def get_tasks():
symbols = ['MSFT', 'AMZN', 'GOOG', 'CRM']
fromdate, todate = '2017-09-01', '2017-09-30'
df_prices = psp.stockpricesstacked(symbols, fromdate, todate)
list_of_dicts = df_prices.T.to_dict().values()
return jsonify(list_of_dicts)
def setclassdictionaries(self,list_of_tickersigns = [],fromdate = '2017-01-01',todate = '2017-12-31'):
print 'started def setclassdictionaries'
df_tickers = pd.DataFrame(list_of_tickersigns, columns = ['ticker','sign','weight'])
df_tickers.set_index("ticker", drop=True, inplace=True)
self.TickerSignDataframe = df_tickers
list_of_symbols = df_tickers.index.tolist()
self.SymbolsList = list_of_symbols
list_of_signs = df_tickers['sign'].tolist()
self.SignsList = list_of_signs
print 'go to pull prices'
import pullstackedprices as pp
df = pp.stockpricesstacked(symbols=list_of_symbols,fromdate = fromdate,todate = todate)
if len(list_of_tickersigns) == 0:
columns = list(df.columns.values)
else:
columns = list_of_symbols
df = df[columns]
self.ClosePricesDataframe = df
print 'finished def setclassdictionaries'
return True
def prices():
try:
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 pullstackedprices as psp
#symbols = ['MSFT','AMZN','GOOG','MS']
symbols = tickerstring.split('-')
fromdate, todate = fromdatestring, todatestring
df = psp.stockpricesstacked(symbols, fromdate, todate)
df.index = df.index.map(str)
#df_end = df[:-1]
list_of_dicts = df.T.to_dict()
ret = jsonify({'prices': list_of_dicts})
except Exception as inst:
ret = 'Main.py My error was caught: ' + str(inst)
return ret
def adjclose():
try:
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 pullstackedprices as psp
#symbols = ['MSFT','AMZN','GOOG','MS']
symbols = tickerstring.split(',')
fromdate, todate = fromdatestring, todatestring
df = psp.stockpricesstacked(symbols,
fromdate,
todate,
pricechangeortotal='total')
df.index = df.index.map(str)
#df_end = df[:-1]
list_of_dicts = df.T.to_dict()
ret = jsonify({'prices': list_of_dicts})
#ret = render_template('prices',tables=[df.to_html(classes='prices')]) #, titles = ['na', 'Female surfers', 'Male surfers'])
except Exception as inst:
ret = 'Main.py My error was caught: ' + str(inst)
return ret
def stockreturnsstacked(symbols,fromdate,todate,pricechangeortotal='pricechange',logorarithmetic='log'):
import pullstackedprices as psp
df_00 = psp.stockpricesstacked(symbols,fromdate,todate,pricechangeortotal)
df_01 = pd.DataFrame(index=df_00.index.copy())
list_of_symbols_good = list(df_00.columns)
print 'list_of_symbols_good',list_of_symbols_good
for s in list_of_symbols_good:
if not logorarithmetic == 'log':
df_01[s] = df_00[s].pct_change()
else:
df_01[s] = np.log(1.0 + df_00[s].pct_change())
return df_01
def execute():
#This was up on the server before being pulled via git'
try:
import pullstackedprices as psp
symbols = ['MSFT', 'AMZN', 'GOOG']
fromdate, todate = '2017-09-01', '2017-09-30'
prices = psp.stockpricesstacked(symbols, fromdate, todate)
ret = str(prices[:-1])
except Exception as inst:
ret = 'Main.py My error was caught: ' + str(inst)
return ret
def execute():
#This was up on the server before being pulled via git'
try:
import pullstackedprices as psp
symbols = ['MSFT', 'AMZN', 'GOOG']
fromdate, todate = '2017-09-1', '2017-09-30'
df_prices = psp.stockpricesstacked(symbols, fromdate, todate)
df_prices_end = df_prices[:-1]
list_of_dicts = df_prices_end.T.to_dict().values()
ret = jsonify({'prices': 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
def coint(df2column):
import statsmodels.tsa.stattools as ts
symbols = list(df2column.columns.values)
print symbols
x1=df2column[symbols[0]]
y1=df2column[symbols[1]]
#print 'x1',x1
#print 'y1',y1
coin_result = ts.coint(x1, y1)
return coin_result
if __name__=='__main__':
#symbols = ['LAZ', 'LMT', 'RTN', 'MAS', 'AMAT', 'INTC', 'LPX', 'GRMN', 'PCLN', 'KSS', 'JWN', 'M', 'GPS', 'LOW', 'PEP', 'CVS', 'CL', 'KMB', 'MO', 'PM', 'CVX', 'BAC', 'BEN', 'MS', 'AXP', 'CELG', 'AMGN', 'JNJ', 'LLY', 'MMM', 'UNP', 'CSCO', 'SWKS', 'CA', 'STX', 'LYB', 'APD', 'T', 'TGT', 'HD', 'ETR', 'AES', 'HOG', 'F', 'GPC', 'LEG', 'WHR', 'NWL', 'TRIP', 'HAS', 'BC', 'CMCSA', 'DIS', 'VIA', 'DISH', 'NWS', 'PAG', 'CRI', 'COLM', 'SKX', 'NKE', 'TAP', 'CASY', 'HRL', 'HAIN', 'SJM', 'ADM', 'KHC', 'MDLZ', 'FTI', 'SLB', 'NFX', 'KMI', 'CXO', 'MUR', 'WPX', 'EGN', 'XOM', 'LNG', 'FCNCA', 'LUK', 'Y', 'WTM', 'AXS', 'ALKS', 'MDT', 'XRAY', 'CAH', 'MD', 'PDCO', 'UHS', 'AGN', 'ARNC', 'UAL', 'AAL', 'GE', 'SNA', 'WAB', 'FLS', 'VRSK', 'GWR', 'GWW', 'VSAT', 'AVT', 'TWTR', 'AMD', 'QCOM', 'FSLR', 'OTEX', 'NUAN', 'HPE', 'RPM', 'MLM', 'VMC', 'SEE', 'SON', 'HHC', 'LVLT', 'LVLT', 'S', 'JLL']
#symbols = ['MAR', 'MON', 'NOV', 'A', 'AAL', 'AAP', 'AAPL', 'ABBV', 'ABC', 'ABT', 'ACN', 'ADBE', 'ADI', 'ADM', 'ADP', 'ADS', 'ADSK', 'AEE', 'AEP', 'AES', 'AET', 'AFL', 'AGN', 'AIG', 'AIV', 'AIZ', 'AJG', 'AKAM', 'ALB', 'ALGN', 'ALK', 'ALL', 'ALLE', 'ALXN', 'AMAT', 'AMD', 'AME', 'AMG', 'AMGN', 'AMP', 'AMT', 'AMZN', 'ANDV', 'ANSS', 'ANTM', 'AON', 'AOS', 'APA', 'APC', 'APD', 'APH', 'ARE', 'ARNC', 'ATVI', 'AVB', 'AVGO', 'AVY', 'AWK', 'AXP', 'AYI', 'AZO', 'BA', 'BAC', 'BAX', 'BBT', 'BBY', 'BCR', 'BDX', 'BEN', 'BF.B', 'BHF', 'BHGE', 'BIIB', 'BK', 'BLK', 'BLL', 'BMY', 'BRK.B', 'BSX', 'BWA', 'BXP', 'C', 'CA', 'CAG', 'CAH', 'CAT', 'CB', 'CBG', 'CBOE', 'CBS', 'CCI', 'CCL', 'CDNS', 'CELG', 'CERN', 'CF', 'CFG', 'CHD', 'CHK', 'CHRW', 'CHTR', 'CI', 'CINF', 'CL', 'CLX', 'CMA', 'CMCSA', 'CME', 'CMG', 'CMI', 'CMS', 'CNC', 'CNP', 'COF', 'COG', 'COH', 'COL', 'COO', 'COP', 'COST', 'COTY', 'CPB', 'CRM', 'CSCO', 'CSRA', 'CSX', 'CTAS', 'CTL', 'CTSH', 'CTXS', 'CVS', 'CVX', 'CXO', 'D', 'DAL', 'DE', 'DFS', 'DG', 'DGX', 'DHI', 'DHR', 'DIS', 'DISCA', 'DISCK', 'DISH', 'DLPH', 'DLR', 'DLTR', 'DOV', 'DPS', 'DRE', 'DRI', 'DTE', 'DUK', 'DVA', 'DVN', 'DWDP', 'DXC', 'EA', 'EBAY', 'ECL', 'ED', 'EFX', 'EIX', 'EL', 'EMN', 'EMR', 'EOG', 'EQIX', 'EQR', 'EQT', 'ES', 'ESRX', 'ESS', 'ETFC', 'ETN', 'ETR', 'EVHC', 'EW', 'EXC', 'EXPD', 'EXPE', 'EXR', 'F', 'FAST', 'FB', 'FBHS', 'FCX', 'FDX', 'FE', 'FFIV', 'FIS', 'FISV', 'FITB', 'FL', 'FLIR', 'FLR', 'FLS', 'FMC', 'FOX', 'FOXA', 'FRT', 'FTI', 'FTV', 'GD', 'GE', 'GGP', 'GILD', 'GIS', 'GLW', 'GM', 'GOOG', 'GOOGL', 'GPC', 'GPN', 'GPS', 'GRMN', 'GS', 'GT', 'GWW', 'HAL', 'HAS', 'HBAN', 'HBI', 'HCA', 'HCN', 'HCP', 'HD', 'HES', 'HIG', 'HLT', 'HOG', 'HOLX', 'HON', 'HP', 'HPE', 'HPQ', 'HRB', 'HRL', 'HRS', 'HSIC', 'HST', 'HSY', 'HUM', 'IBM', 'ICE', 'IDXX', 'IFF', 'ILMN', 'INCY', 'INFO', 'INTC', 'INTU', 'IP', 'IPG', 'IR', 'IRM', 'ISRG', 'IT', 'ITW', 'IVZ', 'JBHT', 'JCI', 'JEC', 'JNJ', 'JNPR', 'JPM', 'JWN', 'K', 'KEY', 'KHC', 'KIM', 'KLAC', 'KMB', 'KMI', 'KMX', 'KO', 'KORS', 'KR', 'KSS', 'KSU', 'L', 'LB', 'LEG', 'LEN', 'LH', 'LKQ', 'LLL', 'LLY', 'LMT', 'LNC', 'LNT', 'LOW', 'LRCX', 'LUK', 'LUV', 'LVLT', 'LYB', 'M', 'MA', 'MAA', 'MAC', 'MAS', 'MAT', 'MCD', 'MCHP', 'MCK', 'MCO', 'MDLZ', 'MDT', 'MET', 'MGM', 'MHK', 'MKC', 'MLM', 'MMC', 'MMM', 'MNST', 'MO', 'MOS', 'MPC', 'MRK', 'MRO', 'MS', 'MSFT', 'MSI', 'MTB', 'MTD', 'MU', 'MYL', 'NAVI', 'NBL', 'NDAQ', 'NEE', 'NEM', 'NFLX', 'NFX', 'NI', 'NKE', 'NLSN', 'NOC', 'NRG', 'NSC', 'NTAP', 'NTRS', 'NUE', 'NVDA', 'NWL', 'NWS', 'NWSA', 'O', 'OKE', 'OMC', 'ORCL', 'ORLY', 'OXY', 'PAYX', 'PBCT', 'PCAR', 'PCG', 'PCLN', 'PDCO', 'PEG', 'PEP', 'PFE', 'PFG', 'PG', 'PGR', 'PH', 'PHM', 'PKG', 'PKI', 'PLD', 'PM', 'PNC', 'PNR', 'PNW', 'PPG', 'PPL', 'PRGO', 'PRU', 'PSA', 'PSX', 'PVH', 'PWR', 'PX', 'PXD', 'PYPL', 'Q', 'QCOM', 'QRVO', 'RCL', 'RE', 'REG', 'REGN', 'RF', 'RHI', 'RHT', 'RJF', 'RL', 'RMD', 'ROK', 'ROP', 'ROST', 'RRC', 'RSG', 'RTN', 'SBAC', 'SBUX', 'SCG', 'SCHW', 'SEE', 'SHW', 'SIG', 'SJM', 'SLB', 'SLG', 'SNA', 'SNI', 'SNPS', 'SO', 'SPG', 'SPGI', 'SPLS', 'SRCL', 'SRE', 'STI', 'STT', 'STX', 'STZ', 'SWK', 'SWKS', 'SYF', 'SYK', 'SYMC', 'SYY', 'T', 'TAP', 'TDG', 'TEL', 'TGT', 'TIF', 'TJX', 'TMK', 'TMO', 'TRIP', 'TROW', 'TRV', 'TSCO', 'TSN', 'TSS', 'TWX', 'TXN', 'TXT', 'UA', 'UAA', 'UAL', 'UDR', 'UHS', 'ULTA', 'UNH', 'UNM', 'UNP', 'UPS', 'URI', 'USB', 'UTX', 'V', 'VAR', 'VFC', 'VIAB', 'VLO', 'VMC', 'VNO', 'VRSK', 'VRSN', 'VRTX', 'VTR', 'VZ', 'WAT', 'WBA', 'WDC', 'WEC', 'WFC', 'WHR', 'WLTW', 'WM', 'WMB', 'WMT', 'WRK', 'WU', 'WY', 'WYN', 'WYNN', 'XEC', 'XEL', 'XL', 'XLNX', 'XOM', 'XRAY', 'XRX', 'XYL', 'YUM', 'ZBH', 'ZION', 'ZTS']
#symbols = ['MAR', 'MON', 'NOV', 'A', 'AAL', 'AAP', 'AAPL',]
symbols = ['AAPL','FB','KO','PEP','GOOG','GOOGL']
fromdate,todate = '2016-07-01','2017-08-05'
import pullstackedprices as psp
df1 = psp.stockpricesstacked(symbols,fromdate,todate )
#import pullstackedreturns as psr
#df1 = psr.stockreturnsstacked(symbols,fromdate,todate )
#df1 = df1.dropna()
print 'df1',df1
ret = coint(df1[['GOOG','GOOGL']])
print ret
#if i2 >= 6:
# break
print 'finished creating class dictionaries...'
self.PairPricesDiffDictionary = dict_pairdiff_prices
self.PairRunningMaxDiffDictionary = dict_pairdiff_runningmax
self.PairRunningMinDiffDictionary = dict_pairdiff_runningmin
self.PairBetweenMaxMinDiffDictionary = dict_pairdiff_betweenmaxmin
self.PairRunningPctDiffDictionary = dict_pairdiff_runningpct
self.PairMovingAverageDiffDictionary = dict_pairdiff_movingaverage
self.PairMovingStdevDiffDictionary = dict_pairdiff_standarddeviation
self.SymbolsList = columns
return True
if __name__ == '__main__':
#symbols = ['MAR', 'MON', 'NOV', 'A', 'AAL', 'AAP', 'AAPL', ]
symbols = [
'GOOG',
'GOOGL',
]
print symbols
import pullstackedprices as psp
df = psp.stockpricesstacked(symbols, '2013-07-01', '2017-08-05')
o = analyze()
o.setclassdictionaries(pairdataframe=df, movingaveragewindow=50)
for k, v in o.PairMovingStdevDiffDictionary.items():
print k, v
def setclassdictionaries(symbols, fromdate, todate, movingaveragewindow=70):
print 'started def setclassdictionaries'
import pullstackedprices as pp1
df = pp1.stockpricesstacked(
symbols,
fromdate,
todate,
)
import pandas as pd
list_of_dates = list(df.index)
list_of_dates_sorted = sorted(list_of_dates)
df2 = pd.DataFrame({'Date': list_of_dates_sorted})
ClosePricesDataframe = df
columns = list(df.columns.values)
df_openshares = 10000.0 / df.iloc[[0]]
df_shares2 = df_openshares.append([df_openshares] * (len(df) - 1),
ignore_index=True)
df_shares3 = pd.concat([df2, df_shares2], axis=1)
df_shares3.set_index("Date", drop=True, inplace=True)
#print '----------------------------'
#print df_shares3
#stop
df_dollarized = df.multiply(df_shares3, axis=1)
dict_pairdiff_runningmax = {}
dict_pairdiff_runningmin = {}
dict_pairdiff_betweenmaxmin = {}
dict_pairdiff_runningpct = {}
dict_pairdiff_prices = {}
dict_pairdiff_dollarized = {}
dict_pairdiff_movingaverage = {}
dict_pairdiff_standarddeviation = {}
print 'started creating class dictionaries...'
i2 = 0
for column in columns:
df_diff_runningmax = pd.DataFrame(index=df.index)
df_diff_runningmin = pd.DataFrame(index=df.index)
df_diff_movingaverage = pd.DataFrame(index=df.index)
df_diff_stdev = pd.DataFrame(index=df.index)
print 'setclassdictionaries', column
df_diff_prices = df[columns].sub(df[column], axis=0)
#df_diff_prices = df_diff_prices.abs()
#df_diff_prices = df_diff #df[columns].sub(df[column], axis=0)
i3 = 0
for column1 in columns:
df_diff_prices1 = df_diff_prices[column1].to_frame(column1)
df_diff_runningmax[column1] = df_diff_prices1.rolling(
window=20).max() #df_diff1[column1].cummax().to_frame(column1)
df_diff_runningmin[column1] = df_diff_prices1.rolling(
window=20).min(
) #df_diff1[column1].cummin().to_frame(column1) #ssss
#df_diff_runningmax[column1] = df_diff1[column1].cummax().to_frame(column1)
#df_diff_runningmin[column1] = df_diff1[column1].cummin().to_frame(column1) #ssss
df_diff_movingaverage[column1] = df_diff_prices1.rolling(
window=movingaveragewindow).mean()
df_diff_stdev[column1] = df_diff_prices1.rolling(
window=movingaveragewindow).std()
i3 += 1
df_diff_betweenmaxmin = df_diff_runningmax[columns].sub(
df_diff_runningmin[columns], axis=0)
df_diff_runningpct = (df_diff_prices - df_diff_runningmin) / (
df_diff_runningmax - df_diff_runningmin)
df_diff_dollarized = df_dollarized[columns].sub(df_dollarized[column],
axis=0)
dict_pairdiff_prices[column] = df_diff_prices
dict_pairdiff_runningmax[column] = df_diff_runningmax
dict_pairdiff_runningmin[column] = df_diff_runningmin
dict_pairdiff_betweenmaxmin[column] = df_diff_betweenmaxmin
dict_pairdiff_runningpct[column] = df_diff_runningpct
dict_pairdiff_dollarized[column] = df_diff_dollarized
dict_pairdiff_movingaverage[column] = df_diff_movingaverage
dict_pairdiff_standarddeviation[column] = df_diff_stdev
i2 += 1
#if i2 >= 6:
# break
print 'finished creating class dictionaries...'
PairPricesDiffDictionary = dict_pairdiff_prices
for k, v in PairPricesDiffDictionary.items():
print k
PairRunningMaxDiffDictionary = dict_pairdiff_runningmax
PairRunningMinDiffDictionary = dict_pairdiff_runningmin
PairBetweenMaxMinDiffDictionary = dict_pairdiff_betweenmaxmin
PairRunningPctDiffDictionary = dict_pairdiff_runningpct
PairDollarizedDiffDictionary = dict_pairdiff_dollarized
PairMovingAverageDiffDictionary = dict_pairdiff_movingaverage
PairMovingStdevDiffDictionary = dict_pairdiff_standarddeviation
SymbolsList = columns
return True
