mirror of
https://github.com/Magnus167/msyrs.git
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301 lines
7.3 KiB
Plaintext
301 lines
7.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# ! uv pip install E:\\Work\\ruzt\\msyrs --upgrade"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Import Python packages\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import macrosynergy\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import polars as pl\n",
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"import os\n",
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"\n",
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"from macrosynergy.panel import view_timelines\n",
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"from macrosynergy.management.types import QuantamentalDataFrame\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Import Python bindings - `msyrs`\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import msyrs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"DATA_FOLDER_PATH = \"E:/Work/jpmaqs-data\"\n",
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"# DATA_FOLDER_PATH = \"C:/Users/PalashTyagi/Code/go-dataquery/jpmaqs-data\"\n",
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"DQ_CLIENT_ID = os.getenv(\"DQ_CLIENT_ID\")\n",
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"DQ_CLIENT_SECRET = os.getenv(\"DQ_CLIENT_SECRET\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import time\n",
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"\n",
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"nb_start_time = time.time()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dfpath = f\"{DATA_FOLDER_PATH}/data/ADPEMPL_SA_P1M1ML1/USD_ADPEMPL_SA_P1M1ML1.csv\"\n",
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"\n",
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"starttime = time.time()\n",
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"ldf: pl.DataFrame = msyrs.qdf.load_qdf(dfpath)\n",
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"print(f\"Time taken to load qdf: {time.time() - starttime}\")\n",
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"ldf.head(5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"cids_dm = \"AUD.CAD.CHF.EUR.GBP.JPY.NOK.NZD.SEK.USD\".split(\".\")\n",
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"cids_em = \"CLP.COP.CZK.HUF.IDR.ILS.INR.KRW.MXN.PLN.THB.TRY.TWD.ZAR\".split(\".\")\n",
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"cids = cids_dm + cids_em\n",
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"cids_dux = list(set(cids) - set([\"IDR\", \"NZD\"]))\n",
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"ecos = (\n",
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" \"CPIC_SA_P1M1ML12.CPIC_SJA_P3M3ML3AR.CPIC_SJA_P6M6ML6AR.CPIH_SA_P1M1ML12.\"\n",
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" \"CPIH_SJA_P3M3ML3AR.CPIH_SJA_P6M6ML6AR.INFTEFF_NSA.INTRGDP_NSA_P1M1ML12_3MMA.\"\n",
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" \"INTRGDPv5Y_NSA_P1M1ML12_3MMA.PCREDITGDP_SJA_D1M1ML12.RGDP_SA_P1Q1QL4_20QMA.\"\n",
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" \"RYLDIRS02Y_NSA.RYLDIRS05Y_NSA.PCREDITBN_SJA_P1M1ML12\".split(\".\")\n",
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")\n",
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"\n",
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"mkts = (\n",
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" \"DU02YXR_NSA.DU05YXR_NSA.DU02YXR_VT10.DU05YXR_VT10.EQXR_NSA.EQXR_VT10.\"\n",
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" \"FXXR_NSA.FXXR_VT10.FXCRR_NSA.FXTARGETED_NSA.FXUNTRADABLE_NSA\".split(\".\")\n",
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")\n",
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"xcats = ecos + mkts\n",
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"\n",
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"tickers = [f\"{c}_{x}\" for c in cids for x in xcats]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"starttime = time.time()\n",
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"\n",
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"big_df: pl.DataFrame = msyrs.qdf.load_qdf_from_download_bank(\n",
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" folder_path=DATA_FOLDER_PATH,\n",
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" xcats=xcats,\n",
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")\n",
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"print(f\"Time taken to load qdf batch: {time.time() - starttime}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"big_df.estimated_size(\"mb\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"starttime = time.time()\n",
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"\n",
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"test_df = msyrs.qdf.reduce_dataframe(df=big_df, xcats=mkts)\n",
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"\n",
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"print(f\"Time taken to reduce qdf: {time.time() - starttime}\")\n",
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"print(test_df.estimated_size(\"mb\"))\n",
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"test_df = None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sel_cids = [\"USD\", \"EUR\", \"GBP\", \"AUD\", \"CAD\", \"CHF\", \"JPY\", \"INR\"]\n",
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"start = \"2000-01-01\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"eq_xcats = [xc for xc in xcats if xc.startswith(\"EQ\")]\n",
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"starttime = time.time()\n",
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"\n",
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"eq_df = msyrs.qdf.reduce_dataframe(\n",
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" df=big_df, cids=sel_cids, xcats=eq_xcats, start=start\n",
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")\n",
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"\n",
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"print(f\"Time taken to reduce qdf: {time.time() - starttime}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fx_xcats = [xc for xc in xcats if xc.startswith(\"FX\")]\n",
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"\n",
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"starttime = time.time()\n",
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"\n",
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"fx_df = msyrs.qdf.reduce_dataframe(\n",
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" df=big_df, cids=sel_cids, xcats=fx_xcats, start=start\n",
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")\n",
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"new_df: pl.DataFrame = msyrs.qdf.update_dataframe(df=eq_df, df_add=fx_df)\n",
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"\n",
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"print(f\"Time taken to reduce qdf: {time.time() - starttime}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"starttime = time.time()\n",
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"\n",
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"new_df: pl.DataFrame = msyrs.qdf.update_dataframe(df=eq_df, df_add=fx_df)\n",
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"\n",
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"print(f\"Time taken to update qdf: {time.time() - starttime}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"msyrs.utils.get_bdates_series_default_opt(start_date='1971-01-01', end_date='2040-05-01', freq='D')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"msyrs.utils.get_bdates_series_default_pl(start_date='2000-01-01', end_date='2020-05-01', freq='D').dtype"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"wdf = QuantamentalDataFrame(new_df.to_pandas()).to_wide()\n",
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"# wdf.values[np.random.rand(*wdf.shape) < 0.0001] = np.nan\n",
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"wdf = QuantamentalDataFrame.from_wide(wdf, categorical=False)\n",
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"# cast column 'real_date' to pl.Date\n",
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"new_df = pl.DataFrame(wdf).with_columns(\n",
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" pl.col(\"real_date\").cast(pl.Date, strict=True)\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"msyrs.utils.create_blacklist_from_qdf(new_df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"view_timelines(df=new_df.to_pandas())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"end_time = time.time()\n",
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"print(f\"Time taken: {end_time - nb_start_time} seconds\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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