def aggregatedreturnsdataframe(symbols, fromdate, todate):
import random
import datetime
ls_final = []
ls_final.append([
'symbol', 'start_date', 'end_date', 'annualized_return',
'cumulative_return', 'random_return', 'start_price', 'end_price',
'stdev', 'stdev_np', 'mean_np'
])
i = 0
df_pricechange = psr.stockreturnsstacked(symbols, fromdate, todate,
'PriceChange')
df_total = psr.stockreturnsstacked(symbols, fromdate, todate, 'Total')
print '-----------df lastdate ----------------'
fromdate_derived = df_total.iloc[:1].index.strftime('%Y-%m-%d').item(0)
todate_derived = df_total.iloc[-1:].index.strftime('%Y-%m-%d').item(0)
time1 = datetime.datetime.strptime(fromdate_derived + ' 00:00:00.00',
"%Y-%m-%d %H:%M:%S.%f")
time2 = datetime.datetime.strptime(todate_derived + ' 23:59:59.999999',
"%Y-%m-%d %H:%M:%S.%f")
#print 'times',time1,time2
elapsedTime = time2 - time1
yrs = float(
divmod(elapsedTime.total_seconds(), 60.0)[0] / 60.0 / 24.0 / 365.0)
#print df + 1
df_cumprod = np.cumprod(df_total + 1) - 1
df_adr = np.cumprod(df_total + 1).iloc[-1:]**(float(1) / (yrs)) - 1.0
print '-----------df adr ----------------'
print df_adr
#print '-----------df_cumprod----------------'
#print df.len(df)
stdev_np = np.std(df_pricechange, ddof=1)
mean_np = np.mean(df_pricechange)
df = pd.DataFrame(mean_np, columns=['mean'])
df['stdev'] = stdev_np
df = df.T
df = pd.concat([
df,
pd.DataFrame(np.array(df_cumprod.iloc[-1:]),
index=['cumreturn'],
columns=df_cumprod.columns)
])
df = pd.concat([
df,
pd.DataFrame(np.array(df_adr.iloc[-1:]),
index=['annreturn'],
columns=df_adr.columns)
])
return df.T
def correlationmatrix(symbols, fromdate, todate):
print 'Running correlationmatrix'
date14 = str(datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
df_alignedpricechangereturns = psr.stockreturnsstacked(
symbols, fromdate, todate, pricechangeortotal='pricechange')
df_alignedpricechangereturns = df_alignedpricechangereturns.dropna() #eeee
covmatrix_array = np.cov(df_alignedpricechangereturns, None, 0)
rows = np.array(list(df_alignedpricechangereturns))[:np.newaxis]
corrmatrix_array = np.corrcoef(
df_alignedpricechangereturns.T.values.tolist())
df_correlationmatrix = pd.DataFrame(
corrmatrix_array,
index=rows,
columns=list(df_alignedpricechangereturns))
#cachedfilepathname = mycachefolder
myfilename = 'correlation_' + date14 + '.csv'
cachedfilepathname = os.path.join(app.config['UPLOAD_FOLDER'], myfilename)
df_correlationmatrix.to_csv(cachedfilepathname,
columns=(list(
df_correlationmatrix.columns.values)))
#import time
time.sleep(1.5)
print myfilename
return myfilename
def cov():
try:
print 'got here xx'
todatestring = str(datetime.datetime.now().date())
fromdatestring = todatestring[:-2] + '01'
tickerstring = request.args.get('tickerstring',
default='MSFT-AAPL',
type=str)
fromdatestring = request.args.get('fromdate',
default=fromdatestring,
type=str)
todatestring = request.args.get('todate',
default=todatestring,
type=str)
import pullstackedreturns as psp
#symbols = ['MSFT','AMZN','GOOG','MS']
symbols = tickerstring.split('-')
fromdate, todate = fromdatestring, todatestring
df_returns = psp.stockreturnsstacked(symbols,
fromdate,
todate,
pricechangeortotal='pricechange')
df_alignedpricechangereturns = df_returns
df_alignedpricechangereturns = df_alignedpricechangereturns.dropna(
) #eeee
covmatrix_array = np.cov(df_alignedpricechangereturns, None, 0)
rows = np.array(list(df_alignedpricechangereturns))[:np.newaxis]
df = pd.DataFrame(covmatrix_array,
index=rows,
columns=list(df_alignedpricechangereturns))
#df.index = df.index.map(str)
list_of_dicts = df.T.to_dict()
ret = jsonify({'cov': list_of_dicts})
except Exception as inst:
ret = 'Main.py My error was caught: ' + str(inst)
return ret
def getfile():
try:
if request.method == 'POST':
clearcontentsofcache()
print 'yyyyy'
date14 = str(datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
feature = request.form['feature']
tickerstring = request.form['tickerstring']
fromdate = request.form['fromdate']
todate = request.form['todate']
tickerstring = "".join(tickerstring.split())
symbols = tickerstring.split(',')
feature = feature[:3].lower()
print 'check', tickerstring, feature
print symbols
if feature == 'cov':
myfilename = covariancematrix(symbols, fromdate, todate)
elif feature == 'cor':
myfilename = correlationmatrix(symbols, fromdate, todate)
elif feature == 'pri':
myfilename = 'prices_' + date14 + '.csv'
cachedfilepathname = os.path.join(app.config['UPLOAD_FOLDER'],
myfilename)
df = psp.stockpricesstacked(symbols, fromdate, todate)
df.to_csv(cachedfilepathname,
columns=(list(df.columns.values)))
time.sleep(1.5)
elif feature == 'adj':
myfilename = 'adjclose_' + date14 + '.csv'
cachedfilepathname = os.path.join(app.config['UPLOAD_FOLDER'],
myfilename)
df = psp.stockpricesstacked(symbols,
fromdate,
todate,
pricechangeortotal='total')
df.to_csv(cachedfilepathname,
columns=(list(df.columns.values)))
time.sleep(1.5)
elif feature == 'ret':
myfilename = 'returns_' + date14 + '.csv'
cachedfilepathname = os.path.join(app.config['UPLOAD_FOLDER'],
myfilename)
df = psr.stockreturnsstacked(symbols, fromdate, todate)
df.to_csv(cachedfilepathname,
columns=(list(df.columns.values)))
time.sleep(1.5)
elif feature == 'int':
print 'got here xxx'
myfilename = 'intraday_' + date14 + '.csv'
cachedfilepathname = os.path.join(app.config['UPLOAD_FOLDER'],
myfilename)
days = request.args.get('days', default=1, type=int)
df = pd.DataFrame()
for symbol in symbols:
print 'xx', symbol
df_x = pid.intradaystockprices(ticker=symbol,
period=60,
days=days)
if len(df) == 0:
df = df_x
else:
df = df.append(df_x)
df.to_csv(cachedfilepathname,
columns=(list(df.columns.values)))
time.sleep(1.5)
ret = 'Successfully executed.'
return send_file('cache/' + myfilename,
mimetype='text/csv',
attachment_filename=myfilename,
as_attachment=True)
else:
#print 'xxx'
return initial_html
except Exception as inst:
ret = 'getfile My error was caught: ' + str(inst)
return initial_html