# Competitive Securities > 來源：https://uqer.io/community/share/54b5c373f9f06c276f651a18 ## 策略實現： + 計算三只同一行業股票過去4天內前3天的平均成交價（VWAP），這里選用的是中國平安 (601318.XSHG)、中國太保 (601601.XSHG)和中國人壽 (601628.XSHG) + 當某兩只股票的價格低于`0.995 * VWAP`，同時另一只股票價格高于VWAP時，買入后者 + 當某兩只股票的價格高于`1.025 * VWAP`，同時另一只股票價格低于VWAP時，清空后者 ```py 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 * ``` ```py "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) ```  ```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: 0.14708285 volatility: 0.285959506628 information: 0.525131029268 sharpe: 0.395275720443 max_drawdown: 0.391931712536 alpha: 0.089663482291 beta: 1.15117691695 ``` ```py perf['cumulative_return'].plot() perf['benchmark_cumulative_return'].plot() pylab.legend(['current_strategy','HS300']) <matplotlib.legend.Legend at 0x55bf290> ```