trend_classifier
Automated signal segmentation, trend classification and analysis.
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 casesbottom_up- Merge-based, control exact segment countpelt- 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
1 maintainer
Metadata
1 maintainer
Release files for trend-classifier 0.3.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| trend_classifier-0.3.3.tar.gz | 21.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| trend_classifier-0.3.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 45.1 kB
Release files / trend_classifier-0.3.3.tar.gz
| Download URL | trend_classifier-0.3.3.tar.gz |
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| Size | 21.7 kB |
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| Size | 23.5 kB |
| Tags | Python 3 |
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