{ "cells": [ { "cell_type": "markdown", "id": "0ca41be5", "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": "69bc2115", "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": "44258137", "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", " ReturnAttributionReportSettings,\n", " XSRReportSettings,\n", ")" ] }, { "cell_type": "markdown", "id": "213aba0b", "metadata": {}, "source": [ "We will also need to have a Bayesline API client configured." ] }, { "cell_type": "code", "execution_count": null, "id": "83643cdb", "metadata": { "tags": [ "skip-execution" ] }, "outputs": [], "source": [ "bln = BayeslineApiClient.new_client(\n", " endpoint=\"https://[ENDPOINT]\",\n", " api_key=\"[API-KEY]\",\n", ")" ] }, { "cell_type": "markdown", "id": "7ecce8a8", "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/0.14.0/notebooks/tutorial_portfolio_hierarchies.html)." ] }, { "cell_type": "code", "execution_count": 3, "id": "b7bf6f0a", "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": "0bb5be03", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (20, 7)
portfolio_idasset_idasset_id_typedatecurrencyshare_qtynav
strstrstrdatestrf64f64
"AGTHX""02079K305""cusip9"2026-01-01nullnull0.5
"AGTHX""02079K305""cusip9"2026-01-31nullnull0.55
"AGTHX""2588173""sedol7"2026-01-01nullnull0.5
"AGTHX""2588173""sedol7"2026-01-31nullnull0.45
"FCNTX""67066G10""cusip8"2026-01-01nullnull1.0
…………………
"SPX""2588173""sedol7"2026-01-31nullnull0.2
"SPX""67066G10""cusip8"2026-01-01nullnull0.3
"SPX""67066G10""cusip8"2026-01-31nullnull0.25
"SPX""85371710""cusip8"2026-01-01nullnull0.3
"SPX""85371710""cusip8"2026-01-31nullnull0.25
" ], "text/plain": [ "shape: (20, 7)\n", "┌──────────────┬───────────┬───────────────┬────────────┬──────────┬───────────┬──────┐\n", "│ portfolio_id ┆ asset_id ┆ asset_id_type ┆ date ┆ currency ┆ share_qty ┆ nav │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ str ┆ str ┆ str ┆ date ┆ str ┆ f64 ┆ f64 │\n", "╞══════════════╪═══════════╪═══════════════╪════════════╪══════════╪═══════════╪══════╡\n", "│ AGTHX ┆ 02079K305 ┆ cusip9 ┆ 2026-01-01 ┆ null ┆ null ┆ 0.5 │\n", "│ AGTHX ┆ 02079K305 ┆ cusip9 ┆ 2026-01-31 ┆ null ┆ null ┆ 0.55 │\n", "│ AGTHX ┆ 2588173 ┆ sedol7 ┆ 2026-01-01 ┆ null ┆ null ┆ 0.5 │\n", "│ AGTHX ┆ 2588173 ┆ sedol7 ┆ 2026-01-31 ┆ null ┆ null ┆ 0.45 │\n", "│ FCNTX ┆ 67066G10 ┆ cusip8 ┆ 2026-01-01 ┆ null ┆ null ┆ 1.0 │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ SPX ┆ 2588173 ┆ sedol7 ┆ 2026-01-31 ┆ null ┆ null ┆ 0.2 │\n", "│ SPX ┆ 67066G10 ┆ cusip8 ┆ 2026-01-01 ┆ null ┆ null ┆ 0.3 │\n", "│ SPX ┆ 67066G10 ┆ cusip8 ┆ 2026-01-31 ┆ null ┆ null ┆ 0.25 │\n", "│ SPX ┆ 85371710 ┆ cusip8 ┆ 2026-01-01 ┆ null ┆ null ┆ 0.3 │\n", "│ SPX ┆ 85371710 ┆ cusip8 ┆ 2026-01-31 ┆ null ┆ null ┆ 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(2026, 1, 1), dt.date(2026, 1, 31), \n", " dt.date(2026, 1, 1), dt.date(2026, 1, 31),\n", "\n", " # FCNTX\n", " dt.date(2026, 1, 1), dt.date(2026, 1, 31),\n", "\n", " # VADGX\n", " dt.date(2026, 1, 1), dt.date(2026, 1, 31), \n", " dt.date(2026, 1, 1), dt.date(2026, 1, 31), \n", " dt.date(2026, 1, 1), dt.date(2026, 1, 31), \n", " \n", " # SPX\n", " dt.date(2026, 1, 1), dt.date(2026, 1, 31), \n", " dt.date(2026, 1, 1), dt.date(2026, 1, 31), \n", " dt.date(2026, 1, 1), dt.date(2026, 1, 31), \n", " dt.date(2026, 1, 1), dt.date(2026, 1, 31), \n", " ],\n", " \"currency\": [None] * 20,\n", " \"share_qty\": [None] * 20,\n", " \"nav\": [\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", "}).with_columns(pl.col(\"currency\").cast(pl.String), pl.col(\"share_qty\").cast(pl.Float64))\n", "\n", "portfolio_df" ] }, { "cell_type": "code", "execution_count": 5, "id": "838042a1", "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": "c9e81463", "metadata": { "lines_to_next_cell": 2 }, "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, \n", " portfolio_source=\"portfolio-attribution-demo\",\n", ")\n", "portfoliohierarchy_settings.to_polars()" ] }, { "cell_type": "markdown", "id": "e2e735af", "metadata": {}, "source": [ "Now that we have the portfolio hiearachy in place, we define a basic factor model." ] }, { "cell_type": "code", "execution_count": 7, "id": "5abbee63", "metadata": {}, "outputs": [], "source": [ "factorriskmodel_settings = FactorRiskModelSettings(\n", " universe=UniverseSettings(),\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": "b3fd6153", "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": "7bc2a7f8", "metadata": {}, "outputs": [], "source": [ "report_settings = ReturnAttributionReportSettings(\n", " portfolio_hierarchy_settings=portfoliohierarchy_settings,\n", " factor_model_settings=factorriskmodel_settings,\n", " normalize_holdings=False, \n", " return_aggregation_type=\"arithmetic\",\n", ")\n", "report_engine = bln.equity.reports.load(\n", " report_settings.with_dataset(\"bayesline/Bayesline-US-All-1y\")\n", ")\n", "report = report_engine.calculate(start_date=\"2026-01-05\", end_date=\"2026-01-30\")" ] }, { "cell_type": "markdown", "id": "656155d5", "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": 9, "id": "8df9e4bc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (76, 4)
tickerdateinput_asset_idPnL
strdatestrf32
"AGTHX"2026-01-05"02079K305"0.002221
"AGTHX"2026-01-05"2588173"-0.000093
"AGTHX"2026-01-05"67066G10"0.0
"AGTHX"2026-01-05"85371710"0.0
"AGTHX"2026-01-06"02079K305"-0.003514
…………
"AGTHX"2026-01-29"85371710"0.0
"AGTHX"2026-01-30"02079K305"-0.000399
"AGTHX"2026-01-30"2588173"-0.003319
"AGTHX"2026-01-30"67066G10"0.0
"AGTHX"2026-01-30"85371710"0.0
" ], "text/plain": [ "shape: (76, 4)\n", "┌────────┬────────────┬────────────────┬───────────┐\n", "│ ticker ┆ date ┆ input_asset_id ┆ PnL │\n", "│ --- ┆ --- ┆ --- ┆ --- │\n", "│ str ┆ date ┆ str ┆ f32 │\n", "╞════════╪════════════╪════════════════╪═══════════╡\n", "│ AGTHX ┆ 2026-01-05 ┆ 02079K305 ┆ 0.002221 │\n", "│ AGTHX ┆ 2026-01-05 ┆ 2588173 ┆ -0.000093 │\n", "│ AGTHX ┆ 2026-01-05 ┆ 67066G10 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-05 ┆ 85371710 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-06 ┆ 02079K305 ┆ -0.003514 │\n", "│ … ┆ … ┆ … ┆ … │\n", "│ AGTHX ┆ 2026-01-29 ┆ 85371710 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-30 ┆ 02079K305 ┆ -0.000399 │\n", "│ AGTHX ┆ 2026-01-30 ┆ 2588173 ┆ -0.003319 │\n", "│ AGTHX ┆ 2026-01-30 ┆ 67066G10 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-30 ┆ 85371710 ┆ 0.0 │\n", "└────────┴────────────┴────────────────┴───────────┘" ] }, "execution_count": 9, "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=(\"PnL\",), # exclude benchmark / active\n", " )\n", " .select(\"ticker\", \"date\", \"input_asset_id\", \"PnL\")\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "id": "a4e1b603", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (76, 4)
tickerdateinput_asset_idPnL
strdatestrf32
"AGTHX"2026-01-05"02079K305"0.002221
"AGTHX"2026-01-05"2588173"-0.000093
"AGTHX"2026-01-05"67066G10"0.0
"AGTHX"2026-01-05"85371710"0.0
"AGTHX"2026-01-06"02079K305"-0.003514
…………
"AGTHX"2026-01-29"85371710"0.0
"AGTHX"2026-01-30"02079K305"-0.000399
"AGTHX"2026-01-30"2588173"-0.003319
"AGTHX"2026-01-30"67066G10"0.0
"AGTHX"2026-01-30"85371710"0.0
" ], "text/plain": [ "shape: (76, 4)\n", "┌────────┬────────────┬────────────────┬───────────┐\n", "│ ticker ┆ date ┆ input_asset_id ┆ PnL │\n", "│ --- ┆ --- ┆ --- ┆ --- │\n", "│ str ┆ date ┆ str ┆ f32 │\n", "╞════════╪════════════╪════════════════╪═══════════╡\n", "│ AGTHX ┆ 2026-01-05 ┆ 02079K305 ┆ 0.002221 │\n", "│ AGTHX ┆ 2026-01-05 ┆ 2588173 ┆ -0.000093 │\n", "│ AGTHX ┆ 2026-01-05 ┆ 67066G10 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-05 ┆ 85371710 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-06 ┆ 02079K305 ┆ -0.003514 │\n", "│ … ┆ … ┆ … ┆ … │\n", "│ AGTHX ┆ 2026-01-29 ┆ 85371710 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-30 ┆ 02079K305 ┆ -0.000399 │\n", "│ AGTHX ┆ 2026-01-30 ┆ 2588173 ┆ -0.003319 │\n", "│ AGTHX ┆ 2026-01-30 ┆ 67066G10 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-30 ┆ 85371710 ┆ 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=(\"PnL\",), # exclude benchmark / active\n", " )\n", " .select(\"ticker\", \"date\", \"input_asset_id\", \"PnL\")\n", ")" ] }, { "cell_type": "markdown", "id": "4b9bcc80", "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": "7575ccac", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (22, 6)
datebooktypefactor_groupfactorPnL
datestrstrstrstrf32
2026-01-05"book_1""Factors""market""Market"0.014521
2026-01-05"book_1""Factors""style""Dividend"0.000741
2026-01-05"book_1""Factors""style""Growth"0.001469
2026-01-05"book_1""Factors""style""Leverage"-0.00059
2026-01-05"book_1""Factors""style""Momentum"-0.002752
………………
2026-01-05"book_1""Factors""trbc""Institutions, Associations & O…0.0
2026-01-05"book_1""Factors""trbc""Real Estate"0.0
2026-01-05"book_1""Factors""trbc""Technology"-0.004956
2026-01-05"book_1""Factors""trbc""Utilities"0.0
2026-01-05"book_1""Idiosyncratic"""""-0.022892
" ], "text/plain": [ "shape: (22, 6)\n", "┌────────────┬────────┬───────────────┬──────────────┬─────────────────────────────────┬───────────┐\n", "│ date ┆ book ┆ type ┆ factor_group ┆ factor ┆ PnL │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ date ┆ str ┆ str ┆ str ┆ str ┆ f32 │\n", "╞════════════╪════════╪═══════════════╪══════════════╪═════════════════════════════════╪═══════════╡\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ market ┆ Market ┆ 0.014521 │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ style ┆ Dividend ┆ 0.000741 │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ style ┆ Growth ┆ 0.001469 │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ style ┆ Leverage ┆ -0.00059 │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ style ┆ Momentum ┆ -0.002752 │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ trbc ┆ Institutions, Associations & O… ┆ 0.0 │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ trbc ┆ Real Estate ┆ 0.0 │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ trbc ┆ Technology ┆ -0.004956 │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ trbc ┆ Utilities ┆ 0.0 │\n", "│ 2026-01-05 ┆ book_1 ┆ Idiosyncratic ┆ ┆ ┆ -0.022892 │\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\", \"2026-01-05\"), (\"book\", \"book_1\")], # filters\n", " expand=(\"type\", \"factor_group\", \"factor\",),\n", " value_cols=(\"PnL\",),\n", " )\n", " .select(\"date\", \"book\", \"type\", \"factor_group\", \"factor\", \"PnL\")\n", ")" ] }, { "cell_type": "markdown", "id": "1e9d82ad", "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": "392864cf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (88, 7)
datebooktypefactor_groupfactorinput_asset_idPnL
datestrstrstrstrstrf32
2026-01-05"book_1""Factors""market""Market""02079K305"0.003646
2026-01-05"book_1""Factors""market""Market""2588173"0.003541
2026-01-05"book_1""Factors""market""Market""67066G10"0.007334
2026-01-05"book_1""Factors""market""Market""85371710"0.0
2026-01-05"book_1""Factors""style""Dividend""02079K305"0.000107
…………………
2026-01-05"book_1""Factors""trbc""Utilities""85371710"0.0
2026-01-05"book_1""Idiosyncratic""""""02079K305"-0.002582
2026-01-05"book_1""Idiosyncratic""""""2588173"-0.002947
2026-01-05"book_1""Idiosyncratic""""""67066G10"-0.017364
2026-01-05"book_1""Idiosyncratic""""""85371710"0.0
" ], "text/plain": [ "shape: (88, 7)\n", "┌────────────┬────────┬───────────────┬──────────────┬───────────┬────────────────┬───────────┐\n", "│ date ┆ book ┆ type ┆ factor_group ┆ factor ┆ input_asset_id ┆ PnL │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ date ┆ str ┆ str ┆ str ┆ str ┆ str ┆ f32 │\n", "╞════════════╪════════╪═══════════════╪══════════════╪═══════════╪════════════════╪═══════════╡\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ market ┆ Market ┆ 02079K305 ┆ 0.003646 │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ market ┆ Market ┆ 2588173 ┆ 0.003541 │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ market ┆ Market ┆ 67066G10 ┆ 0.007334 │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ market ┆ Market ┆ 85371710 ┆ 0.0 │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ style ┆ Dividend ┆ 02079K305 ┆ 0.000107 │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ 2026-01-05 ┆ book_1 ┆ Factors ┆ trbc ┆ Utilities ┆ 85371710 ┆ 0.0 │\n", "│ 2026-01-05 ┆ book_1 ┆ Idiosyncratic ┆ ┆ ┆ 02079K305 ┆ -0.002582 │\n", "│ 2026-01-05 ┆ book_1 ┆ Idiosyncratic ┆ ┆ ┆ 2588173 ┆ -0.002947 │\n", "│ 2026-01-05 ┆ book_1 ┆ Idiosyncratic ┆ ┆ ┆ 67066G10 ┆ -0.017364 │\n", "│ 2026-01-05 ┆ book_1 ┆ Idiosyncratic ┆ ┆ ┆ 85371710 ┆ 0.0 │\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\", \"2026-01-05\"), (\"book\", \"book_1\")], # filters\n", " expand=(\"type\", \"factor_group\", \"factor\", \"input_asset_id\"),\n", " value_cols=(\"PnL\",),\n", " )\n", " .select(\"date\", \"book\", \"type\", \"factor_group\", \"factor\", \"input_asset_id\", \"PnL\")\n", ")" ] }, { "cell_type": "markdown", "id": "c2500eef", "metadata": {}, "source": [ "By using the filters, we can also isolate the specific return attribution." ] }, { "cell_type": "code", "execution_count": 13, "id": "8af0a0b3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (76, 4)
funddateinput_asset_idPnL
strdatestrf32
"some_fund"2026-01-05"02079K305"-0.005679
"some_fund"2026-01-05"2588173"-0.007663
"some_fund"2026-01-05"67066G10"-0.027782
"some_fund"2026-01-05"85371710"0.0
"some_fund"2026-01-06"02079K305"-0.024212
…………
"some_fund"2026-01-29"85371710"0.0
"some_fund"2026-01-30"02079K305"0.0266
"some_fund"2026-01-30"2588173"-0.001122
"some_fund"2026-01-30"67066G10"0.023794
"some_fund"2026-01-30"85371710"0.0
" ], "text/plain": [ "shape: (76, 4)\n", "┌───────────┬────────────┬────────────────┬───────────┐\n", "│ fund ┆ date ┆ input_asset_id ┆ PnL │\n", "│ --- ┆ --- ┆ --- ┆ --- │\n", "│ str ┆ date ┆ str ┆ f32 │\n", "╞═══════════╪════════════╪════════════════╪═══════════╡\n", "│ some_fund ┆ 2026-01-05 ┆ 02079K305 ┆ -0.005679 │\n", "│ some_fund ┆ 2026-01-05 ┆ 2588173 ┆ -0.007663 │\n", "│ some_fund ┆ 2026-01-05 ┆ 67066G10 ┆ -0.027782 │\n", "│ some_fund ┆ 2026-01-05 ┆ 85371710 ┆ 0.0 │\n", "│ some_fund ┆ 2026-01-06 ┆ 02079K305 ┆ -0.024212 │\n", "│ … ┆ … ┆ … ┆ … │\n", "│ some_fund ┆ 2026-01-29 ┆ 85371710 ┆ 0.0 │\n", "│ some_fund ┆ 2026-01-30 ┆ 02079K305 ┆ 0.0266 │\n", "│ some_fund ┆ 2026-01-30 ┆ 2588173 ┆ -0.001122 │\n", "│ some_fund ┆ 2026-01-30 ┆ 67066G10 ┆ 0.023794 │\n", "│ some_fund ┆ 2026-01-30 ┆ 85371710 ┆ 0.0 │\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=(\"PnL\",),\n", " )\n", " .select(\"fund\", \"date\", \"input_asset_id\", \"PnL\")\n", ")" ] }, { "cell_type": "markdown", "id": "f7788d0c", "metadata": {}, "source": [ "We can now to the same for risk attribution / decomposition" ] }, { "cell_type": "code", "execution_count": 14, "id": "9c91e43e", "metadata": {}, "outputs": [], "source": [ "report_settings = XSRReportSettings(\n", " portfolio_hierarchy_settings=portfoliohierarchy_settings,\n", " factor_model_settings=factorriskmodel_settings,\n", " normalize_holdings=False,\n", ")\n", "report_engine = bln.equity.reports.load(\n", " report_settings.with_dataset(\"bayesline/Bayesline-US-All-1y\")\n", ")\n", "report = report_engine.calculate(start_date=\"2026-01-05\", end_date=\"2026-01-30\")" ] }, { "cell_type": "code", "execution_count": 15, "id": "604fd58a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (22, 10)
tickerdatetypefactor_groupfactorExposureVolatilityCorrelationContributionBeta
strdatestrstrstrf32f32f32f32f32
"AGTHX"2026-01-05"Factors""market""Market"0.994520.121030.5462910.0657550.0
"AGTHX"2026-01-05"Factors""style""Dividend"0.0125410.024745-0.097627-0.000030.0
"AGTHX"2026-01-05"Factors""style""Growth"0.9194130.0149010.1400190.0019180.0
"AGTHX"2026-01-05"Factors""style""Leverage"-0.1387580.025394-0.3983340.0014040.0
"AGTHX"2026-01-05"Factors""style""Momentum"1.1090640.0582160.3071120.0198290.0
…………………………
"AGTHX"2026-01-05"Factors""trbc""Institutions, Associations & O…0.00.2139030.5384920.00.0
"AGTHX"2026-01-05"Factors""trbc""Real Estate"0.00.076078-0.2408730.00.0
"AGTHX"2026-01-05"Factors""trbc""Technology"0.994520.0513840.3951650.0201940.0
"AGTHX"2026-01-05"Factors""trbc""Utilities"0.00.112429-0.1583850.00.0
"AGTHX"2026-01-05"Idiosyncratic"""""0.994520.1483440.6258430.092840.0
" ], "text/plain": [ "shape: (22, 10)\n", "┌────────┬────────────┬─────────────┬────────────┬───┬────────────┬────────────┬────────────┬──────┐\n", "│ ticker ┆ date ┆ type ┆ factor_gro ┆ … ┆ Volatility ┆ Correlatio ┆ Contributi ┆ Beta │\n", "│ --- ┆ --- ┆ --- ┆ up ┆ ┆ --- ┆ n ┆ on ┆ --- │\n", "│ str ┆ date ┆ str ┆ --- ┆ ┆ f32 ┆ --- ┆ --- ┆ f32 │\n", "│ ┆ ┆ ┆ str ┆ ┆ ┆ f32 ┆ f32 ┆ │\n", "╞════════╪════════════╪═════════════╪════════════╪═══╪════════════╪════════════╪════════════╪══════╡\n", "│ AGTHX ┆ 2026-01-05 ┆ Factors ┆ market ┆ … ┆ 0.12103 ┆ 0.546291 ┆ 0.065755 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-05 ┆ Factors ┆ style ┆ … ┆ 0.024745 ┆ -0.097627 ┆ -0.00003 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-05 ┆ Factors ┆ style ┆ … ┆ 0.014901 ┆ 0.140019 ┆ 0.001918 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-05 ┆ Factors ┆ style ┆ … ┆ 0.025394 ┆ -0.398334 ┆ 0.001404 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-05 ┆ Factors ┆ style ┆ … ┆ 0.058216 ┆ 0.307112 ┆ 0.019829 ┆ 0.0 │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ AGTHX ┆ 2026-01-05 ┆ Factors ┆ trbc ┆ … ┆ 0.213903 ┆ 0.538492 ┆ 0.0 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-05 ┆ Factors ┆ trbc ┆ … ┆ 0.076078 ┆ -0.240873 ┆ 0.0 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-05 ┆ Factors ┆ trbc ┆ … ┆ 0.051384 ┆ 0.395165 ┆ 0.020194 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-05 ┆ Factors ┆ trbc ┆ … ┆ 0.112429 ┆ -0.158385 ┆ 0.0 ┆ 0.0 │\n", "│ AGTHX ┆ 2026-01-05 ┆ Idiosyncrat ┆ ┆ … ┆ 0.148344 ┆ 0.625843 ┆ 0.09284 ┆ 0.0 │\n", "│ ┆ ┆ ic ┆ ┆ ┆ ┆ ┆ ┆ │\n", "└────────┴────────────┴─────────────┴────────────┴───┴────────────┴────────────┴────────────┴──────┘" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for a specific ticker and date, the risk attribution across the factors\n", "(\n", " report.accessor.get_data(\n", " [(\"ticker\", \"AGTHX\"), (\"date\", \"2026-01-05\")], # filters: [ticker=AGTHX]\n", " expand=(\"type\", \"factor_group\", \"factor\",), # combinations define the rows\n", " value_cols=report.accessor.metric_cols, # Exposure, Stand-alone Volatility, Variance Contribution\n", " )\n", " .select(\"ticker\", \"date\", \"type\", \"factor_group\", \"factor\", *report.accessor.metric_cols)\n", ")" ] }, { "cell_type": "code", "execution_count": 16, "id": "b4db8a3a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (76, 9)
tickerfactordateinput_asset_idExposureVolatilityCorrelationContributionBeta
strstrdatestrf32f32f32f32f32
"AGTHX""Volatility"2026-01-05"02079K305"0.1181840.1382180.5855080.0095640.0
"AGTHX""Volatility"2026-01-05"2588173"-0.178530.1382180.585508-0.0144480.0
"AGTHX""Volatility"2026-01-05"67066G10"0.00.1382180.5855080.00.0
"AGTHX""Volatility"2026-01-05"85371710"0.00.1382180.5855080.00.0
"AGTHX""Volatility"2026-01-06"02079K305"0.11630.1377890.5861210.0093920.0
………………………
"AGTHX""Volatility"2026-01-29"85371710"0.00.1295390.542120.00.0
"AGTHX""Volatility"2026-01-30"02079K305"0.0552770.1327730.5452790.0040020.0
"AGTHX""Volatility"2026-01-30"2588173"-0.0655280.1327730.545279-0.0047440.0
"AGTHX""Volatility"2026-01-30"67066G10"0.00.1327730.5452790.00.0
"AGTHX""Volatility"2026-01-30"85371710"0.00.1327730.5452790.00.0
" ], "text/plain": [ "shape: (76, 9)\n", "┌────────┬────────────┬────────────┬─────────────┬───┬────────────┬────────────┬────────────┬──────┐\n", "│ ticker ┆ factor ┆ date ┆ input_asset ┆ … ┆ Volatility ┆ Correlatio ┆ Contributi ┆ Beta │\n", "│ --- ┆ --- ┆ --- ┆ _id ┆ ┆ --- ┆ n ┆ on ┆ --- │\n", "│ str ┆ str ┆ date ┆ --- ┆ ┆ f32 ┆ --- ┆ --- ┆ f32 │\n", "│ ┆ ┆ ┆ str ┆ ┆ ┆ f32 ┆ f32 ┆ │\n", "╞════════╪════════════╪════════════╪═════════════╪═══╪════════════╪════════════╪════════════╪══════╡\n", "│ AGTHX ┆ Volatility ┆ 2026-01-05 ┆ 02079K305 ┆ … ┆ 0.138218 ┆ 0.585508 ┆ 0.009564 ┆ 0.0 │\n", "│ AGTHX ┆ Volatility ┆ 2026-01-05 ┆ 2588173 ┆ … ┆ 0.138218 ┆ 0.585508 ┆ -0.014448 ┆ 0.0 │\n", "│ AGTHX ┆ Volatility ┆ 2026-01-05 ┆ 67066G10 ┆ … ┆ 0.138218 ┆ 0.585508 ┆ 0.0 ┆ 0.0 │\n", "│ AGTHX ┆ Volatility ┆ 2026-01-05 ┆ 85371710 ┆ … ┆ 0.138218 ┆ 0.585508 ┆ 0.0 ┆ 0.0 │\n", "│ AGTHX ┆ Volatility ┆ 2026-01-06 ┆ 02079K305 ┆ … ┆ 0.137789 ┆ 0.586121 ┆ 0.009392 ┆ 0.0 │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ AGTHX ┆ Volatility ┆ 2026-01-29 ┆ 85371710 ┆ … ┆ 0.129539 ┆ 0.54212 ┆ 0.0 ┆ 0.0 │\n", "│ AGTHX ┆ Volatility ┆ 2026-01-30 ┆ 02079K305 ┆ … ┆ 0.132773 ┆ 0.545279 ┆ 0.004002 ┆ 0.0 │\n", "│ AGTHX ┆ Volatility ┆ 2026-01-30 ┆ 2588173 ┆ … ┆ 0.132773 ┆ 0.545279 ┆ -0.004744 ┆ 0.0 │\n", "│ AGTHX ┆ Volatility ┆ 2026-01-30 ┆ 67066G10 ┆ … ┆ 0.132773 ┆ 0.545279 ┆ 0.0 ┆ 0.0 │\n", "│ AGTHX ┆ Volatility ┆ 2026-01-30 ┆ 85371710 ┆ … ┆ 0.132773 ┆ 0.545279 ┆ 0.0 ┆ 0.0 │\n", "└────────┴────────────┴────────────┴─────────────┴───┴────────────┴────────────┴────────────┴──────┘" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for a specific ticker and factor, the risk 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, # Exposure, Stand-alone Volatility, Variance Contribution\n", " )\n", " .select(\"ticker\", \"factor\", \"date\", \"input_asset_id\", *report.accessor.metric_cols)\n", ")" ] } ], "metadata": { "jupytext": { "text_representation": { "extension": ".py", "format_name": "percent", "format_version": "1.3", "jupytext_version": "1.19.3" } }, "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": "3.11.15" } }, "nbformat": 4, "nbformat_minor": 5 }