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msyrs/notebooks/funcwise/bdate_range_util.ipynb
Palash Tyagi cfbd54be7a add notebook with some benchmarks
2025-04-13 11:16:03 +01:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# ! uv pip install E:\\Work\\ruzt\\msyrs --upgrade"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Python packages\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import macrosynergy\n",
"import pandas as pd\n",
"import numpy as np\n",
"import polars as pl\n",
"import os\n",
"import time\n",
"\n",
"from macrosynergy.panel import view_timelines\n",
"from macrosynergy.management.types import QuantamentalDataFrame\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Python bindings - `msyrs`\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import msyrs"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bdates</th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2000-01-03</td>\n",
" <td>2000-01-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2000-01-10</td>\n",
" <td>2000-01-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2000-01-17</td>\n",
" <td>2000-01-17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2000-01-24</td>\n",
" <td>2000-01-24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2000-01-31</td>\n",
" <td>2000-01-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1056</th>\n",
" <td>2020-03-30</td>\n",
" <td>2020-03-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1057</th>\n",
" <td>2020-04-06</td>\n",
" <td>2020-04-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1058</th>\n",
" <td>2020-04-13</td>\n",
" <td>2020-04-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1059</th>\n",
" <td>2020-04-20</td>\n",
" <td>2020-04-20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1060</th>\n",
" <td>2020-04-27</td>\n",
" <td>2020-04-27</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1061 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" bdates 0\n",
"0 2000-01-03 2000-01-03\n",
"1 2000-01-10 2000-01-10\n",
"2 2000-01-17 2000-01-17\n",
"3 2000-01-24 2000-01-24\n",
"4 2000-01-31 2000-01-31\n",
"... ... ...\n",
"1056 2020-03-30 2020-03-30\n",
"1057 2020-04-06 2020-04-06\n",
"1058 2020-04-13 2020-04-13\n",
"1059 2020-04-20 2020-04-20\n",
"1060 2020-04-27 2020-04-27\n",
"\n",
"[1061 rows x 2 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = msyrs.utils.get_bdates_series_default_opt(start_date='2000-01-01', end_date='2020-05-01', freq='W').to_pandas()\n",
"y = pd.Series(pd.bdate_range(start='2000-01-01', end='2020-05-01', freq='W-MON'))\n",
"\n",
"pd.concat([x, y], axis=1)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Results for M\t & \tBMS\t are exactly the same\n",
"Results for Q\t & \tBQS\t are exactly the same\n",
"Results for W\t & \tW-MON\t are exactly the same\n",
"Results for WF\t & \tW-FRI\t are exactly the same\n"
]
}
],
"source": [
"for rs_freq, pd_freq in [('M', 'BMS'), ('Q', 'BQS'), ('W', 'W-MON'), ('WF', 'W-FRI')]:\n",
"\n",
"\n",
" x = msyrs.utils.get_bdates_series_default_opt(start_date='2000-01-01', end_date='2020-05-01', freq=rs_freq).to_pandas()\n",
" y = pd.Series(pd.bdate_range(start='2000-01-01', end='2020-05-01', freq=pd_freq))\n",
"\n",
" e = x == y\n",
" res = e.all()\n",
" non_matching_df = pd.concat([x[~e], y[~e]], axis=1)\n",
" assert res, f\"Results for {rs_freq}\\t and \\t{pd_freq}\\t are not the same\\n{non_matching_df}\"\n",
" print(f\"Results for {rs_freq}\\t & \\t{pd_freq}\\t are exactly the same\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"23.5 μs ± 1.02 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
"67.4 μs ± 979 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
"1.97 ms ± 57.3 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
"4.65 ms ± 170 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"28.3 ms ± 898 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
"93.8 ms ± 2.02 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%timeit msyrs.utils.get_bdates_series_default_opt(start_date='2000-01-01', end_date='2020-05-01', freq='D')\n",
"%timeit msyrs.utils.get_bdates_series_default_opt(start_date='1971-01-01', end_date='2040-05-01', freq='D')\n",
"%timeit msyrs.utils.get_bdates_series_default_pl(start_date='2000-01-01', end_date='2020-05-01', freq='D')\n",
"%timeit msyrs.utils.get_bdates_series_default_pl(start_date='1971-01-01', end_date='2040-05-01', freq='D')\n",
"%timeit pd.bdate_range(start='2000-01-01', end='2020-05-01', freq='B')\n",
"%timeit pd.bdate_range(start='1971-01-01', end='2040-05-01', freq='B')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7.95 μs ± 146 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
"17.9 μs ± 108 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
"1.73 ms ± 20.8 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
"4 ms ± 69.3 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"5.69 ms ± 139 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"19.1 ms ± 268 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%timeit msyrs.utils.get_bdates_series_default_opt(start_date='2000-01-01', end_date='2020-05-01', freq='WF')\n",
"%timeit msyrs.utils.get_bdates_series_default_opt(start_date='1971-01-01', end_date='2040-05-01', freq='WF')\n",
"%timeit msyrs.utils.get_bdates_series_default_pl(start_date='2000-01-01', end_date='2020-05-01', freq='WF')\n",
"%timeit msyrs.utils.get_bdates_series_default_pl(start_date='1971-01-01', end_date='2040-05-01', freq='WF')\n",
"%timeit pd.bdate_range(start='2000-01-01', end='2020-05-01', freq='W-FRI')\n",
"%timeit pd.bdate_range(start='1971-01-01', end='2040-05-01', freq='W-FRI')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.9 μs ± 126 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
"13.1 μs ± 93.3 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
"1.73 ms ± 29.3 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
"4.2 ms ± 81.5 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"931 μs ± 14.2 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
"3.05 ms ± 47.5 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%timeit msyrs.utils.get_bdates_series_default_opt(start_date='2000-01-01', end_date='2020-05-01', freq='ME')\n",
"%timeit msyrs.utils.get_bdates_series_default_opt(start_date='1971-01-01', end_date='2040-05-01', freq='ME')\n",
"%timeit msyrs.utils.get_bdates_series_default_pl(start_date='2000-01-01', end_date='2020-05-01', freq='ME')\n",
"%timeit msyrs.utils.get_bdates_series_default_pl(start_date='1971-01-01', end_date='2040-05-01', freq='ME')\n",
"%timeit pd.bdate_range(start='2000-01-01', end='2020-05-01', freq='BME')\n",
"%timeit pd.bdate_range(start='1971-01-01', end='2040-05-01', freq='BME')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.65 μs ± 69.1 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
"4.78 μs ± 38.7 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
"1.73 ms ± 122 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
"4.16 ms ± 286 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"340 μs ± 11.3 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
"1.1 ms ± 11.5 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
"source": [
"%timeit msyrs.utils.get_bdates_series_default_opt(start_date='2000-01-01', end_date='2020-05-01', freq='Q')\n",
"%timeit msyrs.utils.get_bdates_series_default_opt(start_date='1971-01-01', end_date='2040-05-01', freq='Q')\n",
"%timeit msyrs.utils.get_bdates_series_default_pl(start_date='2000-01-01', end_date='2020-05-01', freq='Q')\n",
"%timeit msyrs.utils.get_bdates_series_default_pl(start_date='1971-01-01', end_date='2040-05-01', freq='Q')\n",
"%timeit pd.bdate_range(start='2000-01-01', end='2020-05-01', freq='BQS')\n",
"%timeit pd.bdate_range(start='1971-01-01', end='2040-05-01', freq='BQS')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.21 μs ± 83.4 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
"3.66 μs ± 198 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
"2.67 ms ± 459 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"3.71 ms ± 143 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
"98.7 μs ± 1.47 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
"289 μs ± 15.3 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
"source": [
"%timeit msyrs.utils.get_bdates_series_default_opt(start_date='2000-01-01', end_date='2020-05-01', freq='YE')\n",
"%timeit msyrs.utils.get_bdates_series_default_opt(start_date='1971-01-01', end_date='2040-05-01', freq='YE')\n",
"%timeit msyrs.utils.get_bdates_series_default_pl(start_date='2000-01-01', end_date='2020-05-01', freq='YE')\n",
"%timeit msyrs.utils.get_bdates_series_default_pl(start_date='1971-01-01', end_date='2040-05-01', freq='YE')\n",
"%timeit pd.bdate_range(start='2000-01-01', end='2020-05-01', freq='BYE')\n",
"%timeit pd.bdate_range(start='1971-01-01', end='2040-05-01', freq='BYE')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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