Simple Bollinger Bands

发布于 2022-02-20 22:26:15 字数 14126 浏览 718 评论 0 收藏 0

策略思路：在一段时间内，股票价格可以认为是在一定水平上下波动。例如，可以简单认为股价在过去100天的平均值基础上，上下波动几个标准差。

策略实现： (1) 股价高于这个波动区间，说明股价虚高，故卖出 (2) 股价低于这个波动区间，说明股价虚低，故买入

PS：本策略适用于股市平稳波动时期，在大牛市或者大熊市不太适用

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, 10, 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):

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

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

        upper = mu + 2*sd
        middle = mu
        lower = mu - 2*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	2011-11-01	1000000.00000	{}	1000000.00000	0.000000	[]
1	2011-11-02	1000000.00000	{}	1000000.00000	0.016629	[]
2	2011-11-03	1000000.00000	{}	1000000.00000	0.000697	[]
3	2011-11-04	1000000.00000	{}	1000000.00000	0.007086	[]
4	2011-11-07	1000000.00000	{}	1000000.00000	-0.009950	[]
5	2011-11-08	1000000.00000	{}	1000000.00000	-0.003120	[]
6	2011-11-09	1000000.00000	{}	1000000.00000	0.008778	[]
7	2011-11-10	1000000.00000	{}	1000000.00000	-0.018922	[]
8	2011-11-11	1000000.00000	{}	1000000.00000	-0.001699	[]
9	2011-11-14	1000000.00000	{}	1000000.00000	0.020480	[]
10	2011-11-15	1000000.00000	{}	1000000.00000	-0.002005	[]
11	2011-11-16	1000000.00000	{}	1000000.00000	-0.027167	[]
12	2011-11-17	1000000.00000	{}	1000000.00000	-0.003033	[]
13	2011-11-18	1000000.00000	{}	1000000.00000	-0.020857	[]
14	2011-11-21	1000000.00000	{}	1000000.00000	0.001225	[]
15	2011-11-22	1000000.00000	{}	1000000.00000	-0.000081	[]
16	2011-11-23	1000000.00000	{}	1000000.00000	-0.009758	[]
17	2011-11-24	1000000.00000	{}	1000000.00000	0.001898	[]
18	2011-11-25	1000000.00000	{}	1000000.00000	-0.007317	[]
19	2011-11-28	1000000.00000	{}	1000000.00000	0.001303	[]
20	2011-11-29	1000000.00000	{}	1000000.00000	0.013697	[]
21	2011-11-30	1000000.00000	{}	1000000.00000	-0.033370	[]
22	2011-12-01	1000000.00000	{}	1000000.00000	0.024625	[]
23	2011-12-02	1000000.00000	{}	1000000.00000	-0.010180	[]
24	2011-12-05	1000000.00000	{}	1000000.00000	-0.014048	[]
25	2011-12-06	1000000.00000	{}	1000000.00000	-0.002000	[]
26	2011-12-07	1000000.00000	{}	1000000.00000	0.004724	[]
27	2011-12-08	1000000.00000	{}	1000000.00000	-0.001276	[]
28	2011-12-09	1000000.00000	{}	1000000.00000	-0.008531	[]
29	2011-12-12	1000000.00000	{}	1000000.00000	-0.010293	[]
...	...	...	...	...	...	...
679	2014-08-19	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1149017.27989	0.000087	[]
680	2014-08-20	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1168551.04989	-0.003633	[Order({'transact_price': 0.0, 'symbol': '6000...
681	2014-08-21	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1155719.88989	-0.005028	[]
682	2014-08-22	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1152026.33989	0.004723	[]
683	2014-08-25	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1148250.45989	-0.009513	[]
684	2014-08-26	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1141866.53989	-0.008012	[]
685	2014-08-27	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1133729.60989	0.001507	[]
686	2014-08-28	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1130309.43989	-0.007010	[]
687	2014-08-29	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1144849.97989	0.011686	[]
688	2014-09-01	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1146127.24989	0.007283	[]
689	2014-09-02	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1171994.81989	0.013222	[]
690	2014-09-03	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1186363.06989	0.009377	[Order({'transact_price': 0.0, 'symbol': '6000...
691	2014-09-04	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1188453.82989	0.007218	[]
692	2014-09-05	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1203626.03989	0.009494	[]
693	2014-09-09	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1182303.76989	-0.001647	[]
694	2014-09-10	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1180321.58989	-0.005231	[]
695	2014-09-11	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1168537.55989	-0.003691	[Order({'transact_price': 0.0, 'symbol': '6000...
696	2014-09-12	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1171787.86989	0.006150	[]
697	2014-09-15	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1153269.96989	-0.000479	[]
698	2014-09-16	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1141194.90989	-0.019869	[]
699	2014-09-17	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1145778.58989	0.005258	[]
700	2014-09-18	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1152235.30989	0.003056	[]
701	2014-09-19	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1162000.35989	0.006870	[]
702	2014-09-22	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1136441.48989	-0.019087	[]
703	2014-09-23	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1139988.07989	0.008635	[]
704	2014-09-24	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1157613.46989	0.017671	[]
705	2014-09-25	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1169367.30989	-0.002006	[]
706	2014-09-26	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1169791.29989	0.000094	[]
707	2014-09-29	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1170503.49989	0.004349	[]
708	2014-09-30	96907.47989	{u'601166.XSHG': 3877.0, u'600036.XSHG': 0, u'...	1168692.07989	0.001303	[]

709 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: 0.0594823977045
volatility: 0.214571802928
information: 0.407163653335
sharpe: 0.102806600884
max_drawdown: 0.36585509048
alpha: 0.052294592526
beta: 0.434097117249

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

<matplotlib.legend.Legend at 0x568fe90>