{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Whitelist portfolio simulation"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"from simple_back_test import *\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"import pickle\n",
"from plotnine import *\n",
"from dfply import *"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"ff = \"../../data/portfolio_simulations_many.pickle\"\n",
"br_runs = pickle.load(open(ff, 'rb'))"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"runs = all_runs_to_df(br_runs)"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"runs = runs >> arrange('annualised_ret', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def table_and_chart(df, gg):\n",
" df[gg] = df[gg].astype('category')\n",
" summarised = (df >> \n",
" group_by(gg) >> \n",
" summarise(\n",
" max_ann_ret = colmax(X.annualised_ret),\n",
" mean_ann_ret = mean(X.annualised_ret), \n",
" min_ann_ret = colmin(X.annualised_ret),\n",
" mean_sharpe = mean(X.sharpe), \n",
" mean_total_tracks=mean(X.total_number_tracks)))\n",
" \n",
" display(summarised)\n",
" print(ggplot(df, aes('annualised_ret')) + geom_density(aes(color = gg), alpha = 0.5))\n",
" return summarised\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare difference max prices"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"max_price_diffs = runs >> mask(X.execution_slippage == 0.1, X.clip_individual_returns == 3, X.new_streams_per_date == 1/7)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" max_price | \n",
" max_ann_ret | \n",
" mean_ann_ret | \n",
" min_ann_ret | \n",
" mean_sharpe | \n",
" mean_total_tracks | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 10000.0 | \n",
" 0.557796 | \n",
" 0.314418 | \n",
" 0.135382 | \n",
" 2.316898 | \n",
" 52.00 | \n",
"
\n",
" \n",
" | 1 | \n",
" 20000.0 | \n",
" 0.747912 | \n",
" 0.388193 | \n",
" 0.175065 | \n",
" 2.073243 | \n",
" 51.96 | \n",
"
\n",
" \n",
" | 2 | \n",
" 50000.0 | \n",
" 1.110146 | \n",
" 0.683299 | \n",
" 0.189538 | \n",
" 2.237604 | \n",
" 50.66 | \n",
"
\n",
" \n",
"
\n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"a = table_and_chart(max_price_diffs, 'max_price')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare differences for how many tracks we can sign"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" new_streams_per_date | \n",
" max_ann_ret | \n",
" mean_ann_ret | \n",
" min_ann_ret | \n",
" mean_sharpe | \n",
" mean_total_tracks | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.142857 | \n",
" 1.110146 | \n",
" 0.683299 | \n",
" 0.189538 | \n",
" 2.237604 | \n",
" 50.66 | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.285714 | \n",
" 1.532392 | \n",
" 0.996685 | \n",
" 0.351331 | \n",
" 2.731044 | \n",
" 87.11 | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.428571 | \n",
" 1.754954 | \n",
" 1.186065 | \n",
" 0.716061 | \n",
" 2.988296 | \n",
" 108.60 | \n",
"
\n",
" \n",
"
\n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"signed_deals_diff = runs >> mask(X.execution_slippage == 0.1, X.clip_individual_returns == 3, X.max_price == 50000)\n",
"a = table_and_chart(signed_deals_diff, 'new_streams_per_date')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare clipping"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" clip_individual_returns | \n",
" max_ann_ret | \n",
" mean_ann_ret | \n",
" min_ann_ret | \n",
" mean_sharpe | \n",
" mean_total_tracks | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 3 | \n",
" 1.754954 | \n",
" 1.186065 | \n",
" 0.716061 | \n",
" 2.988296 | \n",
" 108.60 | \n",
"
\n",
" \n",
" | 1 | \n",
" 5 | \n",
" 2.981609 | \n",
" 1.885412 | \n",
" 0.937845 | \n",
" 2.895200 | \n",
" 118.92 | \n",
"
\n",
" \n",
"
\n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"df = runs >> mask(X.execution_slippage == 0.1, X.new_streams_per_date == 3/7, X.max_price == 50000)\n",
"a = table_and_chart(df, 'clip_individual_returns')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare execution slippage"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" execution_slippage | \n",
" max_ann_ret | \n",
" mean_ann_ret | \n",
" min_ann_ret | \n",
" mean_sharpe | \n",
" mean_total_tracks | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.05 | \n",
" 1.817067 | \n",
" 1.258926 | \n",
" 0.681593 | \n",
" 3.084556 | \n",
" 109.61 | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.10 | \n",
" 1.754954 | \n",
" 1.186065 | \n",
" 0.716061 | \n",
" 2.988296 | \n",
" 108.60 | \n",
"
\n",
" \n",
"
\n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"df = runs >> mask(X.clip_individual_returns == 3, X.new_streams_per_date == 3/7, X.max_price == 50000)\n",
"a = table_and_chart(df, 'execution_slippage')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Best portolio"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"artist_advance_fraction 0.800000\n",
"capital 250000.000000\n",
"clip_individual_returns 5.000000\n",
"duration_days 365.000000\n",
"execution_slippage 0.100000\n",
"max_price 50000.000000\n",
"min_price 5000.000000\n",
"new_streams_per_date 0.428571\n",
"payment_per_stream 0.005000\n",
"pop_5_slippage 0.150000\n",
"average_positions 25.382932\n",
"average_position_size 389819.712787\n",
"average_portfolio_return 0.006188\n",
"portfolio_std 0.042138\n",
"cumulative_return 11.251843\n",
"total_number_tracks 116.000000\n",
"max_drawdown -0.047787\n",
"sharpe 2.331084\n",
"annualised_ret 2.981609\n",
"annual_volatility 0.668921\n",
"sortino 42.560849\n",
"downside_risk 0.036637\n",
"tail_risk 45.771678\n",
"Name: 3549, dtype: float64"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runs.iloc[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Actual opportunities"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Number of tracks | \n",
" Min streams | \n",
" Mean streams | \n",
" Median streams | \n",
" Max Streams | \n",
"
\n",
" \n",
" | Artist_Advance | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | <2k | \n",
" 1248 | \n",
" 10 | \n",
" 1.082879e+05 | \n",
" 37760.5 | \n",
" 581970 | \n",
"
\n",
" \n",
" | 5k | \n",
" 171 | \n",
" 588900 | \n",
" 9.344912e+05 | \n",
" 882289.0 | \n",
" 1466000 | \n",
"
\n",
" \n",
" | 10k | \n",
" 93 | \n",
" 1487000 | \n",
" 2.117939e+06 | \n",
" 2039966.0 | \n",
" 2897000 | \n",
"
\n",
" \n",
" | 20k | \n",
" 65 | \n",
" 2949000 | \n",
" 4.042791e+06 | \n",
" 3997000.0 | \n",
" 5767000 | \n",
"
\n",
" \n",
" | 30k | \n",
" 40 | \n",
" 6022000 | \n",
" 8.885636e+06 | \n",
" 8481000.0 | \n",
" 14650000 | \n",
"
\n",
" \n",
" | >50k | \n",
" 64 | \n",
" 14884400 | \n",
" 7.826420e+07 | \n",
" 29884500.0 | \n",
" 740764000 | \n",
"
\n",
" \n",
"
\n",
"