ferro-ta 1.0.1

A fast Technical Analysis library — TA-Lib alternative powered by Rust and PyO3

⚡ ferro-ta

The Python Technical Analysis Library That Beats TA-Lib — Everywhere

Powered by Rust. Driven by O(n) algorithms. Designed for the speed that modern quantitative trading demands.

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"Same API as TA-Lib. 3–5× faster. No C compiler needed. Drop it in today."

ferro-ta is a Rust-powered, PyO3-compiled technical analysis library that replaces TA-Lib with a pure-Rust core that runs 3× to 5× faster on every major indicator. It runs as a pre-compiled Python wheel — no C toolchain, no system dependencies, no compilation headaches.

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🚀 Why ferro-ta?

	TA-Lib	ferro-ta
Speed	C extension, O(n×period) for STOCH/etc.	Rust + O(n) algorithms for most indicators
Installation	Requires C compiler + system libs	pip install ferro-ta — zero deps
Platforms	Linux-only on many CI systems	Windows / macOS (Intel + M-series) / Linux
API	talib.SMA(close, 20)	ferro_ta.SMA(close, 20) — identical
Extra indicators	—	VWAP, SUPERTREND, ICHIMOKU, DONCHIAN, and 10 more
Streaming API	—	Bar-by-bar stateful classes
GPU acceleration	—	Optional PyTorch backend (CUDA / MPS)
WebAssembly	—	Node.js / Browser via WASM
Type stubs	—	Full .pyi + py.typed (PEP 561)

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⚡ Performance vs TA-Lib

ferro-ta is optimized for high throughput and often competitive with TA-Lib, thanks to:

• O(n) sliding max/min (monotonic deque) for STOCH — was O(n×period) in TA-Lib
• Fused TR loop for ATR — no intermediate allocation, single pass
• Branchless gain/loss for RSI — diff.max(0.0) instead of if/else
• O(n) rolling operators for SMA/WMA/BBANDS — sliding window accumulators
• Fused fast+slow EMA loop for MACD — single pass for both EMAs
• Zero-copy NumPy bridging — input arrays read directly from buffer without copying

🏆 Reproducible benchmark workflow

We publish benchmark methodology and generated tables in benchmarks/README.md.

• Cross-library speed suite (62 indicators × available libraries): benchmarks/test_speed.py
• Head-to-head TA-Lib comparison: benchmarks/bench_vs_talib.py
• Table generation from results.json: benchmarks/benchmark_table.py

# Reproduce these numbers yourself
pip install ferro-ta ta-lib
python benchmarks/bench_vs_talib.py --sizes 10000 100000 --json benchmark_vs_talib.json
# or with uv:
uv run python benchmarks/bench_vs_talib.py --sizes 10000 100000 --json benchmark_vs_talib.json
uv run python benchmarks/check_vs_talib_regression.py --input benchmark_vs_talib.json

# full cross-library speed suite (100k bars):
uv run pytest benchmarks/test_speed.py --benchmark-only --benchmark-json=benchmarks/results.json -v
# generate markdown table from results:
uv run python benchmarks/benchmark_table.py

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🎯 Features

• No C-compiler required — pre-compiled wheels for Windows, macOS (Intel & Apple Silicon), and Linux
• Drop-in API compatible with TA-Lib (SMA, EMA, RSI, MACD, BBANDS, and 155+ more)
• Extended Indicators beyond TA-Lib: VWAP, SUPERTREND, ICHIMOKU, DONCHIAN, PIVOT_POINTS, KELTNER_CHANNELS, HULL_MA, CHANDELIER_EXIT, VWMA, CHOPPINESS_INDEX
• Streaming / Live-Trading API — bar-by-bar stateful classes (StreamingSMA, StreamingRSI, etc.)
• NumPy integration — accepts and returns NumPy arrays; reads input buffers without copying data
• Pandas integration — transparently accepts pandas.Series / DataFrame and returns Series with original index preserved
• Polars integration — transparently accepts polars.Series and returns polars.Series; install with pip install "ferro-ta[polars]"
• Indicator pipeline — compose multiple indicators into a reusable pipeline (ferro_ta.pipeline.Pipeline)
• Configuration defaults — set global parameter defaults, per-indicator overrides, and temporary scopes (ferro_ta.config)
• Optional GPU backend — pass a PyTorch tensor to ferro_ta.gpu.sma/ema/rsi and get a tensor back (CUDA or MPS); install with pip install "ferro-ta[gpu]"
• Type stubs (.pyi) + py.typed (PEP 561) for IDE auto-completion and mypy/pyright support
• WebAssembly binding — use ferro-ta in Node.js or the browser via wasm/ (SMA, EMA, BBANDS, RSI, ATR, OBV, MACD, MOM, STOCHF)
• Backtesting utilities — minimal vectorized backtester (ferro_ta.backtest) with RSI, SMA crossover, and MACD crossover strategies; optional commission and slippage
• Plugin registry — register and run custom or built-in indicators by name (ferro_ta.registry)
• Error model — custom exception hierarchy (FerroTAError, FerroTAValueError, FerroTAInputError) with input validation helpers
• Sphinx documentation in docs/ and Jupyter notebook examples in examples/
• OHLCV resampling — time-based and volume-bar resampling, multi-timeframe API (ferro_ta.resampling)
• Tick aggregation — tick/volume/time bar builders from raw trades (ferro_ta.aggregation)
• Strategy DSL — expression-based strategy evaluation (ferro_ta.dsl)
• Signal composition — weighted/rank composite scores and screening (ferro_ta.signals)
• Portfolio analytics — correlation, volatility, beta, drawdown (ferro_ta.portfolio)
• Cross-asset analytics — relative strength, spread, Z-score, rolling beta (ferro_ta.cross_asset)
• Feature matrix — multi-indicator DataFrame for ML pipelines (ferro_ta.features)
• Charting API — matplotlib and plotly charts with indicator subplots (ferro_ta.viz)
• Data adapters — pluggable adapter interface with CSV and in-memory implementations (ferro_ta.adapters)
• Options/IV helpers — IV rank, IV percentile, IV z-score on any IV series (ferro_ta.options)
• Agentic tools — stable LangChain/agent tool wrappers (ferro_ta.tools), end-to-end workflow orchestrator (ferro_ta.workflow)
• MCP server — Model Context Protocol server for Cursor/Claude integration; run with python -m ferro_ta.mcp
• Observability / Logging — ferro_ta.enable_debug(), ferro_ta.log_call(), ferro_ta.benchmark() and ferro_ta.traced() decorator for instrumentation
• API discovery — ferro_ta.indicators(category=None) lists all 160+ indicators with metadata; ferro_ta.info(func) returns full parameter docs
• Structured error codes — every FerroTAError exception now carries a code (FTERR001–FTERR006) and an actionable suggestion hint

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📦 Installation

pip install ferro-ta

Optional extras:

pip install "ferro-ta[pandas]"   # transparent pandas.Series support
pip install "ferro-ta[polars]"   # transparent polars.Series support
pip install "ferro-ta[gpu]"      # GPU-accelerated SMA/EMA/RSI via PyTorch (CUDA/MPS)
pip install "ferro-ta[options]"  # Options/IV helpers (IV rank, percentile, z-score)
pip install "ferro-ta[mcp]"      # MCP server for Cursor/Claude agent integration
pip install "ferro-ta[all]"      # all optional extras (excluding gpu)

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⚡ Quick Start

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

close = np.array([44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.15,
                  43.61, 44.33, 44.83, 45.10, 45.15, 43.61, 44.33])

# Simple Moving Average
sma = SMA(close, timeperiod=5)

# Exponential Moving Average
ema = EMA(close, timeperiod=5)

# Relative Strength Index
rsi = RSI(close, timeperiod=14)

# MACD (returns macd_line, signal_line, histogram)
macd_line, signal, histogram = MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)

# Bollinger Bands (returns upper, middle, lower)
upper, middle, lower = BBANDS(close, timeperiod=5, nbdevup=2.0, nbdevdn=2.0)

Migrating from TA-Lib? Just swap the import — the API is identical:

# Before (TA-Lib)
import talib
sma = talib.SMA(close, timeperiod=20)
rsi = talib.RSI(close, timeperiod=14)

# After (ferro-ta — same call signature, faster result)
import ferro_ta
sma = ferro_ta.SMA(close, timeperiod=20)
rsi = ferro_ta.RSI(close, timeperiod=14)

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🛠️ Development Setup

Requires Rust and Python 3.10–3.13 (PyO3 supports up to 3.13; for Python 3.14+ use a compatible interpreter or set PYO3_USE_ABI3_FORWARD_COMPATIBILITY=1 to attempt a build).

# Create a virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Install build tool and dependencies
pip install maturin numpy pytest pandas

# Compile and install in editable mode
maturin develop --release

# Run tests
pytest tests/unit/ tests/integration/
# or: uv run pytest tests/unit/ tests/integration/

# Run TA-Lib comparison tests (requires ta-lib package)
pip install "ferro-ta[comparison]"   # or: pip install ta-lib
pytest tests/integration/test_vs_talib.py -v

# Build Sphinx documentation (requires sphinx + sphinx-rtd-theme)
pip install "ferro-ta[docs]"
cd docs && make html
# Output: docs/_build/html/index.html

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📊 Full TA-Lib Compatibility

ferro-ta covers 100% of TA-Lib's function set (162+ indicators). The table below shows implementation status and numerical accuracy vs TA-Lib.

Legend

Symbol	Meaning
✅ Exact	Values match TA-Lib to floating-point precision
✅ Close	Values match after a short convergence window (EMA-seed difference)
⚠️ Corr	Strong correlation (> 0.95) but not numerically identical (Wilder smoothing seed or algorithm variant)
⚠️ Shape	Same output shape / NaN structure; values differ due to algorithm variant
❌	Not yet implemented

Overlap Studies

TA-Lib Function	ferro-ta	Accuracy	Notes
BBANDS	✅	✅ Exact	Bollinger Bands
DEMA	✅	✅ Close	Double EMA; converges after ~20 bars
EMA	✅	✅ Close	Exponential Moving Average; converges after ~20 bars
KAMA	✅	✅ Exact	Kaufman Adaptive MA (values match after seed bar)
MA	✅	✅ Exact	Moving average (generic, type-selectable)
MAMA	✅	⚠️ Corr	MESA Adaptive MA
MAVP	✅	✅ Exact	MA with variable period
MIDPOINT	✅	✅ Exact	Midpoint over period
MIDPRICE	✅	✅ Exact	Midpoint price over period
SAR	✅	⚠️ Shape	Parabolic SAR (same shape; reversal history diverges)
SAREXT	✅	⚠️ Shape	Parabolic SAR Extended
SMA	✅	✅ Exact	Simple Moving Average
T3	✅	✅ Close	Triple Exponential MA (T3); converges after ~50 bars
TEMA	✅	✅ Close	Triple EMA; converges after ~20 bars
TRIMA	✅	✅ Exact	Triangular Moving Average
WMA	✅	✅ Exact	Weighted Moving Average

Momentum Indicators

TA-Lib Function	ferro-ta	Accuracy	Notes
ADX	✅	✅ Close	Avg Directional Movement Index (TA-Lib Wilder sum-seeding)
ADXR	✅	✅ Close	ADX Rating (inherits ADX; TA-Lib seeding)
APO	✅	✅ Close	Absolute Price Oscillator (EMA-based)
AROON	✅	✅ Exact	Aroon Up/Down
AROONOSC	✅	✅ Exact	Aroon Oscillator
BOP	✅	✅ Exact	Balance Of Power
CCI	✅	✅ Exact	Commodity Channel Index (TA-Lib–compatible MAD formula)
CMO	✅	✅ Close	Chande Momentum Oscillator (rolling window, TA-Lib–compatible)
DX	✅	✅ Close	Directional Movement Index (TA-Lib Wilder sum-seeding)
MACD	✅	✅ Close	MACD (EMA-based; converges after ~30 bars)
MACDEXT	✅	✅ Close	MACD with controllable MA type (EMA-based; converges)
MACDFIX	✅	✅ Close	MACD Fixed 12/26 (EMA-based; converges)
MFI	✅	✅ Exact	Money Flow Index
MINUS_DI	✅	✅ Close	Minus Directional Indicator (TA-Lib Wilder sum-seeding)
MINUS_DM	✅	✅ Close	Minus Directional Movement (TA-Lib Wilder sum-seeding)
MOM	✅	✅ Exact	Momentum
PLUS_DI	✅	✅ Close	Plus Directional Indicator (TA-Lib Wilder sum-seeding)
PLUS_DM	✅	✅ Close	Plus Directional Movement (TA-Lib Wilder sum-seeding)
PPO	✅	✅ Close	Percentage Price Oscillator (EMA-based)
ROC	✅	✅ Exact	Rate of Change
ROCP	✅	✅ Exact	Rate of Change Percentage
ROCR	✅	✅ Exact	Rate of Change Ratio
ROCR100	✅	✅ Exact	Rate of Change Ratio × 100
RSI	✅	✅ Close	Relative Strength Index (TA-Lib Wilder seeding; converges after ~1 seed bar)
STOCH	✅	✅ Close	Stochastic (TA-Lib–compatible SMA smoothing for slowk and slowd)
STOCHF	✅	✅ Exact	Stochastic Fast (%K exact; %D NaN offset ±2)
STOCHRSI	✅	✅ Close	Stochastic RSI (TA-Lib–compatible; SMA fastd, Wilder-seeded RSI)
TRIX	✅	✅ Close	1-day ROC of Triple EMA (EMA-based; converges)
ULTOSC	✅	✅ Exact	Ultimate Oscillator
WILLR	✅	✅ Exact	Williams' %R

Volume Indicators

TA-Lib Function	ferro-ta	Accuracy	Notes
AD	✅	✅ Exact	Chaikin A/D Line
ADOSC	✅	✅ Exact	Chaikin A/D Oscillator
OBV	✅	✅ Exact	On Balance Volume (increments identical; constant offset at bar 0)

Volatility Indicators

TA-Lib Function	ferro-ta	Accuracy	Notes
ATR	✅	✅ Close	Average True Range (TA-Lib Wilder seeding; matches from bar timeperiod)
NATR	✅	✅ Close	Normalized ATR (TA-Lib Wilder seeding)
TRANGE	✅	✅ Exact	True Range (bar 0 differs; all others identical)

Cycle Indicators

TA-Lib Function	ferro-ta	Accuracy	Notes
HT_DCPERIOD	✅	⚠️ Shape	Hilbert Transform Dominant Cycle Period (Ehlers algorithm)
HT_DCPHASE	✅	⚠️ Shape	Hilbert Transform Dominant Cycle Phase
HT_PHASOR	✅	⚠️ Shape	Hilbert Transform Phasor Components (inphase, quadrature)
HT_SINE	✅	⚠️ Shape	Hilbert Transform SineWave (sine, leadsine)
HT_TRENDLINE	✅	⚠️ Shape	Hilbert Transform Instantaneous Trendline
HT_TRENDMODE	✅	⚠️ Shape	Hilbert Transform Trend vs Cycle Mode (1=trend, 0=cycle)

Price Transformations

TA-Lib Function	ferro-ta	Accuracy	Notes
AVGPRICE	✅	✅ Exact	Average Price
MEDPRICE	✅	✅ Exact	Median Price
TYPPRICE	✅	✅ Exact	Typical Price
WCLPRICE	✅	✅ Exact	Weighted Close Price

Statistic Functions

TA-Lib Function	ferro-ta	Accuracy	Notes
BETA	✅	✅ Close	Beta coefficient (returns-based regression matching TA-Lib)
CORREL	✅	✅ Exact	Pearson Correlation Coefficient
LINEARREG	✅	✅ Exact	Linear Regression
LINEARREG_ANGLE	✅	✅ Exact	Linear Regression Angle
LINEARREG_INTERCEPT	✅	✅ Exact	Linear Regression Intercept
LINEARREG_SLOPE	✅	✅ Exact	Linear Regression Slope
STDDEV	✅	✅ Exact	Standard Deviation
TSF	✅	✅ Exact	Time Series Forecast
VAR	✅	✅ Exact	Variance

Pattern Recognition

ferro-ta implements all 61 candlestick patterns. All return the same {-100, 0, 100} convention as TA-Lib. Pattern thresholds may differ slightly from the full TA-Lib implementation.

TA-Lib Function	ferro-ta	Notes
CDL2CROWS	✅	Two Crows
CDL3BLACKCROWS	✅	Three Black Crows
CDL3INSIDE	✅	Three Inside Up/Down
CDL3LINESTRIKE	✅	Three-Line Strike
CDL3OUTSIDE	✅	Three Outside Up/Down
CDL3STARSINSOUTH	✅	Three Stars In The South
CDL3WHITESOLDIERS	✅	Three Advancing White Soldiers
CDLABANDONEDBABY	✅	Abandoned Baby
CDLADVANCEBLOCK	✅	Advance Block
CDLBELTHOLD	✅	Belt-hold
CDLBREAKAWAY	✅	Breakaway
CDLCLOSINGMARUBOZU	✅	Closing Marubozu
CDLCONCEALBABYSWALL	✅	Concealing Baby Swallow
CDLCOUNTERATTACK	✅	Counterattack
CDLDARKCLOUDCOVER	✅	Dark Cloud Cover
CDLDOJI	✅	Doji
CDLDOJISTAR	✅	Doji Star
CDLDRAGONFLYDOJI	✅	Dragonfly Doji
CDLENGULFING	✅	Engulfing Pattern
CDLEVENINGDOJISTAR	✅	Evening Doji Star
CDLEVENINGSTAR	✅	Evening Star
CDLGAPSIDESIDEWHITE	✅	Up/Down-gap side-by-side white lines
CDLGRAVESTONEDOJI	✅	Gravestone Doji
CDLHAMMER	✅	Hammer
CDLHANGINGMAN	✅	Hanging Man
CDLHARAMI	✅	Harami Pattern
CDLHARAMICROSS	✅	Harami Cross Pattern
CDLHIGHWAVE	✅	High-Wave Candle
CDLHIKKAKE	✅	Hikkake Pattern
CDLHIKKAKEMOD	✅	Modified Hikkake Pattern
CDLHOMINGPIGEON	✅	Homing Pigeon
CDLIDENTICAL3CROWS	✅	Identical Three Crows
CDLINNECK	✅	In-Neck Pattern
CDLINVERTEDHAMMER	✅	Inverted Hammer
CDLKICKING	✅	Kicking
CDLKICKINGBYLENGTH	✅	Kicking by the longer Marubozu
CDLLADDERBOTTOM	✅	Ladder Bottom
CDLLONGLEGGEDDOJI	✅	Long Legged Doji
CDLLONGLINE	✅	Long Line Candle
CDLMARUBOZU	✅	Marubozu
CDLMATCHINGLOW	✅	Matching Low
CDLMATHOLD	✅	Mat Hold
CDLMORNINGDOJISTAR	✅	Morning Doji Star
CDLMORNINGSTAR	✅	Morning Star
CDLONNECK	✅	On-Neck Pattern
CDLPIERCING	✅	Piercing Pattern
CDLRICKSHAWMAN	✅	Rickshaw Man
CDLRISEFALL3METHODS	✅	Rising/Falling Three Methods
CDLSEPARATINGLINES	✅	Separating Lines
CDLSHOOTINGSTAR	✅	Shooting Star
CDLSHORTLINE	✅	Short Line Candle
CDLSPINNINGTOP	✅	Spinning Top
CDLSTALLEDPATTERN	✅	Stalled Pattern
CDLSTICKSANDWICH	✅	Stick Sandwich
CDLTAKURI	✅	Takuri (Dragonfly Doji with very long lower shadow)
CDLTASUKIGAP	✅	Tasuki Gap
CDLTHRUSTING	✅	Thrusting Pattern
CDLTRISTAR	✅	Tristar Pattern
CDLUNIQUE3RIVER	✅	Unique 3 River
CDLUPSIDEGAP2CROWS	✅	Upside Gap Two Crows
CDLXSIDEGAP3METHODS	✅	Upside/Downside Gap Three Methods

Math Operators / Math Transforms

ferro-ta provides TA-Lib–compatible wrappers for all arithmetic and math-transform functions. Rolling functions (SUM, MAX, MIN) produce NaN for the first timeperiod - 1 bars.

TA-Lib Function	ferro-ta	Notes
ADD	✅	Element-wise addition
SUB	✅	Element-wise subtraction
MULT	✅	Element-wise multiplication
DIV	✅	Element-wise division
SUM	✅	Rolling sum over timeperiod
MAX / MAXINDEX	✅	Rolling maximum / index
MIN / MININDEX	✅	Rolling minimum / index
ACOS / ASIN / ATAN	✅	Arc trig transforms
CEIL / FLOOR	✅	Round up / down
COS / SIN / TAN	✅	Trig transforms
COSH / SINH / TANH	✅	Hyperbolic transforms
EXP / LN / LOG10	✅	Exponential / log transforms
SQRT	✅	Square root

Pandas API

Contract: All indicators accept pandas.Series (or 1-D DataFrame columns) and return pandas.Series — or a tuple of Series for multi-output functions like MACD, BBANDS — with the original index preserved.

Default OHLCV column names: When using a DataFrame with OHLCV data, the conventional names are open, high, low, close, volume. To use different column names, use the helper :func:ferro_ta.utils.get_ohlcv (or pass Series/arrays extracted from your DataFrame).

Single Series or tuple of Series:

import pandas as pd
from ferro_ta import SMA, BBANDS, MACD, CDLDOJI

close = pd.Series([44.34, 44.09, 44.15, 43.61, 44.33], index=pd.date_range("2024-01-01", 5))

# Single-output: returns Series
sma = SMA(close, timeperiod=3)  # pd.Series with same index

# Multi-output: returns tuple of Series
upper, mid, lower = BBANDS(close, timeperiod=3)  # all pd.Series

DataFrame with OHLCV columns (configurable names):

import pandas as pd
from ferro_ta import ATR, RSI
from ferro_ta.utils import get_ohlcv  # or: from ferro_ta._utils import get_ohlcv

df = pd.DataFrame({
    "Open": [1, 2, 3], "High": [1.1, 2.1, 3.1],
    "Low": [0.9, 1.9, 2.9], "Close": [1.05, 2.05, 3.05],
}, index=pd.date_range("2024-01-01", periods=3, freq="D"))

# Extract with default names (open, high, low, close, volume)
o, h, l, c, v = get_ohlcv(df, open_col="Open", high_col="High", low_col="Low", close_col="Close")
atr = ATR(h, l, c, timeperiod=2)   # index preserved
rsi = RSI(c, timeperiod=2)         # index preserved

Extended Indicators

ferro-ta includes popular indicators that go beyond the TA-Lib standard set. These are available in ferro_ta.extended and importable directly from ferro_ta.

Function	ferro-ta	Notes
VWAP	✅	Volume Weighted Average Price — cumulative (session) or rolling window
SUPERTREND	✅	ATR-based trend signal; returns (supertrend_line, direction)
ICHIMOKU	✅	Ichimoku Cloud — Tenkan, Kijun, Senkou A/B, Chikou Span
DONCHIAN	✅	Donchian Channels — rolling highest high / lowest low
PIVOT_POINTS	✅	Pivot points — Classic, Fibonacci, Camarilla methods
KELTNER_CHANNELS	✅	EMA ± (ATR × multiplier) bands; returns (upper, middle, lower)
HULL_MA	✅	Hull Moving Average — fast, low-lag WMA-based MA
CHANDELIER_EXIT	✅	ATR-based trailing stop levels; returns (long_exit, short_exit)
VWMA	✅	Volume Weighted Moving Average — rolling sum(close*vol) / sum(vol)
CHOPPINESS_INDEX	✅	Market choppiness/trending strength index (0–100)

from ferro_ta import VWAP, SUPERTREND, ICHIMOKU, DONCHIAN, PIVOT_POINTS
from ferro_ta import KELTNER_CHANNELS, HULL_MA, CHANDELIER_EXIT, VWMA, CHOPPINESS_INDEX
import numpy as np

close = np.array([44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.15])
high  = close + 0.5
low   = close - 0.5
vol   = np.full(len(close), 1_000_000.0)

# Cumulative / rolling VWAP
vwap = VWAP(high, low, close, vol)
rolling_vwap = VWAP(high, low, close, vol, timeperiod=5)

# Supertrend (trend line and direction: 1=up, -1=down)
st_line, direction = SUPERTREND(high, low, close, timeperiod=7, multiplier=3.0)

# Ichimoku Cloud
tenkan, kijun, senkou_a, senkou_b, chikou = ICHIMOKU(high, low, close)

# Donchian Channels
dc_upper, dc_mid, dc_lower = DONCHIAN(high, low, timeperiod=5)

# Pivot Points
pivot, r1, s1, r2, s2 = PIVOT_POINTS(high, low, close, method="classic")
# method options: "classic", "fibonacci", "camarilla"

# Keltner Channels
kc_upper, kc_mid, kc_lower = KELTNER_CHANNELS(high, low, close, timeperiod=20, atr_period=10)

# Hull Moving Average
hull = HULL_MA(close, timeperiod=16)

# Chandelier Exit
long_exit, short_exit = CHANDELIER_EXIT(high, low, close, timeperiod=22, multiplier=3.0)

# Volume Weighted Moving Average
vwma = VWMA(close, vol, timeperiod=20)

# Choppiness Index (100 = choppy, 0 = strong trend)
ci = CHOPPINESS_INDEX(high, low, close, timeperiod=14)

Streaming / Live-Trading API

For real-time / bar-by-bar processing, import classes from ferro_ta.streaming. Each class maintains state internally and returns NaN during the warmup window:

from ferro_ta.streaming import StreamingSMA, StreamingEMA, StreamingRSI, StreamingATR
from ferro_ta.streaming import StreamingBBands, StreamingMACD, StreamingStoch
from ferro_ta.streaming import StreamingVWAP, StreamingSupertrend

sma = StreamingSMA(period=20)
rsi = StreamingRSI(period=14)
atr = StreamingATR(period=14)
bb  = StreamingBBands(period=20, nbdevup=2.0, nbdevdn=2.0)
macd = StreamingMACD(fastperiod=12, slowperiod=26, signalperiod=9)
stoch = StreamingStoch(fastk_period=5, slowk_period=3, slowd_period=3)
vwap = StreamingVWAP()  # reset() at session open
st = StreamingSupertrend(period=7, multiplier=3.0)

for bar in live_data_feed:
    current_sma  = sma.update(bar.close)
    current_rsi  = rsi.update(bar.close)
    current_atr  = atr.update(bar.high, bar.low, bar.close)
    upper, mid, lower = bb.update(bar.close)
    macd_line, signal, histogram = macd.update(bar.close)
    slowk, slowd = stoch.update(bar.high, bar.low, bar.close)
    current_vwap = vwap.update(bar.high, bar.low, bar.close, bar.volume)
    st_line, trend_dir = st.update(bar.high, bar.low, bar.close)  # 1=up, -1=down

📈 Implementation Coverage Summary

Category	Implemented	Not Implemented
Overlap Studies	19	0
Momentum Indicators	28	0
Volume Indicators	3	0
Volatility Indicators	3	0
Cycle Indicators	6	0
Price Transforms	4	0
Statistic Functions	9	0
Pattern Recognition	61	0
Math Operators / Transforms	24	0
Extended Indicators	10	—
Streaming Classes	9	—
Total	162+	0

🎉 100% of TA-Lib's function set is implemented. NaN values are placed at the beginning of each output array for the warmup period.

---

🔄 Batch Execution API

Run indicators on multiple price series (symbols) in a single call. Dedicated Rust-backed functions for SMA, EMA, RSI, ATR, STOCH, and ADX; use batch_apply for any other indicator.

import numpy as np
from ferro_ta.batch import batch_sma, batch_ema, batch_rsi, batch_atr, batch_stoch, batch_adx, batch_apply

# 100 bars × 5 symbols
close = np.random.rand(100, 5) + 50.0
high = close + 0.1
low = close - 0.1

sma_out = batch_sma(close, timeperiod=14)       # (100, 5)
ema_out = batch_ema(close, timeperiod=14)       # (100, 5)
rsi_out = batch_rsi(close, timeperiod=14)       # (100, 5)
atr_out = batch_atr(high, low, close, timeperiod=14)
stoch_k, stoch_d = batch_stoch(high, low, close)
adx_out = batch_adx(high, low, close, timeperiod=14)

# Any single-series function via batch_apply
from ferro_ta import BBANDS
def bbands_upper(c, **kw):
    return BBANDS(c, **kw)[0]
upper = batch_apply(close, bbands_upper, timeperiod=20)

---

🦀 Pure Rust Core Library

ferro-ta is structured as a Cargo workspace with two crates:

Crate	Purpose
ferro_ta (root)	PyO3 #[pyfunction] wrappers — converts numpy ↔ &[f64]; builds the Python wheel
crates/ferro_ta_core	Pure Rust indicators — no PyO3/numpy dependency; usable from any Rust project

# Build and test the core crate directly
cargo build -p ferro_ta_core
cargo test -p ferro_ta_core

use ferro_ta_core::overlap;

let close = vec![1.0, 2.0, 3.0, 4.0, 5.0];
let sma = overlap::sma(&close, 3);

Rust Module Structure

The main ferro_ta crate (src/) uses a consistent directory-based module layout matching the TA-Lib category structure. Every module is a directory with mod.rs declaring sub-modules and a register() function; each indicator (or closely related group) lives in its own .rs file:

src/
├── lib.rs                   # PyModule entry point — calls each module's register()
├── overlap/                 # Overlap Studies (SMA, EMA, BBANDS, MACD, SAR, …)
│   ├── mod.rs
│   ├── sma.rs, ema.rs, wma.rs, dema.rs, tema.rs, trima.rs, kama.rs, t3.rs
│   ├── bbands.rs, macd.rs, macdfix.rs, macdext.rs
│   ├── sar.rs, sarext.rs, mama.rs, midpoint.rs, midprice.rs
│   └── ma_mavp.rs
├── momentum/                # Momentum Indicators (RSI, STOCH, ADX, CCI, …)
│   ├── mod.rs
│   └── rsi.rs, mom.rs, roc.rs, willr.rs, aroon.rs, cci.rs, mfi.rs,
│       bop.rs, stochf.rs, stoch.rs, stochrsi.rs, apo.rs, ppo.rs, cmo.rs,
│       adx.rs, trix.rs, ultosc.rs
├── volatility/              # Volatility Indicators (ATR, NATR, TRANGE)
│   ├── mod.rs
│   ├── common.rs            # shared TR computation
│   ├── trange.rs, atr.rs, natr.rs
├── volume/                  # Volume Indicators (AD, ADOSC, OBV)
│   ├── mod.rs
│   └── ad.rs, adosc.rs, obv.rs
├── statistic/               # Statistic Functions (STDDEV, VAR, LINEARREG*, BETA, CORREL)
│   ├── mod.rs
│   ├── common.rs            # shared linreg() helper
│   └── stddev.rs, var.rs, linearreg.rs, beta.rs, correl.rs
├── price_transform/         # Price Transformations (AVGPRICE, MEDPRICE, TYPPRICE, WCLPRICE)
│   ├── mod.rs
│   └── avgprice.rs, medprice.rs, typprice.rs, wclprice.rs
├── cycle/                   # Cycle Indicators (HT_TRENDLINE, HT_DCPERIOD, …)
│   ├── mod.rs
│   ├── common.rs            # shared HT core pipeline (compute_ht_core)
│   └── ht_trendline.rs, ht_dcperiod.rs, ht_dcphase.rs,
│       ht_phasor.rs, ht_sine.rs, ht_trendmode.rs
└── pattern/                 # Pattern Recognition (CDL2CROWS, CDLDOJI, …)
    ├── mod.rs
    ├── common.rs            # shared candle utilities
    └── cdl*.rs              # one file per pattern (61 patterns)

This layout makes it easy to add, review, or modify individual indicators in isolation — simply edit or add the relevant .rs file and update mod.rs.

Python sub-package layout

The python/ferro_ta/ package is organized into sub-packages by concern. Backward-compat stubs at the old flat paths (e.g. ferro_ta.momentum) re-export from the new locations, so existing code continues to work without changes.

python/ferro_ta/
├── __init__.py        # top-level re-exports and public API
├── core/              # Exceptions, configuration, registry, logging, raw FFI bindings
├── indicators/        # Technical indicators (momentum, overlap, volatility, volume,
│                      #   statistic, cycle, pattern, price_transform, math_ops, extended)
├── data/              # Streaming, batch, chunked, resampling, aggregation, adapters
├── analysis/          # Portfolio, backtest, regime, cross_asset, attribution,
│                      #   signals, features, crypto, options
├── tools/             # Visualisation, alerting, DSL, pipeline, workflow,
│                      #   api_info, GPU support
└── mcp/               # Model Context Protocol server

🌐 Other Languages (WebAssembly / Node.js)

A WebAssembly binding is available in the wasm/ directory, exposing SMA, EMA, BBANDS, RSI, ATR, OBV, and MACD for use in Node.js and browsers.

// Node.js (after `wasm-pack build --target nodejs --out-dir pkg` in wasm/)
const { sma, rsi, macd } = require('./wasm/pkg/ferro_ta_wasm.js');

const close = new Float64Array([44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10]);
const smaOut = sma(close, 3);       // Float64Array — first 2 values are NaN
const rsiOut = rsi(close, 5);       // Float64Array — first 5 values are NaN

// MACD — returns [macd_line, signal_line, histogram] as a js_sys::Array
const [macdLine, signal, hist] = macd(close, 3, 5, 2);

See wasm/README.md for build instructions, the full list of exposed functions, and browser usage examples.

---

🔥 GPU Acceleration (Optional)

For very large arrays (millions of bars), an optional GPU-accelerated path is available via PyTorch. Pass a torch.Tensor on CUDA or MPS and get a tensor back; NumPy in → NumPy out (CPU fallback).

pip install "ferro-ta[gpu]"
# or install PyTorch yourself (e.g. with CUDA or MPS support):
# pip install torch

import torch
from ferro_ta.gpu import sma, ema, rsi

# Use CUDA or MPS (Apple Silicon)
close_gpu = torch.tensor(
    [44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.15, 43.61, 44.33],
    device="cuda",  # or device="mps" on Apple Silicon
    dtype=torch.float64,
)

result = sma(close_gpu, timeperiod=5)   # torch.Tensor on same device
result_cpu = result.cpu().numpy()       # back to NumPy if needed

PyTorch tensors in → PyTorch tensors out; NumPy arrays in → NumPy arrays out (CPU). See docs/gpu-backend.md for supported indicators, limitations, and benchmark data.

---

📉 Backtesting

A minimal vectorized backtester is available at ferro_ta.backtest:

import numpy as np
from ferro_ta.backtest import backtest

np.random.seed(42)
close = np.cumprod(1 + np.random.randn(200) * 0.01) * 100

# Run an RSI 30/70 strategy
result = backtest(close, strategy="rsi_30_70", timeperiod=14)
print(f"Final equity: {result.final_equity:.4f}")
print(f"Number of trades: {result.n_trades}")

# Or use SMA crossover
result2 = backtest(close, strategy="sma_crossover", fast=10, slow=30)
result3 = backtest(close, strategy="macd_crossover", commission_per_trade=0.001, slippage_bps=5)

Note: This is a minimal harness for testing strategies. Optional commission_per_trade and slippage_bps are supported; for margin or full order types consider backtrader, zipline, or vectorbt. For production use consider backtrader, zipline, or vectorbt.

---

🔗 Indicator Pipeline

Compose multiple indicators into a reusable pipeline:

import numpy as np
from ferro_ta import SMA, EMA, RSI, BBANDS
from ferro_ta.pipeline import Pipeline

close = np.cumprod(1 + np.random.randn(200) * 0.01) * 100

pipe = (
    Pipeline()
    .add("sma_20", SMA, timeperiod=20)
    .add("ema_20", EMA, timeperiod=20)
    .add("rsi_14", RSI, timeperiod=14)
    .add("bb", BBANDS, output_keys=["bb_upper", "bb_mid", "bb_lower"],
         timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
)

results = pipe.run(close)
# {'sma_20': array([...]), 'ema_20': array([...]), ..., 'bb_lower': array([...])}
print(list(results.keys()))

---

⚙️ Configuration Defaults

Set global parameter defaults to avoid repeating them on every call:

import ferro_ta.config as config

config.set_default("timeperiod", 20)       # applies to all indicators
config.set_default("RSI.timeperiod", 14)   # RSI-specific override

from ferro_ta import RSI, SMA
# RSI(close) uses timeperiod=14; SMA(close) uses timeperiod=20

# Context manager for temporary overrides
with config.Config(timeperiod=5):
    result = SMA(close)   # timeperiod=5 inside this block
# back to timeperiod=20 after the block

config.reset()   # clear all custom defaults

---

🔌 Plugin Registry

Register and call any indicator (built-in or custom) by name. See the Writing a plugin <docs/plugins.rst>_ doc for the plugin contract and a full example (examples/custom_indicator.py).

import numpy as np
from ferro_ta.registry import register, run, list_indicators

# Call a built-in by name
close = np.array([44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10])
sma = run("SMA", close, timeperiod=3)

# Register a custom indicator
def DOUBLE_RSI(close, timeperiod=14, smooth=3):
    import ferro_ta
    rsi = ferro_ta.RSI(close, timeperiod=timeperiod)
    return ferro_ta.SMA(rsi, timeperiod=smooth)

register("DOUBLE_RSI", DOUBLE_RSI)
result = run("DOUBLE_RSI", close, timeperiod=5, smooth=2)

# List all registered indicators
print(list_indicators()[:5])  # ['AD', 'ADOSC', 'ADX', 'ADXR', 'APO']

---

🛡️ Error Handling

ferro-ta provides a typed exception hierarchy with error codes and actionable suggestions:

from ferro_ta import FerroTAError, FerroTAValueError, FerroTAInputError
from ferro_ta.exceptions import check_timeperiod, check_equal_length

# Catch any ferro-ta error
try:
    result = SMA(close, timeperiod=0)
except FerroTAValueError as e:
    print(e.code)        # "FTERR001"
    print(e.suggestion)  # "Set timeperiod=1 or higher."
    print(e)             # "[FTERR001] timeperiod must be >= 1, got 0\n  Suggestion: ..."

# Validate inputs before calling
check_equal_length(open=open_, close=close)   # raises FerroTAInputError (FTERR004) on mismatch
check_timeperiod(timeperiod)                   # raises FerroTAValueError (FTERR001) if < 1

Error code reference:

Code	Exception	Meaning
FTERR001	FerroTAValueError	Invalid parameter value
FTERR002	FerroTAInputError	Invalid input array
FTERR003	FerroTAInputError	Input array too short
FTERR004	FerroTAInputError	Mismatched array lengths
FTERR005	FerroTAInputError	Array contains NaN/Inf (strict mode)
FTERR006	FerroTAValueError/InputError	Rust-bridge error

🔍 Observability & Logging

ferro-ta ships a lightweight logging module that integrates with Python's standard logging library:

import ferro_ta

# Enable DEBUG-level logging (writes to stderr)
ferro_ta.enable_debug()
result = ferro_ta.SMA(close, timeperiod=20)
# DEBUG [ferro_ta] calling SMA(ndarray(252,) dtype=float64, timeperiod=20)
# DEBUG [ferro_ta] SMA → ndarray(252,)  [0.042 ms]
ferro_ta.disable_debug()

# Context manager: temporary debug output
with ferro_ta.debug_mode():
    ferro_ta.RSI(close, timeperiod=14)

# Call with automatic shape + timing log
result = ferro_ta.log_call(ferro_ta.ATR, high, low, close, timeperiod=14)

# Benchmark: returns {mean_ms, min_ms, max_ms, total_ms, n}
stats = ferro_ta.benchmark(ferro_ta.SMA, close, timeperiod=20, n=500)
print(f"SMA mean: {stats['mean_ms']:.3f} ms")

# Decorator: wrap any function with automatic logging
@ferro_ta.traced
def my_strategy(close):
    sma = ferro_ta.SMA(close, timeperiod=20)
    rsi = ferro_ta.RSI(close, timeperiod=14)
    return sma, rsi

🔎 API Discovery

import ferro_ta

# List all 160+ indicators with metadata
all_indicators = ferro_ta.indicators()
print(len(all_indicators))  # 160+

# Filter by category
overlap = ferro_ta.indicators(category="overlap")
momentum = ferro_ta.indicators(category="momentum")

# Get parameter info for any indicator
d = ferro_ta.info(ferro_ta.SMA)
print(d["signature"])   # (close: ArrayLike, timeperiod: int = 30) -> NDArray[float64]
print(d["params"])      # {"close": {"default": None, ...}, "timeperiod": {"default": 30, ...}}

# By name string
d = ferro_ta.info("MACD")

See PLATFORMS.md for supported OS and Python versions. See CHANGELOG.md and VERSIONING.md for release notes and versioning policy. See RELEASE.md for the step-by-step release playbook. See examples/ for Jupyter notebook examples (quickstart, streaming, backtesting, and more).

🗺️ Multi-Timeframe, Portfolio, and ML Features

OHLCV Resampling and Multi-Timeframe API (ferro_ta.resampling)

from ferro_ta.resampling import resample, volume_bars, multi_timeframe
from ferro_ta import RSI
import pandas as pd

# Resample 1-minute data to 5-minute bars (requires pandas)
df5 = resample(ohlcv_df, '5min')

# Volume bars (every 10,000 units of volume) — Rust backend
vbars = volume_bars(ohlcv_df, volume_threshold=10_000)

# Multi-timeframe RSI in one call
mtf = multi_timeframe(ohlcv_df, ['5min', '15min'], indicator=RSI,
                      indicator_kwargs={'timeperiod': 14})
# mtf = {'5min': array(...), '15min': array(...)}

Tick Aggregation Pipeline (ferro_ta.aggregation)

from ferro_ta.aggregation import aggregate_ticks, TickAggregator

# Tick bars, volume bars, time bars — all Rust-backed
tick_bars   = aggregate_ticks(ticks, rule='tick:100')
volume_bars = aggregate_ticks(ticks, rule='volume:500')
time_bars   = aggregate_ticks(ticks, rule='time:60')

# Class-based API
agg = TickAggregator(rule='tick:100')
bars = agg.aggregate(ticks)  # → pandas DataFrame or dict

Strategy Expression DSL (ferro_ta.dsl)

from ferro_ta.dsl import Strategy, evaluate

# Parse and evaluate expression strings
strat = Strategy("RSI(14) < 30 and close > SMA(20)")
signal = strat.evaluate({"close": close_arr})  # 1/0 integer array

Signal Composition and Screening (ferro_ta.signals)

from ferro_ta.signals import compose, screen, rank_signals

# Weighted combination of signal columns (Rust-backed)
score = compose(signals_df, weights=[0.4, 0.35, 0.25])

# Screening
top2 = screen({'AAPL': 0.8, 'MSFT': 0.9, 'GOOG': 0.5}, top_n=2)
# {'MSFT': 0.9, 'AAPL': 0.8}

Portfolio Analytics (ferro_ta.portfolio)

from ferro_ta.portfolio import correlation_matrix, portfolio_volatility, beta, drawdown

corr   = correlation_matrix(returns_df)               # Pearson corr matrix
vol    = portfolio_volatility(returns_df, weights,     # sqrt(w'Σw)
                              annualise=252)
b      = beta(asset_returns, benchmark_returns)        # OLS beta
rb     = beta(asset_returns, benchmark_returns,        # rolling beta
              window=30)
dd, mx = drawdown(equity_curve)                        # drawdown series + max

Cross-Asset Relative Strength (ferro_ta.cross_asset)

from ferro_ta.cross_asset import relative_strength, spread, ratio, zscore, rolling_beta

rs = relative_strength(asset_rets, bench_rets)  # cumulative return ratio
sp = spread(price_a, price_b, hedge=1.0)        # A - hedge * B
z  = zscore(sp, window=20)                       # rolling Z-score

Feature Matrix for ML (ferro_ta.features)

from ferro_ta.features import feature_matrix

fm = feature_matrix(ohlcv, [
    ('RSI', {'timeperiod': 14}),
    ('SMA', {'timeperiod': 20}),
    ('ATR', {'timeperiod': 14}),
], nan_policy='drop')
# fm is a pandas DataFrame with one column per indicator
# Use with sklearn: clf.fit(fm.values, labels)

Charting and Visualization (ferro_ta.viz)

from ferro_ta.viz import plot
from ferro_ta import RSI, SMA

fig = plot(ohlcv_df, indicators={'RSI(14)': RSI(close), 'SMA(20)': SMA(close)},
           backend='matplotlib', savefig='chart.png')
# Also supports 'plotly' backend for interactive charts

Market Data Adapters (ferro_ta.adapters)

from ferro_ta.adapters import CsvAdapter, InMemoryAdapter, register_adapter, DataAdapter

# Load from CSV
adapter = CsvAdapter('data.csv', index_col='date')
ohlcv = adapter.fetch()

# Custom adapter
class MyAdapter(DataAdapter):
    def fetch(self, **kwargs): return ...

register_adapter('mybroker', MyAdapter)

---

🤝 Community

• GitHub Discussions — Ask questions, share strategies, and request features in our Discussions space. Categories: Q&A, Ideas, Show & Tell, Announcements.

• Contributing: See CONTRIBUTING.md for setup, code style, and PR guidelines.

• Code of Conduct: All participants are expected to follow the CODE_OF_CONDUCT.md.

• Governance: Decision-making process and maintainer info in GOVERNANCE.md.

• Roadmap: Development plan in ROADMAP.md.

• Security: Responsible disclosure policy in SECURITY.md.

• Migration from TA-Lib: Step-by-step guide in the documentation.

• Library Compatibility Guides — drop-in migration instructions and cross-library test results:

  • TA-Lib compatibility — full indicator mapping, API differences, and migration guide
  • pandas-ta compatibility — indicator mapping, known differences, and comparison tests
  • ta (Bukosabino) compatibility — indicator mapping, known differences, and comparison tests
  • Tulipy compatibility — C99 Tulip Indicators: output truncation, memory requirements, signature mapping
  • finta compatibility — pure-Pandas library: DataFrame requirements, speed comparison, migration guide

• Cross-Library Benchmarks — accuracy and speed comparison across all 6 libraries:

  • Benchmarks README — real timing results (µs), accuracy methodology, and known limitations
  • Performance Roadmap — plan to achieve 100x speedup over Tulipy

---

ferro-ta — Built with ❤️ and Rust. Star ⭐ on GitHub to support the project.