for the derivatives_portfolio object just defined:
In [ 23 ]: portfolio.get_statistics()
Out[23]:
name quant. value curr. pos_value pos_delta pos_vega
0 eur_call_pos 5 2.814638 EUR 14.073190 3.3605 42.7900
1 am_put_pos 3 4.472021 EUR 13.416063 -2.0895 30.5181
The sum of the position values, Deltas, and Vegas is also easily calculated. This portfolio
is slightly long Delta (almost neutral) and long Vega:
In [ 24 ]: portfolio.get_statistics()[[‘pos_value’, ‘pos_delta’, ‘pos_vega’]].sum()
# aggregate over all positions
Out[24]: pos_value 27.489253
pos_delta 1.271000
pos_vega 73.308100
dtype: float64
A complete overview of all positions is conveniently obtained by the get_positions
method — such output can, for example, be used for reporting purposes (but is omitted
here due to reasons of space):
In [ 25 ]: portfolio.get_positions()
Of course, you can also access and use all (simulation, valuation, etc.) objects of the
derivatives_portfolio object in direct fashion:
In [ 26 ]: portfolio.valuation_objects[‘am_put_pos’].present_value()
Out[26]: 4.450573
In [ 27 ]: portfolio.valuation_objects[‘eur_call_pos’].delta()
Out[27]: 0.6498
This derivatives portfolio valuation is conducted based on the assumption that the risk
factors are not correlated. This is easily verified by inspecting two simulated paths, one for
each simulation object:
In [ 28 ]: path_no = 777
path_gbm = portfolio.underlying_objects[‘gbm’].get_instrument_values()[
:, path_no]
path_jd = portfolio.underlying_objects[‘jd’].get_instrument_values()[
:, path_no]
Figure 18-1 shows the selected paths in direct comparison — no jump occurs for the jump
diffusion:
In [ 29 ]: import matplotlib.pyplot as plt
%matplotlib inline
In [ 30 ]: plt.figure(figsize=( 7 , 4 ))
plt.plot(portfolio.time_grid, path_gbm, ‘r’, label=‘gbm’)
plt.plot(portfolio.time_grid, path_jd, ‘b’, label=‘jd’)
plt.xticks(rotation= 30 )
plt.legend(loc= 0 ); plt.grid(True)
