msyrs/notebooks/python-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build and install the package\n",
    "\n",
    "First patch `pyo3-polars`:\n",
    "\n",
    "\n",
    "- Use [this diff](https://github.com/pola-rs/pyo3-polars/compare/main...Magnus167:pyo3-polars:main) to make changes to the `pyo3-polars` package.\n",
    "\n",
    "\n",
    "Install the package:\n",
    "\n",
    "```bash\n",
    "python -m venv .venv\n",
    "\n",
    "# source .venv/bin/activate\n",
    "./.venv/Scripts/activate\n",
    "\n",
    "pip install maturin ipywidgets\n",
    "\n",
    "maturin develop --release\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Python packages\n"
   ]
  },
  {
   "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 os"
   ]
  },
  {
   "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\"\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfpath = f\"{DATA_FOLDER_PATH}/data/ADPEMPL_SA_P1M1ML1/USD_ADPEMPL_SA_P1M1ML1.csv\"\n",
    "\n",
    "\n",
    "ldf: pl.DataFrame = msyrs.qdf.load_qdf(dfpath)\n",
    "ldf.head(5)"
   ]
  },
  {
   "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",
    "tickers = [f\"{c}_{x}\" for c in cids for x in xcats]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "downloaded_df: pl.DataFrame = msyrs.download.download_jpmaqs_indicators_as_df(\n",
    "    client_id=DQ_CLIENT_ID,\n",
    "    client_secret=DQ_CLIENT_SECRET,\n",
    "    tickers=tickers,\n",
    ")\n",
    "downloaded_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_df: pl.DataFrame = msyrs.qdf.load_qdf_from_download_bank(\n",
    "    folder_path=DATA_FOLDER_PATH, tickers=tickers\n",
    ")\n",
    "big_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_df.estimated_size(\"mb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_df.to_pandas().memory_usage(deep=True).sum() / 1024**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "macrosynergy.management.types.QuantamentalDataFrame(big_df.to_pandas()).memory_usage(\n",
    "    deep=True\n",
    ").sum() / 1024**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel_cids = [\"USD\", \"EUR\", \"GBP\", \"AUD\", \"CAD\"]\n",
    "start = \"2024-11-14\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eq_df = msyrs.qdf.reduce_dataframe(\n",
    "    df=big_df,\n",
    "    cids=sel_cids,\n",
    "    xcats=[\"EQXR_NSA\", \"EQXR_VT10\"],\n",
    "    start=start,\n",
    ")\n",
    "eq_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fx_xcats = [xc for xc in xcats if xc.startswith(\"FX\")]\n",
    "fx_df = msyrs.qdf.reduce_dataframe(\n",
    "    df=big_df, cids=sel_cids, start=start, xcats=fx_xcats, intersect=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df: pl.DataFrame = msyrs.qdf.update_dataframe(df=eq_df, df_add=fx_df)\n",
    "\n",
    "new_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.tail(10)"
   ]
  }
 ],
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