def test_analytical_gamma(self):
while self.tdi.has_next():
row = self.tdi.next_row()
S, K, t, r, sigma = row['S'], row['K'], row['t'], row['R'], row[
'v']
self.assertAlmostEqual(analytical.gamma('c', S, K, t, r, sigma),
row['CG'],
delta=0.000001)
self.assertAlmostEqual(analytical.gamma('p', S, K, t, r, sigma),
row['PG'],
delta=0.000001)
def test_analytical_gamma(self):
S = 49
K = 50
sigma = .2
r = .05
t = 0.3846
flag = 'p'
c_put = c_analytical.gamma(flag, S, K, t, r, sigma)
py_put = py_analytical.gamma(flag, S, K, t, r, sigma)
self.assertTrue(almost_equal(c_put, py_put))
flag = 'c'
c_call = c_analytical.gamma(flag, S, K, t, r, sigma)
py_call = py_analytical.gamma(flag, S, K, t, r, sigma)
self.assertTrue(almost_equal(c_call, py_call))
def test_gamma(self):
gammas = [
gamma(flag, S, K, t, r, sigma)
for flag, S, K, t, r, sigma in self.arg_combinations
]
ngammas = [
ngamma(flag, S, K, t, r, sigma)
for flag, S, K, t, r, sigma in self.arg_combinations
]
self.assertTrue(self.diff_mean(gammas, ngammas) < self.epsilon)
def Calculate_gamma(each_row):
if each_row['Implied_Volatility'] == 0:
measure= 0
else:
try:
measure = bs_a.gamma(flag=each_row['CallOrPut'], S=each_row['Spot'], K=each_row['Strike_Price'],
t=each_row['Days_to_Expiry']/365, r=.1,sigma=each_row['Implied_Volatility'])
except:
measure = 0
print("Gamma", end=" ")
return measure
def calc_all(self, b):
h = self.all_set(b)
if not h: return False
if not h.get("sigma"): self.civ(h)
if not h.get("sigma"): return False
a = (h["flag"], h["F"], h["K"], h["t"], h["r"], h["sigma"])
r = self.rounding
h["price"] = round(oprice(*a), r)
h["delta"] = round(delta(*a), r)
h["gamma"] = round(gamma(*a) * 100, r)
h["vega"] = round(vega(*a), r)
return h
def gamma(self, option_obj, asset_price, timestamp, expiration,
call_put_flag):
flag = call_put_flag
S = asset_price
K = option_obj.strike
t = self.time_2_expiration(timestamp, expiration)
r = self.risk_free_rate
sigma = option_obj.sigma
gamma = vollib_greeks.gamma(flag, S, K, t, r, sigma)
return gamma
def calculateAllGreeks(ticker, date, type, strike):
S = si.get_live_price(ticker)
K = strike
sigma = calculateAnnualizedVolatility(ticker)
t = optionExpiration(date)
r = calculateTreasuryYield(date)
if type == 'Call' or type == "call" or type == "C" or type == 'c':
flag = 'c'
else:
flag = 'p'
dlta = delta(flag, S, K, t, r, sigma)
gam = gamma(flag, S, K, t, r, sigma)
thta = theta(flag, S, K, t, r, sigma)
vga = vega(flag, S, K, t, r, sigma)
rh = rho(flag, S, K, t, r, sigma)
return ({"delta": dlta, "gamma": gam, "theta": thta, "vega": vga, "rho": rh})
def calculateGreek(ticker, date, type, strike, greek):
grk = 0
S = si.get_live_price(ticker)
K = strike
sigma = calculateAnnualizedVolatility(ticker)
t = optionExpiration(date)
r = calculateTreasuryYield(date)
flag = ''
if type == 'Call' or type == "call" or type == "C" or type == 'c':
flag = 'c'
flag = 'p'
if greek == "Delta" or greek == "delta":
grk = delta(flag, S, K, t, r, sigma)
elif greek == "Gamma" or greek == "gamma":
grk = gamma(flag, S, K, t, r, sigma)
elif greek == "Theta" or greek == "theta":
grk = theta(flag, S, K, t, r, sigma)
elif greek == "Vega" or greek == "vega":
grk = vega(flag, S, K, t, r, sigma)
else:
grk = rho(flag, S, K, t, r, sigma)
return grk
def on_update(msg, order):
now = time.time()
global tick
ms = msg['market_state']
tck = ms['ticker']
last_price = msg['market_state']['last_price']
tickers[tck]['price'].append(last_price)
tick += 1
if tck == fut:
return
S = tickers[fut]['price'][-1] if len(tickers[fut]['price']) > 0 else None
K = int(tck[1:4]) if len(tck) == 5 else int(tck[1:3])
t = (450 - tick) / 450 * 1 / 12
flag = 'c' if tck[-1] == 'C' else 'p'
if S is not None:
try:
iv = implied_volatility.implied_volatility(last_price, S, K, t, r,
flag)
theo_p = black_scholes.black_scholes(flag, S, K, t, r, iv)
delta = analytical.delta(flag, S, K, t, r, iv)
gamma = analytical.gamma(flag, S, K, t, r, iv)
# vega = analytical.vega(flag,S,K,t,r,iv)
tickers[tck]['vol'].append(vol)
tickers[tck]['delta'].append(delta)
tickers[tck]['gamma'].append(gamma)
# tickers[tck]['vega'].append(vega)
if len(tickers[tck]['vol']) > 5:
vol_sma = np.mean(np.array(ticker[tck]['vol'][-6:-1]))
if abs(iv - vol_sma) / vol_sma > .015:
if iv > vol_sma: order.addSell(tck, 10, last_price + dx)
else: order.addBuy(tck, 10, last_price - dx)
except:
print(S, K, last_price, flag)