volas

github.com/kaelzhang/volas

A Rust-backed, pandas-shaped DataFrame for live OHLCV pipelines: a broad trading-indicator set, incremental O(lookback) refresh on each new bar, and NumPy/Torch-ready output. This page is the project's reproducible benchmark report — regenerated from make benchmark on each release. See the repository for docs and installation.On this run, volas beats TA-Lib on 151 / 158 covered indicators by the default ratio, using the full coverage table below.

Benchmark report

OHLCV technical-indicator computation across libraries · 2026-07-29T09:31:35.619804+00:00 · Apple M1 (Virtual) · Python 3.12.10
Lower time is better; Perf shows the slowest candidate as 1.00× and each faster candidate as {slowest ÷ this}×.

pandaspandas_tastock_pandaspolarstalibvolas

Windowed live stream — 1m bars → 15m, bounded window (per-bar)

The live loop: fold a new 1-minute bar into a 15-minute series and refresh atr:14 over a bounded 30-bar window, every bar, for 20 000 bars. volas does this natively (a windowed, tf-aware frame that auto-compacts, so memory stays O(window + lookback)); the other libraries have no such primitive, so each is given a hand-written, non-lazy equivalent — a trimmed ring of the last window + max_lookback 15-minute bars, folded the same way and re-run through that library's ATR (never an O(n) recompute over all history). Times are per bar, amortized over the 20 000-bar stream (so volas's periodic compaction is included).

atr:14

Candidate	Mean	Median	OPS	rounds	Perf
volas	586.3 ns	586.8 ns	1,705,559	3	61.39×
talib	5.72 µs	5.72 µs	174,747	3	6.29×
polars	30.43 µs	30.49 µs	32,867	3	1.18×
pandas	36.43 µs	36.02 µs	27,450	3	1.00×

Append one new bar → updated indicator

A new bar arrives. volas / stock_pandas refresh their cached column incrementally (O(lookback)); the libraries with no indicator cache (pandas / pandas_ta / polars / talib) must recompute the series (O(n)). Every candidate is measured with the same round count so the rounds column is comparable.

atr:14

Candidate	Mean	Median	OPS	rounds	Perf
volas	574.6 ns	542.0 ns	1,740,244	500	881.54×
talib	14.52 µs	14.04 µs	68,881	500	34.03×
pandas_ta	112.04 µs	109.35 µs	8,925	500	4.37×
polars	123.49 µs	122.35 µs	8,098	500	3.90×
stock_pandas	377.83 µs	372.29 µs	2,647	500	1.28×
pandas	487.24 µs	477.79 µs	2,052	500	1.00×

boll.upper

Candidate	Mean	Median	OPS	rounds	Perf
volas	1.56 µs	1.54 µs	639,834	500	256.94×
talib	11.57 µs	11.50 µs	86,441	500	34.45×
polars	92.79 µs	91.40 µs	10,777	500	4.34×
pandas	153.94 µs	151.60 µs	6,496	500	2.61×
stock_pandas	329.85 µs	321.81 µs	3,032	500	1.23×
pandas_ta	400.42 µs	396.21 µs	2,497	500	1.00×

ema:12

Candidate	Mean	Median	OPS	rounds	Perf
volas	495.0 ns	500.0 ns	2,020,153	500	619.87×
talib	6.83 µs	6.67 µs	146,424	500	46.49×
polars	37.92 µs	36.96 µs	26,371	500	8.39×
pandas	40.54 µs	38.79 µs	24,669	500	7.99×
pandas_ta	50.74 µs	50.00 µs	19,710	500	6.20×
stock_pandas	312.83 µs	309.94 µs	3,197	500	1.00×

ma:20

Candidate	Mean	Median	OPS	rounds	Perf
volas	1.28 µs	1.21 µs	779,974	500	255.55×
talib	6.08 µs	6.04 µs	164,477	500	51.09×
polars	34.94 µs	34.67 µs	28,623	500	8.90×
pandas	48.13 µs	46.75 µs	20,779	500	6.60×
pandas_ta	51.40 µs	50.38 µs	19,456	500	6.13×
stock_pandas	311.73 µs	308.71 µs	3,208	500	1.00×

macd

Candidate	Mean	Median	OPS	rounds	Perf
volas	719.5 ns	708.0 ns	1,389,788	500	442.97×
talib	17.71 µs	17.25 µs	56,457	500	18.18×
polars	82.40 µs	73.83 µs	12,136	500	4.25×
pandas	90.25 µs	89.06 µs	11,080	500	3.52×
pandas_ta	262.52 µs	256.04 µs	3,809	500	1.22×
stock_pandas	321.43 µs	313.62 µs	3,111	500	1.00×

macd.signal

Candidate	Mean	Median	OPS	rounds	Perf
volas	641.7 ns	625.0 ns	1,558,327	500	514.90×
talib	17.45 µs	17.29 µs	57,311	500	18.61×
pandas	125.47 µs	120.71 µs	7,970	500	2.67×
polars	119.27 µs	123.23 µs	8,385	500	2.61×
pandas_ta	259.02 µs	253.77 µs	3,861	500	1.27×
stock_pandas	326.56 µs	321.81 µs	3,062	500	1.00×

rsi:14

Candidate	Mean	Median	OPS	rounds	Perf
volas	558.1 ns	542.0 ns	1,791,819	500	760.76×
talib	13.59 µs	13.58 µs	73,560	500	30.36×
pandas_ta	58.25 µs	57.58 µs	17,168	500	7.16×
polars	182.15 µs	180.71 µs	5,490	500	2.28×
stock_pandas	310.37 µs	306.46 µs	3,222	500	1.35×
pandas	415.21 µs	412.33 µs	2,408	500	1.00×

willr:14

Candidate	Mean	Median	OPS	rounds	Perf
volas	1.04 µs	1.04 µs	961,169	500	199.03×
talib	11.96 µs	11.92 µs	83,602	500	17.39×
polars	79.84 µs	71.27 µs	12,525	500	2.91×
pandas_ta	82.63 µs	81.54 µs	12,102	500	2.54×
pandas	209.86 µs	207.19 µs	4,765	500	1.00×

Core DataFrame API (construct / slice / mask / assign / copy)

The data-handling plumbing a live system runs around every indicator call — frame construction, column access, row slicing, boolean masking, column assignment, copy — timed against pandas / polars. Not indicator math; the surrounding core APIs.

construct

Candidate	Mean	Median	OPS	rounds	Perf
volas	4.34 µs	4.21 µs	230,504	39216	10.41×
polars	22.83 µs	22.33 µs	43,810	1585	1.96×
pandas	46.50 µs	43.79 µs	21,506	883	1.00×

copy

Candidate	Mean	Median	OPS	rounds	Perf
volas	315.3 ns	292.0 ns	3,171,539	115381	103.17×
polars	459.2 ns	417.0 ns	2,177,627	1842	72.24×
pandas	31.43 µs	30.12 µs	31,813	3572	1.00×

getcol

Candidate	Mean	Median	OPS	rounds	Perf
volas	344.4 ns	333.0 ns	2,903,517	114286	20.77×
polars	546.0 ns	541.0 ns	1,831,601	909	12.79×
pandas	7.05 µs	6.92 µs	141,894	1427	1.00×

mask

Candidate	Mean	Median	OPS	rounds	Perf
volas	11.30 µs	10.37 µs	88,465	16360	6.68×
polars	53.66 µs	47.67 µs	18,637	134	1.45×
pandas	72.17 µs	69.29 µs	13,855	1483	1.00×

setitem

Candidate	Mean	Median	OPS	rounds	Perf
volas	1.25 µs	1.21 µs	802,105	2184	12.14×
pandas	8.36 µs	7.92 µs	119,622	1834	1.85×
polars	15.38 µs	14.67 µs	65,019	536	1.00×

slice

Candidate	Mean	Median	OPS	rounds	Perf
polars	1.72 µs	1.71 µs	581,130	827	2.95×
volas	2.18 µs	2.13 µs	457,830	2397	2.37×
pandas	5.67 µs	5.04 µs	176,275	2020	1.00×

Batch indicator computation

Compute the indicator over the whole series, across every library.

atr:14

Candidate	Mean	Median	OPS	rounds	Perf
volas	4.22 µs	4.21 µs	237,210	50211	110.12×
talib	14.07 µs	13.96 µs	71,092	29484	33.20×
stock_pandas	93.55 µs	92.71 µs	10,689	5385	5.00×
pandas_ta	109.23 µs	107.04 µs	9,155	3235	4.33×
polars	115.59 µs	111.79 µs	8,651	2562	4.14×
pandas	471.73 µs	463.37 µs	2,120	902	1.00×

boll.upper

Candidate	Mean	Median	OPS	rounds	Perf
volas	5.91 µs	5.75 µs	169,164	49896	68.32×
talib	11.73 µs	11.46 µs	85,255	34238	34.28×
polars	84.40 µs	75.83 µs	11,849	2782	5.18×
pandas	145.66 µs	144.17 µs	6,865	2391	2.73×
stock_pandas	154.67 µs	151.21 µs	6,465	4619	2.60×
pandas_ta	397.35 µs	392.85 µs	2,517	1332	1.00×

ema:12

Candidate	Mean	Median	OPS	rounds	Perf
volas	5.14 µs	5.04 µs	194,420	59260	9.80×
talib	6.69 µs	6.63 µs	149,414	50103	7.46×
stock_pandas	36.16 µs	35.38 µs	27,653	7788	1.40×
polars	36.97 µs	36.71 µs	27,049	6848	1.35×
pandas	39.49 µs	38.33 µs	25,323	3605	1.29×
pandas_ta	51.13 µs	49.42 µs	19,559	4276	1.00×

hhv:10

Candidate	Mean	Median	OPS	rounds	Perf
volas	3.07 µs	3.04 µs	325,738	40610	18.08×
talib	5.63 µs	5.42 µs	177,590	39737	10.15×
polars	30.40 µs	32.42 µs	32,897	6055	1.70×
stock_pandas	47.17 µs	46.71 µs	21,198	6619	1.18×
pandas	56.37 µs	55.00 µs	17,741	3404	1.00×

llv:10

Candidate	Mean	Median	OPS	rounds	Perf
volas	3.90 µs	3.75 µs	256,274	54921	15.13×
talib	6.34 µs	5.83 µs	157,740	22967	9.73×
polars	33.60 µs	30.83 µs	29,759	4882	1.84×
stock_pandas	55.65 µs	51.42 µs	17,969	7016	1.10×
pandas	57.58 µs	56.75 µs	17,368	3837	1.00×

ma:20

Candidate	Mean	Median	OPS	rounds	Perf
volas	4.34 µs	4.25 µs	230,229	37559	15.15×
talib	6.06 µs	5.96 µs	165,025	48193	10.80×
polars	35.04 µs	34.50 µs	28,540	6282	1.87×
pandas	47.47 µs	46.83 µs	21,065	2872	1.37×
pandas_ta	49.32 µs	48.54 µs	20,275	5016	1.33×
stock_pandas	65.95 µs	64.38 µs	15,163	7466	1.00×

macd

Candidate	Mean	Median	OPS	rounds	Perf
volas	6.91 µs	6.71 µs	144,642	43165	38.47×
talib	17.46 µs	17.12 µs	57,289	26144	15.07×
stock_pandas	48.40 µs	47.21 µs	20,659	6945	5.47×
pandas	90.10 µs	87.75 µs	11,099	2463	2.94×
polars	88.35 µs	89.98 µs	11,318	3708	2.87×
pandas_ta	269.68 µs	258.13 µs	3,708	1379	1.00×

macd.signal

Candidate	Mean	Median	OPS	rounds	Perf
volas	11.45 µs	11.29 µs	87,322	40269	23.12×
talib	17.37 µs	17.17 µs	57,583	29889	15.21×
stock_pandas	59.38 µs	58.38 µs	16,840	8014	4.47×
pandas	125.19 µs	123.29 µs	7,988	2907	2.12×
polars	118.43 µs	123.50 µs	8,444	3266	2.11×
pandas_ta	265.10 µs	261.04 µs	3,772	1617	1.00×

rsi:14

Candidate	Mean	Median	OPS	rounds	Perf
volas	11.89 µs	11.54 µs	84,118	35715	35.72×
talib	14.67 µs	14.00 µs	68,188	34935	29.45×
stock_pandas	53.80 µs	53.33 µs	18,586	8194	7.73×
pandas_ta	57.90 µs	56.54 µs	17,270	4611	7.29×
polars	183.42 µs	181.75 µs	5,452	2053	2.27×
pandas	415.05 µs	412.25 µs	2,409	969	1.00×

willr:14

Candidate	Mean	Median	OPS	rounds	Perf
volas	10.80 µs	10.46 µs	92,608	34433	20.62×
talib	11.56 µs	11.33 µs	86,507	27492	19.03×
polars	82.55 µs	71.92 µs	12,114	3344	3.00×
pandas_ta	92.60 µs	87.17 µs	10,799	3231	2.47×
pandas	221.87 µs	215.63 µs	4,507	2123	1.00×

Full coverage — volas vs TA-Lib

Every indicator both volas and TA-Lib implement (the set the parity suite aligns), one row per indicator. The default volas vs TA-Lib column is the Tencent fixture; optional generated lengths and cached append refresh appear as additional ratio columns. Values > 1.00× mean volas is faster.

volas beats TA-Lib on 151 / 158 covered indicators by the default ratio (0 exactly even, 7 slower).

Indicator	volas	TA-Lib	volas vs TA-Lib (after append)	volas vs TA-Lib	volas vs TA-Lib (100)	volas vs TA-Lib (250)	volas vs TA-Lib (20000)
midpoint:14	7.29 µs	71.83 µs	60.64×	9.85×	n/a	n/a	n/a
cdl.stalledpattern	2.92 µs	20.71 µs	21.46×	7.10×	n/a	n/a	n/a
cdl.kicking	4.58 µs	27.50 µs	28.90×	6.00×	n/a	n/a	n/a
cdl.kickingbylength	4.58 µs	26.75 µs	27.14×	5.84×	n/a	n/a	n/a
cdl.3starsinsouth	2.92 µs	15.50 µs	15.33×	5.32×	n/a	n/a	n/a
cdl.risefall3methods	5.54 µs	25.21 µs	22.05×	4.55×	n/a	n/a	n/a
cdl.3whitesoldiers	5.79 µs	26.29 µs	25.86×	4.54×	n/a	n/a	n/a
minmax.min:30	3.69 µs	16.33 µs	10.95×	4.43×	n/a	n/a	n/a
minmax.max:30	3.69 µs	16.29 µs	11.09×	4.42×	n/a	n/a	n/a
linearreg_intercept:14	3.02 µs	13.00 µs	13.18×	4.30×	n/a	n/a	n/a
cdl.upsidegap2crows	3.33 µs	13.92 µs	16.54×	4.18×	n/a	n/a	n/a
cdl.marubozu	3.50 µs	14.50 µs	14.79×	4.14×	n/a	n/a	n/a
tsf:14	3.29 µs	13.50 µs	13.08×	4.10×	n/a	n/a	n/a
cdl.thrusting	3.79 µs	15.46 µs	15.24×	4.08×	n/a	n/a	n/a
linearreg:14	3.33 µs	13.50 µs	13.64×	4.05×	n/a	n/a	n/a
cdl.counterattack	4.46 µs	17.75 µs	16.41×	3.98×	n/a	n/a	n/a
cdl.morningstar	3.50 µs	13.67 µs	12.77×	3.90×	n/a	n/a	n/a
cdl.identical3crows	5.00 µs	19.50 µs	18.83×	3.90×	n/a	n/a	n/a
cdl.matchinglow	3.04 µs	11.67 µs	11.25×	3.84×	n/a	n/a	n/a
linearreg_slope:14	3.02 µs	11.46 µs	11.61×	3.79×	n/a	n/a	n/a
cdl.haramicross	4.04 µs	15.29 µs	16.08×	3.78×	n/a	n/a	n/a
minus_dm:14	3.04 µs	11.50 µs	19.87×	3.78×	n/a	n/a	n/a
plus_dm:14	3.08 µs	11.50 µs	20.60×	3.73×	n/a	n/a	n/a
rocr:10	666.6 ns	2.46 µs	6.21×	3.69×	n/a	n/a	n/a
cdl.eveningstar	3.33 µs	12.21 µs	11.29×	3.66×	n/a	n/a	n/a
accbands:20	4.29 µs	14.46 µs	10.03×	3.37×	n/a	n/a	n/a
cdl.abandonedbaby	6.08 µs	20.17 µs	19.77×	3.31×	n/a	n/a	n/a
cdl.morningdojistar	4.67 µs	15.17 µs	14.40×	3.25×	n/a	n/a	n/a
cdl.separatinglines	6.37 µs	20.62 µs	19.93×	3.24×	n/a	n/a	n/a
atr:14	4.04 µs	12.96 µs	25.17×	3.21×	n/a	n/a	n/a
cdl.ladderbottom	2.75 µs	8.54 µs	7.96×	3.11×	n/a	n/a	n/a
minmaxindex.max:30	6.75 µs	20.04 µs	33.46×	2.97×	n/a	n/a	n/a
natr:14	4.38 µs	12.96 µs	24.26×	2.96×	n/a	n/a	n/a
tema:30	5.08 µs	14.83 µs	26.65×	2.92×	n/a	n/a	n/a
accbands.lower:20	5.17 µs	14.46 µs	10.94×	2.80×	n/a	n/a	n/a
cdl.onneck	3.87 µs	10.71 µs	13.25×	2.76×	n/a	n/a	n/a
mom:10	516.7 ns	1.42 µs	3.81×	2.74×	n/a	n/a	n/a
cdl.sticksandwich	3.75 µs	10.21 µs	10.13×	2.72×	n/a	n/a	n/a
cdl.advanceblock	10.63 µs	28.83 µs	27.03×	2.71×	n/a	n/a	n/a
linearreg_angle:14	12.29 µs	32.92 µs	30.75×	2.68×	n/a	n/a	n/a
cdl.inneck	3.87 µs	10.25 µs	10.96×	2.65×	n/a	n/a	n/a
accbands.upper:20	5.33 µs	14.08 µs	9.54×	2.64×	n/a	n/a	n/a
medprice	570.8 ns	1.46 µs	3.89×	2.55×	n/a	n/a	n/a
cdl.longline	4.79 µs	12.08 µs	13.92×	2.52×	n/a	n/a	n/a
macd	6.37 µs	16.00 µs	25.00×	2.51×	n/a	n/a	n/a
trix:30	6.25 µs	15.58 µs	27.98×	2.49×	n/a	n/a	n/a
ht_dcphase	194.42 µs	477.50 µs	442.21×	2.46×	n/a	n/a	n/a
ht_sine.leadsine	212.46 µs	513.79 µs	500.22×	2.42×	n/a	n/a	n/a
macdfix	6.29 µs	15.21 µs	24.67×	2.42×	n/a	n/a	n/a
ht_sine.sine	213.17 µs	513.75 µs	460.17×	2.41×	n/a	n/a	n/a
boll.middle	4.37 µs	10.25 µs	7.30×	2.34×	n/a	n/a	n/a
wclprice	695.8 ns	1.62 µs	4.01×	2.34×	n/a	n/a	n/a
cdl.dojistar	4.88 µs	11.37 µs	11.47×	2.33×	n/a	n/a	n/a
midprice:14	7.46 µs	17.38 µs	14.92×	2.33×	n/a	n/a	n/a
avgprice	820.8 ns	1.87 µs	4.61×	2.28×	n/a	n/a	n/a
ht_trendmode	232.25 µs	530.12 µs	471.00×	2.28×	n/a	n/a	n/a
cdl.invertedhammer	5.42 µs	12.33 µs	13.17×	2.28×	n/a	n/a	n/a
cdl.highwave	2.67 µs	6.04 µs	5.50×	2.27×	n/a	n/a	n/a
cci:14	10.00 µs	22.63 µs	20.66×	2.26×	n/a	n/a	n/a
typprice	737.5 ns	1.67 µs	4.45×	2.26×	n/a	n/a	n/a
wma:30	2.52 µs	5.67 µs	4.29×	2.25×	n/a	n/a	n/a
cdl.hangingman	4.71 µs	10.46 µs	9.28×	2.22×	n/a	n/a	n/a
cdl.gravestonedoji	5.42 µs	11.79 µs	11.21×	2.18×	n/a	n/a	n/a
cdl.homingpigeon	3.17 µs	6.87 µs	7.00×	2.17×	n/a	n/a	n/a
cdl.dragonflydoji	4.88 µs	10.54 µs	10.77×	2.16×	n/a	n/a	n/a
minmaxindex.min:30	9.46 µs	20.12 µs	33.53×	2.13×	n/a	n/a	n/a
ultosc	7.96 µs	16.67 µs	9.45×	2.09×	n/a	n/a	n/a
dema:30	5.00 µs	10.42 µs	18.79×	2.08×	n/a	n/a	n/a
cdl.3inside	6.79 µs	13.83 µs	13.56×	2.04×	n/a	n/a	n/a
cdl.gapsidesidewhite	4.92 µs	10.00 µs	10.00×	2.03×	n/a	n/a	n/a
tr	925.0 ns	1.87 µs	4.89×	2.03×	n/a	n/a	n/a
cdl.tasukigap	4.46 µs	9.00 µs	9.04×	2.02×	n/a	n/a	n/a
cdl.shortline	5.62 µs	10.96 µs	10.97×	1.95×	n/a	n/a	n/a
dx:14	8.29 µs	16.12 µs	27.87×	1.94×	n/a	n/a	n/a
cdl.eveningdojistar	8.67 µs	16.83 µs	16.67×	1.94×	n/a	n/a	n/a
cdl.spinningtop	2.75 µs	5.21 µs	5.12×	1.89×	n/a	n/a	n/a
cdl.mathold	10.21 µs	19.21 µs	17.19×	1.88×	n/a	n/a	n/a
cdl.harami	3.46 µs	6.50 µs	6.08×	1.88×	n/a	n/a	n/a
beta:5@high,low	6.50 µs	12.21 µs	7.10×	1.88×	n/a	n/a	n/a
boll.lower	5.62 µs	10.21 µs	7.30×	1.81×	n/a	n/a	n/a
boll.upper	5.62 µs	10.21 µs	6.86×	1.81×	n/a	n/a	n/a
cdl.2crows	4.38 µs	7.92 µs	9.17×	1.81×	n/a	n/a	n/a
cdl.closingmarubozu	4.00 µs	7.21 µs	8.71×	1.80×	n/a	n/a	n/a
cdl.unique3river	7.00 µs	12.50 µs	13.21×	1.79×	n/a	n/a	n/a
cdl.rickshawman	4.17 µs	7.21 µs	7.44×	1.73×	n/a	n/a	n/a
minus_di:14	7.33 µs	12.54 µs	21.93×	1.71×	n/a	n/a	n/a
maxindex:30	6.46 µs	11.00 µs	20.22×	1.70×	n/a	n/a	n/a
plus_di:14	7.38 µs	12.54 µs	21.08×	1.70×	n/a	n/a	n/a
cdl.belthold	4.17 µs	7.08 µs	7.44×	1.70×	n/a	n/a	n/a
cdl.3blackcrows	6.13 µs	10.21 µs	9.85×	1.67×	n/a	n/a	n/a
rocr100:10	1.50 µs	2.46 µs	5.73×	1.64×	n/a	n/a	n/a
cdl.doji	2.42 µs	3.96 µs	4.29×	1.64×	n/a	n/a	n/a
cdl.takuri	4.83 µs	7.87 µs	8.28×	1.63×	n/a	n/a	n/a
aroon.up:14	5.83 µs	9.50 µs	8.85×	1.63×	4.00×	2.97×	1.61×
roc:10	1.54 µs	2.50 µs	6.31×	1.62×	3.68×	3.30×	1.07×
trima:30	4.29 µs	6.96 µs	5.09×	1.62×	n/a	n/a	n/a
rocp:10	1.54 µs	2.46 µs	6.21×	1.59×	n/a	n/a	n/a
ad	2.58 µs	4.08 µs	5.88×	1.58×	n/a	n/a	n/a
hhv:10	2.92 µs	4.58 µs	4.95×	1.57×	3.26×	2.62×	1.79×
cdl.tristar	5.08 µs	7.71 µs	7.58×	1.52×	3.12×	2.26×	2.17×
cdl.xsidegap3methods	4.42 µs	6.62 µs	11.66×	1.50×	n/a	n/a	n/a
bop	1.99 µs	2.92 µs	6.55×	1.47×	n/a	n/a	n/a
cdl.shootingstar	5.71 µs	8.37 µs	9.96×	1.47×	n/a	n/a	n/a
macdext	9.29 µs	13.42 µs	7.89×	1.44×	n/a	n/a	n/a
macd.histogram	11.17 µs	16.08 µs	26.94×	1.44×	n/a	n/a	n/a
macd.signal	11.13 µs	16.00 µs	25.07×	1.44×	n/a	n/a	n/a
cdl.piercing	3.33 µs	4.75 µs	8.42×	1.43×	n/a	n/a	n/a
macdfix.signal	10.75 µs	15.21 µs	29.29×	1.41×	n/a	n/a	n/a
cdl.concealbabyswall	7.00 µs	9.88 µs	9.61×	1.41×	n/a	n/a	n/a
adx:14	11.00 µs	15.46 µs	24.93×	1.41×	n/a	n/a	n/a
cdl.engulfing	3.12 µs	4.33 µs	4.36×	1.39×	n/a	n/a	n/a
adxr:14	11.50 µs	15.83 µs	23.18×	1.38×	n/a	n/a	n/a
macdfix.histogram	11.08 µs	15.25 µs	29.11×	1.38×	n/a	n/a	n/a
obv	2.25 µs	3.08 µs	4.80×	1.37×	n/a	n/a	n/a
cdl.3outside	2.83 µs	3.88 µs	3.92×	1.37×	n/a	n/a	n/a
stoch.d	11.12 µs	15.21 µs	14.37×	1.37×	2.76×	2.09×	1.12×
minindex:30	9.33 µs	12.62 µs	21.37×	1.35×	n/a	n/a	n/a
sar	4.88 µs	6.50 µs	9.63×	1.33×	n/a	n/a	n/a
cdl.longleggeddoji	3.54 µs	4.71 µs	5.67×	1.33×	n/a	n/a	n/a
adosc	5.12 µs	6.71 µs	10.07×	1.31×	n/a	n/a	n/a
sarext	5.33 µs	6.96 µs	9.93×	1.30×	n/a	n/a	n/a
llv:10	3.46 µs	4.50 µs	4.46×	1.30×	n/a	n/a	n/a
cdl.hikkakemod	8.29 µs	10.75 µs	10.41×	1.30×	n/a	n/a	n/a
cdl.darkcloudcover	3.25 µs	4.17 µs	4.46×	1.28×	n/a	n/a	n/a
stochf.k	8.87 µs	11.37 µs	11.70×	1.28×	n/a	n/a	n/a
ma:20	4.08 µs	5.08 µs	3.87×	1.24×	n/a	n/a	n/a
sum:30	4.12 µs	5.00 µs	3.78×	1.21×	n/a	n/a	n/a
var:5	4.21 µs	5.08 µs	5.41×	1.21×	n/a	n/a	n/a
cdl.hikkake	3.67 µs	4.42 µs	4.71×	1.20×	3.33×	2.58×	1.13×
stddev:5	4.75 µs	5.71 µs	6.04×	1.20×	n/a	n/a	n/a
cdl.breakaway	3.96 µs	4.75 µs	4.96×	1.20×	3.18×	2.41×	1.85×
aroon.down:14	8.08 µs	9.67 µs	9.13×	1.20×	3.25×	2.79×	1.48×
cdl.3linestrike	5.25 µs	6.25 µs	6.28×	1.19×	3.06×	2.18×	1.18×
t3:5	5.54 µs	6.58 µs	11.86×	1.19×	n/a	n/a	n/a
aroonosc:14	7.75 µs	9.17 µs	8.39×	1.18×	2.91×	2.36×	1.04×
stoch.k	12.83 µs	15.13 µs	13.68×	1.18×	n/a	n/a	n/a
ema:12	4.87 µs	5.67 µs	12.10×	1.16×	n/a	n/a	n/a
kama:30	4.92 µs	5.71 µs	10.32×	1.16×	n/a	n/a	n/a
stochrsi.d	19.29 µs	22.04 µs	28.78×	1.14×	1.87×	1.60×	1.32×
stochf.d	9.96 µs	11.37 µs	11.13×	1.14×	2.88×	2.02×	1.06×
correl:30@high,low	6.29 µs	7.17 µs	3.33×	1.14×	n/a	n/a	n/a
stochrsi.k	19.42 µs	22.08 µs	33.42×	1.14×	n/a	n/a	n/a
ppo	9.79 µs	11.00 µs	6.21×	1.12×	n/a	n/a	n/a
mavp:2,30@close,periods	115.58 µs	128.92 µs	25.44×	1.12×	n/a	n/a	n/a
rsi:14	11.04 µs	12.29 µs	26.47×	1.11×	n/a	n/a	n/a
ht_trendline	115.54 µs	125.33 µs	134.14×	1.08×	n/a	n/a	n/a
cdl.hammer	8.54 µs	9.08 µs	9.45×	1.06×	n/a	n/a	n/a
cmo:14	11.62 µs	12.08 µs	25.42×	1.04×	n/a	n/a	n/a
macdext.signal	13.00 µs	13.50 µs	7.61×	1.04×	n/a	n/a	n/a
willr:14	9.75 µs	10.08 µs	9.85×	1.03×	n/a	n/a	n/a
mfi:14	4.67 µs	4.79 µs	4.58×	1.03×	3.05×	2.43×	3.69×
mama.fama	101.25 µs	100.08 µs	114.29×	0.99×	n/a	n/a	n/a
mama	101.04 µs	99.79 µs	120.55×	0.99×	1.14×	1.02×	1.15×
apo	9.62 µs	9.42 µs	5.24×	0.98×	n/a	n/a	n/a
ht_phasor.inphase	110.29 µs	104.29 µs	123.10×	0.95×	n/a	n/a	n/a
macdext.histogram	14.50 µs	13.50 µs	7.70×	0.93×	n/a	n/a	n/a
ht_phasor.quadrature	110.29 µs	101.67 µs	137.48×	0.92×	n/a	n/a	n/a
ht_dcperiod	117.21 µs	106.00 µs	125.16×	0.90×	1.04×	0.96×	0.90×

Generated by scripts/benchmark_report.py from pytest-benchmark output.