# Conservative Bollinger Bands > 来源：https://uqer.io/community/share/548575def9f06c8e77336728 ```py 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 ``` ```py start = datetime(2011, 1, 1) end = datetime(2014, 8, 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 + 1*sd middle = mu lower = mu - 1*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) ```  ```py 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 | [] | | ... | ... | ... | ... | ... | ... | ``` 868 rows × 6 columns ``` ```py 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: 0.0806072460858 volatility: 0.121542243584 information: 0.967129870018 sharpe: 0.344919139631 max_drawdown: 0.100359317734 alpha: 0.0876204656402 beta: 0.392712356147 ``` ```py perf['cumulative_return'].plot() perf['benchmark_cumulative_return'].plot() pylab.legend(['current_strategy','HS300']) ```