pending_orders(self.main_symbol)
def on_open_bar(self, open_price: float, symbol: str):
self.symbol = symbol
current_time = self.get_current_time()
# --- 1. 取消上一根K线未成交的限价单 ---
if self._last_order_id and self._last_order_id in self.get_pending_orders():
self.cancel_order(self._last_order_id)
self.log(f"已取消上一根K线的挂单: {self._last_order_id}")
self._last_order_id = None
bar_history = self.get_bar_history()
if len(bar_history) < self.atr_period + 3:
return # 数据不足，无法计算指标
# --- 2. 计算指标 ---
# current_atr = calculate_atr(bar_history, self.atr_period)
current_atr = talib.ATR(np.array([bar.high for bar in bar_history]),
np.array([bar.low for bar in bar_history]),
np.array([bar.close for bar in bar_history]),
timeperiod=self.atr_period)[-1]
if current_atr == 0:
return # 避免除以零或在不活跃的市场中交易
fvg_signal = find_fvg(bar_history)
# --- 3. 管理现有持仓 (平仓逻辑) ---
current_positions = self.get_current_positions()
position_volume = current_positions.get(self.symbol, 0)
meta = self.position_meta.get(self.symbol)
if position_volume != 0 and meta:
# 传递当前Bar的最高价和最低价用于更新最优价格
current_bar_high = bar_history[-1].high
current_bar_low = bar_history[-1].low
self.manage_open_position(position_volume, open_price, current_bar_high, current_bar_low, meta, current_atr)
return # 如果正在管理平仓，则不评估进场信号
# --- 4. 评估新机会 (开仓逻辑) ---
if position_volume == 0 and fvg_signal:
self.evaluate_entry_signal(fvg_signal, open_price, current_atr)
def manage_open_position(self, volume: int, current_price: float, current_bar_high: float, current_bar_low: float,
meta: Dict, current_atr: float):
entry_price = meta['entry_price']
entry_atr = meta['entry_atr']
partially_exited = meta.get('partially_exited', False)
optimal_price = meta.get('optimal_price', entry_price) # 初始化或获取最优价格
# 定义基于入场时ATR的止损目标价格
# stop_loss_price = entry_price - entry_atr * self.stop_loss_atr_multiplier if volume > 0 else entry_price + entry_atr * self.stop_loss_atr_multiplier
stop_loss_price = entry_price - 5 if volume > 0 else entry_price + 5
partial_profit_atr_multiplier = self.partial_profit_atr_multiplier[0] if volume > 0 else \
self.partial_profit_atr_multiplier[1]
partial_tp_price = entry_price + entry_atr * partial_profit_atr_multiplier if volume > 0 else entry_price - entry_atr * partial_profit_atr_multiplier
# 检查多头持仓
if volume > 0:
# 更新最优价格 (最高价)
meta['optimal_price'] = max(optimal_price, current_bar_high)
optimal_price = meta['optimal_price']
# # 最大回撤止盈 (只在部分止盈后激活)
# if partially_exited:
# trailing_stop_level = optimal_price - self.trailing_stop_atr_multiplier * current_atr
# if current_price <= trailing_stop_level:
# self.log(f"多头回撤止盈到达 {current_price:.2f}. 关闭剩余仓位。")
# self.close_position("CLOSE_LONG", abs(volume))
# 部分止盈
if current_price >= partial_tp_price and not partially_exited:
self.log(f"多头部分止盈到达 {current_price:.2f}. 关闭仓位。")
# self.send_market_order("CLOSE_LONG", self.trade_volume)
self.close_position("CLOSE_LONG", abs(volume))
meta['partially_exited'] = True
# 首次触发部分止盈时，将当前价格设置为最优价格，作为回撤止盈的起点
meta['optimal_price'] = current_price
# 止损
elif current_price <= stop_loss_price:
self.log(f"多头止损到达 {current_price:.2f}. 关闭全部仓位。")
self.close_position("CLOSE_LONG", abs(volume))
# 检查空头持仓
elif volume < 0:
# 更新最优价格 (最低价)
meta['optimal_price'] = min(optimal_price, current_bar_low)
optimal_price = meta['optimal_price']
# # 最大回撤止盈 (只在部分止盈后激活)
# if partially_exited:
# trailing_stop_level = optimal_price + self.trailing_stop_atr_multiplier * current_atr
# if current_price >= trailing_stop_level:
# self.log(f"空头回撤止盈到达 {current_price:.2f}. 关闭剩余仓位。")
# self.close_position("CLOSE_SHORT", abs(volume))
# 部分止盈
if current_price <= partial_tp_price and not partially_exited:
self.log(f"空头部分止盈到达 {current_price:.2f}. 关闭一半仓位。")
# self.send_market_order("CLOSE_SHORT", self.trade_volume)
self.close_position("CLOSE_SHORT", abs(volume))
meta['partially_exited'] = True
# 首次触发部分止盈时，将当前价格设置为最优价格，作为回撤止盈的起点
meta['optimal_price'] = current_price
# 止损
elif current_price >= stop_loss_price:
self.log(f"空头止损到达 {current_price:.2f}. 关闭全部仓位。")
self.close_position("CLOSE_SHORT", abs(volume))
def evaluate_entry_signal(self, fvg_signal, open_price, current_atr):
direction, fvg_high, fvg_low = fvg_signal
open_atr_multiplier = self.open_atr_multiplier[0] if 'bullish' == direction else self.open_atr_multiplier[1]
# 多头入场信号
if 'bullish' == direction:
# 在FVG上沿挂限价单，等待价格回调
if self.indicator_long is None or self.indicator_long.is_condition_met(*self.get_indicator_tuple()):
limit_price = fvg_low + open_atr_multiplier * current_atr
# 避免追高
if open_price > limit_price:
self.log(f"检测到看涨FVG。在 {limit_price:.2f} 挂限价买单。")
new_order = self.send_limit_order("BUY", limit_price, self.trade_volume, {'entry_atr': current_atr})
if new_order:
self._last_order_id = new_order.id
# 空头入场信号
elif 'bearish' == direction:
# 在FVG下沿挂限价单
if self.indicator_short is None or self.indicator_short.is_condition_met(*self.get_indicator_tuple()):
limit_price = fvg_high - open_atr_multiplier * current_atr
# 避免追低
if open_price < limit_price:
self.log(f"检测到看跌FVG。在 {limit_price:.2f} 挂限价卖单。")
new_order = self.send_limit_order("SELL", limit_price, self.trade_volume, {'entry_atr': current_atr})
if new_order:
self._last_order_id = new_order.id
def close_position(self, direction: str, volume: int):
self.send_market_order(direction, volume)
if self.symbol in self.position_meta:
del self.position_meta[self.symbol] # 完全平仓后清理元数据
def send_limit_order(self, direction: str, limit_price: float, volume: int, meta: Dict) -> Optional[Order]:
if direction not in self.order_direction:
return None
order_id = f"{self.symbol}_{direction}_{self.get_current_time().strftime('%Y%m%d%H%M%S')}_{self.order_id_counter}"
self.order_id_counter += 1
order = Order(
id=order_id,
symbol=self.symbol,
direction=direction,
volume=volume,
price_type="LIMIT",
limit_price=limit_price,
submitted_time=self.get_current_time(),
offset="OPEN",
)
# 存储开仓时的元数据，包括入场价和入场时的ATR
self.position_meta[self.symbol] = {
"entry_price": limit_price,
"entry_atr": meta.get('entry_atr', 0),
"partially_exited": False,
"optimal_price": limit_price # 初始最优价格设置为入场价格
}
entry_price = limit_price
entry_atr = meta.get('entry_atr', 0)
stop_loss_price = entry_price - 5 if direction == "BUY" else entry_price + 5
partial_profit_atr_multiplier = self.partial_profit_atr_multiplier[0] if volume > 0 else \
self.partial_profit_atr_multiplier[1]
partial_tp_price = entry_price + entry_atr * partial_profit_atr_multiplier if volume > 0 else entry_price - entry_atr * partial_profit_atr_multiplier
self.log(
f'atr: {meta.get("entry_atr", 0)}, stop_loss_price: {stop_loss_price}, partial_tp_price: {partial_tp_price}')
return self.send_order(order)
def send_market_order(self, direction: str, volume: int):
order_id = f"{self.symbol}_{direction}_{self.get_current_time().strftime('%Y%m%d%H%M%S')}_{self.order_id_counter}"
self.order_id_counter += 1
order = Order(
id=order_id,
symbol=self.symbol,
direction=direction,
volume=volume,
price_type="MARKET",
submitted_time=self.get_current_time(),
offset="CLOSE" # 在本策略中，所有市价单都是为了平仓
)
self.send_order(order)
def on_rollover(self, old_symbol: str, new_symbol: str):
super().on_rollover(old_symbol, new_symbol)
self._last_order_id = None
self.position_meta = {} # 换月时清空所有元数据
self.log("检测到换月。已清空挂单和持仓元数据。")
def on_close_bar(self, bar: Bar, next_bar_open: Optional[float] = None):
self.cancel_all_pending_orders(self.main_symbol)

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import numpy as np
import talib
from typing import Optional, Dict, Any, List, Tuple, Union
from src.algo.TrendLine import calculate_latest_trendline_values
# 假设这些是你项目中的基础模块
from src.core_data import Bar, Order
from src.indicators.base_indicators import Indicator
from src.indicators.indicators import Empty
from src.strategies.base_strategy import Strategy
class TrendlineBreakoutStrategy(Strategy):
"""
趋势线突破策略 V3 (优化版):
1. 策略逻辑与 V2 相同，但趋势线计算被重构为一个独立的、
高性能的辅助方法。
2. 该方法只计算最新的趋势线值，避免不必要的数组生成。
开仓信号:
- 做多: 上一根收盘价上穿下趋势线
- 做空: 上一根收盘价下穿上趋势线
平仓逻辑:
- 采用 ATR 滑动止损 (Trailing Stop)。
"""
def __init__(
self,
context: Any,
main_symbol: str,
trendline_n: int = 50,
trade_volume: int = 1,
order_direction: Optional[List[str]] = None,
atr_period: int = 14,
atr_multiplier: float = 1.0,
enable_log: bool = True,
indicators: Union[Indicator, List[Indicator]] = None,
):
super().__init__(context, main_symbol, enable_log)
self.main_symbol = main_symbol
self.trendline_n = trendline_n
self.trade_volume = trade_volume
self.order_direction = order_direction or ["BUY", "SELL"]
self.atr_period = atr_period
self.atr_multiplier = atr_multiplier
self.pos_meta: Dict[str, Dict[str, Any]] = {}
if indicators is None:
indicators = [Empty(), Empty()]
self.indicators = indicators
if self.trendline_n < 3:
raise ValueError("trendline_n 必须大于或等于 3")
log_message = (
f"TrendlineBreakoutStrategy (V3 Optimized) 初始化:\n"
f"交易标的={self.main_symbol}, 交易量={self.trade_volume}\n"
f"趋势线周期={self.trendline_n}, ATR周期={self.atr_period}, ATR倍数={self.atr_multiplier}"
)
self.log(log_message)
def _calculate_atr(self, bar_history: List[Bar]) -> Optional[float]:
# (此函数与上一版本完全相同，保持不变)
if len(bar_history) < self.atr_period + 1: return None
highs = np.array([b.high for b in bar_history])
lows = np.array([b.low for b in bar_history])
closes = np.array([b.close for b in bar_history])
atr = talib.ATR(highs, lows, closes, timeperiod=self.atr_period)
return atr[-1] if not np.isnan(atr[-1]) else None
def on_init(self):
super().on_init()
self.pos_meta.clear()
def on_open_bar(self, open_price: float, symbol: str):
bar_history = self.get_bar_history()
min_bars_required = self.trendline_n + 2
if len(bar_history) < min_bars_required:
return
self.cancel_all_pending_orders(symbol)
pos = self.get_current_positions().get(symbol, 0)
# 1. 优先处理平仓逻辑 (逻辑不变)
meta = self.pos_meta.get(symbol)
if meta and pos != 0:
current_atr = self._calculate_atr(bar_history[:-1])
if current_atr:
trailing_stop = meta['trailing_stop']
direction = meta['direction']
last_close = bar_history[-1].close
if direction == "BUY":
new_stop_level = last_close - current_atr * self.atr_multiplier
trailing_stop = max(trailing_stop, new_stop_level)
else: # SELL
new_stop_level = last_close + current_atr * self.atr_multiplier
trailing_stop = min(trailing_stop, new_stop_level)
self.pos_meta[symbol]['trailing_stop'] = trailing_stop
if (direction == "BUY" and open_price <= trailing_stop) or \
(direction == "SELL" and open_price >= trailing_stop):
self.log(f"ATR滑动止损触发: 价格 {open_price:.2f} 触及止损位 {trailing_stop:.2f}")
self.send_market_order("CLOSE_LONG" if direction == "BUY" else "CLOSE_SHORT", abs(pos))
del self.pos_meta[symbol]
return
# 2. 开仓逻辑 (调用优化后的方法)
if pos == 0:
prices_for_trendline = np.array([b.close for b in bar_history[-self.trendline_n - 1:-1]])
# --- 调用新的独立方法 ---
trendline_val_upper, trendline_val_lower = calculate_latest_trendline_values(prices_for_trendline)
if trendline_val_upper is None or trendline_val_lower is None:
return # 无法计算趋势线，跳过
prev_close = bar_history[-2].close
last_close = bar_history[-1].close
current_atr = self._calculate_atr(bar_history[:-1])
if not current_atr:
return
# if "BUY" in self.order_direction and last_close > trendline_val_upper and self.indicators[0].is_condition_met(*self.get_indicator_tuple()):
# self.log(f"做多信号: Close({last_close:.2f}) 上穿下趋势线({trendline_val_upper:.2f})")
# self.send_open_order("BUY", open_price, self.trade_volume, current_atr)
#
# elif "SELL" in self.order_direction and last_close < trendline_val_lower and self.indicators[1].is_condition_met(*self.get_indicator_tuple()):
# self.log(f"做空信号: Close({last_close:.2f}) 下穿上趋势线({trendline_val_lower:.2f})")
# self.send_open_order("SELL", open_price, self.trade_volume, current_atr)
if "BUY" in self.order_direction and last_close > trendline_val_upper and self.indicators[0].is_condition_met(*self.get_indicator_tuple()):
self.log(f"做多信号: Close({last_close:.2f}) 上穿下趋势线({trendline_val_upper:.2f})")
self.send_open_order("BUY", open_price, self.trade_volume, current_atr)
elif "SELL" in self.order_direction and last_close < trendline_val_lower and self.indicators[1].is_condition_met(*self.get_indicator_tuple()):
self.log(f"做空信号: Close({last_close:.2f}) 下穿上趋势线({trendline_val_lower:.2f})")
self.send_open_order("SELL", open_price, self.trade_volume, current_atr)
# send_open_order, send_market_order, on_rollover 等方法与上一版本完全相同，保持不变
def send_open_order(self, direction: str, entry_price: float, volume: int, current_atr: float):
if direction == "BUY":
initial_stop = entry_price - current_atr * self.atr_multiplier
else:
initial_stop = entry_price + current_atr * self.atr_multiplier
current_time = self.get_current_time()
order_id = f"{self.symbol}_{direction}_{current_time.strftime('%Y%m%d%H%M%S')}"
order_direction = "BUY" if direction == "BUY" else "SELL"
order = Order(id=order_id, symbol=self.symbol, direction=order_direction, volume=volume, price_type="MARKET",
submitted_time=current_time, offset="OPEN")
self.send_order(order)
self.pos_meta[self.symbol] = {"direction": direction, "volume": volume, "entry_price": entry_price,
"trailing_stop": initial_stop}
self.log(
f"发送开仓订单: {direction} {volume}手 @ Market Price (执行价约 {entry_price:.2f}), 初始ATR止损位: {initial_stop:.2f}")
def send_market_order(self, direction: str, volume: int):
current_time = self.get_current_time()
order_id = f"{self.symbol}_{direction}_{current_time.strftime('%Y%m%d%H%M%S')}"
order = Order(id=order_id, symbol=self.symbol, direction=direction, volume=volume, price_type="MARKET",
submitted_time=current_time, offset="CLOSE")
self.send_order(order)
self.log(f"发送平仓订单: {direction} {volume}手 @ Market Price")
def on_rollover(self, old_symbol: str, new_symbol: str):
super().on_rollover(old_symbol, new_symbol)
self.cancel_all_pending_orders(new_symbol)
self.pos_meta.clear()

2508
futures_trading_strategies/MA/TrendlineBreakoutStrategy/TrendlineBreakoutStrategy_ML.ipynb Normal file
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226
futures_trading_strategies/MA/TrendlineBreakoutStrategy/TrendlineHawkesStrategy.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"id": "522f09ca7b3fe929",
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-21T13:27:14.609968Z",
"start_time": "2025-10-21T13:27:14.180365Z"
}
},
"source": [
"from datetime import datetime\n",
"\n",
"from src.data_processing import load_raw_data\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import sys\n",
"\n",
"if '/mnt/d/PyProject/NewQuant/' not in sys.path:\n",
" sys.path.append('/mnt/d/PyProject/NewQuant/')"
],
"outputs": [],
"execution_count": 1
},
{
"cell_type": "code",
"id": "4f7e4b438cea750e",
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-21T13:27:14.989537Z",
"start_time": "2025-10-21T13:27:14.615874Z"
}
},
"source": [
"from turtle import down\n",
"from src.analysis.result_analyzer import ResultAnalyzer\n",
"# 导入所有必要的模块\n",
"from src.data_manager import DataManager\n",
"from src.backtest_engine import BacktestEngine\n",
"from src.indicators.indicator_list import INDICATOR_LIST\n",
"from src.indicators.indicators import *\n",
"\n",
"# 导入您自己的 SMC 策略\n",
"from src.strategies.TrendlineBreakoutStrategy.TrendlineHawkesStrategyFast import TrendlineHawkesStrategy\n",
"\n",
"# --- 配置参数 ---\n",
"# 获取当前脚本所在目录，假设数据文件在项目根目录下的 data 文件夹内\n",
"data_file_path = '/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min15.csv'\n"
],
"outputs": [],
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-21T13:27:15.060902Z",
"start_time": "2025-10-21T13:27:14.996119Z"
}
},
"cell_type": "code",
"source": [
"\n",
"initial_capital = 100000.0\n",
"slippage_rate = 0.000 # 假设每笔交易0.1%的滑点\n",
"commission_rate = 0.0000 # 假设每笔交易0.02%的佣金\n",
"\n",
"global_config = {\n",
" 'symbol': 'KQ_m@CZCE_MA', # 确保与数据文件中的 symbol 匹配\n",
"}\n",
"\n",
"# 回测时间范围\n",
"start_time = datetime(2021, 1, 1)\n",
"end_time = datetime(2024, 6, 1)\n",
"\n",
"start_time = datetime(2024, 1, 1)\n",
"end_time = datetime(2025, 8, 1)\n",
"\n",
"\n",
"indicators = INDICATOR_LIST\n",
"indicators = []\n",
"\n",
"# 确保 DataManager 能够重置以进行多次回测\n",
"# data_manager.reset() # 首次运行不需要重置"
],
"id": "9ee53c41eaaefabb",
"outputs": [],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-21T13:27:21.876001Z",
"start_time": "2025-10-21T13:27:15.070471Z"
}
},
"cell_type": "code",
"source": [
"from src.indicators.indicators import ROC_MA\n",
"\n",
"# --- 1. 初始化数据管理器 ---\n",
"print(\"初始化数据管理器...\")\n",
"data_manager = DataManager(file_path=data_file_path, symbol=global_config['symbol'], start_time=start_time,\n",
" end_time=end_time)\n",
"\n",
"strategy_parameters = {\n",
" 'main_symbol': 'MA', # <-- 替换为你的交易品种代码，例如 'GC=F' (黄金期货), 'ZC=F' (玉米期货)\n",
" 'trade_volume': 1,\n",
" 'trendline_n': 70,\n",
" 'hawkes_kappa': 0.1,\n",
" 'order_direction': ['SELL', 'BUY'],\n",
" 'reverse_logic': True,\n",
" # 'indicators': [RateOfChange(10, -2.1, -0.5), ROC_MA(10, 10, -2.7, -0.4)],\n",
" 'enable_log': False\n",
"}\n",
"\n",
"\n",
"\n",
"# --- 2. 初始化回测引擎并运行 ---\n",
"print(\"\\n初始化回测引擎...\")\n",
"engine = BacktestEngine(\n",
" data_manager=data_manager,\n",
" strategy_class=TrendlineHawkesStrategy, # <--- 更改为您的 SMC 策略类\n",
" # current_segment_symbol 参数已从 SMCPureH1LongStrategy 中移除，不需要设置\n",
" strategy_params=strategy_parameters,\n",
" initial_capital=initial_capital,\n",
" slippage_rate=slippage_rate,\n",
" commission_rate=commission_rate,\n",
" roll_over_mode=True,\n",
" start_time=start_time,\n",
" end_time=end_time,\n",
" indicators=indicators # 如果您的 SMC 策略不使用这些指标，也可以考虑移除\n",
")\n",
"\n",
"print(\"\\n开始运行回测...\")\n",
"engine.run_backtest()\n",
"print(\"\\n回测运行完毕。\")\n",
"\n",
"# --- 3. 获取回测结果 ---\n",
"results = engine.get_backtest_results()\n",
"portfolio_snapshots = results[\"portfolio_snapshots\"]\n",
"trade_history = results[\"trade_history\"]\n",
"initial_capital_result = results[\"initial_capital\"]\n",
"bars = results[\"all_bars\"]\n",
"\n",
"# --- 4. 结果分析与可视化 ---\n",
"if portfolio_snapshots:\n",
" analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, bars, initial_capital_result, INDICATOR_LIST)\n",
"\n",
" analyzer.generate_report()\n",
" analyzer.plot_performance()\n",
" metrics = analyzer.calculate_all_metrics()\n",
" print(metrics)\n",
"\n",
" analyzer.analyze_indicators()\n",
"else:\n",
" print(\"\\n没有生成投资组合快照，无法进行结果分析。\")"
],
"id": "f903fd2761d446cd",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"初始化数据管理器...\n",
"数据加载成功: /mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min15.csv\n",
"数据范围从 2020-12-31 14:45:00 到 2025-08-21 14:30:00\n",
"总计 25596 条记录。\n",
"\n",
"初始化回测引擎...\n",
"模拟器初始化：初始资金=100000.00, 滑点率=0.0, 佣金率=0.0\n",
"内存仓储已初始化，管理ID: 'src.strategies.TrendlineBreakoutStrategy.TrendlineHawkesStrategyFast.TrendlineHawkesStrategy_13b1be9c188912b2ee8ccd9e5fb0718d'\n",
"\n",
"--- 回测引擎初始化完成 ---\n",
" 策略: TrendlineHawkesStrategy\n",
" 初始资金: 100000.00\n",
" 换月模式: 启用\n",
"\n",
"开始运行回测...\n",
"\n",
"--- 回测开始 ---\n",
"TrendlineHawkesStrategy 策略初始化回调被调用。\n",
"开始将 DataFrame 转换为 Bar 对象流...\n",
"首次运行，正在初始化霍克斯状态和滚动窗口...\n",
"状态初始化完成。\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mKeyboardInterrupt\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[4]\u001B[39m\u001B[32m, line 38\u001B[39m\n\u001B[32m 23\u001B[39m engine = BacktestEngine(\n\u001B[32m 24\u001B[39m data_manager=data_manager,\n\u001B[32m 25\u001B[39m strategy_class=TrendlineHawkesStrategy, \u001B[38;5;66;03m# <--- 更改为您的 SMC 策略类\u001B[39;00m\n\u001B[32m (...)\u001B[39m\u001B[32m 34\u001B[39m indicators=indicators \u001B[38;5;66;03m# 如果您的 SMC 策略不使用这些指标，也可以考虑移除\u001B[39;00m\n\u001B[32m 35\u001B[39m )\n\u001B[32m 37\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[33m开始运行回测...\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m38\u001B[39m \u001B[43mengine\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrun_backtest\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 39\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[33m回测运行完毕。\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 41\u001B[39m \u001B[38;5;66;03m# --- 3. 获取回测结果 ---\u001B[39;00m\n",
"\u001B[36mFile \u001B[39m\u001B[32m/mnt/d/PyProject/NewQuant/src/backtest_engine.py:166\u001B[39m, in \u001B[36mBacktestEngine.run_backtest\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m 163\u001B[39m \u001B[38;5;28mself\u001B[39m.strategy.on_open_bar(current_bar.open, current_bar.symbol)\n\u001B[32m 165\u001B[39m current_indicator_dict = {}\n\u001B[32m--> \u001B[39m\u001B[32m166\u001B[39m close_array = \u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43marray\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mclose_list\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 167\u001B[39m open_array = np.array(\u001B[38;5;28mself\u001B[39m.open_list)\n\u001B[32m 168\u001B[39m high_array = np.array(\u001B[38;5;28mself\u001B[39m.high_list)\n",
"\u001B[31mKeyboardInterrupt\u001B[39m: "
]
}
],
"execution_count": 4
}
],
"metadata": {
"kernelspec": {
"display_name": "quant",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

178
futures_trading_strategies/MA/TrendlineBreakoutStrategy/TrendlineHawkesStrategy.py Normal file
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@@ -0,0 +1,178 @@
import numpy as np
import pandas as pd
from typing import Optional, Dict, Any, List
# 假设这些是你项目中的模块
from src.core_data import Bar, Order
from src.strategies.base_strategy import Strategy
from src.algo.TrendLine import calculate_latest_trendline_values
from src.algo.HawksProcess import calculate_hawkes_bands
class TrendlineHawkesStrategy(Strategy):
"""
趋势线与霍克斯过程双重确认策略 (V2 - 支持逻辑反转):
入场信号 (双重确认):
1. 趋势线事件: 收盘价突破上轨(标准模式做多)或下轨(标准模式做空)。
2. 霍克斯确认: 同时，成交量霍克斯强度必须高于其近期高位分位数。
出场逻辑 (基于霍克斯过程):
- 当成交量霍克斯强度从高位回落至近期低位分位数以下时，平仓。
逻辑反转 (`reverse_logic=True`):
- 趋势线突破上轨时，开【空】仓。
- 趋势线突破下轨时，开【多】仓。
"""
def __init__(
self,
context: Any,
main_symbol: str,
trade_volume: int = 1,
order_direction: Optional[List[str]] = None,
# --- 新增: 逻辑反转开关 ---
reverse_logic: bool = False,
# --- 趋势线参数 ---
trendline_n: int = 50,
# --- 霍克斯过程参数 ---
hawkes_kappa: float = 0.1,
hawkes_lookback: int = 50,
hawkes_entry_percent: float = 0.95,
hawkes_exit_percent: float = 0.50,
enable_log: bool = True,
):
super().__init__(context, main_symbol, enable_log)
self.main_symbol = main_symbol
self.trade_volume = trade_volume
self.order_direction = order_direction or ["BUY", "SELL"]
# --- 新增 ---
self.reverse_logic = reverse_logic
self.trendline_n = trendline_n
self.hawkes_kappa = hawkes_kappa
self.hawkes_lookback = hawkes_lookback
self.hawkes_entry_percent = hawkes_entry_percent
self.hawkes_exit_percent = hawkes_exit_percent
self.pos_meta: Dict[str, Dict[str, Any]] = {}
if self.trendline_n < 3:
raise ValueError("trendline_n 必须大于或等于 3")
log_message = (
f"TrendlineHawkesStrategy 初始化:\n"
f"【逻辑模式】: {'反转 (Reversal)' if self.reverse_logic else '标准 (Breakout)'}\n"
f"趋势线周期={self.trendline_n}\n"
f"霍克斯参数: kappa={self.hawkes_kappa}, lookback={self.hawkes_lookback}, "
f"entry_pct={self.hawkes_entry_percent}, exit_pct={self.hawkes_exit_percent}"
)
self.log(log_message)
def on_init(self):
# (此函数保持不变)
super().on_init()
self.pos_meta.clear()
def on_open_bar(self, open_price: float, symbol: str):
bar_history = self.get_bar_history()
min_bars_required = max(self.trendline_n + 2, self.hawkes_lookback + 2)
if len(bar_history) < min_bars_required:
return
self.cancel_all_pending_orders(symbol)
pos = self.get_current_positions().get(symbol, 0)
# --- 数据准备 (与之前相同) ---
close_prices = np.array([b.close for b in bar_history])
volume_series = pd.Series(
[b.volume for b in bar_history],
index=pd.to_datetime([b.datetime for b in bar_history])
)
vol_hawkes, hawkes_upper_band, hawkes_lower_band = calculate_hawkes_bands(
volume_series, self.hawkes_lookback, self.hawkes_kappa,
self.hawkes_entry_percent, self.hawkes_exit_percent
)
latest_hawkes_value = vol_hawkes.iloc[-1]
latest_hawkes_upper = hawkes_upper_band.iloc[-1]
latest_hawkes_lower = hawkes_lower_band.iloc[-1]
# 1. 优先处理平仓逻辑 (逻辑保持不变)
meta = self.pos_meta.get(symbol)
if meta and pos != 0:
if latest_hawkes_value < latest_hawkes_lower:
self.log(f"霍克斯出场信号: 强度({latest_hawkes_value:.2f}) < 阈值({latest_hawkes_lower:.2f})")
self.send_market_order("CLOSE_LONG" if meta['direction'] == "BUY" else "CLOSE_SHORT", abs(pos))
del self.pos_meta[symbol]
return
# 2. 开仓逻辑 (加入反转判断)
if pos == 0:
prices_for_trendline = close_prices[-self.trendline_n - 1:-1]
trend_upper, trend_lower = calculate_latest_trendline_values(prices_for_trendline)
if trend_upper is None or trend_lower is None:
return
prev_close = bar_history[-2].close
last_close = bar_history[-1].close
# --- a) 定义基础的突破【事件】 ---
upper_break_event = last_close > trend_upper and prev_close < trend_upper
lower_break_event = last_close < trend_lower and prev_close > trend_lower
# --- b) 定义霍克斯【确认】---
hawkes_confirmation = latest_hawkes_value > latest_hawkes_upper
# 只有当基础事件和霍克斯确认都发生时，才考虑开仓
if hawkes_confirmation and (upper_break_event or lower_break_event):
# --- c) 【核心修改】根据 reverse_logic 决定最终交易方向 ---
trade_direction = None
if upper_break_event: # 价格向上突破上轨
# 标准模式：做多 (动量)
# 反转模式：做空 (力竭反转)
trade_direction = "SELL" if self.reverse_logic else "BUY"
elif lower_break_event: # 价格向下突破下轨
# 标准模式：做空 (动量)
# 反转模式：做多 (恐慌探底反转)
trade_direction = "BUY" if self.reverse_logic else "SELL"
# d) 执行交易
if trade_direction and trade_direction in self.order_direction:
event_type = "向上突破" if upper_break_event else "向下突破"
logic_type = "反转" if self.reverse_logic else "标准"
self.log(
f"{logic_type}模式 {trade_direction} 信号: "
f"价格{event_type} & 霍克斯强度({latest_hawkes_value:.2f}) > 阈值({latest_hawkes_upper:.2f})"
)
self.send_open_order(trade_direction, open_price, self.trade_volume)
# send_open_order, send_market_order, on_rollover 等方法保持不变
def send_open_order(self, direction: str, entry_price: float, volume: int):
current_time = self.get_current_time()
order_id = f"{self.symbol}_{direction}_{current_time.strftime('%Y%m%d%H%M%S')}"
order_direction = "BUY" if direction == "BUY" else "SELL"
order = Order(id=order_id, symbol=self.symbol, direction=order_direction, volume=volume, price_type="MARKET",
submitted_time=current_time, offset="OPEN")
self.send_order(order)
self.pos_meta[self.symbol] = {"direction": direction, "volume": volume, "entry_price": entry_price}
self.log(f"发送开仓订单: {direction} {volume}手 @ Market Price (执行价约 {entry_price:.2f})")
def send_market_order(self, direction: str, volume: int):
current_time = self.get_current_time()
order_id = f"{self.symbol}_{direction}_{current_time.strftime('%Y%m%d%H%M%S')}"
order = Order(id=order_id, symbol=self.symbol, direction=direction, volume=volume, price_type="MARKET",
submitted_time=current_time, offset="CLOSE")
self.send_order(order)
self.log(f"发送平仓订单: {direction} {volume}手 @ Market Price")
def on_rollover(self, old_symbol: str, new_symbol: str):
super().on_rollover(old_symbol, new_symbol)
self.cancel_all_pending_orders(new_symbol)
self.pos_meta.clear()

188
futures_trading_strategies/MA/TrendlineBreakoutStrategy/TrendlineHawkesStrategyFast.py Normal file
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@@ -0,0 +1,188 @@
import numpy as np
import pandas as pd
from typing import Optional, Dict, Any, List
# 假设这些是你项目中的模块
from src.core_data import Bar, Order
from src.strategies.base_strategy import Strategy
from src.algo.TrendLine import calculate_latest_trendline_values
class TrendlineHawkesStrategy(Strategy):
"""
趋势线与霍克斯过程双重确认策略 (V4 - 终极性能版):
- 霍克斯过程和滚动分位数都实现为高效的有状态增量计算。
- 使用固定长度的Numpy数组作为滚动窗口，避免Pandas.rolling的开销和不一致性。
"""
def __init__(
self,
context: Any,
main_symbol: str,
# ... 参数与V3完全相同 ...
trade_volume: int = 1,
order_direction: Optional[List[str]] = None,
reverse_logic: bool = False,
trendline_n: int = 50,
hawkes_kappa: float = 0.1,
hawkes_lookback: int = 50,
hawkes_entry_percent: float = 0.95,
hawkes_exit_percent: float = 0.50,
enable_log: bool = True,
):
super().__init__(context, main_symbol, enable_log)
# ... 参数赋值与V3完全相同 ...
self.main_symbol = main_symbol
self.trade_volume = trade_volume
self.order_direction = order_direction or ["BUY", "SELL"]
self.reverse_logic = reverse_logic
self.trendline_n = trendline_n
self.hawkes_kappa = hawkes_kappa
self.hawkes_lookback = hawkes_lookback
self.hawkes_entry_percent = hawkes_entry_percent
self.hawkes_exit_percent = hawkes_exit_percent
self.pos_meta: Dict[str, Dict[str, Any]] = {}
# --- 【核心修改】状态缓存重构 ---
# 只缓存上一个时间点的霍克斯强度值 (未缩放)
self._last_hawkes_unscaled: float = 0.0
# 只维护一个固定长度的滚动窗口，用于计算分位数
self._hawkes_window: np.ndarray = np.array([], dtype=np.float64)
# 衰减因子
self._hawkes_alpha = np.exp(-self.hawkes_kappa)
# ... 日志与V3相同 ...
def _initialize_state(self, initial_volumes: np.ndarray):
"""
仅在策略开始时调用一次，用于填充初始的滚动窗口。
"""
print("首次运行，正在初始化霍克斯状态和滚动窗口...")
alpha = self._hawkes_alpha
kappa = self.hawkes_kappa
# 完整计算一次历史强度，只为填充窗口
temp_hawkes_history = np.zeros_like(initial_volumes, dtype=np.float64)
if len(initial_volumes) > 0:
temp_hawkes_history[0] = initial_volumes[0] if not np.isnan(initial_volumes[0]) else 0.0
for i in range(1, len(initial_volumes)):
temp_hawkes_history[i] = temp_hawkes_history[i - 1] * alpha + (
initial_volumes[i] if not np.isnan(initial_volumes[i]) else 0.0)
# 记录最后一个点的强度值，作为下一次增量计算的起点
self._last_hawkes_unscaled = temp_hawkes_history[-1] if len(temp_hawkes_history) > 0 else 0.0
# 用历史强度值的最后 hawkes_lookback 个点来填充滚动窗口
self._hawkes_window = (temp_hawkes_history * kappa)[-self.hawkes_lookback:]
print("状态初始化完成。")
def _update_state_incrementally(self, latest_volume: float):
"""
【增量计算】在每个新的Bar上调用，更新强度值和滚动窗口。
"""
# 1. 计算最新的霍克斯强度值 (未缩放)
new_hawkes_unscaled = self._last_hawkes_unscaled * self._hawkes_alpha + (
latest_volume if not np.isnan(latest_volume) else 0.0)
# 2. 更新上一个点的状态，为下一次计算做准备
self._last_hawkes_unscaled = new_hawkes_unscaled
# 3. 将新的缩放后的强度值推入滚动窗口
new_hawkes_scaled = new_hawkes_unscaled * self.hawkes_kappa
# np.roll 会高效地将数组元素移动，然后我们将新值放在第一个位置
# 这比 append + delete 的效率高得多
self._hawkes_window = np.roll(self._hawkes_window, -1)
self._hawkes_window[-1] = new_hawkes_scaled
def on_init(self):
super().on_init()
self.pos_meta.clear()
# 重置状态
self._last_hawkes_unscaled = 0.0
self._hawkes_window = np.array([], dtype=np.float64)
def on_open_bar(self, open_price: float, symbol: str):
bar_history = self.get_bar_history()
min_bars_required = max(self.trendline_n + 2, self.hawkes_lookback + 2)
if len(bar_history) < min_bars_required:
return
# --- 【核心修改】霍克斯过程的状态更新 ---
# 检查是否是第一次运行
if self._hawkes_window.size == 0:
initial_volumes = np.array([b.volume for b in bar_history], dtype=float)
self._initialize_state(initial_volumes[:-1]) # 用到上一根bar为止的数据初始化
# 增量更新当前bar的状态
self._update_state_incrementally(float(bar_history[-1].volume))
# --- 后续逻辑使用更新后的状态进行计算 ---
self.cancel_all_pending_orders(symbol)
pos = self.get_current_positions().get(symbol, 0)
# 【核心修改】直接在固定长度的窗口上计算分位数
# 这比pandas.rolling快几个数量级，且结果稳定
latest_hawkes_value = self._hawkes_window[-1]
latest_hawkes_upper = np.quantile(self._hawkes_window, self.hawkes_entry_percent)
latest_hawkes_lower = np.quantile(self._hawkes_window, self.hawkes_exit_percent)
# 1. 平仓逻辑 (完全不变)
meta = self.pos_meta.get(symbol)
if meta and pos != 0:
if latest_hawkes_value < latest_hawkes_lower:
self.log(f"霍克斯出场信号...") # 日志简化
self.send_market_order("CLOSE_LONG" if meta['direction'] == "BUY" else "CLOSE_SHORT", abs(pos))
del self.pos_meta[symbol]
return
# 2. 开仓逻辑 (完全不变)
if pos == 0:
close_prices = np.array([b.close for b in bar_history])
prices_for_trendline = close_prices[-self.trendline_n - 1:-1]
trend_upper, trend_lower = calculate_latest_trendline_values(prices_for_trendline)
if trend_upper is not None and trend_lower is not None:
prev_close = bar_history[-2].close
last_close = bar_history[-1].close
upper_break_event = last_close > trend_upper and prev_close < trend_upper
lower_break_event = last_close < trend_lower and prev_close > trend_lower
hawkes_confirmation = latest_hawkes_value > latest_hawkes_upper
if hawkes_confirmation and (upper_break_event or lower_break_event):
trade_direction = None
if upper_break_event:
trade_direction = "SELL" if self.reverse_logic else "BUY"
elif lower_break_event:
trade_direction = "BUY" if self.reverse_logic else "SELL"
if trade_direction and trade_direction in self.order_direction:
self.log(f"开仓信号确认...") # 日志简化
self.send_open_order(trade_direction, open_price, self.trade_volume)
# send_open_order, send_market_order, on_rollover 等方法保持不变
# ... (代码省略，与之前版本相同) ...
# send_open_order, send_market_order, on_rollover 等方法保持不变
def send_open_order(self, direction: str, entry_price: float, volume: int):
current_time = self.get_current_time()
order_id = f"{self.symbol}_{direction}_{current_time.strftime('%Y%m%d%H%M%S')}"
order_direction = "BUY" if direction == "BUY" else "SELL"
order = Order(id=order_id, symbol=self.symbol, direction=order_direction, volume=volume, price_type="MARKET",
submitted_time=current_time, offset="OPEN")
self.send_order(order)
self.pos_meta[self.symbol] = {"direction": direction, "volume": volume, "entry_price": entry_price}
self.log(f"发送开仓订单: {direction} {volume}手 @ Market Price (执行价约 {entry_price:.2f})")
def send_market_order(self, direction: str, volume: int):
current_time = self.get_current_time()
order_id = f"{self.symbol}_{direction}_{current_time.strftime('%Y%m%d%H%M%S')}"
order = Order(id=order_id, symbol=self.symbol, direction=direction, volume=volume, price_type="MARKET",
submitted_time=current_time, offset="CLOSE")
self.send_order(order)
self.log(f"发送平仓订单: {direction} {volume}手 @ Market Price")
def on_rollover(self, old_symbol: str, new_symbol: str):
super().on_rollover(old_symbol, new_symbol)
self.cancel_all_pending_orders(new_symbol)
self.pos_meta.clear()

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# =====================================================================================
# 以下是新增的 ValueMigrationStrategy 策略代码
# =====================================================================================
from collections import deque
from datetime import timedelta, time
import numpy as np
import pandas as pd
from typing import List, Any, Optional, Dict
import talib
from src.core_data import Bar, Order
from src.strategies.ValueMigrationStrategy.data_class import ProfileStats, calculate_profile_from_bars
from src.strategies.base_strategy import Strategy
# = ===================================================================
# 全局辅助函数 (Global Helper Functions)
# 将这些函数放在文件顶部，以便所有策略类都能调用
# =====================================================================
def compute_price_volume_distribution(bars: List[Bar], tick_size: float) -> Optional[pd.Series]:
"""
[全局函数] 从K线数据中计算出原始的价格-成交量分布。
"""
if not bars:
return None
data = []
# 为了性能，我们只处理有限数量的bars，防止内存问题
# 在实际应用中，更高效的实现是必要的
for bar in bars[-500:]: # 添加一个安全限制
price_range = np.arange(bar.low, bar.high + tick_size, tick_size)
if len(price_range) == 0 or bar.volume == 0: continue
# 将成交量近似分布到K线覆盖的每个tick上
volume_per_tick = bar.volume / len(price_range)
for price in price_range:
data.append({'price': price, 'volume': volume_per_tick})
if not data:
return None
df = pd.DataFrame(data)
if df.empty:
return None
return df.groupby('price')['volume'].sum().sort_index()
# 确保在文件顶部导入
from scipy.signal import find_peaks
def find_hvns_with_distance(price_volume_dist: pd.Series, distance_in_ticks: int) -> List[float]:
"""
[全局函数] 使用峰值查找算法，根据峰值间的最小距离来识别HVNs。
Args:
price_volume_dist: 价格-成交量分布序列。
distance_in_ticks: 两个HVN之间必须间隔的最小tick数量。
Returns:
一个包含所有被识别出的HVN价格的列表。
"""
if price_volume_dist.empty or len(price_volume_dist) < 3:
return []
# distance参数确保找到的峰值之间至少相隔N个点
peaks_indices, _ = find_peaks(price_volume_dist.values, distance=distance_in_ticks)
if len(peaks_indices) == 0:
return [price_volume_dist.idxmax()] # 默认返回POC
hvn_prices = price_volume_dist.index[peaks_indices].tolist()
return hvn_prices
class ValueMigrationStrategy(Strategy):
# 确保在文件顶部导入
from scipy.signal import find_peaks
# =====================================================================================
# 以下是全新的、基于HVN回测逻辑的 HVNPullbackStrategy 策略代码
# =====================================================================================
"""
一个基于动态HVN突破后回测的量化交易策略。(适配无回调函数的框架)
该策略首先动态识别出市场中重要的成交量密集区(HVNs)。当价格
明确穿越一个HVN后，它并不立即追逐，而是预期价格会有一个短暂的
回测行为，并在HVN附近的一个偏移位置挂限价单，以更高概率顺势入场。
"""
def __init__(
self,
context: Any,
main_symbol: str,
enable_log: bool,
trade_volume: int,
tick_size: float = 1,
profile_period: int = 100,
recalc_interval: int = 4,
hvn_distance_ticks: int = 1,
entry_offset_atr: float = 0.2,
stop_loss_atr: float = 1.0,
take_profit_atr: float = 1.0,
atr_period: int = 14,
order_direction=None,
indicators=[None, None],
):
super().__init__(context, main_symbol, enable_log)
if order_direction is None:
order_direction = ['BUY', 'SELL']
self.trade_volume = trade_volume
self.tick_size = tick_size
self.profile_period = profile_period
self.recalc_interval = recalc_interval
self.hvn_distance_ticks = hvn_distance_ticks
self.entry_offset_atr = entry_offset_atr
self.stop_loss_atr = stop_loss_atr
self.take_profit_atr = take_profit_atr
self.atr_period = atr_period
self.order_direction = order_direction
self.indicator_long = indicators[0]
self.indicator_short = indicators[1]
self.main_symbol = main_symbol
self.order_id_counter = 0
self._bar_counter = 0
self._cached_hvns: List[float] = []
self._last_order_id: Optional[str] = None
# 元数据存储:
self.position_meta: Dict[str, Any] = {} # 存储已成交持仓的止盈止损
self._pending_order_meta: Dict[str, Any] = {} # 存储未成交挂单的预设参数
def on_open_bar(self, open_price: float, symbol: str):
self.symbol = symbol
self._bar_counter += 1
bar_history = self.get_bar_history()
required_len = max(self.profile_period, self.atr_period) + 1
if len(bar_history) < required_len:
return
# # --- 1. 取消上一根K线未成交的限价单 ---
# if self._last_order_id and self._last_order_id in self.get_pending_orders():
# self.cancel_order(self._last_order_id)
# self.log(f"已取消上一根K线的挂单: {self._last_order_id}")
# # 如果挂单被取消，清除对应的预设元数据
# if self._last_order_id in self._pending_order_meta:
# del self._pending_order_meta[self._last_order_id]
# self._last_order_id = None
self.cancel_all_pending_orders(self.symbol)
# --- 2. 管理现有持仓 (逻辑核心调整) ---
position_volume = self.get_current_positions().get(self.symbol, 0)
if position_volume != 0:
self.manage_open_position(position_volume, open_price)
return # 有持仓则不进行新的开仓评估
# --- 3. 周期性地计算并缓存所有的HVNs ---
if self._bar_counter % self.recalc_interval == 1:
profile_bars = bar_history[-self.profile_period:]
dist = compute_price_volume_distribution(profile_bars, self.tick_size)
if dist is not None and not dist.empty:
self._cached_hvns = find_hvns_with_distance(dist, self.hvn_distance_ticks)
self.log(f"New HVNs identified at: {[f'{p:.2f}' for p in self._cached_hvns]}")
if not self._cached_hvns: return
# --- 4. 评估新机会 (穿越后挂单逻辑) ---
self.evaluate_entry_signal(bar_history)
def manage_open_position(self, volume: int, current_price: float):
"""在on_open_bar中主动管理已开仓位的止盈止损。"""
# [关键逻辑]: 检测是否为新成交的持仓
if self.symbol not in self.position_meta:
# 这是一个新持仓。我们必须从挂单的元数据中恢复止盈止损参数。
# 这里假设只有一个挂单能成交。如果有多个，需要更复杂的匹配逻辑。
if not self._pending_order_meta:
self.log("Error: New position detected but no pending order meta found.")
# 紧急情况：立即平仓或设置默认止损
return
# 从挂单元数据中获取参数，并“过户”到持仓元数据
# 由于我们每次只挂一个单，取第一个即可
order_id = next(iter(self._pending_order_meta))
meta = self._pending_order_meta.pop(order_id) # 取出并从pending中删除
self.position_meta[self.symbol] = meta
self.log(f"新持仓确认。已设置TP/SL: {meta}")
# [常规逻辑]: 检查止盈止损
meta = self.position_meta[self.symbol]
sl_price = meta['sl_price']
tp_price = meta['tp_price']
if volume > 0: # 多头
if current_price <= sl_price:
self.log(f"多头止损触发 at {current_price:.2f}")
self.close_position("CLOSE_LONG", abs(volume))
elif current_price >= tp_price:
self.log(f"多头止盈触发 at {current_price:.2f}")
self.close_position("CLOSE_LONG", abs(volume))
elif volume < 0: # 空头
if current_price >= sl_price:
self.log(f"空头止损触发 at {current_price:.2f}")
self.close_position("CLOSE_SHORT", abs(volume))
elif current_price <= tp_price:
self.log(f"空头止盈触发 at {current_price:.2f}")
self.close_position("CLOSE_SHORT", abs(volume))
def evaluate_entry_signal(self, bar_history: List[Bar]):
prev_close = bar_history[-2].close
current_close = bar_history[-1].close
highs = np.array([b.high for b in bar_history], dtype=float)
lows = np.array([b.low for b in bar_history], dtype=float)
closes = np.array([b.close for b in bar_history], dtype=float)
current_atr = talib.ATR(highs, lows, closes, self.atr_period)[-1]
if current_atr < self.tick_size: return
for hvn in sorted(self._cached_hvns):
if "BUY" in self.order_direction and (prev_close < hvn < current_close):
if self.indicator_long is None or self.indicator_long.is_condition_met(*self.get_indicator_tuple()):
limit_price = hvn + self.entry_offset_atr * current_atr
self.log(f"价格向上穿越HVN({hvn:.2f}). 在 {limit_price:.2f} 挂限价买单。")
self.send_hvn_limit_order("BUY", limit_price, current_atr)
return
if "SELL" in self.order_direction and (prev_close > hvn > current_close):
if self.indicator_short is None or self.indicator_short.is_condition_met(
*self.get_indicator_tuple()):
limit_price = hvn - self.entry_offset_atr * current_atr
self.log(f"价格向下穿越HVN({hvn:.2f}). 在 {limit_price:.2f} 挂限价卖单。")
self.send_hvn_limit_order("SELL", limit_price, current_atr)
return
def send_hvn_limit_order(self, direction: str, limit_price: float, entry_atr: float):
# 预先计算止盈止损价格
sl_price = limit_price - self.stop_loss_atr * entry_atr if direction == "BUY" else limit_price + self.stop_loss_atr * entry_atr
tp_price = limit_price + self.take_profit_atr * entry_atr if direction == "BUY" else limit_price - self.take_profit_atr * entry_atr
order_id = f"{self.symbol}_{direction}_LIMIT_{self.order_id_counter}"
self.order_id_counter += 1
# 将这些参数存储到 pending_order_meta 中
self._pending_order_meta[order_id] = {'sl_price': sl_price, 'tp_price': tp_price}
order = Order(
id=order_id, symbol=self.symbol, direction=direction, volume=self.trade_volume,
price_type="LIMIT", limit_price=limit_price, submitted_time=self.get_current_time(),
offset="OPEN"
)
sent_order = self.send_order(order)
if sent_order:
self._last_order_id = sent_order.id
def close_position(self, direction: str, volume: int):
self.send_market_order(direction, volume)
if self.symbol in self.position_meta:
del self.position_meta[self.symbol] # 平仓后清理持仓元数据
def send_market_order(self, direction: str, volume: int, offset: str = "CLOSE"):
order_id = f"{self.symbol}_{direction}_{offset}_{self.get_current_time().strftime('%Y%m%d%H%M%S')}_{self.order_id_counter}"
self.order_id_counter += 1
order = Order(
id=order_id, symbol=self.symbol, direction=direction, volume=volume,
price_type="MARKET", submitted_time=self.get_current_time(), offset=offset
)
self.send_order(order)

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# =====================================================================================
# 以下是新增的 ValueMigrationStrategy 策略代码
# =====================================================================================
from collections import deque
from datetime import timedelta, time
import numpy as np
import pandas as pd
from typing import List, Any, Optional, Dict
import talib
from src.core_data import Bar, Order
from src.strategies.ValueMigrationStrategy.data_class import ProfileStats, calculate_profile_from_bars
from src.strategies.base_strategy import Strategy
# = ===================================================================
# 全局辅助函数 (Global Helper Functions)
# 将这些函数放在文件顶部，以便所有策略类都能调用
# =====================================================================
def compute_price_volume_distribution(bars: List[Bar], tick_size: float) -> Optional[pd.Series]:
"""
[全局函数] 从K线数据中计算出原始的价格-成交量分布。
"""
if not bars:
return None
data = []
# 为了性能，我们只处理有限数量的bars，防止内存问题
# 在实际应用中，更高效的实现是必要的
for bar in bars[-500:]: # 添加一个安全限制
price_range = np.arange(bar.low, bar.high + tick_size, tick_size)
if len(price_range) == 0 or bar.volume == 0: continue
# 将成交量近似分布到K线覆盖的每个tick上
volume_per_tick = bar.volume / len(price_range)
for price in price_range:
data.append({'price': price, 'volume': volume_per_tick})
if not data:
return None
df = pd.DataFrame(data)
if df.empty:
return None
return df.groupby('price')['volume'].sum().sort_index()
# =====================================================================================
# 以下是最终的、以性能为首要考量的、超高速VP计算模块
# =====================================================================================
def compute_fast_volume_profile(bars: List[Bar], tick_size: float) -> Optional[pd.Series]:
"""
[全局核心函数] 使用“离散重心累加”法，超高速地从K线数据中构建成交量剖面图。
该方法将每根K线的全部成交量，一次性地归于其加权中心价
(HLC/3)所对齐的tick上。这在保持核心逻辑精确性的同时，
实现了计算速度的数量级提升。
Args:
bars: 用于计算的K线历史数据。
tick_size: 合约的最小变动价位。
Returns:
一个代表成交量剖面图的Pandas Series，或None。
"""
if not bars:
return None
# 使用字典进行累加，这是Python中最快的操作之一
volume_dict = {}
for bar in bars:
if bar.volume == 0:
continue
# 1. 计算K线的加权中心价
center_price = (bar.high + bar.low + bar.close) / 3
# 2. 将中心价对齐到最近的tick
aligned_price = round(center_price / tick_size) * tick_size
# 3. 将该Bar的全部成交量，一次性累加到对齐后的价格点上
volume_dict[aligned_price] = volume_dict.get(aligned_price, 0) + bar.volume
if not volume_dict:
return None
# 将最终结果转换为一个有序的Pandas Series
return pd.Series(volume_dict).sort_index()
# 确保在文件顶部导入
from scipy.signal import find_peaks
def find_hvns_with_distance(price_volume_dist: pd.Series, distance_in_ticks: int) -> List[float]:
"""
[全局函数] 使用峰值查找算法，根据峰值间的最小距离来识别HVNs。
Args:
price_volume_dist: 价格-成交量分布序列。
distance_in_ticks: 两个HVN之间必须间隔的最小tick数量。
Returns:
一个包含所有被识别出的HVN价格的列表。
"""
if price_volume_dist.empty or len(price_volume_dist) < 3:
return []
# distance参数确保找到的峰值之间至少相隔N个点
peaks_indices, _ = find_peaks(price_volume_dist.values, distance=distance_in_ticks)
if len(peaks_indices) == 0:
return [price_volume_dist.idxmax()] # 默认返回POC
hvn_prices = price_volume_dist.index[peaks_indices].tolist()
return hvn_prices
def find_hvns_strict(price_volume_dist: pd.Series, window_radius: int) -> List[float]:
"""
[全局函数] 使用严格的“滚动窗口最大值”定义来识别HVNs。
一个点是HVN，当且仅当它的成交量大于其左右各 `window_radius` 个点的成交量。
Args:
price_volume_dist: 价格-成交量分布序列。
window_radius: 定义了检查窗口的半径 (即您所说的 N)。
Returns:
一个包含所有被识别出的HVN价格的列表。
"""
if price_volume_dist.empty or window_radius == 0:
return [price_volume_dist.idxmax()] if not price_volume_dist.empty else []
# 1. 确保价格序列是连续的，用0填充缺失的ticks
full_price_range = np.arange(price_volume_dist.index.min(),
price_volume_dist.index.max() + price_volume_dist.index.to_series().diff().min(),
price_volume_dist.index.to_series().diff().min())
continuous_dist = price_volume_dist.reindex(full_price_range, fill_value=0)
# 2. 计算滚动窗口最大值
window_size = 2 * window_radius + 1
rolling_max = continuous_dist.rolling(window=window_size, center=True).max()
# 3. 找到那些自身成交量就等于其窗口最大值的点
is_hvn = (continuous_dist == rolling_max) & (continuous_dist > 0)
hvn_prices = continuous_dist[is_hvn].index.tolist()
# 4. 处理平顶山：如果连续多个点都是HVN，只保留中间那个
if not hvn_prices:
return [price_volume_dist.idxmax()] # 如果找不到，返回POC
final_hvns = []
i = 0
while i < len(hvn_prices):
# 找到一个连续HVN块
j = i
while j + 1 < len(hvn_prices) and (hvn_prices[j + 1] - hvn_prices[j]) < (
2 * price_volume_dist.index.to_series().diff().min()):
j += 1
# 取这个连续块的中间点
middle_index = i + (j - i) // 2
final_hvns.append(hvn_prices[middle_index])
i = j + 1
return final_hvns
# 确保在文件顶部导入
from scipy.signal import find_peaks
# =====================================================================================
# 以下是V2版本的、简化了状态管理的 HVNPullbackStrategy 代码
# =====================================================================================
class ValueMigrationStrategy(Strategy):
"""
一个基于动态HVN突破后回测的量化交易策略。(V2: 简化状态管理)
V2版本简化了内部状态管理，移除了基于order_id的复杂元数据传递，
使用更直接、更健壮的单一状态变量来处理挂单的止盈止损参数，
完美适配“单次单持仓”的策略逻辑。
"""
def __init__(
self,
context: Any,
main_symbol: str,
enable_log: bool,
trade_volume: int,
tick_size: float = 1,
profile_period: int = 100,
recalc_interval: int = 4,
hvn_distance_ticks: int = 20,
entry_offset_atr: float = 0.0,
stop_loss_atr: float = 1.0,
take_profit_atr: float = 2.0,
atr_period: int = 14,
order_direction=None,
indicators=[None, None],
):
super().__init__(context, main_symbol, enable_log)
if order_direction is None:
order_direction = ['BUY', 'SELL']
self.trade_volume = trade_volume
self.tick_size = tick_size
self.profile_period = profile_period
self.recalc_interval = recalc_interval
self.hvn_distance_ticks = hvn_distance_ticks
self.entry_offset_atr = entry_offset_atr
self.stop_loss_atr = stop_loss_atr
self.take_profit_atr = take_profit_atr
self.atr_period = atr_period
self.order_direction = order_direction
self.indicator_long = indicators[0]
self.indicator_short = indicators[1]
self.main_symbol = main_symbol
self.order_id_counter = 0
self._bar_counter = 0
self._cached_hvns: List[float] = []
# --- V2: 简化的状态管理 ---
self._pending_sl_price: Optional[float] = None
self._pending_tp_price: Optional[float] = None
def on_open_bar(self, open_price: float, symbol: str):
self.symbol = symbol
self._bar_counter += 1
bar_history = self.get_bar_history()
required_len = max(self.profile_period, self.atr_period) + 1
if len(bar_history) < required_len:
return
# --- 1. 取消所有挂单并重置挂单状态 ---
self.cancel_all_pending_orders(self.symbol)
# self._pending_sl_price = None
# self._pending_tp_price = None
# --- 2. 管理现有持仓 ---
position_volume = self.get_current_positions().get(self.symbol, 0)
if position_volume != 0:
self.manage_open_position(position_volume, open_price)
return
# --- 3. 周期性地计算HVNs ---
if self._bar_counter % self.recalc_interval == 1:
profile_bars = bar_history[-self.profile_period:]
dist = compute_price_volume_distribution(profile_bars, self.tick_size)
# dist = compute_fast_volume_profile(profile_bars, self.tick_size)
if dist is not None and not dist.empty:
# self._cached_hvns = find_hvns_with_distance(dist, self.hvn_distance_ticks)
self._cached_hvns = find_hvns_strict(dist, self.hvn_distance_ticks)
self.log(f"New HVNs identified at: {[f'{p:.2f}' for p in self._cached_hvns]}")
if not self._cached_hvns: return
# --- 4. 评估新机会 (挂单逻辑) ---
self.evaluate_entry_signal(bar_history)
def manage_open_position(self, volume: int, current_price: float):
"""主动管理已开仓位的止盈止损。"""
# # [V2 关键逻辑]: 检测是否为新持仓
# # 如果这是一个新持仓，并且我们有预设的止盈止损，就将其存入
# if self._pending_sl_price is not None and self._pending_tp_price is not None:
# meta = {'sl_price': self._pending_sl_price, 'tp_price': self._pending_tp_price}
# self.position_meta = meta
# self.log(f"新持仓确认。已设置TP/SL: {meta}")
# else:
# # 这种情况理论上不应发生，但作为保护
# self.log("Error: New position detected but no pending TP/SL values found.")
# self.close_position("CLOSE_LONG" if volume > 0 else "CLOSE_SHORT", abs(volume))
# return
# [常规逻辑]: 检查止盈止损
sl_price = self._pending_sl_price
tp_price = self._pending_tp_price
if volume > 0: # 多头
if current_price <= sl_price or current_price >= tp_price:
action = "止损" if current_price <= sl_price else "止盈"
self.log(f"多头{action}触发 at {current_price:.2f}")
self.close_position("CLOSE_LONG", abs(volume), current_price)
elif volume < 0: # 空头
if current_price >= sl_price or current_price <= tp_price:
action = "止损" if current_price >= sl_price else "止盈"
self.log(f"空头{action}触发 at {current_price:.2f}")
self.close_position("CLOSE_SHORT", abs(volume), current_price)
def evaluate_entry_signal(self, bar_history: List[Bar]):
prev_close = bar_history[-2].close
current_close = bar_history[-1].close
highs = np.array([b.high for b in bar_history], dtype=float)
lows = np.array([b.low for b in bar_history], dtype=float)
closes = np.array([b.close for b in bar_history], dtype=float)
current_atr = talib.ATR(highs, lows, closes, self.atr_period)[-1]
if current_atr < self.tick_size: return
for hvn in sorted(self._cached_hvns):
# (为了简洁，买卖逻辑合并)
direction = None
if "BUY" in self.order_direction and (prev_close < hvn < current_close):
direction = "SELL"
pass_filter = self.indicator_long is None or self.indicator_long.is_condition_met(
*self.get_indicator_tuple())
elif "SELL" in self.order_direction and (prev_close > hvn > current_close):
direction = "BUY"
pass_filter = self.indicator_short is None or self.indicator_short.is_condition_met(
*self.get_indicator_tuple())
else:
continue # 没有触发穿越
if direction and pass_filter:
offset = self.entry_offset_atr * current_atr
limit_price = hvn + offset if direction == "BUY" else hvn - offset
self.log(f"价格穿越HVN({hvn:.2f}). 在 {limit_price:.2f} 挂限价{direction}单。")
# self.send_hvn_limit_order(direction, limit_price + 1 if direction == 'BUY' else -1, current_atr)
self.send_hvn_limit_order(direction, limit_price, current_atr)
return # 每次只挂一个单
def send_hvn_limit_order(self, direction: str, limit_price: float, entry_atr: float):
print(limit_price, self.get_current_time())
# [V2 关键逻辑]: 直接更新实例变量
self._pending_sl_price = limit_price - self.stop_loss_atr * entry_atr if direction == "BUY" else limit_price + self.stop_loss_atr * entry_atr
self._pending_tp_price = limit_price + self.take_profit_atr * entry_atr if direction == "BUY" else limit_price - self.take_profit_atr * entry_atr
order_id = f"{self.symbol}_{direction}_LIMIT_{self.order_id_counter}"
self.order_id_counter += 1
# order = Order(
# id=order_id, symbol=self.symbol, direction=direction, volume=self.trade_volume,
# price_type="LIMIT", limit_price=limit_price, submitted_time=self.get_current_time(),
# offset="OPEN"
# )
order = Order(
id=order_id, symbol=self.symbol, direction=direction, volume=self.trade_volume,
price_type="STOP", stop_price=limit_price, submitted_time=self.get_current_time(),
offset="OPEN"
)
self.send_order(order)
def close_position(self, direction: str, volume: int, current_price: float):
self.send_market_order(direction, volume, current_price)
def send_market_order(self, direction: str, volume: int, current_price: float, offset: str = "CLOSE"):
order_id = f"{self.symbol}_{direction}_{offset}_{self.get_current_time().strftime('%Y%m%d%H%M%S')}_{self.order_id_counter}"
self.order_id_counter += 1
order = Order(
id=order_id, symbol=self.symbol, direction=direction, volume=volume,
price_type="MARKET", submitted_time=self.get_current_time(), offset=offset, limit_price=current_price
)
self.send_order(order)

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{
"cells": [
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": "",
"id": "31b2956abfa86104"
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}

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import numpy as np
import pandas as pd
from dataclasses import dataclass
from scipy.stats import kurtosis, skew
from datetime import time, timedelta
from typing import List, Tuple, Optional
from src.core_data import Bar
@dataclass
class ProfileStats:
"""封装剖面图的所有核心统计量。"""
vah: float
val: float
poc: float
def calculate_profile_from_bars(bars: List[Bar], tick_size: float, va_percentage: int = 70) -> Optional[ProfileStats]:
"""
[全局核心函数] 从给定的K线列表计算剖面图统计量 (VAH, VAL, POC)。
"""
if not bars:
return None
data = []
for bar in bars:
price_range = np.arange(bar.low, bar.high + tick_size, tick_size)
if len(price_range) == 0 or bar.volume == 0: continue
volume_per_tick = bar.volume / len(price_range)
for price in price_range:
data.append({'price': price, 'volume': volume_per_tick})
if not data: return None
df = pd.DataFrame(data)
if df.empty: return None
price_volume_dist = df.groupby('price')['volume'].sum().sort_index()
if price_volume_dist.empty or len(price_volume_dist) < 3: return None
poc = price_volume_dist.idxmax()
total_volume = price_volume_dist.sum()
value_area_volume_target = total_volume * (va_percentage / 100.0)
current_va_volume = price_volume_dist.loc[poc]
vah, val = poc, poc
prices_above = price_volume_dist.index[price_volume_dist.index > poc]
prices_below = price_volume_dist.index[price_volume_dist.index < poc].sort_values(ascending=False)
idx_above, idx_below = 0, 0
while current_va_volume < value_area_volume_target:
vol_above = price_volume_dist.loc[prices_above[idx_above]] if idx_above < len(prices_above) else 0
vol_below = price_volume_dist.loc[prices_below[idx_below]] if idx_below < len(prices_below) else 0
if vol_above == 0 and vol_below == 0: break
if vol_above > vol_below:
current_va_volume += vol_above
vah = prices_above[idx_above]
idx_above += 1
else:
current_va_volume += vol_below
val = prices_below[idx_below]
idx_below += 1
return ProfileStats(vah=vah, val=val, poc=poc)

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "initial_id",
"metadata": {
"ExecuteTime": {
"end_time": "2025-07-22T07:44:51.375234Z",
"start_time": "2025-07-22T07:44:51.352161Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"%load_ext autoreload\n",
"%autoreload 2\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a559dfcf",
"metadata": {
"ExecuteTime": {
"end_time": "2025-07-22T07:44:56.927700Z",
"start_time": "2025-07-22T07:44:51.391111Z"
}
},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'src'",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mturtle\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m down\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01manalysis\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mresult_analyzer\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ResultAnalyzer\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# 导入所有必要的模块\u001b[39;00m\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msrc\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdata_manager\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DataManager\n",
"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'src'"
]
}
],
"source": [
"\n",
"from turtle import down\n",
"from src.analysis.result_analyzer import ResultAnalyzer\n",
"# 导入所有必要的模块\n",
"from src.data_manager import DataManager\n",
"from src.backtest_engine import BacktestEngine\n",
"from src.indicators.indicator_list import INDICATOR_LIST\n",
"from src.indicators.indicators import RSI, BollingerBandwidth, HistoricalRange, NormalizedATR, RateOfChange, StochasticOscillator\n",
"from src.strategies.OpenTwoFactorStrategy import SimpleLimitBuyStrategyLong, SimpleLimitBuyStrategyShort, SimpleLimitBuyStrategy\n",
"\n",
"\n",
"# --- 配置参数 ---\n",
"# 获取当前脚本所在目录，假设数据文件在项目根目录下的 data 文件夹内\n",
"# data_file_path = '/mnt/d/PyProject/NewQuant/data/data/SHFE_rb2510/SHFE_rb2510_min60.csv'\n",
"# data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min60.csv\"\n",
"# data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@SHFE_rb/KQ_m@SHFE_rb_min60.csv\"\n",
"data_file_path = \"/mnt/d/PyProject/NewQuant/data/data/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min60.csv\"\n",
"\n",
"initial_capital = 100000.0\n",
"slippage_rate = 0.000 # 假设每笔交易0.1%的滑点\n",
"commission_rate = 0.0001 # 假设每笔交易0.02%的佣金\n",
"\n",
"global_config = {\n",
" 'symbol': 'KQ_m@CZCE_MA',\n",
"}\n",
"\n",
"# Short 可用\n",
"strategy_parameters = {\n",
" 'main_symbol': \"MA\", # 根据您的数据文件中的品种名称调整\n",
" 'trade_volume': 1,\n",
" 'lag': 7,\n",
" # 'range_factor': 1.8, # 示例值，需要通过网格搜索优化\n",
" # 'profit_factor': 2.8, # 示例值\n",
" # 'range_factor': 1.6, # 示例值，需要通过网格搜索优化\n",
" # 'profit_factor': 2.1, # 示例值\n",
" 'range_factor_l': 1.8, # 示例值，需要通过网格搜索优化\n",
" 'profit_factor_l': 2.8, # 示例值\n",
" 'range_factor_s': 1.6, # 示例值，需要通过网格搜索优化\n",
" 'profit_factor_s': 2.1, # 示例值\n",
" 'max_position': 10,\n",
" 'enable_log': True,\n",
" 'stop_loss_points': 20,\n",
" 'use_indicator': True,\n",
" # 'indicator': HistoricalRange(11, 25, 20),\n",
" # 'indicator': BollingerBandwidth(window=20, nbdev=2.0, down_bound=1.9, up_bound=3.25),\n",
" 'indicator_l': HistoricalRange(11, 25, 20),\n",
" 'indicator_s': BollingerBandwidth(window=20, nbdev=2.0, down_bound=1.9, up_bound=3.25),\n",
"}\n",
"start_time = datetime(2021, 1, 1)\n",
"end_time = datetime(2024, 6, 1)\n",
"\n",
"start_time = datetime(2024, 6, 1)\n",
"end_time = datetime(2025, 8, 1)\n",
"\n",
"\n",
"# --- 1. 初始化数据管理器 ---\n",
"print(\"初始化数据管理器...\")\n",
"data_manager = DataManager(file_path=data_file_path, symbol=global_config['symbol'], start_time=start_time, end_time=end_time)\n",
"# 确保 DataManager 能够重置以进行多次回测\n",
"# data_manager.reset() # 首次运行不需要重置\n",
"\n",
"# --- 2. 初始化回测引擎并运行 ---\n",
"print(\"\\n初始化回测引擎...\")\n",
"engine = BacktestEngine(\n",
" data_manager=data_manager,\n",
" strategy_class=SimpleLimitBuyStrategy,\n",
" # current_segment_symbol=strategy_parameters['symbol'],\n",
" strategy_params=strategy_parameters,\n",
" initial_capital=initial_capital,\n",
" slippage_rate=slippage_rate,\n",
" commission_rate=commission_rate,\n",
" roll_over_mode=True,\n",
" start_time=start_time,\n",
" end_time=end_time,\n",
" indicators=INDICATOR_LIST\n",
")\n",
"\n",
"print(\"\\n开始运行回测...\")\n",
"engine.run_backtest()\n",
"print(\"\\n回测运行完毕。\")\n",
"\n",
"# --- 3. 获取回测结果 ---\n",
"results = engine.get_backtest_results()\n",
"portfolio_snapshots = results[\"portfolio_snapshots\"]\n",
"trade_history = results[\"trade_history\"]\n",
"initial_capital_result = results[\"initial_capital\"]\n",
"bars = results[\"all_bars\"]\n",
"\n",
"# --- 4. 结果分析与可视化 ---\n",
"if portfolio_snapshots:\n",
" analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, bars, initial_capital_result, INDICATOR_LIST)\n",
"\n",
" analyzer.generate_report()\n",
" analyzer.plot_performance()\n",
" metrics = analyzer.calculate_all_metrics()\n",
" print(metrics)\n",
"\n",
" analyzer.analyze_indicators()\n",
"else:\n",
" print(\"\\n没有生成投资组合快照，无法进行结果分析。\")\n",
"\n",
"# --- 4. 结果分析与可视化 (待实现) ---\n",
"# if portfolio_snapshots:\n",
"# analyzer = ResultAnalyzer(portfolio_snapshots, trade_history, initial_capital_result)\n",
"# metrics = analyzer.calculate_all_metrics()\n",
"# print(\"\\n--- 绩效指标 ---\")\n",
"# for key, value in metrics.items():\n",
"# print(f\" {key}: {value:.4f}\")\n",
"#\n",
"# print(\"\\n--- 绘制绩效图表 ---\")\n",
"# analyzer.plot_performance()\n",
"# else:\n",
"# print(\"\\n没有生成投资组合快照，无法进行结果分析。\")\n",
"\n"
]
}
],
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0
futures_trading_strategies/MA/tqsdk_main2.ipynb Normal file
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