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msyrs/notebooks/funcwise/linear_composites.ipynb
Palash Tyagi f84879119b updating linear composites notebook
2025-04-10 00:33:28 +01:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Python packages\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ! uv pip install E:\\Work\\ruzt\\msyrs --upgrade"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import macrosynergy\n",
"import pandas as pd\n",
"import numpy as np\n",
"import polars as pl\n",
"import time\n",
"import os\n",
"\n",
"from macrosynergy.panel import view_timelines\n",
"from macrosynergy.management.types import QuantamentalDataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Python bindings - `msyrs`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import msyrs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"DATA_FOLDER_PATH = \"E:/Work/jpmaqs-data\""
]
},
{
"cell_type": "code",
"execution_count": null,
"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",
"cpi_xcats = \"CPIC_SA_P1M1ML12.CPIC_SJA_P3M3ML3AR.CPIC_SJA_P6M6ML6AR.CPIH_SA_P1M1ML12.CPIH_SJA_P3M3ML3AR.CPIH_SJA_P6M6ML6AR\".split(\n",
" \".\"\n",
")\n",
"\n",
"tickers = [f\"{c}_{x}\" for c in cids for x in xcats]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"big_df.estimated_size(\"mb\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sel_cids = [\"USD\", \"EUR\", \"GBP\", \"AUD\", \"CAD\"]\n",
"start = \"1990-01-01\"\n",
"nb_start_time = time.time()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Running with uniform weights, 2 xcats, 5 cids\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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 + cpi_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}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_cids = [\"USD\", \"CAD\"]\n",
"\n",
"_df = new_df.to_pandas()\n",
"starttime = time.time()\n",
"\n",
"\n",
"mx = macrosynergy.panel.linear_composite(\n",
"\n",
" df=_df,\n",
"\n",
" xcats=[\"EQXR_NSA\", \"FXXR_NSA\"],\n",
" cids=_cids,\n",
"\n",
" weights=None,\n",
"\n",
" signs=None,\n",
"\n",
" normalize_weights=False,\n",
" start=None,\n",
" end=None,\n",
"\n",
" blacklist=None,\n",
"\n",
" complete_xcats=False,\n",
"\n",
" complete_cids=False,\n",
"\n",
" new_xcat=\"COMPOSITE\",\n",
"\n",
" new_cid=\"GLB\",\n",
"\n",
")\n",
"print(f\"Time taken to run linear composite: {time.time() - starttime}\")\n",
"\n",
"\n",
"# view_timelines(QuantamentalDataFrame(mx), cids=_cids)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_cids = [\"USD\", \"CAD\"]\n",
"starttime = time.time()\n",
"\n",
"x = msyrs.panel.linear_composite(\n",
"\n",
" df=new_df,\n",
"\n",
" xcats=[\"EQXR_NSA\", \"FXXR_NSA\"],\n",
" cids=_cids,\n",
"\n",
" weights=None,\n",
"\n",
" signs=None,\n",
"\n",
" weight_xcats=None,\n",
"\n",
" normalize_weights=False,\n",
" start=None,\n",
" end=None,\n",
"\n",
" blacklist=None,\n",
"\n",
" complete_xcats=False,\n",
"\n",
" complete_cids=False,\n",
"\n",
" new_xcat=\"COMPOSITE\",\n",
"\n",
" new_cid=\"GLB\",\n",
"\n",
")\n",
"print(f\"Time taken to run linear composite rs: {time.time() - starttime}\")\n",
"\n",
"# view_timelines(QuantamentalDataFrame(x.to_pandas()), cids=_cids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Running with variable weights\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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",
"view_timelines(QuantamentalDataFrame(mx), cids=_cids)\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": "markdown",
"metadata": {},
"source": [
"### Running with variable weights, normalized\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x = msyrs.panel.linear_composite(\n",
" df=new_df,\n",
" xcats=cpi_xcats,\n",
" cids=_cids,\n",
" weights=list(range(1, len(cpi_xcats) + 1)),\n",
" normalize_weights=True,\n",
" new_xcat=\"COMPOSITE\",\n",
" new_cid=\"GLB\",\n",
")\n",
"x.to_pandas().dropna(how=\"any\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"view_timelines(x.to_pandas().dropna(how=\"any\"), cids=_cids)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mx = macrosynergy.panel.linear_composite(\n",
" df=new_df.to_pandas(),\n",
" xcats=cpi_xcats,\n",
" cids=_cids,\n",
" weights=list(range(1, len(cpi_xcats) + 1)),\n",
" normalize_weights=True,\n",
" new_xcat=\"COMPOSITE\",\n",
" new_cid=\"GLB\",\n",
")\n",
"view_timelines(mx.dropna(how=\"any\"), cids=_cids)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": "markdown",
"metadata": {},
"source": [
"### Running with categorical weights, normalized\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"raise NotImplementedError(\"Not implemented yet\")"
]
}
],
"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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