Competitive Securities
策略实现:
计算三只同一行业股票过去4天内前3天的平均成交价(VWAP),这里选用的是中国平安 (601318.XSHG)、中国太保 (601601.XSHG)和中国人寿 (601628.XSHG)
当某两只股票的价格低于
0.995 * VWAP
,同时另一只股票价格高于VWAP时,买入后者当某两只股票的价格高于
1.025 * VWAP
,同时另一只股票价格低于VWAP时,清空后者
import pandas as pd
import numpy as np
from datetime import datetime
from matplotlib import pylab
import quartz
import quartz.backtest as qb
import quartz.performance as qp
from quartz.api import *
"Competitive Securities"
start = pd.datetime(2012, 1, 1)
end = pd.datetime(2014, 12, 1)
bm = 'HS300'
universe = ['601601.XSHG', '601318.XSHG', '601628.XSHG']
csvs = []
capital_base = 5000
window = 4
threshold_dn = 0.995
threshold_up = 1.025
refresh_rate = 4
def initialize(account):
account.amount = 1000
account.universe = universe
add_history('hist', window)
def handle_data(account):
vwap3, price = {}, {}
for stk in account.universe:
if stk not in account.hist:
continue
vwap3[stk] = sum(account.hist[stk]['turnoverValue'][:3])/sum(account.hist[stk]['turnoverVol'][:3])
price[stk] = account.hist[stk].iloc[window-1,:]['closePrice']
if len(vwap3)!=3:
return
stk_0 = account.universe[0]
stk_1 = account.universe[1]
stk_2 = account.universe[2]
if price[stk_1] <= threshold_dn * vwap3[stk_1] and price[stk_2] <= threshold_dn * vwap3[stk_2] and price[stk_0] > vwap3[stk_0]:
order(stk_0, account.amount)
if price[stk_2] <= threshold_dn * vwap3[stk_2] and price[stk_0] <= threshold_dn * vwap3[stk_0] and price[stk_1] > vwap3[stk_1]:
order(stk_1, account.amount)
if price[stk_0] <= threshold_dn * vwap3[stk_0] and price[stk_1] <= threshold_dn * vwap3[stk_1] and price[stk_2] > vwap3[stk_2]:
order(stk_2, account.amount)
if price[stk_1] >= threshold_up * vwap3[stk_1] and price[stk_2] >= threshold_up * vwap3[stk_2] and price[stk_0] < vwap3[stk_0]:
order_to(stk_0, 0)
if price[stk_2] >= threshold_up * vwap3[stk_2] and price[stk_0] >= threshold_up * vwap3[stk_0] and price[stk_1] < vwap3[stk_1]:
order_to(stk_1, 0)
if price[stk_0] >= threshold_up * vwap3[stk_0] and price[stk_1] >= threshold_up * vwap3[stk_1] and price[stk_2] < vwap3[stk_2]:
order_to(stk_2, 0)
perf = qp.perf_parse(bt)
out_keys = ['annualized_return', 'volatility', 'information',
'sharpe', 'max_drawdown', 'alpha', 'beta']
for k in out_keys:
print '%s: %s' % (k, perf[k])
annualized_return: 0.14708285
volatility: 0.285959506628
information: 0.525131029268
sharpe: 0.395275720443
max_drawdown: 0.391931712536
alpha: 0.089663482291
beta: 1.15117691695
perf['cumulative_return'].plot()
perf['benchmark_cumulative_return'].plot()
pylab.legend(['current_strategy','HS300'])
<matplotlib.legend.Legend at 0x55bf290>