def build(self, symbol, param_numberofyears, showresults):
import pandas as pd
import numpy as np
print('mysymbol', symbol, '------------------------------------')
#param_numberofyears = 4
numberofweekstorun = param_numberofyears * 52
from datetime import datetime, timedelta
dStart = datetime.now().date() + timedelta(weeks=-numberofweekstorun)
#print(dStart)
index = pd.date_range(dStart + timedelta(days=0),
periods=numberofweekstorun,
freq='W-Fri')
#print(index)
columns = [
'd1', 'd2', 'pRef', 'p1', 'p2', 'Delta2to1', 'DrawDownMax',
'DrawUpMax', 'DrawDownPctChange', 'DrawUpPctChange'
]
df_ = pd.DataFrame(index=index, columns=columns)
df_ = df_.fillna(0) # with 0s rather than NaNs
#df_ = df_.fillna(method='pad')
# ------------------------------------------------------------
# Change this number after * if changing the number of columns
data = np.array([np.arange(len(index))] * 10).T
# ------------------------------------------------------------
df = pd.DataFrame(data, index=index, columns=columns)
#-------------------------------------------------------------------------------
dEnd = index[-1]
import pullprices
fmt = '%Y-%m-%d'
dDataStart = dStart + timedelta(days=-33)
f = pullprices.stockhistory(symbol, dDataStart, dEnd)
for idx, row in df.iterrows():
pDraw_downmax = 0
pDraw_upmax = 0
#print(idx)
dRef = idx
d17 = dRef + timedelta(days=-17)
d32 = dRef + timedelta(days=-32)
dRef_string = dRef.strftime(fmt)
d17_string = d17.strftime(fmt)
d32_string = d32.strftime(fmt)
try:
p32 = f.ix[d32_string]['Adj Close']
#print(d32_string,p32)
except:
p32 = float('NaN')
#print('no data')
pass
try:
p17 = f.ix[d17_string]['Adj Close']
#print(d17_string,p17)
except:
p17 = float('NaN')
#print('no data')
pass
try:
pRef = f.ix[dRef_string]['Adj Close']
#print(dRef_string,pRef)
except:
pRef = float('NaN')
#print('no data')
pass
try:
pdelta32to17 = p17 - p32
#print('pricedelta',pdelta32to17)
except:
pdelta32to17 = float('NaN')
#print('no delta')
for iday in range(1, 32 - 17 + 1):
dDraw = d32 + timedelta(days=iday)
dDraw_string = dDraw.strftime(fmt)
#print(dDraw_string)
try:
pDraw = f.ix[dDraw_string]['Adj Close']
pDelta = pDraw - p32
if pDelta < pDraw_downmax:
pDraw_downmax = pDelta
if pDelta > pDraw_upmax:
pDraw_upmax = pDelta
if p32 == float('NaN'):
pDraw_downmax = float('NaN')
pDraw_upmax = float('NaN')
except:
pDraw = float('Nan')
#print(dRef_string,d17_string,d32_string)
pctchangeDraw_downmax = pDraw_downmax / p32
pctchangeDraw_upmax = pDraw_upmax / p32
df.loc[dRef] = pd.Series({
'd1': d17.strftime('%Y-%m-%d'),
'd2': d32.strftime('%Y-%m-%d'),
'pRef': pRef,
'p1': p17,
'p2': p32,
'Delta2to1': pdelta32to17,
'DrawDownMax': pDraw_downmax,
'DrawUpMax': pDraw_upmax,
'DrawDownPctChange': pctchangeDraw_downmax,
'DrawUpPctChange': pctchangeDraw_upmax
})
#print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
#df = df.fillna(method='pad')
print(df)
print(len(data))
print(len(df))
print('------------------------')
print(dStart)
print(dEnd)
print('dRef', dRef)
self.DataFrameResult = df
def build(self,
symbol,
param_numberofyears ,
showresults
):
import pandas as pd
import numpy as np
print('mysymbol',symbol,'------------------------------------')
#param_numberofyears = 4
numberofweekstorun = param_numberofyears * 52
from datetime import datetime, timedelta
dStart = datetime.now().date() + timedelta(weeks = -numberofweekstorun)
#print(dStart)
index = pd.date_range(dStart+timedelta(days = 0), periods=numberofweekstorun, freq='W-Fri')
#print(index)
columns = ['d1','d2','pRef','p1','p2','Delta2to1','DrawDownMax','DrawUpMax','DrawDownPctChange','DrawUpPctChange']
df_ = pd.DataFrame(index=index, columns=columns)
df_ = df_.fillna(0) # with 0s rather than NaNs
#df_ = df_.fillna(method='pad')
# ------------------------------------------------------------
# Change this number after * if changing the number of columns
data = np.array([np.arange(len(index))]*10).T
# ------------------------------------------------------------
df = pd.DataFrame(data, index=index, columns=columns)
#-------------------------------------------------------------------------------
dEnd = index[-1]
import pullprices
fmt = '%Y-%m-%d'
dDataStart = dStart+timedelta(days = -33)
f=pullprices.stockhistory(symbol, dDataStart, dEnd)
for idx, row in df.iterrows():
pDraw_downmax = 0
pDraw_upmax = 0
#print(idx)
dRef = idx
d17 = dRef + timedelta(days = -17)
d32 = dRef + timedelta(days = -32)
dRef_string = dRef.strftime(fmt)
d17_string = d17.strftime(fmt)
d32_string = d32.strftime(fmt)
try:
p32 = f.ix[d32_string]['Adj Close']
#print(d32_string,p32)
except:
p32 = float('NaN')
#print('no data')
pass
try:
p17 = f.ix[d17_string]['Adj Close']
#print(d17_string,p17)
except:
p17 = float('NaN')
#print('no data')
pass
try:
pRef = f.ix[dRef_string]['Adj Close']
#print(dRef_string,pRef)
except:
pRef = float('NaN')
#print('no data')
pass
try:
pdelta32to17 = p17 - p32
#print('pricedelta',pdelta32to17)
except:
pdelta32to17 = float('NaN')
#print('no delta')
for iday in range(1, 32 - 17 + 1):
dDraw = d32 + timedelta(days = iday)
dDraw_string = dDraw.strftime(fmt)
#print(dDraw_string)
try:
pDraw = f.ix[dDraw_string]['Adj Close']
pDelta = pDraw - p32
if pDelta < pDraw_downmax:
pDraw_downmax = pDelta
if pDelta > pDraw_upmax:
pDraw_upmax = pDelta
if p32 == float('NaN'):
pDraw_downmax = float('NaN')
pDraw_upmax = float('NaN')
except:
pDraw = float('Nan')
#print(dRef_string,d17_string,d32_string)
pctchangeDraw_downmax = pDraw_downmax / p32
pctchangeDraw_upmax = pDraw_upmax / p32
df.loc[dRef] = pd.Series({'d1':d17.strftime('%Y-%m-%d'),'d2':d32.strftime('%Y-%m-%d'), 'pRef':pRef, 'p1':p17,'p2':p32,'Delta2to1':pdelta32to17,'DrawDownMax':pDraw_downmax,'DrawUpMax':pDraw_upmax,'DrawDownPctChange':pctchangeDraw_downmax,'DrawUpPctChange':pctchangeDraw_upmax})
#print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
#df = df.fillna(method='pad')
print(df)
print(len(data))
print(len(df))
print('------------------------')
print(dStart)
print(dEnd)
print('dRef',dRef)
self.DataFrameResult = df
# self.DictionaryOfSerializedQuadCandidateXMLPathNames = dSerializedQuadCandidateXMLPathNames
data = np.array([np.arange(len(index))]*8).T
# ------------------------------------------------------------
df = pd.DataFrame(data, index=index, columns=columns)
#-------------------------------------------------------------------------------
dEnd = index[-1]
import pullprices
fmt = '%Y-%m-%d'
dDataStart = dStart+timedelta(days = -33)
f=pullprices.stockhistory("AAPL", dDataStart, dEnd)
for idx, row in df.iterrows():
pDraw_downmax = 0
pDraw_upmax = 0
#print(idx)
dRef = idx
d17 = dRef + timedelta(days = -17)
d32 = dRef + timedelta(days = -32)
dRef_string = dRef.strftime(fmt)
d17_string = d17.strftime(fmt)
d32_string = d32.strftime(fmt)
try:
#dRef = datetime.strptime(day, '%d/%b/%Y')
import pullprices
from dateutil.relativedelta import relativedelta, FR
dCurrent = datetime.now() + relativedelta(weekday=FR(-1))
dCurrent = dCurrent.replace(hour=0,minute=0,second=0,microsecond=0)
print('dCurrent',dCurrent)
fmt = '%Y-%m-%d'
#dRef = datetime.now()
#start = dRef - timedelta(days = dRef.weekday())
dDataStart = dCurrent + timedelta(days = -900)
RefMinus7 = 0
f=pullprices.stockhistory("AAPL", dDataStart, dCurrent)
lsRefDates=[]
while True:
dRef = dCurrent + timedelta(days = RefMinus7)
lsRefDates.append(dRef)
d17 = dRef + timedelta(days = -17)
d32 = dRef + timedelta(days = -32)
dRef_string = dRef.strftime(fmt)
d17_string = d17.strftime(fmt)
d32_string = d32.strftime(fmt)
dDataStart_string = dDataStart.strftime(fmt)
print('dRef',dRef)
