trend-classifier 0.3.0

Package for automated signal segmentation, trend classification and analysis.

trend_classifier

Automated signal segmentation, trend classification and analysis.

Documentation | Tutorials | API Reference

Quick Start

from trend_classifier import Segmenter

seg = Segmenter(x=x_data, y=y_data, n=20)
seg.calculate_segments()
seg.plot_segments()

Installation

pip install trend-classifier

With optional dependencies:

pip install trend-classifier[pelt]         # PELT algorithm (ruptures)
pip install trend-classifier[optimization] # Hyperparameter tuning (optuna)
pip install trend-classifier[all]          # All extras

Features

• Multiple detection algorithms:

  • sliding_window - Original algorithm, interpretable, good for most cases
  • bottom_up - Merge-based, control exact segment count
  • pelt - Optimal segmentation via ruptures library

• Rich segment information: slope, offset, volatility, trend consistency

• DataFrame export: seg.segments.to_dataframe()

• Visualization: plot_segments(), plot_segment()

• Configurable: Fine-tune sensitivity with alpha, beta, window size

Example with Stock Data

import yfinance as yf
from trend_classifier import Segmenter

# Download data
df = yf.download("AAPL", start="2020-01-01", end="2023-01-01", progress=False)

# Segment and visualize
seg = Segmenter(df=df, column="Close", n=20)
seg.calculate_segments()
seg.plot_segments()

# Export to DataFrame
seg.segments.to_dataframe()

Using Different Detectors

from trend_classifier import Segmenter

# PELT algorithm (requires: pip install trend-classifier[pelt])
seg = Segmenter(x=x, y=y, detector="pelt", detector_params={"penalty": 10})
seg.calculate_segments()

# Bottom-up with target segment count
seg = Segmenter(x=x, y=y, detector="bottom_up", detector_params={"max_segments": 10})
seg.calculate_segments()

Segment Properties

Each segment contains:

Property	Description
start, stop	Index range
slope	Trend direction and steepness
std	Volatility (after detrending)
reason_for_new_segment	Why segment boundary was placed

segment = seg.segments[0]
print(f"Slope: {segment.slope:.4f}, Volatility: {segment.std:.4f}")

Documentation

Full documentation with tutorials and API reference:

https://izikeros.github.io/trend_classifier/

License

MIT © Krystian Safjan