配对交易最初代码(不能运行版本)
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PairsTrading.ipynb
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1678
PairsTrading.ipynb
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PairsTrading.py
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PairsTrading.py
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import vectorbt as vbt
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import akshare as ak
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import datetime
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from numba import njit
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from collections import namedtuple
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# 设置中文显示
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['axes.unicode_minus'] = False
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# 获取股票数据
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print("正在获取股票数据...")
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stock_00981 = ak.stock_hk_daily(symbol="00981")
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stock_01347 = ak.stock_hk_daily(symbol="01347")
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# 数据预处理
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def preprocess_data(df, symbol):
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"""预处理股票数据"""
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df = df.copy()
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df['date'] = pd.to_datetime(df['日期'])
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df.set_index('date', inplace=True)
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df = df.sort_index()
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# 重命名列以符合vectorbt要求
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df = df.rename(columns={
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'开盘': 'open',
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'最高': 'high',
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'最低': 'low',
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'收盘': 'close',
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'成交量': 'volume'
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})
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return df[['open', 'high', 'low', 'close', 'volume']]
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# 预处理数据
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smic_data = preprocess_data(stock_00981, "00981")
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hhic_data = preprocess_data(stock_01347, "01347")
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# 对齐数据时间索引
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common_index = smic_data.index.intersection(hhic_data.index)
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smic_data = smic_data.loc[common_index]
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hhic_data = hhic_data.loc[common_index]
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print(f"数据时间范围: {common_index.min()} 到 {common_index.max()}")
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print(f"总交易日数: {len(common_index)}")
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# 配对交易参数
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PERIOD = 30 # 回看周期
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UPPER = 2.0 # 上界
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LOWER = -2.0 # 下界
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ORDER_PCT1 = 0.1 # 中芯国际交易比例
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ORDER_PCT2 = 0.1 # 华虹半导体交易比例
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COMMPERC = 0.002 # 手续费率 0.2%
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MODE = 'OLS' # 使用OLS方法
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INITIAL_CASH = 100000 # 初始资金
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# 定义数据结构
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Memory = namedtuple("Memory", ('spread', 'zscore', 'status'))
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Params = namedtuple("Params", ('period', 'upper', 'lower', 'order_pct1', 'order_pct2'))
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@njit
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def ols_spread_nb(a, b):
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"""计算OLS价差"""
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a = np.log(a)
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b = np.log(b)
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_b = np.vstack((b, np.ones(len(b)))).T
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slope, intercept = np.dot(np.linalg.inv(np.dot(_b.T, _b)), np.dot(_b.T, a))
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spread = a - (slope * b + intercept)
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return spread[-1]
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@njit
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def pre_group_func_nb(c, _period, _upper, _lower, _order_pct1, _order_pct2):
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"""准备当前组（资产对）"""
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assert c.group_len == 2
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# 初始化内存数组
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spread = np.full(c.target_shape[0], np.nan, dtype=np.float64)
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zscore = np.full(c.target_shape[0], np.nan, dtype=np.float64)
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status = np.full(1, 0, dtype=np.int64)
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memory = Memory(spread, zscore, status)
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# 选择参数
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period = _period[0] if len(_period) > 1 else _period
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upper = _upper[0] if len(_upper) > 1 else _upper
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lower = _lower[0] if len(_lower) > 1 else _lower
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order_pct1 = _order_pct1[0] if len(_order_pct1) > 1 else _order_pct1
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order_pct2 = _order_pct2[0] if len(_order_pct2) > 1 else _order_pct2
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params = Params(period, upper, lower, order_pct1, order_pct2)
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# 创建仓位大小数组
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size = np.empty(c.group_len, dtype=np.float64)
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return (memory, params, size)
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@njit
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def pre_segment_func_nb(c, memory, params, size, mode):
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"""准备当前段（组内行）"""
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# 等待足够的数据
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if c.i < params.period - 1:
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size[0] = np.nan
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size[1] = np.nan
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return (size,)
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# 计算窗口切片
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window_slice = slice(max(0, c.i + 1 - params.period), c.i + 1)
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# 根据模式计算价差
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if mode == 'OLS':
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a = c.close[window_slice, c.from_col] # 中芯国际
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b = c.close[window_slice, c.from_col + 1] # 华虹半导体
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memory.spread[c.i] = ols_spread_nb(a, b)
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elif mode == 'log_return':
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# 对数收益率方法
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logret_a = np.log(c.close[c.i, c.from_col] / c.close[c.i - 1, c.from_col])
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logret_b = np.log(c.close[c.i, c.from_col + 1] / c.close[c.i - 1, c.from_col + 1])
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memory.spread[c.i] = logret_a - logret_b
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else:
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raise ValueError("Unknown mode")
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# 计算z-score
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spread_mean = np.mean(memory.spread[window_slice])
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spread_std = np.std(memory.spread[window_slice])
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memory.zscore[c.i] = (memory.spread[c.i] - spread_mean) / spread_std
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# 使用前一个z-score生成交易信号（避免未来数据）
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if c.i > 0 and not np.isnan(memory.zscore[c.i - 1]):
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# 做空信号：z-score > 上界 且 当前不是做空状态
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if memory.zscore[c.i - 1] > params.upper and memory.status[0] != 1:
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size[0] = -params.order_pct1 # 卖空中芯国际
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size[1] = params.order_pct2 # 买入华虹半导体
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# 执行顺序：先卖后买
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c.call_seq_now[0] = 0
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c.call_seq_now[1] = 1
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memory.status[0] = 1 # 设置为做空状态
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# 做多信号：z-score < 下界 且 当前不是做多状态
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elif memory.zscore[c.i - 1] < params.lower and memory.status[0] != 2:
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size[0] = params.order_pct1 # 买入中芯国际
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size[1] = -params.order_pct2 # 卖空华虹半导体
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# 执行顺序：先卖后买
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c.call_seq_now[0] = 1
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c.call_seq_now[1] = 0
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memory.status[0] = 2 # 设置为做多状态
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else:
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size[0] = np.nan
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size[1] = np.nan
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else:
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size[0] = np.nan
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size[1] = np.nan
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# 设置估值价格
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c.last_val_price[c.from_col] = c.close[c.i - 1, c.from_col]
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c.last_val_price[c.from_col + 1] = c.close[c.i - 1, c.from_col + 1]
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return (size,)
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@njit
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def order_func_nb(c, size, price, commperc):
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"""执行订单"""
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group_col = c.col - c.from_col
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return vbt.portfolio.nb.order_nb(
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size=size[group_col],
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price=price[c.i, c.col],
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size_type=vbt.portfolio.enums.SizeType.TargetPercent,
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fees=commperc
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)
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# 准备价格数据
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print("准备回测数据...")
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price_data = pd.DataFrame({
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'SMIC': smic_data['close'],
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'HHIC': hhic_data['close']
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})
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# 运行配对交易回测
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print("运行配对交易回测...")
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portfolio = vbt.Portfolio.from_order_func(
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price_data,
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order_func_nb,
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pre_group_func_nb=pre_group_func_nb,
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pre_segment_func_nb=pre_segment_func_nb,
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pre_group_args=(
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np.array([PERIOD]), # period
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np.array([UPPER]), # upper
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np.array([LOWER]), # lower
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np.array([ORDER_PCT1]), # order_pct1
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np.array([ORDER_PCT2]) # order_pct2
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),
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pre_segment_args=(MODE,), # mode
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order_args=(COMMPERC,), # commperc
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group_by=np.array([0, 0]), # 将两列分为同一组
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cash_sharing=True, # 共享现金
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cash=INITIAL_CASH,
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freq='1D'
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)
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# 计算额外指标
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def calculate_additional_metrics(portfolio, price_data):
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"""计算额外指标"""
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# 价差和z-score（需要重新计算用于分析）
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smic_close = price_data['SMIC'].values
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hhic_close = price_data['HHIC'].values
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if MODE == 'OLS':
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# 使用OLS方法计算价差
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spread = np.full(len(smic_close), np.nan)
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for i in range(PERIOD, len(smic_close)):
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window_slice = slice(i - PERIOD + 1, i + 1)
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a = smic_close[window_slice]
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b = hhic_close[window_slice]
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spread[i] = ols_spread_nb(a, b)
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else:
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# 对数收益率方法
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spread = np.full(len(smic_close), np.nan)
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spread[1:] = np.log(smic_close[1:] / smic_close[:-1]) - np.log(hhic_close[1:] / hhic_close[:-1])
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# 计算z-score
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zscore = np.full(len(spread), np.nan)
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for i in range(PERIOD, len(spread)):
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window_slice = slice(i - PERIOD + 1, i + 1)
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spread_mean = np.nanmean(spread[window_slice])
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spread_std = np.nanstd(spread[window_slice])
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if spread_std > 0:
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zscore[i] = (spread[i] - spread_mean) / spread_std
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return pd.Series(spread, index=price_data.index), pd.Series(zscore, index=price_data.index)
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# 计算指标
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spread_series, zscore_series = calculate_additional_metrics(portfolio, price_data)
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# 生成交易信号
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short_signals = (zscore_series > UPPER).rename('short_signals')
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long_signals = (zscore_series < LOWER).rename('long_signals')
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# 可视化结果
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print("生成分析图表...")
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# 创建子图
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fig = plt.figure(figsize=(15, 12))
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# 1. 价格走势
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ax1 = plt.subplot(3, 1, 1)
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plt.plot(price_data.index, price_data['SMIC'], label='中芯国际(00981)', linewidth=1)
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plt.plot(price_data.index, price_data['HHIC'], label='华虹半导体(01347)', linewidth=1)
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plt.title('股票价格走势')
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plt.ylabel('价格(港元)')
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plt.legend()
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plt.grid(True, alpha=0.3)
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# 2. 价差和z-score
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ax2 = plt.subplot(3, 1, 2)
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plt.plot(spread_series.index, spread_series.values, label='价差', color='blue', linewidth=1)
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plt.title('价差走势')
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plt.ylabel('价差')
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plt.legend()
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plt.grid(True, alpha=0.3)
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ax3 = ax2.twinx()
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plt.plot(zscore_series.index, zscore_series.values, label='Z-Score', color='red', linewidth=1, alpha=0.7)
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plt.axhline(y=UPPER, color='red', linestyle='--', alpha=0.5, label=f'上界({UPPER})')
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plt.axhline(y=0, color='black', linestyle='-', alpha=0.3)
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plt.axhline(y=LOWER, color='green', linestyle='--', alpha=0.5, label=f'下界({LOWER})')
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plt.ylabel('Z-Score')
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plt.legend()
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# 标记交易信号
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short_points = zscore_series[short_signals]
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long_points = zscore_series[long_signals]
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plt.scatter(short_points.index, short_points.values, color='red', marker='v', s=30, label='做空信号')
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plt.scatter(long_points.index, long_points.values, color='green', marker='^', s=30, label='做多信号')
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# 3. 资产价值
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ax4 = plt.subplot(3, 1, 3)
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portfolio.value().vbt.plot(ax=ax4, title='投资组合价值')
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plt.ylabel('资产价值(港元)')
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plt.xlabel('日期')
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.show()
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# 输出回测结果
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print("\n" + "="*50)
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print("配对交易回测结果")
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print("="*50)
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# 基本统计
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stats = portfolio.stats()
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print(f"初始资金: {INITIAL_CASH:,.2f} 港元")
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print(f"最终资产: {portfolio.value().iloc[-1]:,.2f} 港元")
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print(f"总收益率: {stats['Total Return']:.2%}")
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print(f"年化收益率: {stats['Annual Return']:.2%}")
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print(f"夏普比率: {stats['Sharpe Ratio']:.2f}")
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print(f"最大回撤: {stats['Max Drawdown']:.2%}")
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print(f"总交易次数: {stats['Total Trades']}")
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# 交易统计
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orders = portfolio.orders()
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if len(orders) > 0:
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print(f"\n交易统计:")
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print(f"中芯国际交易次数: {len(orders[orders['Column'] == 'SMIC'])}")
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print(f"华虹半导体交易次数: {len(orders[orders['Column'] == 'HHIC'])}")
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# 计算平均持仓时间
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positions = portfolio.positions()
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if len(positions) > 0:
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avg_holding = positions.duration.mean()
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print(f"平均持仓时间: {avg_holding:.1f} 天")
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# 信号统计
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print(f"\n信号统计:")
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print(f"做空信号数量: {short_signals.sum()}")
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print(f"做多信号数量: {long_signals.sum()}")
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print(f"总信号数量: {short_signals.sum() + long_signals.sum()}")
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# 相关性分析
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correlation = price_data['SMIC'].corr(price_data['HHIC'])
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print(f"\n股票相关性: {correlation:.4f}")
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# 保存结果到文件
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results_df = pd.DataFrame({
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'Date': price_data.index,
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'SMIC_Price': price_data['SMIC'],
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'HHIC_Price': price_data['HHIC'],
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'Spread': spread_series,
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'ZScore': zscore_series,
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'Portfolio_Value': portfolio.value(),
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'Short_Signals': short_signals,
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'Long_Signals': long_signals
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})
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results_df.to_csv('pair_trading_results.csv', index=False)
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print(f"\n详细结果已保存到: pair_trading_results.csv")
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# 显示最近交易
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recent_trades = orders.tail(10)
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if len(recent_trades) > 0:
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print(f"\n最近10笔交易:")
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print(recent_trades[['Timestamp', 'Column', 'Size', 'Price', 'Fees']])
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127
comtom_indicator_example.py
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127
comtom_indicator_example.py
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import vectorbt as vbt
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import akshare as ak
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import datetime
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stock_00981 = ak.stock_hk_daily(symbol="00981")
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stock_01347 = ak.stock_hk_daily(symbol="01347")
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# 数据预处理 - 设置日期索引
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def prepare_data(df):
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"""准备数据，设置日期索引"""
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df = df.copy()
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df['date'] = pd.to_datetime(df['date'])
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df = df.set_index('date')
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return df
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stock_00981 = prepare_data(stock_00981)
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stock_01347 = prepare_data(stock_01347)
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stock_00981_close = stock_00981['close']
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print("中芯国际最近5个交易日收盘价:")
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print(stock_00981_close.tail(5))
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def custom_indicator(close, rsi_window=14, ma_window=50):
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# 计算日线RSI
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rsi = vbt.RSI.run(close, window=rsi_window).rsi
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# 计算日线移动平均
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ma = vbt.MA.run(close, window=ma_window).ma
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# 确保所有数组都是numpy数组，避免pandas索引问题
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close_np = close.to_numpy() if hasattr(close, 'to_numpy') else close
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rsi_np = rsi.to_numpy() if hasattr(rsi, 'to_numpy') else rsi
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ma_np = ma.to_numpy() if hasattr(ma, 'to_numpy') else ma
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||||
|
||||
# 使用numpy数组进行计算
|
||||
trend = np.where(rsi_np > 70, -1, 0)
|
||||
trend = np.where((rsi_np < 30) & (close_np < ma_np), 1, trend)
|
||||
return trend
|
||||
|
||||
# 创建自定义指标 - 移除 keep_pd=True
|
||||
ind = vbt.IndicatorFactory(
|
||||
class_name="Combination",
|
||||
short_name="comb",
|
||||
input_names=["close"],
|
||||
param_names=["rsi_window", "ma_window"],
|
||||
output_names=["value"]
|
||||
).from_apply_func(
|
||||
custom_indicator,
|
||||
rsi_window=14,
|
||||
ma_window=50
|
||||
# 移除 keep_pd=True
|
||||
)
|
||||
|
||||
# 运行指标
|
||||
res = ind.run(
|
||||
stock_00981_close,
|
||||
rsi_window=21,
|
||||
ma_window=[21,50,100]
|
||||
)
|
||||
|
||||
print("指标结果:")
|
||||
print(res.value)
|
||||
|
||||
# 生成交易信号
|
||||
entries = res.value == 1.0
|
||||
exits = res.value == -1.0
|
||||
|
||||
print(f"买入信号数量: {entries.sum().sum()}")
|
||||
print(f"卖出信号数量: {exits.sum().sum()}")
|
||||
|
||||
# 如果结果有多列，选择第一列
|
||||
if entries.ndim > 1 and entries.shape[1] > 1:
|
||||
entries = entries.iloc[:, 0]
|
||||
exits = exits.iloc[:, 0]
|
||||
print("使用第一组参数")
|
||||
|
||||
# 创建投资组合
|
||||
pf = vbt.Portfolio.from_signals(stock_00981_close, entries, exits, freq='D')
|
||||
|
||||
# 绘图
|
||||
try:
|
||||
pf.plot().show()
|
||||
except Exception as e:
|
||||
print(f"绘图错误: {e}")
|
||||
# 简化绘图
|
||||
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))
|
||||
|
||||
# 价格和信号
|
||||
ax1.plot(stock_00981_close.index, stock_00981_close.values, label='Close Price', color='blue')
|
||||
|
||||
# 买入信号
|
||||
buy_signals = entries.fillna(False)
|
||||
if buy_signals.any():
|
||||
buy_dates = stock_00981_close.index[buy_signals]
|
||||
buy_prices = stock_00981_close[buy_signals]
|
||||
ax1.scatter(buy_dates, buy_prices, color='green', marker='^', s=100, label='Buy')
|
||||
|
||||
# 卖出信号
|
||||
sell_signals = exits.fillna(False)
|
||||
if sell_signals.any():
|
||||
sell_dates = stock_00981_close.index[sell_signals]
|
||||
sell_prices = stock_00981_close[sell_signals]
|
||||
ax1.scatter(sell_dates, sell_prices, color='red', marker='v', s=100, label='Sell')
|
||||
|
||||
ax1.set_title('Price and Trading Signals')
|
||||
ax1.legend()
|
||||
ax1.grid(True)
|
||||
|
||||
# 投资组合价值
|
||||
portfolio_value = pf.value()
|
||||
ax2.plot(portfolio_value.index, portfolio_value.values, label='Portfolio Value', color='orange')
|
||||
ax2.set_title('Portfolio Value')
|
||||
ax2.legend()
|
||||
ax2.grid(True)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
print("-------------------")
|
||||
print("投资组合统计:")
|
||||
stats = pf.stats()
|
||||
print(stats)
|
||||
print(f"总收益率: {pf.total_return():.2%}")
|
||||
print("-------------------")
|
||||
BIN
python-for-algorithmic-trading-cookbook-main.zip
Normal file
BIN
python-for-algorithmic-trading-cookbook-main.zip
Normal file
Binary file not shown.
132
t.py
132
t.py
@ -1,127 +1,17 @@
|
||||
import vectorbt as vbt
|
||||
import akshare as ak
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import datetime
|
||||
|
||||
stock_00981 = ak.stock_hk_daily(symbol="00981")
|
||||
stock_01347 = ak.stock_hk_daily(symbol="01347")
|
||||
# 创建两个DataFrame
|
||||
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=['x', 'y', 'z'])
|
||||
df2 = pd.DataFrame({'A': [7, 8, 9], 'C': [10, 11, 12]}, index=['y', 'z', 'w'])
|
||||
|
||||
# 数据预处理 - 设置日期索引
|
||||
def prepare_data(df):
|
||||
"""准备数据，设置日期索引"""
|
||||
df = df.copy()
|
||||
df['date'] = pd.to_datetime(df['date'])
|
||||
df = df.set_index('date')
|
||||
return df
|
||||
print(df1)
|
||||
print(df2)
|
||||
|
||||
stock_00981 = prepare_data(stock_00981)
|
||||
stock_01347 = prepare_data(stock_01347)
|
||||
# 对齐两个DataFrame
|
||||
aligned_df1, aligned_df2 = df1.align(df2, broadcast_axis=0)
|
||||
|
||||
stock_00981_close = stock_00981['close']
|
||||
print("中芯国际最近5个交易日收盘价:")
|
||||
print(stock_00981_close.tail(5))
|
||||
|
||||
def custom_indicator(close, rsi_window=14, ma_window=50):
|
||||
# 计算日线RSI
|
||||
rsi = vbt.RSI.run(close, window=rsi_window).rsi
|
||||
|
||||
# 计算日线移动平均
|
||||
ma = vbt.MA.run(close, window=ma_window).ma
|
||||
|
||||
# 确保所有数组都是numpy数组，避免pandas索引问题
|
||||
close_np = close.to_numpy() if hasattr(close, 'to_numpy') else close
|
||||
rsi_np = rsi.to_numpy() if hasattr(rsi, 'to_numpy') else rsi
|
||||
ma_np = ma.to_numpy() if hasattr(ma, 'to_numpy') else ma
|
||||
|
||||
# 使用numpy数组进行计算
|
||||
trend = np.where(rsi_np > 70, -1, 0)
|
||||
trend = np.where((rsi_np < 30) & (close_np < ma_np), 1, trend)
|
||||
return trend
|
||||
|
||||
# 创建自定义指标 - 移除 keep_pd=True
|
||||
ind = vbt.IndicatorFactory(
|
||||
class_name="Combination",
|
||||
short_name="comb",
|
||||
input_names=["close"],
|
||||
param_names=["rsi_window", "ma_window"],
|
||||
output_names=["value"]
|
||||
).from_apply_func(
|
||||
custom_indicator,
|
||||
rsi_window=14,
|
||||
ma_window=50
|
||||
# 移除 keep_pd=True
|
||||
)
|
||||
|
||||
# 运行指标
|
||||
res = ind.run(
|
||||
stock_00981_close,
|
||||
rsi_window=21,
|
||||
ma_window=50
|
||||
)
|
||||
|
||||
print("指标结果:")
|
||||
print(res.value)
|
||||
|
||||
# 生成交易信号
|
||||
entries = res.value == 1.0
|
||||
exits = res.value == -1.0
|
||||
|
||||
print(f"买入信号数量: {entries.sum().sum()}")
|
||||
print(f"卖出信号数量: {exits.sum().sum()}")
|
||||
|
||||
# 如果结果有多列，选择第一列
|
||||
if entries.ndim > 1 and entries.shape[1] > 1:
|
||||
entries = entries.iloc[:, 0]
|
||||
exits = exits.iloc[:, 0]
|
||||
print("使用第一组参数")
|
||||
|
||||
# 创建投资组合
|
||||
pf = vbt.Portfolio.from_signals(stock_00981_close, entries, exits, freq='D')
|
||||
|
||||
# 绘图
|
||||
try:
|
||||
pf.plot().show()
|
||||
except Exception as e:
|
||||
print(f"绘图错误: {e}")
|
||||
# 简化绘图
|
||||
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))
|
||||
|
||||
# 价格和信号
|
||||
ax1.plot(stock_00981_close.index, stock_00981_close.values, label='Close Price', color='blue')
|
||||
|
||||
# 买入信号
|
||||
buy_signals = entries.fillna(False)
|
||||
if buy_signals.any():
|
||||
buy_dates = stock_00981_close.index[buy_signals]
|
||||
buy_prices = stock_00981_close[buy_signals]
|
||||
ax1.scatter(buy_dates, buy_prices, color='green', marker='^', s=100, label='Buy')
|
||||
|
||||
# 卖出信号
|
||||
sell_signals = exits.fillna(False)
|
||||
if sell_signals.any():
|
||||
sell_dates = stock_00981_close.index[sell_signals]
|
||||
sell_prices = stock_00981_close[sell_signals]
|
||||
ax1.scatter(sell_dates, sell_prices, color='red', marker='v', s=100, label='Sell')
|
||||
|
||||
ax1.set_title('Price and Trading Signals')
|
||||
ax1.legend()
|
||||
ax1.grid(True)
|
||||
|
||||
# 投资组合价值
|
||||
portfolio_value = pf.value()
|
||||
ax2.plot(portfolio_value.index, portfolio_value.values, label='Portfolio Value', color='orange')
|
||||
ax2.set_title('Portfolio Value')
|
||||
ax2.legend()
|
||||
ax2.grid(True)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
print("-------------------")
|
||||
print("投资组合统计:")
|
||||
stats = pf.stats()
|
||||
print(stats)
|
||||
print(f"总收益率: {pf.total_return():.2%}")
|
||||
print("-------------------")
|
||||
print("对齐后的df1:")
|
||||
print(aligned_df1)
|
||||
print("\n对齐后的df2:")
|
||||
print(aligned_df2)
|
||||
Reference in New Issue
Block a user