Question

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>