{ "cells": [ { "cell_type": "markdown", "id": "50635b8f", "metadata": {}, "source": [ "# Factor Attribution\n", "\n", "In this tutorial we are going to show how to run a factor-based attribution (return and risk) using the Reporting API. This API is currently under active development.\n", "\n", "The steps are as follows:\n", "- Uploading a set of portfolios\n", "- Organizing the portfolios into a hierarchy\n", "- Creating a factor model\n", "- Running a factor return attribution report\n", "- Pulling out different aggregations and drill-downs\n", "- Running a factor risk attribution report" ] }, { "cell_type": "markdown", "id": "7c709920", "metadata": {}, "source": [ "## Imports & Setup\n", "\n", "For this tutorial notebook, you will need to import the following packages." ] }, { "cell_type": "code", "execution_count": 2, "id": "90936ecc", "metadata": {}, "outputs": [], "source": [ "import datetime as dt\n", "import polars as pl\n", "\n", "from bayesline.apiclient import BayeslineApiClient\n", "from bayesline.api.equity import (\n", " FactorRiskModelSettings,\n", " UniverseSettings,\n", " ExposureSettings,\n", " ContinuousExposureGroupSettings,\n", " CategoricalExposureGroupSettings,\n", " ModelConstructionSettings,\n", " PortfolioHierarchySettings,\n", " ReportSettings,\n", " FactorAttributionDrilldownReportSettings,\n", " FactorAttributionDrilldownMeasureSettings,\n", " XSRDrillDownReportSettings,\n", ")" ] }, { "cell_type": "markdown", "id": "a4fa0e5d", "metadata": {}, "source": [ "We will also need to have a Bayesline API client configured." ] }, { "cell_type": "code", "execution_count": null, "id": "d3c3ce1c", "metadata": { "tags": [ "skip-execution" ] }, "outputs": [], "source": [ "bln = BayeslineApiClient.new_client(\n", " endpoint=\"https://[ENDPOINT]\",\n", " api_key=\"[API-KEY]\",\n", ")" ] }, { "cell_type": "markdown", "id": "d6473290", "metadata": {}, "source": [ "We will first upload a portfolio and set a hierarchy. The steps followed here are the same as in the [Portfolio Hierarchies Tutorial](https://docs.bayesline.com/0.9.2/notebooks/tutorial_portfolio_hierarchies.html)." ] }, { "cell_type": "code", "execution_count": 3, "id": "19d6711f", "metadata": {}, "outputs": [], "source": [ "uploader = bln.equity.uploaders.get_data_type(\"portfolios\").create_or_replace_dataset(\"portfolio-attribution-demo\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "27a5ada2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (20, 5)
portfolio_idasset_idasset_id_typedatevalue
strstrstrdatef64
"AGTHX""02079K305""cusip9"2025-01-010.5
"AGTHX""02079K305""cusip9"2025-01-310.55
"AGTHX""2588173""sedol7"2025-01-010.5
"AGTHX""2588173""sedol7"2025-01-310.45
"FCNTX""67066G10""cusip8"2025-01-011.0
……………
"SPX""2588173""sedol7"2025-01-310.2
"SPX""67066G10""cusip8"2025-01-010.3
"SPX""67066G10""cusip8"2025-01-310.25
"SPX""85371710""cusip8"2025-01-010.3
"SPX""85371710""cusip8"2025-01-310.25
" ], "text/plain": [ "shape: (20, 5)\n", "┌──────────────┬───────────┬───────────────┬────────────┬───────┐\n", "│ portfolio_id ┆ asset_id ┆ asset_id_type ┆ date ┆ value │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ str ┆ str ┆ str ┆ date ┆ f64 │\n", "╞══════════════╪═══════════╪═══════════════╪════════════╪═══════╡\n", "│ AGTHX ┆ 02079K305 ┆ cusip9 ┆ 2025-01-01 ┆ 0.5 │\n", "│ AGTHX ┆ 02079K305 ┆ cusip9 ┆ 2025-01-31 ┆ 0.55 │\n", "│ AGTHX ┆ 2588173 ┆ sedol7 ┆ 2025-01-01 ┆ 0.5 │\n", "│ AGTHX ┆ 2588173 ┆ sedol7 ┆ 2025-01-31 ┆ 0.45 │\n", "│ FCNTX ┆ 67066G10 ┆ cusip8 ┆ 2025-01-01 ┆ 1.0 │\n", "│ … ┆ … ┆ … ┆ … ┆ … │\n", "│ SPX ┆ 2588173 ┆ sedol7 ┆ 2025-01-31 ┆ 0.2 │\n", "│ SPX ┆ 67066G10 ┆ cusip8 ┆ 2025-01-01 ┆ 0.3 │\n", "│ SPX ┆ 67066G10 ┆ cusip8 ┆ 2025-01-31 ┆ 0.25 │\n", "│ SPX ┆ 85371710 ┆ cusip8 ┆ 2025-01-01 ┆ 0.3 │\n", "│ SPX ┆ 85371710 ┆ cusip8 ┆ 2025-01-31 ┆ 0.25 │\n", "└──────────────┴───────────┴───────────────┴────────────┴───────┘" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolio_df = pl.DataFrame({\n", " \"portfolio_id\": [\n", " \"AGTHX\", \"AGTHX\", \"AGTHX\", \"AGTHX\",\n", " \"FCNTX\", \"FCNTX\",\n", " \"VADGX\", \"VADGX\", \"VADGX\", \"VADGX\", \"VADGX\", \"VADGX\",\n", " \"SPX\", \"SPX\", \"SPX\", \"SPX\", \"SPX\", \"SPX\", \"SPX\", \"SPX\",\n", " ],\n", " \"asset_id\": [\n", " # AGTHX\n", " \"02079K305\", \"02079K305\", \n", " \"2588173\", \"2588173\",\n", "\n", " # FCNTX\n", " \"67066G10\", \"67066G10\",\n", "\n", " # VADGX\n", " \"02079K305\", \"02079K305\", \n", " \"2588173\", \"2588173\", \n", " \"67066G10\", \"67066G10\",\n", "\n", " # SPX\n", " \"02079K305\", \"02079K305\", \n", " \"2588173\", \"2588173\",\n", " \"67066G10\", \"67066G10\",\n", " \"85371710\", \"85371710\",\n", "\n", " ], \n", " \"asset_id_type\": [\n", " # AGTHX\n", " \"cusip9\", \"cusip9\", \"sedol7\", \"sedol7\",\n", "\n", " # FCNTX\n", " \"cusip8\", \"cusip8\",\n", "\n", " # VADGX\n", " \"cusip9\", \"cusip9\", \"sedol7\", \"sedol7\", \"cusip8\", \"cusip8\",\n", "\n", " # SPX\n", " \"cusip9\", \"cusip9\", \"sedol7\", \"sedol7\", \"cusip8\", \"cusip8\", \"cusip8\", \"cusip8\",\n", " \n", " ],\n", " \"date\": [\n", " # AGTHX\n", " dt.date(2025, 1, 1), dt.date(2025, 1, 31), \n", " dt.date(2025, 1, 1), dt.date(2025, 1, 31),\n", "\n", " # FCNTX\n", " dt.date(2025, 1, 1), dt.date(2025, 1, 31),\n", "\n", " # VADGX\n", " dt.date(2025, 1, 1), dt.date(2025, 1, 31), \n", " dt.date(2025, 1, 1), dt.date(2025, 1, 31), \n", " dt.date(2025, 1, 1), dt.date(2025, 1, 31), \n", " \n", " # SPX\n", " dt.date(2025, 1, 1), dt.date(2025, 1, 31), \n", " dt.date(2025, 1, 1), dt.date(2025, 1, 31), \n", " dt.date(2025, 1, 1), dt.date(2025, 1, 31), \n", " dt.date(2025, 1, 1), dt.date(2025, 1, 31), \n", " ],\n", " \"value\": [\n", " # AGTHX\n", " 0.5, 0.55, \n", " 0.5, 0.45,\n", "\n", " # FCNTX\n", " 1.0, 1.0,\n", "\n", " # VADGX\n", " 0.3, 0.5, \n", " 0.4, 0.2,\n", " 0.3, 0.25,\n", "\n", " # SPX\n", " 0.3, 0.3, \n", " 0.4, 0.2,\n", " 0.3, 0.25,\n", " 0.3, 0.25,\n", " ],\n", "})\n", "\n", "portfolio_df" ] }, { "cell_type": "code", "execution_count": 5, "id": "feb75d1c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "UploadCommitResult(version=1, committed_names=[])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "uploader.fast_commit(portfolio_df, mode=\"append\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "265b9020", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (4, 5)
fundbooktickerportfolio_idbenchmark_id
strstrstrstrnull
"some_fund""book_1""AGTHX""AGTHX"null
"some_fund""book_1""FCNTX""FCNTX"null
"some_fund""book_2""VADGX""VADGX"null
"some_fund""book_2""SPX""SPX"null
" ], "text/plain": [ "shape: (4, 5)\n", "┌───────────┬────────┬────────┬──────────────┬──────────────┐\n", "│ fund ┆ book ┆ ticker ┆ portfolio_id ┆ benchmark_id │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ str ┆ str ┆ str ┆ str ┆ null │\n", "╞═══════════╪════════╪════════╪══════════════╪══════════════╡\n", "│ some_fund ┆ book_1 ┆ AGTHX ┆ AGTHX ┆ null │\n", "│ some_fund ┆ book_1 ┆ FCNTX ┆ FCNTX ┆ null │\n", "│ some_fund ┆ book_2 ┆ VADGX ┆ VADGX ┆ null │\n", "│ some_fund ┆ book_2 ┆ SPX ┆ SPX ┆ null │\n", "└───────────┴────────┴────────┴──────────────┴──────────────┘" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolio_hierarchy_df = pl.DataFrame(\n", " {\n", " \"fund\": [\"some_fund\", \"some_fund\", \"some_fund\", \"some_fund\"],\n", " \"book\": [\"book_1\", \"book_1\", \"book_2\", \"book_2\"],\n", " \"ticker\": [\"AGTHX\", \"FCNTX\", \"VADGX\", \"SPX\"],\n", " \"portfolio_id\": [\"AGTHX\", \"FCNTX\", \"VADGX\", \"SPX\"],\n", " }\n", ")\n", "portfoliohierarchy_settings = PortfolioHierarchySettings.from_polars(\n", " portfolio_hierarchy_df, portfolio_source=\"portfolio-attribution-demo\"\n", ")\n", "portfoliohierarchy_settings.to_polars()\n" ] }, { "cell_type": "markdown", "id": "4bf4999a", "metadata": {}, "source": [ "Now that we have the portfolio hiearachy in place, we define a basic factor model." ] }, { "cell_type": "code", "execution_count": 7, "id": "349dea55", "metadata": {}, "outputs": [], "source": [ "factorriskmodel_settings = FactorRiskModelSettings(\n", " universe=UniverseSettings(dataset=\"Bayesline-US-All-1y\"),\n", " exposures=ExposureSettings(\n", " exposures=[\n", " ContinuousExposureGroupSettings(hierarchy=\"market\"),\n", " CategoricalExposureGroupSettings(hierarchy=\"trbc\"),\n", " ContinuousExposureGroupSettings(hierarchy=\"style\"),\n", " ]\n", " ),\n", " modelconstruction=ModelConstructionSettings(\n", " estimation_universe=None,\n", " zero_sum_constraints={\"trbc\": \"mcap_weighted\"}\n", " ),\n", ")" ] }, { "cell_type": "markdown", "id": "b37a35a0", "metadata": {}, "source": [ "We then define and load the report. We have different multi-period aggregation settings available, which we do not go into here." ] }, { "cell_type": "code", "execution_count": 8, "id": "78da2620", "metadata": {}, "outputs": [], "source": [ "# construct the report\n", "report_settings = ReportSettings(\n", " report=FactorAttributionDrilldownReportSettings(\n", " measures=[\n", " FactorAttributionDrilldownMeasureSettings( # return attribution\n", " normalize_holdings=True,\n", " return_aggregation_type=\"geometric\",\n", " ),\n", " FactorAttributionDrilldownMeasureSettings( # PnL attribution\n", " normalize_holdings=False,\n", " return_aggregation_type=\"arithmetic\",\n", " ),\n", " ],\n", " ),\n", " risk_model=factorriskmodel_settings,\n", ")\n", "report_engine = bln.equity.portfolioreport.load(\n", " report_settings,\n", " hierarchy_ref_or_settings=portfoliohierarchy_settings,\n", ")" ] }, { "cell_type": "markdown", "id": "5c73fe88", "metadata": {}, "source": [ "The order defines the axes over which we can aggregate. For example by putting fund, book and ticker in our portfolio axis, we can filter and aggregate along this dimension at a later stage. The concepts of fund, book, and ticker were defined through the PortfolioHierarchySettings above." ] }, { "cell_type": "code", "execution_count": 9, "id": "3135f018", "metadata": {}, "outputs": [], "source": [ "order = {\n", " \"date\": [\"date\"],\n", " \"portfolio\": [\"fund\", \"book\", \"ticker\"],\n", " \"factor_and_idio\": [\"type\", \"factor\", \"factor_group\"],\n", " \"asset\": [\"input_asset_id\"],\n", "}\n", "report_accessor = report_engine.get_report(\n", " order,\n", " date_start=\"2025-01-03\",\n", " date_end=\"2025-01-31\",\n", " subtotals=[\"date\", \"portfolio\", \"factor_and_idio\", \"asset\"],\n", ")" ] }, { "cell_type": "markdown", "id": "d1338c47", "metadata": {}, "source": [ "Let's first look at the return attribution from all sources at the ticker level (recall that ticker is defined in the portfolio hierarchy). For a specific ticker, we want to know the return attribution to different assets at each date." ] }, { "cell_type": "code", "execution_count": 10, "id": "155d459d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (57, 5)
tickerdateinput_asset_idReturnPnL
strstrstrf32f32
"AGTHX""2025-01-03""02079K305"0.0062530.006234
"AGTHX""2025-01-03""2588173"0.0056760.005658
"AGTHX""2025-01-03""67066G10"0.00.0
"AGTHX""2025-01-06""02079K305"0.0133010.013418
"AGTHX""2025-01-06""2588173"0.0052920.005338
……………
"AGTHX""2025-01-30""2588173"-0.031159-0.032432
"AGTHX""2025-01-30""67066G10"0.00.0
"AGTHX""2025-01-31""02079K305"0.0081340.00832
"AGTHX""2025-01-31""2588173"0.0000810.000083
"AGTHX""2025-01-31""67066G10"0.00.0
" ], "text/plain": [ "shape: (57, 5)\n", "┌────────┬────────────┬────────────────┬───────────┬───────────┐\n", "│ ticker ┆ date ┆ input_asset_id ┆ Return ┆ PnL │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ str ┆ str ┆ str ┆ f32 ┆ f32 │\n", "╞════════╪════════════╪════════════════╪═══════════╪═══════════╡\n", "│ AGTHX ┆ 2025-01-03 ┆ 02079K305 ┆ 0.006253 ┆ 0.006234 │\n", "│ AGTHX ┆ 2025-01-03 ┆ 2588173 ┆ 0.005676 ┆ 0.005658 │\n", "│ AGTHX ┆ 2025-01-03 ┆ 67066G10 ┆ 0.0 ┆ 0.0 │\n", "│ AGTHX ┆ 2025-01-06 ┆ 02079K305 ┆ 0.013301 ┆ 0.013418 │\n", "│ AGTHX ┆ 2025-01-06 ┆ 2588173 ┆ 0.005292 ┆ 0.005338 │\n", "│ … ┆ … ┆ … ┆ … ┆ … │\n", "│ AGTHX ┆ 2025-01-30 ┆ 2588173 ┆ -0.031159 ┆ -0.032432 │\n", "│ AGTHX ┆ 2025-01-30 ┆ 67066G10 ┆ 0.0 ┆ 0.0 │\n", "│ AGTHX ┆ 2025-01-31 ┆ 02079K305 ┆ 0.008134 ┆ 0.00832 │\n", "│ AGTHX ┆ 2025-01-31 ┆ 2588173 ┆ 0.000081 ┆ 0.000083 │\n", "│ AGTHX ┆ 2025-01-31 ┆ 67066G10 ┆ 0.0 ┆ 0.0 │\n", "└────────┴────────────┴────────────────┴───────────┴───────────┘" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for a specific ticker, the return attribution across dates and assets\n", "(\n", " report_accessor.get_data(\n", " [(\"ticker\", \"AGTHX\")], # filters: [ticker=AGTHX]\n", " expand=(\"date\", \"input_asset_id\",), # combinations define the rows\n", " value_cols=(\"Return\", \"PnL\"), # exclude benchmark / active\n", " )\n", " .select(\"ticker\", \"date\", \"input_asset_id\", \"Return\", \"PnL\")\n", ")" ] }, { "cell_type": "markdown", "id": "8926e0ee", "metadata": {}, "source": [ "Using different settings, we can split out the returns across all sources (factors and idio) at a different level. Below we show the returns on a specific date, for a specific book, split out by factor." ] }, { "cell_type": "code", "execution_count": 11, "id": "e2b0ecca", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (22, 7)
datebooktypefactor_groupfactorReturnPnL
strstrstrstrstrf32f32
"2025-01-03""book_1""Factors""market""Market"0.0136630.027693
"2025-01-03""book_1""Factors""trbc""Energy"0.00.0
"2025-01-03""book_1""Factors""trbc""Basic Materials"0.00.0
"2025-01-03""book_1""Factors""trbc""Industrials"0.00.0
"2025-01-03""book_1""Factors""trbc""Consumer Cyclicals"0.00.0
…………………
"2025-01-03""book_1""Factors""style""Volatility"-0.000889-0.001802
"2025-01-03""book_1""Factors""style""Momentum"0.0022960.004653
"2025-01-03""book_1""Factors""style""Dividend"0.0000910.000185
"2025-01-03""book_1""Factors""style""Leverage"0.0005910.001198
"2025-01-03""book_1""Idiosyncratic"""""0.0065210.013217
" ], "text/plain": [ "shape: (22, 7)\n", "┌────────────┬────────┬───────────────┬──────────────┬────────────────────┬───────────┬───────────┐\n", "│ date ┆ book ┆ type ┆ factor_group ┆ factor ┆ Return ┆ PnL │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ str ┆ str ┆ str ┆ str ┆ str ┆ f32 ┆ f32 │\n", "╞════════════╪════════╪═══════════════╪══════════════╪════════════════════╪═══════════╪═══════════╡\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ market ┆ Market ┆ 0.013663 ┆ 0.027693 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ trbc ┆ Energy ┆ 0.0 ┆ 0.0 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ trbc ┆ Basic Materials ┆ 0.0 ┆ 0.0 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ trbc ┆ Industrials ┆ 0.0 ┆ 0.0 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ trbc ┆ Consumer Cyclicals ┆ 0.0 ┆ 0.0 │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ style ┆ Volatility ┆ -0.000889 ┆ -0.001802 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ style ┆ Momentum ┆ 0.002296 ┆ 0.004653 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ style ┆ Dividend ┆ 0.000091 ┆ 0.000185 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ style ┆ Leverage ┆ 0.000591 ┆ 0.001198 │\n", "│ 2025-01-03 ┆ book_1 ┆ Idiosyncratic ┆ ┆ ┆ 0.006521 ┆ 0.013217 │\n", "└────────────┴────────┴───────────────┴──────────────┴────────────────────┴───────────┴───────────┘" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for a specific book on a specific date, the factor return attribution across assets\n", "(\n", " report_accessor.get_data(\n", " [(\"date\", \"2025-01-03\"), (\"book\", \"book_1\")], # filters\n", " expand=(\"type\", \"factor_group\", \"factor\",),\n", " value_cols=(\"Return\", \"PnL\"),\n", " )\n", " .select(\"date\", \"book\", \"type\", \"factor_group\", \"factor\", \"Return\", \"PnL\")\n", ")" ] }, { "cell_type": "markdown", "id": "55682bbc", "metadata": {}, "source": [ "We can drill down further by including the asset dimension. This decomposes the returns of the assets in a specific book, on a specific date." ] }, { "cell_type": "code", "execution_count": 12, "id": "bf5ab534", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (66, 8)
datebooktypefactor_groupfactorinput_asset_idReturnPnL
strstrstrstrstrstrf32f32
"2025-01-03""book_1""Factors""market""Market""02079K305"0.0033730.006836
"2025-01-03""book_1""Factors""market""Market""2588173"0.0033470.006784
"2025-01-03""book_1""Factors""market""Market""67066G10"0.0069430.014072
"2025-01-03""book_1""Factors""trbc""Energy""02079K305"0.00.0
"2025-01-03""book_1""Factors""trbc""Energy""2588173"0.00.0
……………………
"2025-01-03""book_1""Factors""style""Leverage""2588173"-0.000065-0.000131
"2025-01-03""book_1""Factors""style""Leverage""67066G10"0.0002210.000449
"2025-01-03""book_1""Idiosyncratic""""""02079K305"-0.001356-0.002748
"2025-01-03""book_1""Idiosyncratic""""""2588173"-0.000086-0.000175
"2025-01-03""book_1""Idiosyncratic""""""67066G10"0.0079630.016139
" ], "text/plain": [ "shape: (66, 8)\n", "┌────────────┬────────┬─────────────┬─────────────┬──────────┬─────────────┬───────────┬───────────┐\n", "│ date ┆ book ┆ type ┆ factor_grou ┆ factor ┆ input_asset ┆ Return ┆ PnL │\n", "│ --- ┆ --- ┆ --- ┆ p ┆ --- ┆ _id ┆ --- ┆ --- │\n", "│ str ┆ str ┆ str ┆ --- ┆ str ┆ --- ┆ f32 ┆ f32 │\n", "│ ┆ ┆ ┆ str ┆ ┆ str ┆ ┆ │\n", "╞════════════╪════════╪═════════════╪═════════════╪══════════╪═════════════╪═══════════╪═══════════╡\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ market ┆ Market ┆ 02079K305 ┆ 0.003373 ┆ 0.006836 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ market ┆ Market ┆ 2588173 ┆ 0.003347 ┆ 0.006784 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ market ┆ Market ┆ 67066G10 ┆ 0.006943 ┆ 0.014072 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ trbc ┆ Energy ┆ 02079K305 ┆ 0.0 ┆ 0.0 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ trbc ┆ Energy ┆ 2588173 ┆ 0.0 ┆ 0.0 │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ style ┆ Leverage ┆ 2588173 ┆ -0.000065 ┆ -0.000131 │\n", "│ 2025-01-03 ┆ book_1 ┆ Factors ┆ style ┆ Leverage ┆ 67066G10 ┆ 0.000221 ┆ 0.000449 │\n", "│ 2025-01-03 ┆ book_1 ┆ Idiosyncrat ┆ ┆ ┆ 02079K305 ┆ -0.001356 ┆ -0.002748 │\n", "│ ┆ ┆ ic ┆ ┆ ┆ ┆ ┆ │\n", "│ 2025-01-03 ┆ book_1 ┆ Idiosyncrat ┆ ┆ ┆ 2588173 ┆ -0.000086 ┆ -0.000175 │\n", "│ ┆ ┆ ic ┆ ┆ ┆ ┆ ┆ │\n", "│ 2025-01-03 ┆ book_1 ┆ Idiosyncrat ┆ ┆ ┆ 67066G10 ┆ 0.007963 ┆ 0.016139 │\n", "│ ┆ ┆ ic ┆ ┆ ┆ ┆ ┆ │\n", "└────────────┴────────┴─────────────┴─────────────┴──────────┴─────────────┴───────────┴───────────┘" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for a specific book on a specific date, the factor return attribution by asset\n", "(\n", " report_accessor.get_data(\n", " [(\"date\", \"2025-01-03\"), (\"book\", \"book_1\")], # filters\n", " expand=(\"type\", \"factor_group\", \"factor\", \"input_asset_id\"),\n", " value_cols=(\"Return\", \"PnL\"),\n", " )\n", " .select(\"date\", \"book\", \"type\", \"factor_group\", \"factor\", \"input_asset_id\", \"Return\", \"PnL\")\n", ")" ] }, { "cell_type": "markdown", "id": "9d0ad898", "metadata": {}, "source": [ "By using the filters, we can also isolate the specific return attribution." ] }, { "cell_type": "code", "execution_count": 13, "id": "101e3c80", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (57, 5)
funddateinput_asset_idReturnPnL
strstrstrf32f32
"some_fund""2025-01-03""02079K305"-0.001496-0.006045
"some_fund""2025-01-03""2588173"-0.000112-0.000454
"some_fund""2025-01-03""67066G10"0.0063920.025823
"some_fund""2025-01-06""02079K305"0.0040320.016697
"some_fund""2025-01-06""2588173"0.0007160.002964
……………
"some_fund""2025-01-30""2588173"-0.023051-0.091597
"some_fund""2025-01-30""67066G10"-0.001804-0.00717
"some_fund""2025-01-31""02079K305"0.0050310.019784
"some_fund""2025-01-31""2588173"0.0006530.002569
"some_fund""2025-01-31""67066G10"-0.013499-0.053083
" ], "text/plain": [ "shape: (57, 5)\n", "┌───────────┬────────────┬────────────────┬───────────┬───────────┐\n", "│ fund ┆ date ┆ input_asset_id ┆ Return ┆ PnL │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ str ┆ str ┆ str ┆ f32 ┆ f32 │\n", "╞═══════════╪════════════╪════════════════╪═══════════╪═══════════╡\n", "│ some_fund ┆ 2025-01-03 ┆ 02079K305 ┆ -0.001496 ┆ -0.006045 │\n", "│ some_fund ┆ 2025-01-03 ┆ 2588173 ┆ -0.000112 ┆ -0.000454 │\n", "│ some_fund ┆ 2025-01-03 ┆ 67066G10 ┆ 0.006392 ┆ 0.025823 │\n", "│ some_fund ┆ 2025-01-06 ┆ 02079K305 ┆ 0.004032 ┆ 0.016697 │\n", "│ some_fund ┆ 2025-01-06 ┆ 2588173 ┆ 0.000716 ┆ 0.002964 │\n", "│ … ┆ … ┆ … ┆ … ┆ … │\n", "│ some_fund ┆ 2025-01-30 ┆ 2588173 ┆ -0.023051 ┆ -0.091597 │\n", "│ some_fund ┆ 2025-01-30 ┆ 67066G10 ┆ -0.001804 ┆ -0.00717 │\n", "│ some_fund ┆ 2025-01-31 ┆ 02079K305 ┆ 0.005031 ┆ 0.019784 │\n", "│ some_fund ┆ 2025-01-31 ┆ 2588173 ┆ 0.000653 ┆ 0.002569 │\n", "│ some_fund ┆ 2025-01-31 ┆ 67066G10 ┆ -0.013499 ┆ -0.053083 │\n", "└───────────┴────────────┴────────────────┴───────────┴───────────┘" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# the idiosyncratic returns in a specific fund\n", "(\n", " report_accessor.get_data(\n", " [(\"fund\", \"some_fund\"), (\"type\", \"Idiosyncratic\")], # filters\n", " expand=(\"date\", \"input_asset_id\",),\n", " value_cols=(\"Return\", \"PnL\"),\n", " )\n", " .select(\"fund\", \"date\", \"input_asset_id\", \"Return\", \"PnL\")\n", ")" ] }, { "cell_type": "markdown", "id": "7c418fe1", "metadata": {}, "source": [ "We can now to the same for risk attribution / decomposition" ] }, { "cell_type": "code", "execution_count": 14, "id": "7b46e364", "metadata": {}, "outputs": [], "source": [ "# construct the report\n", "report_settings = ReportSettings(\n", " report=XSRDrillDownReportSettings(), # uses default measures & forecasting settings\n", " risk_model=factorriskmodel_settings,\n", ")\n", "report_engine = bln.equity.portfolioreport.load(\n", " report_settings,\n", " hierarchy_ref_or_settings=portfoliohierarchy_settings,\n", ")" ] }, { "cell_type": "markdown", "id": "61bd00e3", "metadata": {}, "source": [ "Orders work the same as for attribution" ] }, { "cell_type": "code", "execution_count": 15, "id": "3b7d6da3", "metadata": {}, "outputs": [], "source": [ "order = {\n", " \"date\": [\"date\"],\n", " \"portfolio\": [\"fund\", \"book\", \"ticker\"],\n", " \"factor_and_idio\": [\"type\", \"factor\", \"factor_group\"],\n", " \"asset\": [\"input_asset_id\"],\n", "}\n", "report_accessor = report_engine.get_report(\n", " order,\n", " date_start=\"2025-01-03\",\n", " date_end=\"2025-01-31\",\n", " subtotals=[\"date\", \"portfolio\", \"factor_and_idio\", \"asset\"],\n", ")" ] }, { "cell_type": "code", "execution_count": 16, "id": "4f600683", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (22, 9)
tickerdatetypefactor_groupfactorExposure (X)Volatility (σ)Correlation (ρ)Contribution (Xσρ)
strstrstrstrstrf32f32f32f32
"AGTHX""2025-01-03""Factors""market""Market"1.00.1455710.5817240.084682
"AGTHX""2025-01-03""Factors""trbc""Energy"1.0000e-160.161093-0.195762-3.1536e-18
"AGTHX""2025-01-03""Factors""trbc""Basic Materials"1.0000e-160.084038-0.158261-1.3300e-18
"AGTHX""2025-01-03""Factors""trbc""Industrials"1.0000e-160.051383-0.013657-7.0171e-20
"AGTHX""2025-01-03""Factors""trbc""Consumer Cyclicals"1.0000e-160.058483-0.278516-1.6288e-18
………………………
"AGTHX""2025-01-03""Factors""style""Volatility"-0.6156080.0944330.593388-0.034496
"AGTHX""2025-01-03""Factors""style""Momentum"0.6717580.0488240.2741030.00899
"AGTHX""2025-01-03""Factors""style""Dividend"-0.088540.024852-0.3166760.000697
"AGTHX""2025-01-03""Factors""style""Leverage"-0.226360.021849-0.3650770.001806
"AGTHX""2025-01-03""Idiosyncratic"""""1.00.1433070.6636460.095105
" ], "text/plain": [ "shape: (22, 9)\n", "┌────────┬────────────┬────────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", "│ ticker ┆ date ┆ type ┆ factor_gr ┆ … ┆ Exposure ┆ Volatilit ┆ Correlati ┆ Contribut │\n", "│ --- ┆ --- ┆ --- ┆ oup ┆ ┆ (X) ┆ y (σ) ┆ on (ρ) ┆ ion (Xσρ) │\n", "│ str ┆ str ┆ str ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ ┆ ┆ ┆ str ┆ ┆ f32 ┆ f32 ┆ f32 ┆ f32 │\n", "╞════════╪════════════╪════════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", "│ AGTHX ┆ 2025-01-03 ┆ Factors ┆ market ┆ … ┆ 1.0 ┆ 0.145571 ┆ 0.581724 ┆ 0.084682 │\n", "│ AGTHX ┆ 2025-01-03 ┆ Factors ┆ trbc ┆ … ┆ 1.0000e-1 ┆ 0.161093 ┆ -0.195762 ┆ -3.1536e- │\n", "│ ┆ ┆ ┆ ┆ ┆ 6 ┆ ┆ ┆ 18 │\n", "│ AGTHX ┆ 2025-01-03 ┆ Factors ┆ trbc ┆ … ┆ 1.0000e-1 ┆ 0.084038 ┆ -0.158261 ┆ -1.3300e- │\n", "│ ┆ ┆ ┆ ┆ ┆ 6 ┆ ┆ ┆ 18 │\n", "│ AGTHX ┆ 2025-01-03 ┆ Factors ┆ trbc ┆ … ┆ 1.0000e-1 ┆ 0.051383 ┆ -0.013657 ┆ -7.0171e- │\n", "│ ┆ ┆ ┆ ┆ ┆ 6 ┆ ┆ ┆ 20 │\n", "│ AGTHX ┆ 2025-01-03 ┆ Factors ┆ trbc ┆ … ┆ 1.0000e-1 ┆ 0.058483 ┆ -0.278516 ┆ -1.6288e- │\n", "│ ┆ ┆ ┆ ┆ ┆ 6 ┆ ┆ ┆ 18 │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ AGTHX ┆ 2025-01-03 ┆ Factors ┆ style ┆ … ┆ -0.615608 ┆ 0.094433 ┆ 0.593388 ┆ -0.034496 │\n", "│ AGTHX ┆ 2025-01-03 ┆ Factors ┆ style ┆ … ┆ 0.671758 ┆ 0.048824 ┆ 0.274103 ┆ 0.00899 │\n", "│ AGTHX ┆ 2025-01-03 ┆ Factors ┆ style ┆ … ┆ -0.08854 ┆ 0.024852 ┆ -0.316676 ┆ 0.000697 │\n", "│ AGTHX ┆ 2025-01-03 ┆ Factors ┆ style ┆ … ┆ -0.22636 ┆ 0.021849 ┆ -0.365077 ┆ 0.001806 │\n", "│ AGTHX ┆ 2025-01-03 ┆ Idiosyncra ┆ ┆ … ┆ 1.0 ┆ 0.143307 ┆ 0.663646 ┆ 0.095105 │\n", "│ ┆ ┆ tic ┆ ┆ ┆ ┆ ┆ ┆ │\n", "└────────┴────────────┴────────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for a specific ticker and date, the return attribution across the factors\n", "(\n", " report_accessor.get_data(\n", " [(\"ticker\", \"AGTHX\"), (\"date\", \"2025-01-03\")], # filters: [ticker=AGTHX]\n", " expand=(\"type\", \"factor_group\", \"factor\",), # combinations define the rows\n", " value_cols=report_accessor.metric_cols, # X-Sigma-Rho and Contribution\n", " )\n", " .select(\"ticker\", \"date\", \"type\", \"factor_group\", \"factor\", *report_accessor.metric_cols)\n", ")" ] }, { "cell_type": "code", "execution_count": 17, "id": "a58a911c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (57, 8)
tickerfactordateinput_asset_idExposure (X)Volatility (σ)Correlation (ρ)Contribution (Xσρ)
strstrstrstrf32f32f32f32
"AGTHX""Volatility""2025-01-03""02079K305"-0.2116090.0944330.593388-0.011858
"AGTHX""Volatility""2025-01-03""2588173"-0.4039990.0944330.593388-0.022638
"AGTHX""Volatility""2025-01-03""67066G10"1.0000e-160.0944330.5933885.6035e-18
"AGTHX""Volatility""2025-01-06""02079K305"-0.2137430.0941540.577862-0.011629
"AGTHX""Volatility""2025-01-06""2588173"-0.415070.0941540.577862-0.022583
……………………
"AGTHX""Volatility""2025-01-30""2588173"-0.2617930.098530.587572-0.015156
"AGTHX""Volatility""2025-01-30""67066G10"1.0000e-160.098530.5875725.7893e-18
"AGTHX""Volatility""2025-01-31""02079K305"-0.1466310.097580.599562-0.008579
"AGTHX""Volatility""2025-01-31""2588173"-0.2423580.097580.599562-0.014179
"AGTHX""Volatility""2025-01-31""67066G10"1.0000e-160.097580.5995625.8505e-18
" ], "text/plain": [ "shape: (57, 8)\n", "┌────────┬────────────┬────────────┬────────────┬────────────┬────────────┬────────────┬───────────┐\n", "│ ticker ┆ factor ┆ date ┆ input_asse ┆ Exposure ┆ Volatility ┆ Correlatio ┆ Contribut │\n", "│ --- ┆ --- ┆ --- ┆ t_id ┆ (X) ┆ (σ) ┆ n (ρ) ┆ ion (Xσρ) │\n", "│ str ┆ str ┆ str ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ ┆ ┆ ┆ str ┆ f32 ┆ f32 ┆ f32 ┆ f32 │\n", "╞════════╪════════════╪════════════╪════════════╪════════════╪════════════╪════════════╪═══════════╡\n", "│ AGTHX ┆ Volatility ┆ 2025-01-03 ┆ 02079K305 ┆ -0.211609 ┆ 0.094433 ┆ 0.593388 ┆ -0.011858 │\n", "│ AGTHX ┆ Volatility ┆ 2025-01-03 ┆ 2588173 ┆ -0.403999 ┆ 0.094433 ┆ 0.593388 ┆ -0.022638 │\n", "│ AGTHX ┆ Volatility ┆ 2025-01-03 ┆ 67066G10 ┆ 1.0000e-16 ┆ 0.094433 ┆ 0.593388 ┆ 5.6035e-1 │\n", "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 8 │\n", "│ AGTHX ┆ Volatility ┆ 2025-01-06 ┆ 02079K305 ┆ -0.213743 ┆ 0.094154 ┆ 0.577862 ┆ -0.011629 │\n", "│ AGTHX ┆ Volatility ┆ 2025-01-06 ┆ 2588173 ┆ -0.41507 ┆ 0.094154 ┆ 0.577862 ┆ -0.022583 │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ AGTHX ┆ Volatility ┆ 2025-01-30 ┆ 2588173 ┆ -0.261793 ┆ 0.09853 ┆ 0.587572 ┆ -0.015156 │\n", "│ AGTHX ┆ Volatility ┆ 2025-01-30 ┆ 67066G10 ┆ 1.0000e-16 ┆ 0.09853 ┆ 0.587572 ┆ 5.7893e-1 │\n", "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 8 │\n", "│ AGTHX ┆ Volatility ┆ 2025-01-31 ┆ 02079K305 ┆ -0.146631 ┆ 0.09758 ┆ 0.599562 ┆ -0.008579 │\n", "│ AGTHX ┆ Volatility ┆ 2025-01-31 ┆ 2588173 ┆ -0.242358 ┆ 0.09758 ┆ 0.599562 ┆ -0.014179 │\n", "│ AGTHX ┆ Volatility ┆ 2025-01-31 ┆ 67066G10 ┆ 1.0000e-16 ┆ 0.09758 ┆ 0.599562 ┆ 5.8505e-1 │\n", "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 8 │\n", "└────────┴────────────┴────────────┴────────────┴────────────┴────────────┴────────────┴───────────┘" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for a specific ticker and factor, the return attribution across dates and assets\n", "(\n", " report_accessor.get_data(\n", " [(\"ticker\", \"AGTHX\"), (\"factor\", \"Volatility\")], # filters\n", " expand=(\"date\", \"input_asset_id\",), # combinations define the rows\n", " value_cols=report_accessor.metric_cols, # X-Sigma-Rho and Contribution\n", " )\n", " .select(\"ticker\", \"factor\", \"date\", \"input_asset_id\", *report_accessor.metric_cols)\n", ")" ] } ], "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": "3.11.14" } }, "nbformat": 4, "nbformat_minor": 5 }