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 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 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 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 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')
#!/usr/bin/env python
from p4defs import *
import p4fns
import math
import os.path as path
cnx500 = p4fns.read_csv(NSEEQDIR + 'CNX500.csv')
cnxlist = [row[PXL['SYMBOL']] for row in cnx500]
ixclist = ['NIFTY', 'BANKNIFTY']
# General: Prices, Turnover, Volatility
# ============================================== ##
#for symbol in cnxlist:
# genldata = [['TIMESTAMP','OPEN','HIGH','LOW','CLOSE','TURNOVER','VOLATILITY']]
# eqsdb = p4fns.filterdf(p4fns.read_csv(NSEEQSDBDIR+symbol+CSV), PQS, 'SERIES', REGEQSERIES)
#
# if path.isfile(NSEDVDIR+'TECH/'+symbol+CSV):
# dvsdb = p4fns.read_csv(NSEDVDIR+'TECH/'+symbol+CSV)
# ivdict = {row[0]:float(row[1]) for row in dvsdb}
# else:
# ivdict = {}
#
# timestamp = [row[PQS['TIMESTAMP']] for row in eqsdb]
# avgiv = []
# for i in range (0, len(timestamp)):
# if timestamp[i] in ivdict:
# avgiv.append(ivdict[timestamp[i]])
# else:
# avgiv.append(0)
# gain = [float(row[PQS['GAIN']]) for row in eqsdb]
# cum_gain = p4fns.rolling_sum(gain, 21)
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')
## ============================================================================================= ##
## Date for which database is to be built ##
## ============================================================================================= ##
tgtdate = dp.parse(str(sys.argv[1]), dayfirst=True)
timestamp = tgtdate.strftime('%Y-%m-%d')
rawixfile = NSEIXRAWDIR + timestamp + CSV
## ============================================================================================= ##
## NSE Index DB ##
## ============================================================================================= ##
## Read the Catalog Files
## ============================================================================================= ##
ixc0df = p4fns.read_csv(NSEIXCatalog)
ixc0dict = {row[PXC['NAME']]: row[PXC['SYMBOL']] for row in ixc0df}
ixclist = list(ixc0dict.keys())
## Append the days values to the Temporal Index DB
## ============================================================================================= ##
rawixdf = p4fns.read_csv(rawixfile)
ixdf = []
for rrow in rawixdf:
trow = [''] * len(PXT)
name = rrow[PXR['NAME']]
if name in ixclist:
trow[PXT['TIMESTAMP']] = timestamp
trow[PXT['SYMBOL']] = ixc0dict[rrow[PXR['NAME']]]
trow[PXT['OPEN']] = rrow[PXR['OPEN']] if (
rrow[PXR['OPEN']] != '-') else ''
#!/usr/bin/env python
from p4defs import *
import p4fns
import dateutil.parser as dp
import math
import os.path as path
TECHCOLTYP = {
'SYMBOL': 'S',
'PRICE': 'F',
'GAIN': 'F',
'MKT_CAP': 'I',
'MC_PERCENT': 'I'
}
today = p4fns.read_csv(NSEIXSDBDIR + 'NIFTY' + CSV)[-1][PXS['TIMESTAMP']]
## ********************************************************************************************* ##
## Daily Closing Data
## ********************************************************************************************* ##
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)
## ********************************************************************************************* ##
## General Information
#!/usr/bin/env python
from p4defs import *
import m4fns
import p4fns
cnx500 = p4fns.read_csv(NSEEQDIR + 'CNX500.csv')
cnxlist = [row[PXL['SYMBOL']] for row in cnx500]
## ============================================================================================= ##
## Daily Candlestick and Volume chart
## ============================================================================================= ##
#donelist = [row[0] for row in p4fns.read_csv(IMGDLYLOG)]
#todolist = [sym for sym in cnxlist if sym not in donelist]
#periods = {'3M':63,'6M':126,'1Y':252,'2Y':504,'4Y':1008}
#for symbol in todolist:
# m4fns.pltcandle(symbol, periods)
# p4fns.write_csv(IMGDLYLOG, [[symbol]], 'a')
#
## ============================================================================================= ##
## Volume and Volatility Bar
## ============================================================================================= ##
#donelist = [row[0] for row in p4fns.read_csv(IMGVOLLOG)]
#todolist = [sym for sym in cnxlist if sym not in donelist]
#for symbol in todolist:
# m4fns.pltvol(symbol)
# p4fns.write_csv(IMGVOLLOG, [[symbol]], 'a')
#
### ============================================================================================= ##
## Bollinger Bands
## ============================================================================================= ##
#donelist = [row[0] for row in p4fns.read_csv(IMGBOBLOG)]
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:
#!/usr/bin/env python
from p4defs import *
import p4fns
import q4fns
import json
cnx500 = p4fns.read_csv(NSEEQDIR+'CNX500.csv')
cnxlist = [row[PXL['SYMBOL']] for row in cnx500]
ixclist = ['NIFTY','BANKNIFTY']
catalog = p4fns.read_csv(NSEEQCatalog)
eqnamdict = {}
eqsecdict = {}
eqinddict = {}
eqmkcdict = {}
eqmkclist = []
for row in catalog:
eqnamdict[row[PCAT['SYMBOL']]] = row[PCAT['NAME']]
eqsecdict[row[PCAT['SYMBOL']]] = row[PCAT['SECTOR']]
eqinddict[row[PCAT['SYMBOL']]] = row[PCAT['INDUSTRY']]
mktcap = float(row[PCAT['MKT_CAP']]) if row[PCAT['MKT_CAP']] else 0
eqmkcdict[row[PCAT['SYMBOL']]] = mktcap
eqmkclist.append(mktcap)
donelist = [row[0] for row in p4fns.read_csv(NSEQNTLOG)]
## ********************************************************************************************* ##
## Catalog
## ********************************************************************************************* ##
if 'CATALOG' not in donelist:
eqcatdf = p4fns.read_csv(NSEEQCatalog)
catdf = []
for item in ixclist:
## ============================================================================================= ##
## Date for which database is to be built ##
## ============================================================================================= ##
tgtdate = dp.parse(str(sys.argv[1]), dayfirst=True)
timestamp = tgtdate.strftime('%Y-%m-%d')
rawdvfile = NSEDVRAWDIR + timestamp + CSV
## ============================================================================================= ##
## NSE Temporal Deriv DB
## ============================================================================================= ##
## Append the days values to the Temporal DERIV DB
## ============================================================================================= ##
rawdvdf = p4fns.read_csv(rawdvfile)
dvdf = []
for rrow in rawdvdf:
if (rrow[PDR['CONTRACTS']] != '0'):
trow = [''] * len(PDT)
trow[PDT['TIMESTAMP']] = timestamp
trow[PDT['INSTRUMENT']] = rrow[PDR['INSTRUMENT']]
trow[PDT['SYMBOL']] = rrow[PDR['SYMBOL']]
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']]
## ============================================================================================= ##
## Date for which database is to be built ##
## ============================================================================================= ##
tgtdate = dp.parse(str(sys.argv[1]), dayfirst=True)
timestamp = tgtdate.strftime('%Y-%m-%d')
raweqfile = NSEEQRAWDIR + timestamp + CSV
## ============================================================================================= ##
## NSE Temporal Equity DB
## ============================================================================================= ##
## Append the days values to the Temporal EQUITY DB
## ============================================================================================= ##
raweqdf = p4fns.read_csv(raweqfile)
eqdf = []
for rrow in raweqdf:
if rrow[PQR['SERIES']] in VALEQSERIES:
trow = [''] * len(PQT)
trow[PQT['TIMESTAMP']] = timestamp
trow[PQT['ISIN']] = rrow[PQR['ISIN']]
trow[PQT['SYMBOL']] = rrow[PQR['SYMBOL']]
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']]
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from p4defs import *
import p4fns
import sys
mode = str(sys.argv[1])
csvdb = str(sys.argv[2])
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]
if (mode == 'WRITE'):
if (csvdb == SQLEQCatalog):
inputdf = p4fns.readall_csv(NSEEQCatalog)
p4fns.write_sql(NSEULDB, csvdb, inputdf, MCAT, EQCATCOL)
elif (csvdb == SQLIXCatalog):
inputdf = p4fns.readall_csv(NSEIXCatalog)
p4fns.write_sql(NSEULDB, csvdb, inputdf, MXC, IXCATCOL)
elif (csvdb in ixclist):
inputdf = p4fns.readall_csv(NSEIXSDBDIR + csvdb + CSV)
p4fns.write_sql(NSEULDB, csvdb, inputdf, MXS, SYIXCOL)
elif (csvdb in eqclist):
inputdf = p4fns.readall_csv(NSEEQSDBDIR + csvdb + CSV)
p4fns.write_sql(NSEULDB, csvdb, inputdf, MQS, SYEQCOL)
elif (csvdb == SQLNSEBONUS):
inputdf = p4fns.readall_csv(NSEBONUS)
import sys
import math
import dateutil.parser as dp
import pandas as pd
mode = 'T'
symbol1 = str(sys.argv[1])
symbol2 = str(sys.argv[2])
mwindow = int(sys.argv[3])
deltaP = float(sys.argv[4])
deltaN = float(sys.argv[5])
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'
#!/usr/bin/env python
# Usage ./nsecadb.py
from p4defs import *
import p4fns
from datetime import datetime
import dateutil.parser as dp
import re
## ============================================================================================= ##
## General
## ============================================================================================= ##
eqschdf = p4fns.read_csv(NSESCHCatalog)
eqschdict = {row[PSCH['OLDSYMBOL']]: row[PSCH['SYMBOL']] for row in eqschdf}
## ============================================================================================= ##
## Parsing NSE Corporate Actions and Updating the Corporate Actions Catalogs
## ============================================================================================= ##
def cadates(row):
try:
rcdate = (datetime.strptime(row[PRCA['RECDATE']],
'%d-%b-%Y')).strftime('%Y-%m-%d')
except:
try:
rcdate = (datetime.strptime(row[PRCA['BCSTART']],
'%d-%b-%Y')).strftime('%Y-%m-%d')
except:
rcdate = ''
try:
bcdate = (datetime.strptime(row[PRCA['BCEND']],