indikator 0.2.0

A Python library for technical indicator calculations, optimized with Numba for high performance.

Indikator

High-Performance Technical Indicators for Python

indikator is a powerful, type-safe Python library for financial market analysis. It provides a comprehensive suite of technical indicators optimized with Numba for high performance, validated with validated, and configurable via nonfig.

Key Features

• 🚀 High Performance: Critical calculations are JIT-compiled using Numba for near-C speeds.
• 🛡️ Type-Safe & Validated: Built with strict type checking (basedpyright) and runtime data validation (validated).
• ⚙️ Configurable: Flexible parameter management using nonfig's hierarchical configuration system.
• 🐼 Pandas Integration: Seamlessly works with pandas DataFrames and Series.
• 📦 Modern Stack: Managed with uv, linted with ruff, and tested with pytest.

Installation

Install using pip or uv:

pip install indikator
# or
uv add indikator

Usage

Basic Usage

Indicators can be used directly as functions. They validate input data automatically.

import pandas as pd
from indikator import atr

# Load your OHLCV data
data = pd.DataFrame({
    'high': [...],
    'low': [...],
    'close': [...]
})

# Calculate ATR with default parameters (window=14)
result = atr(data)

# Result is a DataFrame with 'atr' and 'true_range' columns
print(result.head())

Configuration

You can override parameters directly or create reusable configurations using .make().

# 1. Direct override
result = atr(data, window=20)

# 2. Reusable configuration (Factory pattern)
# Create a specialized ATR calculator
fast_atr = atr.Config(window=5).make()

# Apply it to multiple datasets
result1 = fast_atr(data1)
result2 = fast_atr(data2)

Validation

indikator ensures your data is correct before calculation. It checks for:

• Required columns (e.g., 'high', 'low', 'close')
• Data types (numeric)
• Data quality (non-empty, non-NaN where required)

# This will raise a helpful error if 'high' column is missing
try:
    atr(data[['close', 'low']])
except ValueError as e:
    print(f"Validation Error: {e}")

Available Indicators

Indicator	Description
ATR	Average True Range (Volatility) (Standard & Intraday)
Bollinger Bands	Volatility bands based on SMA and standard deviation
Churn Factor	Volume efficiency measure
Legs	Zigzag/Swing point detection
MACD	Moving Average Convergence Divergence
MFI	Money Flow Index (Volume-weighted RSI)
OBV	On-Balance Volume
Opening Range	High/Low of the first N minutes
Pivots	Support/Resistance pivot points
RSI	Relative Strength Index
RVOL	Relative Volume (Standard & Intraday)
Sector Correlation	Correlation with a benchmark/sector
Slope	Linear regression slope
VWAP	Volume Weighted Average Price (Standard & Anchored)
Z-Score	Standard deviation from mean (Standard & Intraday)

Development

This project uses uv for dependency management and poe for task running.

Setup

1. Install uv:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Clone and sync:

   git clone https://github.com/yourusername/indikator.git
   cd indikator
   uv sync
   

3. Install pre-commit hooks:

   uv run pre-commit install
   

Common Tasks

• Test: uv run pytest
• Lint: uv run ruff check
• Format: uv run ruff format
• Type Check: uv run basedpyright
• Run All Checks: uv run poe quality
• Generate Docs: uv run poe docs

License

MIT License. See LICENSE for details.