mirror of
https://github.com/Magnus167/msyrs.git
synced 2025-08-20 04:20:00 +00:00
updating linear composites notebook
This commit is contained in:
Palash Tyagi
parent
f63eedb50a
commit
f84879119b
@ -9,34 +9,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[2mUsing Python 3.12.4 environment at: E:\\Work\\ruzt\\msyrs\\.venv\u001b[0m\n",
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"\u001b[2mResolved \u001b[1m34 packages\u001b[0m \u001b[2min 121ms\u001b[0m\u001b[0m\n",
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" \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m msyrs\u001b[2m @ file:///E:/Work/ruzt/msyrs\u001b[0m\n",
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" \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m msyrs\u001b[2m @ file:///E:/Work/ruzt/msyrs\u001b[0m\n",
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"\u001b[2mPrepared \u001b[1m1 package\u001b[0m \u001b[2min 14.72s\u001b[0m\u001b[0m\n",
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"\u001b[2mUninstalled \u001b[1m1 package\u001b[0m \u001b[2min 4ms\u001b[0m\u001b[0m\n",
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"\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",
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" If the cache and target directories are on different filesystems, hardlinking may not be supported.\n",
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" If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.\u001b[0m\n",
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"\u001b[2mInstalled \u001b[1m1 package\u001b[0m \u001b[2min 30ms\u001b[0m\u001b[0m\n",
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" \u001b[33m~\u001b[39m \u001b[1mmsyrs\u001b[0m\u001b[2m==0.0.1 (from file:///E:/Work/ruzt/msyrs)\u001b[0m\n"
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]
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}
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],
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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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"# ! 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": 2,
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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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@ -44,10 +26,11 @@
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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 time\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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"from macrosynergy.management.types import QuantamentalDataFrame"
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]
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},
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{
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@ -59,7 +42,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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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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@ -68,83 +51,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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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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"DATA_FOLDER_PATH = \"E:/Work/jpmaqs-data\""
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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": 5,
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Time taken to load qdf: 0.004000425338745117\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div><style>\n",
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".dataframe > thead > tr,\n",
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".dataframe > tbody > tr {\n",
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" text-align: right;\n",
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" white-space: pre-wrap;\n",
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"}\n",
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"</style>\n",
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"<small>shape: (5, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>real_date</th><th>cid</th><th>xcat</th><th>value</th><th>grading</th><th>eop_lag</th><th>mop_lag</th></tr><tr><td>date</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>2010-03-03</td><td>"USD"</td><td>"ADPEMPL_SA_P1M1ML1"</td><td>-0.173806</td><td>3.0</td><td>3</td><td>33</td></tr><tr><td>2010-03-04</td><td>"USD"</td><td>"ADPEMPL_SA_P1M1ML1"</td><td>-0.173806</td><td>3.0</td><td>4</td><td>34</td></tr><tr><td>2010-03-05</td><td>"USD"</td><td>"ADPEMPL_SA_P1M1ML1"</td><td>-0.173806</td><td>3.0</td><td>5</td><td>35</td></tr><tr><td>2010-03-08</td><td>"USD"</td><td>"ADPEMPL_SA_P1M1ML1"</td><td>-0.173806</td><td>3.0</td><td>8</td><td>38</td></tr><tr><td>2010-03-09</td><td>"USD"</td><td>"ADPEMPL_SA_P1M1ML1"</td><td>-0.173806</td><td>3.0</td><td>9</td><td>39</td></tr></tbody></table></div>"
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],
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"text/plain": [
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"shape: (5, 7)\n",
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"┌────────────┬─────┬────────────────────┬───────────┬─────────┬─────────┬─────────┐\n",
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"│ real_date ┆ cid ┆ xcat ┆ value ┆ grading ┆ eop_lag ┆ mop_lag │\n",
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"│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
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"│ date ┆ str ┆ str ┆ f64 ┆ f64 ┆ i64 ┆ i64 │\n",
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"╞════════════╪═════╪════════════════════╪═══════════╪═════════╪═════════╪═════════╡\n",
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"│ 2010-03-03 ┆ USD ┆ ADPEMPL_SA_P1M1ML1 ┆ -0.173806 ┆ 3.0 ┆ 3 ┆ 33 │\n",
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"│ 2010-03-04 ┆ USD ┆ ADPEMPL_SA_P1M1ML1 ┆ -0.173806 ┆ 3.0 ┆ 4 ┆ 34 │\n",
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"│ 2010-03-05 ┆ USD ┆ ADPEMPL_SA_P1M1ML1 ┆ -0.173806 ┆ 3.0 ┆ 5 ┆ 35 │\n",
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"│ 2010-03-08 ┆ USD ┆ ADPEMPL_SA_P1M1ML1 ┆ -0.173806 ┆ 3.0 ┆ 8 ┆ 38 │\n",
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"│ 2010-03-09 ┆ USD ┆ ADPEMPL_SA_P1M1ML1 ┆ -0.173806 ┆ 3.0 ┆ 9 ┆ 39 │\n",
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"└────────────┴─────┴────────────────────┴───────────┴─────────┴─────────┴─────────┘"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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": 7,
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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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@ -162,22 +78,18 @@
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")\n",
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"xcats = ecos + mkts\n",
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"\n",
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"cpi_xcats = \"CPIC_SA_P1M1ML12.CPIC_SJA_P3M3ML3AR.CPIC_SJA_P6M6ML6AR.CPIH_SA_P1M1ML12.CPIH_SJA_P3M3ML3AR.CPIH_SJA_P6M6ML6AR\".split(\n",
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" \".\"\n",
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")\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": 8,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Time taken to load qdf batch: 1.3058679103851318\n"
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]
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}
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],
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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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@ -185,52 +97,41 @@
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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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"print(f\"Time taken to load qdf batch: {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": 9,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"286.69339656829834"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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": 10,
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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\"]\n",
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"start = \"1990-01-01\""
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"start = \"1990-01-01\"\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": "markdown",
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"metadata": {},
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"source": [
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"### Running with uniform weights, 2 xcats, 5 cids\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": 11,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Time taken to reduce qdf: 0.9705278873443604\n"
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]
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}
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],
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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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"eq_xcats = [xc for xc in xcats if xc.startswith(\"EQ\")]\n",
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@ -239,7 +140,7 @@
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"eq_df = msyrs.qdf.reduce_dataframe(\n",
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" df=big_df,\n",
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" cids=sel_cids,\n",
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" xcats=fx_xcats + eq_xcats,\n",
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" xcats=fx_xcats + eq_xcats + cpi_xcats,\n",
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" start=start,\n",
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")\n",
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"\n",
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@ -248,133 +149,107 @@
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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}\")\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": 12,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Time taken: 2.3365907669067383 seconds\n"
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]
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}
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],
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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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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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",
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" warnings.warn(wrn_msg.format(cidx=cidx, missing_xcats=missing_xcats))\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"_cids = [\"USD\", \"CAD\"]\n",
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"\n",
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"_df = new_df.to_pandas()\n",
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"starttime = time.time()\n",
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"\n",
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"\n",
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"mx = macrosynergy.panel.linear_composite(\n",
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" df=new_df.to_pandas(),\n",
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"\n",
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" df=_df,\n",
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"\n",
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" xcats=[\"EQXR_NSA\", \"FXXR_NSA\"],\n",
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" cids=_cids,\n",
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"\n",
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" weights=None,\n",
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"\n",
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" signs=None,\n",
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"\n",
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" normalize_weights=False,\n",
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" start=None,\n",
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" end=None,\n",
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"\n",
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" blacklist=None,\n",
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"\n",
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" complete_xcats=False,\n",
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"\n",
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" complete_cids=False,\n",
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"\n",
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" new_xcat=\"COMPOSITE\",\n",
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"\n",
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" new_cid=\"GLB\",\n",
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"\n",
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")\n",
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"print(f\"Time taken to run linear composite: {time.time() - starttime}\")\n",
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"\n",
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"\n",
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"# view_timelines(QuantamentalDataFrame(mx), cids=_cids)"
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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": 14,
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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 = [\"USD\", \"CAD\"]\n",
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"starttime = time.time()\n",
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"\n",
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"x = msyrs.panel.linear_composite(\n",
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"\n",
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" df=new_df,\n",
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"\n",
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" xcats=[\"EQXR_NSA\", \"FXXR_NSA\"],\n",
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" cids=_cids,\n",
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"\n",
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" weights=None,\n",
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"\n",
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" signs=None,\n",
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"\n",
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" weight_xcats=None,\n",
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"\n",
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" normalize_weights=False,\n",
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" start=None,\n",
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" end=None,\n",
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"\n",
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" blacklist=None,\n",
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"\n",
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" complete_xcats=False,\n",
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"\n",
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" complete_cids=False,\n",
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"\n",
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" new_xcat=\"COMPOSITE\",\n",
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"\n",
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" new_cid=\"GLB\",\n",
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"\n",
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")\n",
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"print(f\"Time taken to run linear composite rs: {time.time() - starttime}\")\n",
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"\n",
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"# view_timelines(QuantamentalDataFrame(x.to_pandas()), cids=_cids)"
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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": 15,
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"cell_type": "markdown",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mwide = QuantamentalDataFrame(mx).to_wide().sort_index()\n",
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"rwide = QuantamentalDataFrame(x.to_pandas()).to_wide().sort_index()\n",
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"np.allclose((mwide - rwide).sum(axis=1), 0)"
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"### Running with variable weights\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": 16,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"_cids = [\"USD\", \"CAD\", \"EUR\", \"AUD\"]\n",
|
||||
"_xcats = [\"EQXR_NSA\", \"FXXR_NSA\"]\n",
|
||||
@ -397,72 +272,90 @@
|
||||
" 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": "code",
|
||||
"execution_count": 17,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>real_date</th>\n",
|
||||
" <th>value</th>\n",
|
||||
" <th>cid</th>\n",
|
||||
" <th>xcat</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"Empty DataFrame\n",
|
||||
"Columns: [real_date, value, cid, xcat]\n",
|
||||
"Index: []"
|
||||
"source": [
|
||||
"### Running with variable weights, normalized\n"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"x = msyrs.panel.linear_composite(\n",
|
||||
" df=new_df,\n",
|
||||
" xcats=_xcats,\n",
|
||||
" xcats=cpi_xcats,\n",
|
||||
" cids=_cids,\n",
|
||||
" weights=[1, 9],\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": {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user