Python

Python bindings for ferro-ta. The package wraps ferro_ta_core via PyO3 and adds NumPy conversion, optional pandas/polars wrappers, and Sphinx autodoc.

Installation

pip install ferro-ta

# For Pandas support:
pip install "ferro-ta[pandas]"

# For benchmarks:
pip install "ferro-ta[benchmark]"

Basic Usage

Vectorized indicator functions accept NumPy arrays and return NumPy arrays. SMA, RSI, MACD, and BBANDS are the shared examples used on every language page.

import numpy as np
from ferro_ta import SMA, RSI, MACD, BBANDS

close = np.linspace(44.0, 48.0, 40)

sma = SMA(close, timeperiod=5)
rsi = RSI(close, timeperiod=14)
macd_line, signal, histogram = MACD(close)
upper, middle, lower = BBANDS(close, timeperiod=5)

Pandas Integration

Vectorized indicator functions transparently accept pandas.Series and preserve the index:

import pandas as pd
from ferro_ta import SMA, BBANDS

idx   = pd.date_range("2024-01-01", periods=40, freq="D")
close = pd.Series(list(range(40)), index=idx, dtype="float64") + 44.0

sma = SMA(close, timeperiod=3)           # → pd.Series, same index
upper, mid, lower = BBANDS(close, timeperiod=3)  # → tuple of pd.Series

Streaming / Live Trading

Streaming classes take scalar bar fields and return scalars, not arrays. Use the ferro_ta.streaming module for bar-by-bar processing:

from ferro_ta.streaming import StreamingSMA, StreamingRSI, StreamingATR

sma = StreamingSMA(period=5)
rsi = StreamingRSI(period=14)
atr = StreamingATR(period=14)

for bar in live_feed:
    current_sma = sma.update(bar.close)
    current_rsi = rsi.update(bar.close)
    current_atr = atr.update(bar.high, bar.low, bar.close)

Extended Indicators

from ferro_ta import VWAP, SUPERTREND, ICHIMOKU, DONCHIAN, PIVOT_POINTS
import numpy as np

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

vwap = VWAP(high, low, close, vol)
rolling_vwap = VWAP(high, low, close, vol, timeperiod=14)

st_line, direction = SUPERTREND(high, low, close, timeperiod=7, multiplier=3.0)
tenkan, kijun, senkou_a, senkou_b, chikou = ICHIMOKU(high, low, close)
dc_upper, dc_mid, dc_lower = DONCHIAN(high, low, timeperiod=20)
pivot, r1, s1, r2, s2 = PIVOT_POINTS(high, low, close, method="classic")

Derivatives Analytics

Analytics helpers take scalar arguments and return scalars or small result objects, not NumPy arrays.

from ferro_ta.analysis.options import greeks, option_price
from ferro_ta.analysis.futures import basis

call_price = option_price(100.0, 100.0, 0.05, 1.0, 0.20, option_type="call")
call_greeks = greeks(100.0, 100.0, 0.05, 1.0, 0.20, option_type="call")
front_basis = basis(100.0, 103.0)

See Derivatives Analytics for the full analytics surface, including implied volatility inversion, smile metrics, strike selection, futures curve tools, strategy schemas, and multi-leg payoff helpers.

API map

Python keeps TA-Lib-style UPPER_CASE names. The generated reference lives under API Reference:

Category	Module	Notes
Overlap	`ferro_ta.overlap` / `ferro_ta.SMA`	SMA, EMA, MACD, BBANDS, …
Momentum	`ferro_ta.momentum`	RSI, STOCH, ADX, `PLUS_DI`, …
Pattern	`ferro_ta.pattern`	61 `CDL*` candlestick functions
Streaming	`ferro_ta.streaming`	`StreamingSMA`, `StreamingRSI`, …
Batch	`ferro_ta.batch`	2-D multi-series helpers

Cross-language coverage (including names that exist only on Python) is generated in Cross-language coverage.