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Event-Based Targets

You can find several examples on how to create these targets in the educational notebooks provided by Quantreo.

import quantreo.target_engineering as te

Peaks & Valleys Detection

The detect_peaks_valleys function identifies structural turning points in a price series by labeling local maxima (peaks) and local minima (valleys). It is particularly useful to define event-driven targets that are independent of fixed time intervals.

  • The function returns a label of 1 for a peak, -1 for a valley, and 0 elsewhere.
  • Internally, it leverages scipy.signal.find_peaks for robustness.

Tip

You can combine this function with magnitude-based targets like future_returns() to detect how strong the move is after a turning point.

te.event_based.detect_peaks_valleys(df: pd.DataFrame, col: str = 'close', **kwargs)

"""
Detect peaks and valleys in a time series using scipy's find_peaks.

This function labels turning points in a price series:
- 1 for local maxima (**peaks**),
- -1 for local minima (**valleys**),
- 0 for all other points.

It internally uses `scipy.signal.find_peaks` for both peak and valley detection.

Parameters
----------
df : pd.DataFrame
    DataFrame containing the price data.
col : str, optional
    The column name of the series to analyze (default is 'close').
**kwargs :
        Additional keyword arguments passed directly to scipy.signal.find_peaks
        (e.g., distance=5, prominence=0.5, wlen=20, height=1.0, etc.)

Returns
-------
pd.Series
    A Series of labels with the same index as `df`:
    - 1 for peaks,
    - -1 for valleys,
    - 0 for neutral points.
"""

df["label"] = te.event_based.detect_peaks_valleys(df, col='close', distance=10, prominence=1)

📢 "For a practical example, check out the educational notebook."