Polars in Finance Cheat Sheet

Polars expressions and contexts applied to S&P 500 OHLCV data. If you know Pandas, the patterns here should click fast.

1. Loading Financial Data

import polars as pl
import polars.selectors as cs
from pathlib import Path
import os

1root = Path(os.path.abspath("")).parent.parent

2df = pl.read_ipc(root / "assets" / "data" / "ticker_data.arrow")
3df = df.sort(["ticker", "date"])

df.head(5)

1

Set root path to project directory.

2

Load ticker data from an Arrow file.

3

Sort by ticker and date — required for shifts and rolling windows.

Could not memory_map compressed IPC file, defaulting to normal read. Toggle off 'memory_map' to silence this warning.

shape: (5, 7)

date	ticker	open	high	low	close	volume
date	str	f32	f32	f32	f32	f32
2006-01-03	"A"	19.918699	20.025999	19.5728	19.9783	5.307088e6
2006-01-04	"A"	20.008101	20.1751	19.900801	20.032	4.195817e6
2006-01-05	"A"	19.9485	20.556801	19.9485	20.556801	4.835402e6
2006-01-06	"A"	20.574699	20.747601	20.3302	20.664101	6.146307e6
2006-01-09	"A"	20.664101	20.753599	20.527	20.6045	4.082859e6

2. Expressions: The Atomic Units of Logic

Expressions are reusable, composable chunks of logic. Define once, apply anywhere.

1log_return = (pl.col("close") / pl.col("close").shift(1)).log().over("ticker")

realized_vol = (log_return.rolling_std(window_size=21) * (252**0.5)).over(
    "ticker"
2)


def min_max_scale(name: str) -> pl.Expr:
    col = pl.col(name)
3    return (col - col.min()) / (col.max() - col.min())

1

Log returns: ln(P_t / P_{t-1}), calculated within each ticker via .over().

2

Realized volatility: annualized rolling std (21-day window ≈ 1 trading month).

3

Min-max scaling for ML features.

3. Context: Selection & Feature Engineering

with_columns adds multiple indicators in a single pass.

df = df.with_columns(
1    ret=log_return,
    vol=realized_vol,
2    typical_price=(pl.col("high") + pl.col("low") + pl.col("close")) / 3,
)

df.tail(5)

1

Append log returns and realized volatility.

2

Typical price — common input for Money Flow Index.

shape: (5, 10)

date	ticker	open	high	low	close	volume	ret	vol	typical_price
date	str	f32	f32	f32	f32	f32	f32	f32	f32
2026-04-22	"ZTS"	118.949997	119.910004	116.599998	117.519997	3.5146e6	-0.0056	0.272725	118.010002
2026-04-23	"ZTS"	117.190002	117.599998	114.949997	116.059998	4.5328e6	-0.012501	0.276069	116.203331
2026-04-24	"ZTS"	116.010002	117.050003	115.410004	116.870003	4.1851e6	0.006955	0.276144	116.443344
2026-04-27	"ZTS"	116.620003	119.68	116.599998	117.870003	3.1603e6	0.00852	0.277579	118.050003
2026-04-28	"ZTS"	117.230003	118.290001	116.080002	116.650002	2.9725e6	-0.010404	0.260076	117.006668

4. Context: Aggregation (group_by)

risk_summary = df.group_by("ticker").agg(
    annual_return=(pl.col("ret").mean() * 252),
    annual_vol=(pl.col("ret").std() * (252**0.5)),
1    max_drawdown=((pl.col("close") / pl.col("close").cum_max() - 1).min()),
2    sharpe_ratio=(pl.col("ret").mean() / pl.col("ret").std()) * (252**0.5),
)

risk_summary.sort("sharpe_ratio", descending=True).head(5)

1

Max drawdown via cumulative maximum.

2

Sharpe ratio (assuming 0% risk-free rate).

shape: (5, 5)

ticker	annual_return	annual_vol	max_drawdown	sharpe_ratio
str	f32	f32	f32	f32
"SNDK"	2.785047	0.98521	-0.475009	2.826855
"GEV"	1.023233	0.533674	-0.382856	1.917337
"Q"	0.743194	0.545531	-0.271231	1.362331
"CEG"	0.475289	0.490132	-0.507023	0.969717
"AVGO"	0.351236	0.378535	-0.483	0.927882

5. Power Selectors (polars.selectors)

Target columns by properties instead of hardcoding names — handy for scenario shocks.

1price_shock = cs.starts_with("open", "high", "low", "close") * 1.01

df.select(
    "date",
    "ticker",
2    price_shock.name.suffix("_shock"),
).head(3)

1

Apply a 1% upward shock to all OHLC columns at once.

2

Suffix the new column names to keep originals intact.

shape: (3, 6)

date	ticker	open_shock	high_shock	low_shock	close_shock
date	str	f32	f32	f32	f32
2006-01-03	"A"	20.117886	20.226259	19.768528	20.178083
2006-01-04	"A"	20.208181	20.376852	20.099808	20.232319
2006-01-05	"A"	20.147984	20.762369	20.147984	20.762369

6. Window Functions (over)

Compute group-level stats and project them back onto individual rows — no merge needed.

df.with_columns(
    vol_z=(pl.col("volume") - pl.col("volume").mean().over("ticker"))
1    / pl.col("volume").std().over("ticker"),
2    rel_strength=pl.col("close") / pl.col("close").mean().over("date"),
).head(5)

1

Volume z-score per ticker — flags unusual trading activity.

2

Cross-sectional relative strength: price vs. market average that day.

shape: (5, 12)

date	ticker	open	high	low	close	volume	ret	vol	typical_price	vol_z	rel_strength
date	str	f32	f32	f32	f32	f32	f32	f32	f32	f32	f32
2006-01-03	"A"	19.918699	20.025999	19.5728	19.9783	5.307088e6	null	null	19.859035	1.077447	0.749611
2006-01-04	"A"	20.008101	20.1751	19.900801	20.032	4.195817e6	0.002684	null	20.035969	0.542916	0.748959
2006-01-05	"A"	19.9485	20.556801	19.9485	20.556801	4.835402e6	0.025861	null	20.354034	0.850562	0.766769
2006-01-06	"A"	20.574699	20.747601	20.3302	20.664101	6.146307e6	0.005206	null	20.580635	1.481119	0.764833
2006-01-09	"A"	20.664101	20.753599	20.527	20.6045	4.082859e6	-0.002888	null	20.628368	0.488583	0.754984

7. Time Series: Rolling Metrics & Asof Joins

df.with_columns(
    vwap=(pl.col("close") * pl.col("volume")).rolling_sum(20)
1    / pl.col("volume").rolling_sum(20)
).head(5)

1

20-day rolling VWAP.

shape: (5, 11)

date	ticker	open	high	low	close	volume	ret	vol	typical_price	vwap
date	str	f32	f32	f32	f32	f32	f32	f32	f32	f32
2006-01-03	"A"	19.918699	20.025999	19.5728	19.9783	5.307088e6	null	null	19.859035	null
2006-01-04	"A"	20.008101	20.1751	19.900801	20.032	4.195817e6	0.002684	null	20.035969	null
2006-01-05	"A"	19.9485	20.556801	19.9485	20.556801	4.835402e6	0.025861	null	20.354034	null
2006-01-06	"A"	20.574699	20.747601	20.3302	20.664101	6.146307e6	0.005206	null	20.580635	null
2006-01-09	"A"	20.664101	20.753599	20.527	20.6045	4.082859e6	-0.002888	null	20.628368	null

8. Reshaping for Correlation Analysis

Pivot from long to wide for correlation / covariance matrices.

wide_returns = df.pivot(
    index="date",
    on="ticker",
    values="ret",
1).drop_nulls()

2corr_matrix = wide_returns.select(cs.numeric()).corr()
corr_matrix.head(5)

1

One column per ticker’s returns.

2

Correlation matrix across all assets.

shape: (5, 501)

A	AAPL	ABBV	ABNB	ABT	ACGL	ACN	ADBE	ADI	ADM	ADP	ADSK	AEE	AEP	AES	AFL	AIG	AIZ	AJG	AKAM	ALB	ALGN	ALL	ALLE	AMAT	AMCR	AMD	AME	AMGN	AMP	AMT	AMZN	ANET	AON	AOS	APA	APD	…	VICI	VLO	VLTO	VMC	VRSK	VRSN	VRT	VRTX	VST	VTR	VTRS	VZ	WAB	WAT	WBD	WDAY	WDC	WEC	WELL	WFC	WM	WMB	WMT	WRB	WSM	WST	WTW	WY	WYNN	XEL	XOM	XYL	XYZ	YUM	ZBH	ZBRA	ZTS
f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	…	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
1.0	0.252005	0.159886	0.387158	0.291319	0.051918	0.314924	0.220464	0.365594	0.082219	0.250166	0.352096	0.01177	-0.145537	0.20628	0.149047	-0.048577	0.142817	0.096217	0.21591	0.184219	0.467583	0.129767	0.202938	0.322944	0.333373	0.084619	0.375688	0.321947	0.361348	0.011551	0.34124	0.173192	0.162829	0.229952	-0.147347	0.205694	…	0.016885	-0.049898	0.323352	0.333929	0.135326	0.065517	0.177153	0.315086	0.133985	-0.169327	0.253472	-0.07502	0.337305	0.62843	0.056035	0.257507	0.22573	-0.032459	-0.059922	0.214895	0.004414	-0.119114	0.066659	0.013125	0.385525	0.357492	0.103034	0.16268	0.289355	0.007724	-0.119016	0.23687	0.381205	0.216098	0.196699	0.274606	0.362176
0.252005	1.0	0.032607	0.353655	0.207605	0.10381	0.050583	0.132521	0.365957	-0.057716	0.06369	0.121071	-0.158196	-0.146113	0.018032	0.205258	0.216694	0.115361	0.003543	-0.085321	0.028355	0.376391	0.127262	0.008084	0.178174	0.273976	0.092632	0.342363	0.248612	0.294118	0.025718	0.231091	0.098731	-0.052414	0.18999	-0.127354	0.106166	…	0.109663	-0.125555	0.149918	0.116569	-0.076417	0.007996	0.242075	0.07343	-0.009934	0.081029	0.401407	-0.069197	0.336963	0.267525	0.150331	0.083502	0.115886	-0.190388	0.044669	0.36324	-0.072876	-0.057587	0.033092	0.117635	0.27519	0.175975	0.068427	0.108035	0.365579	-0.149933	-0.108486	0.194755	0.151375	0.146997	0.06224	0.188564	0.252188
0.159886	0.032607	1.0	0.025042	0.17823	0.1801	0.051987	0.065414	0.029073	0.030087	-0.049793	0.074998	0.27563	0.201132	0.01249	0.162784	0.032822	0.151542	0.089796	0.13993	0.098696	0.106788	0.131893	0.054494	0.021722	0.108147	0.003968	0.181843	0.389602	-0.032594	0.105185	-0.112818	-0.002974	0.035057	0.120331	-0.133418	0.137989	…	0.028821	-0.092267	0.206458	0.08881	0.081341	-0.101082	0.004792	0.433486	-0.154702	0.184427	0.14594	0.123576	0.107561	0.05267	0.036274	-0.130214	-0.060284	0.194187	0.229703	-0.040875	0.105738	0.055912	0.229642	0.057308	0.04018	0.160114	0.013678	0.040071	-0.121142	0.183901	-0.111634	0.084572	0.053288	0.195927	0.009359	-0.03683	-0.018306
0.387158	0.353655	0.025042	1.0	0.179758	0.069289	0.408465	0.469951	0.41908	-0.003487	0.454314	0.500417	-0.086092	-0.187378	-0.005041	0.155108	0.131687	0.26926	0.201723	0.092757	0.196378	0.559737	0.112407	0.127923	0.296559	0.276444	0.173123	0.297822	0.195929	0.433715	-0.006856	0.417421	0.322316	0.226075	0.217916	-0.153585	-0.079396	…	0.053546	-0.091131	0.448981	0.12535	0.24754	0.193942	0.155101	0.2933	0.108703	-0.09358	0.293449	-0.223877	0.354082	0.348563	0.159664	0.396202	0.156269	-0.177693	-0.075159	0.368768	0.012266	-0.014705	-0.084896	-0.146465	0.44977	0.284118	0.182924	0.037971	0.439027	-0.130453	-0.272673	0.284202	0.451018	0.021685	0.142714	0.384437	0.45244
0.291319	0.207605	0.17823	0.179758	1.0	0.135045	0.082257	-0.01935	0.121082	0.129791	0.053078	-0.057555	0.235583	0.179526	0.038046	0.225051	0.124796	0.100274	0.090429	0.032748	-0.07607	0.23032	0.184531	0.133566	0.092136	0.224374	-0.145982	0.167634	0.177653	0.073567	0.151422	-0.07323	-0.16457	0.005134	0.142195	-0.005853	0.199751	…	0.141295	-0.051328	0.31528	0.121736	0.047432	0.100863	0.08491	0.126726	0.014749	0.292106	0.13791	0.088639	0.223678	0.151275	0.095275	-0.110802	0.028947	0.160011	0.325483	-0.008272	0.135166	-0.005325	0.131658	0.12927	0.217428	0.32369	0.019537	0.198712	0.092924	0.155969	-0.001896	0.056342	-0.007221	0.300103	0.180629	-0.065155	0.275692

9. Performance: Categoricals & Streaming

Cast string columns to Categorical for memory savings. For datasets that exceed RAM, use LazyFrame + streaming to process in batches.

df = pl.scan_ipc("large_dataset.arrow")
.filter(pl.col("vol") > 0.3)
.group_by("ticker")
.agg(pl.col("ret").mean())
1.with_columns(pl.col("ticker").cast(pl.Categorical))
2.collect(streaming=True)

1

Tickers as Categorical — integers under the hood, much less memory.

2

Streaming execution — processes in batches instead of loading everything at once.

Note

Download the companion script here.