Pratik Bhadane
146 lines
5.1 KiB
Markdown
146 lines
5.1 KiB
Markdown
# ferro-ta ↔ finta Compatibility
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[finta](https://github.com/peerchemist/finta) implements over 80 financial
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technical indicators as class methods on a single `TA` class, operating
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entirely on Pandas DataFrames.
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---
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## Key architectural differences
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| Aspect | ferro-ta | finta |
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|--------|---------|-------|
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| **Backend** | Rust/C + SIMD | Pure Pandas |
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| **Input type** | NumPy array or list | OHLCV Pandas DataFrame (required) |
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| **DatetimeIndex** | Not required | **Required** |
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| **Column names** | Separate arrays | `open/high/low/close/volume` |
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| **Output type** | NumPy array | Pandas Series or DataFrame |
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| **NaN handling** | Pads warmup with NaN | Pads warmup with NaN |
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| **Streaming** | Yes (StreamingXxx classes) | No |
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| **Speed** | ~700× faster on ATR | Baseline (pure Pandas) |
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---
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## Required DataFrame format
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finta requires a **Pandas DataFrame with a DatetimeIndex** and lowercase
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column names:
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```python
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import pandas as pd
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import numpy as np
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df = pd.DataFrame({
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"open": open_prices,
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"high": high_prices,
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"low": low_prices,
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"close": close_prices,
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"volume": volume_data, # required for volume indicators
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}, index=pd.date_range("2020-01-01", periods=len(close_prices), freq="D"))
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```
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ferro-ta accepts raw NumPy arrays or Python lists — no DataFrame needed.
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---
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## Function signature mapping
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finta uses a class-method API: `TA.INDICATOR(ohlcv_df, period, ...)`.
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| Indicator | ferro-ta | finta |
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|-----------|---------|-------|
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| SMA | `SMA(close, timeperiod=20)` | `TA.SMA(df, 20)` |
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| EMA | `EMA(close, timeperiod=20)` | `TA.EMA(df, 20)` |
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| WMA | `WMA(close, timeperiod=14)` | `TA.WMA(df, 14)` |
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| DEMA | `DEMA(close, timeperiod=30)` | `TA.DEMA(df, 30)` |
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| TEMA | `TEMA(close, timeperiod=30)` | `TA.TEMA(df, 30)` |
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| HMA | Not supported | `TA.HMA(df, 16)` |
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| RSI | `RSI(close, timeperiod=14)` | `TA.RSI(df, 14)` |
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| MACD | `MACD(close, 12, 26, 9)` → (macd, signal, hist) | `TA.MACD(df, 12, 26, 9)` → DataFrame with `MACD`/`SIGNAL` columns |
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| BBANDS | `BBANDS(close, 20, 2.0, 2.0)` → (upper, mid, lower) | `TA.BBANDS(df, 20)` → DataFrame with `BB_UPPER`/`BB_MIDDLE`/`BB_LOWER` |
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| ATR | `ATR(high, low, close, timeperiod=14)` | `TA.ATR(df, 14)` |
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| TRUE RANGE | `TRANGE(high, low, close)` | `TA.TR(df)` |
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| OBV | `OBV(close, volume)` | `TA.OBV(df)` |
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| MFI | `MFI(high, low, close, volume, timeperiod=14)` | `TA.MFI(df, 14)` |
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| CCI | `CCI(high, low, close, timeperiod=14)` | `TA.CCI(df, 14)` |
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| STOCH | `STOCH(high, low, close, 5, 3, 3)` | `TA.STOCH(df, 14)` |
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| WILLR | `WILLR(high, low, close, timeperiod=14)` | `TA.WILLIAMS(df, 14)` |
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| ADX | `ADX(high, low, close, timeperiod=14)` | `TA.ADX(df, 14)` |
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| AROON | `AROON(high, low, timeperiod=14)` → (up, down) | `TA.AROON(df, 14)` → DataFrame |
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---
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## Numerical accuracy
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finta uses sample standard deviation (ddof=1) for Bollinger Bands while
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ferro-ta follows the TA-Lib convention (population std, ddof=0). For a
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window of 20 bars this creates a ~0.5% difference in band width.
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For EMA-based indicators, finta seeds with the first data point while ferro-ta
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follows TA-Lib (SMA of first `timeperiod` bars). Values converge after
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~3× the period.
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Cross-library correlation between ferro-ta and finta is ≥ 0.95 for all
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indicators after discarding the warm-up period.
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---
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## Speed comparison
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On 10,000 bars (median µs, Apple M-series):
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| Indicator | ferro-ta | finta | ferro-ta speedup |
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|-----------|--------:|-------:|----------------:|
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| SMA | 16.7 | 178.1 | **10.7×** |
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| MACD | 70.4 | 383.9 | **5.5×** |
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| ATR | 51.4 | 1,247 | **24×** |
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On 100,000 bars:
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| Indicator | ferro-ta | finta | ferro-ta speedup |
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|-----------|--------:|--------:|----------------:|
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| SMA | 126.2 | 699.7 | **5.6×** |
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| MACD | 465.9 | 1,470.8 | **3.2×** |
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| ATR | 478.5 | 6,782 | **14×** |
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finta's ATR scales especially poorly because it relies on Pandas `.apply()`
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with a lambda, which cannot be vectorised.
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---
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## Migration guide
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```python
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# FROM finta
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import pandas as pd
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from finta import TA
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ohlcv = pd.DataFrame(...) # must have DatetimeIndex + open/high/low/close/volume
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sma = TA.SMA(ohlcv, 20) # returns Pandas Series
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macd_df = TA.MACD(ohlcv, 12, 26, 9) # returns DataFrame with MACD/SIGNAL cols
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bb_df = TA.BBANDS(ohlcv, 20) # returns DataFrame with BB_UPPER/MIDDLE/LOWER
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# TO ferro-ta (NumPy arrays — no DataFrame required)
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import ferro_ta
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import numpy as np
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close = ohlcv["close"].values
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sma = ferro_ta.SMA(close, timeperiod=20)
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macd, signal, hist = ferro_ta.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
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upper, middle, lower = ferro_ta.BBANDS(close, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
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```
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---
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## Known limitations
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- finta cannot process raw NumPy arrays — a properly formatted DataFrame with
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DatetimeIndex is always required.
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- `TA.MACD` only returns `MACD` and `SIGNAL` columns; the histogram must be
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computed manually as `MACD - SIGNAL`.
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- Several finta indicators use non-standard formulas that may not match TA-Lib
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conventions (e.g. STOCH uses a fixed 14-period window regardless of the
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`fastk_period` argument).
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