ferro-ta ↔ finta Compatibility

finta implements over 80 financial technical indicators as class methods on a single TA class, operating entirely on Pandas DataFrames.

Key architectural differences

Aspect	ferro-ta	finta
Backend	Rust/C + SIMD	Pure Pandas
Input type	NumPy array or list	OHLCV Pandas DataFrame (required)
DatetimeIndex	Not required	Required
Column names	Separate arrays	`open/high/low/close/volume`
Output type	NumPy array	Pandas Series or DataFrame
NaN handling	Pads warmup with NaN	Pads warmup with NaN
Streaming	Yes (StreamingXxx classes)	No
Speed	~700× faster on ATR	Baseline (pure Pandas)

Required DataFrame format

finta requires a Pandas DataFrame with a DatetimeIndex and lowercase column names:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "open":   open_prices,
    "high":   high_prices,
    "low":    low_prices,
    "close":  close_prices,
    "volume": volume_data,   # required for volume indicators
}, index=pd.date_range("2020-01-01", periods=len(close_prices), freq="D"))

ferro-ta accepts raw NumPy arrays or Python lists — no DataFrame needed.

Function signature mapping

finta uses a class-method API: TA.INDICATOR(ohlcv_df, period, ...).

Indicator	ferro-ta	finta
SMA	`SMA(close, timeperiod=20)`	`TA.SMA(df, 20)`
EMA	`EMA(close, timeperiod=20)`	`TA.EMA(df, 20)`
WMA	`WMA(close, timeperiod=14)`	`TA.WMA(df, 14)`
DEMA	`DEMA(close, timeperiod=30)`	`TA.DEMA(df, 30)`
TEMA	`TEMA(close, timeperiod=30)`	`TA.TEMA(df, 30)`
HMA	Not supported	`TA.HMA(df, 16)`
RSI	`RSI(close, timeperiod=14)`	`TA.RSI(df, 14)`
MACD	`MACD(close, 12, 26, 9)` → (macd, signal, hist)	`TA.MACD(df, 12, 26, 9)` → DataFrame with `MACD`/`SIGNAL` columns
BBANDS	`BBANDS(close, 20, 2.0, 2.0)` → (upper, mid, lower)	`TA.BBANDS(df, 20)` → DataFrame with `BB_UPPER`/`BB_MIDDLE`/`BB_LOWER`
ATR	`ATR(high, low, close, timeperiod=14)`	`TA.ATR(df, 14)`
TRUE RANGE	`TRANGE(high, low, close)`	`TA.TR(df)`
OBV	`OBV(close, volume)`	`TA.OBV(df)`
MFI	`MFI(high, low, close, volume, timeperiod=14)`	`TA.MFI(df, 14)`
CCI	`CCI(high, low, close, timeperiod=14)`	`TA.CCI(df, 14)`
STOCH	`STOCH(high, low, close, 5, 3, 3)`	`TA.STOCH(df, 14)`
WILLR	`WILLR(high, low, close, timeperiod=14)`	`TA.WILLIAMS(df, 14)`
ADX	`ADX(high, low, close, timeperiod=14)`	`TA.ADX(df, 14)`
AROON	`AROON(high, low, timeperiod=14)` → (up, down)	`TA.AROON(df, 14)` → DataFrame

Numerical accuracy

finta uses sample standard deviation (ddof=1) for Bollinger Bands while ferro-ta follows the TA-Lib convention (population std, ddof=0). For a window of 20 bars this creates a ~0.5% difference in band width.

For EMA-based indicators, finta seeds with the first data point while ferro-ta follows TA-Lib (SMA of first timeperiod bars). Values converge after ~3× the period.

Cross-library correlation between ferro-ta and finta is ≥ 0.95 for all indicators after discarding the warm-up period.

Speed comparison

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

Indicator	ferro-ta	finta	ferro-ta speedup
SMA	16.7	178.1	10.7×
MACD	70.4	383.9	5.5×
ATR	51.4	1,247	24×

On 100,000 bars:

Indicator	ferro-ta	finta	ferro-ta speedup
SMA	126.2	699.7	5.6×
MACD	465.9	1,470.8	3.2×
ATR	478.5	6,782	14×

finta's ATR scales especially poorly because it relies on Pandas .apply() with a lambda, which cannot be vectorised.

Migration guide

# FROM finta
import pandas as pd
from finta import TA

ohlcv = pd.DataFrame(...)  # must have DatetimeIndex + open/high/low/close/volume
sma = TA.SMA(ohlcv, 20)            # returns Pandas Series
macd_df = TA.MACD(ohlcv, 12, 26, 9)  # returns DataFrame with MACD/SIGNAL cols
bb_df = TA.BBANDS(ohlcv, 20)       # returns DataFrame with BB_UPPER/MIDDLE/LOWER

# TO ferro-ta (NumPy arrays — no DataFrame required)
import ferro_ta
import numpy as np

close = ohlcv["close"].values
sma = ferro_ta.SMA(close, timeperiod=20)

macd, signal, hist = ferro_ta.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)

upper, middle, lower = ferro_ta.BBANDS(close, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)

Known limitations

finta cannot process raw NumPy arrays — a properly formatted DataFrame with DatetimeIndex is always required.
TA.MACD only returns MACD and SIGNAL columns; the histogram must be computed manually as MACD - SIGNAL.
Several finta indicators use non-standard formulas that may not match TA-Lib conventions (e.g. STOCH uses a fixed 14-period window regardless of the fastk_period argument).

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