def driptest(numlist):
'''
input: a list of numbers (nums corrsp to stocks listed in tckrlist)
output: the value of a portfolio containing stocks specified in numlist starting on 9/19/2000 and ending on 7/15/2013
divs reinvested at price on close of pay dates
'''
#TODO add semi-annual contributions, invested evenly between all stocks, every 130 days
cashperstock = 50000/len(numlist) #start with $50,000 dollars divided by number of stocks
holdingslist = []
for i in numlist: #buy initial shares
price = mf.parse_yahoo_historical(mf.fetch_historical_yahoo(tckrlist[i], (2000, 9, 19), (2000, 9, 19), dividends=False), adjusted=False)
iprice = price[0][2]
ishares = round(cashperstock/iprice, 2)
holdingslist.append(ishares)
for i in fulldatelist: #for every day in window
hcount = 0
p = i.split('/')
d = int(p[1])
yahoodate = (int(p[2]), int(p[0]), d) # a yahoo fetch compatible date
hcount = 0
for j in numlist: #look at every stock in the numlist
paycount = 0
icurrprice = 0
d = int(p[1])
yahoodate = (int(p[2]), int(p[0]), d)
for k in paydatemaster[j]: #for each stock, look at payment date master list
k = k.strip()
if k == i: #check if current day is a dividend pay day
paymentmaster[j][paycount] = float(paymentmaster[j][paycount])
divpaymt = round(paymentmaster[j][paycount]*holdingslist[hcount], 2)
try:
currprice = mf.parse_yahoo_historical(mf.fetch_historical_yahoo(tckrlist[j], yahoodate, yahoodate, dividends=False), adjusted=False)
icurrprice = currprice[0][2]
holdingslist[hcount] += round(divpaymt/icurrprice, 3) #reinvest, using yahoo data, tckrlist[j]
except: #sometimes paydates are on the weekend, in which case the next available day's close price is used
while icurrprice == 0:
d+= 1
try:
yahoodate = (int(p[2]), int(p[0]), d)
currprice = mf.parse_yahoo_historical(mf.fetch_historical_yahoo(tckrlist[j], yahoodate, yahoodate, dividends=False), adjusted=False)
icurrprice = currprice[0][2]
holdingslist[hcount] += round(divpaymt/icurrprice, 3)
except:
pass
paycount += 1
hcount += 1
finaldate = (2013, 7, 15)
count = 0
value = 0
for i in numlist:
finalprice = mf.parse_yahoo_historical(mf.fetch_historical_yahoo(tckrlist[i], finaldate, finaldate, dividends=False), adjusted=False)
ifinalprice = finalprice[0][2]
value += round(ifinalprice*holdingslist[count], 2) #calculate final value
count += 1
return value
def manualtest(numlist):
'''
input: a list of numbers (nums corrsp to stocks listed in tckrlist)
output: the value of a portfolio containing stocks specified in numlist starting on 9/19/2000 and ending on 7/15/2013
using a manual reinvestment strategy
'''
daycount = 0
cash = 0
transactions = 0
cashperstock = 50000/len(numlist) #start with $50,000 dollars divided by number of stocks
holdingslist = []
for i in numlist: #buy initial shares
price = mf.parse_yahoo_historical(mf.fetch_historical_yahoo(tckrlist[i], (2000, 9, 19), (2000, 9, 19), dividends=False), adjusted=False)
iprice = price[0][2]
ishares = round(cashperstock/iprice, 2)
holdingslist.append(ishares)
for i in fulldatelist: #for every day in window
fiftydaylist = []
buyconditiontf =[]
tendaylist= []
mrdaystckprice = []
hcount = 0
for j in numlist: #look at every stock in the randomly chosen numlist
paycount = 0
for k in paydatemaster[j]: #for each stock, look at payment date master list
k = k.strip()
if k == i: #check if current day is a div pay day
paymentmaster[j][paycount] = float(paymentmaster[j][paycount])
divpaymt = round(paymentmaster[j][paycount]*holdingslist[hcount], 2)
cash += divpaymt # if it's a pay day, add the div payment to cash
paycount += 1
hcount += 1
if daycount > 50: # if more than 50 days have passed
for u in numlist:
tempolist = []
dcount = 0
for p in stockdatemaster[u]:
if p == i:
tempolist = stockpricemaster[u][dcount:dcount+50] #stock prices for last 50 days
dcount += 1
if len(tempolist) != 0:
fstdv, favg = meanstdv(tempolist) #calculate fifty day moving average
tstdv, tavg = meanstdv(tempolist[:10]) #calculate 10 day moving average
fiftydaylist.append(favg)
tendaylist.append(tavg)
mrdaystckprice.append(tempolist[0])
fcount = 0
truelist = []
for s in mrdaystckprice:
if float(s) < fiftydaylist[fcount] and float(s) < tendaylist[fcount] and tendaylist[fcount] < fiftydaylist[fcount]: #buy criteria defined
buyconditiontf.append(True)
truelist.append(fcount)
# print i
else:
buyconditiontf.append(False)
fcount += 1
fcount = 0
if cash != 0 and len(truelist) != 0: #if there is cash available and one or more buy criteria have been met
if cash/len(truelist) > 500: #if there's more than $500 cash
for e in buyconditiontf:
if e == True:
holdingslist[fcount] += (cash/len(truelist))/float(mrdaystckprice[fcount]) #for all stocks meeting buy criteria, add to holdings using available cash
transactions += 1 #add one transaction to the list
fcount += 1
cash = 0 #reset cash to zero; all cash was spent adding to stocks meeting buy criteria
daycount += 1
finaldate = (2013, 7, 15)
count = 0
value = 0
for i in numlist:
finalprice = mf.parse_yahoo_historical(mf.fetch_historical_yahoo(tckrlist[i], finaldate, finaldate, dividends=False), adjusted=False)
ifinalprice = finalprice[0][2]
value += round((ifinalprice*holdingslist[count])+cash, 2) #calculate final value for each stock
count += 1
return value, transactions
import matplotlib.pyplot as plt
import matplotlib.finance as f
import numpy as np
spy = f.fetch_historical_yahoo('SPY', (2007,1,1), (2008,12,31))
agg = f.fetch_historical_yahoo('AGG', (2007,1,1,), (2008,12,31))
spy_l = f.parse_yahoo_historical(spy, adjusted=True, asobject=False)
agg_l = f.parse_yahoo_historical(agg, adjusted=True, asobject=False)
print spy_l
print agg_l
ndays = min(len(spy_l),len(agg_l)) - 1
print 'ndays', ndays
spy_daily = []
agg_daily = []
for i in range(1, ndays):
spy_daily.append(float(spy_l[i][4]) / float(spy_l[i+1][4]) - 1)
agg_daily.append(float(agg_l[i][4]) / float(agg_l[i+1][4]) - 1)
print spy_daily
print agg_daily
x = np.array(spy_daily, dtype='float')
y = np.array(agg_daily, dtype='float')
plt.axis('equal')
plt.grid(True)
plt.xlabel('spy')
plt.ylabel('agg')
plt.title('daily returns during financial crisis')
