文章采用均值为SMA(close, time_period = 3日)，利用(收盘价- 三日均线)计算偏离程度。
如果大于阈值(首个收盘价的2%)则开仓买入（卖出）
如果收盘价穿过均线说明均值偏离情况消失平仓。
文章采用Tick高频数据、也可以切换日收盘价数据进行改写。
话不多说上代码：
导出必要包和数据
import pandas as pdimport numpy as npimport datetimeimport talib as tlimport warningsfrom pyecharts import options as optsfrom pyecharts.globals import ThemeTypefrom pyecharts.charts import Kline,Line, Bar, Grid,Scatterwarnings.filterwarnings("ignore")data = pd.read_csv('你的数据路径')
使用Ta-Lib库计算SMA均线(本文采用Tick数据，如果换成日数据请注意修改参数)
查看一下偏离度与阈值的变化情况
data = xdata['month'] = data.index.map(lambda x: x.strftime('%Y.%m'))data['time'] = data.index.map(lambda x: x.time())# 选取 阈值threshold = data.close[0]*0.02data['SMA'] = tl.SMA(data.close, len(data[(data.index.date==data.index.date[0])])*3)data['distance'] = data['close'] - data['SMA']data['threshold'] = thresholddf = data[['threshold', 'distance']].resample('1D').last().round(2)df = df.dropna()L_ = (Line(init_opts=opts.InitOpts(theme=ThemeType.MACARONS, width='900px', height='500px')).add_xaxis(df.index.tolist()).add_yaxis("距离",df['distance'].tolist()).add_yaxis("下轨",(df['threshold']*-1).tolist()).add_yaxis("上轨",df['threshold'].tolist()).set_global_opts(yaxis_opts=opts.AxisOpts(is_scale=True),xaxis_opts=opts.AxisOpts(is_scale=True),title_opts=opts.TitleOpts(title="收盘价到均值的距离&阈值情况"),datazoom_opts=[opts.DataZoomOpts(type_='inside')]))L_.render_notebook()
目测来看交易次数不多
遍历所有单元格查看买卖情况，文章使用tick级数据，预设实时计算均值、查看举止和收盘价之间的偏离度，一旦出现开仓信号则以买一(卖一)价格报单，在下一个3秒内成交。此处为了简化暂时没有考虑报单成交的情况，假设以下一个tick的收盘价成交。（如果使用日级别数据、可以将s_init, s 删除）
# 记录当前位置position = 1# 记录当日可出售股票持仓s_init = 5# 记录整体股票持仓s = s_init# 记录买单卖单buy = []sell = []for i in range(0, len(data)-2):# 开仓判断# 偏离距离大于阈值，卖出if ((data['distance'][i] > threshold) & (data['distance'][i-1] < threshold) & (position==1) & (s_init>0)):sell.append([data.index[i+1], data['close'][i+1],200])position -= 1s_init -= 1s -= 1# 向下偏离且偏离距离大于阈值，买入elif ((data['distance'][i] < (-threshold)) & (data['distance'][i-1] > (-threshold)) & (position==1)):buy.append([data.index[i+1], data['close'][i+1],200])position += 1s += 1# 止盈平仓判断# 收盘价下穿均值，买入平仓elif ((position == 2) & (data['distance'][i]<= 0) & (data['distance'][i-1]>0) & (s_init>0)):sell.append([data.index[i+1], data['close'][i+1],200])position -= 1s_init -= 1s -= 1# 收盘价上穿均值，买入平仓elif ((position == 0) & (data['distance'][i]>= 0) & (data['distance'][i-1]<0)):buy.append([data.index[i+1], data['close'][i+1],200])position += 1s += 1if data.index[i].time() == datetime.time(15, 0):s_init = s# 最后收盘时平仓if len(sell)< len(buy):sell.append([data.index[-1],data['close'][-1], 200])elif len(sell)> len(buy):buy.append([data.index[-1],data['close'][-1], 200])
数据处理及盈亏计算
sell = pd.DataFrame(sell, columns = ['卖出时间', '卖出价格', '卖出数量'])buy = pd.DataFrame(buy, columns = ['买入时间', '买入价格', '买入数量'])sell.index = sell['卖出时间']buy.index = buy['买入时间']data = data.join(sell[['卖出价格','卖出数量']])data = data.join(buy[['买入价格', '买入数量']])data = data.round(2)net_profit = (sell['卖出价格']*sell['卖出数量']*0.998).sum()-(buy['买入价格']*buy['买入数量']).sum()
画图查看部分买入卖出情况，确保策略代码正常
data_plot = data[(data.month == data.month.unique()[3])]L_ = (Line(init_opts=opts.InitOpts(theme=ThemeType.MACARONS, width='900px', height='500px')).add_xaxis(data_plot.index.tolist()).add_yaxis("收盘价",data_plot['close'].tolist()).add_yaxis("均线上轨",(data_plot['SMA']+data_plot.threshold).round(2).tolist()).add_yaxis("均线",data_plot['SMA'].tolist()).add_yaxis("均线下轨",(data_plot['SMA']-data_plot.threshold).round(2).tolist()).set_global_opts(yaxis_opts=opts.AxisOpts(is_scale=True),xaxis_opts=opts.AxisOpts(is_scale=True),title_opts=opts.TitleOpts(title="均线&收盘价情况"),datazoom_opts=[opts.DataZoomOpts(type_='inside')]))S_ = (Scatter().add_xaxis(data_plot.index.tolist()).add_yaxis("卖出",data_plot['卖出价格'].tolist()).add_yaxis("买入",data_plot['买入价格'].tolist()).set_global_opts(yaxis_opts=opts.AxisOpts(is_scale=True),xaxis_opts=opts.AxisOpts(is_scale=True),datazoom_opts=[opts.DataZoomOpts(type_='inside')]))L_.overlap(S_)L_.render_notebook()
抽取的部分数据看出：不考虑配对情况的话，大部分买点是在买点上方。
画图查看股价变动和净盈亏之间的波动关系
data = data.fillna(0)data['敞口'] = (data['买入数量'].cumsum()-data['卖出数量'].cumsum())data['实现盈利'] = (data['卖出数量']*data['卖出价格']).cumsum()*0.998-(data['买入数量']*data['买入价格']).cumsum()data['净盈亏'] = (data['敞口'])*data['close']+(data['实现盈利'])data['净盈亏'] = data['净盈亏'].round(2)df = data[['close','净盈亏']].resample('1D').last()df = df.dropna()L_ = (Line(init_opts=opts.InitOpts(theme=ThemeType.MACARONS, width='900px', height='500px')).add_xaxis(df.index.tolist()).add_yaxis("收盘价",df['close'].tolist(),yaxis_index=1).add_yaxis("盈亏",df['净盈亏'].tolist()).extend_axis(yaxis=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value}元"))) # 添加一条蓝色的y轴.set_global_opts(yaxis_opts=opts.AxisOpts(is_scale=True),xaxis_opts=opts.AxisOpts(is_scale=True),title_opts=opts.TitleOpts(title="均值回归交易策略--盈亏、股价变化情况）"),datazoom_opts=[opts.DataZoomOpts(type_='inside')]))L_.render_notebook()
均值回归策略在震荡市表现优异，单边行情时表现较差，文章选取标的震荡行情较多，获得正向盈利且跑赢单纯持有。
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