Library for automated signal segmentation, trend classification and analysis.
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The package is pip-installable. To install it, run:
pip3 install trend-classifier
usage:
import yfinance as yf
from trend_classifier import Segmenter
# download data from yahoo finance
df = yf.download("AAPL", start="2018-09-15", end="2022-09-05", interval="1d", progress=False)
x_in = list(range(0, len(df.index.tolist()), 1))
y_in = df["Adj Close"].tolist()
seg = Segmenter(x_in, y_in, n=20)
seg.calculate_segments()For graphical output use Segmenter.plot_segments():
seg.plot_segments()
After calling method Segmenter.calculate_segments() segments are identified and information is stored in Segmenter.segments as list of Segment objects. Each Segment object. Each Segment object has attributes such as 'start', 'stop' - range of indices for the extracted segment, slope and many more attributes that might be helpful for further analysis.
Exemplary info on one segment:
from devtools import debug
debug(seg.segments[3])and you should see something like this:
seg.segments[3]: Segment(
start=154,
stop=177,
slope=-0.37934038908585044,
offset=109.54630934894907,
slopes=[
-0.45173184100846725,
-0.22564684358754555,
0.15555037018051593,
0.34801127785130714,
],
offsets=[
121.65628807526804,
83.56079272220015,
17.32660986821478,
-17.86417581658647,
],
slopes_std=0.31334199799377654,
offsets_std=54.60900279722876,
std=0.933497081795997,
span=82.0,
reason_for_new_segment='offset',
)
export results to tabular format (pandas DataFrame):
seg.segments.to_dataframe()
(NOTE: for clarity reasons, not all columns are shown in the screenshot above)
- Smooth out the price data using the Savitzky-Golay filter,
- label the highs and lows.
- higher highs and higher lows indicates an uptrend.
The requirement here is than you need OHLC data for the assets you would like to analyse.