def main(*args):
OPT_N = 4000000
iterations = 10
if len(args) >= 2:
iterations = int(args[0])
callResultNumpy = np.zeros(OPT_N)
putResultNumpy = -np.ones(OPT_N)
stockPrice = randfloat(np.random.random(OPT_N), 5.0, 30.0)
optionStrike = randfloat(np.random.random(OPT_N), 1.0, 100.0)
optionYears = randfloat(np.random.random(OPT_N), 0.25, 10.0)
callResultNumba = np.zeros(OPT_N)
putResultNumba = -np.ones(OPT_N)
time0 = time.time()
for i in range(iterations):
black_scholes(callResultNumpy, putResultNumpy, stockPrice,
optionStrike, optionYears, RISKFREE, VOLATILITY)
time1 = time.time()
print("Numpy Time: %f msec" % ((1000 * (time1 - time0)) / iterations))
time0 = time.time()
for i in range(iterations):
black_scholes_numba(callResultNumba, putResultNumba, stockPrice,
optionStrike, optionYears, RISKFREE, VOLATILITY)
time1 = time.time()
print("Numba Time: %f msec" % ((1000 * (time1 - time0)) / iterations))
delta = np.abs(callResultNumpy - callResultNumba)
L1norm = delta.sum() / np.abs(callResultNumpy).sum()
print("L1 norm: %E" % L1norm)
print("Max absolute error: %E" % delta.max())
def main (*args):
OPT_N = 4000000
iterations = 10
if len(args) >= 2:
iterations = int(args[0])
callResultNumpy = np.zeros(OPT_N)
putResultNumpy = -np.ones(OPT_N)
stockPrice = randfloat(np.random.random(OPT_N), 5.0, 30.0)
optionStrike = randfloat(np.random.random(OPT_N), 1.0, 100.0)
optionYears = randfloat(np.random.random(OPT_N), 0.25, 10.0)
callResultNumba = np.zeros(OPT_N)
putResultNumba = -np.ones(OPT_N)
time0 = time.time()
for i in range(iterations):
black_scholes(callResultNumpy, putResultNumpy, stockPrice,
optionStrike, optionYears, RISKFREE, VOLATILITY)
time1 = time.time()
print("Numpy Time: %f msec" %
((1000 * (time1 - time0)) / iterations))
time0 = time.time()
for i in range(iterations):
black_scholes_numba(callResultNumba, putResultNumba, stockPrice,
optionStrike, optionYears, RISKFREE, VOLATILITY)
time1 = time.time()
print("Numba Time: %f msec" %
((1000 * (time1 - time0)) / iterations))
delta = np.abs(callResultNumpy - callResultNumba)
L1norm = delta.sum() / np.abs(callResultNumpy).sum()
print("L1 norm: %E" % L1norm)
print("Max absolute error: %E" % delta.max())
def leverage(self, strike):
return 1.0 / black_scholes ((-1 if self.style == "put" else 1),
self.price,
strike,
self.t/365.0,
self.vol,
self.r,
self.d)
def main(*args):
OPT_N = 40000
callResult = np.zeros(OPT_N)
putResult = -np.ones(OPT_N)
stockPrice = randfloat(np.random.random(OPT_N), 5.0, 30.0)
optionStrike = randfloat(np.random.random(OPT_N), 1.0, 100.0)
optionYears = randfloat(np.random.random(OPT_N), 0.25, 10.0)
c = black_scholes(stockPrice, optionStrike, optionYears, RISKFREE, VOLATILITY)
p = None
return c, p
def portfolio_items(self,
prices=None,
mtm=False,
payoff_asset=None,
date=None,
dt=0.0):
retval = []
if prices == None:
prices = self.prices
for asset in self.portfolio:
if asset[1] == "cash":
retval.append(asset[0])
elif asset[1] == "spot":
quantity = asset[0]
underlying = asset[2]
purchase = asset[3]
if purchase < 0.0:
raise ValueError
retval.append(quantity * prices[underlying])
elif asset[1] == "put" or asset[1] == "call":
quantity = asset[0]
style = asset[1]
expiry = asset[2]
strike = asset[3]
underlying = asset[4]
purchase = asset[5]
price = prices[underlying]
value = 0.0
if strike < 0.0 or purchase < 0.0:
raise ValueError
if not mtm or underlying == payoff_asset:
if asset[1] == "put" and price < strike:
value = strike - price
if asset[1] == "call" and price > strike:
value = price - strike
else:
t = (date_fraction(date, expiry) - dt / 365.0)
if (t < 0.0):
t = 0.0
vol = self.vols[underlying]
if (price < 0.0):
price = 0.0
value = black_scholes((-1 if style == "put" else 1), price,
strike, t, vol, self.r, 0.0)
retval.append(quantity * value)
elif asset[1] == "comment":
pass
else:
raise Exception("unknown asset")
return retval
def portfolio_items(self, prices = None, mtm=False, payoff_asset=None, date=None, dt = 0.0):
retval = []
if prices == None:
prices = self.prices
for asset in self.portfolio:
if asset[1] == "cash":
retval.append(asset[0])
elif asset[1] == "spot":
quantity = asset[0]
underlying = asset[2]
purchase = asset[3]
if purchase < 0.0:
raise ValueError
retval.append(quantity * prices[underlying])
elif asset[1] == "put" or asset[1] == "call":
quantity = asset[0]
style = asset[1]
expiry = asset[2]
strike = asset[3]
underlying = asset[4]
purchase = asset[5]
price = prices[underlying]
value = 0.0
if strike < 0.0 or purchase < 0.0:
raise ValueError
if not mtm or underlying == payoff_asset:
if asset[1] == "put" and price < strike:
value = strike - price
if asset[1] == "call" and price > strike:
value = price - strike
else:
t = (date_fraction(date, expiry) -dt/365.0)
if (t < 0.0):
t = 0.0
vol = self.vols[underlying]
if (price < 0.0):
price = 0.0
value = black_scholes ((-1 if style == "put" else 1), price, strike, t, vol, self.r, 0.0)
retval.append(quantity * value )
elif asset[1] == "comment":
pass
else:
raise Exception ("unknown asset")
return retval
def gearing(self, strike):
"""Calculate return dependence for given strike"""
options = self.leverage(strike)
return (lambda price_new, dt : options * black_scholes ((-1 if self.style == "put" else 1), price_new, strike, ((self.t-dt)/365.0), self.vol, self.r, self.d))
for o in calls:
for o2 in puts:
if o['expiration'] == o2['expiration']:
strikec.append(int(o['strike']))
strikep.append(int(o2['strike']))
exp = datetime.strptime(o['expiration'][:-13], '%Y-%m-%d')
pexps.append(exp.strftime('%s'))
abc = 0
oldp = 0
while abc < len(calls):
now = time.time()
diff = (int(pexps[abc]) - int(now)) / 60 / 60 / 24 / 365
p1 = black_scholes(spot, strikep[abc], diff,
ivs[puts[abc]['instrumentName']], 0.03, 0.0, -1)
c1 = black_scholes(spot, strikec[abc], diff,
ivs[calls[abc]['instrumentName']], 0.03, 0.0, 1)
c2 = black_scholes(spot * 1.11, strikep[abc], diff,
ivs[puts[abc]['instrumentName']], 0.03, 0.0, -1)
p2 = black_scholes(spot * 1.11, strikec[abc], diff,
ivs[calls[abc]['instrumentName']], 0.03, 0.0, 1)
c3 = black_scholes(spot * 0.89, strikep[abc], diff,
ivs[puts[abc]['instrumentName']], 0.03, 0.0, -1)
p3 = black_scholes(spot * 0.89, strikec[abc], diff,
ivs[calls[abc]['instrumentName']], 0.03, 0.0, 1)
cost1 = (c1 + p1)
cost2 = (c2 + p2)
cost3 = (c3 + p3)
profit = (cost2 - cost1) + (cost3 - cost1)
def gearing(self, strike):
"""Calculate return dependence for given strike"""
options = self.leverage(strike)
return (lambda price_new, dt: options * black_scholes(
(-1 if self.style == "put" else 1), price_new, strike, (
(self.t - dt) / 365.0), self.vol, self.r, self.d))
def leverage(self, strike):
return 1.0 / black_scholes(
(-1 if self.style == "put" else 1), self.price, strike,
self.t / 365.0, self.vol, self.r, self.d)
puts.append(s)
#print(puts)
civs[s] = iv
pivs[s] = iv
costp.append(has[options[o]['instrumentName']])
instsp.append(options[o]['instrumentName'])
#print(len(puts))
#print(len(calls))
ccount = -1
for c in calls:
ccount = ccount + 1
pcount = -1
for p in puts:
pcount = pcount + 1
p1 = black_scholes(spot, p, diff, pivs[p], 0.03, 0.0, -1)
c1 = black_scholes(spot, c, diff, civs[c], 0.03, 0.0, 1)
if c1 > costc[ccount] * spot:
print('c1 underpriced!')
print(c1)
print({
'price': costc[ccount],
'call s': c,
'put s': p,
'instrument': instsc[ccount],
'e': e
})
if p1 > costp[pcount] * spot:
print('p1 underpriced!')
print(p1)
print({
data = requests.get(
'https://www.malaysiawarrants.com.my/mqmy/ScreenerJSONServlet?undefined&underlying=all&type=all&issuer=all&maturity=all&moneyness=all&moneynessPercent=all&effectiveGearing=all&expiry=all&indicator=all&sortBy=&sortOrder=asc'
).json()["data"]
for x in data:
warrant_name = x["dwSymbol"]
price = float(x["underlying_price"].replace(",", ""))
ex_price = float(x["exercisePrice"].replace(",", ""))
cr = float(x["conv_ratio"].replace(",", ""))
exdate = x["maturity"]
vola = float(x["impliedVolalitiy"])
bid = float(x["bidPrice"].replace(",", ""))
if vola == 0 or cr > 1000:
continue
d = datetime.datetime.strptime(exdate, '%d %b %y')
exdate = d.strftime('%Y-%m-%d')
exdate = datetime.datetime.strptime(exdate, '%Y-%m-%d')
remaining_days = (exdate.date() - datetime.datetime.now().date()).days
time = float(remaining_days / 365)
bs = black_scholes(1, price, ex_price, time, vola / 100, rf / 100, cr)
margin = round(bs / bid, 2)
if margin < 1.7 and margin > 1.2 and remaining_days > 60:
print("%s\nblackscholes: %s\nmarket: %s\nmargin: %s\nexpired: %s\n" %
(warrant_name, bs, bid, margin, exdate))