| name | fundamental-universes |
| description | Use when selecting or screening a QuantConnect/LEAN Equity universe on Morningstar fundamentals — the py`add_universe(...)`cs`AddUniverse(...)` pattern, the `Fundamental` object and how its data is organized, period accessors for `MultiPeriodField` values, and year-over-year deltas. Covers the Piotroski F-Score, Altman Z-score, Magic Formula, Graham filters, and custom screens. For the exact path of any field, it points to the equity-fundamental-data skill. Skip when — the universe is index/ETF-constituent only (py`self.universe.etf(...)`cs`Universe.ETF(...)`). |
Fundamental universes in QuantConnect / LEAN
Select or screen an Equity universe on Morningstar fundamentals by passing a Fundamental callback to pyadd_universe(...)csAddUniverse(...). Each Fundamental object f is one company's snapshot; the Morningstar data hangs off it in a large, deeply nested tree. pyf.financial_statements.net_incomecsf.FinancialStatements.NetIncome does not exist — net income lives on IncomeStatement, one level deeper. For the exact path of any field — net income, operating cash flow, PE ratio, sector code, and so on — see the equity-fundamental-data skill; guessing a path wastes a backtest run.
Static type checking
Type-hint your Fundamental parameters so the IDE autocompletes paths and flags typos like netincom before the backtest runs:
- Callback parameter:
def select(self, fundamentals: list[Fundamental]) -> list[Symbol]:.
- Helpers that take one snapshot:
def get_metrics(self, f: Fundamental):.
The Fundamental object
f is passed into your pyadd_universe(...)csAddUniverse(...) selection callback (one per company); you can also pull a snapshot per-security with pyf = self.securities["SPY"].fundamentalscsvar f = Securities["SPY"].Fundamentals, or request it from history. Beyond price/volume basics (pyf.market_capcsf.MarketCap, pyf.dollar_volumecsf.DollarVolume, pyf.pricecsf.Price), the Morningstar tree hangs off it: financial statements (income statement, balance sheet, cash flow statement), operation / valuation / earning ratios, earning reports, company profile, company & security reference, and asset classification. The equity-fundamental-data skill lists every one of those fields as a full path from f — look the path up there.
Period accessors for MultiPeriodField properties
Statement and ratio-growth attributes are MultiPeriodField wrappers — append a period accessor to read the number:
- py
.three_monthscs.ThreeMonths, py.six_monthscs.SixMonths, py.nine_monthscs.NineMonths, py.twelve_monthscs.TwelveMonths — period-aggregated value (TTM at py.twelve_monthscs.TwelveMonths).
- py
.valuecs.Value — most recent reported period (quarterly or annual, whichever filed last).
- py
.one_monthcs.OneMonth, py.two_monthscs.TwoMonths — short-window aggregates (rare).
How to choose:
- Income statement / cash flow: prefer py
.twelve_monthscs.TwelveMonths for cross-company comparability. py.valuecs.Value mixes quarterlies and annuals.
- Balance sheet: py
.valuecs.Value — point-in-time snapshots; period aggregation is meaningless.
- Operation / earning ratios: also
MultiPeriodField — py.valuecs.Value unless you specifically want a longer window.
Forgetting an accessor is silent: the leaf compares as truthy and inequalities against numbers produce nonsense without raising. Valuation ratios and the company/security-reference, company-profile, and asset-classification fields are not wrapped — read those directly, with no accessor.
Year-over-year deltas
Fundamental exposes only the latest reported period. Piotroski / Altman / changes-in-X screens need an earlier snapshot — store one per symbol keyed on pyf.financial_statements.period_ending_date.valuecsf.FinancialStatements.PeriodEndingDate.Value and compare against it on later bars.
