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')
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:]
price2 = [math.log(float(row[PXS['CLOSE']])) for row in datadb]
instrm2 = 'IX'
dsize = min(len(price1), len(price2))
mu = p4fns.rolling_smean(vwap1[-dsize:], mwindow)
sg = p4fns.rolling_sstdd(vwap1[-dsize:], mwindow)
mlen = len(sg)
timeser = [row[PQS['TIMESTAMP']] for row in datadb][-mlen:][:dur]
price1 = price1[-mlen:][:dur]
price2 = price2[-mlen:][:dur]
mu = mu[-mlen:][:dur]
sg = sg[-mlen:][:dur]
tlen = len(timeser)
startdate = timeser[0]
enddate = timeser[-1]
capital = 1000000
#startprice1 = float(p4fns.findvalue(startdate, instrm1, symbol1, 'XX', 'XX', 'XX', 'CLOSE'))
#startprice2 = float(p4fns.findvalue(startdate, instrm2, symbol2, 'XX', 'XX', 'XX', 'CLOSE'))
startprice1 = price1[0]
startprice2 = price1[0]
mwindow = 94
close = [float(row[PQS['CLOSE']]) for row in eqsdb]
pvwap = [math.log(float(row[PQS['VWAP']])) for row in eqsdb]
if (eqsize >= rwindow + mwindow + 40):
title = ['TIMESTAMP', 'CLOSE', 'MEAN', 'SIGMA']
reffdb = p4fns.read_csv(NSEIXSDBDIR + 'NIFTY' + CSV)[-eqsize:]
pvwapR = [math.log(float(row[PXS['CLOSE']])) for row in reffdb]
regr = p4fns.rolling_regress(pvwap[-eqsize:], pvwapR[-eqsize:],
rwindow)
predict = [round(math.exp(x), 2) for x in regr]
mu = p4fns.rolling_smean(predict, mwindow)
rlen = len(predict)
error = [
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')
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 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)
price2 = [math.log(float(row[PQS['CLOSE']])) for row in datadb]
vwap2 = [math.log(float(row[PQS['VWAP']])) for row in datadb]
else:
datadb = p4fns.read_csv(NSEIXSDBDIR + symbol2 + CSV)[-days:]
close2 = [float(row[PXS['CLOSE']]) for row in datadb]
price2 = [math.log(float(row[PXS['CLOSE']])) for row in datadb]
vwap2 = price2
dsize = min(len(price1), len(price2))
regrP = p4fns.rolling_regress(vwap1[-dsize:], vwap2[-dsize:], rwindow)
rlen = len(regrP)
errorP = [
round((a / b - 1) * 100, 2) for a, b in zip(price1[-rlen:], regrP[-rlen:])
]
timeser = [row[PQS['TIMESTAMP']] for row in datadb][-rlen:]
muP = p4fns.rolling_smean(errorP, mwindow)
sgP = p4fns.rolling_sstdd(errorP, mwindow)
mlen = len(sgP)
errorP = errorP[-mlen:][:dur]
timeser = timeser[-mlen:][:dur]
muP = muP[-mlen:][:dur]
sgP = sgP[-mlen:][:dur]
tlen = len(timeser)
close1 = close1[-mlen:][:dur]
close2 = close2[-mlen:][:dur]
if symbol1 in cnxlist:
instrm1 = 'EQ'
else:
instrm1 = 'IX'
if symbol2 in cnxlist: