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Hexital - Incremental Technical Analysis Library

Python Version PyPi Version ⧉ Package Status ⧉ GitHub Release Date Downloads ⧉ Downloads ⧉ GitHub Repo stars Unit Tests - master ⧉ Unit Tests - development ⧉ license

Source Code: https://github.com/MerlinR/Hexital ⧉

Hexital is a fast, zero-dependency Python library for technical analysis. It computes indicators incrementally — append a candle, get the new reading — instead of recalculating the entire series each time.

  • Fast — built for live feeds and append-one-candle workflows
  • Easy — dicts, lists, or Candle objects as input
  • Versatile — indicators, patterns, candlestick transforms, analysis helpers
  • Lightweight — no pandas or numpy required at runtime

Beta: Breaking changes are still possible. See the Release Notes.

Installation

pip install hexital

Development branch:

pip install git+https://github.com/merlinr/hexital.git@development

Choose your path

I want to… Use Guide
Compute one indicator on a live feed EMA(...).append() Quick Start
Run several indicators on one candle stream Hexital(...) Strategies
Typed access to indicator objects HexitalCol + indicator_field() Strategies
Save / reload strategy config settings / from_settings() Strategies
Load candles from CSV, Pandas, timestamps Candle.from_dicts() etc. Candles
Build 5m bars from 1m data timeframe= on indicator + label on candles Candles · Features
Check crossovers, rising/falling hexital.analysis Analysis
Heikin-Ashi or other candle transforms candlestick= Candlesticks
Write my own indicator subclass Indicator Custom indicators
Browse what's built in catalogues Indicators · Patterns

New here? Quick Start walks through the examples below step by step.

Getting started

Single indicator

from hexital import EMA, Candle

candles = Candle.from_dicts([
    {"open": 17213, "high": 2395, "low": 7813, "close": 3615, "volume": 19661},
    {"open": 1301, "high": 3007, "low": 11626, "close": 19048, "volume": 28909},
])

ema = EMA(candles=candles, period=3)
ema.calculate()
print(ema.reading())  # 8408.7552

# Append updates the reading automatically
ema.append(Candle.from_dict({"open": 19723, "high": 4837, "low": 11631, "close": 6231, "volume": 38993}))
print(ema.reading())  # 7319.8776

Hexital — multiple indicators, one candle stream

Use Hexital when a strategy needs several indicators fed from the same candles:

from hexital import EMA, WMA, Candle, Hexital

candles = Candle.from_dicts([
    {"open": 17213, "high": 2395, "low": 7813, "close": 3615, "volume": 19661},
    {"open": 1301, "high": 3007, "low": 11626, "close": 19048, "volume": 28909},
    {"open": 12615, "high": 923, "low": 7318, "close": 1351, "volume": 33765},
])

strategy = Hexital("Demo Strat", candles, [
    WMA(name="WMA", period=8),
    EMA(period=3),
])
strategy.calculate()

print(strategy.reading("EMA_3"))  # 8408.7552
print(strategy.reading("WMA"))    # 9316.4722

strategy.append(Candle.from_dict({"open": 19723, "high": 4837, "low": 11631, "close": 6231, "volume": 38993}))
print(strategy.reading("EMA_3"))  # 7319.8776
print(strategy.reading("WMA"))    # 8934.9722

Named indicators keep stable keys (WMA). Unnamed indicators get generated names from type and settings (EMA_3 = EMA with period 3). Nested dict fields use : at lookup time (e.g. MACD_12_26_9:signal).

What's included

Indicators

40+ incremental indicators for common strategies. Full reference: indicator catalogue.

ADX · AO · Amorph · AROON · ATR · BBANDS / BandWidth · CCI · ChandelierExit / CKSP · CMF · CMO · COPC · Counter · DEMA · Donchian · EMA · Fisher · HL / HLA / HLCA · HMA · Ichimoku · JMA · KAMA · KC · KST · LinearRegression / RegressionSlope / RegressionChannel · MACD · MFI · MOP · NATR · OBV · PPO · PSAR · PivotPoints · RMA · ROC · RSI · RVI · SMA · Squeeze / SqueezePro · STDEV / STDEVT · STOCH · Supertrend · TEMA · TR · TRIX · TSI · UO · Vortex · VWAP · VWMA · WillR · WMA · ZScore

Candlestick patterns

Pattern detection on candle sequences — full catalogue.

doji · dojistar · hammer · inverted_hammer

Candlestick types

Transform incoming candles before indicators run (e.g. Heikin-Ashi) — catalogue.

HeikinAshi

Movements

Pine Script–style helpers for indicator behaviour over time — full catalogue.

positive / negative · rising / falling · mean_rising / mean_falling · highest / lowest · highestbar / lowestbar · cross / crossover / crossunder · value_range

from hexital.analysis import cross, rising

rising(ema, "EMA_3", length=8)
cross(strategy, "EMA_3", "WMA")

Testing & performance

Accuracy

Every built-in indicator is unit tested against Pandas-TA ⧉ as a source of truth. Values are compared with a small tolerance where floating-point or formula differences apply.

When to use Hexital vs Pandas-TA

Use case Better fit
Live / streaming — append one candle at a time Hexital
Large bulk backtest — load full history once, vectorise Pandas-TA

Hexital only calculates missing readings on append (O(1) per update). Libraries built on pandas typically recompute or reshape the full frame on each append, which gets slower as history grows.

In internal benchmarks, Hexital stays roughly flat as candle count increases during incremental updates, while Pandas-TA time grows with series length. For bulk calculation on large static datasets, Pandas-TA is often faster.

Chart of bulk calculations. Chart of all calculations.

More detail and charts: Features.

Learn more

License

MIT — see LICENSE.