{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# backtrader教程\n",
"\n",
"**by Zoe**\n",
"\n",
"
\n",
"
\n",
"
\n",
"\n",
"backtrader是基于Python的量化框架平台，内置了talib指标库、analyzer分析库等功能，具有回测速度快、易用性高以及扩展性好等特点。相较于网上已有教程，本教程从数据至分析进行了全面的介绍，着重阐述了数据清洗以及框架调整等部分，点出了backtrader的某些不足并提供了解决方法。\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"import backtrader as bt\n",
"import tushare as ts\n",
"import pandas as pd\n",
"from backtrader.feeds import PandasData"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. 数据加载\n",
"### 1.1. data collecting\n",
"\n",
"backtrader支持Yahoo等online的数据源和本地数据源。大家也可以自己造轮子，接wind,tushare等。\n",
"backtrader要求至少含有7列数据，包括datetime，open，high，low，close，volume，openinterest。本文把datetime设为了index。\n",
"\n",
"本文拉前复权数据，前复权就是从后往前复权，所以复权后最近的价格保持一致。至于后复权，vice versa。有些数据源的前复权机制会有负数的存在，这是因为计算复权的时候是价格减去了分红，而不是用的累计复权因子，https://xueqiu.com/3488649239/62074848 这篇文章已经介绍得很详细，本文就不抛砖引玉了。注意：在用累计因子的情况下，前后复权的选择不影响收益率。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# tushare\n",
"def get_single_kdata(code, start='2020-01-01', end='2021-08-18', index=False):\n",
" df = ts.get_k_data(code, autype='qfq', start=start, end=end, index=index)\n",
" df['date'] = pd.to_datetime(df['date'])\n",
" df['openinterest'] = 0\n",
" return df[['date', 'open', 'high', 'low', 'close', 'volume', 'openinterest']]\n",
"\n",
"# # yahoo\n",
"# def get_single_kdata(code, start='2020-01-01', end='2021-08-18'):\n",
"# df = bt.feeds.YahooFinanceData(\n",
"# dataname='AAPL',\n",
"# fromdate=datetime.strptime(start,'%Y-%m-%d'),\n",
"# todate=datetime.strptime(end,'%Y-%m-%d'),\n",
"# adjclose=True,\n",
"# adjvolume=True)\n",
"# return df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" date open high low close volume openinterest\n",
"0 2020-01-02 16.65 16.95 16.55 16.87 1530231.0 0\n",
"1 2020-01-03 16.94 17.31 16.92 17.18 1116194.0 0\n",
"2 2020-01-06 17.01 17.34 16.91 17.07 862083.0 0\n",
"3 2020-01-07 17.13 17.28 16.95 17.15 728607.0 0\n",
"4 2020-01-08 17.00 17.05 16.63 16.66 847824.0 0\n"
]
}
],
"source": [
"secu_lst = ['600000','000001']\n",
"kdata = {}\n",
"for secu in secu_lst:\n",
" kdata[secu] = get_single_kdata(secu)\n",
" \n",
"print(kdata['000001'].head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"对于一个策略，股票池常常是不断变化的，不可能只有一个起点和终点。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" date open high low close volume openinterest\n",
"0 2020-02-03 13.99 14.70 13.99 13.99 2259194.0 0\n",
"1 2020-02-04 14.05 14.66 14.02 14.60 1706172.0 0\n",
"2 2020-02-05 14.59 14.89 14.32 14.63 1491380.0 0\n",
"3 2020-02-06 14.81 14.87 14.51 14.77 1185815.0 0\n",
"4 2020-02-07 14.60 14.69 14.41 14.62 924852.0 0\n"
]
}
],
"source": [
"# 如果你想要的不同的start和end\n",
"# 自己定义\n",
"secu_lst = {'600000':{'start':'2020-01-01','end':'2020-07-18'},\n",
" '000001':{'start':'2020-02-01','end':'2020-08-18'}}\n",
"kdata = {}\n",
"for secu in secu_lst.keys():\n",
" kdata[secu] = get_single_kdata(secu, secu_lst[secu]['start'], secu_lst[secu]['end']).reset_index(drop=True)\n",
" \n",
"print(kdata['000001'].head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2. data cleansing and processing\n",
"\n",
"上述股票的日期并没有对齐，造成开始停止日不同的原因还有股池的筛选条件、新股上市、股票停牌等。而backtrader的运作机制next()需要all data都满足最小周期，否则需要调用prenext()。本文建议一个更简单的方法，补充数据使所有股票拥有同样的周期，并同时标注停牌日。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"start: 2020-01-01, end 2020-08-18\n",
"start: 2020-01-02 00:00:00, end 2020-08-18 00:00:00\n"
]
}
],
"source": [
"# 先拿大盘trading_dates\n",
"benchmark_start = min(secu_lst.values(), key=lambda x : x['start'])['start']\n",
"benchmark_end = max(secu_lst.values(), key=lambda x : x['end'])['end']\n",
"print(f\"start: {benchmark_start}, end {benchmark_end}\")\n",
"\n",
"benchmark = '399905' # 中证500\n",
"kdata['benchmark'] = get_single_kdata(benchmark, benchmark_start, benchmark_end, index=True) \n",
"trading_dates = kdata['benchmark']['date'].tolist()\n",
"print(f\"start: {trading_dates[0]}, end {trading_dates[-1]}\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"000001\n",
" open high low close volume openinterest suspend\n",
"date \n",
"2020-01-02 0.00 0.00 0.00 0.00 0.0 0.0 1\n",
"2020-01-03 0.00 0.00 0.00 0.00 0.0 0.0 1\n",
"2020-01-06 0.00 0.00 0.00 0.00 0.0 0.0 1\n",
"2020-01-07 0.00 0.00 0.00 0.00 0.0 0.0 1\n",
"2020-01-08 0.00 0.00 0.00 0.00 0.0 0.0 1\n",
"... ... ... ... ... ... ... ...\n",
"2020-08-12 14.21 14.50 14.15 14.38 1596812.0 0.0 0\n",
"2020-08-13 14.40 14.46 14.14 14.18 837262.0 0.0 0\n",
"2020-08-14 14.10 14.51 14.06 14.47 1103216.0 0.0 0\n",
"2020-08-17 14.60 15.35 14.55 15.19 3268028.0 0.0 0\n",
"2020-08-18 15.20 15.30 14.91 15.15 1350261.0 0.0 0\n",
"\n",
"[152 rows x 7 columns]\n",
"600000\n",
" open high low close volume openinterest suspend\n",
"date \n",
"2020-01-02 12.47 12.64 12.45 12.47 516290.0 0.0 0\n",
"2020-01-03 12.57 12.63 12.47 12.60 380188.0 0.0 0\n",
"2020-01-06 12.52 12.65 12.42 12.46 410011.0 0.0 0\n",
"2020-01-07 12.51 12.60 12.46 12.50 284214.0 0.0 0\n",
"2020-01-08 12.41 12.45 12.25 12.32 352405.0 0.0 0\n",
"... ... ... ... ... ... ... ...\n",
"2020-08-12 11.37 11.39 11.17 11.27 686395.0 0.0 1\n",
"2020-08-13 11.37 11.39 11.17 11.27 686395.0 0.0 1\n",
"2020-08-14 11.37 11.39 11.17 11.27 686395.0 0.0 1\n",
"2020-08-17 11.37 11.39 11.17 11.27 686395.0 0.0 1\n",
"2020-08-18 11.37 11.39 11.17 11.27 686395.0 0.0 1\n",
"\n",
"[152 rows x 7 columns]\n"
]
}
],
"source": [
"for secu in set(kdata.keys())-set(['benchmark']):\n",
" print(secu)\n",
" secu_kdata = kdata['benchmark'][['date']].merge(kdata[secu],how='left')\n",
" secu_kdata['suspend'] = 0 \n",
" secu_kdata.loc[secu_kdata['open'].isnull(), 'suspend'] = 1 # 标记为停盘日\n",
" secu_kdata.set_index(['date'], inplace = True) # 设date为index\n",
" end = secu_lst[secu]['end']\n",
" secu_kdata.fillna(method='ffill',inplace = True) # start后的数据用前日数据进行补充\n",
" secu_kdata.fillna(value = 0,inplace = True) #start前的数据用0补充\n",
" secu_kdata.loc[(secu_kdata.index > end), 'suspend'] = 1\n",
" print(secu_kdata)\n",
" kdata[secu] = secu_kdata"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"股票池的筛选也同理，补充数据就ok，但要注意，end是剔除日的后一根bar的日期。\n",
"整理成一个class会更清晰一点"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class GetKdatas(object):\n",
" def __init__(self, secu_lst, benchmark='000001'):\n",
" \"\"\"\n",
" :parameter secu_lst: a dict contained stocks with starts and ends\n",
" :parameter benchmark: the name of benchmark\n",
" \"\"\"\n",
" self.secu_lst = secu_lst\n",
" self.benchmark = benchmark\n",
"\n",
" @staticmethod\n",
" def get_single_kdata(code, start='2020-01-01', end='2021-08-18', index=False):\n",
" df = ts.get_k_data(code, autype='qfq', start=start, end=end, index=index)\n",
" df['date'] = pd.to_datetime(df['date'])\n",
" df['openinterest'] = 0\n",
" return df[['date', 'open', 'high', 'low', 'close', 'volume', 'openinterest']]\n",
"\n",
" def get_all_kdata(self):\n",
" kdata = {}\n",
" for secu in set(self.secu_lst):\n",
" secu_kdata = self.get_single_kdata(secu, self.secu_lst[secu]['start'], self.secu_lst[secu]['end'])\n",
" kdata[secu] = secu_kdata.reset_index(drop=True)\n",
" return kdata\n",
"\n",
" def merge_period(self):\n",
" all_kdata = self.get_all_kdata()\n",
" benchmark_start = min(self.secu_lst.values(), key=lambda x: x['start'])['start']\n",
" benchmark_end = max(self.secu_lst.values(), key=lambda x: x['end'])['end']\n",
" all_kdata['benchmark'] = self.get_single_kdata(self.benchmark, benchmark_start, benchmark_end,True)\n",
"\n",
" for secu in set(all_kdata.keys()) - set(['benchmark']):\n",
" secu_kdata = all_kdata['benchmark'][['date']].merge(all_kdata[secu], how='left')\n",
" secu_kdata['suspend'] = 0\n",
" secu_kdata.loc[secu_kdata['open'].isnull(), 'suspend'] = 1 # 标记为停盘日\n",
" secu_kdata.set_index(['date'], inplace=True) # 设date为index\n",
" end = secu_lst[secu]['end']\n",
" secu_kdata.fillna(method='ffill', inplace=True) # start后的数据用前日数据进行补充\n",
" secu_kdata.fillna(value=0, inplace=True) # start前的数据用0补充\n",
" secu_kdata.loc[(secu_kdata.index > end), 'suspend'] = 1\n",
" all_kdata[secu] = secu_kdata\n",
"\n",
" _ = all_kdata.pop('benchmark')\n",
" return all_kdata"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'000001': open high low close volume openinterest suspend\n",
"date \n",
"2020-01-02 0.00 0.00 0.00 0.00 0.0 0.0 1\n",
"2020-01-03 0.00 0.00 0.00 0.00 0.0 0.0 1\n",
"2020-01-06 0.00 0.00 0.00 0.00 0.0 0.0 1\n",
"2020-01-07 0.00 0.00 0.00 0.00 0.0 0.0 1\n",
"2020-01-08 0.00 0.00 0.00 0.00 0.0 0.0 1\n",
"... ... ... ... ... ... ... ...\n",
"2020-08-12 14.21 14.50 14.15 14.38 1596812.0 0.0 0\n",
"2020-08-13 14.40 14.46 14.14 14.18 837262.0 0.0 0\n",
"2020-08-14 14.10 14.51 14.06 14.47 1103216.0 0.0 0\n",
"2020-08-17 14.60 15.35 14.55 15.19 3268028.0 0.0 0\n",
"2020-08-18 15.20 15.30 14.91 15.15 1350261.0 0.0 0\n",
"\n",
"[152 rows x 7 columns], '600000': open high low close volume openinterest suspend\n",
"date \n",
"2020-01-02 12.47 12.64 12.45 12.47 516290.0 0.0 0\n",
"2020-01-03 12.57 12.63 12.47 12.60 380188.0 0.0 0\n",
"2020-01-06 12.52 12.65 12.42 12.46 410011.0 0.0 0\n",
"2020-01-07 12.51 12.60 12.46 12.50 284214.0 0.0 0\n",
"2020-01-08 12.41 12.45 12.25 12.32 352405.0 0.0 0\n",
"... ... ... ... ... ... ... ...\n",
"2020-08-12 11.37 11.39 11.17 11.27 686395.0 0.0 1\n",
"2020-08-13 11.37 11.39 11.17 11.27 686395.0 0.0 1\n",
"2020-08-14 11.37 11.39 11.17 11.27 686395.0 0.0 1\n",
"2020-08-17 11.37 11.39 11.17 11.27 686395.0 0.0 1\n",
"2020-08-18 11.37 11.39 11.17 11.27 686395.0 0.0 1\n",
"\n",
"[152 rows x 7 columns]}\n"
]
}
],
"source": [
"secu_lst = {'600000': {'start': '2020-01-01', 'end': '2020-07-18'},\n",
" '000001': {'start': '2020-02-01', 'end': '2020-08-18'}}\n",
"\n",
"kdata = GetKdatas(secu_lst).merge_period()\n",
"print(kdata)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### 1.3. data feeding\n",
"\n",
"接下来就是放入数据，backtrader默认仅datetime，open，high，low，close，volume，openinterest这7列。\n",
"
\n",
"默认顺序为open (default: 1) , high (default: 2), low (default: 3), close (default: 4), volume (default: 5), openinterest (default: 6)。\n",
"
\n",
"一定，一定要记得在add时加入*name=secu*，不然回溯股票的代码很麻烦"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"cerebro = bt.Cerebro()\n",
"for secu in set(kdata.keys())-set(['benchmark']):\n",
" df = PandasData(dataname=kdata[secu], fromdate=kdata[secu].index[0],todate=kdata[secu].index[-1])\n",
" cerebro.adddata(df, name=secu) \n",
"\n",
"# 如果是本地csv，如果你的column是和默认一样的，那从datetime开始都不用写，否则必须指明\n",
"# data = btfeeds.GenericCSVData(\n",
"# dataname='mydata.csv',\n",
"# fromdate=benchmark_start,\n",
"# todate=benchmark_end,\n",
"# dtformat=('%Y-%m-%d'),\n",
"# datetime=0,\n",
"# high=1,\n",
"# low=2,\n",
"# open=3,\n",
"# close=4,\n",
"# volume=5,\n",
"# openinterest=-1\n",
"# )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**如果你要增添其他的指标，可以根据以下模块。注意放入数据的时候是PandasData_Extend。如果是load本地文件，就把PandasData换成GenericCSVData，序号从序列8开始。"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"class PandasData_Extend(PandasData):\n",
" # Add a 'suspend' line to the inherited ones from the base class\n",
" lines = ('suspend',)\n",
" # 现在是(open,0), ... , (openinterest,5)这6列，所以增加1\n",
" # add the parameter to the parameters inherited from the base class\n",
" params = (('suspend', 6),)\n",
" \n",
" # 如果是csv文档，openinterest 在 GenericCSVData index是7，所以1\n",
" # params = (('suspend', 8),)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. 框架介绍\n",
"### 2.1. 初始资金、买卖佣金设置\n",
"\n",
"初始资金默认10000，但众所周知，初始金额的大小会影响整个策略收益。如果过小，会买不进某些大盘股。如果过大，会导致小盘股的买入划价增加很多，甚至无法购买预期数量的小盘股，因此需要设置初始资金。"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"初始资金1000000\n"
]
}
],
"source": [
"cerebro = bt.Cerebro()\n",
"# 设置初始资本为1 million\n",
"startcash = 10**6\n",
"cerebro.broker.setcash(startcash)\n",
"print(f\"初始资金{cerebro.broker.getvalue()}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"费率也是一大问题，券商佣金最低收五元，沪市过户费最低收一元。不要小看这些费率，当你做高频交易的时候，你的利润可能大都被这些费率吃掉了。\n",
"
\n",
"本文为简略，过户费没有进行对市场的判断。如需，加上判断即可。"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"class CommInfoPro(bt.CommInfoBase):\n",
"\n",
" params = (\n",
" ('stamp_duty', 0.001), # 印花税率\n",
" ('stamp_duty_fe', 1), # 最低印花税\n",
" ('commission', 0.001), # 佣金率\n",
" ('commission_fee', 5), # 最低佣金费\n",
" ('stocklike', True), # 股票\n",
" ('commtype', bt.CommInfoBase.COMM_PERC), # 按比例收\n",
" )\n",
"\n",
" def _getcommission(self, size, price, pseudoexec):\n",
" '''\n",
" If size is greater than 0, this indicates a long / buying of shares.\n",
" If size is less than 0, it idicates a short / selling of shares.\n",
" '''\n",
"\n",
" if size > 0: # 买入，不考虑印花税\n",
" return max(size * price * self.p.commission, self.p.commission_fee)\n",
" elif size < 0: # 卖出，考虑印花税\n",
" return max(size * price * (self.p.stamp_duty + self.p.commission), self.p.stamp_duty_fe)\n",
" else:\n",
" return 0 # just in case for some reason the size is 0.\n",
"\n",
" \n",
"cerebro.broker.addcommissioninfo(CommInfoPro())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2. 下单函数调整\n",
"众所周知，A股市以每手100股成交的，所以order size必须为100的整数倍。因此我们在计算的时候也应该注意这一点。一种方法是在backtrader内部修改order函数，在计算amount的时候downcast，因为jupyter不好展示这一步骤。本文通过在order的时候计算这一amount来解决。\n",
"\n",
"注意：都往下约，保持现金充足"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"400\n",
"500\n"
]
}
],
"source": [
"# 原有\n",
"#self.buy(data, amount)\n",
"#self.sell(data, amount)\n",
"\n",
"#改为\n",
"def downcast(amount, lot):\n",
" return abs(amount//lot*lot)\n",
"\n",
"print(downcast(418,100))\n",
"print(downcast(-418,100))\n",
"#self.buy(data, downcast(amount, lot))\n",
"#self.sell(data, downcast(amount, lot))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3. 增加股票信息\n",
"\n",
"backtrader把股票名设为了data._name，本文不建议通过这种方式进行抽取。最好是在linseries.py里加入装饰器@property\n",
"![](\"img1.png\")
"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# in linseries.py\n",
"# @property\n",
"# def name(self):\n",
"# return self._name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4 data和line的介绍\n",
"\n",
"line是backtrader的核心，是储存数据的重要形式。多条line组成lines,包括在data feed模块放入的open（开盘价）、close（收盘价）等6条line。可以把line看成是一条时间线，0代表当下的数据，-1代表前一根bar的数据，-2代表前两根bar的数据；同样，1代表后一根bar的数据，2代表后两根bar的数据，以此内推。\n",
"\n",
"data是line的集合，是line的展现形式。data代表单个个股（商品、债券等）的数据，包括open、close等lines，而多个data组成list形式的datas。在取某只股票的data时，可以通过self.datas[x]或者self.datax，x代表该股票被放入的顺序号（从0开始），也可以通过self.datasbyname[data._name]取得。\n",
"\n",
"backtrader也提供了指标的不同提取方式。以data0的close举例，包括：self.data0.close[0],self.data0_close[0],self.data0_0[0]（第二个0代表close的顺序号0）。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. 策略回测\n",
"### 3.1. 单只股票\n",
"接下来就开始单只股票的回测示范。本策略逻辑收盘价>20日均线就买入，否则卖出。\n",
"由于指标简单，可以在init里直接，计算节省时间。否则需要在next里每次进行计算\n",
"1. self.params.your_param等价于self.params.maperiod\n",
"2. self.datas是list格式，因为仅单只股票，所有长度为1。self.datas[0]代表的是这只股票的数据\n",
"\n",
"\n",
"在backtrader的order属性里，也是不包含股票的name。一种方法是修改order的class，本文从简，将name作为参数传入。\n",
"
\n",
"在买入卖出时，可以根据以下常用方法\n",
"1. self.order_target_percent(secu_data, target_pct, name=secu)\n",
"2. self.order_target_value(secu_data, target_val, name=secu)\n",
"3. self.buy(secu_data, order_amount, name=secu)\n",
"4. self.sell(secu_data, order_amount, name=secu)\n",
"\n",
"注意：本策略是以日为单位运行，以第二日开盘价作为交割价。backtrader的运行机制是以购买的资金/今日收盘价，得到购买数量后第二日开盘买入。所以买卖时为避免第二日开盘价与今日收盘价的差价，不能以全仓买入。 且在最后一天不做任何下单买卖。\n",
"
\n",
"注意：股票剔除是当下bar做出的决定，但是在后一根bar，也就是end_date进行的买卖。所以一定要放入后一根bar的开盘数据作为交割价，不然有未来函数。在策略购买卖中，需要规范在股票剔除日（end_date）必须卖出。"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"初始资金1000000\n",
"2020-02-06, 1000000.00元\n",
"2020-02-07, 1000000.00元\n",
"2020-02-10, 1000000.00元\n",
"2020-02-11, 1000000.00元\n",
"2020-02-12, 1000000.00元\n",
"2020-02-13, 1000000.00元\n",
"2020-02-14, 1000000.00元\n",
"2020-02-17, 1000000.00元\n",
"2020-02-18, 1000000.00元\n",
"2020-02-19, 1000000.00元\n",
"2020-02-20, 1000000.00元\n",
"2020-02-20, 买600000, price:11.23, amout: 87200.0\n",
"2020-02-21, 买入600000, 成交量87200.0，成交价11.23\n",
"2020-02-21, 1006094.21元\n",
"2020-02-21, 卖600000, price:11.30, pct: 0\n",
"2020-02-24, 卖出600000, 成交量-87200.0，成交价11.23\n",
"2020-02-24, 999989.21元\n",
"2020-02-24, 买600000, price:11.16, amout: 87800.0\n",
"2020-02-25, 买入600000, 成交量87800.0，成交价11.07\n",
"2020-02-25, 1001735.49元\n",
"2020-02-25, 卖600000, price:11.09, pct: 0\n",
"2020-02-26, 卖出600000, 成交量-87800.0，成交价11.01\n",
"2020-02-26, 994710.49元\n",
"2020-02-26, 买600000, price:11.20, amout: 87000.0\n",
"2020-02-27, 买入600000, 成交量87000.0，成交价11.20\n",
"2020-02-27, 995570.74元\n",
"2020-02-27, 卖600000, price:11.21, pct: 0\n",
"2020-02-28, 卖出600000, 成交量-87000.0，成交价11.11\n",
"2020-02-28, 986869.74元\n",
"2020-03-02, 986869.74元\n",
"2020-03-02, 买600000, price:11.04, amout: 87600.0\n",
"2020-03-03, 买入600000, 成交量87600.0，成交价11.13\n",
"2020-03-03, 980727.99元\n",
"2020-03-03, 卖600000, price:11.06, pct: 0\n",
"2020-03-04, 卖出600000, 成交量-87600.0，成交价11.01\n",
"2020-03-04, 976346.99元\n",
"2020-03-04, 买600000, price:11.03, amout: 86700.0\n",
"2020-03-05, 买入600000, 成交量86700.0，成交价11.08\n",
"2020-03-05, 997145.39元\n",
"2020-03-05, 卖600000, price:11.32, pct: 0\n",
"2020-03-06, 卖出600000, 成交量-86700.0，成交价11.23\n",
"2020-03-06, 989341.39元\n",
"2020-03-06, 买600000, price:11.12, amout: 87100.0\n",
"2020-03-09, 买入600000, 成交量87100.0，成交价11.00\n",
"2020-03-09, 970169.81元\n",
"2020-03-10, 978008.81元\n",
"2020-03-11, 969298.81元\n",
"2020-03-12, 957975.81元\n",
"2020-03-13, 963201.81元\n",
"2020-03-16, 946652.81元\n",
"2020-03-17, 939684.81元\n",
"2020-03-18, 920522.81元\n",
"2020-03-19, 897005.81元\n",
"2020-03-20, 910070.81元\n",
"2020-03-23, 888295.81元\n",
"2020-03-24, 907457.81元\n",
"2020-03-25, 915296.81元\n",
"2020-03-26, 922264.81元\n",
"2020-03-27, 923135.81元\n",
"2020-03-30, 926619.81元\n",
"2020-03-31, 915296.81元\n",
"2020-04-01, 910070.81元\n",
"2020-04-02, 919651.81元\n",
"2020-04-03, 915296.81元\n",
"2020-04-07, 926619.81元\n",
"2020-04-08, 923135.81元\n",
"2020-04-09, 919651.81元\n",
"2020-04-10, 920522.81元\n",
"2020-04-10, 卖600000, price:10.21, pct: 0\n",
"2020-04-13, 卖出600000, 成交量-87100.0，成交价10.15\n",
"2020-04-13, 915295.81元\n",
"2020-04-14, 915295.81元\n",
"2020-04-14, 买600000, price:10.25, amout: 87500.0\n",
"2020-04-15, 买入600000, 成交量87500.0，成交价10.20\n",
"2020-04-15, 913536.88元\n",
"2020-04-15, 卖600000, price:10.18, pct: 0\n",
"2020-04-16, 卖出600000, 成交量-87500.0，成交价10.10\n",
"2020-04-16, 906535.88元\n",
"2020-04-16, 买600000, price:10.16, amout: 87400.0\n",
"2020-04-17, 买入600000, 成交量87400.0，成交价10.18\n",
"2020-04-17, 908274.98元\n",
"2020-04-17, 卖600000, price:10.20, pct: 0\n",
"2020-04-20, 卖出600000, 成交量-87400.0，成交价10.15\n",
"2020-04-20, 903903.98元\n",
"2020-04-21, 903903.98元\n",
"2020-04-22, 903903.98元\n",
"2020-04-23, 903903.98元\n",
"2020-04-24, 903903.98元\n",
"2020-04-27, 903903.98元\n",
"2020-04-27, 买600000, price:10.34, amout: 85600.0\n",
"2020-04-28, 买入600000, 成交量85600.0，成交价10.36\n",
"2020-04-28, 901327.12元\n",
"2020-04-28, 卖600000, price:10.33, pct: 0\n",
"2020-04-29, 卖出600000, 成交量-85600.0，成交价10.35\n",
"2020-04-29, 903038.12元\n",
"2020-04-29, 买600000, price:10.61, amout: 83400.0\n",
"2020-04-30, 买入600000, 成交量83400.0，成交价10.58\n",
"2020-04-30, 907199.29元\n",
"2020-04-30, 卖600000, price:10.63, pct: 0\n",
"2020-05-06, 卖出600000, 成交量-83400.0，成交价10.44\n",
"2020-05-06, 891352.29元\n",
"2020-05-06, 买600000, price:10.46, amout: 83500.0\n",
"2020-05-07, 买入600000, 成交量83500.0，成交价10.45\n",
"2020-05-07, 886333.57元\n",
"2020-05-07, 卖600000, price:10.39, pct: 0\n",
"2020-05-08, 卖出600000, 成交量-83500.0，成交价10.44\n",
"2020-05-08, 890507.57元\n",
"2020-05-08, 买600000, price:10.44, amout: 83500.0\n",
"2020-05-11, 买入600000, 成交量83500.0，成交价10.44\n",
"2020-05-11, 889663.85元\n",
"2020-05-11, 卖600000, price:10.43, pct: 0\n",
"2020-05-12, 卖出600000, 成交量-83500.0，成交价10.44\n",
"2020-05-12, 890497.85元\n",
"2020-05-12, 买600000, price:10.34, amout: 84300.0\n",
"2020-05-13, 买入600000, 成交量84300.0，成交价10.31\n",
"2020-05-13, 896390.16元\n",
"2020-05-13, 卖600000, price:10.38, pct: 0\n",
"2020-05-14, 卖出600000, 成交量-84300.0，成交价10.32\n",
"2020-05-14, 891331.16元\n",
"2020-05-14, 买600000, price:10.30, amout: 84800.0\n",
"2020-05-15, 买入600000, 成交量84800.0，成交价10.36\n",
"2020-05-15, 884538.37元\n",
"2020-05-18, 887930.37元\n",
"2020-05-18, 卖600000, price:10.32, pct: 0\n",
"2020-05-19, 卖出600000, 成交量-84800.0，成交价10.45\n",
"2020-05-19, 898953.37元\n",
"2020-05-19, 买600000, price:10.35, amout: 85100.0\n",
"2020-05-20, 买入600000, 成交量85100.0，成交价10.37\n",
"2020-05-20, 902348.55元\n",
"2020-05-20, 卖600000, price:10.41, pct: 0\n",
"2020-05-21, 卖出600000, 成交量-85100.0，成交价10.44\n",
"2020-05-21, 904900.55元\n",
"2020-05-21, 买600000, price:10.33, amout: 85800.0\n",
"2020-05-22, 买入600000, 成交量85800.0，成交价10.30\n",
"2020-05-22, 891163.71元\n",
"2020-05-25, 902317.71元\n",
"2020-05-26, 902317.71元\n",
"2020-05-27, 909181.71元\n",
"2020-05-28, 924625.71元\n",
"2020-05-28, 卖600000, price:10.53, pct: 0\n",
"2020-05-29, 卖出600000, 成交量-85800.0，成交价10.45\n",
"2020-05-29, 917760.71元\n",
"2020-05-29, 买600000, price:10.57, amout: 85000.0\n",
"2020-06-01, 买入600000, 成交量85000.0，成交价10.63\n",
"2020-06-01, 919451.68元\n",
"2020-06-01, 卖600000, price:10.65, pct: 0\n",
"2020-06-02, 卖出600000, 成交量-85000.0，成交价10.58\n",
"2020-06-02, 913500.68元\n",
"2020-06-02, 买600000, price:10.68, amout: 83800.0\n",
"2020-06-03, 买入600000, 成交量83800.0，成交价10.75\n",
"2020-06-03, 909301.67元\n",
"2020-06-03, 卖600000, price:10.70, pct: 0\n",
"2020-06-04, 卖出600000, 成交量-83800.0，成交价10.80\n",
"2020-06-04, 917680.67元\n",
"2020-06-04, 买600000, price:10.64, amout: 84500.0\n",
"2020-06-05, 买入600000, 成交量84500.0，成交价10.68\n",
"2020-06-05, 916826.64元\n",
"2020-06-05, 卖600000, price:10.67, pct: 0\n",
"2020-06-08, 卖出600000, 成交量-84500.0，成交价10.64\n",
"2020-06-08, 914290.64元\n",
"2020-06-08, 买600000, price:10.61, amout: 84400.0\n",
"2020-06-09, 买入600000, 成交量84400.0，成交价10.62\n",
"2020-06-09, 919345.68元\n",
"2020-06-09, 卖600000, price:10.68, pct: 0\n",
"2020-06-10, 卖出600000, 成交量-84400.0，成交价10.71\n",
"2020-06-10, 921876.68元\n",
"2020-06-10, 买600000, price:10.58, amout: 85300.0\n",
"2020-06-11, 买入600000, 成交量85300.0，成交价10.58\n",
"2020-06-11, 913337.65元\n",
"2020-06-11, 卖600000, price:10.48, pct: 0\n",
"2020-06-12, 卖出600000, 成交量-85300.0，成交价10.58\n",
"2020-06-12, 921866.65元\n",
"2020-06-12, 买600000, price:10.55, amout: 85600.0\n",
"2020-06-15, 买入600000, 成交量85600.0，成交价10.41\n",
"2020-06-15, 916721.74元\n",
"2020-06-16, 925281.74元\n",
"2020-06-17, 926993.74元\n",
"2020-06-18, 929561.74元\n",
"2020-06-19, 938977.74元\n",
"2020-06-19, 卖600000, price:10.61, pct: 0\n",
"2020-06-22, 卖出600000, 成交量-85600.0，成交价10.57\n",
"2020-06-22, 935552.74元\n",
"2020-06-22, 买600000, price:10.55, amout: 86900.0\n",
"2020-06-23, 买入600000, 成交量86900.0，成交价10.51\n",
"2020-06-23, 932936.61元\n",
"2020-06-24, 943364.61元\n",
"2020-06-24, 卖600000, price:10.60, pct: 0\n",
"2020-06-29, 卖出600000, 成交量-86900.0，成交价10.63\n",
"2020-06-29, 945970.61元\n",
"2020-06-29, 买600000, price:10.57, amout: 87700.0\n",
"2020-06-30, 买入600000, 成交量87700.0，成交价10.59\n",
"2020-06-30, 945084.32元\n",
"2020-06-30, 卖600000, price:10.58, pct: 0\n",
"2020-07-01, 卖出600000, 成交量-87700.0，成交价10.59\n",
"2020-07-01, 945960.32元\n",
"2020-07-01, 买600000, price:10.74, amout: 86300.0\n",
"2020-07-02, 买入600000, 成交量86300.0，成交价10.73\n",
"2020-07-02, 973567.06元\n",
"2020-07-02, 卖600000, price:11.05, pct: 0\n",
"2020-07-03, 卖出600000, 成交量-86300.0，成交价11.08\n",
"2020-07-03, 976155.06元\n",
"2020-07-03, 买600000, price:11.19, amout: 85400.0\n",
"2020-07-06, 买入600000, 成交量85400.0，成交价11.30\n",
"2020-07-06, 1053005.41元\n",
"2020-07-06, 卖600000, price:12.20, pct: 0\n",
"2020-07-07, 卖出600000, 成交量-85400.0，成交价12.45\n",
"2020-07-07, 1074354.41元\n",
"2020-07-07, 买600000, price:12.11, amout: 86900.0\n",
"2020-07-08, 买入600000, 成交量86900.0，成交价12.17\n",
"2020-07-08, 1071736.84元\n",
"2020-07-08, 卖600000, price:12.14, pct: 0\n",
"2020-07-09, 卖出600000, 成交量-86900.0，成交价12.26\n",
"2020-07-09, 1082163.84元\n",
"2020-07-09, 买600000, price:11.99, amout: 88400.0\n",
"2020-07-10, 买入600000, 成交量88400.0，成交价11.98\n",
"2020-07-10, 1048561.25元\n",
"2020-07-10, 卖600000, price:11.60, pct: 0\n",
"2020-07-13, 卖出600000, 成交量-88400.0，成交价11.60\n",
"2020-07-13, 1048560.25元\n",
"2020-07-13, 买600000, price:11.66, amout: 88100.0\n",
"2020-07-14, 买入600000, 成交量88100.0，成交价11.61\n",
"2020-07-14, 1028287.02元\n",
"2020-07-14, 卖600000, price:11.38, pct: 0\n",
"2020-07-15, 卖出600000, 成交量-88100.0，成交价11.47\n",
"2020-07-15, 1036215.02元\n",
"2020-07-15, 买600000, price:11.18, amout: 90800.0\n",
"2020-07-16, 买入600000, 成交量90800.0，成交价11.25\n",
"2020-07-16, 1031664.80元\n",
"2020-07-16, 卖600000, price:11.20, pct: 0\n",
"2020-07-17, 卖出600000, 成交量-90800.0，成交价11.37\n",
"结束资金: 1047099.8\n"
]
}
],
"source": [
"class single_strategy(bt.Strategy):\n",
" # 全局设定交易策略的参数\n",
" params = (\n",
" ('maperiod', 20),\n",
" )\n",
"\n",
" def __init__(self):\n",
" # 初始化交易指令\n",
" self.order = None\n",
"\n",
" # 添加移动均线指标，内置了talib模块\n",
" self.sma = bt.ind.SMA(self.datas[0], period=self.params.maperiod)\n",
"\n",
" # 可以不要，但如果你数据未对齐，需要在这里检验\n",
" def prenext(self):\n",
" pass\n",
" \n",
" def downcast(amount, lot):\n",
" return abs(amount//lot*lot)\n",
" \n",
" \n",
" def next(self):\n",
" if self.order: # 检查是否有指令等待执行,如果有就不执行这根bar\n",
" return\n",
" \n",
" # 回测最后一天不进行买卖\n",
" if self.datas[0].datetime.date(0) == end_date:\n",
" return \n",
" \n",
" # 拿这根bar时期的所有资产价值（如果按日K数据放入，即代表今日的资产价值）\n",
" self.log(\"%.2f元\" % self.broker.getvalue()) \n",
" if not self.position: # 没有持仓\n",
" \n",
" # 执行买入条件判断：收盘价格上涨突破20日均线；\n",
" # 不要在股票剔除日前一天进行买入\n",
" if self.datas[0].close > self.sma and data.datetime.date(1) < end_date:\n",
" # 永远不要满仓买入某只股票\n",
" order_value = self.broker.getvalue()*0.98\n",
" order_amount = downcast(order_value/self.datas[0].close[0], 100)\n",
" self.order = self.buy(self.datas[0], size=order_amount, name=self.datas[0]._name)\n",
" self.log(f\"买{self.datas[0]._name}, price:{self.datas[0].close[0]:.2f}, amout: {order_amount}\")\n",
" # self.order = self.order_target_percent(self.datas[0], 0.98, name=self.datas[0]._name)\n",
" # self.log(f\"买{self.datas[0]._name}, price:{self.datas[0].close[0]:.2f}, pct: 0.98\")\n",
" else:\n",
" \n",
" # 执行卖出条件判断：收盘价格跌破20日均线，或者股票剔除\n",
" if self.datas[0].close > self.sma or data.datetime.date(1) >= end_date:\n",
" # 执行卖出\n",
" self.order = self.order_target_percent(self.datas[0], 0, name=self.datas[0]._name)\n",
" self.log(f\"卖{self.datas[0]._name}, price:{self.datas[0].close[0]:.2f}, pct: 0\")\n",
"\n",
" def log(self, txt, dt=None):\n",
" ''' 输出日志'''\n",
" dt = dt or self.datas[0].datetime.date(0) # 拿现在的日期\n",
" print('%s, %s' % (dt.isoformat(), txt))\n",
"\n",
" def notify_order(self, order):\n",
" if order.status in [order.Submitted, order.Accepted]:\n",
" # Buy/Sell order submitted/accepted to/by broker - Nothing to do\n",
" return\n",
"\n",
" # Check if an order has been completed\n",
" # Attention: broker could reject order if not enough cash\n",
" if order.status in [order.Completed, order.Canceled, order.Margin]:\n",
" if order.isbuy():\n",
" self.log(f\"\"\"买入{order.info['name']}, 成交量{order.executed.size}，成交价{order.executed.price:.2f}\"\"\")\n",
" elif order.issell():\n",
" self.log(f\"\"\"卖出{order.info['name']}, 成交量{order.executed.size}，成交价{order.executed.price:.2f}\"\"\")\n",
" self.bar_executed = len(self)\n",
"\n",
" # Write down: no pending order\n",
" self.order = None\n",
"\n",
"\n",
"cerebro = bt.Cerebro()\n",
"cerebro.addstrategy(single_strategy)\n",
"secu_lst = {'600000': {'start': '2020-01-01', 'end': '2020-07-18'}}\n",
"df = GetKdatas(secu_lst).merge_period()['600000']\n",
"data = PandasData_Extend(dataname=df, fromdate=df.index[0], todate=df.index[-1])\n",
"cerebro.adddata(data, name='600000')\n",
"end_date = df.index[-1] # 股票剔除日\n",
"\n",
"# 设置初始资本为1 million\n",
"startcash = 10**6\n",
"cerebro.broker.setcash(startcash)\n",
"print(f\"初始资金{cerebro.broker.getvalue()}\")\n",
"# 设置交易手续费\n",
"cerebro.broker.addcommissioninfo(CommInfoPro())\n",
"# 运行回测系统\n",
"cerebro.run()\n",
"# 获取回测结束后的总资金\n",
"portvalue = cerebro.broker.getvalue()\n",
"# 打印结果\n",
"print(f'结束资金: {round(portvalue, 2)}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2. 多只股票\n",
"策略逻辑和上面的相同。\n",
"计算均线的时候用了dict循环计算每只股票的指标。\n",
"1. self.getdatanames()按顺序返回所有股票的名称list\n",
"2. self.getdatabyname(secu_name):返回该股票的data"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"初始资金1000000\n",
"2020-02-06, 1000000.00, []\n",
"2020-02-06, 买000001, price:14.77, amout: 32400.0\n",
"2020-02-07, 买入000001, 成交量32400.0，成交价14.60\n",
"2020-02-07, 1000643.00, [('000001', 32400.0)]\n",
"2020-02-10, 996755.00, [('000001', 32400.0)]\n",
"2020-02-11, 1006151.00, [('000001', 32400.0)]\n",
"2020-02-12, 1005503.00, [('000001', 32400.0)]\n",
"2020-02-13, 1001615.00, [('000001', 32400.0)]\n",
"2020-02-14, 1013927.00, [('000001', 32400.0)]\n",
"2020-02-17, 1024943.00, [('000001', 32400.0)]\n",
"2020-02-18, 1019435.00, [('000001', 32400.0)]\n",
"2020-02-19, 1020731.00, [('000001', 32400.0)]\n",
"2020-02-20, 1032071.00, [('000001', 32400.0)]\n",
"2020-02-20, 买600000, price:11.23, amout: 44100.0\n",
"2020-02-21, 买入600000, 成交量44100.0，成交价11.23\n",
"2020-02-21, 1034829.00, [('000001', 32400.0), ('600000', 44100.0)]\n",
"2020-02-24, 1017315.00, [('000001', 32400.0), ('600000', 44100.0)]\n",
"2020-02-25, 1008072.00, [('000001', 32400.0), ('600000', 44100.0)]\n",
"2020-02-26, 1011303.00, [('000001', 32400.0), ('600000', 44100.0)]\n",
"2020-02-27, 1015632.00, [('000001', 32400.0), ('600000', 44100.0)]\n",
"2020-02-28, 979992.00, [('000001', 32400.0), ('600000', 44100.0)]\n",
"2020-02-28, 卖000001, price:14.50, pct: 0\n",
"2020-02-28, 卖600000, price:10.85, pct: 0\n",
"2020-03-02, 卖出000001, 成交量-32400.0，成交价14.55\n",
"2020-03-02, 卖出600000, 成交量-44100.0，成交价10.95\n",
"2020-03-02, 986020.00, []\n",
"2020-03-02, 买600000, price:11.04, amout: 42800.0\n",
"2020-03-03, 买入600000, 成交量42800.0，成交价11.13\n",
"2020-03-03, 983019.00, [('600000', 42800.0)]\n",
"2020-03-04, 981735.00, [('600000', 42800.0)]\n",
"2020-03-05, 994147.00, [('600000', 42800.0)]\n",
"2020-03-05, 买000001, price:15.39, amout: 31000.0\n",
"2020-03-06, 买入000001, 成交量31000.0，成交价15.18\n",
"2020-03-06, 980932.00, [('600000', 42800.0), ('000001', 31000.0)]\n",
"2020-03-09, 948400.00, [('600000', 42800.0), ('000001', 31000.0)]\n",
"2020-03-09, 卖600000, price:10.78, pct: 0\n",
"2020-03-09, 卖000001, price:14.45, pct: 0\n",
"2020-03-10, 卖出600000, 成交量-42800.0，成交价10.71\n",
"2020-03-10, 卖出000001, 成交量-31000.0，成交价14.38\n",
"2020-03-10, 943232.00, []\n",
"2020-03-11, 943232.00, []\n",
"2020-03-12, 943232.00, []\n",
"2020-03-13, 943232.00, []\n",
"2020-03-16, 943232.00, []\n",
"2020-03-17, 943232.00, []\n",
"2020-03-18, 943232.00, []\n",
"2020-03-19, 943232.00, []\n",
"2020-03-20, 943232.00, []\n",
"2020-03-23, 943232.00, []\n",
"2020-03-24, 943232.00, []\n",
"2020-03-25, 943232.00, []\n",
"2020-03-26, 943232.00, []\n",
"2020-03-27, 943232.00, []\n",
"2020-03-30, 943232.00, []\n",
"2020-03-31, 943232.00, []\n",
"2020-04-01, 943232.00, []\n",
"2020-04-02, 943232.00, []\n",
"2020-04-03, 943232.00, []\n",
"2020-04-07, 943232.00, []\n",
"2020-04-08, 943232.00, []\n",
"2020-04-09, 943232.00, []\n",
"2020-04-10, 943232.00, []\n",
"2020-04-10, 买600000, price:10.21, amout: 44300.0\n",
"2020-04-13, 买入600000, 成交量44300.0，成交价10.15\n",
"2020-04-13, 941455.00, [('600000', 44300.0)]\n",
"2020-04-14, 947657.00, [('600000', 44300.0)]\n",
"2020-04-14, 买000001, price:12.86, amout: 35300.0\n",
"2020-04-15, 买入000001, 成交量35300.0，成交价12.86\n",
"2020-04-15, 944904.00, [('600000', 44300.0), ('000001', 35300.0)]\n",
"2020-04-16, 937311.00, [('600000', 44300.0), ('000001', 35300.0)]\n",
"2020-04-16, 卖000001, price:12.68, pct: 0\n",
"2020-04-17, 卖出000001, 成交量-35300.0，成交价12.77\n",
"2020-04-17, 942259.00, [('600000', 44300.0)]\n",
"2020-04-17, 买000001, price:12.89, amout: 35000.0\n",
"2020-04-20, 买入000001, 成交量35000.0，成交价12.86\n",
"2020-04-20, 942817.00, [('600000', 44300.0), ('000001', 35000.0)]\n",
"2020-04-20, 卖600000, price:10.11, pct: 0\n",
"2020-04-21, 卖出600000, 成交量-44300.0，成交价10.07\n",
"2020-04-21, 957144.00, [('000001', 35000.0)]\n",
"2020-04-22, 949444.00, [('000001', 35000.0)]\n",
"2020-04-23, 949444.00, [('000001', 35000.0)]\n",
"2020-04-24, 949794.00, [('000001', 35000.0)]\n",
"2020-04-27, 958894.00, [('000001', 35000.0)]\n",
"2020-04-27, 买600000, price:10.34, amout: 44500.0\n",
"2020-04-28, 买入600000, 成交量44500.0，成交价10.36\n",
"2020-04-28, 958254.00, [('000001', 35000.0), ('600000', 44500.0)]\n",
"2020-04-29, 988214.00, [('000001', 35000.0), ('600000', 44500.0)]\n",
"2020-04-30, 985954.00, [('000001', 35000.0), ('600000', 44500.0)]\n",
"2020-05-06, 972789.00, [('000001', 35000.0), ('600000', 44500.0)]\n",
"2020-05-07, 966874.00, [('000001', 35000.0), ('600000', 44500.0)]\n",
"2020-05-08, 978199.00, [('000001', 35000.0), ('600000', 44500.0)]\n",
"2020-05-11, 979504.00, [('000001', 35000.0), ('600000', 44500.0)]\n",
"2020-05-12, 968149.00, [('000001', 35000.0), ('600000', 44500.0)]\n",
"2020-05-13, 964329.00, [('000001', 35000.0), ('600000', 44500.0)]\n",
"2020-05-14, 949219.00, [('000001', 35000.0), ('600000', 44500.0)]\n",
"2020-05-14, 卖000001, price:13.30, pct: 0\n",
"2020-05-15, 卖出000001, 成交量-35000.0，成交价13.39\n",
"2020-05-15, 951478.00, [('600000', 44500.0)]\n",
"2020-05-18, 953258.00, [('600000', 44500.0)]\n",
"2020-05-19, 954593.00, [('600000', 44500.0)]\n",
"2020-05-20, 957263.00, [('600000', 44500.0)]\n",
"2020-05-21, 953703.00, [('600000', 44500.0)]\n",
"2020-05-22, 945248.00, [('600000', 44500.0)]\n",
"2020-05-25, 951033.00, [('600000', 44500.0)]\n",
"2020-05-26, 951033.00, [('600000', 44500.0)]\n",
"2020-05-27, 954593.00, [('600000', 44500.0)]\n",
"2020-05-28, 962603.00, [('600000', 44500.0)]\n",
"2020-05-29, 964383.00, [('600000', 44500.0)]\n",
"2020-06-01, 967943.00, [('600000', 44500.0)]\n",
"2020-06-02, 969278.00, [('600000', 44500.0)]\n",
"2020-06-02, 买000001, price:13.55, amout: 34300.0\n",
"2020-06-03, 买入000001, 成交量34300.0，成交价13.64\n",
"2020-06-03, 966733.00, [('600000', 44500.0), ('000001', 34300.0)]\n",
"2020-06-04, 965092.00, [('600000', 44500.0), ('000001', 34300.0)]\n",
"2020-06-05, 967113.00, [('600000', 44500.0), ('000001', 34300.0)]\n",
"2020-06-08, 965472.00, [('600000', 44500.0), ('000001', 34300.0)]\n",
"2020-06-09, 970302.00, [('600000', 44500.0), ('000001', 34300.0)]\n",
"2020-06-10, 959678.00, [('600000', 44500.0), ('000001', 34300.0)]\n",
"2020-06-11, 941165.00, [('600000', 44500.0), ('000001', 34300.0)]\n",
"2020-06-11, 卖000001, price:13.08, pct: 0\n",
"2020-06-12, 卖出000001, 成交量-34300.0，成交价12.90\n",
"2020-06-12, 938105.00, [('600000', 44500.0)]\n",
"2020-06-15, 929205.00, [('600000', 44500.0)]\n",
"2020-06-16, 933655.00, [('600000', 44500.0)]\n",
"2020-06-17, 934545.00, [('600000', 44500.0)]\n",
"2020-06-18, 935880.00, [('600000', 44500.0)]\n",
"2020-06-19, 940775.00, [('600000', 44500.0)]\n",
"2020-06-22, 938105.00, [('600000', 44500.0)]\n",
"2020-06-23, 934990.00, [('600000', 44500.0)]\n",
"2020-06-24, 940330.00, [('600000', 44500.0)]\n",
"2020-06-29, 938995.00, [('600000', 44500.0)]\n",
"2020-06-30, 939440.00, [('600000', 44500.0)]\n",
"2020-07-01, 946560.00, [('600000', 44500.0)]\n",
"2020-07-01, 买000001, price:13.12, amout: 34600.0\n",
"2020-07-02, 买入000001, 成交量34600.0，成交价13.08\n",
"2020-07-02, 972460.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-03, 1007062.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-06, 1101485.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-07, 1090560.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-08, 1101583.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-09, 1086950.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-10, 1046413.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-13, 1050121.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-14, 1030395.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-15, 1007309.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-16, 1004047.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-17, 1006816.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-20, 1038355.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-21, 1030941.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-22, 1031733.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-23, 983183.00, [('600000', 44500.0), ('000001', 34600.0)]\n",
"2020-07-23, 卖600000, price:10.84, pct: 0\n",
"2020-07-23, 卖000001, price:14.01, pct: 0\n",
"2020-07-24, 卖出600000, 成交量-44500.0，成交价10.78\n",
"2020-07-24, 卖出000001, 成交量-34600.0，成交价13.97\n",
"2020-07-24, 979127.00, []\n",
"2020-07-27, 979127.00, []\n",
"2020-07-28, 979127.00, []\n",
"2020-07-29, 979127.00, []\n",
"2020-07-30, 979127.00, []\n",
"2020-07-31, 979127.00, []\n",
"2020-08-03, 979127.00, []\n",
"2020-08-04, 979127.00, []\n",
"2020-08-05, 979127.00, []\n",
"2020-08-06, 979127.00, []\n",
"2020-08-07, 979127.00, []\n",
"2020-08-10, 979127.00, []\n",
"2020-08-10, 买000001, price:13.95, amout: 33600.0\n",
"2020-08-11, 买入000001, 成交量33600.0，成交价13.97\n",
"2020-08-11, 984498.00, [('000001', 33600.0)]\n",
"2020-08-12, 992898.00, [('000001', 33600.0)]\n",
"2020-08-13, 986178.00, [('000001', 33600.0)]\n",
"2020-08-14, 995922.00, [('000001', 33600.0)]\n",
"2020-08-17, 1020114.00, [('000001', 33600.0)]\n",
"2020-08-17, 买600000, price:10.84, amout: 45100.0\n",
"2020-08-18, 买入600000, 成交量45100.0，成交价10.85\n",
"2020-08-18, 1011549.00, [('000001', 33600.0), ('600000', 45100.0)]\n",
"2020-08-19, 1004908.00, [('000001', 33600.0), ('600000', 45100.0)]\n",
"2020-08-20, 982811.00, [('000001', 33600.0), ('600000', 45100.0)]\n",
"2020-08-20, 卖600000, price:10.47, pct: 0\n",
"2020-08-21, 卖出600000, 成交量-45100.0，成交价10.56\n",
"2020-08-21, 982165.00, [('000001', 33600.0)]\n",
"2020-08-24, 982501.00, [('000001', 33600.0)]\n",
"2020-08-25, 987205.00, [('000001', 33600.0)]\n",
"2020-08-26, 979477.00, [('000001', 33600.0)]\n",
"2020-08-27, 982501.00, [('000001', 33600.0)]\n",
"2020-08-28, 1005013.00, [('000001', 33600.0)]\n",
"2020-08-31, 1003333.00, [('000001', 33600.0)]\n",
"2020-09-01, 1005349.00, [('000001', 33600.0)]\n",
"2020-09-02, 1011397.00, [('000001', 33600.0)]\n",
"2020-09-03, 997285.00, [('000001', 33600.0)]\n",
"2020-09-04, 999301.00, [('000001', 33600.0)]\n",
"2020-09-07, 998629.00, [('000001', 33600.0)]\n",
"2020-09-08, 1015093.00, [('000001', 33600.0)]\n",
"2020-09-09, 1007701.00, [('000001', 33600.0)]\n",
"2020-09-10, 1012069.00, [('000001', 33600.0)]\n",
"2020-09-11, 1000981.00, [('000001', 33600.0)]\n",
"2020-09-14, 1010725.00, [('000001', 33600.0)]\n",
"2020-09-15, 1012405.00, [('000001', 33600.0)]\n",
"2020-09-16, 1015429.00, [('000001', 33600.0)]\n",
"2020-09-17, 1019797.00, [('000001', 33600.0)]\n",
"2020-09-18, 1036597.00, [('000001', 33600.0)]\n",
"2020-09-21, 1029541.00, [('000001', 33600.0)]\n",
"2020-09-22, 1019797.00, [('000001', 33600.0)]\n",
"2020-09-23, 1021813.00, [('000001', 33600.0)]\n",
"2020-09-24, 1004677.00, [('000001', 33600.0)]\n",
"2020-09-25, 1007029.00, [('000001', 33600.0)]\n",
"2020-09-28, 1011061.00, [('000001', 33600.0)]\n",
"2020-09-29, 993925.00, [('000001', 33600.0)]\n",
"2020-09-30, 1006357.00, [('000001', 33600.0)]\n",
"2020-10-09, 1006693.00, [('000001', 33600.0)]\n",
"2020-10-12, 1030885.00, [('000001', 33600.0)]\n",
"2020-10-13, 1036261.00, [('000001', 33600.0)]\n",
"2020-10-14, 1035253.00, [('000001', 33600.0)]\n",
"2020-10-15, 1053061.00, [('000001', 33600.0)]\n",
"2020-10-16, 1071205.00, [('000001', 33600.0)]\n",
"2020-10-16, 买600000, price:9.72, amout: 52800.0\n",
"2020-10-19, 买入600000, 成交量0，成交价0.00\n",
"2020-10-19, 1083973.00, [('000001', 33600.0), ('600000', 0.0)]\n",
"2020-10-20, 1085989.00, [('000001', 33600.0), ('600000', 0.0)]\n",
"2020-10-20, 卖600000, price:9.58, pct: 0\n",
"2020-10-21, 1098421.00, [('000001', 33600.0)]\n",
"2020-10-21, 买600000, price:9.70, amout: 54300.0\n",
"2020-10-22, 买入600000, 成交量0，成交价0.00\n",
"2020-10-22, 1086661.00, [('000001', 33600.0), ('600000', 0.0)]\n",
"2020-10-23, 1105813.00, [('000001', 33600.0), ('600000', 0.0)]\n",
"2020-10-26, 1091365.00, [('000001', 33600.0), ('600000', 0.0)]\n",
"2020-10-27, 1093381.00, [('000001', 33600.0), ('600000', 0.0)]\n",
"2020-10-27, 卖600000, price:9.48, pct: 0\n",
"2020-10-28, 1089013.00, [('000001', 33600.0)]\n",
"2020-10-29, 1093717.00, [('000001', 33600.0)]\n",
"2020-10-30, 1093045.00, [('000001', 33600.0)]\n",
"2020-11-02, 1089013.00, [('000001', 33600.0)]\n",
"2020-11-03, 1100101.00, [('000001', 33600.0)]\n",
"2020-11-04, 1112197.00, [('000001', 33600.0)]\n",
"2020-11-05, 1091365.00, [('000001', 33600.0)]\n",
"2020-11-06, 1089349.00, [('000001', 33600.0)]\n",
"2020-11-09, 1096069.00, [('000001', 33600.0)]\n",
"2020-11-10, 1105141.00, [('000001', 33600.0)]\n",
"2020-11-11, 1095061.00, [('000001', 33600.0)]\n",
"2020-11-11, 买600000, price:9.56, amout: 54900.0\n",
"2020-11-12, 买入600000, 成交量0，成交价0.00\n",
"2020-11-12, 1090021.00, [('000001', 33600.0), ('600000', 0.0)]\n",
"2020-11-12, 卖000001, price:17.66, pct: 0\n",
"2020-11-12, 卖600000, price:9.43, pct: 0\n",
"2020-11-13, 卖出000001, 成交量-33600.0，成交价17.42\n",
"2020-11-13, 1081956.00, []\n",
"2020-11-16, 1081956.00, []\n",
"2020-11-17, 1081956.00, []\n",
"2020-11-17, 买000001, price:17.83, amout: 29100.0\n",
"2020-11-17, 买600000, price:9.53, amout: 54400.0\n",
"2020-11-18, 买入000001, 成交量29100.0，成交价17.78\n",
"2020-11-18, 买入600000, 成交量54400.0，成交价9.53\n",
"2020-11-18, 1110981.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-11-19, 1124506.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-11-20, 1120989.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-11-23, 1155617.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-11-24, 1145331.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-11-25, 1134425.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-11-26, 1156477.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-11-27, 1174265.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-11-30, 1167813.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-12-01, 1189890.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-12-02, 1174948.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-12-03, 1171785.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-12-04, 1160993.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-12-07, 1139852.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-12-08, 1133488.64, [('000001', 29100.0), ('600000', 54400.0)]\n",
"2020-12-08, 卖000001, price:18.71, pct: 0\n",
"2020-12-09, 卖出000001, 成交量-29100.0，成交价18.79\n",
"2020-12-09, 1132007.64, [('600000', 54400.0)]\n",
"2020-12-10, 1127111.64, [('600000', 54400.0)]\n",
"2020-12-11, 1120583.64, [('600000', 54400.0)]\n",
"2020-12-14, 1123303.64, [('600000', 54400.0)]\n",
"2020-12-15, 1121127.64, [('600000', 54400.0)]\n",
"2020-12-16, 1117863.64, [('600000', 54400.0)]\n",
"2020-12-17, 1124391.64, [('600000', 54400.0)]\n",
"2020-12-18, 1123847.64, [('600000', 54400.0)]\n",
"2020-12-21, 1123303.64, [('600000', 54400.0)]\n",
"2020-12-22, 1115687.64, [('600000', 54400.0)]\n",
"2020-12-23, 1112423.64, [('600000', 54400.0)]\n",
"2020-12-24, 1112423.64, [('600000', 54400.0)]\n",
"2020-12-25, 1114055.64, [('600000', 54400.0)]\n",
"2020-12-28, 1113511.64, [('600000', 54400.0)]\n",
"2020-12-28, 买000001, price:18.85, amout: 28300.0\n",
"2020-12-29, 买入000001, 成交量28300.0，成交价18.87\n",
"2020-12-29, 1119820.30, [('600000', 54400.0), ('000001', 28300.0)]\n",
"2020-12-29, 卖600000, price:9.53, pct: 0\n",
"2020-12-30, 卖出600000, 成交量-54400.0，成交价9.52\n",
"2020-12-30, 1120124.30, [('000001', 28300.0)]\n",
"2020-12-31, 1124086.30, [('000001', 28300.0)]\n",
"2021-01-04, 1103144.30, [('000001', 28300.0)]\n",
"2021-01-05, 1090975.30, [('000001', 28300.0)]\n",
"2021-01-06, 1130312.30, [('000001', 28300.0)]\n",
"2021-01-06, 买600000, price:9.82, amout: 55200.0\n",
"2021-01-07, 买入600000, 成交量55200.0，成交价9.83\n",
"2021-01-07, 1138824.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-08, 1138513.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-11, 1145784.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-12, 1168298.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-13, 1163120.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-14, 1149777.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-15, 1176026.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-18, 1232968.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-19, 1227196.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-20, 1220939.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-21, 1213595.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-22, 1197999.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-25, 1207153.88, [('000001', 28300.0), ('600000', 55200.0)]\n",
"2021-01-25, 卖600000, price:9.72, pct: 0\n",
"2021-01-26, 卖出600000, 成交量-55200.0，成交价9.69\n",
"2021-01-26, 1202100.88, [('000001', 28300.0)]\n",
"2021-01-27, 1222193.88, [('000001', 28300.0)]\n",
"2021-01-28, 1214552.88, [('000001', 28300.0)]\n",
"2021-01-29, 1222476.88, [('000001', 28300.0)]\n",
"2021-01-29, 买600000, price:9.96, amout: 58900.0\n",
"2021-02-01, 买入600000, 成交量0，成交价0.00\n",
"2021-02-01, 1263794.88, [('000001', 28300.0), ('600000', 0.0)]\n",
"2021-02-02, 1227853.88, [('000001', 28300.0), ('600000', 0.0)]\n",
"2021-02-03, 1275114.88, [('000001', 28300.0), ('600000', 0.0)]\n",
"2021-02-04, 1265209.88, [('000001', 28300.0), ('600000', 0.0)]\n",
"2021-02-05, 1274548.88, [('000001', 28300.0), ('600000', 0.0)]\n",
"2021-02-08, 1270869.88, [('000001', 28300.0), ('600000', 0.0)]\n",
"2021-02-09, 1264643.88, [('000001', 28300.0), ('600000', 0.0)]\n",
"2021-02-10, 1243135.88, [('000001', 28300.0), ('600000', 0.0)]\n",
"2021-02-18, 1256719.88, [('000001', 28300.0), ('600000', 0.0)]\n",
"2021-02-19, 1243984.88, [('000001', 28300.0), ('600000', 0.0)]\n",
"2021-02-22, 1202383.88, [('000001', 28300.0), ('600000', 0.0)]\n",
"2021-02-22, 卖000001, price:22.38, pct: 0\n",
"2021-02-23, 卖出000001, 成交量-28300.0，成交价22.38\n",
"2021-02-23, 1202382.88, [('600000', 0.0)]\n",
"2021-02-24, 1202382.88, [('600000', 0.0)]\n",
"2021-02-25, 1202382.88, [('600000', 0.0)]\n",
"2021-02-26, 1202382.88, [('600000', 0.0)]\n",
"2021-03-01, 1202382.88, [('600000', 0.0)]\n",
"2021-03-02, 1202382.88, [('600000', 0.0)]\n",
"2021-03-03, 1202382.88, [('600000', 0.0)]\n",
"2021-03-04, 1202382.88, [('600000', 0.0)]\n",
"2021-03-05, 1202382.88, [('600000', 0.0)]\n",
"2021-03-08, 1202382.88, [('600000', 0.0)]\n",
"2021-03-09, 1202382.88, [('600000', 0.0)]\n",
"2021-03-10, 1202382.88, [('600000', 0.0)]\n",
"2021-03-11, 1202382.88, [('600000', 0.0)]\n",
"2021-03-12, 1202382.88, [('600000', 0.0)]\n",
"2021-03-15, 1202382.88, [('600000', 0.0)]\n",
"2021-03-16, 1202382.88, [('600000', 0.0)]\n",
"2021-03-17, 1202382.88, [('600000', 0.0)]\n",
"2021-03-18, 1202382.88, [('600000', 0.0)]\n",
"2021-03-19, 1202382.88, [('600000', 0.0)]\n",
"2021-03-22, 1202382.88, [('600000', 0.0)]\n",
"2021-03-23, 1202382.88, [('600000', 0.0)]\n",
"2021-03-24, 1202382.88, [('600000', 0.0)]\n",
"2021-03-25, 1202382.88, [('600000', 0.0)]\n",
"2021-03-26, 1202382.88, [('600000', 0.0)]\n",
"2021-03-29, 1202382.88, [('600000', 0.0)]\n",
"2021-03-29, 买000001, price:21.49, amout: 26800.0\n",
"2021-03-30, 买入000001, 成交量26800.0，成交价21.38\n",
"2021-03-30, 1217117.15, [('600000', 0.0), ('000001', 26800.0)]\n",
"2021-03-31, 1219261.15, [('600000', 0.0), ('000001', 26800.0)]\n",
"2021-04-01, 1213097.15, [('600000', 0.0), ('000001', 26800.0)]\n",
"2021-04-02, 1205593.15, [('600000', 0.0), ('000001', 26800.0)]\n",
"2021-04-02, 卖600000, price:10.76, pct: 0\n",
"2021-04-06, 1210417.15, [('000001', 26800.0)]\n",
"2021-04-07, 1209345.15, [('000001', 26800.0)]\n",
"2021-04-08, 1207201.15, [('000001', 26800.0)]\n",
"2021-04-09, 1200233.15, [('000001', 26800.0)]\n",
"2021-04-12, 1184153.15, [('000001', 26800.0)]\n",
"2021-04-13, 1186297.15, [('000001', 26800.0)]\n",
"2021-04-14, 1183349.15, [('000001', 26800.0)]\n",
"2021-04-15, 1175041.15, [('000001', 26800.0)]\n",
"2021-04-16, 1172361.15, [('000001', 26800.0)]\n",
"2021-04-19, 1196213.15, [('000001', 26800.0)]\n",
"2021-04-20, 1210685.15, [('000001', 26800.0)]\n",
"2021-04-21, 1246061.15, [('000001', 26800.0)]\n",
"2021-04-22, 1245257.15, [('000001', 26800.0)]\n",
"2021-04-23, 1253565.15, [('000001', 26800.0)]\n",
"2021-04-26, 1244185.15, [('000001', 26800.0)]\n",
"2021-04-27, 1244185.15, [('000001', 26800.0)]\n",
"2021-04-28, 1255173.15, [('000001', 26800.0)]\n",
"2021-04-29, 1261605.15, [('000001', 26800.0)]\n",
"2021-04-30, 1253565.15, [('000001', 26800.0)]\n",
"2021-05-06, 1259193.15, [('000001', 26800.0)]\n",
"2021-05-07, 1273933.15, [('000001', 26800.0)]\n",
"2021-05-10, 1268841.15, [('000001', 26800.0)]\n",
"2021-05-11, 1259997.15, [('000001', 26800.0)]\n",
"2021-05-12, 1260533.15, [('000001', 26800.0)]\n",
"2021-05-13, 1247669.15, [('000001', 26800.0)]\n",
"2021-05-14, 1254369.15, [('000001', 26800.0)]\n",
"2021-05-17, 1261873.15, [('000001', 26800.0)]\n",
"2021-05-18, 1269913.15, [('000001', 26800.0)]\n",
"2021-05-19, 1261873.15, [('000001', 26800.0)]\n",
"2021-05-20, 1267769.15, [('000001', 26800.0)]\n",
"2021-05-21, 1258925.15, [('000001', 26800.0)]\n",
"2021-05-24, 1258657.15, [('000001', 26800.0)]\n",
"2021-05-25, 1288673.15, [('000001', 26800.0)]\n",
"2021-05-25, 买600000, price:10.32, amout: 59900.0\n",
"2021-05-26, 买入600000, 成交量59900.0，成交价10.31\n",
"2021-05-26, 1302050.97, [('000001', 26800.0), ('600000', 59900.0)]\n",
"2021-05-27, 1292560.97, [('000001', 26800.0), ('600000', 59900.0)]\n",
"2021-05-28, 1288382.97, [('000001', 26800.0), ('600000', 59900.0)]\n",
"2021-05-31, 1275550.97, [('000001', 26800.0), ('600000', 59900.0)]\n",
"2021-06-01, 1263254.97, [('000001', 26800.0), ('600000', 59900.0)]\n",
"2021-06-02, 1264247.97, [('000001', 26800.0), ('600000', 59900.0)]\n",
"2021-06-03, 1261630.97, [('000001', 26800.0), ('600000', 59900.0)]\n",
"2021-06-03, 卖000001, price:23.77, pct: 0\n",
"2021-06-04, 卖出000001, 成交量-26800.0，成交价23.99\n",
"2021-06-04, 1267525.97, [('600000', 59900.0)]\n",
"2021-06-04, 买000001, price:24.54, amout: 24700.0\n",
"2021-06-07, 买入000001, 成交量24700.0，成交价24.48\n",
"2021-06-07, 1263073.92, [('600000', 59900.0), ('000001', 24700.0)]\n",
"2021-06-08, 1273546.92, [('600000', 59900.0), ('000001', 24700.0)]\n",
"2021-06-09, 1270520.92, [('600000', 59900.0), ('000001', 24700.0)]\n",
"2021-06-09, 卖600000, price:10.21, pct: 0\n",
"2021-06-10, 卖出600000, 成交量-59900.0，成交价10.21\n",
"2021-06-10, 1259404.92, [('000001', 24700.0)]\n",
"2021-06-10, 买600000, price:10.23, amout: 59000.0\n",
"2021-06-11, 买入600000, 成交量59000.0，成交价10.24\n",
"2021-06-11, 1234767.88, [('000001', 24700.0), ('600000', 59000.0)]\n",
"2021-06-11, 卖000001, price:23.37, pct: 0\n",
"2021-06-11, 卖600000, price:10.17, pct: 0\n",
"2021-06-15, 卖出000001, 成交量-24700.0，成交价23.35\n",
"2021-06-15, 卖出600000, 成交量-59000.0，成交价10.15\n",
"2021-06-15, 1233091.88, []\n",
"2021-06-16, 1233091.88, []\n",
"2021-06-17, 1233091.88, []\n",
"2021-06-18, 1233091.88, []\n",
"2021-06-21, 1233091.88, []\n",
"2021-06-22, 1233091.88, []\n",
"2021-06-23, 1233091.88, []\n",
"2021-06-24, 1233091.88, []\n",
"2021-06-25, 1233091.88, []\n",
"2021-06-28, 1233091.88, []\n",
"2021-06-29, 1233091.88, []\n",
"2021-06-30, 1233091.88, []\n",
"2021-07-01, 1233091.88, []\n",
"2021-07-02, 1233091.88, []\n",
"2021-07-05, 1233091.88, []\n",
"2021-07-06, 1233091.88, []\n",
"2021-07-07, 1233091.88, []\n",
"2021-07-08, 1233091.88, []\n",
"2021-07-09, 1233091.88, []\n",
"2021-07-12, 1233091.88, []\n",
"2021-07-13, 1233091.88, []\n",
"2021-07-14, 1233091.88, []\n",
"2021-07-15, 1233091.88, []\n",
"2021-07-16, 1233091.88, []\n",
"2021-07-16, 买600000, price:9.98, amout: 59300.0\n",
"2021-07-19, 买入600000, 成交量59300.0，成交价9.98\n",
"2021-07-19, 1234864.96, [('600000', 59300.0)]\n",
"2021-07-20, 1233678.96, [('600000', 59300.0)]\n",
"2021-07-21, 1233678.96, [('600000', 59300.0)]\n",
"2021-07-22, 1233678.96, [('600000', 59300.0)]\n",
"2021-07-23, 1233678.96, [('600000', 59300.0)]\n",
"2021-07-26, 1233678.96, [('600000', 59300.0)]\n",
"2021-07-27, 1233678.96, [('600000', 59300.0)]\n",
"2021-07-28, 1233678.96, [('600000', 59300.0)]\n",
"2021-07-29, 1233678.96, [('600000', 59300.0)]\n",
"2021-07-30, 1233678.96, [('600000', 59300.0)]\n",
"2021-08-02, 1233678.96, [('600000', 59300.0)]\n",
"2021-08-03, 1233678.96, [('600000', 59300.0)]\n",
"2021-08-04, 1233678.96, [('600000', 59300.0)]\n",
"2021-08-05, 1233678.96, [('600000', 59300.0)]\n",
"2021-08-06, 1233678.96, [('600000', 59300.0)]\n",
"2021-08-09, 1233678.96, [('600000', 59300.0)]\n",
"2021-08-10, 1233678.96, [('600000', 59300.0)]\n",
"2021-08-10, 买000001, price:19.73, amout: 30000.0\n",
"2021-08-11, 买入000001, 成交量30000.0，成交价19.99\n",
"2021-08-11, 1228272.97, [('600000', 59300.0), ('000001', 30000.0)]\n",
"2021-08-11, 卖600000, price:9.99, pct: 0\n",
"2021-08-12, 卖出600000, 成交量-59300.0，成交价10.01\n",
"2021-08-12, 1231857.97, [('000001', 30000.0)]\n",
"2021-08-13, 1231857.97, [('000001', 30000.0)]\n",
"2021-08-16, 1233657.97, [('000001', 30000.0)]\n",
"2021-08-17, 1225257.97, [('000001', 30000.0)]\n",
"结束资金: 1253757.97\n"
]
}
],
"source": [
"class multi_strategy(bt.Strategy):\n",
" # 全局设定交易策略的参数\n",
" params = (\n",
" ('maperiod', 20),\n",
" )\n",
"\n",
" def __init__(self):\n",
" # 初始化交易指令\n",
" self.order = None\n",
" self.buy_lst = []\n",
"\n",
" # 添加移动均线指标，内置了talib模块\n",
" # 循环计算每只股票的指标\n",
" self.sma = {x: bt.ind.SMA(self.getdatabyname(x), period=self.p.maperiod) for x in self.getdatanames()}\n",
"\n",
" def prenext(self):\n",
" pass\n",
" \n",
" \n",
" def downcast(self, amount, lot):\n",
" return abs(amount//lot*lot)\n",
" \n",
" \n",
" def next(self):\n",
" if self.order: # 检查是否有指令等待执行,\n",
" return\n",
"\n",
" # 回测最后一天不进行买卖\n",
" if self.datas[0].datetime.date(0) == end_date:\n",
" return \n",
" \n",
" # 检查是否持仓\n",
" self.log(f'{self.broker.getvalue():.2f}, {[(x, self.getpositionbyname(x).size) for x in self.buy_lst]}')\n",
" if len(self.buy_lst) < 2: # 没有持仓\n",
" for secu in set(self.getdatanames()) - set(self.buy_lst):\n",
" data = self.getdatabyname(secu)\n",
" # 执行买入条件判断：收盘价格上涨突破20日均线\n",
" # 不要在股票剔除日前一天进行买入\n",
" if data.close > self.sma[secu] and \\\n",
" data.datetime.date(1) < pd.Timestamp(secu_lst[secu]['end']):\n",
" # 执行买入\n",
" order_value = self.broker.getvalue()*0.48\n",
" order_amount = self.downcast(order_value/data.close[0], 100)\n",
" self.order = self.buy(data, size=order_amount, name=secu)\n",
" self.log(f\"买{secu}, price:{data.close[0]:.2f}, amout: {order_amount}\")\n",
" self.buy_lst.append(secu)\n",
" elif self.position:\n",
" now_lst = []\n",
" for secu in self.buy_lst:\n",
" data = self.getdatabyname(secu)\n",
" # 执行卖出条件判断：收盘价格跌破20日均线，或者股票剔除\n",
" if (data.close < self.sma[secu]) or \\\n",
" (data.datetime.date(1) >= pd.Timestamp(secu_lst[secu]['end'])):\n",
" # 执行卖出\n",
" self.order = self.order_target_percent(data, 0, name=secu)\n",
" # 也可以用 self.sell(data, size = self.getposition(data).size)\n",
" # or self.sell(data, size = self.getpositionbyname(secu).size)\n",
" self.log(f\"卖{secu}, price:{data.close[0]:.2f}, pct: 0\")\n",
" continue\n",
" now_lst.append(secu)\n",
" self.buy_lst = now_lst.copy()\n",
"\n",
" def log(self, txt, dt=None):\n",
" ''' 输出日志'''\n",
" dt = dt or self.datas[0].datetime.date(0)\n",
" print('%s, %s' % (dt.isoformat(), txt))\n",
"\n",
" def notify_order(self, order):\n",
" if order.status in [order.Submitted, order.Accepted]:\n",
" # Buy/Sell order submitted/accepted to/by broker - Nothing to do\n",
" return\n",
"\n",
" # Check if an order has been completed\n",
" # Attention: broker could reject order if not enough cash\n",
" if order.status in [order.Completed, order.Canceled, order.Margin]:\n",
" if order.isbuy():\n",
" self.log(f\"\"\"买入{order.info['name']}, 成交量{order.executed.size}，成交价{order.executed.price:.2f}\"\"\")\n",
" elif order.issell():\n",
" self.log(f\"\"\"卖出{order.info['name']}, 成交量{order.executed.size}，成交价{order.executed.price:.2f}\"\"\")\n",
" self.bar_executed = len(self)\n",
"\n",
" # Write down: no pending order\n",
" self.order = None\n",
"\n",
" \n",
"secu_lst = {'600000': {'start': '2020-01-01', 'end': '2021-07-20'},\n",
" '000001': {'start': '2020-02-01', 'end': '2021-08-18'}}\n",
"# 拿对齐的数据\n",
"kdata = GetKdatas(secu_lst).merge_period()\n",
"kdata = dict(sorted(kdata.items()))\n",
"\n",
"# 开始回测\n",
"cerebro = bt.Cerebro()\n",
"cerebro.addstrategy(multi_strategy)\n",
"\n",
"for secu in kdata.keys():\n",
" df = kdata[secu]\n",
" data = PandasData_Extend(dataname=df, fromdate=df.index[0], todate=df.index[-1])\n",
" cerebro.adddata(data, name=secu)\n",
"end_date = df.index[-1]\n",
"\n",
"# 设置初始资本为1 million\n",
"startcash = 10**6\n",
"cerebro.broker.setcash(startcash)\n",
"print(f\"初始资金{cerebro.broker.getvalue()}\")\n",
"# 设置交易手续费\n",
"cerebro.broker.addcommissioninfo(CommInfoPro())\n",
"\n",
"# 加入指标\n",
"cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='_sharpe')\n",
"cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='_annrtn')\n",
"cerebro.addanalyzer(bt.analyzers.DrawDown, _name='_dd')\n",
"cerebro.addanalyzer(bt.analyzers.PyFolio, _name='_pyfolio')\n",
"# 运行回测系统\n",
"thestrats = cerebro.run()\n",
"# 获取回测结束后的总资金\n",
"portvalue = cerebro.broker.getvalue()\n",
"# 打印结果\n",
"print(f'结束资金: {round(portvalue, 2)}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. 策略评估\n",
"下图较为详细得展示了各个买入点和卖出点，方便我们进行买入卖出点的直观判断"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '');\n",
" var titletext = $(\n",
" '');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('');\n",
"\n",
" var fmt_picker = $('');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('![](\"')
');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('');\n",
" var button = $('');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"![](\"data:image/png;base64,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\")
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[[]]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cerebro.plot(style='candlestick')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"展示夏普、年化收益率以及最大回撤等。本策略以日为单位进行计算，所以取252作为基准进行指标计算。同理可推，如果是以月为基准，则将252修改为12。"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"thestrat = thestrats[0]\n",
"pyfolio = thestrat.analyzers._pyfolio.get_analysis()\n",
"def plot_strategy(pyfolio):\n",
" returns = pyfolio['returns'].values()\n",
" returns = pd.DataFrame(list(zip(pyfolio['returns'].keys(),pyfolio['returns'].values())), columns=['date','total_value'])\n",
" \n",
"\n",
" sharpe = np.round(np.sqrt(252) * returns['total_value'].mean() / returns['total_value'].std(), 4)\n",
" returns['total_value']=returns['total_value']+1\n",
" returns['total_value'] = returns['total_value'].cumprod()\n",
" annal_rtn = np.round(returns['total_value'].iloc[-1]**(252/len(returns))-1, 4)*100\n",
" dd = 1-returns['total_value']/np.maximum.accumulate(returns['total_value'])\n",
" end_idx = np.argmax(dd)\n",
" start_idx = np.argmax(returns['total_value'].iloc[:end_idx])\n",
" maxdd_days = end_idx-start_idx\n",
" maxdd = np.round(max(dd), 4)*100\n",
" \n",
" returns = returns.set_index('date')\n",
" ax=returns.plot(y='total_value')\n",
" plt.text(0.01,0.8, f'sharpe: {sharpe:.2f}', transform=ax.transAxes)\n",
" plt.text(0.01,0.75, f'maxdd: {maxdd:.2f}%', transform=ax.transAxes)\n",
" plt.text(0.01,0.7, f'maxdd_days: {maxdd_days:}days', transform=ax.transAxes)\n",
" plt.text(0.01,0.65, f'annal_rtn: {(annal_rtn):.2f}%', transform=ax.transAxes)\n",
" plt.scatter([returns.index[start_idx], returns.index[end_idx]], [returns.iloc[start_idx], returns.iloc[end_idx]],\n",
" s = 80, c = 'g', marker = 'v', label = 'MaxDrawdown Duration')\n",
" plt.title('portfolio')\n",
" plt.show()\n",
"\n",
"plot_strategy(pyfolio)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.6"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": true,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {
"height": "calc(100% - 180px)",
"left": "10px",
"top": "150px",
"width": "307.2px"
},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}