{ "cells": [ { "cell_type": "markdown", "id": "992223c7", "metadata": {}, "source": [ "# Portfolio Aligned Universes\n", "\n", "In this notebook we'll demonstrate how to define universes based on existing portfolio data. \n", "\n", "For this purpose we will first upload some sample portfolio holdings and subsequently use them to define a universe filter." ] }, { "cell_type": "code", "execution_count": 2, "id": "112d39e5", "metadata": {}, "outputs": [], "source": [ "import io\n", "\n", "import polars as pl\n", "\n", "from bayesline.api.equity import (\n", " PortfolioOrganizerSettings,\n", " UniverseSettings,\n", ")\n", "from bayesline.apiclient import BayeslineApiClient" ] }, { "cell_type": "code", "execution_count": null, "id": "7fc19806", "metadata": { "tags": [ "skip-execution" ] }, "outputs": [], "source": [ "bln = BayeslineApiClient.new_client(\n", " endpoint=\"https://[ENDPOINT]\",\n", " api_key=\"[API-KEY]\",\n", ")" ] }, { "cell_type": "markdown", "id": "d43df41f", "metadata": {}, "source": [ "## Uploading Portfolios\n", "\n", "We'll upload two portfolios `PORT_1` and `PORT_2` which contain assets *Apple* (`IC83A1B819`), *Microsoft* (`ICF982536B`) and *Alphabet* (`ICA17F00B9`)." ] }, { "cell_type": "code", "execution_count": 3, "id": "42971f80", "metadata": {}, "outputs": [], "source": [ "portfolio_uploaders = bln.equity.uploaders.get_data_type(\"portfolios\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "fa9144b9", "metadata": {}, "outputs": [], "source": [ "portfolio_uploader = portfolio_uploaders.create_or_replace_dataset(\"US-Portfolios\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "564efafe", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (4, 5)
dateportfolio_idIC83A1B819ICA17F00B9ICF982536B
datestrf64f64f64
2026-01-01"PORT_1"0.30.70.0
2026-01-01"PORT_2"0.40.00.6
2026-02-01"PORT_1"1.00.00.0
2026-02-01"PORT_2"0.00.01.0
" ], "text/plain": [ "shape: (4, 5)\n", "┌────────────┬──────────────┬────────────┬────────────┬────────────┐\n", "│ date ┆ portfolio_id ┆ IC83A1B819 ┆ ICA17F00B9 ┆ ICF982536B │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ date ┆ str ┆ f64 ┆ f64 ┆ f64 │\n", "╞════════════╪══════════════╪════════════╪════════════╪════════════╡\n", "│ 2026-01-01 ┆ PORT_1 ┆ 0.3 ┆ 0.7 ┆ 0.0 │\n", "│ 2026-01-01 ┆ PORT_2 ┆ 0.4 ┆ 0.0 ┆ 0.6 │\n", "│ 2026-02-01 ┆ PORT_1 ┆ 1.0 ┆ 0.0 ┆ 0.0 │\n", "│ 2026-02-01 ┆ PORT_2 ┆ 0.0 ┆ 0.0 ┆ 1.0 │\n", "└────────────┴──────────────┴────────────┴────────────┴────────────┘" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_csv = \"\"\"\n", "portfolio_id\tdate\tasset_id\tasset_id_type\tcurrency\tshare_qty\tnav\n", "PORT_1\t2026-01-01\tIC83A1B819\tbayesid\t\t\t.3\n", "PORT_1\t2026-01-01\tICA17F00B9\tbayesid\t\t\t.7\n", "PORT_2\t2026-01-01\tIC83A1B819\tbayesid\t\t\t.4\n", "PORT_2\t2026-01-01\tICF982536B\tbayesid\t\t\t.6\n", "PORT_1\t2026-02-01\tIC83A1B819\tbayesid\t\t\t1\n", "PORT_2\t2026-02-01\tICF982536B\tbayesid\t\t\t1\n", "\"\"\"\n", "\n", "portfolios_df = pl.read_csv(\n", " io.StringIO(data_csv.strip()), \n", " separator=\"\\t\", \n", " try_parse_dates=True,\n", " schema_overrides={\n", " \"currency\": pl.String,\n", " \"share_qty\": pl.Float64,\n", " \"nav\": pl.Float64,\n", " },\n", ")\n", "\n", "portfolios_df.pivot(index=[\"date\", \"portfolio_id\"], on=\"asset_id\", values=\"nav\").fill_null(0)" ] }, { "cell_type": "code", "execution_count": 6, "id": "b80b8359", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "UploadCommitResult(version=1, committed_names=[])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "portfolio_uploader.fast_commit(portfolios_df, mode=\"append\")" ] }, { "cell_type": "markdown", "id": "7d5dfb26", "metadata": {}, "source": [ "## Creating the Universe\n", "\n", "Note that below we could still use other filters in addition to the portfolio filter. This way we could for instance express *the energy sector within the Russell 3000*." ] }, { "cell_type": "code", "execution_count": 7, "id": "3b83a604", "metadata": {}, "outputs": [], "source": [ "universe_settings = UniverseSettings(\n", " dataset=\"Bayesline-US-All-1y\",\n", " portfolio_filter=PortfolioOrganizerSettings(\n", " # filters against the point in time superset \n", " # of all portfolios contained in `US-Portfolios`\n", " enabled_portfolios=\"US-Portfolios\"\n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "id": "73b8a7ae", "metadata": {}, "outputs": [], "source": [ "universe_api = bln.equity.universes.load(universe_settings)" ] }, { "cell_type": "markdown", "id": "b6c628a4", "metadata": {}, "source": [ "When obtaining the universe data note how even though we only specified holdings for `2026-01-01` and `2026-02-01` (which could be rebalance dates) the resulting universe contains entries for each day. Underneath the holdings are forward filled and delisted assets are dropped." ] }, { "cell_type": "code", "execution_count": 9, "id": "3074fb96", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
value
bayesidIC83A1B819ICA17F00B9ICF982536B
date
2026-01-011.01.01.0
2026-01-021.01.01.0
2026-01-031.01.01.0
2026-01-041.01.01.0
2026-01-051.01.01.0
............
2026-03-271.0NaN1.0
2026-03-281.0NaN1.0
2026-03-291.0NaN1.0
2026-03-301.0NaN1.0
2026-03-311.0NaN1.0
\n", "

90 rows × 3 columns

\n", "
" ], "text/plain": [ " value \n", "bayesid IC83A1B819 ICA17F00B9 ICF982536B\n", "date \n", "2026-01-01 1.0 1.0 1.0\n", "2026-01-02 1.0 1.0 1.0\n", "2026-01-03 1.0 1.0 1.0\n", "2026-01-04 1.0 1.0 1.0\n", "2026-01-05 1.0 1.0 1.0\n", "... ... ... ...\n", "2026-03-27 1.0 NaN 1.0\n", "2026-03-28 1.0 NaN 1.0\n", "2026-03-29 1.0 NaN 1.0\n", "2026-03-30 1.0 NaN 1.0\n", "2026-03-31 1.0 NaN 1.0\n", "\n", "[90 rows x 3 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "universe_api.get().to_pandas().assign(value=1.).set_index([\"date\", \"bayesid\"]).unstack()" ] } ], "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": "3.11.15" } }, "nbformat": 4, "nbformat_minor": 5 }