def qntdaily(symbol):
eqsdbdf = p4fns.readhdr_csv(NSEEQSDBDIR + symbol + CSV)
eqsdb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR + symbol + CSV), PQS,
'SERIES', REGEQSERIES)[-252:]
eqsdbdf = eqsdbdf + eqsdb
inputdf = p4fns.rearrange(eqsdbdf, PQS, JSONCOL)
p4fns.write_json(JSONDLYDIR + symbol + JSON, inputdf, EQCOLTYP)
def qntresult(symbol, resdf):
techtitle = ['SYMBOL']
techtable = []
result = [
dp.parse(row[PRES['TIMESTAMP']]).strftime('%Y-%m-%d') for row in resdf
if row[PRES['SYMBOL']] == symbol
][-8:]
srow = []
srow.append(symbol)
eqsdb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR + symbol + CSV), PQS,
'SERIES', REGEQSERIES)
for res in result:
backmean = p4fns.smean([
float(row[PQS['CLOSE']])
for row in p4fns.blockdf(eqsdb, PQS, res, 21, 'be')
])
fronmean = p4fns.smean([
float(row[PQS['CLOSE']])
for row in p4fns.blockdf(eqsdb, PQS, res, 21, 'fe')
])
effect = round(math.log(fronmean / backmean) * 100, 2)
srow.append(effect)
techtitle.append(res)
techtable.append(srow)
techtable = [techtitle] + techtable
p4fns.write_json(JSONRESDIR + symbol + JSON, techtable, TECHCOLTYP)
def qntperf(symbol, name):
perftable = []
eqsdb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR + symbol + CSV), PQS,
'SERIES', REGEQSERIES)
## Price Values
## ============================================== ##
price = [float(row[PQS['CLOSE']]) for row in eqsdb]
perf1w = round(math.log(price[-1] / price[-5]) *
100, 2) if len(price) > 5 else '-'
perf1m = round(math.log(price[-1] / price[-21]) *
100, 2) if len(price) > 21 else '-'
perf3m = round(math.log(price[-1] / price[-63]) *
100, 2) if len(price) > 63 else '-'
perf6m = round(math.log(price[-1] / price[-126]) *
100, 2) if len(price) > 126 else '-'
perf1y = round(math.log(price[-1] / price[-252]) *
100, 2) if len(price) > 252 else '-'
perf2y = round(math.log(price[-1] / price[-504]) *
100, 2) if len(price) > 504 else '-'
perf4y = round(math.log(price[-1] / price[-1008]) *
100, 2) if len(price) > 1008 else '-'
## Volatility Values
## ============================================== ##
gain = [float(row[PQS['GAIN']]) for row in eqsdb]
stdd1w = round(p4fns.sstdd(gain[-5:]) *
math.sqrt(252), 2) if len(price) > 5 else '-'
stdd1m = round(p4fns.sstdd(gain[-21:]) *
math.sqrt(252), 2) if len(price) > 21 else '-'
stdd3m = round(p4fns.sstdd(gain[-63:]) *
math.sqrt(252), 2) if len(price) > 63 else '-'
stdd6m = round(p4fns.sstdd(gain[-126:]) *
math.sqrt(252), 2) if len(price) > 126 else '-'
stdd1y = round(p4fns.sstdd(gain[-252:]) *
math.sqrt(252), 2) if len(price) > 252 else '-'
stdd2y = round(p4fns.sstdd(gain[-504:]) *
math.sqrt(252), 2) if len(price) > 504 else '-'
stdd4y = round(p4fns.sstdd(gain[-1008:]) *
math.sqrt(252), 2) if len(price) > 1008 else '-'
perftable.append([symbol,name,perf1w,perf1m,perf3m,perf6m,perf1y,perf2y,perf4y,\
stdd1w,stdd1m,stdd3m,stdd6m,stdd1y,stdd2y,stdd4y])
p4fns.write_csv(NSETECHDIR + 'NSEPerf' + CSV, perftable, 'a')
def qntpair(symbol, period, deltaP, deltaN, rwindow, mwindow, pairlist):
title = ['PAIR', 'NORM', 'DWSTAT']
maxper = period + rwindow + mwindow - 1
table = []
datadb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR + symbol + CSV), PQS,
'SERIES', REGEQSERIES)[-maxper:]
pclose = [math.log(float(row[PQS['CLOSE']])) for row in datadb]
pvwap = [math.log(float(row[PQS['VWAP']])) for row in datadb]
dsize = len(pclose)
if (dsize >= rwindow + mwindow + 40):
for pair in pairlist:
reffdb = p4fns.read_csv(NSEEQSDBDIR + pair + CSV)[-maxper:]
pvwapR = [math.log(float(row[PQS['VWAP']])) for row in reffdb]
regr = p4fns.rolling_regress(pvwap[-dsize:], pvwapR[-dsize:],
rwindow)
rlen = len(regr)
error = [
round((a / b - 1) * 100, 2)
for a, b in zip(pclose[-rlen:], regr[-rlen:])
]
mu = p4fns.rolling_smean(error, mwindow)
sg = p4fns.rolling_sstdd(error, mwindow)
mlen = len(sg)
error = error[-mlen:]
normdist = int(
p4fns.cumnormdist((error[-1] - mu[-1]) / sg[-1]) * 100)
et_t1 = sum([
math.pow((error[i] - error[i - 1]), 2) for i in range(1, mlen)
])
et_sq = sum([math.pow(error[i], 2) for i in range(0, mlen)])
dwstat = round(et_t1 / et_sq, 2)
table.append([pair, normdist, dwstat])
p4fns.write_csv(NSEPAIRDIR + symbol + CSV, [title] + table, 'w')
p4fns.write_json(JSONPAIRDIR + symbol + JSON, [title] + table, [])
def qnttech(symbol, name):
techtable = []
eqsdb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR + symbol + CSV), PQS,
'SERIES', REGEQSERIES)
## Price Bands
## ============================================== ##
price = [float(row[PQS['CLOSE']]) for row in eqsdb]
vwap = [float(row[PQS['VWAP']]) for row in eqsdb]
pb1m = int((price[-1] - min(price[-21:])) /
(max(price[-21:]) - min(price[-21:])) *
100) if len(price) > 21 else '-'
pb3m = int((price[-1] - min(price[-63:])) /
(max(price[-63:]) - min(price[-63:])) *
100) if len(price) > 63 else '-'
pb6m = int((price[-1] - min(price[-126:])) /
(max(price[-126:]) - min(price[-126:])) *
100) if len(price) > 126 else '-'
pb1y = int((price[-1] - min(price[-252:])) /
(max(price[-252:]) - min(price[-252:])) *
100) if len(price) > 252 else '-'
## Bollinger Bands
## ============================================== ##
dsize = len(price)
period = [21, 63, 126, 252]
bb = ['-'] * 4
for i in range(0, 4):
if (dsize > period[i] + 1):
mu = p4fns.rolling_emean(vwap[-(period[i] + 1):], period[i])[-1]
sg = p4fns.rolling_sstdd(vwap[-(period[i] + 1):], period[i])[-1]
bb[i] = int(p4fns.cumnormdist((price[-1] - mu) / sg) * 100)
techtable.append(
[symbol, name, pb1m, pb3m, pb6m, pb1y, bb[0], bb[1], bb[2], bb[3]])
p4fns.write_csv(NSETECHDIR + 'NSETech' + CSV, techtable, 'a')
years = int(sys.argv[6])
dur = int(sys.argv[7]) * 252
ixlist = ['NIFTY', 'BANKNIFTY']
cnx500 = [row[2] for row in p4fns.read_csv(NSEEQDIR + 'CNX500.csv')]
cnx100 = [row[2] for row in p4fns.read_csv(NSEEQDIR + 'CNX100.csv')]
cnx50 = [row[2] for row in p4fns.read_csv(NSEEQDIR + 'CNX50.csv')]
cnxlist = cnx500
days = 252 * years + mwindow
result = []
# ============================================================================================= ##
# Bollinger Band
# ============================================================================================= ##
if symbol1 in cnxlist:
datadb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR + symbol1 + CSV), PQS,
'SERIES', REGEQSERIES)[-days:]
price1 = [math.log(float(row[PQS['CLOSE']])) for row in datadb]
vwap1 = [math.log(float(row[PQS['VWAP']])) for row in datadb]
instrm1 = 'EQ'
else:
datadb = p4fns.read_csv(NSEIXSDBDIR + symbol1 + CSV)[-days:]
price1 = [math.log(float(row[PXS['CLOSE']])) for row in datadb]
vwap1 = price1
instrm1 = 'IX'
if symbol2 in cnxlist:
datadb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR + symbol2 + CSV), PQS,
'SERIES', REGEQSERIES)[-days:]
price2 = [math.log(float(row[PQS['CLOSE']])) for row in datadb]
instrm2 = 'EQ'
else:
datadb = p4fns.read_csv(NSEIXSDBDIR + symbol2 + CSV)[-days:]
# srow.append(eqsdb[i][PQS['OPEN']])
# srow.append(eqsdb[i][PQS['HIGH']])
# srow.append(eqsdb[i][PQS['LOW']])
# srow.append(eqsdb[i][PQS['CLOSE']])
# srow.append(round(float(eqsdb[i][PQS['TURNOVER']])/10000000, 2))
# srow.append(avgiv[i] if avgiv[i] != 0 else volatility[i])
# genldata.append(srow)
#
# p4fns.write_csv(NSEDIR+'TECHNICAL/GENL/'+symbol+CSV, genldata, 'w')
# Bollinger Bands + AutoRegression + Nifty Regression
# =================================================== ##
#for symbol in cnxlist:
for symbol in ['HDFCBANK']:
eqsdb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR + symbol + CSV), PQS,
'SERIES', REGEQSERIES)
eqsize = len(eqsdb)
dsize = 252
# # Bollinger Bands
# # --------------------------------------------------- #
# genldata = p4fns.read_csv(NSEDIR+'TECHNICAL/GENL/'+symbol+CSV)
# title = ['TIMESTAMP','CLOSE','MEAN','SIGMA']
# dsize = min(252, len(genldata))
# vwap = [float(row[PQS['VWAP']]) for row in eqsdb]
# emean = p4fns.rolling_emean(vwap, 21)[-dsize:]
# eqdata = eqsdb[-dsize:]
# genldata = genldata[-dsize:]
# vwap = vwap[-dsize:]
# table = []
# sigma = [round(float(genldata[i][6])*vwap[i]/(math.sqrt(252)*100),2) for i in range(dsize)]
#
def qntgenl(symbol, name, sector, industry, mktcap, mcpercent):
techtitle = ['SYMBOL','PRICE','GAIN','NAME','SECTOR','INDUSTRY','MKT_CAP','MC_PERCENT',\
'VOLATILITY','MAX_VTY','MIN_VTY','VOLUME','MAX_VOL','MIN_VOL']
techtable = []
srow = []
srow.append(symbol)
eqsdb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR + symbol + CSV), PQS,
'SERIES', REGEQSERIES)
## Current Values
## ============================================== ##
curprice = eqsdb[-1][PQS['CLOSE']]
srow.append(curprice)
change = round(float(eqsdb[-1][PQS['GAIN']]), 2)
srow.append(change)
srow.append(name)
srow.append(sector)
srow.append(industry)
srow.append(mktcap)
srow.append(mcpercent)
## Volatility
## ============================================== ##
if path.isfile(NSEDVSDBDIR + symbol + CSV):
dvsdb = p4fns.filterdf(p4fns.filterdf(p4fns.filterdf(p4fns.read_csv(NSEDVSDBDIR+symbol+CSV),\
PDS, 'INSTRUMENT', ['OPTSTK']),\
PDS, 'TIMESTAMP', [today]),\
PDS, 'T2E', [str(x) for x in range(1,50)])
ivlist = [float(row[PDS['IV']]) for row in dvsdb]
wtlist = [float(row[PDS['VAL_INLAKH']]) for row in dvsdb]
if sum(wtlist) >= 100:
avgiv = round(p4fns.wmean(ivlist, wtlist), 2)
else:
avgiv = 0
else:
avgiv = 0
eqdata = eqsdb[-756:]
gain = [float(row[PQS['GAIN']]) for row in eqdata]
cum_gain = p4fns.rolling_sum(gain, 21)
rol_stdd = p4fns.rolling_sstdd(cum_gain, 21)
if (avgiv == 0):
stdd1m = round(p4fns.sstdd(cum_gain) * math.sqrt(12), 2)
volatility = stdd1m
else:
volatility = avgiv
max_stdd = max([volatility, round(max(rol_stdd) * math.sqrt(12), 2)])
min_stdd = min([volatility, round(min(rol_stdd) * math.sqrt(12), 2)])
srow.append(volatility)
srow.append(max_stdd)
srow.append(min_stdd)
## Volume
## ============================================== ##
eqdata = eqsdb[-252:]
turnover = [
round(float(row[PQS['TURNOVER']]) / 10000000, 2) for row in eqdata
]
volume = p4fns.rolling_emean(turnover, 3)
max_vol = max(volume)
min_vol = min(volume)
srow.append(turnover[-1])
srow.append(max_vol)
srow.append(min_vol)
## Create JSON File
## ============================================== ##
techtable.append(srow)
p4fns.write_csv(NSEGENLDIR + symbol + CSV, [techtitle] + techtable, 'w')
p4fns.write_json(JSONGENLDIR + symbol + JSON, [techtitle] + techtable,
TECHCOLTYP)
genltable = []
grow = []
grow.append(symbol)
grow.append(name)
grow.append(sector)
grow.append(industry)
grow.append(curprice)
grow.append(change)
grow.append(mktcap)
grow.append(turnover[-1])
grow.append(volatility)
genltable.append(grow)
p4fns.write_csv(NSETECHDIR + 'NSEGenl' + CSV, genltable, 'a')
def qntevent(symbol, bonuspst, bonusfut, splitpst, splitfut, rightpst,
rightfut, divdnpst, divdnfut, resltpst, resltfut):
eventsum = []
bpst = p4fns.filterdf(bonuspst, PBON, 'SYMBOL', [symbol])
bfut = p4fns.filterdf(bonusfut, PBON, 'SYMBOL', [symbol])
spst = p4fns.filterdf(splitpst, PSPL, 'SYMBOL', [symbol])
sfut = p4fns.filterdf(splitfut, PSPL, 'SYMBOL', [symbol])
gpst = p4fns.filterdf(rightpst, PRGT, 'SYMBOL', [symbol])
gfut = p4fns.filterdf(rightfut, PRGT, 'SYMBOL', [symbol])
dpst = p4fns.filterdf(divdnpst, PDIV, 'SYMBOL', [symbol])
dfut = p4fns.filterdf(divdnfut, PDIV, 'SYMBOL', [symbol])
rpst = p4fns.filterdf(resltpst, PRES, 'SYMBOL', [symbol])
rfut = p4fns.filterdf(resltfut, PRES, 'SYMBOL', [symbol])
for row in bpst:
eventsum.append([
dp.parse(row[PBON['EXDATE']]), 'Bonus Shares', row[PBON['RATIO']],
'P'
])
for row in bfut:
eventsum.append([
dp.parse(row[PBON['EXDATE']]), 'Bonus Shares', row[PBON['RATIO']],
'F'
])
for row in spst:
eventsum.append([
dp.parse(row[PSPL['EXDATE']]), 'Stock Split', row[PSPL['RATIO']],
'P'
])
for row in sfut:
eventsum.append([
dp.parse(row[PSPL['EXDATE']]), 'Stock Split', row[PSPL['RATIO']],
'F'
])
for row in gpst:
eventsum.append([
dp.parse(row[PRGT['EXDATE']]), 'Rights Issue', row[PRGT['RATIO']],
'P'
])
for row in gpst:
eventsum.append([
dp.parse(row[PRGT['EXDATE']]), 'Rights Issue', row[PRGT['RATIO']],
'F'
])
for row in dpst:
eventsum.append([
dp.parse(row[PDIV['EXDATE']]), 'Dividend Declaration',
row[PDIV['DIVIDEND']] + ' Rs', 'P'
])
for row in dfut:
eventsum.append([
dp.parse(row[PDIV['EXDATE']]), 'Dividend Declaration',
row[PDIV['DIVIDEND']] + ' Rs', 'F'
])
for row in rpst:
eventsum.append(
[dp.parse(row[PRES['TIMESTAMP']]), 'Result Declaration', '-', 'P'])
for row in rfut:
eventsum.append(
[dp.parse(row[PRES['TIMESTAMP']]), 'Result Declaration', '-', 'F'])
eventsum.sort(key=lambda x: x[0])
eventsum = [[row[0].strftime('%Y-%m-%d')] + row[1:] for row in eventsum]
fname = JSONEVNTDIR + symbol + JSON
with open(fname, 'w') as fjson:
json.dump(eventsum, fjson)
def pltcrosregres(symbol, period, deltaP, deltaN, rwindow, mwindow):
maxper = period + rwindow + mwindow - 1
datadb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR + symbol + CSV), PQS,
'SERIES', REGEQSERIES)[-maxper:]
ptimestamp = [
date2num(datetime.strptime(row[PQS['TIMESTAMP']], '%Y-%m-%d'))
for row in datadb
]
pclose = [math.log(float(row[PQS['CLOSE']])) for row in datadb]
pvwap = [math.log(float(row[PQS['VWAP']])) for row in datadb]
dsize = len(ptimestamp)
if (dsize >= rwindow + mwindow + 40):
# pairlist = [row[0] for row in p4fns.read_csv(NSEPAIRDIR+symbol+CSV)]+['NIFTY']
pairlist = ['NIFTY']
for pair in pairlist:
if pair in ixlist:
reffdb = p4fns.read_csv(NSEIXSDBDIR + pair + CSV)[-maxper:]
pvwapR = [math.log(float(row[PXS['CLOSE']])) for row in reffdb]
else:
reffdb = p4fns.read_csv(NSEEQSDBDIR + pair + CSV)[-maxper:]
pvwapR = [math.log(float(row[PQS['VWAP']])) for row in reffdb]
regr = p4fns.rolling_regress(pvwap[-dsize:], pvwapR[-dsize:],
rwindow)
rlen = len(regr)
error = [
round((a / b - 1) * 100, 2)
for a, b in zip(pclose[-rlen:], regr[-rlen:])
]
stimestamp = ptimestamp[-rlen:]
mu = p4fns.rolling_smean(error, mwindow)
sg = p4fns.rolling_sstdd(error, mwindow)
mlen = len(sg)
error = error[-mlen:]
stimestamp = stimestamp[-mlen:]
mu = mu[-mlen:]
sg = sg[-mlen:]
upl = [mu[i] + sg[i] * deltaP for i in range(mlen)]
lwl = [mu[i] - sg[i] * deltaN for i in range(mlen)]
majorl = MonthLocator()
xformat = DateFormatter('%b')
fig = plt.figure(figsize=(6, 3))
gs = gridspec.GridSpec(1, 1)
ax1 = plt.subplot(gs[0])
plt.title(symbol, loc='left', color=textc, weight='bold')
plt.title('StatArb [' + symbol + ' vs ' + pair + ']',
loc='left',
color=textc,
weight='bold',
size='small')
ax1.xaxis.set_major_locator(majorl)
ax1.xaxis.set_major_formatter(xformat)
ax1.yaxis.tick_right()
ax1.grid(b=True, which='major', color=gridc, linestyle=':')
ax1.patch.set_facecolor(backc)
ax1.spines['bottom'].set_color(labelc)
ax1.spines['top'].set_color(backc)
ax1.spines['right'].set_color(labelc)
ax1.spines['left'].set_color(backc)
ax1.tick_params(axis='x', colors=labelc)
ax1.tick_params(axis='y', colors=labelc)
for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):
label.set_fontsize(6)
ax1.plot(stimestamp, error, color='deepskyblue', linewidth=1.5)
ax1.xaxis_date()
ax1.autoscale_view()
ax1.set_aspect('auto')
plt.setp(ax1.get_xticklabels(),
horizontalalignment='center',
fontsize=8)
ax2 = plt.subplot(gs[0])
ax2.plot(stimestamp, mu, color='royalblue', linewidth=1.5)
ax3 = plt.subplot(gs[0])
ax3.plot(stimestamp, upl, color='yellowgreen')
ax4 = plt.subplot(gs[0])
ax4.plot(stimestamp, lwl, color='orangered')
plt.figtext(0.94,
0.94,
'$\copyright$ piby4.com ' + today,
color=sitec,
size='xx-small',
ha='right')
gs.tight_layout(fig)
# plt.savefig(IMGCRRDIR+symbol+'_'+pair+'.png', facecolor=(backc))
plt.savefig('aaa.png', facecolor=(backc))
plt.close(fig)
#!/usr/bin/env python
from p4defs import *
import p4fns
import math
import os.path as path
cnxlist = [row[PCAT['SYMBOL']] for row in p4fns.read_csv(NSEEQCatalog)]
ixclist = ['NIFTY', 'BANKNIFTY']
count = 0
for symbol in cnxlist + ixclist:
count += 1
print count
if path.isfile(NSEDVSDBDIR + symbol + CSV):
dvxdata = [['TIMESTAMP', 'AVGIV']]
eqsdb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR + symbol + CSV), PQS,
'SERIES', REGEQSERIES)
## Volatility
## ============================================== ##
dvsdb = p4fns.read_csv(NSEDVSDBDIR + symbol + CSV)
for row in eqsdb:
timestamp = row[PQS['TIMESTAMP']]
dvtdb = p4fns.filterdf(p4fns.filterdf(p4fns.filterdf(dvsdb,\
PDS, 'INSTRUMENT', ['OPTSTK']),\
PDS, 'TIMESTAMP', [timestamp]),\
PDS, 'T2E', [str(x) for x in range(1,50)])
ivlist = [float(row[PDS['IV']]) for row in dvtdb]
wtlist = [float(row[PDS['VAL_INLAKH']]) for row in dvtdb]
if sum(wtlist) >= 100:
avgiv = round(p4fns.wmean(ivlist, wtlist), 2)
def updateledger(ledgerin, timestamp, operator):
ledgerdf = ledgerin
ledger = p4fns.filterdf(ledgerin[1:], PLS, 'INSTRUMENT',
['OPTIDX', 'OPTSTK', 'FUTIVX', 'FUTSTK', 'FUTIDX'])
ledger = p4fns.filterts(ledger, PLS, 'EXPIRY_DT', timestamp, operator)
appenddf = []
for row in ledger:
instrument = row[PLS['INSTRUMENT']]
symbol = row[PLS['SYMBOL']]
optiontyp = row[PLS['OPTION_TYP']]
expirydt = row[PLS['EXPIRY_DT']]
strikepr = row[PLS['STRIKE_PR']]
idf = p4fns.filterdf(ledger, PLS, 'INSTRUMENT', [instrument])
idf = p4fns.filterdf(idf, PLS, 'SYMBOL', [symbol])
idf = p4fns.filterdf(idf, PLS, 'OPTION_TYP', [optiontyp])
idf = p4fns.filterdf(idf, PLS, 'EXPIRY_DT', [expirydt])
idf = p4fns.filterdf(idf, PLS, 'STRIKE_PR', [strikepr])
cumvol = 0
for irow in idf:
cumvol = cumvol + irow[PLS['VOLUME']]
adf = p4fns.filterdf(appenddf, PLS, 'INSTRUMENT', [instrument])
adf = p4fns.filterdf(adf, PLS, 'SYMBOL', [symbol])
adf = p4fns.filterdf(adf, PLS, 'OPTION_TYP', [optiontyp])
adf = p4fns.filterdf(adf, PLS, 'EXPIRY_DT', [expirydt])
adf = p4fns.filterdf(adf, PLS, 'STRIKE_PR', [strikepr])
appvol = 0
for prow in adf:
appvol = appvol + prow[PLS['VOLUME']]
if (appvol + cumvol != 0):
expvalue = float(
p4fns.findvalue(expirydt, instrument, symbol, optiontyp,
expirydt, strikepr, 'CLOSE'))
appenddf = appendledger(appenddf, expirydt, symbol, instrument, optiontyp, expirydt,\
strikepr, (-appvol-cumvol), expvalue)
ledgerdf = ledgerdf + appenddf
return ledgerdf
## Calculate Time to Expiry
## ============================================================================================= ##
srow[PDS['T2E']] = (dp.parse(trow[PDT['EXPIRY_DT']]) -
dp.parse(trow[PDT['TIMESTAMP']])).days
## Calculate Implied Volatility
## ============================================================================================= ##
instrument = trow[PDT['INSTRUMENT']]
strikepr = float(trow[PDT['STRIKE_PR']])
optyp = trow[PDT['OPTION_TYP']]
expirydt = trow[PDT['EXPIRY_DT']]
opprice = float(trow[PDT['CLOSE']])
days2exp = srow[PDS['T2E']]
if (instrument == 'OPTSTK') and (symbol in eqclist):
ulprice = float(p4fns.filterdf(p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR+symbol+CSV),\
PQS, 'SERIES', REGEQSERIES), PQS, 'TIMESTAMP', [timestamp])[-1][PQS['CLOSE']])
impvol = p4fns.ivcalc(optyp, ulprice, strikepr, opprice, days2exp)
elif (instrument == 'OPTIDX') and (symbol in ixclist):
ulprice = float(p4fns.filterdf(p4fns.read_csv(NSEIXSDBDIR+symbol+CSV),\
PXS, 'TIMESTAMP', [timestamp])[-1][PXS['CLOSE']])
impvol = p4fns.ivcalc(optyp, ulprice, strikepr, opprice, days2exp)
else:
impvol = 0
srow[PDS['IV']] = impvol
## Calculate Option Distance
## ============================================================================================= ##
if ((instrument == 'OPTSTK') and (symbol in eqclist)) or \
((instrument == 'OPTIDX') and (symbol in ixclist)):
subdf = []
for xrow in dvdf: