def main():
# if np_output is True, the output will be np.ndarray, otherwise pd.DataFrame
np_output = False # True
t = "30-04-2021"
r = 0.10
S = 14661
sigma = 0.2375
# sigma = 0.23
K = 14600
T = "06-05-2021"
# default market environment
market_env = MarketEnvironment(t, r, S, sigma)
# print(market_env)
# define option style and type
opt_style = "plain_vanilla" # "digital" # "plain_vanilla"
opt_type = "put" # "put"
option = option_factory(market_env, opt_style, opt_type, K, T)
# print(option)
# CalcualtePricesForRange(S, option)
# print("Metrics:")
# print("Payoff:", option.payoff())
# print("Price upper limit:", option.price_upper_limit())
# print("Price lower limit:", option.price_lower_limit())
print("Price:", option.price())
print("P&L:", option.PnL())
print("Delta:", option.delta())
print("Theta:", option.theta())
print("Gamma:", option.gamma())
print("Vega:", option.vega())
print("Rho:", option.rho())
def main():
# Bull-Spread implementation example
# default market environment
market_env = MarketEnvironment()
print(market_env)
# options strikes
K_long = 80
K_short = 110
# bull-spread portfolio initialized (as empty portfolio)
bull_spread_ptf = Portfolio(name="Bull Spread Strategy")
print(bull_spread_ptf)
# 80-call
Vanilla_Call_long = PlainVanillaOption(market_env, K=K_long, T='31-12-2021')
print(Vanilla_Call_long)
# 110-call
Vanilla_Call_short = PlainVanillaOption(market_env, K=K_short, T='31-12-2021')
print(Vanilla_Call_short)
# creation of bull-spread portfolio strategy
bull_spread_ptf.add_instrument(Vanilla_Call_long, 1)
bull_spread_ptf.add_instrument(Vanilla_Call_short, -1)
print(bull_spread_ptf)
# portfolio plotter instance
bull_spread_ptf_plotter = PortfolioPlotter(bull_spread_ptf)
# select metrics to plot
for plot_metrics in ["price", "PnL", "delta", "theta", "gamma", "vega", "rho"]:
plot_details_flag = True if plot_metrics == "price" else False
# Bull-Spread price plot
bull_spread_ptf_plotter.plot(t='01-06-2020', plot_metrics=plot_metrics,
plot_details=plot_details_flag)
for time_kind in ['date', 'tau']:
# set time-parameter to plot
multiple_valuation_dates = get_time_parameter(bull_spread_ptf, kind=time_kind)
print(multiple_valuation_dates)
# Plot at multiple dates
bull_spread_ptf_plotter.plot(t=multiple_valuation_dates, plot_metrics=plot_metrics)
# Surface plot
bull_spread_ptf_plotter.plot(t=multiple_valuation_dates, plot_metrics=plot_metrics,
surf_plot=True)
# Surface plot (rotate) - Underlying value side
bull_spread_ptf_plotter.plot(t=multiple_valuation_dates, plot_metrics=plot_metrics,
surf_plot=True, view=(0, 180))
# Price surface plot (rotate) - Date side
bull_spread_ptf_plotter.plot(t=multiple_valuation_dates, plot_metrics=plot_metrics,
surf_plot=True, view=(0, -90))
def main():
# numeric greeks example
# default market environment
market_env = MarketEnvironment()
print(market_env)
# define option style and type
opt_style = "plain_vanilla" # "digital"
opt_type = "call" # "call"
option = option_factory(market_env, opt_style, opt_type)
print(option)
# numeric greeks instance
NumGreeks = NumericGreeks(option)
# underlying range at which compute greeks
S_range = np.linspace(50, 150, 2000)
# time-to-maturity range at which compute greeks
tau_range = np.linspace(1e-4, 1.0, 1000)
tau_range = homogenize(tau_range, reverse_order=True)
# select greek
for greek_type in ["delta", "theta", "gamma", "vega", "rho"]:
#
# greek Vs Underlying level S
#
# numeric greek calculation
greek_numeric_Vs_S = greeks_factory(NumGreeks, greek_type)(S=S_range)
# labels
label_numeric_S = greeks_label_factory(greek_type, opt_type, kind="num")
# plot title
plot_title_S = greeks_title_factory(option, greek_type)
# plot
plot(x=S_range, f=greek_numeric_Vs_S, x_label=r"$S$",
f_label=label_numeric_S, title=plot_title_S)
#
# greek Vs residual time to maturity tau
#
# numeric greek calculation
greek_numeric_Vs_tau = greeks_factory(NumGreeks, greek_type)(tau=tau_range)
# labels
label_numeric_tau = greeks_label_factory(greek_type, opt_type,
kind="num", underlying=r"\tau")
# plot title
plot_title_tau = greeks_title_factory(option, greek_type, underlying=r"\tau")
# plot
plot(x=tau_range, f=greek_numeric_Vs_tau, x_label=r"$\tau$",
f_label=label_numeric_tau, title=plot_title_tau)
def main():
# if np_output is True, the output will be np.ndarray, otherwise pd.DataFrame
np_output = False # True
# default market environment
market_env = MarketEnvironment()
print(market_env)
# define option style and type
opt_style = "plain_vanilla" # "digital"
opt_type = "call" # "put"
option = option_factory(market_env, opt_style, opt_type)
print(option)
for case in ['All_scalar', 'S', 'S.sigma_distributed', 'S.r_distributed', 'S.sigma_and_r_distributed',
'K', 'K.sigma_distributed', 'K.r_distributed', 'K.sigma_and_r_distributed',
't', 't.sigma_distributed', 't.r_distributed', 't.sigma_and_r_distributed',
'S.t', 'S.t.sigma_distributed_as_Sxt_grid', 'S.t.r_distributed_as_Sxt_grid',
'S.t.sigma_and_r_distributed_as_Sxt_grid',
'K.t', 'K.t.sigma_distributed_as_Kxt_grid', 'K.t.r_distributed_as_Kxt_grid',
'K.t.sigma_and_r_distributed_as_Kxt_grid',
't.sigma_axis', 't.r_axis']:
# get parameters dictionary for case considered
param_dict, case_info = get_param_dict(option, np_output, case)
print("\n--------------------------------------------\n")
print("\n" + case_info + "\n")
print("Parameters:")
print("S: {}".format(param_dict["S"]))
print("K: {}".format(param_dict["K"]))
print("t: {}".format(param_dict["t"]))
print("sigma: {}".format(param_dict["sigma"]))
print("r: {}\n".format(param_dict["r"]))
print("Metrics:")
print("Payoff:\n", option.payoff(**param_dict))
print("\nPrice upper limit:\n", option.price_upper_limit(**param_dict))
print("\nPrice lower limit:\n", option.price_lower_limit(**param_dict))
print("\nPrice:\n", option.price(**param_dict))
print("\nP&L:\n", option.PnL(**param_dict))
print("\nDelta:\n", option.delta(**param_dict))
print("\nTheta:\n", option.theta(**param_dict))
print("\nGamma:\n", option.gamma(**param_dict))
print("\nVega:\n", option.vega(**param_dict))
print("\nRho:\n", option.rho(**param_dict))
# Implied volatility calculation is not implemented for x-axis
# (columns) spanned by sigma
if ('sigma_axis' not in param_dict) or (param_dict['sigma_axis'] is False):
print("\nExpected Implied Volatility: \n{}\n".format(param_dict["sigma"]))
print("\nImplied Volatility - Newton method:\n{}\n".format(option.implied_volatility(**param_dict)))
param_dict["minimization_method"] = "Least-Squares"
print("\nImplied Volatility - Least-Squares constrained method:\n{}\n"
.format(option.implied_volatility(**param_dict)))
def main():
# vanilla call implementation example
# default market environment
market_env = MarketEnvironment()
print(market_env)
# define option style and type
opt_style = "plain_vanilla" # "digital"
opt_type = "call" # "put"
option = option_factory(market_env, opt_style, opt_type)
print(option)
# option plotter instance
plotter = OptionPlotter(option)
# valuation date of the option
emission_date = option.get_t()
print(emission_date)
# select dependency to plot as x-axis of the plot
for dependency_type in ["S", "K", "sigma", "r"]:
# keyboard parameter and corresponding range to test
x_axis_dict = options_x_axis_parameters_factory(
option, dependency_type)
# select metrics to plot
for plot_metrics in [
"price", "PnL", "delta", "theta", "gamma", "vega", "rho"
]:
plot_details_flag = True if plot_metrics == "price" else False
# Plot at t
plotter.plot(**x_axis_dict,
t=[emission_date],
plot_metrics=plot_metrics,
plot_details=plot_details_flag)
# Plot at another date-string date
plotter.plot(**x_axis_dict,
t="01-06-2020",
plot_metrics=plot_metrics,
plot_details=plot_details_flag)
for time_kind in ['date', 'tau']:
# set time-parameter to plot
multiple_valuation_dates = get_time_parameter(option,
kind=time_kind)
print(multiple_valuation_dates)
# Plot at multiple dates
plotter.plot(**x_axis_dict,
t=multiple_valuation_dates,
plot_metrics=plot_metrics)
def setUp(self) -> None:
warnings.filterwarnings("ignore")
# common market environment
mkt_env = MarketEnvironment(t="01-06-2020")
# underlying values to test
S_vector = [60, 90, 120]
# options maturities
T_call = "31-12-2020"
T_put = "30-06-2021"
# time parameter
t_range = pd.date_range(start=mkt_env.get_t(), end=T_call, periods=3)
# pricing parameters
self.params = {"S": S_vector, "t": t_range, "np_output": False}
# options strikes
K_put = 80
K_call = 110
# portfolio options positions
self.call_pos = 2
self.put_pos = -5
# empty portfolio initialized
self.ptf = Portfolio()
# adding 2 long plain-vanilla call contracts
self.call_opt = PlainVanillaOption(mkt_env, K=K_call, T=T_call)
# adding 5 short plain-vanilla put contracts
self.put_opt = PlainVanillaOption(mkt_env, option_type="put", K=K_put, T=T_put)
# adding contracts to portfolio
self.ptf.add_instrument(self.call_opt, self.call_pos)
self.ptf.add_instrument(self.put_opt, self.put_pos)
def main():
# vanilla call implementation example
# default market environment
market_env = MarketEnvironment()
print(market_env)
# define option style and type
opt_style = "plain_vanilla" # "digital"
opt_type = "call" # "put"
option = option_factory(market_env, opt_style, opt_type)
print(option)
# option plotter instance
plotter = OptionPlotter(option)
# select dependency to plot as x-axis of the plot
for dependency_type in ["S", "K", "sigma", "r"]:
# keyboard parameter and corresponding range to test
x_axis_dict = options_x_axis_parameters_factory(option, dependency_type)
# appropriate azimut angle for best viewing
azimut_angle = get_azimut_angle(dependency_type)
# select metrics to plot
for plot_metrics in ["price", "PnL", "delta", "theta", "gamma", "vega", "rho"]:
for time_kind in ['date', 'tau']:
# set time-parameter to plot
multiple_valuation_dates = get_time_parameter(option, kind=time_kind)
print(multiple_valuation_dates)
# Surface plot
plotter.plot(**x_axis_dict, t=multiple_valuation_dates,
plot_metrics=plot_metrics, surf_plot=True)
# Surface plot (rotate) - x-axis side
plotter.plot(**x_axis_dict, t=multiple_valuation_dates,
plot_metrics=plot_metrics, surf_plot=True,
view=(0, azimut_angle["x-axis side"]))
# Price surface plot (rotate) - Date side
plotter.plot(**x_axis_dict, t=multiple_valuation_dates,
plot_metrics=plot_metrics, surf_plot=True,
view=(0, azimut_angle["Date side"]))
def CreateExpectedSpotLists(OptionType, currSymbol="Sheet1"):
data = pd.read_excel(OptionsConstants.Location + "\\" +
OptionsConstants.TradeDate + "\\" + OptionType + "_" +
OptionsConstants.FileSuffix + ".xlsx",
sheet_name=currSymbol)
OutputList = []
for index, row in data.iterrows():
t = OptionsConstants.TradeDate
r = 0.1
S = row['underlyingValue']
sigma = float((row['impliedVolatility']) / 100)
K = row['strikePrice']
T = (datetime.datetime.strptime(row['expiryDate'],
'%d-%b-%Y')).strftime('%d-%m-%Y')
# default market environment
market_env = MarketEnvironment(t, r, S, sigma)
# define option style and type
opt_style = "plain_vanilla" # "digital" # "plain_vanilla"
opt_type = OptionType
option = option_factory(market_env, opt_style, opt_type, K, T)
ProjectedPriceList = CalcualtePricesForRange(S, option)
OutputList.append({
'OptionType': OptionType,
'StrikePrice': row['strikePrice'],
'Underlying': row['underlyingValue'],
'LastPrice': row['lastPrice'],
'ExpiryDate': row['expiryDate'],
'ProjectedSpot': ProjectedPriceList
})
newCalldf = pd.DataFrame(OutputList).dropna()
filemode = 'w'
if (os.path.exists(OptionsConstants.Location + "\\" +
OptionsConstants.TradeDate + "\\" + OptionType +
"Projected_" + OptionsConstants.FileSuffix + ".xlsx")):
filemode = 'a'
with pd.ExcelWriter(OptionsConstants.Location + "\\" +
OptionsConstants.TradeDate + "\\" + OptionType +
"Projected_" + OptionsConstants.FileSuffix + ".xlsx",
engine='openpyxl',
mode=filemode) as writer:
newCalldf.to_excel(writer, sheet_name=currSymbol, index=False)
return (OutputList)
def setPortfolio():
mktEnv = MarketEnvironment()
# strikes
kl = 90
ks = 110
print(mktEnv)
option_spread_ptf = Portfolio(name="Option Spread")
print(option_spread_ptf)
# 90-call
vcl = PlainVanillaOption(mktEnv, K=kl, T='31-12-2021')
print(vcl)
vcs = PlainVanillaOption(mktEnv, K=ks, T='31-12-2021')
print(vcs)
# creation of bull-spread portfolio strategy
option_spread_ptf.add_instrument(vcl, 1)
option_spread_ptf.add_instrument(vcs, -1)
print(option_spread_ptf)
def main():
#
# portfolio instantiation example
#
# if np_output is True, the output will be np.ndarray, otherwise pd.DataFrame
np_output = False # True
# default market environment
market_env = MarketEnvironment(t="01-06-2020")
print(market_env)
# underlying values to test
S_vector = [60, 90, 120]
print("S_vector: {}\n".format(S_vector))
# options maturities
T_call = "31-12-2020"
T_put = "30-06-2021" # T_call
# choose the kind of time-parameter to use: either a date ('date') or a
# time-to-maturity ('ttm'). Time-to-maturity time parameter is not allowed
# for multi-horizon portfolios.
time_parameter = 'date' # 'ttm'
# get time parameter
t_range = get_time_parameter(market_env,
end_date=min(T_call, T_put, key=date_string_to_datetime_obj),
periods=5,
kind=time_parameter,
multi_horizon_ptf=T_call != T_put)
# options strikes
K_put = 80
K_call = 110
# portfolio options positions
call_pos = 2
put_pos = -5
#
# Step 0: empty portfolio initialized
#
ptf = Portfolio()
print(ptf)
#
# Step 1: adding 2 long plain-vanilla call contracts
#
# plain-vanilla call option
call = PlainVanillaOption(market_env, K=K_call, T=T_call)
print(call)
# adding contract to portfolio
ptf.add_instrument(call, call_pos)
print(ptf)
# metrics to compare
for metrics in ["price", "PnL", "delta", "theta", "gamma", "vega", "rho"]:
# portfolio metrics
ptf_metrics = getattr(ptf, metrics)(S=S_vector, t=t_range, np_output=np_output)
print("\nPortfolio {}:\n{}".format(metrics, ptf_metrics))
# verification with benchmark metrics
call_metrics = getattr(call, metrics)(S=S_vector, t=t_range, np_output=np_output)
benchmark_metrics = call_pos * call_metrics
print("\nBenchmark {}:\n{}".format(metrics, benchmark_metrics))
# check effective match
diff = (ptf_metrics - benchmark_metrics).astype('float')
num_nonzero_diff = np.count_nonzero(diff) - np.isnan(diff).sum().sum()
exact_match = True if num_nonzero_diff == 0 else False
print("\nIs replication exact (NaN excluded)? {}\n".format(exact_match))
#
# Step 2: adding 5 short plain-vanilla put contracts
#
# plain-vanilla put option
put = PlainVanillaOption(market_env, option_type="put", K=K_put, T=T_put)
print(put)
# adding contract to portfolio
ptf.add_instrument(put, put_pos)
print(ptf)
# metrics to compare
for metrics in ["price", "PnL", "delta", "theta", "gamma", "vega", "rho"]:
# portfolio metrics
ptf_metrics = getattr(ptf, metrics)(S=S_vector, t=t_range, np_output=np_output)
print("\nPortfolio {}:\n{}".format(metrics, ptf_metrics))
# verification with benchmark metrics
call_metrics = getattr(call, metrics)(S=S_vector, t=t_range, np_output=np_output)
put_metrics = getattr(put, metrics)(S=S_vector, t=t_range, np_output=np_output)
benchmark_metrics = call_pos * call_metrics + put_pos * put_metrics
print("\nBenchmark {}:\n{}".format(metrics, benchmark_metrics))
# check effective match
diff = (ptf_metrics - benchmark_metrics).astype('float')
num_nonzero_diff = np.count_nonzero(diff) - np.isnan(diff).sum().sum()
exact_match = True if num_nonzero_diff == 0 else False
print("\nIs replication exact (NaN excluded)? {}\n".format(exact_match))
def main():
# Bull-Spread implementation example
# default market environment
market_env = MarketEnvironment()
print(market_env)
# options strikes
K_long = 80
K_short = 110
# bull-spread portfolio initialized (as empty portfolio)
bull_spread_ptf = Portfolio(name="Bull Spread Strategy")
print(bull_spread_ptf)
# 80-call
Vanilla_Call_long = PlainVanillaOption(market_env, K=K_long, T='31-12-2021')
print(Vanilla_Call_long)
# 110-call
Vanilla_Call_short = PlainVanillaOption(market_env, K=K_short, T='31-12-2021')
print(Vanilla_Call_short)
# creation of bull-spread portfolio strategy
bull_spread_ptf.add_instrument(Vanilla_Call_long, 1)
bull_spread_ptf.add_instrument(Vanilla_Call_short, -1)
print(bull_spread_ptf)
# portfolio plotter instance
bull_spread_ptf_plotter = PortfolioPlotter(bull_spread_ptf)
# select dependency to plot as x-axis of the plot
# (strike 'K' is skipped because a bull-spread is a multi-strike portfolio)
for dependency_type in ["S", "sigma", "r"]:
# keyboard parameter and corresponding range to test
x_axis_dict = options_x_axis_parameters_factory(bull_spread_ptf, dependency_type)
# appropriate azimut angle for best viewing
azimut_angle = get_azimut_angle(dependency_type)
# select metrics to plot
for plot_metrics in ["price", "PnL", "delta", "theta", "gamma", "vega", "rho"]:
plot_details_flag = True if plot_metrics == "price" else False
# Bull-Spread price plot
bull_spread_ptf_plotter.plot(**x_axis_dict, t='01-06-2020', plot_metrics=plot_metrics,
plot_details=plot_details_flag)
for time_kind in ['date', 'tau']:
# set time-parameter to plot
multiple_valuation_dates = get_time_parameter(bull_spread_ptf, kind=time_kind)
print(multiple_valuation_dates)
# Plot at multiple dates
bull_spread_ptf_plotter.plot(**x_axis_dict, t=multiple_valuation_dates,
plot_metrics=plot_metrics)
# Surface plot
bull_spread_ptf_plotter.plot(**x_axis_dict, t=multiple_valuation_dates,
plot_metrics=plot_metrics, surf_plot=True)
# Surface plot (rotate) - x-axis side
bull_spread_ptf_plotter.plot(**x_axis_dict, t=multiple_valuation_dates,
plot_metrics=plot_metrics, surf_plot=True,
view=(0, azimut_angle["x-axis side"]))
# Price surface plot (rotate) - Date side
bull_spread_ptf_plotter.plot(**x_axis_dict, t=multiple_valuation_dates,
plot_metrics=plot_metrics, surf_plot=True,
view=(0, azimut_angle["Date side"]))
def main():
# output format: pd.DataFrame
np_output = False
# choose whether to plot expected IV or reconstructed one (takes longer)
plot_expected_iv = True # False
# default market environment
market_env = MarketEnvironment()
print(market_env)
# define option style and type
opt_style = "plain_vanilla" # "digital"
opt_type = "call" # "call" # "put"
option = option_factory(market_env, opt_style, opt_type)
print(option)
# K
K_vector = np.linspace(50, 150, 100)
# tau: a date-range of 5 valuation dates between t and T-10d
n = 6
valuation_date = option.get_t()
expiration_date = option.get_T()
t_vector = pd.date_range(start=valuation_date,
end=expiration_date - pd.Timedelta(days=25),
periods=n)
# sigma (qualitatively reproducing the smile)
k, tau = np.meshgrid(K_vector, option.time_to_maturity(t=t_vector))
sigma_grid_K = 0.01 + ((k - 100)**2) / (100 * k) / tau
# pricing parameters
param_dict = {
"S": 100,
"K": K_vector,
"t": t_vector,
"sigma": sigma_grid_K,
"r": 0.01,
"np_output": np_output
}
print("Parameters:")
print("S: {}".format(param_dict["S"]))
print("K: {}".format(param_dict["K"]))
print("t: {}".format(param_dict["t"]))
print("sigma: \n{}".format(param_dict["sigma"]))
print("r: {}\n".format(param_dict["r"]))
# expected implied volatility: is the 'sigma' parameter with which the
# target price has been generated
expected_IV = pd.DataFrame(data=param_dict["sigma"],
columns=K_vector,
index=t_vector)
expected_IV.rename_axis('K', axis='columns', inplace=True)
expected_IV.rename_axis('t', axis='rows', inplace=True)
print("\nExpected Kxt Implied volatility Surface: \n", expected_IV)
if plot_expected_iv:
IV_plot = expected_IV
else:
# compute target price
target_price = option.price(**param_dict)
print("\nTarget Price in input: \n", target_price)
# Add target_price to parameters dictionary:
param_dict['target_price'] = target_price
# Least=Squares method
param_dict["minimization_method"] = "Least-Squares"
ls_IV = option.implied_volatility(**param_dict)
RMSE_ls = np.sqrt(np.nanmean((ls_IV - expected_IV)**2))
RMSRE_ls = np.sqrt(np.nanmean(
((ls_IV - expected_IV) / expected_IV)**2))
print(
"\nImplied Volatility - Least-Squares constrained method - Metrics (NaN excluded): RMSE={:.1E}, "
"RMSRE={:.1E}:\n".format(RMSE_ls, RMSRE_ls), ls_IV)
IV_plot = ls_IV
# option plotter instance
plotter = OptionPlotter(option)
plotter.plot(plot_metrics="implied_volatility", IV=IV_plot)
def main():
# Calendar-Spread implementation example
# default market environment
market_env = MarketEnvironment()
print(market_env)
# options expirations
T_short = "31-05-2020"
T_long = "30-08-2020"
# current underlying level
S_t = market_env.get_S()
# calendar-spread portfolio initialized (as empty portfolio)
calendar_spread_ptf = Portfolio(name="Calendar Spread Strategy")
print(calendar_spread_ptf)
# T_long-call
Vanilla_Call_long = PlainVanillaOption(market_env, T=T_long, K=S_t)
print(Vanilla_Call_long)
# T_short-call
Vanilla_Call_short = PlainVanillaOption(market_env, T=T_short, K=S_t)
print(Vanilla_Call_short)
# creation of Calendar-Spread portfolio strategy
calendar_spread_ptf.add_instrument(Vanilla_Call_long, 1)
calendar_spread_ptf.add_instrument(Vanilla_Call_short, -1)
print(calendar_spread_ptf)
# portfolio plotter instance
calendar_spread_ptf_plotter = PortfolioPlotter(calendar_spread_ptf)
# valuation date of the portfolio
valuation_date = calendar_spread_ptf.get_t()
print(valuation_date)
# select metrics to plot
for plot_metrics in [
"price", "PnL", "delta", "theta", "gamma", "vega", "rho"
]:
plot_details_flag = True if plot_metrics == "price" else False
# time-parameter as a date-range of 5 valuation dates between t and T_short
# being the Calendar-Spread a multi-horizon portfolio, time-to-maturity
# time parameters are not allowed.
last_date = T_short if plot_metrics in ["price", "PnL"] else date_string_to_datetime_obj(T_short) - \
pd.Timedelta(days=1)
multiple_valuation_dates = pd.date_range(start=valuation_date,
end=last_date,
periods=5)
print(multiple_valuation_dates)
# Bull-Spread price plot
calendar_spread_ptf_plotter.plot(t=last_date,
plot_metrics=plot_metrics,
plot_details=plot_details_flag)
# Plot at multiple dates
calendar_spread_ptf_plotter.plot(t=multiple_valuation_dates,
plot_metrics=plot_metrics)
# Surface plot
calendar_spread_ptf_plotter.plot(t=multiple_valuation_dates,
plot_metrics=plot_metrics,
surf_plot=True)
# Surface plot (rotate) - Underlying value side
calendar_spread_ptf_plotter.plot(t=multiple_valuation_dates,
plot_metrics=plot_metrics,
surf_plot=True,
view=(0, 180))
# Price surface plot (rotate) - Date side
calendar_spread_ptf_plotter.plot(t=multiple_valuation_dates,
plot_metrics=plot_metrics,
surf_plot=True,
view=(0, -90))
def main():
#
# portfolio instantiation example
#
# if np_output is True, the output will be np.ndarray, otherwise pd.DataFrame
np_output = False # True
# default market environment
market_env = MarketEnvironment(t="01-06-2020")
print(market_env)
# underlying values to test
S_vector = [60, 90, 120]
print("S_vector: {}\n".format(S_vector))
# options maturities
T_call = "31-12-2020"
T_put = "30-06-2021"
# options strikes are the same: single-strike portfolio can be evaluated
# at different strike-price too
K = 90
K_put = K
K_call = K
# portfolio options positions
call_pos = 2
put_pos = -5
#
# Step 0: empty portfolio initialized
#
ptf = Portfolio()
print(ptf)
#
# Step 1: adding 2 long plain-vanilla call contracts
#
# plain-vanilla call option
opt1_style = "plain_vanilla" # "digital"
opt1_type = "call" # "put"
call = option_factory(market_env,
opt1_style,
opt1_type,
K=K_call,
T=T_call)
print(call)
# adding contract to portfolio
ptf.add_instrument(call, call_pos)
print(ptf)
#
# Step 2: adding 5 short plain-vanilla put contracts
#
# plain-vanilla put option
opt2_style = "plain_vanilla" # "digital"
opt2_type = "put" # "call"
put = option_factory(market_env, opt2_style, opt2_type, K=K_put, T=T_put)
print(put)
# plain-vanilla put option
put = PlainVanillaOption(market_env, option_type="put", K=K_put, T=T_put)
print(put)
# adding contract to portfolio
ptf.add_instrument(put, put_pos)
print(ptf)
#
# Step 3: portfolio evaluation
#
for case in [
'All_scalar', 'S', 'S.sigma_distributed', 'S.r_distributed',
'S.sigma_and_r_distributed', 'K', 'K.sigma_distributed',
'K.r_distributed', 'K.sigma_and_r_distributed', 't',
't.sigma_distributed', 't.r_distributed',
't.sigma_and_r_distributed', 'S.t',
'S.t.sigma_distributed_as_Sxt_grid',
'S.t.r_distributed_as_Sxt_grid',
'S.t.sigma_and_r_distributed_as_Sxt_grid', 'K.t',
'K.t.sigma_distributed_as_Kxt_grid',
'K.t.r_distributed_as_Kxt_grid',
'K.t.sigma_and_r_distributed_as_Kxt_grid', 't.sigma_axis',
't.r_axis'
]:
# get parameters dictionary for case considered
param_dict, case_info = get_param_dict_single_K_ptf(
ptf,
np_output,
case,
T=min(T_call, T_put, key=date_string_to_datetime_obj))
print("\n--------------------------------------------\n")
print("\n" + case_info + "\n")
print("Parameters:")
print("S: {}".format(param_dict["S"]))
print("K: {}".format(param_dict["K"]))
print("t: {}".format(param_dict["t"]))
print("sigma: {}".format(param_dict["sigma"]))
print("r: {}\n".format(param_dict["r"]))
print("Metrics:")
# metrics to compare
for metrics in [
"price", "PnL", "delta", "theta", "gamma", "vega", "rho"
]:
# portfolio metrics
ptf_metrics = getattr(ptf, metrics)(**param_dict)
print("\nPortfolio {}:\n{}".format(metrics, ptf_metrics))
# verification with benchmark metrics
call_metrics = getattr(call, metrics)(**param_dict)
put_metrics = getattr(put, metrics)(**param_dict)
benchmark_metrics = call_pos * call_metrics + put_pos * put_metrics
print("\nBenchmark {}:\n{}".format(metrics, benchmark_metrics))
# check effective match
diff = (ptf_metrics - benchmark_metrics).astype('float')
num_nonzero_diff = np.count_nonzero(diff) - np.isnan(
diff).sum().sum()
exact_match = True if num_nonzero_diff == 0 else False
print("\nIs replication exact (NaN excluded)? {}\n".format(
exact_match))
def test_ReadURL(self):
# url = "https://www.nseindia.com/api/option-chain-indices?symbol=NIFTY"
# # url = "https://www.nseindia.com/get-quotes/derivatives?symbol=NIFTY&identifier=OPTIDXNIFTY"
# headers = {
# 'User-agent': 'Mozilla/5.0'
# }
# t = requests.get(url,headers=headers)
# # print(t.text)
# test = t.json()
# # path="100%.data.expiryDate"
# # for expiryDate in test.values():
# # try:
# # print(expiryDate)
# # except KeyError:
# # temp="dummy"
#
# print(test["records"]["data"])
with open('C:\\Users\\srepswal\\Desktop\\test.json') as f:
data = json.load(f)
df = pd.DataFrame(list(data["records"]["data"][0:]),columns=['PE','CE']).dropna()
df2 = pd.DataFrame(df['PE'].values.tolist())
df2 = df2[(df2['openInterest']>2000) & (df2['impliedVolatility']>0 )& (df2['totalTradedVolume']>2000 )]
df3 = pd.DataFrame(df['CE'].values.tolist())
df3 = df3[(df3['openInterest'] > 2000) & (df3['impliedVolatility']>0) & (df3['totalTradedVolume']>2000 )]
# print(data["records"]["data"][0]["PE"]["openInterest"])
with pd.ExcelWriter("C:\\Users\\srepswal\\PycharmProjects\\EquityModelling\\Main\\DriverObject\\Trading\\Data\\Option\\02Apr2021\\temp.xlsx", engine='xlsxwriter') as writer:
df2.to_excel(writer, index=False)
with pd.ExcelWriter(
"C:\\Users\\srepswal\\PycharmProjects\\EquityModelling\\Main\\DriverObject\\Trading\\Data\\Option\\02Apr2021\\call.xlsx",
engine='xlsxwriter') as writer:
df3.to_excel(writer, index=False)
callList = []
for index, row in df3.iterrows():
print(row['strikePrice'], row['lastPrice'],row['impliedVolatility'],row['expiryDate'])
t = "26-03-2021"
r = 0.03
S = row['underlyingValue']
sigma = row['impliedVolatility']/100
K = row['strikePrice']
T =(datetime.datetime.strptime(row['expiryDate'], '%d-%b-%Y')).strftime('%d-%m-%Y')
# default market environment
market_env = MarketEnvironment(t, r, S, sigma)
# print(market_env)
# define option style and type
opt_style = "plain_vanilla" # "digital" # "plain_vanilla"
opt_type = "call" # "put"
option = option_factory(market_env, opt_style, opt_type, K, T)
ProjectedPriceList =CalcualtePricesForRange(S, option)
callList.append(
{'OptionType': 'Call', 'StrikePrice': row['strikePrice'], 'Underlying': row['underlyingValue'],
'LastPrice': row['lastPrice'], 'ExpiryDate': row['expiryDate'],
'ProjectedSpot': ProjectedPriceList})
newCalldf= pd.DataFrame(callList).dropna()
with pd.ExcelWriter(
"C:\\Users\\srepswal\\PycharmProjects\\EquityModelling\\Main\\DriverObject\\Trading\\Data\\Option\\02Apr2021\\CallProjected.xlsx",
engine='xlsxwriter') as writer:
newCalldf.to_excel(writer, index=False)
putList = []
for index, row in df2.iterrows():
print(row['strikePrice'], row['lastPrice'], row['impliedVolatility'], row['expiryDate'])
t = "01-04-2021"
r = 0.03
S = row['underlyingValue']
sigma = row['impliedVolatility'] / 100
K = row['strikePrice']
T = (datetime.datetime.strptime(row['expiryDate'], '%d-%b-%Y')).strftime('%d-%m-%Y')
# default market environment
market_env = MarketEnvironment(t, r, S, sigma)
# print(market_env)
# define option style and type
opt_style = "plain_vanilla" # "digital" # "plain_vanilla"
opt_type = "put" # "put"
option = option_factory(market_env, opt_style, opt_type, K, T)
ProjectedPriceList = CalcualtePricesForRange(S, option)
putList.append(
{'OptionType': 'Put', 'StrikePrice': row['strikePrice'], 'Underlying': row['underlyingValue'],
'LastPrice': row['lastPrice'], 'ExpiryDate': row['expiryDate'],
'ProjectedSpot': ProjectedPriceList})
newPutdf = pd.DataFrame(putList).dropna()
with pd.ExcelWriter(
"C:\\Users\\srepswal\\PycharmProjects\\EquityModelling\\Main\\DriverObject\\Trading\\Data\\Option\\02Apr2021\\PutProjected.xlsx",
engine='xlsxwriter') as writer:
newPutdf.to_excel(writer, index=False)
# CalculateMaxLossAndMinProfit(callList,putList)
CalculateLossAndProfit(callList, putList)
def main():
# if np_output is True, the output will be np.ndarray, otherwise pd.DataFrame
np_output = False # True
# default market environment
market_env = MarketEnvironment()
print(market_env)
# define option style and type
opt_style = "plain_vanilla" # "digital" # "plain_vanilla"
opt_type = "call" # "put"
option = option_factory(market_env, opt_style, opt_type)
print(option)
# loop over different cases:
for case in [
"S_scalar_default.t_scalar_default", "S_scalar.t_scalar_dt",
"S_scalar.t_scalar_str", "S_scalar.tau_scalar",
"S_vector.t_scalar_default", "S_scalar_default.t_date_range",
"S_vector.t_date_range", "S_vector.t_str_list",
"S_vector.tau_list",
"S_scalar_default.t_scalar_default.sigma_axis",
"S_scalar_default.t_scalar_default.r_axis"
]:
# get parameters dictionary for case considered
param_dict, case_info = get_param_dict(option, np_output, case)
not_sigma_axis = ('sigma_axis' not in param_dict) or (
param_dict['sigma_axis'] is False)
not_r_axis = ('r_axis'
not in param_dict) or (param_dict['r_axis'] is False)
print("\n--------------------------------------------\n")
print("\n" + case_info + "\n")
print("Parameters:")
print(
"S: {}".format(param_dict["S"] if "S" in
param_dict else str(option.get_S()) + " (default)"))
print(
"K: {}".format(param_dict["K"] if "K" in
param_dict else str(option.get_K()) + " (default)"))
print("t: {}".format(param_dict["t"] if "t" in
param_dict else str(option.get_tau()) +
" (default)"))
print("sigma: {}".format(param_dict["sigma"] if "sigma" in
param_dict else str(option.get_sigma()) +
" (default)"))
print("r: {}\n".format(param_dict["r"] if "r" in
param_dict else str(option.get_r()) +
" (default)"))
print("Metrics:")
print("Payoff:\n", option.payoff(**param_dict))
print("\nPrice upper limit:\n", option.price_upper_limit(**param_dict))
print("\nPrice lower limit:\n", option.price_lower_limit(**param_dict))
print("\nPrice:\n", option.price(**param_dict))
print("\nP&L:\n", option.PnL(**param_dict))
print("\nDelta:\n", option.delta(**param_dict))
print("\nTheta:\n", option.theta(**param_dict))
print("\nGamma:\n", option.gamma(**param_dict))
print("\nVega:\n", option.vega(**param_dict))
print("\nRho:\n", option.rho(**param_dict))
# Implied volatility calculation is not implemented for x-axis (columns)
# spanned by parameters different from S or K (like sigma or r)
if not_sigma_axis and not_r_axis:
print("\nExpected Implied Volatility: \n{}\n".format(
option.get_sigma()))
print("\nImplied Volatility - Newton method:\n{}\n".format(
option.implied_volatility(**param_dict)))
param_dict["minimization_method"] = "Least-Squares"
print(
"\nImplied Volatility - Least-Squares constrained method:\n{}\n"
.format(option.implied_volatility(**param_dict)))
def CreateSpotListsWithGreeks(OptionType, currSymbol="Sheet1"):
data = pd.read_excel(OptionsConstants.Location + "\\" +
OptionsConstants.TradeDate + "\\" + OptionType + "_" +
OptionsConstants.FileSuffix + ".xlsx",
sheet_name=currSymbol)
OutputList = []
# OutputList[OptionType+'_Delta']=0.000000
# OutputList[OptionType + '_Theta'] = 0.000000
# OutputList[OptionType + '_Gamma'] = 0.000000
# OutputList[OptionType + '_Vega'] = 0.000000
# OutputList[OptionType + '_Rho'] = 0.000000
for index, row in data.iterrows():
t = OptionsConstants.TradeDate
r = 0.1
S = row['underlyingValue']
sigma = float((row['impliedVolatility']) / 100)
K = row['strikePrice']
T = (datetime.datetime.strptime(row['expiryDate'],
'%d-%b-%Y')).strftime('%d-%m-%Y')
# default market environment
market_env = MarketEnvironment(t, r, S, sigma)
# define option style and type
opt_style = "plain_vanilla" # "digital" # "plain_vanilla"
opt_type = OptionType
option = option_factory(market_env, opt_style, opt_type, K, T)
Delta = option.delta()
Theta = option.theta()
Gamma = option.gamma()
Vega = option.vega()
Rho = option.rho()
OutputList.append({
'OptionType':
OptionType,
'StrikePrice':
row['strikePrice'],
'Underlying':
row['underlyingValue'],
'LastPrice':
row['lastPrice'],
'ExpiryDate':
row['expiryDate'],
OptionType + '_Delta':
'%.4f' % float(option.delta()[0]),
OptionType + '_Theta':
'%.4f' % float(option.theta()[0]),
OptionType + '_Gamma':
'%.4f' % float(option.gamma()[0]),
OptionType + '_Vega':
'%.4f' % float(option.vega()[0]),
OptionType + '_Rho':
'%.4f' % float(option.rho()[0])
})
newCalldf = pd.DataFrame(OutputList).dropna()
filemode = 'w'
if (os.path.exists(OptionsConstants.Location + "\\" +
OptionsConstants.TradeDate + "\\" + OptionType +
"Greeks" + OptionsConstants.FileSuffix + ".xlsx")):
filemode = 'a'
with pd.ExcelWriter(OptionsConstants.Location + "\\" +
OptionsConstants.TradeDate + "\\" + OptionType +
"Greeks" + OptionsConstants.FileSuffix + ".xlsx",
engine='openpyxl',
mode=filemode) as writer:
newCalldf.to_excel(writer, sheet_name=currSymbol, index=False)
return (OutputList)
def main():
#
# Black-Scholes implied volatility calculation with user-defined 'sigma'
# parameter surface, used to evaluate the quality of the implied volatility
# calculation.
#
# output format: pd.DataFrame
np_output = False
# default market environment
market_env = MarketEnvironment()
print(market_env)
# define option style and type
opt_style = "plain_vanilla" # "digital"
opt_type = "call" # "call" # "put"
option = option_factory(market_env, opt_style, opt_type)
print(option)
# K
K_vector = [50, 75, 100, 125, 150]
# tau: a date-range of 5 valuation dates between t and T-10d
n = 6
valuation_date = option.get_t()
expiration_date = option.get_T()
t_vector = pd.date_range(start=valuation_date,
end=expiration_date - pd.Timedelta(days=25),
periods=n)
# sigma (qualitatively reproducing the smile)
k, tau = np.meshgrid(K_vector, option.time_to_maturity(t=t_vector))
sigma_grid_K = 0.01 + ((k - 100) ** 2) / (100 * k) / tau
# pricing parameters
param_dict = {"S": 100,
"K": K_vector,
"t": t_vector,
"sigma": sigma_grid_K,
"r": 0.01,
"np_output": np_output}
print("Parameters:")
print("S: {}".format(param_dict["S"]))
print("K: {}".format(param_dict["K"]))
print("t: {}".format(param_dict["t"]))
print("sigma: \n{}".format(param_dict["sigma"]))
print("r: {}\n".format(param_dict["r"]))
# expected implied volatility: is the 'sigma' parameter with which the
# target price has been generated
expected_IV = pd.DataFrame(data=param_dict["sigma"],
columns=K_vector,
index=t_vector)
expected_IV.rename_axis('K', axis='columns', inplace=True)
expected_IV.rename_axis('t', axis='rows', inplace=True)
print("\nExpected Kxt Implied volatility Surface: \n", expected_IV)
#
# Without target_price in input: param_dict['sigma'] parameter is
# used to construct target price, used in minimization
#
print("\n--- WITHOUT target_price in input ---\n")
# newton method
param_dict["minimization_method"] = "Newton"
newton_IV = option.implied_volatility(**param_dict)
RMSE_newton = np.sqrt(np.nanmean((newton_IV - expected_IV) ** 2))
RMSRE_newton = np.sqrt(np.nanmean(((newton_IV - expected_IV) / expected_IV) ** 2))
print("\nImplied Volatility - Newton method - Metrics (NaN excluded): RMSE={:.1E}, RMSRE={:.1E}:\n"
.format(RMSE_newton, RMSRE_newton), newton_IV)
# Least-Squares method
param_dict["minimization_method"] = "Least-Squares"
ls_IV = option.implied_volatility(**param_dict)
RMSE_ls = np.sqrt(np.nanmean((ls_IV - expected_IV) ** 2))
RMSRE_ls = np.sqrt(np.nanmean(((ls_IV - expected_IV) / expected_IV) ** 2))
print(
"\nImplied Volatility - Least-Squares constrained method - Metrics (NaN excluded): RMSE={:.1E}, RMSRE={:.1E}:\n"
.format(RMSE_ls, RMSRE_ls), ls_IV)
#
# With target_price in input: target_price, but no param_dict['sigma'],
# is used in minimization.
#
print("\n--- WITH target_price in input ---\n")
# compute target price
target_price = option.price(**param_dict)
print("\nTarget Price in input: \n", target_price)
# Add target_price to parameters dictionary:
param_dict['target_price'] = target_price
# newton method
param_dict["minimization_method"] = "Newton"
newton_IV = option.implied_volatility(**param_dict)
RMSE_newton = np.sqrt(np.nanmean((newton_IV - expected_IV) ** 2))
RMSRE_newton = np.sqrt(np.nanmean(((newton_IV - expected_IV) / expected_IV) ** 2))
print("\nImplied Volatility - Newton method - Metrics (NaN excluded): RMSE={:.1E}, RMSRE={:.1E}:\n"
.format(RMSE_newton, RMSRE_newton), newton_IV)
# Least-Squares method
param_dict["minimization_method"] = "Least-Squares"
ls_IV = option.implied_volatility(**param_dict)
RMSE_ls = np.sqrt(np.nanmean((ls_IV - expected_IV) ** 2))
RMSRE_ls = np.sqrt(np.nanmean(((ls_IV - expected_IV) / expected_IV) ** 2))
print(
"\nImplied Volatility - Least-Squares constrained method - Metrics (NaN excluded): RMSE={:.1E}, RMSRE={:.1E}:\n"
.format(RMSE_ls, RMSRE_ls), ls_IV)
