IntrinsicTime 0.1.1

Utilities for intrinsic time decomposition and fractal event analysis

IntrinsicTime

Utilities for decomposing intrinsic time events and analyzing fractal scaling behavior in price or signal data.

This package provides:

• dcos_core: A Directional Change and Overshoot (DcOS) event detector.
• dcos_fractal: Tools for fractal scaling and multi-threshold analysis.
• dcos_plot: Plotly visualization for interactive fractal plots.

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Installation

From GitHub

pip install git+https://github.com/THouwe/IntrinsicTime.git

Local Install

git clone https://github.com/THouwe/IntrinsicTime.git
cd IntrinsicTime
pip install -e .

Dependencies

See requirements.txt.

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Overview

IntrinsicTime decomposes time series into Directional Changes (DCs) and Overshoots (OSs) based on log-scale thresholds. It then explores the fractal scaling law between event frequency and detection threshold.

Example Usage

import numpy as np
import pandas as pd
from IntrinsicTime import DcOS_fractal

# Example input DataFrame
df = pd.DataFrame({
    "Timestamp": range(1000),
    "Price": 100 + np.cumsum(np.random.randn(1000))
})

# Initialize and run
analyzer = DcOS_fractal(debugMode=True)
results, ranges = analyzer.run(df)

# Display results
print(results.head())
print(ranges)