{ "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 }