Target Engineering - Event Based¶

Event-based targets are designed to capture meaningful market transitions or structural changes by focusing on specific events rather than fixed time intervals. These targets help identify key price movements, such as breakouts, reversals, or volatility shifts, which are often more relevant for trading decisions than time-based signals. In this notebook, we will create and analyze event-driven targets using the quantreo package to detect actionable patterns and enhance the timing of model predictions.

In [1]:

# Import the Target Engineering Package from Quantreo
import quantreo.target_engineering as te

# To display the graphics
import matplotlib.pyplot as plt
plt.style.use("seaborn-v0_8")

# Import the Target Engineering Package from Quantreo import quantreo.target_engineering as te # To display the graphics import matplotlib.pyplot as plt plt.style.use("seaborn-v0_8")

In [2]:

# Import a dataset to test the functions and create new ones easily
from quantreo.datasets import load_generated_ohlcv
df = load_generated_ohlcv()
df = df.loc["2016"]

# Show the data
df

# Import a dataset to test the functions and create new ones easily from quantreo.datasets import load_generated_ohlcv df = load_generated_ohlcv() df = df.loc["2016"] # Show the data df

Out[2]:

	open	high	low	close	volume
time
2016-01-04 00:00:00	104.944241	105.312073	104.929735	105.232289	576.805768
2016-01-04 04:00:00	105.233361	105.252139	105.047564	105.149357	485.696723
2016-01-04 08:00:00	105.159851	105.384745	105.141110	105.330306	403.969745
2016-01-04 12:00:00	105.330306	105.505799	104.894155	104.923404	1436.917324
2016-01-04 16:00:00	104.914147	105.023293	104.913252	105.014347	1177.672605
...	...	...	...	...	...
2016-12-30 04:00:00	103.632257	103.711884	103.495896	103.564574	563.932484
2016-12-30 08:00:00	103.564574	103.629321	103.555581	103.616731	697.707475
2016-12-30 12:00:00	103.615791	103.628165	103.496810	103.515847	1768.926665
2016-12-30 16:00:00	103.515847	103.692243	103.400370	103.422193	921.437054
2016-12-30 20:00:00	103.420285	103.429955	103.195921	103.226868	764.291608

1548 rows × 5 columns

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.

In [4]:

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

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

Out[4]:

time
2016-01-04 00:00:00    0
2016-01-04 04:00:00    0
2016-01-04 08:00:00    0
2016-01-04 12:00:00    0
2016-01-04 16:00:00    0
                      ..
2016-12-30 04:00:00    0
2016-12-30 08:00:00    0
2016-12-30 12:00:00    0
2016-12-30 16:00:00    0
2016-12-30 20:00:00    0
Name: label, Length: 1548, dtype: int64

In [7]:

price_col = "close"
plt.figure(figsize=(15, 6))
plt.plot(df[price_col], label='Price', linewidth=1)

# Display peaks (label == 1)
plt.scatter(df.index[df['label'] == 1], df[price_col][df['label'] == 1], marker='^', color='green', s=100, label='Peak (High)')

# Display valleys (label == -1)
plt.scatter(df.index[df['label'] == -1], df[price_col][df['label'] == -1], marker='v', color='red', s=100, label='Valley (Low)')

plt.title('Peaks and Valleys')
plt.legend()
plt.grid(True)
plt.xlabel('Time / Index')
plt.ylabel('Price')
plt.tight_layout()
plt.show()

price_col = "close" plt.figure(figsize=(15, 6)) plt.plot(df[price_col], label='Price', linewidth=1) # Display peaks (label == 1) plt.scatter(df.index[df['label'] == 1], df[price_col][df['label'] == 1], marker='^', color='green', s=100, label='Peak (High)') # Display valleys (label == -1) plt.scatter(df.index[df['label'] == -1], df[price_col][df['label'] == -1], marker='v', color='red', s=100, label='Valley (Low)') plt.title('Peaks and Valleys') plt.legend() plt.grid(True) plt.xlabel('Time / Index') plt.ylabel('Price') plt.tight_layout() plt.show()