quant23rg 0.0.1

Package for quantitative analysis

Welcome on the quant23rg package

quant23rg is python package for quantitative analysis

Install it by typing this command in your command prompt

pip install quant23rg

The package is still in progress

Author : Adrien Calas - Le23RayGorbella (Freelancer)

Click here to contact by mail, Location : Paris and Nice, France

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Available : european options pricing, implied volatilities, geometrical brownian motion for assets and portfolios, value at risk & conditional value at risk (work in progress for portfolio)

European options pricing

from pricingCallEuropBS import PricingCallEuropBS
from pricingPutEuropBS import PricingPutEuropBS


import pandas as pd
from implied_volatility import ImpliedVolatility
from pricingCallEuropBS import PricingCallEuropBS
from pricingPutEuropBS import PricingPutEuropBS

spy_opt = pd.read_csv(
    "spy-options.csv",
).dropna()
spy_opt.columns = [
    col + "_call" if ".1" not in col and col != "Strike" else col
    for col in spy_opt.columns
]
spy_opt.columns = [col.replace(".1", "_put") for col in spy_opt.columns]
spy_opt["IV_call"] = spy_opt["IV_call"].apply(lambda x: float(x.replace("%", "")) / 100)
spy_opt["IV_put"] = spy_opt["IV_put"].apply(lambda x: float(x.replace("%", "")) / 100)
spy_opt["Last_call"] = spy_opt["Last_call"].apply(lambda x: float(x))
spy_opt["Last_put"] = spy_opt["Last_put"].apply(lambda x: float(x))
call_example = spy_opt.sort_values("Volume_call", ascending=False).iloc[0]

# Last_call              27.9
# Bid_call              27.85
# Ask_call              28.18
# Change_call            27.9
# Volume_call             962
# Open Int_call         2,741
# IV_call              0.1815
# Last Trade_call    05/26/23
# Strike                410.0
# Last_put              12.45
# Bid_put               12.24
# Ask_put                12.4
# Change_put            12.45
# Volume_put            1,234
# Open Int_put         20,292
# IV_put               0.1831
# Last Trade_put     05/26/23
# Name: 47, dtype: object

s0 = 420.02  # 29 may 2023
dt = 146 / 252  # in years, expiration date : 20 october 2023
call_pricing = PricingCallEuropBS(
    s0=s0,
    strike=call_example.Strike,
    dt=dt,
    interest_rate=0.05 * dt,  # fed rate multiplied by our period of time
    volatility=0.1326,
)
call_pricing.payoff_sigma_fixed()


put_pricing = PricingPutEuropBS(
    s0=s0,
    strike=call_example.Strike,
    dt=dt,
    interest_rate=0.05 * dt,  # fed rate multiplied by our period of time
    volatility=0.1326,
)
put_pricing.payoff_sigma_fixed()

Implied volatilities

### See volatility smile (Strike -> IV(Strike)) ###

ivs = ImpliedVolatility(
    s0=s0,
    strike=call_example.Strike,
    dt=dt,
    interest_rate=0.05 * dt,  # fed rate  multiplied by our period of time
    volatility=0.1326,
)

ivs.show_implied_vol_and_compare(
    marketPrices_call=spy_opt["Last_call"].tolist(),
    marketPrices_put=spy_opt["Last_put"].tolist(),
    strikes=spy_opt.Strike.tolist(),
    marketVols=spy_opt.IV_call.tolist(),
)

Geometrical Brownian Motion for assets and portfolios

from pricingGBM import PricingGBM, PricingGBMPortfolio
import yfinance as yf


sp500 = yf.Ticker("^GSPC")
hist_sp500 = sp500.history("1y")
hist_sp500["returns"] = hist_sp500.Close / hist_sp500.Close.shift(1) - 1
returns_serie = hist_sp500.dropna().returns

#################################################


### Pricing with Geometric Brownian Motion ###
pricer = PricingGBM.create_pricing_GBM_from_time_series(
    hist_sp500.Close,
    nb_steps=100,
    maturity=1,
    returns=returns_serie,
    nb_simulations=1000,
)
pricer.simulate_and_see()

Value at Risk & Conditional Value at Risk (work in progress for portfolio)

### Value at Risk (one asset) ###
from riskManagement import RiskManagementOneAsset

rkManageHistoric = RiskManagementOneAsset(returns_serie, input_type="returns")
rkManageNormal = RiskManagementOneAsset(
    returns_serie,
    mode="normal",
)
rkManageMonteCarlo = RiskManagementOneAsset(
    hist_sp500.Close, "Monte-Carlo", returns=returns_serie, input_type="value"
)

print(
    f"VaR Historic 1 day : {rkManageHistoric.value_at_risk(nb_past_days=len(returns_serie),confiance=.95, nb_days=1 )}"
)
print(
    f"VaR Normal 1 day  : {rkManageNormal.value_at_risk(nb_past_days=len(returns_serie),confiance=.95, nb_days=1 )}"
)
print(
    f"VaR Monte-Carlo 1 day : {rkManageMonteCarlo.value_at_risk(nb_past_days=len(returns_serie),confiance=.95, nb_days=1 )}"
)

print(
    f"Cond-VaR Historic 1 day : {rkManageHistoric.conditional_value_at_risk(nb_past_days=len(returns_serie),confiance=.95, nb_days=1 )}"
)
print(
    f"Cond-VaR Normal 1 day  : {rkManageNormal.conditional_value_at_risk(nb_past_days=len(returns_serie),confiance=.95, nb_days=1 )}"
)
print(
    f"Cond-VaR Monte-Carlo 1 day : {rkManageMonteCarlo.conditional_value_at_risk(nb_past_days=len(returns_serie),confiance=.95, nb_days=1 )}"
)


#################################################


### Value at Risk multiple assets (Cholesky) ###

import yfinance as yf
import numpy as np
from riskManagement import RiskManagementPortfolio

## Datas for the test
sp500 = yf.Ticker("^GSPC")
hist_sp500 = sp500.history("1y")
aapl = yf.Ticker("AAPL")
hist_aapl = aapl.history("1y")
hist_sp500["returns"] = hist_sp500.Close / hist_sp500.Close.shift(1) - 1
###############################@


pf_risk_Test = RiskManagementPortfolio.init_and_instantiate_RK_PF(
    portfolio_time_series=np.array(
        [
            hist_sp500.Close.tolist(),
            hist_aapl.Close.tolist(),
        ]
    ),
    interest_free_rate=0.05 / 252,
    mode="Monte-Carlo",
    nb_days=1,
    input_type="value",
)
print("Correlation matrix :")
print(pf_risk_Test.correlation_matrix)
print(
    "Value-at-risk 1 day",
    pf_risk_Test.value_at_risk(
        nb_past_days=1,  # not important here
        confiance=0.95,
    ),
)

pf_risk_Test = RiskManagementPortfolio.init_and_instantiate_RK_PF(
    portfolio_time_series=np.array(
        [
            hist_sp500.Close.tolist(),
            hist_aapl.Close.tolist(),
        ]
    ),
    interest_free_rate=0.05 / 252,
    mode="normal",
    nb_days=1,
    input_type="value",
)
print("Correlation matrix :")
print(pf_risk_Test.correlation_matrix)
print(
    "Value-at-risk 1 day",
    pf_risk_Test.value_at_risk(
        nb_past_days=1,  # not important here
        confiance=0.95,
    ),
)

################################################