ferro-ta ↔ Tulipy Compatibility

Tulipy is the Python binding for Tulip Indicators — 104 technical analysis functions written in pure ANSI C99, designed for absolute speed with zero external dependencies.

Key architectural differences

Aspect	ferro-ta	Tulipy
Backend	Rust/C + SIMD	ANSI C99
Input type	NumPy array or list	`np.float64` contiguous array
Output length	Same as input (NaN-padded)	Truncated (lookback bars shorter)
NaN handling	Pads warmup with NaN	Strips warmup entirely
Multi-output	Returns tuple	Returns tuple
Pandas support	Yes (via `ArrayLike`)	No
Streaming	Yes (StreamingXxx classes)	No

Output length difference

Tulipy truncates output instead of NaN-padding. When comparing results you must align by the trailing elements:

import tulipy as ti
import ferro_ta
import numpy as np

close = np.ascontiguousarray(np.random.randn(100).cumsum() + 100, dtype=np.float64)

ti_sma = ti.sma(close, period=20)          # len = 81
ft_sma = ferro_ta.SMA(close, timeperiod=20)  # len = 100 (19 leading NaN)

# Align: compare last 81 values
n = len(ti_sma)
assert np.allclose(ti_sma, ft_sma[-n:][np.isfinite(ft_sma[-n:])], atol=1e-8)

Function signature mapping

Tulipy uses lowercase function names. The period argument is always a positional-or-keyword integer.

Indicator	ferro-ta	Tulipy
SMA	`SMA(close, timeperiod=20)`	`sma(close, period=20)`
EMA	`EMA(close, timeperiod=20)`	`ema(close, period=20)`
WMA	`WMA(close, timeperiod=14)`	`wma(close, period=14)`
RSI	`RSI(close, timeperiod=14)`	`rsi(close, period=14)`
MACD	`MACD(close, 12, 26, 9)`	`macd(close, short_period=12, long_period=26, signal_period=9)`
BBANDS	`BBANDS(close, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)` → (upper, mid, lower)	`bbands(close, period=20, stddev=2.0)` → (lower, mid, upper) ⚠️ reversed!
ATR	`ATR(high, low, close, timeperiod=14)`	`atr(high, low, close, period=14)`
OBV	`OBV(close, volume)`	`obv(close, volume)`
CCI	`CCI(high, low, close, timeperiod=14)`	`cci(high, low, close, period=14)`
WILLR	`WILLR(high, low, close, timeperiod=14)`	`willr(high, low, close, period=14)`
STOCH	`STOCH(high, low, close, 5, 3, 3)`	`stoch(high, low, close, ...)`
HMA	Not supported	`hma(close, period=14)`
DEMA	`DEMA(close, timeperiod=30)`	`dema(close, period=30)`
TEMA	`TEMA(close, timeperiod=30)`	`tema(close, period=30)`
AROON	`AROONOSC(high, low, timeperiod=14)`	`aroonosc(high, low, period=14)`
MFI	`MFI(high, low, close, volume, timeperiod=14)`	`mfi(high, low, close, volume, period=14)`
TRANGE	`TRANGE(high, low, close)`	`tr(high, low, close)`

⚠️ BBANDS tuple order: Tulipy returns (lower, middle, upper); ferro-ta and TA-Lib return (upper, middle, lower).

Memory requirements

Tulipy requires strictly contiguous np.float64 arrays. Passing a Pandas Series slice or a non-contiguous array causes an error:

# Wrong — may be a non-contiguous view
close = df["close"].values
ti.sma(close, period=20)  # may raise ValueError

# Correct — explicit contiguous cast
close = np.ascontiguousarray(df["close"].values, dtype=np.float64)
ti.sma(close, period=20)  # always works

ferro-ta accepts any ArrayLike and handles the conversion internally.

Numerical accuracy

Tulipy and ferro-ta agree closely for SMA, WMA, and other non-recursive indicators (differences < 1e-8). For EMA-based indicators the first timeperiod values differ due to initialisation seed choice:

Tulipy: uses the first data value as the EMA seed.
ferro-ta: follows TA-Lib convention (SMA of first timeperiod bars).

Values converge after approximately 2–3× the timeperiod.

Speed comparison

On 10,000 bars (median µs, Apple M-series):

Indicator	ferro-ta	Tulipy	Winner
SMA	16.7	21.2	ferro-ta
MACD	70.4	30.2	Tulipy
ATR	51.4	27.6	Tulipy

Tulipy's C99 implementation excels for recursive indicators (ATR, MACD). ferro-ta is faster for sliding-window indicators (SMA) thanks to SIMD vectorisation.

Migration guide

# FROM Tulipy
import tulipy as ti
import numpy as np

close = np.ascontiguousarray(close_series.values, dtype=np.float64)
sma_values = ti.sma(close, period=20)  # length: n - 19

# TO ferro-ta (drop-in, same numeric result in the tail)
import ferro_ta

sma_values = ferro_ta.SMA(close, timeperiod=20)  # length: n (19 leading NaN)
# Strip warmup if needed:
sma_values = sma_values[~np.isnan(sma_values)]

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