Financial derivatives and portfolio analysis tools for python
- Free software: MIT license
- Documentation: https://derpy.readthedocs.io.
- PyPi: https://pypi.org/project/derpy/
- Gitub: https://github.com/rjdscott/derpy
to include the module in your project, you can simply use pip install derpy then in your python project
import derpy
print(derpy.__version__) # returns '0.0.1'from derpy import bond as bd
px = 95.0428
face_val = 100.0
mat = 1.5
cpn_frq = 2
cpn_rate = 5.25
ytm = 5.5
print(' Price: {}'.format(bd.bond_price(face_val, mat, ytm, cpn_rate, cpn_frq)))
print(' Yield: {}'.format(bd.bond_ytm(px, face_val, mat, cpn_rate, cpn_frq)))
print(' ModDur: {}'.format(bd.bond_duration(px, face_val, mat, cpn_rate, cpn_frq)[0]))
print(' MacDur: {}'.format(bd.bond_duration(px, face_val, mat, cpn_rate, cpn_frq)[1]))
print('Convexity: {}'.format(bd.bond_convexity(px, face_val, mat, cpn_rate, cpn_frq)))from derpy.options import black_scholes_merton as bsm
# usage method 1: use function wrapper
input = ['call', 20, 21, 0.20, 0.1, 0.0002, 0]
call_price = bsm.option_pricing(bsm.euro_option, input)
call_gamma = bsm.option_pricing(bsm.gamma, input)
# usage method 2: call individual functions
put_price = bsm.euro_option('put', 20, 21, 0.2, 0.1, 0.0002) # div_yield is optional
put_gamma = bsm.gamma('put', 20, 21, 0.2, 0.1, 0.0002, 0.0001)
print(call_price) # return 0.16384395..
print(call_gamma) # return 0.23993880..
print(put_price) # return 1.16342..
print(put_gamma) # return 0.2399107..from derpy import portfolio as pt
securities = ['AAA', 'BBB']
positions = [[11, 10], [12, 10], [13, 10], [13, 11], [13, 12]]
prices = [[10, 10], [11, 10], [12, 10], [12, 10], [12, 10]]
dates = ['2018-07-01', '2018-08-01', '2018-09-01', '2018-10-01', '2018-11-01']
df_positions = pd.DataFrame(data=positions, columns=securities, index=dates)
df_prices = pd.DataFrame(data=prices, columns=securities, index=dates)
p = pt.Portfolio(names=securities, positions=df_positions, prices=df_prices)
print(p.sec_values())
print(p.sec_weights())
print(p.portfolio_value())
print(p.portfolio_returns())