SMI — Swiss Market Index Equities

The SMI class provides access to all 20 Swiss Market Index constituents: current prices, historical prices, and daily returns. Data sourced from Yahoo Finance via yfinance.

from swiss_finance import SMI

SMI Constituents

All 20 SMI constituents (as of 2025):

Ticker	Company
`NESN.SW`	Nestlé
`ROG.SW`	Roche Holding
`NOVN.SW`	Novartis
`UBSG.SW`	UBS Group
`ZURN.SW`	Zurich Insurance
`ABBN.SW`	ABB
`SREN.SW`	Swiss Re
`GIVN.SW`	Givaudan
`LONN.SW`	Lonza Group
`SIKA.SW`	Sika
`GEBN.SW`	Geberit
`SLHN.SW`	Swiss Life
`SCMN.SW`	Swisscom
`HOLN.SW`	Holcim
`PGHN.SW`	Partners Group
`CFR.SW`	Compagnie Financière Richemont
`ALC.SW`	Alcon
`SDZ.SW`	Sandoz
`STMN.SW`	Straumann Holding
`VACN.SW`	VAT Group

`SMI.get_constituents`

SMI.get_constituents() -> dict

Get all SMI constituents as a {ticker: company_name} dictionary.

Returns: dict mapping yfinance ticker to company name.

Example:

constituents = SMI.get_constituents()
print(constituents["NESN.SW"])
# Nestlé

print(list(constituents.keys()))
# ['NESN.SW', 'ROG.SW', 'NOVN.SW', ...]

`SMI.get_prices`

SMI.get_prices() -> pd.DataFrame

Get current prices for all 20 SMI constituents.

Returns: DataFrame with ticker index and name, price columns.

Example:

prices = SMI.get_prices()
print(prices)

          name             price
NESN.SW   Nestlé            94.50
ROG.SW    Roche Holding    264.30
NOVN.SW   Novartis          94.82
UBSG.SW   UBS Group         28.45
ZURN.SW   Zurich Insurance 490.10
...

# Most expensive stocks
print(prices.sort_values("price", ascending=False).head(5))

`SMI.get_historical_prices`

SMI.get_historical_prices(
    tickers=None,
    period='1y',
    start=None,
    end=None
) -> pd.DataFrame

Get historical daily closing prices for SMI constituents.

Parameters:

Parameter	Type	Description
`tickers`	`list`	List of tickers (default: all 20)
`period`	`str`	`'1y'`, `'6mo'`, `'2y'`, etc. Ignored if `start`/`end` provided
`start`	`str`	Start date `YYYY-MM-DD` (optional)
`end`	`str`	End date `YYYY-MM-DD` (optional)

Returns: DataFrame with DatetimeIndex and one column per ticker.

Raises: ValueError if unknown tickers or start > end.

Example:

# All 20 constituents, last year
prices = SMI.get_historical_prices(period="1y")
print(prices.shape)
# (252, 20)

# Specific tickers with date range
prices = SMI.get_historical_prices(
    tickers=["NESN.SW", "ROG.SW", "NOVN.SW"],
    start="2023-01-01",
    end="2024-12-31"
)
print(prices.tail(3))

            NESN.SW   ROG.SW   NOVN.SW
Date
2024-12-27    94.12   262.80    93.50
2024-12-28    94.20   263.10    93.75
2024-12-29    94.50   264.30    94.82

`SMI.get_returns`

SMI.get_returns(
    tickers=None,
    period='1y',
    start=None,
    end=None
) -> pd.DataFrame

Get daily returns for SMI constituents (simple percentage change, as decimals).

Parameters:

Parameter	Type	Description
`tickers`	`list`	List of tickers (default: all 20)
`period`	`str`	`'1y'`, `'6mo'`, `'2y'`, etc. Ignored if `start`/`end` provided
`start`	`str`	Start date `YYYY-MM-DD` (optional)
`end`	`str`	End date `YYYY-MM-DD` (optional)

Returns: DataFrame with DatetimeIndex and daily returns as decimals (e.g. 0.012 = +1.2%).

Raises: ValueError if unknown tickers or start > end.

Example:

from swiss_finance import SMI, SNB

returns = SMI.get_returns(period="1y")
print(returns.head(3))

            NESN.SW    ROG.SW   NOVN.SW  ...
Date
2024-01-03  0.00821  -0.01203  0.00345  ...
2024-01-04  0.00120   0.00400 -0.00210  ...
2024-01-05 -0.00500   0.00050  0.00180  ...

# Annualised statistics
mean_ret = returns.mean() * 252
std_ret  = returns.std() * (252 ** 0.5)
rf_daily = SNB.get_saron_daily() / 100 / 252

sharpe = (returns.mean() - rf_daily) / returns.std() * (252 ** 0.5)
print(sharpe.sort_values(ascending=False))

Data source

SMI equity data is fetched from Yahoo Finance via the yfinance library. Data is adjusted for splits and dividends.

Note

Yahoo Finance ToS apply. This module is intended for research and educational use.