Even More Conservative Bollinger Bands

来源：https://uqer.io/community/share/54859edff9f06c8e77336729

import quartz
import quartz.backtest    as qb
import quartz.performance as qp
from   quartz.api         import *

import pandas as pd
import numpy  as np
from datetime   import datetime
from matplotlib import pylab

import talib

start = datetime(2011, 1, 1)
end = datetime(2014, 12, 1)
benchmark = 'HS300'
universe = ['601398.XSHG', '600028.XSHG', '601988.XSHG', '600036.XSHG', '600030.XSHG',
            '601318.XSHG', '600000.XSHG', '600019.XSHG', '600519.XSHG', '601166.XSHG']
capital_base = 1000000
refresh_rate = 5
window = 200

def initialize(account):
    account.amount = 10000
    account.universe = universe
    add_history('hist', window)

def handle_data(account, data):

    for stk in account.universe:
        prices = account.hist[stk]['closePrice']
        if prices is None:
            return

        mu = prices.mean()
        sd = prices.std()

        upper = mu + .5*sd 
        middle = mu
        lower = mu - .5*sd


        cur_pos = account.position.stkpos.get(stk, 0)
        cur_prc = prices[-1]
        if cur_prc > upper and cur_pos >= 0:
            order_to(stk, 0)
        if cur_prc < lower and cur_pos <= 0:
            order(stk, account.amount)


bt
tradeDate cash stock_position portfolio_value benchmark_return blotter
0 2011-01-04 1000000 {} 1000000 0.000000 []
1 2011-01-05 1000000 {} 1000000 -0.004395 []
2 2011-01-06 1000000 {} 1000000 -0.005044 []
3 2011-01-07 1000000 {} 1000000 0.002209 []
4 2011-01-10 1000000 {} 1000000 -0.018454 []
5 2011-01-11 1000000 {} 1000000 0.005384 []
6 2011-01-12 1000000 {} 1000000 0.005573 []
7 2011-01-13 1000000 {} 1000000 -0.000335 []
8 2011-01-14 1000000 {} 1000000 -0.015733 []
9 2011-01-17 1000000 {} 1000000 -0.038007 []
10 2011-01-18 1000000 {} 1000000 0.001109 []
11 2011-01-19 1000000 {} 1000000 0.022569 []
12 2011-01-20 1000000 {} 1000000 -0.032888 []
13 2011-01-21 1000000 {} 1000000 0.013157 []
14 2011-01-24 1000000 {} 1000000 -0.009795 []
15 2011-01-25 1000000 {} 1000000 -0.005273 []
16 2011-01-26 1000000 {} 1000000 0.013536 []
17 2011-01-27 1000000 {} 1000000 0.016128 []
18 2011-01-28 1000000 {} 1000000 0.003393 []
19 2011-01-31 1000000 {} 1000000 0.013097 []
20 2011-02-01 1000000 {} 1000000 0.000252 []
21 2011-02-09 1000000 {} 1000000 -0.011807 []
22 2011-02-10 1000000 {} 1000000 0.020788 []
23 2011-02-11 1000000 {} 1000000 0.005410 []
24 2011-02-14 1000000 {} 1000000 0.031461 []
25 2011-02-15 1000000 {} 1000000 -0.000457 []
26 2011-02-16 1000000 {} 1000000 0.009590 []
27 2011-02-17 1000000 {} 1000000 -0.000807 []
28 2011-02-18 1000000 {} 1000000 -0.010484 []
29 2011-02-21 1000000 {} 1000000 0.014332 []
30 2011-02-22 1000000 {} 1000000 -0.028954 []
31 2011-02-23 1000000 {} 1000000 0.003529 []
32 2011-02-24 1000000 {} 1000000 0.005101 []
33 2011-02-25 1000000 {} 1000000 0.002094 []
34 2011-02-28 1000000 {} 1000000 0.013117 []
35 2011-03-01 1000000 {} 1000000 0.004733 []
36 2011-03-02 1000000 {} 1000000 -0.003562 []
37 2011-03-03 1000000 {} 1000000 -0.006654 []
38 2011-03-04 1000000 {} 1000000 0.015193 []
39 2011-03-07 1000000 {} 1000000 0.019520 []
40 2011-03-08 1000000 {} 1000000 0.000884 []
41 2011-03-09 1000000 {} 1000000 0.000420 []
42 2011-03-10 1000000 {} 1000000 -0.017551 []
43 2011-03-11 1000000 {} 1000000 -0.010025 []
44 2011-03-14 1000000 {} 1000000 0.004787 []
45 2011-03-15 1000000 {} 1000000 -0.018069 []
46 2011-03-16 1000000 {} 1000000 0.013806 []
47 2011-03-17 1000000 {} 1000000 -0.015730 []
48 2011-03-18 1000000 {} 1000000 0.005813 []
49 2011-03-21 1000000 {} 1000000 -0.002667 []
50 2011-03-22 1000000 {} 1000000 0.004942 []
51 2011-03-23 1000000 {} 1000000 0.013021 []
52 2011-03-24 1000000 {} 1000000 -0.004155 []
53 2011-03-25 1000000 {} 1000000 0.013263 []
54 2011-03-28 1000000 {} 1000000 -0.001188 []
55 2011-03-29 1000000 {} 1000000 -0.009905 []
56 2011-03-30 1000000 {} 1000000 -0.000583 []
57 2011-03-31 1000000 {} 1000000 -0.010071 []
58 2011-04-01 1000000 {} 1000000 0.015339 []
59 2011-04-06 1000000 {} 1000000 0.011714 []
... ... ... ... ... ... 
948 rows × 6 columns

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: 0.118291633101
volatility: 0.134550735738
information: 0.776689524517
sharpe: 0.591647698281
max_drawdown: 0.135222029922
alpha: 0.109380091075
beta: 0.429849284472

perf['cumulative_return'].plot()
perf['benchmark_cumulative_return'].plot()
pylab.legend(['current_strategy','HS300'])

<matplotlib.legend.Legend at 0x49c0b10>

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