Modeleon

Write Python. Ship real Excel formulas.

Variable — a value.
MultiVariable — a concept, modeled as a set of Variables and sub-concepts.
Excel — one cell-traceable snapshot of a MultiVariable.

import modeleon as mo

forecast = mo.MultiVariable("Forecast")
forecast.pnl = mo.MultiVariable("P&L")
with forecast.pnl as pnl:
    pnl.revenue  = mo.Variable(1_000_000, unit="$")
    pnl.cogs_pct = mo.Variable(0.6, display_name="COGS %")
    pnl.cogs     = pnl.revenue * pnl.cogs_pct
    pnl.profit   = pnl.revenue - pnl.cogs

forecast.to_excel("forecast.xlsx")

The resulting forecast.xlsx:

B3 is =B1*B2 — a real Excel formula, not the baked value 600000.

How is this different from openpyxl / xlsxwriter / pandas?

The lower stack writes spreadsheets. Modeleon writes the model that produces a spreadsheet — every cell, every formula, every dependency.

Install

pip install modeleon

Python 3.12+.

Variable

A value or formula. Arithmetic tracks dependencies automatically.

revenue = mo.Variable(1_000_000)
cogs    = revenue * mo.Variable(0.6)
cogs.value         # 600_000.0
cogs.formula       # 'revenue * 0.6'
cogs.dependencies  # {'revenue'}

Scalar, list, or unit-bearing — all the same Variable.

MultiVariable

A concept. Variables are its attributes; sub-MultiVariables are its inner concepts.

forecast = mo.MultiVariable("Forecast")
forecast.assumptions = mo.MultiVariable("Assumptions", tax_rate=mo.Variable(0.25))
forecast.pnl = mo.MultiVariable("P&L")
forecast.pnl.revenue = mo.Variable(1_000_000)
forecast.pnl.taxes   = forecast.pnl.revenue * forecast.assumptions.tax_rate

Cross-sheet refs resolve automatically.
forecast.pnl.taxes = forecast.pnl.revenue * forecast.assumptions.tax_rate — the engine emits =B1 * Assumptions!B2 in Excel. Change the tax rate on Assumptions, P&L recalculates.

Reusable concepts subclass MultiVariableClass:

class Cohort(mo.MultiVariableClass):
    def compute(self, start, churn, price):
        self.users   = mo.recurrence(start, "{prev} * (1 - {c})", c=churn)
        self.revenue = self.users * price

Recurrence — period-over-period series

recurrence builds a list-valued Variable where every period references the previous period via a formula. The Excel output is a chain of cells, not a static list of values.

forecast.assumptions = mo.MultiVariable("Assumptions",
    start_rev=mo.Variable(1_000_000, unit="$"),
    growth=mo.Variable([0.05] * 12),
)

forecast.pnl = mo.MultiVariable("P&L")
forecast.pnl.revenue = mo.recurrence(
    start=forecast.assumptions.start_rev,
    formula="{prev} * (1 + {growth})",
    growth=forecast.assumptions.growth,
)

The 12 emitted Excel cells:

B1: =Assumptions!B1                          (period 0 = start)
C1: =B1 * (1 + Assumptions!C2)               (compounded period 1)
D1: =C1 * (1 + Assumptions!D2)               ...
... through M1

Change a growth value on Assumptions — every later cell recalculates.

Excel as a snapshot

.to_excel() walks the MultiVariable tree and writes a workbook where every Variable is a cell, every formula is =A1*B2, every cross-sheet reference is =Sheet!Cell.

Standard library functions emit live formulas, not values:

mo.IRR(cf)       # =IRR(B2:F2, 0.1)
mo.NPV(0.1, cf)  # =NPV(0.1, B2:F2)
mo.IF(rev > 0, rev * 0.25, 0)   # =IF(B1>0, B1*0.25, 0)
mo.EOMONTH(start, 1)            # =EOMONTH(B1, 1)

Also: IRR, NPV, XIRR, PMT, FV, PV · SUM, MAX, MIN, AVERAGE · IF, AND, OR, NOT · ABS, ROUND, INT, MOD · YEAR, MONTH, DAY, EDATE, EOMONTH, DATE, TODAY · CONCAT, UPPER, LOWER, LEN. All emit live formulas.

In a notebook

Every Variable, MultiVariable, and Model has a built-in _repr_html_. In Jupyter you see a live grid of values + formulas with click-to-trace-precedents — Excel's "Trace Precedents" button, in the browser. Toggle "Formulas" to flip every cell to its underlying expression.

Try it without installing anything: Open the first-model notebook in Colab.

If Modeleon resonates — a GitHub ⭐ helps more people find it.

License

Apache 2.0. See LICENSE and NOTICE.

Why this exists → The case for Financial Model Engineering

Releases: CHANGELOG · GitHub releases

modeleon.ai · PyPI

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