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Copy path03-日内加强策略
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03-日内加强策略
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# 导入函数库
from jqdata import *
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
def initialize(context):
set_option('use_real_price', True) # 真实价格交易
set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003,\
close_commission=0.0003, min_commission=5), type='stock')
log.set_level('system','error') # 取消日志
g.stocks = ['601877.XSHG','601155.XSHG','000895.XSHE','600196.XSHG','002311.XSHE']
set_universe(g.stocks)
g.num = len(g.stocks)
set_benchmark({'002939.XSHE':0})
data = {'win':np.zeros(g.num),'equal':np.zeros(g.num),
'lose':np.zeros(g.num),'times':np.zeros(g.num)}
g.df = pd.DataFrame(data,index = g.stocks)
g.days,g.minutes = 0,0 # 建仓日，分钟计时
g.win,g.equal,g.lose = 0,0,0 # 交易胜负次数
g.times,g.cost,g.amount = np.zeros(g.num),np.zeros(g.num),np.zeros(g.num)
# 每日交易次数，累计交易次数，交易成本，每次交易股票数
g.status = np.zeros(g.num)
# 股票交易状态：未交易(0)，可交易(1)，持有待平仓(2)，需马上平仓(3）
g.period = np.zeros(g.num)
# 每只股票计时，一小时后强制卖掉
g.zs,g.zy = 0.02,0.03
g.money1,g.money2 = 0.01,0.01
g.length = 60
run_daily(trade, time='every_bar')
run_daily(cover, time='14:57')
run_daily(after_trade, time='15:30')
def trade(context):
if g.days > 0 and g.minutes < 237:
g.minutes += 1
if g.minutes > 31:
close = history(g.minutes, '1m', 'close')
volume = history(g.minutes, '1m', 'volume')
high = history(g.minutes, '1m', 'high')
low = history(g.minutes, '1m', 'low')
dt = context.current_dt
current_data = get_current_data()
signal(close,volume,high,low,dt)
# 交易
for i in range(g.num):
stock = g.stocks[i]
new_price = current_data[stock].last_price # 获取当前市价
if g.status[i] == 1 and g.times[i] < 1: # 每日最多交易次数
orders = order(stock,g.amount[i])
if orders is None or str(orders.status) != 'held':
print(str(stock)+' 下单未成功')
g.status[i] = 0
else:
g.times[i] += 1
g.df.loc[stock].times += 1
g.cost[i] = orders.price
print('买入股票:'+str(stock)+'，交易成本价为：'+str(orders.price)+'，买入数量为：'+str(orders.amount))
g.status[i] = 2 # 需要平仓的股票状态
g.period[i] = g.minutes
elif g.status[i] == 2:
# 止损止盈
if g.cost[i]*(1+g.zy) < new_price or g.cost[i]*(1-g.zs) > new_price:
orders = order(stock,-1 * g.amount[i])
if orders is None or str(orders.status) != 'held':
print(str(stock)+' 未平仓成功')
else:
g.status[i] = 0
g.period[i] = 0
if orders.price > g.cost[i]:
g.win += 1
g.df.loc[stock].win += 1
print('止盈平仓股票（赚）:'+str(stock)+'，平仓价为：'+str(orders.price)+'，成本价为：'+str(g.cost[i])\
+'，收益率为：'+str(round((orders.price-g.cost[i])/g.cost[i],3)))
else:
g.lose += 1
g.df.loc[stock].lose += 1
print('止损平仓股票（亏）:'+str(stock)+'，平仓价为：'+str(orders.price)+'，成本价为：'+str(g.cost[i])\
+'，收益率为：'+str(round((orders.price-g.cost[i])/g.cost[i],3)))
elif g.status[i] == 3:
# 离场信号
orders = order(stock,-1 * g.amount[i])
if orders is None or str(orders.status) != 'held':
print(str(stock)+' 未平仓成功')
else:
g.status[i] = 0
g.period[i] = 0
if orders.price > g.cost[i]:
g.win += 1
g.df.loc[stock].win += 1
print('平仓股票（赚）:'+str(stock)+'，平仓价为：'+str(orders.price)+'，成本价为：'+str(g.cost[i])\
+'，收益率为：'+str(round((orders.price-g.cost[i])/g.cost[i],3)))
elif orders.price == g.cost[i]:
g.equal += 1
g.df.loc[stock].equal += 1
print('平仓股票（平）:'+str(stock)+'，平仓价为：'+str(orders.price)+'，成本价为：'+str(g.cost[i])\
+'，收益率为：'+str(round((orders.price-g.cost[i])/g.cost[i],3)))
else:
g.lose += 1
g.df.loc[stock].lose += 1
print('平仓股票（亏）:'+str(stock)+'，平仓价为：'+str(orders.price)+'，成本价为：'+str(g.cost[i])\
+'，收益率为：'+str(round((orders.price-g.cost[i])/g.cost[i],3)))
def cover(context):
# 首日建仓
if g.days == 0:
W = np.ones(g.num)*0.5/g.num
cash_list = context.portfolio.cash * W
for i in range(g.num):
stock = g.stocks[i]
orders = order_value(stock,cash_list[i])
if orders is None or str(orders.status) != 'held':
print(str(stock)+' 下单未成功')
else:
g.amount[i] = int(orders.amount)
print(str(stock)+' 下单手数 '+str(orders.amount))
elif g.days > 0:
for i in range(g.num):
stock = g.stocks[i]
if g.status[i] == 2:
orders = order(stock,-1*g.amount[i])
if orders is None or str(orders.status) != 'held':
print(str(stock)+' 未平仓成功')
else:
g.status[i] = 0
if orders.price == g.cost[i]:
g.equal += 1
g.df.loc[stock].equal += 1
print('尾盘平仓股票（平）:'+str(stock)+'，平仓价为：'+str(orders.price)+'，成本价为：'+str(g.cost[i])\
+'，收益率为：'+str(round((orders.price-g.cost[i])/g.cost[i],3)))
elif orders.price < g.cost[i]:
g.lose += 1
g.df.loc[stock].lose += 1
print('尾盘平仓股票（亏）:'+str(stock)+'，平仓价为：'+str(orders.price)+'，成本价为：'+str(g.cost[i])\
+'，收益率为：'+str(round((orders.price-g.cost[i])/g.cost[i],3)))
elif orders.price > g.cost[i]:
g.win += 1
g.df.loc[stock].win += 1
print('尾盘平仓股票（赚）:'+str(stock)+'，平仓价为：'+str(orders.price)+'，成本价为：'+str(g.cost[i])\
+'，收益率为：'+str(round((orders.price-g.cost[i])/g.cost[i],3)))
def after_trade(context):
print('累计胜负情况：赢'+str(g.win)+'，平'+str(g.equal)+'，输'+str(g.lose))
# 每日参数清零
g.minutes = 0
g.times = np.zeros(g.num)
print(g.df)
print('='*75)
g.days += 1
# 进出场信号
def signal(close,volume,high,low,dt):
for i in range(g.num):
stock = g.stocks[i]
p_open = close[stock].iloc[0] # 开盘价
p1 = close[stock].iloc[-1] # 最新价
p20_max = high[stock].iloc[-21:-1].max()
p20_mean = close[stock].iloc[-21:-1].mean()
p10_mean = close[stock].iloc[-11:-1].mean()
p5_mean = close[stock].iloc[-6:-1].mean()
p_min = low[stock].iloc[-31:-1].min()
p_max = close[stock].iloc[-31:-1].max()
cash_f = get_ticks(stock,dt,count=60,fields=['a1_v','b1_v','a1_p','b1_p',\
'a2_v','b2_v','a2_p','b2_p','a3_v','b3_v','a3_p','b3_p',\
'a4_v','b4_v','a4_p','b4_p','a5_v','b5_v','a5_p','b5_p'])
# tick级
a1_v,a1_p = cash_f['a1_v'][-3:],cash_f['a1_p']
b1_v,b1_p = cash_f['b1_v'][-3:],cash_f['b1_p']
a2_v,a2_p = cash_f['a2_v'][-3:],cash_f['a2_p']
b2_v,b2_p = cash_f['b2_v'][-3:],cash_f['b2_p']
a3_v,a3_p = cash_f['a3_v'][-3:],cash_f['a3_p']
b3_v,b3_p = cash_f['b3_v'][-3:],cash_f['b3_p']
a4_v,a4_p = cash_f['a4_v'][-3:],cash_f['a4_p']
b4_v,b4_p = cash_f['b4_v'][-3:],cash_f['b4_p']
a5_v,a5_p = cash_f['a5_v'][-3:],cash_f['a5_p']
b5_v,b5_p = cash_f['b5_v'][-3:],cash_f['b5_p']
if g.status[i] == 0 and g.minutes < 180:
bool_1 = p1 < p_min and p5_mean < p10_mean < p20_mean and p1 > p_open*0.93
bool_2 = b1_p[-2]+g.money1 < b1_p[-1] and b1_p[-3]+g.money2 < b1_p[-2]
bool_3 = (a1_v[-1]+a2_v[-1]+a3_v[-1]+a4_v[-1]+a5_v[-1])/2 < b1_v[-1]+b2_v[-1]+b3_v[-1]+b4_v[-1]+b5_v[-1]
bool_4 = (b1_v[-2]+b2_v[-2]+b3_v[-2]+b4_v[-2]+b5_v[-2])/2 < b1_v[-1]+b2_v[-1]+b3_v[-1]+b4_v[-1]+b5_v[-1]
if bool_1 and bool_2 and bool_3 and bool_4:
g.status[i] = 1
elif g.status[i] == 2:
bool1 = p1 > p_max and p5_mean > p10_mean > p20_mean and p1 > g.cost[i] * 1.015
bool2 = b1_p[-1] < b1_p[-2]
if g.minutes-g.period[i] > g.length:
g.status[i] = 3
elif bool1 and bool2 or p1 > p_open*1.09:
g.status[i] = 3