Compatibility: ferro-ta vs pandas-ta

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

Installation

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

API Comparison

pandas-ta style (accessor)

import pandas as pd
import pandas_ta as ta

close = pd.Series([...])
sma = close.ta.sma(length=20)
ema = close.ta.ema(length=14)
rsi = close.ta.rsi(length=14)

ferro-ta equivalent

import numpy as np
import ferro_ta as ft

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

pandas-ta	ferro-ta	Notes
`ta.sma(length=N)`	`ft.SMA(close, timeperiod=N)`	Exact match
`ta.ema(length=N)`	`ft.EMA(close, timeperiod=N)`	Tail convergence within 1e-6
`ta.wma(length=N)`	`ft.WMA(close, timeperiod=N)`	Exact match
`ta.rsi(length=N)`	`ft.RSI(close, timeperiod=N)`	Tail convergence
`ta.macd(fast, slow, signal)`	`ft.MACD(close, fastperiod, slowperiod, signalperiod)`	Tail convergence
`ta.bbands(length=N, std=2)`	`ft.BBANDS(close, timeperiod=N, nbdevup=2, nbdevdn=2)`	Exact match
`ta.stoch(high, low, close)`	`ft.STOCH(high, low, close, ...)`	Tail convergence
`ta.cci(high, low, close, length=N)`	`ft.CCI(high, low, close, timeperiod=N)`	Exact match
`ta.mom(length=N)`	`ft.MOM(close, timeperiod=N)`	Exact match
`ta.roc(length=N)`	`ft.ROC(close, timeperiod=N)`	Exact match
`ta.trima(length=N)`	`ft.TRIMA(close, timeperiod=N)`	Exact match
`ta.hma(length=N)`	`ft.HT_MA(close, timeperiod=N)`	Hull MA variant
`ta.ichimoku(...)`	`ft.ICHIMOKU(high, low, close)`	Tenkan/Kijun match
`ta.kc(high, low, close, ...)`	`ft.KELTNER(high, low, close, ...)`	Tail convergence

Batch Execution

ferro-ta supports running many indicators at once via the batch API:

import numpy as np
import ferro_ta as ft

data = np.random.randn(1000, 50)  # 50 instruments × 1000 bars

# Run SMA(20) across all 50 instruments in one call
results = ft.batch_compute(data, "SMA", timeperiod=20)

Running the Cross-Library Tests

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

# Install pandas-ta first
pip install pandas-ta

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

Known Differences

Seeding period: EMA results during the first timeperiod bars may differ due to different initialization strategies (SMA seed vs EMA seed). Results converge after the seeding window.
MACD signal line: The signal EMA is seeded from the first valid MACD value. Exact match begins after 2× slowperiod bars.
STOCH smoothing: ferro-ta defaults match TA-Lib (SMA slowk, SMA slowd). pandas-ta uses different defaults; pass matching parameters explicitly.

Performance Comparison

ferro-ta is 10–100× faster than pandas-ta for large arrays because the core computation is written in Rust:

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

3.5 KiB Raw Blame History Unescape Escape