Compatibility: ferro-ta vs ta (Bukosabino)

ferro-ta provides indicators that match ta (Bukosabino's library) results to within numerical tolerance. This guide explains how to migrate from ta and how to run the cross-library validation tests.

Installation

pip install ferro-ta
# Optional: install ta to run comparison tests
pip install ta

API Comparison

ta style

import pandas as pd
from ta.momentum import RSIIndicator, StochasticOscillator
from ta.volatility import AverageTrueRange, BollingerBands
from ta.trend import SMAIndicator, EMAIndicator, MACD, CCIIndicator
from ta.volume import OnBalanceVolumeIndicator
from ta.others import DailyReturnIndicator

close = pd.Series([...])
high = pd.Series([...])
low = pd.Series([...])
volume = pd.Series([...])

rsi = RSIIndicator(close, window=14).rsi()
sma = SMAIndicator(close, window=20).sma_indicator()
ema = EMAIndicator(close, window=14).ema_indicator()

ferro-ta equivalent

import numpy as np
import ferro_ta as ft

close = np.array([...])
high = np.array([...])
low = np.array([...])
volume = np.array([...])

rsi = ft.RSI(close, timeperiod=14)
sma = ft.SMA(close, timeperiod=20)
ema = ft.EMA(close, timeperiod=14)

Note

: ferro-ta operates on NumPy arrays. If you have a pd.Series, pass it directly — ferro-ta will convert it automatically.

Indicator Mapping

ta	ferro-ta	Notes
`SMAIndicator(close, window=N).sma_indicator()`	`ft.SMA(close, timeperiod=N)`	Exact match
`EMAIndicator(close, window=N).ema_indicator()`	`ft.EMA(close, timeperiod=N)`	Tail convergence
`BollingerBands(close, window=N, window_dev=2)`	`ft.BBANDS(close, timeperiod=N, nbdevup=2, nbdevdn=2)`	Exact match
`RSIIndicator(close, window=N).rsi()`	`ft.RSI(close, timeperiod=N)`	Tail convergence
`MACD(close, window_slow, window_fast, window_sign)`	`ft.MACD(close, fastperiod, slowperiod, signalperiod)`	Tail convergence
`StochasticOscillator(high, low, close, window, smooth_window)`	`ft.STOCH(high, low, close, ...)`	Tail convergence
`AverageTrueRange(high, low, close, window=N)`	`ft.ATR(high, low, close, timeperiod=N)`	Tail convergence
`WilliamsRIndicator(high, low, close, lbp=N)`	`ft.WILLR(high, low, close, timeperiod=N)`	Exact match
`OnBalanceVolumeIndicator(close, volume)`	`ft.OBV(close, volume)`	Exact match
`CCIIndicator(high, low, close, window=N)`	`ft.CCI(high, low, close, timeperiod=N)`	Exact match

Running the Cross-Library Tests

Cross-library comparison tests live in tests/integration/test_vs_ta.py. They are automatically skipped when ta is not installed.

# Install ta first
pip install ta

# Run comparison tests
pytest tests/integration/test_vs_ta.py -v

Known Differences

EMA seeding: ta uses pandas ewm with adjust=True by default, which produces different warm-up values. Results converge after 2 × timeperiod bars.
ATR: ta uses a simple rolling mean for ATR by default; ferro-ta uses Wilder's smoothing (same as TA-Lib). Values converge after the warm-up window.
STOCH: ta and ferro-ta use different default smoothing periods. Pass matching window / smooth_window values to get tail convergence.

Performance Comparison

ferro-ta is significantly faster than ta for large arrays because the core computation is written in Rust:

pytest benchmarks/test_speed.py --benchmark-only --benchmark-json=benchmarks/results.json

ta is a pure-Python/pandas library; ferro-ta processes 100k-bar arrays in microseconds vs milliseconds for pandas-based implementations.

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