def testATM(self):
put = Option(100, 100, 5/365, 0.05, is_call=False)
print(put.getInfo())
print(put.getPrice(0.21))
print(put.getImpliedVol(1))
call = Option(100, 100, 5/365, 0.05, is_call=True)
print(call.getInfo())
print(call.getPrice(0.21))
print(call.getImpliedVol(1))
def testOTM(self):
put = Option(105, 100, 1/365, 0.05, is_call=False)
print(put.getInfo())
print(put.getPrice(0.25))
print(put.getImpliedVol(0))
call = Option(95, 100, 1/365, 0.05, is_call=True)
print(call.getInfo())
print(call.getPrice(0.25))
print(call.getImpliedVol(0))
def testITM(self):
put = Option(95, 100, 30/365, 0.05, is_call=False)
print(put.getInfo())
print(put.getPrice(0.25))
print(put.getImpliedVol(5.6577))
call = Option(105, 100, 30/365, 0.05, is_call=True)
print(call.getInfo())
print(call.getPrice(0.25))
print(call.getImpliedVol(6.391))
def testSELLSimpleDynaDeltaCall(self):
pf_shares = []
shares_purchased = []
trxn_costs = []
deltas = []
option_price = []
underlying_price = []
vol = 0.20
T = 20 / 365
stk = 50
underlyingP = 49
underlying_price.append(underlyingP)
call = Option(underlyingP, stk, T, .05, is_call=True)
delta_start = -call.getGreeks(vol)[
'delta'] #NEGATIVE BECAUSE SOLD OPTION
deltas.append(delta_start)
opt_cost = -call.getPrice(vol) #NEGATIVE BECAUSE SOLD
option_price.append(opt_cost)
shares_owned = -delta_start * 100
pf_shares.append(shares_owned)
trxn_costs.append(shares_owned * underlyingP)
shares_purchased.append(shares_owned)
for i in range(20):
T = max(T - 1 / 365, 0)
if i % 2 == 0:
underlyingP = underlyingP + 0.8
underlying_price.append(underlyingP)
else:
underlyingP = underlyingP - 0.5
underlying_price.append(underlyingP)
call.setUnderlyingP(underlyingP)
call.setTimetoMaturity(T)
opt_prc = -call.getPrice(vol) #NEG BC SOLD
option_price.append(opt_prc)
delta = -call.getGreeks(vol)[
'delta'] #NEGATIVE BECAUSE SOLD OPTION
deltas.append(delta)
purchased_shares = -100 * delta - shares_owned
shares_purchased.append(purchased_shares)
shares_owned = shares_owned + purchased_shares
pf_shares.append(shares_owned)
cost_of_shares = purchased_shares * underlyingP
trxn_costs.append(cost_of_shares)
df = pd.DataFrame([
underlying_price, deltas, pf_shares, shares_purchased, trxn_costs,
option_price
]).T
df.columns = [
'underlying_price', 'deltas', 'pf_shares', 'shares_purchased',
'trxn_costs', 'option_price'
]
#assume position is closed at mkt close or hedged at mkt close
df['cum_trxn_costs'] = df['trxn_costs'].cumsum()
df['earlyexit_share_pnl'] = df['pf_shares'].shift(
1) * df['underlying_price'] - df['cum_trxn_costs'].shift(1)
df['earlyexit_option_pnl'] = (df['option_price'] - opt_cost) * 100
df['earlyexit_pnl'] = df['earlyexit_share_pnl'] + df[
'earlyexit_option_pnl']
expiration_pnl = df['earlyexit_pnl'].loc[len(df['earlyexit_pnl']) - 1]
df['underlying_price'].plot()
plt.axhline(stk, color='red')
plt.show()
print("PNL on Expiration: {}".format(expiration_pnl))
print("Realized Vol: {}".format(
pd.Series(underlying_price).pct_change().std() * np.sqrt(252)))
print(df)
self.assertEquals(round(expiration_pnl, 5), -18.85561)
def testBUYSimpleDynaDeltaCall(self):
pf_shares = []
shares_purchased = []
trxn_costs = []
deltas = []
option_price = []
underlying_price = []
vol = 0.20
T = 20 / 365
stk = 50
underlyingP = 49
underlying_price.append(underlyingP)
call = Option(underlyingP, stk, T, .05, is_call=True)
delta_start = call.getGreeks(vol)['delta']
deltas.append(delta_start)
opt_cost = call.getPrice(vol)
option_price.append(opt_cost)
shares_owned = -delta_start * 100
pf_shares.append(shares_owned)
trxn_costs.append(shares_owned * underlyingP)
shares_purchased.append(shares_owned)
for i in range(20):
T = max(T - 1 / 365, 0)
if i % 2 == 0:
underlyingP = underlyingP + 0.5
underlying_price.append(underlyingP)
else:
underlyingP = underlyingP - 0.5
underlying_price.append(underlyingP)
call.setUnderlyingP(underlyingP)
call.setTimetoMaturity(T)
opt_prc = call.getPrice(vol)
option_price.append(opt_prc)
delta = call.getGreeks(vol)['delta']
deltas.append(delta)
purchased_shares = -100 * delta - shares_owned
shares_purchased.append(purchased_shares)
shares_owned = shares_owned + purchased_shares
pf_shares.append(shares_owned)
cost_of_shares = purchased_shares * underlyingP
trxn_costs.append(cost_of_shares)
df = pd.DataFrame([
underlying_price, deltas, pf_shares, shares_purchased, trxn_costs,
option_price
]).T
df.columns = [
'underlying_price', 'deltas', 'pf_shares', 'shares_purchased',
'trxn_costs', 'option_price'
]
df['pct_chg'] = df['underlying_price'].pct_change()
#assume position is closed at mkt close or hedged at mkt close
df['cum_trxn_costs'] = df['trxn_costs'].cumsum()
df['earlyexit_share_pnl'] = df['pf_shares'].shift(
1) * df['underlying_price'] - df['cum_trxn_costs'].shift(1)
df['earlyexit_option_pnl'] = (df['option_price'] - opt_cost) * 100
df['earlyexit_pnl'] = df['earlyexit_share_pnl'] + df[
'earlyexit_option_pnl']
expiration_pnl = df['earlyexit_pnl'].loc[len(df['earlyexit_pnl']) - 1]
self.assertEquals(round(expiration_pnl, 5), 18.85561)
def testMCDynaDelta(self):
pf_shares = []
shares_purchased = []
trxn_costs = []
deltas = []
option_price = []
underlying_price = []
chng = []
#Approx historical returns with SPY
SPY_df = web.DataReader('GLD', 'yahoo', dt.datetime(2019, 3, 29),
dt.datetime.today()).reset_index()
SPY = SPY_df[['Date', 'Close']].set_index(
'Date', drop=True).copy() * 10 #to convert to index val
rets = SPY['Close'].pct_change().dropna()
dist = CurveFitter(rets)
#dist.fitGaussian()
vol = 0.2157
T = 31 / 365
stk = 2000
timesteps = int(T * 365)
underlyingP = 1933
underlying_price.append(underlyingP)
call = Option(underlyingP, stk, T, .05, is_call=True)
delta_start = call.getGreeks(vol)['delta']
deltas.append(delta_start)
opt_cost = call.getPrice(vol)
option_price.append(opt_cost)
shares_owned = -round(delta_start, 2) * 100 #int(round(b/5.0)*5.0)
pf_shares.append(shares_owned)
trxn_costs.append(shares_owned * underlyingP)
shares_purchased.append(shares_owned)
for i in range(timesteps):
T = max(T - 1 / 365, 0)
if i % 2 == 0:
pct_chg = np.random.choice(
rets
) #round(dist.sampledist(st.t, 1.8768166491405975, 0.0, 0.01, 1).item(), 8) #dist.samplenorm(1).item()
chng.append(pct_chg)
underlyingP = underlyingP + underlyingP * pct_chg # + 30
underlying_price.append(underlyingP)
else:
pct_chg = np.random.choice(
rets
) #round(dist.sampledist(st.t, 1.8768166491405975, 0.0, 0.01, 1).item(), 8) #dist.samplenorm(1).item()
chng.append(pct_chg)
underlyingP = underlyingP + underlyingP * pct_chg # - 30
underlying_price.append(underlyingP)
call.setUnderlyingP(underlyingP)
call.setTimetoMaturity(T)
opt_prc = call.getPrice(vol)
option_price.append(opt_prc)
delta = call.getGreeks(vol)['delta']
deltas.append(delta)
purchased_shares = -100 * round(delta, 2) - shares_owned
shares_purchased.append(purchased_shares)
shares_owned = shares_owned + purchased_shares
pf_shares.append(shares_owned)
cost_of_shares = purchased_shares * underlyingP
trxn_costs.append(cost_of_shares)
df = pd.DataFrame([
underlying_price, chng, deltas, pf_shares, shares_purchased,
trxn_costs, option_price
]).T
df.columns = [
'underlying_price', 'pct_chg', 'deltas', 'pf_shares',
'shares_purchased', 'trxn_costs', 'option_price'
]
#assume position is closed at mkt close or hedged at mkt close
df['cum_trxn_costs'] = df['trxn_costs'].cumsum()
df['earlyexit_share_pnl'] = df['pf_shares'].shift(
1) * df['underlying_price'] - df['cum_trxn_costs'].shift(1)
df['earlyexit_option_pnl'] = (df['option_price'] - opt_cost) * 100
df['earlyexit_pnl'] = df['earlyexit_share_pnl'] + df[
'earlyexit_option_pnl']
expiration_pnl = df['earlyexit_pnl'].loc[len(df['earlyexit_pnl']) - 1]
df['underlying_price'].plot()
plt.axhline(stk, color='red')
plt.show()
print("PNL on Expiration: {}".format(expiration_pnl))
print("Realized Vol: {}".format(
pd.Series(underlying_price).pct_change().std() * np.sqrt(252)))
print(df)
def testTheta(self):
put = Option(110, 100, 2, 0.05, is_call=False)
print(put.getInfo())
print(put.getPrice(0.25))
print(put.getGreeks(.25))
def testVega(self):
put = Option(50, 100, 1, 0.05, is_call=False)
print(put.getInfo())
print(put.getPrice(0.25))
print(put.getGreeks(.25))
def main():
ticker = 'TGT'
SPY_df = web.DataReader(ticker, 'yahoo', dt.datetime(2018, 3, 29),
dt.datetime.today()).reset_index()
SPY = SPY_df[['Date', 'Close']].set_index(
'Date', drop=True).copy() * 10 # to convert to index val
data = SPY['Close'].pct_change().dropna()
best_fit_name, best_fit_params = best_fit_distribution(data, 200, ax=None)
best_dist = getattr(st, best_fit_name)
pdf = make_pdf(best_dist, best_fit_params)
plt.figure(figsize=(12, 8))
ax = pdf.plot(lw=2, label='Fitted PDF', legend=True)
data.plot(kind='hist',
bins=50,
density=True,
alpha=0.5,
label='Raw Data',
legend=True,
ax=ax)
param_names = (
best_dist.shapes +
', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']
param_str = ', '.join([
'{}={:0.2f}'.format(k, v) for k, v in zip(param_names, best_fit_params)
])
dist_str = '{}({})'.format(best_fit_name, param_str)
ax.set_title(u'{} Returns Best Fit Distribution: \n'.format(ticker) +
dist_str)
ax.set_xlabel(u'Returns (%)')
ax.set_ylabel('Frequency')
ax.set_xlim([-.25, .25])
dist = CurveFitter(data)
sample_size = 100000
resampled = dist.sampledist(st.nct, best_fit_params[0], best_fit_params[1],
best_fit_params[2], best_fit_params[3],
sample_size)
resampled = pd.Series(resampled)
bins = int(sample_size / 100)
resampled.plot(kind='hist',
bins=bins,
density=True,
alpha=0.25,
label='Resampled Data',
legend=True,
ax=ax)
plt.savefig("BestFit.png")
plt.show()
pf_shares = []
shares_purchased = []
trxn_costs = []
deltas = []
option_price = []
underlying_price = []
chng = []
vol = 0.30
T = 123 / 365
stk = 170
timesteps = int(T * 365)
underlyingP = 149.58
underlying_price.append(underlyingP)
call = Option(underlyingP, stk, T, .05, is_call=True)
delta_start = call.getGreeks(vol)['delta']
deltas.append(delta_start)
opt_cost = call.getPrice(vol)
option_price.append(opt_cost)
shares_owned = -round(delta_start, 2) * 100 # int(round(b/5.0)*5.0)
pf_shares.append(shares_owned)
trxn_costs.append(shares_owned * underlyingP)
shares_purchased.append(shares_owned)
for i in range(timesteps):
T = max(T - 1 / 365, 0)
if i % 2 == 0:
pct_chg = round(
dist.sampledist(best_dist, best_fit_params[0],
best_fit_params[1], best_fit_params[2],
best_fit_params[3], 1).item(),
8) * 2 / 3 #dist.samplenorm(1).item()
chng.append(pct_chg)
underlyingP = underlyingP + underlyingP * pct_chg # + 30
underlying_price.append(underlyingP)
else:
pct_chg = round(
dist.sampledist(best_dist, best_fit_params[0],
best_fit_params[1], best_fit_params[2],
best_fit_params[3], 1).item(),
8) #dist.samplenorm(1).item()
chng.append(pct_chg)
underlyingP = underlyingP + underlyingP * pct_chg # - 30
underlying_price.append(underlyingP)
call.setUnderlyingP(underlyingP)
call.setTimetoMaturity(T)
opt_prc = call.getPrice(vol)
option_price.append(opt_prc)
delta = call.getGreeks(vol)['delta']
deltas.append(delta)
purchased_shares = -100 * round(delta, 2) - shares_owned
shares_purchased.append(purchased_shares)
shares_owned = shares_owned + purchased_shares
pf_shares.append(shares_owned)
cost_of_shares = purchased_shares * underlyingP
trxn_costs.append(cost_of_shares)
df = pd.DataFrame([
underlying_price, chng, deltas, pf_shares, shares_purchased,
trxn_costs, option_price
]).T
df.columns = [
'underlying_price', 'pct_chg', 'deltas', 'pf_shares',
'shares_purchased', 'trxn_costs', 'option_price'
]
# assume position is closed at mkt close or hedged at mkt close
df['cum_trxn_costs'] = df['trxn_costs'].cumsum()
df['earlyexit_share_pnl'] = df['pf_shares'].shift(
1) * df['underlying_price'] - df['cum_trxn_costs'].shift(1)
df['earlyexit_option_pnl'] = (df['option_price'] - opt_cost) * 100
df['earlyexit_pnl'] = df['earlyexit_share_pnl'] + df['earlyexit_option_pnl']
expiration_pnl = df['earlyexit_pnl'].loc[len(df['earlyexit_pnl']) - 1]
df['underlying_price'].plot(
color='blue',
linewidth=3.0,
)
plt.axhline(stk, color='red', linewidth=5.0, linestyle='--')
plt.title("Underlying Price Monte Carlo Simulation")
plt.ylabel("Price")
plt.xlabel("Days")
plt.savefig("UnderlyingPrice.png")
plt.show()
print("PNL on Expiration: {}".format(expiration_pnl))
print("Realized Vol: {}".format(
pd.Series(underlying_price).pct_change().std() * np.sqrt(252)))
print(df)