量化双均线策略：（二）判断买入卖出信号
上篇已经介绍了data的获取，此篇介绍ma5与ma10的双均线策略具体实现。双均线策略是一个趋势策略，基本思路是金叉买入，死叉卖出，也就是当ma5向上穿过ma10时，则买入，向下穿过ma10时，则卖出。
主要实现在Strategy类中，输入的变量格式如下：code_list = ['002415', '002416', '000333']，init_cash = 100000，starttime = '2014-01-01'，endtime = '2017-11-30'，其他几个重要成员变量如下：cash为还剩余的现金；capital_market_value为持仓的市值，按每天的收盘价计算；limit_cash为每只股票分得的仓位，双均线策略中，codelist中的股票平分持仓；position_list为持仓的情况，类型为dict，股票的code为key，持仓的数量为value；data_range为一个回测的日期list，pd.period_range的freq参数选择为B，即工作日；benchmark赋值为sh，即上证指数，以便比对策略结果。
#coding=utf-8
from trade import Trade
from data_repository import DataRepository
import tushare as ts
import pandas as pd
import numpy as np
import math
class Strategy(object):
def __init__(self, code_list, init_cash, start_time, end_time):
self.start_time = start_time
self.end_time = end_time
self.data_repository = DataRepository.get_instance(code_list, self.start_time, self.end_time)
self.code_list = code_list
self.benchmark_code = 'sh'
self.init_cash = init_cash
self.cash = init_cash
self.limited_cash = init_cash/len(code_list)
self.position_list = {}
for code in self.code_list:
self.position_list[code] = 0
#存储trade对象
self.trade = Trade()
d = list(pd.period_range(start=start_time, end=end_time, freq='B'))
self.date_range = list(map(str, d))
self.res_df = pd.DataFrame()
self.res_df['date'] = self.date_range
self.capital_market_value = []所有天数回测结束后，将结果记录到res_df中，该变量为pandas类型，以便分析结果。res_df中数据有每天capital_market_value，每天的涨跌幅，每天对应的benchmark的值（因为回测的date_range已经剔除了周末，节假日等情况会存在停市，故capital_market_value与benchmark的值均使用之前第一个值填充），benchmark采用先bfill，然后还可能存在最开头的值是空的情况，在ffill填充，而capital_market_value则是使用df[df['date'] <= date].tail(1)['close']获得。
def run_simulation(self):
#按天循环
for date in self.date_range:
for code in self.code_list:
sell_signal, sell_open_price = self.get_sell_signal(code, date)
direction = -1
if sell_signal == 1:
amount = self.get_sell_amount(code)
if amount > 0:
commission = self.cal_cost_function(sell_open_price, amount)
#更改现金
self.cash += sell_open_price*amount
self.cash -= commission
#更改持仓
self.position_list[code] -= amount
#加入trade记录
self.trade.add_trade(code, sell_open_price, amount, date, direction, commission)
for code in self.code_list:
buy_signal, buy_open_price = self.get_buy_signal(code, date)
direction = 1
if buy_signal == 1:
amount = self.get_buy_amount(code, buy_open_price)
if amount > 0:
commission = self.cal_cost_function(buy_open_price, amount)
#更改现金
self.cash -= buy_open_price*amount
self.cash -= commission
#更改持仓
self.position_list[code] += amount
#加入trade记录
self.trade.add_trade(code, buy_open_price, amount, date, direction, commission)
#计算每天的市值
self.capital_market_value.append(self.get_market_value(date))
#所有天循环结束后，加入到res_df
self.res_df['capital_market_value'] = pd.Series(self.capital_market_value)
self.res_df['profolio_daily_return'] = round((self.res_df['capital_market_value']/\
self.res_df['capital_market_value'].shift(1)-1),4)
self.res_df['benchmark'] = self.get_benchmark_index()
self.res_df['benchmark'].fillna(method='bfill', inplace=True)
self.res_df['benchmark'].fillna(method='ffill', inplace=True)
self.res_df.to_csv('./datares.csv') def get_benchmark_index(self):
df = ts.get_k_data(self.benchmark_code, start=self.start_time, end=self.end_time)
benchmark_list = []
for date in self.date_range:
if df[df['date'] == date].empty:
benchmark_list.append(np.nan)
else:
benchmark_list.append(float(df[df['date'] == date]['close']))
return benchmark_list
#得到某天的持仓股票的市值，加上cash，一并返回
def get_market_value(self, date):
market_value = 0
for code in self.position_list:
df = self.data_repository.get_onecode_df(code)
if self.position_list[code] != 0:
close_price = df[df['date'] <= date].tail(1)['close']
market_value += self.position_list[code]*float(close_price)
return round(market_value+self.cash, 2)
def get_sell_signal(self, code, date):
df = self.data_repository.get_onecode_df(code)
sell_signal = 0
sell_open_price = 0
if df[df['date'] == date].empty:
return sell_signal, sell_open_price
df = df[df['date'] <= date].tail(3)
if len(df) == 3 and df.iloc[0]['ma5'] > df.iloc[0]['ma10'] and df.iloc[1]['ma5'] < df.iloc[1]['ma10']:
sell_signal = 1
sell_open_price = df.iloc[1]['open']
return sell_signal, sell_open_price
#以后还要加入判断止盈的方法
def get_buy_signal(self, code, date):
df = self.data_repository.get_onecode_df(code)
buy_signal = 0
buy_open_price = 0
if df[df['date'] == date].empty:
return buy_signal, buy_open_price
df = df[df['date'] <= date].tail(3)
if len(df) == 3 and df.iloc[0]['ma5'] < df.iloc[0]['ma10'] and df.iloc[1]['ma5'] > df.iloc[1]['ma10']:
buy_signal = 1
buy_open_price = df.iloc[1]['open']
return buy_signal, buy_open_pric
def get_sell_amount(self, code):
return self.position_list[code]
def get_buy_amount(self, code, price):
if self.position_list[code] == 0:
amount = math.floor(self.limited_cash/(price*100))*100
return amount
else:
return 0
def cal_cost_function(self, price, amount):
commission = price*amount*0.0003
#最低5元手续费
if commission > 5:
return commission
else:
return 5