Migrating from TA-Lib

A quick lookup table for users porting code from TA-Lib (the C library, or its Python binding talib) to Wickra. Replace talib.X(...) with the matching Wickra expression and the rest of your code keeps working.

Argument-order conventions

The two libraries take the same numeric arguments but differ in shape:

TA-Lib (Python) is functional and pass-by-array. talib.RSI(close, n) is a recompute-everything call: it walks the entire close series each time, even when you only want the latest value.
Wickra is a state machine. wickra.RSI(n) returns an instance; you call .batch(close) for the full series or .update(price) one price at a time. The same instance, fed one price per minute, drives a live trading bot — see Streaming vs Batch.

Multi-output indicators (MACD, Bollinger Bands, Stochastic, ADX, Aroon, Keltner, Donchian, SuperTrend, …) return a tuple from update and a Matrix (one column per output, with .shape and [i, j] access) from batch — no NumPy required.

TA-Lib's functions reorder nothing and promise no agreement with a streaming path; Wickra's batch is bit for bit its update. For a backfill where throughput matters more than that, batch_fast takes the same arguments and runs SIMD kernels within a few units in the last place of batch — see Streaming vs Batch.

The names in the table below are the Python/Node.js/WASM aliases. C#, Go, Java and R use the canonical PascalCase name instead — see Naming across bindings for the full alias map.

Mapping table

TA-Lib	Wickra (Python)
`talib.SMA(close, n)`	`wickra.SMA(n).batch(close)`
`talib.EMA(close, n)`	`wickra.EMA(n).batch(close)`
`talib.WMA(close, n)`	`wickra.WMA(n).batch(close)`
`talib.DEMA(close, n)`	`wickra.DEMA(n).batch(close)`
`talib.TEMA(close, n)`	`wickra.TEMA(n).batch(close)`
`talib.KAMA(close, n)`	`wickra.KAMA(n).batch(close)`
`talib.T3(close, n, vfactor)`	`wickra.T3(n, vfactor).batch(close)`
`talib.RSI(close, n)`	`wickra.RSI(n).batch(close)`
`talib.STOCH(high, low, close, k, smooth, d)`	`wickra.Stochastic(k_period, d_period).batch(high, low, close)` → shape `(n, 2)`
`talib.STOCHRSI(close, n, k, d)`	`wickra.StochRSI(rsi_period, stoch_period).batch(close)`
`talib.CCI(high, low, close, n)`	`wickra.CCI(n).batch(high, low, close)`
`talib.WILLR(high, low, close, n)`	`wickra.WilliamsR(n).batch(high, low, close)`
`talib.MFI(high, low, close, volume, n)`	`wickra.MFI(n).batch(high, low, close, volume)`
`talib.ROC(close, n)`	`wickra.ROC(n).batch(close)`
`talib.MOM(close, n)`	`wickra.MOM(n).batch(close)`
`talib.CMO(close, n)`	`wickra.CMO(n).batch(close)`
`talib.MACD(close, fast, slow, signal)`	`wickra.MACD(fast, slow, signal).batch(close)` → shape `(n, 3)`
`talib.PPO(close, fast, slow)`	`wickra.PPO(fast, slow).batch(close)`
`talib.APO(close, fast, slow)`	`wickra.PPO(fast, slow).batch(close)` (PPO is APO scaled to percent)
`talib.TRIX(close, n)`	`wickra.TRIX(n).batch(close)`
`talib.ADX(high, low, close, n)`	`wickra.ADX(n).batch(high, low, close)` → shape `(n, 3)` (`+DI`, `−DI`, `ADX`)
`talib.AROON(high, low, n)`	`wickra.Aroon(n).batch(high, low, close)` → shape `(n, 2)`
`talib.AROONOSC(high, low, n)`	`wickra.AroonOscillator(n).batch(high, low, close)`
`talib.BBANDS(close, n, dev_up, dev_dn)`	`wickra.BollingerBands(n, multiplier).batch(close)` → shape `(n, 4)` (`upper`, `middle`, `lower`, `stddev`)
`talib.ATR(high, low, close, n)`	`wickra.ATR(n).batch(high, low, close)`
`talib.NATR(high, low, close, n)`	`wickra.NATR(n).batch(high, low, close)`
`talib.STDDEV(close, n)`	`wickra.StdDev(n).batch(close)`
`talib.TRANGE(high, low, close)`	`wickra.TrueRange().batch(high, low, close)`
`talib.OBV(close, volume)`	`wickra.OBV().batch(close, volume)`
`talib.AD(high, low, close, volume)`	`wickra.ADL().batch(high, low, close, volume)`
`talib.ADOSC(high, low, close, volume, fast, slow)`	`wickra.ChaikinOscillator(fast, slow).batch(high, low, close, volume)`
`talib.SAR(high, low, accel, max)`	`wickra.PSAR(accel_start, accel_step, accel_max).batch(high, low, close)`
`talib.SAREXT(high, low, start, offset, ...)`	`wickra.SAREXT(start, offset, ...).batch(high, low, close)` (signed, like TA-Lib)
`talib.MACDFIX(close, signal)`	`wickra.MACDFIX(signal).batch(close)` → shape `(n, 3)`
`talib.LINEARREG(close, n)`	`wickra.LinearRegression(n).batch(close)`
`talib.LINEARREG_SLOPE(close, n)`	`wickra.LinRegSlope(n).batch(close)`
`talib.LINEARREG_ANGLE(close, n)`	`wickra.LinRegAngle(n).batch(close)`
`talib.TYPPRICE(high, low, close)`	`wickra.TypicalPrice().batch(high, low, close)`
`talib.MEDPRICE(high, low)`	`wickra.MedianPrice().batch(high, low, close)`
`talib.WCLPRICE(high, low, close)`	`wickra.WeightedClose().batch(high, low, close)`
`talib.ULTOSC(high, low, close, p1, p2, p3)`	`wickra.UltimateOscillator(p1, p2, p3).batch(high, low, close)`

Verified against TA-Lib

The Wickra repository carries a TA-Lib reference test suite (crates/wickra-core/tests/talib_reference.rs). A generator script (scripts/gen_talib_reference.py) runs the real TA-Lib library over fixed input series — an 80-bar golden series, a 3000-bar OHLCV series and a candlestick pattern series — and commits TA-Lib's output as CSV fixtures under testdata/talib/. The test recomputes every series with Wickra, both streaming and in one batch call, checks that the two paths are bit-identical, and compares the result with TA-Lib's to a tolerance of 1e-9 (absolute below magnitude 1, relative above it). Candlestick signals must match exactly, with Wickra's +1 / -1 / 0 against TA-Lib's +100 / -100 / 0.

TA-Lib	Wickra (Python)	Agreement
`ACCBANDS(20)`	`AccelerationBands(20, 4.0)`	exact from the first bar
`SAR(0.02, 0.2)`	`PSAR(0.02, 0.02, 0.2)`	exact from the first bar
`SAREXT` (defaults)	`SAREXT()`	exact from the first bar, including the signed output
`MACDFIX(9)`	`MACDFIX(9)`	identical from bar 164
`ADOSC(3, 10)`	`ChaikinOscillator(3, 10)`	identical from bar 120
`HT_DCPERIOD`	`HilbertDominantCycle()`	identical from bar 186
`HT_DCPHASE`	`HT_DCPHASE()`	identical from bar 202
`HT_PHASOR`	`HT_PHASOR()`	identical from bar 189
`HT_SINE`	`SineWave()`	identical from bar 193 (sine and lead sine)
`HT_TRENDMODE`	`HT_TRENDMODE()`	identical from bar 75
`MAMA(0.5, 0.05)`	`MAMA(0.5, 0.05)`	MAMA from bar 152, FAMA from bar 316
`CDLMORNINGSTAR` / `CDLEVENINGSTAR` (`penetration = 0.3`)	`MorningEveningStar()`	identical signals
`CDLRISEFALL3METHODS`	`RisingThreeMethods()` + `FallingThreeMethods()`	identical signals
`CDLLADDERBOTTOM`	`LadderBottom()`	identical signals
`CDLTRISTAR`	`Tristar()`	identical signals
`CDLHIKKAKEMOD`	`HikkakeModified()`	identical setup-bar signals

The bar numbers are 0-based indices on the 3000-bar series; from that bar to the end of the series every value is within 1e-9. Where a function is not exact from its first bar, the difference is in the start-up only:

EMA seeds (MACDFIX, ADOSC). Both emit their first value on the same bar as TA-Lib, but Wickra seeds each EMA with the SMA of its own first window. TA-Lib aligns the fast MACD EMA's seed with the slow one, and seeds both ADOSC EMAs with the first A/D value. The seed difference decays geometrically and is below 1e-9 from bar 164 (MACDFIX) and bar 120 (ADOSC) on. (TA-Lib's ADOSC is Wickra's ChaikinOscillator, not AdOscillator.)
Hilbert start-up (HT_*, MAMA). TA-Lib primes its Hilbert-transform state with zeros after a WMA burn-in; Wickra waits for its tap buffers to fill. The two then run the same recursion and converge by the bars listed above (186 / 202 / 189 / 193 / 75 / 152 / 316 for HT_DCPERIOD, HT_DCPHASE, HT_PHASOR, HT_SINE, HT_TRENDMODE, MAMA, FAMA). These are properties of the start-up, not of the series length.
Candlestick sizing. TA-Lib sizes bodies and shadows against 5- or 10-bar rolling averages ("candle settings"); Wickra sizes them against the pattern's own bars (for example, a doji is a body of at most a tenth of the bar's range). The pattern series is built from neutral bars with constant body and range so that both schemes agree on which bodies are long, short or doji; the pattern rules themselves (colours, gaps, penetration, nesting, closes) give identical signals, and the near misses next to each pattern fire in neither library. On real data, where bar sizes vary, the two sizing schemes can classify a borderline body differently.
CDLHIKKAKEMOD confirmation. TA-Lib emits ±100 on the setup bar and ±200 on a later confirmation bar. Wickra's HikkakeModified flags only the setup bar.

What Wickra has that TA-Lib does not

Wickra ships several whole families that have no TA-Lib equivalent. These are the main reasons to reach for Wickra over a TA-Lib port:

Risk & performance metrics — a full streaming risk suite TA-Lib has nothing comparable to: SharpeRatio, SortinoRatio, CalmarRatio, TreynorRatio, InformationRatio, OmegaRatio, MaxDrawdown, AverageDrawdown, DrawdownDuration, UlcerIndex, PainIndex, ValueAtRisk, ConditionalValueAtRisk, KellyCriterion, ProfitFactor, GainLossRatio, RecoveryFactor, plus Beta, Alpha and RSquared.
DeMark studies — the full Tom DeMark toolkit: TdSequential, TdSetup, TdCountdown, TdCombo, TdDeMarker, TdDifferential, TdLines, TdOpen, TdPressure, TdRei, TdRangeProjection, TdRiskLevel and DemarkPivots.
Candlestick patterns — Wickra implements the common single- and multi-bar patterns directly (each emits a signed signal, no separate CDL* call per pattern): Doji, Hammer, InvertedHammer, HangingMan, ShootingStar, Engulfing, Harami, PiercingDarkCloud, MorningEveningStar, Marubozu, SpinningTop, Tweezer, ThreeInside, ThreeOutside and ThreeSoldiersOrCrows.
Market-profile / session studies — ValueArea, InitialBalance, OpeningRange and MarketFacilitationIndex.
Trailing stops — SuperTrend, ChandelierExit, ChandeKrollStop, AtrTrailingStop (TA-Lib only has SAR).
Volume oscillators — ChaikinMoneyFlow, ForceIndex, EaseOfMovement, VolumePriceTrend, plus the windowed RollingVwap.
Other modern indicators — ChoppinessIndex, VerticalHorizontalFilter, Coppock, Pmo, ZScore, MassIndex, Vortex, Tsi, Smma, Trima, Zlema, Vwma, BollingerBandwidth, PercentB.

See the Indicators Overview for the complete catalogue organised by family.

What TA-Lib has that Wickra does not (yet)

The long tail of CDL* candlestick patterns. Wickra covers the common ones (see the candlestick list above), but TA-Lib's ~60-pattern catalogue includes many rare formations Wickra does not yet implement.
A few trivial transforms (AVGPRICE, MIDPOINT, MIDPRICE).

(Hilbert-transform studies are covered: Wickra ships HilbertDominantCycle, InstantaneousTrendline and the related Ehlers cycle indicators.)

If you need one of these, open an issue — most are short additions on top of the existing engine.

Migrating from TA-Lib ​

Argument-order conventions ​

Mapping table ​

Verified against TA-Lib ​

What Wickra has that TA-Lib does not ​

What TA-Lib has that Wickra does not (yet) ​

See also ​