Event Study实现

这一部分除了对事件进行展示，还要按照之前提到的规则生成下单指令：

#how to work? evalate the command below in order
#execfile('hw4_event.py')
#execfile('hw4_order.py')
#execfile('hw4_anylize.py')#change edge_price to change eventimport pandas as pd
import numpy as np
import math
import copy
import QSTK.qstkutil.qsdateutil as du
import datetime as dt
import QSTK.qstkutil.DataAccess as da
import QSTK.qstkutil.tsutil as tsu
import QSTK.qstkstudy.EventProfiler as epdef find_events(ls_symbols, d_data):''' Finding the event dataframe '''df_close = d_data['actual_close']ts_market = df_close['SPY']print "Finding Events"#events defined hereedge_price=7.0buy_sell_order=''# Creating an empty dataframedf_events = copy.deepcopy(df_close)df_events = df_events * np.NAN# Time stamps for the event rangeldt_timestamps = df_close.indexfor s_sym in ls_symbols:for i in range(1, len(ldt_timestamps)):# Calculating the returns for this timestamp#f_symprice_today = df_close[s_sym].ix[ldt_timestamps[i]]#f_symprice_yest = df_close[s_sym].ix[ldt_timestamps[i - 1]]#f_marketprice_today = ts_market.ix[ldt_timestamps[i]]#f_marketprice_yest = ts_market.ix[ldt_timestamps[i - 1]]#f_symreturn_today = (f_symprice_today / f_symprice_yest) - 1#f_marketreturn_today = (f_marketprice_today / f_marketprice_yest) - 1# Event is found if the symbol is down more then 3% while the# market is up more then 2%#if f_symreturn_today <= -0.03 and f_marketreturn_today >= 0.02:if df_close[s_sym].ix[ldt_timestamps[i-1]]>=edge_price and df_close[s_sym].ix[ldt_timestamps[i]]<edge_price:df_events[s_sym].ix[ldt_timestamps[i]] = 1tmp_time=ldt_timestamps[i]tmp_day=dt.date(tmp_time.year,tmp_time.month,tmp_time.day)buy_sell_order+=str(tmp_day).replace('-',',')+','+s_sym+',Buy,'+str(100)+','if i+5<len(ldt_timestamps):tmp_time=ldt_timestamps[i+5]else:tmp_time=ldt_timestamps[-1]tmp_day=dt.date(tmp_time.year,tmp_time.month,tmp_time.day)buy_sell_order+=str(tmp_day).replace('-',',')+','+s_sym+',Sell,'+str(100)+','order_list=list(buy_sell_order.split(','))del order_list[-1]order_array=np.array(order_list)order_array.resize((order_array.size/6,6))np.savetxt("myorder.csv",order_array,delimiter=',',fmt='%s')return df_eventsif __name__ == '__main__':dt_start = dt.datetime(2008, 1, 1)dt_end = dt.datetime(2009, 12, 31)ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt.timedelta(hours=16))dataobj = da.DataAccess('Yahoo')ls_symbols = dataobj.get_symbols_from_list('sp5002012')ls_symbols.append('SPY')ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']ldf_data = dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)d_data = dict(zip(ls_keys, ldf_data))for s_key in ls_keys:d_data[s_key] = d_data[s_key].fillna(method='ffill')d_data[s_key] = d_data[s_key].fillna(method='bfill')d_data[s_key] = d_data[s_key].fillna(1.0)df_events = find_events(ls_symbols, d_data)print "Creating Study"ep.eventprofiler(df_events, d_data, i_lookback=20, i_lookforward=20,s_filename='eventstudy.pdf', b_market_neutral=True, b_errorbars=True,s_market_sym='SPY')

股值计算

下单指令上一个模块已经生成，据此计算资产价值：

#how to work? evalate the command below in order
#execfile('hw4_event.py')
#execfile('hw4_order.py')
#execfile('hw4_anylize.py')#change currentCash,dt_start,dt_endimport pandas as pd
import numpy as np
import math
import copy
import QSTK.qstkutil.qsdateutil as du
import datetime as dt
import QSTK.qstkutil.DataAccess as da
import QSTK.qstkutil.tsutil as tsu
import QSTK.qstkstudy.EventProfiler as ep#get order
#sys.argv to get comman parameter
na_data = np.loadtxt('myorder.csv',dtype=np.str,delimiter=',')
#dtype={'names':('year','month','day','equity','buorsell','count'),    'formats':('i4','i4','i4','S5','S5','i4')},
na_dates=np.int_(na_data[:,0:3])
order=na_data[:,3:6]
ls_symbols=set(order[:,0])#get equity price
dt_start = dt.datetime(2008, 1, 1)
dt_end = dt.datetime(2009, 12, 31)
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt.timedelta(hours=16))dataobj = da.DataAccess('Yahoo')#why close?
#close for Adjusted Close ;actual_close for actual close
ls_keys = 'close'#['open', 'high', 'low', 'close', 'volume', 'actual_close']
ldf_data = dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)#calc portfolio
currentCash=50000
currentEquity=dict()
byOrSellDict={'Buy':1,'Sell':-1}#dateInd=0
#currentDate=dt.datetime(na_dates[dateInd,0],na_dates[dateInd,1],na_dates[dateInd,2])+dt.timedelta(hours=16)
#orders=[dt.datetime(na_dates[dateInd,0],na_dates[dateInd,1],na_dates[dateInd,2])+dt.timedelta(hours=16),
#    [order[dateInd,0],order[dateInd,1],int(order[dateInd,2])] for dateInd in range(na_data.shape[0])]
orders={}
for dateInd in range(na_data.shape[0]):tmpDate=dt.datetime(na_dates[dateInd,0],na_dates[dateInd,1],na_dates[dateInd,2])+dt.timedelta(hours=16)if tmpDate in orders.keys():orders[tmpDate].append([order[dateInd,0],order[dateInd,1],int(order[dateInd,2])])else:orders[tmpDate]=[[order[dateInd,0],order[dateInd,1],int(order[dateInd,2])]]#calc first and last day of order
order_date_list=list(orders.keys())
order_date_list.sort()
first_day=order_date_list[0]
last_day=order_date_list[-1]values=''
for i in ldt_timestamps:if i<first_day or i>last_day:continueif i in orders.keys():for singleOrder in orders[i]:equity=singleOrder[0]byOrSell=singleOrder[1]count=singleOrder[2]if equity in currentEquity.keys():currentEquity[equity]+=count*byOrSellDict[byOrSell]else:currentEquity[equity]=count*byOrSellDict[byOrSell]currentCash+=-ldf_data[equity][i]*count*byOrSellDict[byOrSell]print '----------------------',i,equity,byOrSell,countprint currentEquity#dateInd+=1#currentDate=dt.datetime(na_dates[dateInd,0],na_dates[dateInd,1],na_dates[dateInd,2])+dt.timedelta(hours=16)#calc portfolia valueportfValue=currentCashfor tmpEqui in currentEquity.keys():portfValue+=ldf_data[tmpEqui][i]*currentEquity[tmpEqui]print i,portfValuevalues+=str(dt.date(i.year,i.month,i.day)).replace('-',',')+','+str(portfValue)+','values_list=list(values.split(','))
del values_list[-1]
values_array=np.array(values_list)
values_array.resize((values_array.size/4,4))
np.savetxt("hw4values.csv",values_array,delimiter=',',fmt='%s')

策略评估

根据上一模块生成了日期--价值文件计算策略的各种指标：

#how to work? evalate the command below in order
#execfile('hw4_event.py')
#execfile('hw4_order.py')
#execfile('hw4_anylize.py')from math import sqrtdate_values = np.loadtxt('hw4values.csv',dtype=np.str,delimiter=',')
values=np.float_(date_values[:,3])total_return=values[-1]/values[0]#dayly_return=values[1:]/values[:-1]-1
dayly_return=tsu.returnize0(values)std=np.std(dayly_return)ave_dayly_ret=np.mean(dayly_return)sharp_ratio=ave_dayly_ret/std*sqrt(252)print 'Total Return of Fund: ',total_return
print 'Standard Deviation of Fund: ',std
print 'Average Daily Return of Fund: ',ave_dayly_ret
print 'Sharpe Ratio of Fund: ',sharp_ratio

平台整合

execfile('hw4_event.py')
execfile('hw4_order.py')
execfile('hw4_anylize.py')