From 973fb076010d7cfde58e69e204fcb41ca1a012ec Mon Sep 17 00:00:00 2001 From: Palash Tyagi <23239946+Magnus167@users.noreply.github.com> Date: Wed, 9 Apr 2025 00:22:01 +0100 Subject: [PATCH] updating notebook --- notebooks/funcwise/linear_composites.ipynb | 489 +++++++++++++++++++++ 1 file changed, 489 insertions(+) create mode 100644 notebooks/funcwise/linear_composites.ipynb diff --git a/notebooks/funcwise/linear_composites.ipynb b/notebooks/funcwise/linear_composites.ipynb new file mode 100644 index 0000000..86feb2f --- /dev/null +++ b/notebooks/funcwise/linear_composites.ipynb @@ -0,0 +1,489 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Python packages\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[2mUsing Python 3.12.4 environment at: E:\\Work\\ruzt\\msyrs\\.venv\u001b[0m\n", + "\u001b[2mResolved \u001b[1m34 packages\u001b[0m \u001b[2min 121ms\u001b[0m\u001b[0m\n", + " \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m msyrs\u001b[2m @ file:///E:/Work/ruzt/msyrs\u001b[0m\n", + " \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m msyrs\u001b[2m @ file:///E:/Work/ruzt/msyrs\u001b[0m\n", + "\u001b[2mPrepared \u001b[1m1 package\u001b[0m \u001b[2min 14.72s\u001b[0m\u001b[0m\n", + "\u001b[2mUninstalled \u001b[1m1 package\u001b[0m \u001b[2min 4ms\u001b[0m\u001b[0m\n", + "\u001b[1m\u001b[33mwarning\u001b[39m\u001b[0m\u001b[1m:\u001b[0m \u001b[1mFailed to hardlink files; falling back to full copy. This may lead to degraded performance.\n", + " If the cache and target directories are on different filesystems, hardlinking may not be supported.\n", + " If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.\u001b[0m\n", + "\u001b[2mInstalled \u001b[1m1 package\u001b[0m \u001b[2min 30ms\u001b[0m\u001b[0m\n", + " \u001b[33m~\u001b[39m \u001b[1mmsyrs\u001b[0m\u001b[2m==0.0.1 (from file:///E:/Work/ruzt/msyrs)\u001b[0m\n" + ] + } + ], + "source": [ + "! uv pip install E:\\Work\\ruzt\\msyrs --upgrade" + ] + }, + { + "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", + "\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": [], + "source": [ + "DATA_FOLDER_PATH = \"E:/Work/jpmaqs-data\"\n", + "# DATA_FOLDER_PATH = \"C:/Users/PalashTyagi/Code/go-dataquery/jpmaqs-data\"\n", + "DQ_CLIENT_ID = os.getenv(\"DQ_CLIENT_ID\")\n", + "DQ_CLIENT_SECRET = os.getenv(\"DQ_CLIENT_SECRET\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "\n", + "nb_start_time = time.time()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time taken to load qdf: 0.004000425338745117\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (5, 7)
real_datecidxcatvaluegradingeop_lagmop_lag
datestrstrf64f64i64i64
2010-03-03"USD""ADPEMPL_SA_P1M1ML1"-0.1738063.0333
2010-03-04"USD""ADPEMPL_SA_P1M1ML1"-0.1738063.0434
2010-03-05"USD""ADPEMPL_SA_P1M1ML1"-0.1738063.0535
2010-03-08"USD""ADPEMPL_SA_P1M1ML1"-0.1738063.0838
2010-03-09"USD""ADPEMPL_SA_P1M1ML1"-0.1738063.0939
" + ], + "text/plain": [ + "shape: (5, 7)\n", + "┌────────────┬─────┬────────────────────┬───────────┬─────────┬─────────┬─────────┐\n", + "│ real_date ┆ cid ┆ xcat ┆ value ┆ grading ┆ eop_lag ┆ mop_lag │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ date ┆ str ┆ str ┆ f64 ┆ f64 ┆ i64 ┆ i64 │\n", + "╞════════════╪═════╪════════════════════╪═══════════╪═════════╪═════════╪═════════╡\n", + "│ 2010-03-03 ┆ USD ┆ ADPEMPL_SA_P1M1ML1 ┆ -0.173806 ┆ 3.0 ┆ 3 ┆ 33 │\n", + "│ 2010-03-04 ┆ USD ┆ ADPEMPL_SA_P1M1ML1 ┆ -0.173806 ┆ 3.0 ┆ 4 ┆ 34 │\n", + "│ 2010-03-05 ┆ USD ┆ ADPEMPL_SA_P1M1ML1 ┆ -0.173806 ┆ 3.0 ┆ 5 ┆ 35 │\n", + "│ 2010-03-08 ┆ USD ┆ ADPEMPL_SA_P1M1ML1 ┆ -0.173806 ┆ 3.0 ┆ 8 ┆ 38 │\n", + "│ 2010-03-09 ┆ USD ┆ ADPEMPL_SA_P1M1ML1 ┆ -0.173806 ┆ 3.0 ┆ 9 ┆ 39 │\n", + "└────────────┴─────┴────────────────────┴───────────┴─────────┴─────────┴─────────┘" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfpath = f\"{DATA_FOLDER_PATH}/data/ADPEMPL_SA_P1M1ML1/USD_ADPEMPL_SA_P1M1ML1.csv\"\n", + "\n", + "starttime = time.time()\n", + "ldf: pl.DataFrame = msyrs.qdf.load_qdf(dfpath)\n", + "print(f\"Time taken to load qdf: {time.time() - starttime}\")\n", + "ldf.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "cids_dm = \"AUD.CAD.CHF.EUR.GBP.JPY.NOK.NZD.SEK.USD\".split(\".\")\n", + "cids_em = \"CLP.COP.CZK.HUF.IDR.ILS.INR.KRW.MXN.PLN.THB.TRY.TWD.ZAR\".split(\".\")\n", + "cids = cids_dm + cids_em\n", + "cids_dux = list(set(cids) - set([\"IDR\", \"NZD\"]))\n", + "ecos = \"CPIC_SA_P1M1ML12.CPIC_SJA_P3M3ML3AR.CPIC_SJA_P6M6ML6AR.CPIH_SA_P1M1ML12.CPIH_SJA_P3M3ML3AR.CPIH_SJA_P6M6ML6AR.INFTEFF_NSA.INTRGDP_NSA_P1M1ML12_3MMA.INTRGDPv5Y_NSA_P1M1ML12_3MMA.PCREDITGDP_SJA_D1M1ML12.RGDP_SA_P1Q1QL4_20QMA.RYLDIRS02Y_NSA.RYLDIRS05Y_NSA.PCREDITBN_SJA_P1M1ML12\".split(\n", + " \".\"\n", + ")\n", + "\n", + "\n", + "mkts = \"DU02YXR_NSA.DU05YXR_NSA.DU02YXR_VT10.DU05YXR_VT10.EQXR_NSA.EQXR_VT10.FXXR_NSA.FXXR_VT10.FXCRR_NSA.FXTARGETED_NSA.FXUNTRADABLE_NSA\".split(\n", + " \".\"\n", + ")\n", + "xcats = ecos + mkts\n", + "\n", + "tickers = [f\"{c}_{x}\" for c in cids for x in xcats]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time taken to load qdf batch: 1.3058679103851318\n" + ] + } + ], + "source": [ + "starttime = time.time()\n", + "\n", + "big_df: pl.DataFrame = msyrs.qdf.load_qdf_from_download_bank(\n", + " folder_path=DATA_FOLDER_PATH,\n", + " xcats=xcats,\n", + ")\n", + "print(f\"Time taken to load qdf batch: {time.time() - starttime}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "286.69339656829834" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "big_df.estimated_size(\"mb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "sel_cids = [\"USD\", \"EUR\", \"GBP\", \"AUD\", \"CAD\"]\n", + "start = \"1990-01-01\"" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time taken to reduce qdf: 0.9705278873443604\n" + ] + } + ], + "source": [ + "fx_xcats = [xc for xc in xcats if xc.startswith(\"FX\")]\n", + "eq_xcats = [xc for xc in xcats if xc.startswith(\"EQ\")]\n", + "starttime = time.time()\n", + "\n", + "eq_df = msyrs.qdf.reduce_dataframe(\n", + " df=big_df,\n", + " cids=sel_cids,\n", + " xcats=fx_xcats + eq_xcats,\n", + " start=start,\n", + ")\n", + "\n", + "fx_df = msyrs.qdf.reduce_dataframe(\n", + " df=big_df, cids=sel_cids, start=start, xcats=fx_xcats, intersect=True\n", + ")\n", + "new_df: pl.DataFrame = msyrs.qdf.update_dataframe(df=eq_df, df_add=fx_df)\n", + "\n", + "print(f\"Time taken to reduce qdf: {time.time() - starttime}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time taken: 2.3365907669067383 seconds\n" + ] + } + ], + "source": [ + "end_time = time.time()\n", + "print(f\"Time taken: {end_time - nb_start_time} seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "e:\\Work\\ruzt\\msyrs\\.venv\\Lib\\site-packages\\macrosynergy\\panel\\linear_composite.py:437: UserWarning: USD does not have complete xcat data for ['FXXR_NSA']. These will be filled with NaNs for the calculation.\n", + " warnings.warn(wrn_msg.format(cidx=cidx, missing_xcats=missing_xcats))\n" + ] + } + ], + "source": [ + "_cids = [\"USD\", \"CAD\"]\n", + "mx = macrosynergy.panel.linear_composite(\n", + " df=new_df.to_pandas(),\n", + " xcats=[\"EQXR_NSA\", \"FXXR_NSA\"], \n", + " cids=_cids,\n", + " weights=None,\n", + " signs=None,\n", + " normalize_weights=False,\n", + " start=None,\n", + " end=None,\n", + " blacklist=None,\n", + " complete_xcats=False,\n", + " complete_cids=False,\n", + " new_xcat=\"COMPOSITE\",\n", + " new_cid=\"GLB\",\n", + ")\n", + "# view_timelines(QuantamentalDataFrame(mx), cids=_cids)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "_cids = [\"USD\", \"CAD\"]\n", + "x = msyrs.panel.linear_composite(\n", + " df=new_df,\n", + " xcats=[\"EQXR_NSA\", \"FXXR_NSA\"],\n", + " cids=_cids,\n", + " weights=None,\n", + " signs=None,\n", + " weight_xcats=None,\n", + " normalize_weights=False,\n", + " start=None,\n", + " end=None,\n", + " blacklist=None,\n", + " complete_xcats=False,\n", + " complete_cids=False,\n", + " new_xcat=\"COMPOSITE\",\n", + " new_cid=\"GLB\",\n", + ")\n", + "# view_timelines(QuantamentalDataFrame(x.to_pandas()), cids=_cids)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mwide = QuantamentalDataFrame(mx).to_wide().sort_index()\n", + "rwide = QuantamentalDataFrame(x.to_pandas()).to_wide().sort_index()\n", + "np.allclose((mwide - rwide).sum(axis=1), 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "e:\\Work\\ruzt\\msyrs\\.venv\\Lib\\site-packages\\macrosynergy\\panel\\linear_composite.py:437: UserWarning: USD does not have complete xcat data for ['FXXR_NSA']. These will be filled with NaNs for the calculation.\n", + " warnings.warn(wrn_msg.format(cidx=cidx, missing_xcats=missing_xcats))\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "_cids = [\"USD\", \"CAD\", \"EUR\", \"AUD\"]\n", + "_xcats = [\"EQXR_NSA\", \"FXXR_NSA\"]\n", + "\n", + "mx = macrosynergy.panel.linear_composite(\n", + " df=new_df.to_pandas(),\n", + " xcats=_xcats,\n", + " cids=_cids,\n", + " weights=[1, 9],\n", + " normalize_weights=False,\n", + " new_xcat=\"COMPOSITE\",\n", + " new_cid=\"GLB\",\n", + ")\n", + "x = msyrs.panel.linear_composite(\n", + " df=new_df,\n", + " xcats=_xcats,\n", + " cids=_cids,\n", + " weights=[1, 9],\n", + " normalize_weights=False,\n", + " new_xcat=\"COMPOSITE\",\n", + " new_cid=\"GLB\",\n", + ")\n", + "mwide = QuantamentalDataFrame(mx).to_wide().sort_index()\n", + "rwide = QuantamentalDataFrame(x.to_pandas()).to_wide().sort_index()\n", + "np.allclose((mwide - rwide).sum(axis=1), 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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real_datevaluecidxcat
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [real_date, value, cid, xcat]\n", + "Index: []" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "x = msyrs.panel.linear_composite(\n", + " df=new_df,\n", + " xcats=_xcats,\n", + " cids=_cids,\n", + " weights=[1, 9],\n", + " normalize_weights=True,\n", + " new_xcat=\"COMPOSITE\",\n", + " new_cid=\"GLB\",\n", + ")\n", + "x.to_pandas().dropna(how=\"any\")" + ] + } + ], + "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 +}