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')
if (mode == 'T') or (mode == 'C') or (mode == 'D'):
print profit2
# Ownership statistics
# ==============================================================
if (mode == 'T') or (mode == 'C') or (mode == 'D'):
print 'Hold1 ' + str(round(float(own.count(1)) / len(own) * 100))
print 'Hold2 ' + str(round(float(own.count(-1)) / len(own) * 100))
result.append([
symbol1, symbol2, startdate, enddate, dur, mwindow, trades, profit,
profit1, profit2
])
if (mode == 'W'):
p4fns.write_csv('RESULTS/temp.csv', result, 'a')
elif (mode == 'C'):
# Plot
# ==============================================================
df = pd.DataFrame()
df['TIME'] = [dp.parse(row) for row in timeser]
df['ERR'] = errorP
df['OWN'] = own
df[symbol1] = close1
df[symbol2] = close2
df['MU'] = muP
df['PL'] = [muP[i] + deltaP * sgP[i] for i in range(0, tlen)]
df['NL'] = [muP[i] - deltaN * sgP[i] for i in range(0, tlen)]
df = df.set_index(['TIME'])
p8fns.plot3axis(df, ['ERR', 'MU', 'PL', 'NL'], 'OWN', symbol1, symbol2)
round((a - b), 2) for a, b in zip(close[-rlen:], predict[-rlen:])
]
sg = p4fns.rolling_sstdd(error, mwindow)
mu = mu[-dsize:]
sg = sg[-dsize:]
eqdata = eqsdb[-dsize:]
table = []
for i in range(0, dsize):
srow = []
srow.append(eqdata[i][PQS['TIMESTAMP']])
srow.append(eqdata[i][PQS['CLOSE']])
srow.append(mu[i])
srow.append(sg[i])
table.append(srow)
p4fns.write_csv(NSEDIR + 'TECHNICAL/CRR/' + symbol + '_NIFTY' + CSV,
[title] + table, 'w')
# Correlelation between pairs of stocks
# =================================================== ##
#for symbol in cnxlist:
#
# eqsdb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR+symbol+CSV), PQS, 'SERIES', REGEQSERIES)
# symclose = [float(row[PQS['CLOSE']]) for row in eqsdb]
# eqsize = len(eqsdb)
# title = ['SYMBOL', 'CORR126']
# table = []
#
# for pair in [x for x in cnxlist if x != symbol]:
# pairsdb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR+pair+CSV), PQS, 'SERIES', REGEQSERIES)
# pairclose = [float(row[PQS['CLOSE']]) for row in pairsdb]
# pairsize = len(pairsdb)
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')
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)
else:
avgiv = 0
dvxdata.append([timestamp, avgiv])
p4fns.write_csv(NSEDVDIR + 'TECH/' + symbol + CSV, dvxdata, 'w')
catdf = []
for item in ixclist:
catdf.append(item+' ['+item+']')
fname = NSEJSONDIR+'NSEIXCatalog'+JSON
with open(fname, 'w') as fjson:
json.dump(catdf, fjson)
catdf = []
for row in eqcatdf:
if row[PCAT['SYMBOL']] in cnxlist:
catdf.append(row[2]+' ['+row[1]+']')
fname = NSEJSONDIR+'NSECatalog'+JSON
with open(fname, 'w') as fjson:
json.dump(catdf, fjson)
p4fns.write_csv(NSEQNTLOG, [['CATALOG']], 'a')
## ********************************************************************************************* ##
## Daily Closing Data
## ********************************************************************************************* ##
#if 'DAILY' not in donelist:
# for symbol in cnxlist:
# q4fns.qntdaily(symbol)
# p4fns.write_csv(NSEQNTLOG, [['DAILY']], 'a')
## ********************************************************************************************* ##
## General Information
## ********************************************************************************************* ##
if 'GENL' not in donelist:
techhover = ['Symbol','Company Name','Sector','Industry','Closing Price','% Change',\
'Market Capitalization (Rs Cr)','Volume (Rs Cr)','Volatility']
#!/usr/bin/env python from p4defs import * import p4fns clearlog = p4fns.readhdr_csv(NSEQNTLOG) p4fns.write_csv(NSEQNTLOG, clearlog, 'w') clearlog = p4fns.readhdr_csv(IMGDLYLOG) p4fns.write_csv(IMGDLYLOG, clearlog, 'w') clearlog = p4fns.readhdr_csv(IMGVOLLOG) p4fns.write_csv(IMGVOLLOG, clearlog, 'w') clearlog = p4fns.readhdr_csv(IMGBOBLOG) p4fns.write_csv(IMGBOBLOG, clearlog, 'w') clearlog = p4fns.readhdr_csv(IMGAURLOG) p4fns.write_csv(IMGAURLOG, clearlog, 'w') clearlog = p4fns.readhdr_csv(IMGCRRLOG) p4fns.write_csv(IMGCRRLOG, clearlog, 'w')
trow[PDT['EXPIRY_DT']] = dp.parse(rrow[PDR['EXPIRY_DT']],
dayfirst=True).strftime('%Y-%m-%d')
trow[PDT['STRIKE_PR']] = rrow[PDR['STRIKE_PR']]
trow[PDT['OPTION_TYP']] = rrow[PDR['OPTION_TYP']]
trow[PDT['OPEN']] = rrow[PDR['OPEN']]
trow[PDT['HIGH']] = rrow[PDR['HIGH']]
trow[PDT['LOW']] = rrow[PDR['LOW']]
trow[PDT['CLOSE']] = rrow[PDR['CLOSE']]
trow[PDT['SETTLE_PR']] = rrow[PDR['SETTLE_PR']]
trow[PDT['CONTRACTS']] = rrow[PDR['CONTRACTS']]
trow[PDT['VAL_INLAKH']] = rrow[PDR['VAL_INLAKH']]
trow[PDT['OPEN_INT']] = rrow[PDR['OPEN_INT']]
trow[PDT['CHG_IN_OI']] = rrow[PDR['CHG_IN_OI']]
dvdf.append(trow)
p4fns.write_csv(NSEDVTDBFILE, dvdf, 'a')
## ============================================================================================= ##
## NSE Nominal Deriv DB
## ============================================================================================= ##
ixc0df = p4fns.read_csv(NSEIXCatalog)
ixclist = [row[PXC['SYMBOL']] for row in ixc0df]
eqc0df = p4fns.read_csv(NSEEQCatalog)
eqclist = [row[PCAT['SYMBOL']] for row in eqc0df]
## Append the days values to the Nominal DERIV DB
## ============================================================================================= ##
dvdfsdb = []
dvlist = []
for trow in dvdf:
srow = [''] * len(PDS)
trow[PQT['SERIES']] = rrow[PQR['SERIES']]
trow[PQT['OPEN_U']] = rrow[PQR['OPEN_U']]
trow[PQT['HIGH_U']] = rrow[PQR['HIGH_U']]
trow[PQT['LOW_U']] = rrow[PQR['LOW_U']]
trow[PQT['CLOSE_U']] = rrow[PQR['CLOSE_U']]
trow[PQT['LAST_U']] = rrow[PQR['LAST_U']]
trow[PQT['PREV_U']] = rrow[PQR['PREV_U']]
trow[PQT['VOLUME']] = rrow[PQR['VOLUME']]
trow[PQT['TURNOVER']] = rrow[PQR['TURNOVER']]
trow[PQT['CONTRACTS']] = rrow[PQR['CONTRACTS']]
eqdf.append(trow)
eqdlyis = {row[PQT['SYMBOL']]: row[PQT['ISIN']] for row in eqdf}
eqdlysy = {row[PQT['ISIN']]: row[PQT['SYMBOL']] for row in eqdf}
eqdlycp = {row[PQT['SYMBOL']]: row[PQT['CLOSE_U']] for row in eqdf}
p4fns.write_csv(NSEEQTDBFILE, eqdf, 'a')
## Read the Catalog Files
## ============================================================================================= ##
eqc0df = p4fns.read_csv(NSEEQCatalog)
eqi0df = p4fns.read_csv(NSEISINCatalog)
## Update ISINCatalog with ISIN Changes
## ============================================================================================= ##
eqi1df = []
for row in eqc0df:
if row[PCAT['SYMBOL']] in set(eqdlyis.keys()):
if row[PCAT['ISIN']] != eqdlyis[row[PCAT['SYMBOL']]]:
eqi1df.append(
[row[PCAT['ISIN']], eqdlyis[row[PCAT['SYMBOL']]], timestamp])
eqisdict = {row[PISN['OLDISIN']]: row[PISN['ISIN']] for row in eqi1df}
rrow[PXR['CLOSE']] != '-') else ''
trow[PXT['PREV']] = "%.2f" % (float(rrow[PXR['CLOSE']]) -
float(rrow[PXR['CHANGE']]))
trow[PXT['GAIN']] = "%.4f" % (math.log(
float(rrow[PXR['CLOSE']]) / float(trow[PXT['PREV']])) * 100)
trow[PXT['VOLUME']] = rrow[PXR['VOLUME']] if (
rrow[PXR['VOLUME']] != '-') else ''
trow[PXT['TURNOVER']] = rrow[PXR['TURNOVER']] if (
rrow[PXR['TURNOVER']] != '-') else ''
trow[PXT['PE']] = rrow[PXR['PE']] if (rrow[PXR['PE']] != '-') else ''
trow[PXT['PB']] = rrow[PXR['PB']] if (rrow[PXR['PB']] != '-') else ''
trow[PXT['DIVYIELD']] = rrow[PXR['DIVYIELD']] if (
rrow[PXR['DIVYIELD']] != '-') else ''
ixdf.append(trow)
p4fns.write_csv(NSEIXTDBFILE, ixdf, 'a')
## Append the days values to the Nominal Index DB
## ============================================================================================= ##
for name in ixclist:
symbol = ixc0dict[name]
sdbdf = []
sdbcsv = NSEIXSDBDIR + symbol + CSV
newdb = False
if not path.isfile(sdbcsv):
sdbdf.append(SYIXCOL)
newdb = True
for trow in ixdf:
if (trow[PXT['SYMBOL']] == symbol):
srow = [''] * len(PXS)
srow[PXS['TIMESTAMP']] = trow[PXT['TIMESTAMP']]
print profit0
# Ownership statistics
# ==============================================================
if (mode == 'T') or (mode == 'C') or (mode == 'D'):
print 'Hold0 ' + str(round(float(own.count(0)) / len(own) * 100))
print 'Hold1 ' + str(round(float(own.count(1)) / len(own) * 100))
print 'Hold2 ' + str(round(float(own.count(-1)) / len(own) * 100))
result.append([
symbolX, symbolF, symbol1, symbol2, symbol0, startdate, enddate, dur,
rwindow, mwindow, trades, profit, profit1, profit2, profit0
])
if (mode == 'W'):
p4fns.write_csv('temp.csv', result, 'a')
elif (mode == 'C') or (mode == 'D'):
# Plot
# ==============================================================
df = pd.DataFrame()
df['TIME'] = [dp.parse(row) for row in timeser]
df['ERR'] = errorP
df['OWN'] = own
df[symbol1] = close1
df[symbol2] = close2
df['MU'] = muP
df['PL'] = [muP[i] + deltaP * sgP[i] for i in range(0, tlen)]
df['NL'] = [muP[i] - deltaN * sgP[i] for i in range(0, tlen)]
df = df.set_index(['TIME'])
p8fns.plot3axis(df, ['ERR', 'MU', 'PL', 'NL'], 'OWN', symbol1, symbol2)
if bonus and origl:
ratio = "%.4f" % ((bonus + origl) / origl)
bonus0df.append([
row[PRCA['SYMBOL']], row[PRCA['FACEVALUE']], ratio,
exdate, rcdate, bcdate, 'F'
])
return bonus0df
bonus0df = p4fns.read_csv(NSEBONUS)
bonus0df = p4fns.symchange(bonus0df, PBON['SYMBOL'], eqschdict)
bonusrdf = p4fns.read_csv(NSERBONUS)
bonusrdf = p4fns.symchange(bonusrdf, PRCA['SYMBOL'], eqschdict)
bonus1df = p4fns.readhdr_csv(NSEBONUS)
bonus1df.extend(genbonus(bonus0df, bonusrdf))
p4fns.write_csv(NSEBONUS, bonus1df, 'w')
#p4fns.write_sql(NSEULDB, SQLNSEBONUS, bonus1df, MBON, BONUSCOL)
## SPLIT
## ============================================================================================= ##
def parsesplit(purpose):
extract = re.search(
'([Ff]ace|[Ss]plit|[Ff]rom)[^0-9]+([0-9]+)[^0-9]+([Tt]o|\-)+[^0-9]+([0-9]+)',
purpose)
if extract:
split = float(extract.group(4))
origl = float(extract.group(2))
else:
split = 0