Portfolio simulation summary

import pickle
from simple_back_test import *

import pandas as pd
import numpy as np

ff = "../../data/portfolio_simulations.pickle"
runs_per_cap = pickle.load( open(ff, 'rb'))
all_summaries = convert_to_summary_df(runs_per_cap)

all_summaries.head()
all_summaries['cc'] = all_summaries['capital'].astype('category')

all_summaries.groupby('cc').apply( lambda x: display(x.describe()))

	average_positions	average_position_size	average_portfolio_return	portfolio_std	cumulative_return	max_drawdown	sharpe	annualised_ret	annual_volatility	sortino	downside_risk	tail_risk	capital
count	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	7.000000	100.0
mean	11.227572	76261.179200	0.003186	0.029285	2.923035	-0.112575	1.738981	1.067099	0.464886	12.285392	0.087883	inf	50000.0
std	2.387138	16357.811663	0.000859	0.005941	1.334308	0.054738	0.424342	0.390467	0.094315	9.728410	0.037878	NaN	0.0
min	6.881720	46324.010802	0.000388	0.017816	0.090291	-0.279671	0.310099	0.047962	0.282812	0.459498	0.017895	inf	50000.0
25%	9.677419	64819.824511	0.002654	0.025122	1.935018	-0.142298	1.478694	0.792317	0.398802	6.436859	0.062798	inf	50000.0
50%	10.967742	74948.548828	0.003254	0.028624	2.854072	-0.109124	1.740068	1.077449	0.454388	10.405729	0.083313	NaN	50000.0
75%	12.903226	86202.950005	0.003707	0.031923	3.601407	-0.071364	2.003689	1.286883	0.506764	15.333557	0.111164	inf	50000.0
max	17.204301	119485.022168	0.005179	0.049274	6.619313	-0.018933	3.038153	2.005671	0.782194	72.937218	0.212767	inf	50000.0

	average_positions	average_position_size	average_portfolio_return	portfolio_std	cumulative_return	max_drawdown	sharpe	annualised_ret	annual_volatility	sortino	downside_risk	tail_risk	capital
count	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	7.000000	100.0
mean	11.227572	76261.179200	0.003186	0.029285	2.923035	-0.112575	1.738981	1.067099	0.464886	12.285392	0.087883	inf	50000.0
std	2.387138	16357.811663	0.000859	0.005941	1.334308	0.054738	0.424342	0.390467	0.094315	9.728410	0.037878	NaN	0.0
min	6.881720	46324.010802	0.000388	0.017816	0.090291	-0.279671	0.310099	0.047962	0.282812	0.459498	0.017895	inf	50000.0
25%	9.677419	64819.824511	0.002654	0.025122	1.935018	-0.142298	1.478694	0.792317	0.398802	6.436859	0.062798	inf	50000.0
50%	10.967742	74948.548828	0.003254	0.028624	2.854072	-0.109124	1.740068	1.077449	0.454388	10.405729	0.083313	NaN	50000.0
75%	12.903226	86202.950005	0.003707	0.031923	3.601407	-0.071364	2.003689	1.286883	0.506764	15.333557	0.111164	inf	50000.0
max	17.204301	119485.022168	0.005179	0.049274	6.619313	-0.018933	3.038153	2.005671	0.782194	72.937218	0.212767	inf	50000.0

	average_positions	average_position_size	average_portfolio_return	portfolio_std	cumulative_return	max_drawdown	sharpe	annualised_ret	annual_volatility	sortino	downside_risk	tail_risk	capital
count	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	85.000000	100.0
mean	22.095755	152105.583576	0.003102	0.020401	2.997059	-0.075536	2.438061	1.099897	0.323859	17.271529	0.060525	inf	100000.0
std	4.202118	29768.122340	0.000614	0.003056	1.099452	0.055530	0.470058	0.312612	0.048510	11.947575	0.028424	NaN	0.0
min	10.967742	75796.184773	0.001644	0.014937	0.913678	-0.344750	1.117171	0.421526	0.237123	2.690172	0.015151	inf	100000.0
25%	19.386123	133127.178638	0.002590	0.018518	2.066658	-0.094928	2.085404	0.835444	0.293967	9.897639	0.042804	NaN	100000.0
50%	21.935484	151008.499414	0.003170	0.020063	2.992918	-0.060960	2.457380	1.117651	0.318490	13.571511	0.059796	NaN	100000.0
75%	24.354839	169166.858804	0.003417	0.021696	3.471873	-0.042117	2.789298	1.251763	0.344417	21.010622	0.071644	NaN	100000.0
max	34.838710	243069.963378	0.004669	0.032978	6.175693	-0.015566	3.491875	1.909531	0.523504	61.634420	0.169604	inf	100000.0

	average_positions	average_position_size	average_portfolio_return	portfolio_std	cumulative_return	max_drawdown	sharpe	annualised_ret	annual_volatility	sortino	downside_risk	tail_risk	capital
count	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	1.000000e+02	100.0
mean	57.014906	397606.320751	0.003110	0.012709	3.125492	-0.037299	3.894212	1.149219	0.201744	23.192545	0.037259	inf	250000.0
std	6.076068	42456.304857	0.000352	0.000990	0.660183	0.017999	0.423458	0.186585	0.015720	8.662841	0.010520	NaN	0.0
min	39.784946	280732.172990	0.002077	0.010466	1.530757	-0.122648	2.636441	0.654001	0.166145	8.166482	0.017061	6.247094e+00	250000.0
25%	53.118280	370699.642483	0.002867	0.012096	2.637426	-0.044308	3.607025	1.013326	0.192012	16.905889	0.030618	7.239463e+01	250000.0
50%	56.881720	397630.079189	0.003123	0.012659	3.091242	-0.032778	3.877471	1.145784	0.200956	22.243098	0.035643	1.149488e+14	250000.0
75%	60.752688	424563.055292	0.003352	0.013125	3.550711	-0.026424	4.179165	1.273194	0.208349	27.362399	0.043842	2.340766e+14	250000.0
max	69.462366	484395.413443	0.003892	0.016192	4.841764	-0.010759	4.760423	1.602660	0.257042	54.078001	0.067758	inf	250000.0

	average_positions	average_position_size	average_portfolio_return	portfolio_std	cumulative_return	max_drawdown	sharpe	annualised_ret	annual_volatility	sortino	downside_risk	tail_risk	capital
count	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.0
mean	108.142173	757858.917202	0.003004	0.008665	2.974238	-0.024289	5.517803	1.109975	0.137545	29.262258	0.027377	8.434089	500000.0
std	5.975605	42080.579071	0.000225	0.000540	0.414133	0.009613	0.447654	0.118912	0.008571	8.139811	0.005945	3.278267	0.0
min	94.838710	657017.077313	0.002479	0.007387	2.106593	-0.067696	4.318615	0.848359	0.117259	14.526830	0.014125	4.174481	500000.0
25%	104.731183	733825.599755	0.002865	0.008225	2.718569	-0.027682	5.259526	1.037539	0.130569	23.953173	0.023157	6.215086	500000.0
50%	107.982749	757549.169910	0.003017	0.008654	2.971819	-0.022286	5.522452	1.114192	0.137384	27.795763	0.027407	7.598986	500000.0
75%	112.365591	784202.891142	0.003140	0.009025	3.212689	-0.018426	5.836667	1.180731	0.143268	34.350296	0.030205	9.980531	500000.0
max	123.870968	878138.554750	0.003627	0.010137	4.252630	-0.010073	6.492623	1.461723	0.160922	62.807220	0.048422	25.779303	500000.0

from plotnine import *

ggplot(all_summaries, aes('annualised_ret')) + geom_density(aes(color = 'cc'), alpha = 0.5)

/home/sergiusz/anaconda3/envs/whitelist/lib/python3.6/site-packages/plotnine/utils.py:281: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  ndistinct = ids.apply(len_unique, axis=0).as_matrix()
/home/sergiusz/anaconda3/envs/whitelist/lib/python3.6/site-packages/pandas/core/generic.py:4384: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.
  object.__getattribute__(self, name)
/home/sergiusz/anaconda3/envs/whitelist/lib/python3.6/site-packages/pandas/core/generic.py:4385: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.
  return object.__setattr__(self, name, value)

<ggplot: (8747888107882)>

ggplot(all_summaries, aes('annualised_ret')) + geom_histogram(aes(fill = 'cc')) + facet_wrap('~cc')

/home/sergiusz/anaconda3/envs/whitelist/lib/python3.6/site-packages/plotnine/utils.py:281: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  ndistinct = ids.apply(len_unique, axis=0).as_matrix()
/home/sergiusz/anaconda3/envs/whitelist/lib/python3.6/site-packages/plotnine/stats/stat_bin.py:90: UserWarning: 'stat_bin()' using 'bins = 27'. Pick better value with 'binwidth'.
  warn(msg.format(params['bins']))
/home/sergiusz/anaconda3/envs/whitelist/lib/python3.6/site-packages/pandas/core/generic.py:4384: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.
  object.__getattribute__(self, name)
/home/sergiusz/anaconda3/envs/whitelist/lib/python3.6/site-packages/pandas/core/generic.py:4385: FutureWarning: Attribute 'is_copy' is deprecated and will be removed in a future version.
  return object.__setattr__(self, name, value)
/home/sergiusz/anaconda3/envs/whitelist/lib/python3.6/site-packages/plotnine/positions/position.py:188: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  intervals = data[xminmax].drop_duplicates().as_matrix().flatten()

<ggplot: (-9223363289036985933)>