Target Engineering - Magnitude¶

Magnitude targets focus on predicting the amplitude of future price movements rather than their direction. These targets are key components for building regression models aimed at estimating how large a return or volatility could be over a given horizon. In this notebook, we will engineer and extract magnitude-based targets using the quantreo package to enhance trading signals, optimize risk-adjusted returns, and support advanced portfolio management techniques.

In [5]:

# 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

Future Returns¶

The future_returns function computes the amplitude of the future return for each observation.

• You can choose between log-returns or simple returns.
• It’s particularly useful for regression models or for further feature engineering.

In [3]:

df["label"] = te.magnitude.future_returns(df, close_col='close', window_size=10)
df["label"]

df["label"] = te.magnitude.future_returns(df, close_col='close', window_size=10) df["label"]

Out[3]:

time
2016-01-04 00:00:00    0.001054
2016-01-04 04:00:00    0.002075
2016-01-04 08:00:00    0.000521
2016-01-04 12:00:00    0.005400
2016-01-04 16:00:00    0.004218
                         ...   
2016-12-30 04:00:00         NaN
2016-12-30 08:00:00         NaN
2016-12-30 12:00:00         NaN
2016-12-30 16:00:00         NaN
2016-12-30 20:00:00         NaN
Name: label, Length: 1548, dtype: float64

In [4]:

plt.figure(figsize=(15,6))
plt.plot(df["label"])
plt.title("Future Returns Target", size=15)
plt.show()

plt.figure(figsize=(15,6)) plt.plot(df["label"]) plt.title("Future Returns Target", size=15) plt.show()

Future Volatility¶

The future_volatility function estimates future market volatility using several common models: close_to_close, parkinson, rogers_satchell, yang_zhang.

In [6]:

df["label"] = te.magnitude.future_volatility(df, method='yang_zhang', window_size=10, open_col="open", high_col="high", low_col="low", close_col="close")
df["label"]

df["label"] = te.magnitude.future_volatility(df, method='yang_zhang', window_size=10, open_col="open", high_col="high", low_col="low", close_col="close") df["label"]

Out[6]:

time
2016-01-04 00:00:00         NaN
2016-01-04 04:00:00         NaN
2016-01-04 08:00:00         NaN
2016-01-04 12:00:00         NaN
2016-01-04 16:00:00         NaN
                         ...   
2016-12-30 04:00:00    0.001797
2016-12-30 08:00:00    0.001842
2016-12-30 12:00:00    0.001724
2016-12-30 16:00:00    0.001629
2016-12-30 20:00:00    0.001601
Name: label, Length: 1548, dtype: float64

In [7]:

plt.figure(figsize=(15,6))
plt.plot(df["label"])
plt.title("Future Volatility Target", size=15)
plt.show()

plt.figure(figsize=(15,6)) plt.plot(df["label"]) plt.title("Future Volatility Target", size=15) plt.show()

Continuous Barrier Labeling¶

The continuous_barrier_labeling function estimates the exact time (in hours) it takes for the price to hit either a Take Profit (TP) or a Stop Loss (SL) level, starting from each index.
It belongs to the family of event-based targets and provides continuous labels that reflect the speed of a movement rather than just its direction.

This labeling approach is useful for timing analysis, position sizing, and training models that incorporate the notion of time-to-event.

Warning "⏱ Time-based requirement"¶

Unlike other targets, this method requires:

• A DatetimeIndex (named 'time').
• Two timestamp columns: high_time and low_time, indicating when the high and low of the candle occurred (not the end of the bar).

These columns are essential to compute the more accurate label possible without using the ticks.

In [2]:

# Import a dataset to test the next functions more easily
from quantreo.datasets import load_generated_ohlcv_with_time
df = load_generated_ohlcv_with_time()
df = df.loc["2016"]

# Show the data
df

# Import a dataset to test the next functions more easily from quantreo.datasets import load_generated_ohlcv_with_time df = load_generated_ohlcv_with_time() df = df.loc["2016"] # Show the data df

Out[2]:

	open	high	low	close	volume	low_time	high_time
time
2016-01-04 00:00:00	104.944241	105.312073	104.929735	105.232289	576.805768	2016-01-04 03:21:00	2016-01-04 00:23:00
2016-01-04 04:00:00	105.233361	105.252139	105.047564	105.149357	485.696723	2016-01-04 04:15:00	2016-01-04 07:48:00
2016-01-04 08:00:00	105.159851	105.384745	105.141110	105.330306	403.969745	2016-01-04 09:07:00	2016-01-04 10:21:00
2016-01-04 12:00:00	105.330306	105.505799	104.894155	104.923404	1436.917324	2016-01-04 15:50:00	2016-01-04 12:01:00
2016-01-04 16:00:00	104.914147	105.023293	104.913252	105.014347	1177.672605	2016-01-04 18:22:00	2016-01-04 16:09:00
...	...	...	...	...	...	...	...
2016-12-30 04:00:00	103.632257	103.711884	103.495896	103.564574	563.932484	2016-12-30 05:34:00	2016-12-30 04:00:00
2016-12-30 08:00:00	103.564574	103.629321	103.555581	103.616731	697.707475	2016-12-30 10:00:00	2016-12-30 11:59:00
2016-12-30 12:00:00	103.615791	103.628165	103.496810	103.515847	1768.926665	2016-12-30 14:57:00	2016-12-30 12:00:00
2016-12-30 16:00:00	103.515847	103.692243	103.400370	103.422193	921.437054	2016-12-30 16:36:00	2016-12-30 17:43:00
2016-12-30 20:00:00	103.420285	103.429955	103.195921	103.226868	764.291608	2016-12-30 21:02:00	2016-12-30 20:00:00

1548 rows × 7 columns

In [9]:

df["label"] = te.magnitude.continuous_barrier_labeling(df, open_col="open", high_col="high", low_col="low", high_time_col="high_time",
    low_time_col="low_time", tp=0.015, sl=-0.015, buy=True)

df["label"] = te.magnitude.continuous_barrier_labeling(df, open_col="open", high_col="high", low_col="low", high_time_col="high_time", low_time_col="low_time", tp=0.015, sl=-0.015, buy=True)

100%|███████████████████████████████████| 1548/1548 [00:00<00:00, 509677.57it/s]

In [10]:

plt.figure(figsize=(15,6))
plt.plot(df["label"])
plt.title("Continuous Barrier Labeling", size=15)
plt.show()

plt.figure(figsize=(15,6)) plt.plot(df["label"]) plt.title("Continuous Barrier Labeling", size=15) plt.show()