量化交易9backtrader回测十字星K线形态图

十字星形态图&＃xff0c;在股市中的应用&＃xff1a;

十字星是一种K线基本型态。十字星&＃xff0c;是一种只有上下影线&＃xff0c;没有实体的K线图。开盘价即是收盘价&＃xff0c;表示在交易中&＃xff0c;股价出现高于或低于开盘价成交&＃xff0c;但收盘价与开盘价相等。其中&＃xff1a;上影线越长&＃xff0c;表示卖压越重。下影线越长&＃xff0c;表示买盘旺盛。通常在股价高位或低位出现十字线&＃xff0c;可称为转机线&＃xff0c;意味着出现反转。

以上的文字笔者截取自百度百科&＃xff0c;笔者对以上的定义做个解读&＃xff1a;十字星作为反转形态出现&＃xff0c;出现在底部那么就会上涨&＃xff0c;出现在顶部&＃xff0c;那么就会下跌&＃xff0c;根据这个解读&＃xff0c;就得出一个策略&＃xff1a;底部十字星&＃xff0c;全仓买入&＃xff0c;顶部十字星&＃xff0c;全仓卖出。

综上&＃xff1a;今天笔者要回测的策略为&＃xff1a;底部十字星&＃xff0c;全仓买入&＃xff0c;顶部十字星&＃xff0c;全仓卖出

先上代码和结论&＃xff1a;

import tushare as ts
import pandas as pdimport datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
# Import the backtrader platform
import backtrader as bt
import talib as talib
import numpy as npclass MyStrategy(bt.Strategy):# 策略参数params &＃61; dict(printlog&＃61;False)def __init__(self):self.star &＃61; dict()# 定义全局变量self.count &＃61; 0for data in self.datas:# 转为tabib要求的数据格式opens &＃61; np.array(data.open.array)highs &＃61; np.array(data.high.array)lows &＃61; np.array(data.low.array)closes &＃61; np.array(data.close.array)print(opens)# 计算十字星数据&＃xff0c;结果为-100底部十字星&＃xff0c;结果为100顶部十字星&＃xff0c;0非十字星res &＃61; talib.CDLDOJISTAR(opens, highs, lows, closes)# 数据放入self中self.star[data._id] &＃61; resdef next(self):# 得到当前的账户价值total_value &＃61; self.broker.getcash()for data in self.datas:pos &＃61; self.getposition(data).size# 函数出现100就代表出现十字星形态&＃xff0c;做买入if total_value > 0 and self.star[data._id][self.count] &＃61;&＃61; -100:p_value &＃61; total_value * 0.9 / 10size &＃61; ((int(total_value / self.data.close[0]))) - ((int(total_value / self.data.close[0])) % 100) - 100self.buy(data&＃61;data, size&＃61;size)print(&＃39;出现底部十字星&＃xff0c;全仓买入,买入数量&＃39; &＃43; str(size) )# 买入后的出现顶部十字星全仓后卖出if pos > 0 and self.star[data._id][self.count] &＃61;&＃61; 100:# 全部卖出# 跟踪订单避免重复self.sell(data&＃61;data, size&＃61;pos)print(&＃39;出现顶部十字星&＃xff0c;卖出数量&＃39; &＃43; str(pos) )#自增处理self.count &＃61; self.count &＃43; 1def log(self, txt, dt&＃61;None, doprint&＃61;False):if self.params.printlog or doprint:dt &＃61; dt or self.datas[0].datetime.date(0)print(f&＃39;{dt.isoformat()},{txt}&＃39;)# 记录交易执行情况&＃xff08;可省略&＃xff0c;默认不输出结果&＃xff09;def notify_order(self, order):# 如果order为submitted/accepted,返回空if order.status in [order.Submitted, order.Accepted]:return# 如果order为buy/sell executed,报告价格结果if order.status in [order.Completed]:if order.isbuy():self.log(f&＃39;买入:\n价格:{order.executed.price:.2f},\成本:{order.executed.value:.2f},\数量:{order.executed.size:.2f},\手续费:{order.executed.comm:.2f}&＃39;)self.buyprice &＃61; order.executed.priceself.buycomm &＃61; order.executed.commelse:self.log(f&＃39;卖出:\n价格&＃xff1a;{order.executed.price:.2f},\成本: {order.executed.value:.2f},\数量:{order.executed.size:.2f},\手续费{order.executed.comm:.2f}&＃39;)self.bar_executed &＃61; len(self)# 如果指令取消/交易失败, 报告结果elif order.status in [order.Canceled, order.Margin, order.Rejected]:self.log(&＃39;交易失败&＃39;)self.order &＃61; None# 记录交易收益情况&＃xff08;可省略&＃xff0c;默认不输出结果&＃xff09;def notify_trade(self, trade):if not trade.isclosed:returnself.log(f&＃39;策略收益&＃xff1a;\n毛收益 {trade.pnl:.2f}, 净收益 {trade.pnlcomm:.2f}&＃39;)pro &＃61; ts.pro_api(&＃39;cbb257058b7cb228769b4949437c27c27e5132e882747dc80f01a5a5&＃39;)def ts_get_daily_stock(code, start_dt, end_dt):start_dt &＃61; start_dt.replace("&＃39;", "", 3);end_dt &＃61; end_dt.replace("&＃39;", "", 3);# start_dt &＃61; &＃39;20190101&＃39;# end_dt&＃61;&＃39;&＃39;print(code, start_dt, end_dt)data &＃61; pro.daily(ts_code&＃61;code, start_date&＃61;start_dt, end_date&＃61;end_dt)data[&＃39;trade_date&＃39;] &＃61; pd.to_datetime(data[&＃39;trade_date&＃39;])data[&＃39;trade_date&＃39;] &＃61; pd.to_datetime(data[&＃39;trade_date&＃39;])data &＃61; data.sort_values(by&＃61;&＃39;trade_date&＃39;)data.index &＃61; data[&＃39;trade_date&＃39;]data[&＃39;openinterest&＃39;] &＃61; 0data[&＃39;volume&＃39;] &＃61; data[&＃39;vol&＃39;]data &＃61; data[[&＃39;open&＃39;, &＃39;close&＃39;, &＃39;high&＃39;, &＃39;low&＃39;, &＃39;volume&＃39;]]return data# 读取选股的结果df &＃61; pd.read_csv(&＃39;stock_alpha.csv&＃39;)
df.columns &＃61; [&＃39;ts_code&＃39;, &＃39;name&＃39;, &＃39;alpha&＃39;, &＃39;start_dt&＃39;, &＃39;end_dt&＃39;]
min_a &＃61; df.sort_values(by&＃61;&＃39;alpha&＃39;)
min_a &＃61; min_a.iloc[:10, :]code &＃61; []
code &＃61; min_a[&＃39;ts_code&＃39;] # 股票代码start_dts &＃61; []
start_dts &＃61; min_a[&＃39;start_dt&＃39;] # 股票代码起始时间end_dts &＃61; []
end_dts &＃61; min_a[&＃39;end_dt&＃39;] # 股票代码结束时间for i in range(len(code)):data &＃61; ts_get_daily_stock(code.iloc[i], start_dts.iloc[i], end_dts.iloc[i]) # 字段分别为股票代码、开始日期、结束日期data.to_csv(code.iloc[i] &＃43; &＃39;.csv&＃39;)cerebro &＃61; bt.Cerebro()
for i in range(len(code)): # 循环获取股票历史数据dataframe &＃61; pd.read_csv(code.iloc[i] &＃43; &＃39;.csv&＃39;, index_col&＃61;0, parse_dates&＃61;True)dataframe[&＃39;openinterest&＃39;] &＃61; 0data &＃61; bt.feeds.PandasData(dataname&＃61;dataframe,fromdate&＃61;datetime.datetime(2008, 2, 20),todate&＃61;datetime.datetime(2022, 4, 5))
cerebro.adddata(data)# 回测设置
startcash &＃61; 100000.0
cerebro.broker.setcash(startcash)
# 设置佣金为千分之一
cerebro.broker.setcommission(commission&＃61;0.001)
# 添加策略
cerebro.addstrategy(MyStrategy, printlog&＃61;True)
cerebro.run()
# 获取回测结束后的总资金
portvalue &＃61; cerebro.broker.getvalue()
pnl &＃61; portvalue - startcash
# 打印结果
print(f&＃39;总资金: {round(portvalue,2)}&＃39;)
print(f&＃39;净收益: {round(pnl,2)}&＃39;)cerebro.plot()

总资金: 85693.93
净收益: -14306.07

笔者的数据是依旧是中国国航 601111&＃xff0c;csv的文件&＃xff0c;前面的文章可以获取到&＃xff0c;此处不再放出连接

结论是亏损&＃xff0c;看来单纯的用十字星来作为反转形态&＃xff0c;作为买卖点&＃xff0c;针对中国国航是不可靠的&＃xff0c;如果还有同学想要回测其他股票&＃xff0c;可以下载csv修改&＃xff0c;非软件专业的也可以&＃xff0c;联系笔者&＃xff0c;我帮着回测。

本文重点&＃xff1a;介绍一个函数

talib.CDLDOJISTAR(opens, highs, lows, closes)

计算十字星数据的函数&＃xff0c;结果为-100底部十字星&＃xff0c;结果为100顶部十字星&＃xff0c;0非十字星