{
"cells": [
{
"cell_type": "markdown",
"id": "0a586e6f",
"metadata": {},
"source": [
"# Custom Dataset From Scratch\n",
"\n",
"Most of the other tutorials start from a **reference** risk dataset that\n",
"Bayesline maintains — `bayesline/Bayesline-US-All-1y`, `Bayesline-Global`,\n",
"etc. You layer your own exposures or filters on top via\n",
"`DerivedRiskDatasetSettings` and the engine pulls everything else (master\n",
"data, prices, calendars, FX) from the reference.\n",
"\n",
"This tutorial covers the other path: **building a risk dataset from scratch\n",
"with no reference**. You bring every piece of input yourself. This is the\n",
"path you take when you have your own house-alpha factors, your own price\n",
"feed, your own asset master, and you want Bayesline to fit a factor model\n",
"on top of all of it without any external data dependency.\n",
"\n",
"The mechanism is `RootRiskDatasetSettings` and the six (+1 optional) upload\n",
"data types that feed it:\n",
"\n",
"```{mermaid}\n",
"erDiagram\n",
" idmap ||--o{ market_cap : \"asset_id\"\n",
" idmap ||--o{ price : \"asset_id\"\n",
" idmap ||--o{ exposures : \"asset_id\"\n",
" exchange_rates ||--o{ market_cap : \"ccy\"\n",
" exchange_rates ||--o{ price : \"ccy\"\n",
"\n",
" idmap {\n",
" date start_date\n",
" date end_date\n",
" string from_id_type\n",
" string from_id\n",
" string to_id_type\n",
" string to_id\n",
" }\n",
" market_cap {\n",
" date date PK\n",
" string asset_id PK\n",
" string asset_id_type PK\n",
" string ccy\n",
" float market_cap\n",
" float volume\n",
" float idio_vol\n",
" }\n",
" price {\n",
" date date PK\n",
" string asset_id PK\n",
" string asset_id_type PK\n",
" string ccy PK\n",
" float close\n",
" float return\n",
" bool delisted\n",
" }\n",
" exposures {\n",
" date date PK\n",
" string asset_id PK\n",
" string asset_id_type PK\n",
" string factor_group PK\n",
" string factor PK\n",
" float exposure\n",
" }\n",
" exchange_dates {\n",
" date date PK\n",
" string exchange PK\n",
" }\n",
" exchange_rates {\n",
" date date PK\n",
" string ccy PK\n",
" float fx_rate\n",
" }\n",
"```\n",
"\n",
"One sentence per box:\n",
"\n",
"* `idmap` — *which ids refer to the same asset?* (ticker → ticker_core,\n",
" ISIN → ticker, etc.)\n",
"* `market_cap` — *who is each asset?* Slow-changing master data: id, ccy,\n",
" market cap, volume, idio vol.\n",
"* `price` — *what did each asset do today?* The daily market record:\n",
" close, daily return, delisted flag.\n",
"* `exposures` — *what factors does each asset load on?* Long format:\n",
" `(date, asset, group, factor, value)`.\n",
"* `exchange_dates` — *which days is each exchange closed?* Non-trading\n",
" days, per exchange.\n",
"* `exchange_rates` — *how do we get to USD?* USD-base FX per `(date, ccy)`.\n",
"\n",
"The rest of this tutorial walks one box at a time, simulates a small US\n",
"universe so everything runs offline, builds a dataset from all six\n",
"uploads, fits a factor model on top, and checks that the engine recovers\n",
"the factor returns we simulated.\n",
"\n",
"If you already have a reference dataset that meets your needs, you want\n",
"[Model Onboarding](recipe_model_onboarding.ipynb) instead — same final\n",
"shape, derived path.\n"
]
},
{
"cell_type": "markdown",
"id": "1abbd6ca",
"metadata": {},
"source": [
"## Imports and client\n",
"\n",
"We pull in `polars` for frame construction, `numpy` for the simulation,\n",
"and the public `bayesline.api.equity` settings types we'll need below.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8269c713",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [],
"source": [
"import datetime as dt\n",
"\n",
"import numpy as np\n",
"import polars as pl\n",
"\n",
"from bayesline.apiclient import BayeslineApiClient\n",
"from bayesline.api.equity import (\n",
" CategoricalExposureGroupSettings,\n",
" CategoricalFilterSettings,\n",
" ContinuousExposureGroupSettings,\n",
" ExposureSettings,\n",
" FactorRiskModelSettings,\n",
" ModelConstructionSettings,\n",
" RootRiskDatasetSettings,\n",
" UniverseSettings,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "b61e2b0a",
"metadata": {},
"source": [
"A real script would connect to your deployment via\n",
"`BayeslineApiClient.new_client(endpoint=..., api_key=...)`. This tutorial\n",
"uses the in-process app the docs build provides — every API call below\n",
"runs end-to-end against the real backend.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "71b4f4db",
"metadata": {
"lines_to_next_cell": 2,
"tags": [
"skip-execution"
]
},
"outputs": [],
"source": [
"bln = BayeslineApiClient.new_client(\n",
" endpoint=\"https://[ENDPOINT]\",\n",
" api_key=\"[API-KEY]\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "11e3c646",
"metadata": {},
"source": [
"## Simulating a universe\n",
"\n",
"To keep the tutorial self-contained we generate everything in-notebook:\n",
"30 assets on the NYSE, one year of business days, six industries, three\n",
"style factors, plus a market intercept. Every input frame downstream is\n",
"derived from the constants in this cell.\n",
"\n",
"The simulation also gives us **ground truth**: we draw factor returns\n",
"ourselves, so at the end we can plot the engine's estimated `fret()`\n",
"against what we know the answer should be.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bebde078",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"30 assets × 261 dates (2024-01-02 → 2024-12-31)\n",
"industry counts: {np.str_('CONSUMER'): np.int64(8), np.str_('ENERGY'): np.int64(2), np.str_('FINS'): np.int64(6), np.str_('HEALTH'): np.int64(6), np.str_('MATERIALS'): np.int64(4), np.str_('TECH'): np.int64(4)}\n"
]
}
],
"source": [
"rng = np.random.default_rng(42)\n",
"\n",
"N_ASSETS = 30\n",
"INDUSTRIES = [\"TECH\", \"ENERGY\", \"FINS\", \"HEALTH\", \"CONSUMER\", \"MATERIALS\"]\n",
"STYLES = [\"momentum\", \"value\", \"size\"]\n",
"\n",
"# 1 year of weekdays ending 2024-12-31. We filter weekends here so the\n",
"# simulation doesn't generate prices on Sat/Sun; both weekends and US\n",
"# holidays are declared non-trading in the `exchange_dates` upload\n",
"# below (the engine's calendar contract).\n",
"all_days = pl.date_range(\n",
" dt.date(2024, 1, 2), dt.date(2024, 12, 31), interval=\"1d\", eager=True\n",
").to_list()\n",
"dates: list[dt.date] = [d for d in all_days if d.weekday() < 5]\n",
"T = len(dates)\n",
"\n",
"# Stable, readable asset ids. In production these would be tickers, ISINs,\n",
"# or your house ids — anything as long as it's a stable string.\n",
"assets = [f\"A{i:04d}\" for i in range(N_ASSETS)]\n",
"\n",
"# Per-asset static profile: which industry, what style loadings, what base\n",
"# market cap. These are the \"who is each asset\" attributes that flow into\n",
"# market_cap and exposures.\n",
"asset_industry = rng.choice(INDUSTRIES, size=N_ASSETS)\n",
"asset_style_loadings = rng.standard_normal((N_ASSETS, len(STYLES)))\n",
"asset_base_mcap = rng.lognormal(mean=22.0, sigma=1.0, size=N_ASSETS).astype(\"float32\")\n",
"\n",
"print(f\"{N_ASSETS} assets × {T} dates ({dates[0]} → {dates[-1]})\")\n",
"print(\"industry counts:\", dict(zip(*np.unique(asset_industry, return_counts=True))))"
]
},
{
"cell_type": "markdown",
"id": "f9b349ae",
"metadata": {},
"source": [
"### Ground-truth factor returns\n",
"\n",
"We draw factor returns directly, then build per-asset returns as\n",
"\n",
"$$\n",
"r_{i,t} \\;=\\; f^{\\text{market}}_t\n",
" \\;+\\; f^{\\text{industry}}_{g(i),\\,t}\n",
" \\;+\\; \\sum_k \\beta^{\\text{style}}_{i,k}\\, f^{\\text{style}}_{k,t}\n",
" \\;+\\; \\varepsilon_{i,t}.\n",
"$$\n",
"\n",
"Industry returns are constrained to a mcap-weighted zero-sum across\n",
"industries each day — the same constraint we'll apply in the regression\n",
"below. That keeps the industry block orthogonal to the market intercept,\n",
"which is what the engine assumes when `zero_sum_constraints={\"industry\":\n",
"\"mcap_weighted\"}` is set.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "91776a4f",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
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"shape: (5, 3)\n",
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},
"execution_count": 4,
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"output_type": "execute_result"
}
],
"source": [
"SIGMA_MARKET = 0.010 # ~16% annualized\n",
"SIGMA_INDUSTRY = 0.006\n",
"SIGMA_STYLE = 0.004\n",
"SIGMA_IDIO = 0.015 # ~24% annualized\n",
"\n",
"# Market intercept: one return per day.\n",
"f_market = rng.normal(0.0005, SIGMA_MARKET, size=T).astype(\"float32\")\n",
"\n",
"# Industry returns: shape (T, n_industries), then mcap-weighted-zero-summed.\n",
"ind_mcap = np.zeros(len(INDUSTRIES), dtype=\"float32\")\n",
"for k, g in enumerate(INDUSTRIES):\n",
" ind_mcap[k] = asset_base_mcap[asset_industry == g].sum()\n",
"ind_weights = ind_mcap / ind_mcap.sum()\n",
"\n",
"f_industry_raw = rng.normal(0.0, SIGMA_INDUSTRY, size=(T, len(INDUSTRIES))).astype(\n",
" \"float32\"\n",
")\n",
"f_industry = f_industry_raw - (f_industry_raw @ ind_weights)[:, None]\n",
"# Sanity: each day's mcap-weighted industry return is ~0.\n",
"assert np.allclose((f_industry @ ind_weights), 0.0, atol=1e-6)\n",
"\n",
"# Style returns: shape (T, n_styles).\n",
"f_style = rng.normal(0.0, SIGMA_STYLE, size=(T, len(STYLES))).astype(\"float32\")\n",
"\n",
"# Per-asset realized returns: shape (T, N_ASSETS).\n",
"ind_idx = np.array([INDUSTRIES.index(g) for g in asset_industry])\n",
"r = (\n",
" f_market[:, None]\n",
" + f_industry[:, ind_idx]\n",
" + f_style @ asset_style_loadings.T\n",
" + rng.normal(0.0, SIGMA_IDIO, size=(T, N_ASSETS)).astype(\"float32\")\n",
").astype(\"float32\")\n",
"\n",
"# Stash truth as a tidy frame so the final tie-out chart can join against\n",
"# fret(). fret labels factors as `{hierarchy}.{factor}` — `market.market`,\n",
"# `industry.TECH`, `style.momentum`, etc. — so we follow the same scheme\n",
"# here.\n",
"df_truth = pl.concat(\n",
" [\n",
" pl.DataFrame(\n",
" {\"date\": dates, \"factor\": \"market.market\", \"true_return\": f_market}\n",
" ),\n",
" *[\n",
" pl.DataFrame(\n",
" {\n",
" \"date\": dates,\n",
" \"factor\": f\"industry.{g}\",\n",
" \"true_return\": f_industry[:, k],\n",
" }\n",
" )\n",
" for k, g in enumerate(INDUSTRIES)\n",
" ],\n",
" *[\n",
" pl.DataFrame(\n",
" {\"date\": dates, \"factor\": f\"style.{s}\", \"true_return\": f_style[:, k]}\n",
" )\n",
" for k, s in enumerate(STYLES)\n",
" ],\n",
" ]\n",
")\n",
"df_truth.head()"
]
},
{
"cell_type": "markdown",
"id": "28819348",
"metadata": {},
"source": [
"## Step 1 — `idmap`\n",
"\n",
"`idmap` is a temporal table of identifier mappings. It does two jobs in\n",
"a root dataset:\n",
"\n",
"1. **Resolve foreign ids** — ISIN, SEDOL, CUSIP, your house id —\n",
" onto your canonical **master id** at query time.\n",
"2. **Collapse share classes and cross-listings** onto a single\n",
" **master_core id** so the engine treats them as the same underlying\n",
" entity for exposures, market-cap aggregation, and portfolio\n",
" netting.\n",
"\n",
"Job (2) is what the **master vs master_core** distinction is for.\n",
"`master` is your day-to-day asset id — one row per *listing*.\n",
"`master_core` is the canonical id per *entity* — one row per\n",
"underlying company. They are the same id type when every listing is\n",
"its own entity, and they diverge when one entity has multiple\n",
"listings.\n",
"\n",
"The textbook example is Alphabet, which trades as two NASDAQ tickers:\n",
"\n",
"* `GOOGL` — Class A, voting shares\n",
"* `GOOG` — Class C, non-voting shares\n",
"\n",
"Two listings, one company. To bring both into the dataset and collapse\n",
"them onto the same entity, you pick one as the core (say `GOOG`) and\n",
"upload **one projection row per master id**:\n",
"\n",
"| from_id_type | from_id | to_id_type | to_id |\n",
"| ------------ | ------- | ------------- | ------ |\n",
"| `ticker` | `GOOGL` | `ticker_core` | `GOOG` |\n",
"| `ticker` | `GOOG` | `ticker_core` | `GOOG` |\n",
"\n",
"The second row is the **identity projection** for `GOOG` — it tells\n",
"the engine that `GOOG` itself participates as a master id in your\n",
"dataset (and is its own core). Without it, `GOOG` would only be known\n",
"as the *target* of `GOOGL`'s projection, and any `market_cap` /\n",
"`price` / `exposures` row keyed by `GOOG` would be silently dropped\n",
"during dataset construction.\n",
"\n",
"After both rows are in place, `GOOGL` and `GOOG` share one set of\n",
"factor exposures and net to one position when a portfolio is\n",
"aggregated up — even though they continue to carry independent\n",
"prices and market caps in the `price` and `market_cap` uploads.\n",
"\n",
"**A quick warning about using `ticker` as a master_core type.** We\n",
"use `ticker_core` in this tutorial because tickers read well, but\n",
"it's a poor choice in production: tickers can change for the same\n",
"company over time. Facebook re-ticker'd from `FB` to `META` in 2022,\n",
"and any dataset using `ticker_core` as its master_core would split\n",
"that history into two entities — breaking time-series exposures,\n",
"returns, and any portfolio that held it across the rename. A good\n",
"`master_core` id is **permanent** across renames, splits, and\n",
"class consolidations: a vendor permanent id (PERMID, FIGI compid,\n",
"FactSet entity id) or your house's stable id. Bayesline's own\n",
"datasets use `bayesid_core`.\n",
"\n",
"Our toy universe has no share-class or cross-listing pairs, so each\n",
"asset is its own core (the projection is identity). The validity\n",
"window `[0001-01-01, 9999-12-31]` keeps each row valid under any\n",
"`as_of_date`.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "550eb7de",
"metadata": {
"lines_to_next_cell": 2
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"outputs": [
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"└────────────┴────────────┴──────────────┴─────────────┴─────────┴───────┘"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_idmap = pl.DataFrame(\n",
" {\n",
" \"start_date\": [dt.date.min] * N_ASSETS,\n",
" \"end_date\": [dt.date.max] * N_ASSETS,\n",
" \"from_id_type\": [\"ticker\"] * N_ASSETS,\n",
" \"to_id_type\": [\"ticker_core\"] * N_ASSETS,\n",
" \"from_id\": assets,\n",
" \"to_id\": assets,\n",
" }\n",
")\n",
"df_idmap.head()"
]
},
{
"cell_type": "markdown",
"id": "3e6d75bb",
"metadata": {},
"source": [
"## Step 2 — `market_cap` (entity-level data)\n",
"\n",
"`market_cap` answers *who is each asset?* It carries the asset's id,\n",
"functional currency, market cap, daily volume, and an idiosyncratic-vol\n",
"estimate. Primary key is `(date, asset_id, asset_id_type)`.\n",
"\n",
"**Why is this separate from `price`?** Different levels of aggregation.\n",
"`market_cap` (and the rest of this row — `volume`, `idio_vol`) is an\n",
"**entity-level** quantity: one number per company per day. `price` is\n",
"a **listing-level** quantity: one number per traded ticker per\n",
"currency. Alphabet has two listings — `GOOGL` and `GOOG` — that\n",
"quote at different prices but share a single Alphabet market cap.\n",
"The convention is to upload one `market_cap` row per entity, on the\n",
"master listing (here `GOOG`, whose master id equals its master_core);\n",
"repeating it on every listing would double-count when the engine\n",
"aggregates over the master_core collapse.\n",
"\n",
"In the simulation we build a static per-asset profile, then broadcast\n",
"across dates with a small random-walk perturbation on market cap so the\n",
"frame isn't perfectly constant. `volume` and `idio_vol` are constant in\n",
"this toy — production pipelines fill these from a vendor feed.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5a9061b2",
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"lines_to_next_cell": 2
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"│ 2024-01-02 ┆ A0002 ┆ ticker ┆ USD ┆ 2.5720e9 ┆ 2.1954332e7 ┆ 0.015 │\n",
"│ 2024-01-02 ┆ A0003 ┆ ticker ┆ USD ┆ 8.3242e9 ┆ 3.8139168e7 ┆ 0.015 │\n",
"│ 2024-01-02 ┆ A0004 ┆ ticker ┆ USD ┆ 6.36904e8 ┆ 2.5839e6 ┆ 0.015 │\n",
"└────────────┴──────────┴───────────────┴─────┴────────────┴─────────────┴──────────┘"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Per-asset static profile (one row per asset).\n",
"df_assets = pl.DataFrame(\n",
" {\n",
" \"asset_id\": assets,\n",
" \"base_mcap\": asset_base_mcap,\n",
" \"volume\": rng.uniform(5e5, 5e7, N_ASSETS).astype(\"float32\"),\n",
" \"idio_vol\": np.full(N_ASSETS, SIGMA_IDIO, dtype=\"float32\"),\n",
" }\n",
")\n",
"\n",
"# Mcap drifts by a small lognormal walk over time so the upload isn't trivially flat.\n",
"mcap_walk = np.exp(\n",
" np.cumsum(rng.normal(0.0, 0.005, size=(T, N_ASSETS)), axis=0)\n",
").astype(\"float32\")\n",
"\n",
"# Cross-join dates × assets, then attach the time-varying market cap as a\n",
"# column. Polars cross-join order is `(date0, asset0), (date0, asset1), …,\n",
"# (date1, asset0), …`, which is exactly the C-order of `mcap_walk[t, i]`.\n",
"df_market_cap = (\n",
" pl.DataFrame({\"date\": dates})\n",
" .join(df_assets, how=\"cross\")\n",
" .with_columns(\n",
" pl.lit(\"ticker\").alias(\"asset_id_type\"),\n",
" pl.lit(\"USD\").alias(\"ccy\"),\n",
" (pl.col(\"base_mcap\") * pl.Series(mcap_walk.reshape(-1)))\n",
" .cast(pl.Float32)\n",
" .alias(\"market_cap\"),\n",
" )\n",
" .select(\n",
" \"date\", \"asset_id\", \"asset_id_type\", \"ccy\", \"market_cap\", \"volume\", \"idio_vol\"\n",
" )\n",
")\n",
"df_market_cap.head()"
]
},
{
"cell_type": "markdown",
"id": "4811ac8a",
"metadata": {},
"source": [
"## Step 3 — `price` (the daily market record)\n",
"\n",
"`price` answers *what did each asset do today?* It carries close, daily\n",
"total return, and a delisted flag. Primary key is `(date, asset_id,\n",
"asset_id_type, ccy)` — `ccy` is part of the key so the same asset can\n",
"carry prices in multiple currencies simultaneously.\n",
"\n",
"We compound the simulated per-asset returns from a base of 100. There's\n",
"one wrinkle worth calling out: each asset's **first** row must have\n",
"`return = null` and serve as the price base. Without that base row the\n",
"engine has no `p[t-1]` against which to reconstruct the first realized\n",
"return. We use a single base row per asset at `dates[0]` because every\n",
"asset is alive on day zero in this simulation — a real pipeline with\n",
"new listings places the base row at each asset's first appearance.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7890dc6d",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"price rows: 7830 (= 30 base + 7800 observed)\n"
]
},
{
"data": {
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"
\n",
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"shape: (5, 7)\n",
"┌────────────┬──────────┬───────────────┬─────┬───────┬────────┬──────────┐\n",
"│ date ┆ asset_id ┆ asset_id_type ┆ ccy ┆ close ┆ return ┆ delisted │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ date ┆ str ┆ str ┆ str ┆ f32 ┆ f32 ┆ bool │\n",
"╞════════════╪══════════╪═══════════════╪═════╪═══════╪════════╪══════════╡\n",
"│ 2024-01-02 ┆ A0000 ┆ ticker ┆ USD ┆ 100.0 ┆ null ┆ false │\n",
"│ 2024-01-02 ┆ A0001 ┆ ticker ┆ USD ┆ 100.0 ┆ null ┆ false │\n",
"│ 2024-01-02 ┆ A0002 ┆ ticker ┆ USD ┆ 100.0 ┆ null ┆ false │\n",
"│ 2024-01-02 ┆ A0003 ┆ ticker ┆ USD ┆ 100.0 ┆ null ┆ false │\n",
"│ 2024-01-02 ┆ A0004 ┆ ticker ┆ USD ┆ 100.0 ┆ null ┆ false │\n",
"└────────────┴──────────┴───────────────┴─────┴───────┴────────┴──────────┘"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Compound returns to a price path starting at 100.\n",
"close = np.empty((T, N_ASSETS), dtype=\"float32\")\n",
"close[0] = 100.0\n",
"close[1:] = 100.0 * np.cumprod(1.0 + r[1:], axis=0)\n",
"\n",
"PRICE_COLS = [\"date\", \"asset_id\", \"asset_id_type\", \"ccy\", \"close\", \"return\", \"delisted\"]\n",
"\n",
"# Base rows: t=0 for every asset, close=100, return=null (no prior price).\n",
"df_price_base = pl.DataFrame({\"asset_id\": assets}).select(\n",
" pl.lit(dates[0]).alias(\"date\"),\n",
" \"asset_id\",\n",
" pl.lit(\"ticker\").alias(\"asset_id_type\"),\n",
" pl.lit(\"USD\").alias(\"ccy\"),\n",
" pl.lit(100.0, dtype=pl.Float32).alias(\"close\"),\n",
" pl.lit(None, dtype=pl.Float32).alias(\"return\"),\n",
" pl.lit(False).alias(\"delisted\"),\n",
")\n",
"\n",
"# Realized rows: t=1..T-1, close compounded, return = r[t]. Cross-join\n",
"# emits rows in (date, asset) order, matching the C-order reshape of\n",
"# close[1:] and r[1:].\n",
"df_price_obs = (\n",
" pl.DataFrame({\"date\": dates[1:]})\n",
" .join(pl.DataFrame({\"asset_id\": assets}), how=\"cross\")\n",
" .select(\n",
" \"date\",\n",
" \"asset_id\",\n",
" pl.lit(\"ticker\").alias(\"asset_id_type\"),\n",
" pl.lit(\"USD\").alias(\"ccy\"),\n",
" pl.Series(\"close\", close[1:].reshape(-1), dtype=pl.Float32),\n",
" pl.Series(\"return\", r[1:].reshape(-1), dtype=pl.Float32),\n",
" pl.lit(False).alias(\"delisted\"),\n",
" )\n",
")\n",
"\n",
"df_price = pl.concat([df_price_base, df_price_obs]).sort(\"date\", \"asset_id\")\n",
"print(\n",
" f\"price rows: {df_price.height} (= {N_ASSETS} base + {N_ASSETS * (T - 1)} observed)\"\n",
")\n",
"df_price.head()"
]
},
{
"cell_type": "markdown",
"id": "a72b02a7",
"metadata": {},
"source": [
"## Step 4 — `exposures` (long format)\n",
"\n",
"The exposures upload is long-format with schema `(date, asset_id,\n",
"asset_id_type, factor_group, factor, exposure)`. Dense vs sparse routing\n",
"is decided by the engine at dataset-construction time based on which\n",
"factor groups you declare in `dense_factor_groups` vs\n",
"`sparse_factor_groups` on `RootRiskDatasetSettings` — there's no\n",
"per-row flag.\n",
"\n",
"Conceptually:\n",
"\n",
"* **Dense factor groups** (`market`, `style` here) carry a continuous\n",
" value per asset per day. The engine treats every asset as loading on\n",
" every factor in a dense group. We upload one row per asset-day-factor.\n",
"* **Sparse factor groups** (`industry`, `estimation_universe` here)\n",
" only carry the *nonzero* loadings — structural zeros are never\n",
" uploaded. The typical case is one-hot (a pure-play company has a\n",
" single row per day naming its industry), but multi-row spreads\n",
" are valid too: a conglomerate with 40% `TECH` and 60% `MATERIALS`\n",
" contributes two rows per day with fractional exposures. The\n",
" engine routes the sparse block through the path where\n",
" `thin_category_shrinkage` actually fires.\n",
"\n",
"We build the three long frames from the static profile and concatenate.\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "88722956",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"exposures rows: 46980 (market=7830, style=23490, industry=7830, estu=7830)\n"
]
},
{
"data": {
"text/html": [
"
\n",
"shape: (5, 6)
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2024-01-02
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2024-01-02
"A0001"
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"market"
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2024-01-02
"A0002"
"ticker"
"market"
"market"
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2024-01-02
"A0003"
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"A0004"
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"shape: (5, 6)\n",
"┌────────────┬──────────┬───────────────┬──────────────┬────────┬──────────┐\n",
"│ date ┆ asset_id ┆ asset_id_type ┆ factor_group ┆ factor ┆ exposure │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ date ┆ str ┆ str ┆ str ┆ str ┆ f32 │\n",
"╞════════════╪══════════╪═══════════════╪══════════════╪════════╪══════════╡\n",
"│ 2024-01-02 ┆ A0000 ┆ ticker ┆ market ┆ market ┆ 1.0 │\n",
"│ 2024-01-02 ┆ A0001 ┆ ticker ┆ market ┆ market ┆ 1.0 │\n",
"│ 2024-01-02 ┆ A0002 ┆ ticker ┆ market ┆ market ┆ 1.0 │\n",
"│ 2024-01-02 ┆ A0003 ┆ ticker ┆ market ┆ market ┆ 1.0 │\n",
"│ 2024-01-02 ┆ A0004 ┆ ticker ┆ market ┆ market ┆ 1.0 │\n",
"└────────────┴──────────┴───────────────┴──────────────┴────────┴──────────┘"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Per-asset static frames used by the cross joins below. Keeping the\n",
"# static profile separate from the date axis is the canonical way to\n",
"# build long-format exposures in Polars.\n",
"df_asset_industry = pl.DataFrame({\"asset_id\": assets, \"industry\": list(asset_industry)})\n",
"df_asset_style = pl.DataFrame(\n",
" {\n",
" \"asset_id\": np.repeat(assets, len(STYLES)),\n",
" \"style\": STYLES * N_ASSETS,\n",
" \"loading\": asset_style_loadings.reshape(-1).astype(\"float32\"),\n",
" }\n",
")\n",
"df_dates = pl.DataFrame({\"date\": dates})\n",
"\n",
"# market: dense, every asset loads 1.0 (this is the intercept).\n",
"df_exp_market = df_dates.join(\n",
" pl.DataFrame({\"asset_id\": assets}), how=\"cross\"\n",
").with_columns(\n",
" pl.lit(\"ticker\").alias(\"asset_id_type\"),\n",
" pl.lit(\"market\").alias(\"factor_group\"),\n",
" pl.lit(\"market\").alias(\"factor\"),\n",
" pl.lit(1.0, dtype=pl.Float32).alias(\"exposure\"),\n",
")\n",
"\n",
"# style: dense, one row per (date, asset, style). Loadings are constant\n",
"# per asset in this simulation — production pipelines would update them\n",
"# daily.\n",
"df_exp_style = df_dates.join(df_asset_style, how=\"cross\").select(\n",
" \"date\",\n",
" \"asset_id\",\n",
" pl.lit(\"ticker\").alias(\"asset_id_type\"),\n",
" pl.lit(\"style\").alias(\"factor_group\"),\n",
" pl.col(\"style\").alias(\"factor\"),\n",
" pl.col(\"loading\").alias(\"exposure\"),\n",
")\n",
"\n",
"# industry: sparse / categorical. One row per asset-day naming the\n",
"# asset's industry.\n",
"df_exp_industry = df_dates.join(df_asset_industry, how=\"cross\").select(\n",
" \"date\",\n",
" \"asset_id\",\n",
" pl.lit(\"ticker\").alias(\"asset_id_type\"),\n",
" pl.lit(\"industry\").alias(\"factor_group\"),\n",
" pl.col(\"industry\").alias(\"factor\"),\n",
" pl.lit(1.0, dtype=pl.Float32).alias(\"exposure\"),\n",
")\n",
"\n",
"# estimation_universe: sparse, every asset is in our tutorial's estu.\n",
"# Production deployments use this group to declare which assets enter\n",
"# the regression on each day (e.g. \"top 3000 by mcap\").\n",
"df_exp_estu = df_dates.join(\n",
" pl.DataFrame({\"asset_id\": assets}), how=\"cross\"\n",
").with_columns(\n",
" pl.lit(\"ticker\").alias(\"asset_id_type\"),\n",
" pl.lit(\"estimation_universe\").alias(\"factor_group\"),\n",
" pl.lit(\"estimation_universe\").alias(\"factor\"),\n",
" pl.lit(1.0, dtype=pl.Float32).alias(\"exposure\"),\n",
")\n",
"\n",
"df_exposures = pl.concat([df_exp_market, df_exp_style, df_exp_industry, df_exp_estu])\n",
"print(\n",
" f\"exposures rows: {df_exposures.height} \"\n",
" f\"(market={df_exp_market.height}, style={df_exp_style.height}, \"\n",
" f\"industry={df_exp_industry.height}, estu={df_exp_estu.height})\"\n",
")\n",
"df_exposures.head()"
]
},
{
"cell_type": "markdown",
"id": "9fbd2616",
"metadata": {},
"source": [
"## Step 5 — `exchange_dates` (non-trading days)\n",
"\n",
"`exchange_dates` carries every **non-trading day** per exchange —\n",
"weekends *and* holidays. The engine doesn't infer weekends; if a\n",
"Saturday isn't in this upload it's treated as a trading day. The\n",
"trading calendar for an exchange is `(every date in the dataset) ∖\n",
"(rows in this upload for that exchange)`.\n",
"\n",
"We mark both — every Sat/Sun in 2024 plus the US federal holidays for\n",
"XNYS. A production pipeline would generate this from\n",
"`pandas.tseries.holiday` plus a weekday filter, or an\n",
"exchange-calendar service.\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a5207af0",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"non-trading days: 113 (104 weekends + 9 holidays)\n"
]
},
{
"data": {
"text/html": [
"
\n",
"shape: (5, 2)
date
exchange
date
str
2024-01-06
"XNYS"
2024-01-07
"XNYS"
2024-01-13
"XNYS"
2024-01-14
"XNYS"
2024-01-15
"XNYS"
"
],
"text/plain": [
"shape: (5, 2)\n",
"┌────────────┬──────────┐\n",
"│ date ┆ exchange │\n",
"│ --- ┆ --- │\n",
"│ date ┆ str │\n",
"╞════════════╪══════════╡\n",
"│ 2024-01-06 ┆ XNYS │\n",
"│ 2024-01-07 ┆ XNYS │\n",
"│ 2024-01-13 ┆ XNYS │\n",
"│ 2024-01-14 ┆ XNYS │\n",
"│ 2024-01-15 ┆ XNYS │\n",
"└────────────┴──────────┘"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"us_holidays_2024 = {\n",
" dt.date(2024, 1, 1), # New Year\n",
" dt.date(2024, 1, 15), # MLK\n",
" dt.date(2024, 2, 19), # Presidents'\n",
" dt.date(2024, 3, 29), # Good Friday\n",
" dt.date(2024, 5, 27), # Memorial\n",
" dt.date(2024, 6, 19), # Juneteenth\n",
" dt.date(2024, 7, 4), # Independence\n",
" dt.date(2024, 9, 2), # Labor\n",
" dt.date(2024, 11, 28), # Thanksgiving\n",
" dt.date(2024, 12, 25), # Christmas\n",
"}\n",
"# Weekends + holidays = every non-trading day in range. New Year's Day\n",
"# (Jan 1) precedes the Jan-2 start, so only 9 of the 10 listed holidays\n",
"# fall in range — recompute the split from the rows so the parts sum to\n",
"# the total.\n",
"non_trading_days = [d for d in all_days if d.weekday() >= 5 or d in us_holidays_2024]\n",
"df_exchange_dates = pl.DataFrame(\n",
" {\n",
" \"date\": non_trading_days,\n",
" \"exchange\": [\"XNYS\"] * len(non_trading_days),\n",
" }\n",
")\n",
"n_weekend = sum(1 for d in non_trading_days if d.weekday() >= 5)\n",
"n_holiday = len(non_trading_days) - n_weekend\n",
"print(\n",
" f\"non-trading days: {len(non_trading_days)} \"\n",
" f\"({n_weekend} weekends + {n_holiday} holidays)\"\n",
")\n",
"df_exchange_dates.head()"
]
},
{
"cell_type": "markdown",
"id": "a9e4dd0a",
"metadata": {},
"source": [
"## Step 6 — `exchange_rates` (USD-base FX)\n",
"\n",
"`exchange_rates` carries USD-base FX per `(date, ccy)`: **how many\n",
"units of `ccy` equal one USD on that date**. So JPY is a *large*\n",
"number (≈ 150) and EUR is a *small* one (≈ 0.93), because one USD\n",
"buys many yen but less than one euro. Equivalently: to convert a\n",
"foreign-currency price to USD you **divide** by `fx_rate`.\n",
"\n",
"We're USD-only in this tutorial, so every row has `fx_rate = 1.0`.\n",
"A multi-currency pipeline uploads one row per currency per date.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "232a5a5f",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"shape: (5, 3)
date
ccy
fx_rate
date
str
f32
2024-01-02
"USD"
1.0
2024-01-03
"USD"
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2024-01-04
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"shape: (5, 3)\n",
"┌────────────┬─────┬─────────┐\n",
"│ date ┆ ccy ┆ fx_rate │\n",
"│ --- ┆ --- ┆ --- │\n",
"│ date ┆ str ┆ f32 │\n",
"╞════════════╪═════╪═════════╡\n",
"│ 2024-01-02 ┆ USD ┆ 1.0 │\n",
"│ 2024-01-03 ┆ USD ┆ 1.0 │\n",
"│ 2024-01-04 ┆ USD ┆ 1.0 │\n",
"│ 2024-01-05 ┆ USD ┆ 1.0 │\n",
"│ 2024-01-08 ┆ USD ┆ 1.0 │\n",
"└────────────┴─────┴─────────┘"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_exchange_rates = pl.DataFrame(\n",
" {\n",
" \"date\": dates,\n",
" \"ccy\": [\"USD\"] * T,\n",
" \"fx_rate\": np.ones(T, dtype=\"float32\"),\n",
" }\n",
")\n",
"df_exchange_rates.head()"
]
},
{
"cell_type": "markdown",
"id": "cd3bbf34",
"metadata": {
"lines_to_next_cell": 2
},
"source": [
"## Upload\n",
"\n",
"Each of the six frames goes to its own upload data type via the\n",
"Uploaders API. `create_or_replace_dataset(name)` creates the upload\n",
"dataset (or wipes and recreates if it already exists);\n",
"`fast_commit(df, mode=\"overwrite\")` stages and commits in one shot.\n",
"Every frame is built complete in-memory, so each upload is a single\n",
"`\"overwrite\"` commit.\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "736af124",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" idmap → tutorial-idmap ( 30 rows)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" market_cap → tutorial-market-cap ( 7,830 rows)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" price → tutorial-price ( 7,830 rows)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" exposures → tutorial-exposures ( 46,980 rows)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" exchange_dates → tutorial-exchange-dates ( 113 rows)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" exchange_rates → tutorial-exchange-rates ( 261 rows)\n"
]
}
],
"source": [
"def upload(data_type: str, name: str, df: pl.DataFrame) -> None:\n",
" uploader = bln.equity.uploaders.get_data_type(data_type)\n",
" ds = uploader.create_or_replace_dataset(name)\n",
" ds.fast_commit(df, mode=\"overwrite\")\n",
" print(f\" {data_type:15s} → {name:28s} ({df.height:>7,} rows)\")\n",
"\n",
"\n",
"upload(\"idmap\", \"tutorial-idmap\", df_idmap)\n",
"upload(\"market_cap\", \"tutorial-market-cap\", df_market_cap)\n",
"upload(\"price\", \"tutorial-price\", df_price)\n",
"upload(\"exposures\", \"tutorial-exposures\", df_exposures)\n",
"upload(\"exchange_dates\", \"tutorial-exchange-dates\", df_exchange_dates)\n",
"upload(\"exchange_rates\", \"tutorial-exchange-rates\", df_exchange_rates)"
]
},
{
"cell_type": "markdown",
"id": "0903d220",
"metadata": {},
"source": [
"## Create the root dataset\n",
"\n",
"`RootRiskDatasetSettings` ties the six uploads into a single risk\n",
"dataset that the rest of the API targets.\n",
"\n",
"**Source references.** Each `*_source=` parameter is the name of an\n",
"upload dataset you created above. The engine resolves them lazily —\n",
"no data is actually read here.\n",
"\n",
"**`dense_factor_groups` vs `sparse_factor_groups`.** These two dicts\n",
"decide which regression block the engine routes each factor group\n",
"through:\n",
"\n",
"* **Dense** — continuous loading per asset per factor (`market`,\n",
" `style`). Every asset loads on every factor in the group.\n",
"* **Sparse** — only the *nonzero* loadings are uploaded\n",
" (`industry`, `estimation_universe`). One-hot membership is the\n",
" common case, but multi-row spreads are valid: a conglomerate with\n",
" partial loadings across several industries contributes one row\n",
" per nonzero entry. The sparse block is also where\n",
" `thin_category_shrinkage` fires when the model runs (Step 8).\n",
"\n",
"**The dict *value* — `None` vs a hierarchy upload source.** Each\n",
"entry pairs a factor-group name with an optional **factor\n",
"hierarchy**: a tree that organises the group's leaves into parent\n",
"buckets so reports can roll up (GICS sub-industry → industry →\n",
"sector, value sub-styles → value, etc.). The value is either:\n",
"\n",
"* the *name* of a hierarchy upload dataset you committed alongside\n",
" the other six (a separate recipe covers this), or\n",
"* `None`, which tells the engine to **synthesise a flat one-level\n",
" hierarchy** on the fly: each factor in the group becomes its own\n",
" leaf with no parent, no roll-up structure.\n",
"\n",
"`None` is the right choice for `market` and `estimation_universe`\n",
"(one factor each — nothing to roll up) and a perfectly fine\n",
"starting point for `style` and `industry` if you don't have a tree\n",
"yet. You can replace `None` with a hierarchy source later without\n",
"touching any of the six base uploads.\n",
"\n",
"**`as_of_date`** snapshots the `idmap` — only rows whose\n",
"`start_date <= as_of_date` are retained when the dataset's\n",
"`IdMapper` is built. Use the most recent date the idmap is\n",
"authoritative for.\n",
"\n",
"**`master_id_type` / `master_id_core_type`** must match the\n",
"`from_id_type` / `to_id_type` of the master-to-core projection rows\n",
"in your idmap upload (Step 1).\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3acaf930",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id types: ['ticker', 'ticker_core']\n",
"exchanges: ['XNYS']\n",
"categorical hierarchies: ['industry', 'estimation_universe']\n",
"continuous hierarchies: ['market', 'style']\n"
]
}
],
"source": [
"settings = RootRiskDatasetSettings(\n",
" market_cap_source=\"tutorial-market-cap\",\n",
" price_source=\"tutorial-price\",\n",
" exposures_source=\"tutorial-exposures\",\n",
" exchange_dates_source=\"tutorial-exchange-dates\",\n",
" exchange_rates_source=\"tutorial-exchange-rates\",\n",
" idmap_source=\"tutorial-idmap\",\n",
" dense_factor_groups={\"market\": None, \"style\": None},\n",
" sparse_factor_groups={\"industry\": None, \"estimation_universe\": None},\n",
" master_id_type=\"ticker\",\n",
" master_id_core_type=\"ticker_core\",\n",
" as_of_date=dates[-1],\n",
")\n",
"\n",
"bln.equity.riskdatasets.delete_dataset_if_exists(\"tutorial-custom\")\n",
"dataset = bln.equity.riskdatasets.create_dataset(\"tutorial-custom\", settings)\n",
"props = dataset.describe()\n",
"print(\"id types: \", props.universe_settings_menu.id_types)\n",
"print(\n",
" \"exchanges: \",\n",
" props.universe_settings_menu.calendar_settings_menu.exchanges,\n",
")\n",
"print(\n",
" \"categorical hierarchies:\",\n",
" list(props.universe_settings_menu.categorical_hierarchies.keys()),\n",
")\n",
"print(\n",
" \"continuous hierarchies: \",\n",
" list(props.exposure_settings_menu.continuous_hierarchies.keys()),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "8d281d00",
"metadata": {},
"source": [
"## Fit a factor model\n",
"\n",
"`FactorRiskModelSettings` declares one exposure group per factor block we\n",
"uploaded. We build the settings dataset-agnostic, then bind them to our\n",
"new dataset at load time via `.with_dataset(\"tutorial-custom\")` — the\n",
"same pattern the other tutorials use.\n",
"\n",
"`zero_sum_constraints={\"industry\": \"mcap_weighted\"}` matches the\n",
"constraint we applied in the ground-truth simulation, so the engine\n",
"estimates industry returns in the same gauge.\n",
"\n",
"`thin_category_shrinkage={\"industry\": 10.0}` shrinks any industry whose\n",
"mcap-weighted effective sample size falls below `N_min = 10` toward\n",
"zero — a guard against thin-industry blow-ups. With six industries and\n",
"30 assets we're nowhere near that threshold here, but setting it makes\n",
"the configuration realistic.\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "1655cf6c",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fret shape: (252, 11)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"factors: {'industry': 6, 'style': 3, 'market': 1}\n"
]
},
{
"data": {
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"text/plain": [
"shape: (5, 11)\n",
"┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
"│ date ┆ industry. ┆ industry. ┆ industry. ┆ … ┆ market.ma ┆ style.mom ┆ style.siz ┆ style.va │\n",
"│ --- ┆ CONSUMER ┆ ENERGY ┆ FINS ┆ ┆ rket ┆ entum ┆ e ┆ lue │\n",
"│ date ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n",
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"└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"riskmodel_settings = FactorRiskModelSettings(\n",
" universe=UniverseSettings(id_type=\"ticker\"),\n",
" exposures=ExposureSettings(\n",
" exposures=[\n",
" ContinuousExposureGroupSettings(hierarchy=\"market\"),\n",
" CategoricalExposureGroupSettings(hierarchy=\"industry\"),\n",
" ContinuousExposureGroupSettings(hierarchy=\"style\"),\n",
" ]\n",
" ),\n",
" modelconstruction=ModelConstructionSettings(\n",
" estimation_universe=UniverseSettings(\n",
" id_type=\"ticker\",\n",
" categorical_filters=[\n",
" CategoricalFilterSettings(hierarchy=\"estimation_universe\")\n",
" ],\n",
" ),\n",
" zero_sum_constraints={\"industry\": \"mcap_weighted\"},\n",
" thin_category_shrinkage={\"industry\": 10.0},\n",
" return_clip_bounds=(None, None),\n",
" ),\n",
")\n",
"\n",
"model = bln.equity.riskmodels.load(\n",
" riskmodel_settings.with_dataset(\"tutorial-custom\")\n",
").get_model()\n",
"df_fret = model.fret()\n",
"print(\"fret shape:\", df_fret.shape)\n",
"print(\"factors: \", {k: len(v) for k, v in model.factors().items()})\n",
"df_fret.head()"
]
},
{
"cell_type": "markdown",
"id": "14c30d50",
"metadata": {},
"source": [
"## Sanity check: did the engine recover what we simulated?\n",
"\n",
"Because we generated the factor returns ourselves we know what `fret()`\n",
"should look like. Join the estimated returns against `df_truth` and plot\n",
"estimated vs true per factor. The points should hug a 45° line —\n",
"not exactly (idio noise plus regression-weighting choices add scatter),\n",
"but tightly enough that the dataset is clearly wired correctly.\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7a515862",
"metadata": {
"lines_to_next_cell": 2
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"df_compare rows: 2510 (dates kept: 251)\n",
"market n= 251 corr=+0.951\n",
"industry n= 1506 corr=+0.581\n",
"style n= 753 corr=+0.666\n"
]
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# fret returns 0.0 on edge dates where the regression can't run (no prior\n",
"# price). Drop those rows so the scatter only contains real comparisons.\n",
"nonzero_dates = (\n",
" df_fret.unpivot(index=\"date\", variable_name=\"factor\", value_name=\"estimated\")\n",
" .group_by(\"date\")\n",
" .agg(pl.col(\"estimated\").abs().sum().alias(\"absum\"))\n",
" .filter(pl.col(\"absum\") > 0)\n",
" .select(\"date\")\n",
")\n",
"\n",
"df_compare = (\n",
" df_fret.unpivot(index=\"date\", variable_name=\"factor\", value_name=\"estimated\")\n",
" .join(nonzero_dates, on=\"date\", how=\"inner\")\n",
" .join(df_truth, on=(\"date\", \"factor\"), how=\"inner\")\n",
")\n",
"print(f\"df_compare rows: {df_compare.height} (dates kept: {nonzero_dates.height})\")\n",
"\n",
"groups = [\"market\", \"industry\", \"style\"]\n",
"fig, axes = plt.subplots(1, len(groups), figsize=(13, 4))\n",
"for ax, g in zip(axes, groups):\n",
" sub = df_compare.filter(pl.col(\"factor\").str.starts_with(g + \".\"))\n",
" x = sub[\"true_return\"].to_numpy()\n",
" y = sub[\"estimated\"].to_numpy()\n",
" ax.scatter(x, y, s=8, alpha=0.5)\n",
" if x.size:\n",
" lo = float(min(x.min(), y.min()))\n",
" hi = float(max(x.max(), y.max()))\n",
" ax.plot([lo, hi], [lo, hi], color=\"black\", linewidth=0.5)\n",
" corr = float(np.corrcoef(x, y)[0, 1])\n",
" ax.set_title(f\"{g} (n={x.size}, corr={corr:.3f})\")\n",
" print(f\"{g:8s} n={x.size:5d} corr={corr:+.3f}\")\n",
" else:\n",
" ax.set_title(f\"{g} (no rows)\")\n",
" ax.set_xlabel(\"true\")\n",
" ax.set_ylabel(\"estimated\")\n",
"fig.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "f26d4229",
"metadata": {},
"source": [
"## Out of scope (and where to look next)\n",
"\n",
"A few capabilities of `RootRiskDatasetSettings` we deliberately skipped\n",
"to keep this tutorial focused:\n",
"\n",
"* **`series_source`** — optional seventh upload of scalar time series\n",
" (risk-free rate, region-aggregate returns, etc.). Pass\n",
" `series_source=\"...\"` on `RootRiskDatasetSettings` after uploading a\n",
" frame with `(date, series, value)`.\n",
"* **Uploaded hierarchies** — both `dense_factor_groups` and\n",
" `sparse_factor_groups` accept an upload source name in place of\n",
" `None` to load a custom factor hierarchy (e.g. sector → industry →\n",
" sub-industry trees). The default `None` gives a flat one-level\n",
" hierarchy, which is what we used.\n",
"* **Catch-up / incremental uploads** — see\n",
" [Exposure Catch-up Upload](recipe_exposure_catch_up_upload.ipynb) for\n",
" the pattern of appending new dates to an existing upload dataset.\n",
"* **Building a derived dataset on top of this one** — once\n",
" `tutorial-custom` exists, you can pass `reference_dataset=\n",
" \"tutorial-custom\"` on `DerivedRiskDatasetSettings` to layer additional\n",
" exposures / filters / hierarchies. See\n",
" [Model Onboarding](recipe_model_onboarding.ipynb) and\n",
" [Risk Datasets](tutorial_datasets.ipynb).\n"
]
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