03 - Visualization Methods¶

Complete guide to all plotting functions in trend_classifier.

What You'll Learn¶

• plot_segments() - Overview of all segments
• plot_segment() - Highlight specific segments
• plot_segment_with_trendlines_no_context() - Detailed trendline view
• plot_detrended_signal() - Residuals visualization
• Customizing figure sizes

Setup¶

In [1]:

import yfinance as yf

from trend_classifier import Segmenter

# Download data
df = yf.download("AAPL", start="2018-09-15", end="2022-09-05", interval="1d", progress=False)

# Create and calculate segments
seg = Segmenter(df=df, column="Close", n=20)
seg.calculate_segments()

print(f"Working with {len(seg.segments)} segments")

import yfinance as yf from trend_classifier import Segmenter # Download data df = yf.download("AAPL", start="2018-09-15", end="2022-09-05", interval="1d", progress=False) # Create and calculate segments seg = Segmenter(df=df, column="Close", n=20) seg.calculate_segments() print(f"Working with {len(seg.segments)} segments")

Working with 49 segments

1. plot_segments() - Full Overview¶

Shows the entire time series with all detected segments and their trend lines.

Color coding:

• 🟢 Green: Consistent uptrend (all window slopes positive)
• 🔴 Red: Consistent downtrend (all window slopes negative)
• 🟤 Brown/Mixed: Inconsistent trend direction

In [2]:

# Default size
seg.plot_segments()

# Default size seg.plot_segments()

In [3]:

# Custom figure size - wider for presentations
seg.plot_segments(fig_size=(16, 5))

# Custom figure size - wider for presentations seg.plot_segments(fig_size=(16, 5))

In [4]:

# Compact view
seg.plot_segments(fig_size=(10, 3))

# Compact view seg.plot_segments(fig_size=(10, 3))

2. plot_segment() - Highlight Specific Segments¶

Highlights one or more segments against the full time series background. Useful for examining segments of interest.

Single Segment¶

In [5]:

# Highlight segment 3
seg.plot_segment(idx=3)

# Highlight segment 3 seg.plot_segment(idx=3)

In [6]:

# With custom color and size
seg.plot_segment(idx=5, col="blue", fig_size=(12, 4))

# With custom color and size seg.plot_segment(idx=5, col="blue", fig_size=(12, 4))

Multiple Segments¶

Pass a list of indices to highlight multiple segments at once.

In [7]:

# Highlight segments 0, 2, and 4
seg.plot_segment(idx=[0, 2, 4], col="green", fig_size=(14, 4))

# Highlight segments 0, 2, and 4 seg.plot_segment(idx=[0, 2, 4], col="green", fig_size=(14, 4))

In [8]:

# Highlight all uptrend segments
uptrend_indices = [i for i, s in enumerate(seg.segments) if s.slope > 0.1]
print(f"Uptrend segments: {uptrend_indices}")
seg.plot_segment(idx=uptrend_indices, col="green", fig_size=(14, 4))

# Highlight all uptrend segments uptrend_indices = [i for i, s in enumerate(seg.segments) if s.slope > 0.1] print(f"Uptrend segments: {uptrend_indices}") seg.plot_segment(idx=uptrend_indices, col="green", fig_size=(14, 4))

Uptrend segments: [0, 2, 3, 6, 9, 10, 12, 14, 16, 17, 18, 20, 22, 23, 24, 26, 30, 33, 34, 35, 36, 38, 39, 41, 43, 45, 47]

In [9]:

# Highlight all downtrend segments
downtrend_indices = [i for i, s in enumerate(seg.segments) if s.slope < -0.1]
print(f"Downtrend segments: {downtrend_indices}")
seg.plot_segment(idx=downtrend_indices, col="red", fig_size=(14, 4))

# Highlight all downtrend segments downtrend_indices = [i for i, s in enumerate(seg.segments) if s.slope < -0.1] print(f"Downtrend segments: {downtrend_indices}") seg.plot_segment(idx=downtrend_indices, col="red", fig_size=(14, 4))

Downtrend segments: [1, 4, 5, 15, 19, 21, 25, 27, 28, 29, 31, 32, 37, 40, 42, 44, 46, 48]

3. plot_segment_with_trendlines_no_context() - Detailed View¶

Shows a single segment zoomed in, with all individual window trendlines visible. This helps understand how the segment was constructed from overlapping windows.

In [10]:

# View segment 2 with its constituent trendlines
seg.plot_segment_with_trendlines_no_context(idx=2)

# View segment 2 with its constituent trendlines seg.plot_segment_with_trendlines_no_context(idx=2)

In [11]:

# Larger view
seg.plot_segment_with_trendlines_no_context(idx=2, fig_size=(14, 5))

# Larger view seg.plot_segment_with_trendlines_no_context(idx=2, fig_size=(14, 5))

In [12]:

# Compare different segments
import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 3, figsize=(18, 4))
for ax_idx, seg_idx in enumerate([1, 4, 7]):
    plt.sca(axes[ax_idx])
    # Note: This method creates its own figure, so we'll use manual plotting
    segment = seg.segments[seg_idx]
    x_seg = seg.x[segment.start:segment.stop]
    y_seg = seg.y[segment.start:segment.stop]
    axes[ax_idx].plot(x_seg, y_seg, 'gray', linewidth=2)
    axes[ax_idx].set_title(f"Segment {seg_idx} (slope={segment.slope:.3f})")
plt.tight_layout()
plt.show()

# Compare different segments import matplotlib.pyplot as plt fig, axes = plt.subplots(1, 3, figsize=(18, 4)) for ax_idx, seg_idx in enumerate([1, 4, 7]): plt.sca(axes[ax_idx]) # Note: This method creates its own figure, so we'll use manual plotting segment = seg.segments[seg_idx] x_seg = seg.x[segment.start:segment.stop] y_seg = seg.y[segment.start:segment.stop] axes[ax_idx].plot(x_seg, y_seg, 'gray', linewidth=2) axes[ax_idx].set_title(f"Segment {seg_idx} (slope={segment.slope:.3f})") plt.tight_layout() plt.show()

4. plot_detrended_signal() - Residuals¶

Shows the signal after removing the linear trend from each segment. This visualizes the "noise" or volatility within each trend.

Interpretation:

• Values close to 0 = price following the trend closely
• Large deviations = price deviating from the trend

In [13]:

# Plot detrended signal
seg.plot_detrended_signal()

# Plot detrended signal seg.plot_detrended_signal()

In [14]:

# Larger view to see detail
seg.plot_detrended_signal(fig_size=(16, 5))

# Larger view to see detail seg.plot_detrended_signal(fig_size=(16, 5))

5. Custom Visualizations¶

You can also create custom plots using matplotlib and segment data.

In [15]:

import matplotlib.pyplot as plt
import numpy as np

# Create a subplot with original and detrended signals
fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True)

# Original signal with segment boundaries
axes[0].plot(seg.x, seg.y, 'b-', alpha=0.7)
for segment in seg.segments:
    axes[0].axvline(x=segment.start, color='gray', linestyle='--', alpha=0.5)
axes[0].set_ylabel("Price")
axes[0].set_title("Original Signal with Segment Boundaries")

# Detrended signal
axes[1].plot(seg.x, seg.y_de_trended, 'g-', alpha=0.7)
axes[1].axhline(y=0, color='black', linestyle='-', alpha=0.3)
for segment in seg.segments:
    axes[1].axvline(x=segment.start, color='gray', linestyle='--', alpha=0.5)
axes[1].set_xlabel("Index")
axes[1].set_ylabel("Detrended Value")
axes[1].set_title("Detrended Signal (Residuals)")

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt import numpy as np # Create a subplot with original and detrended signals fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True) # Original signal with segment boundaries axes[0].plot(seg.x, seg.y, 'b-', alpha=0.7) for segment in seg.segments: axes[0].axvline(x=segment.start, color='gray', linestyle='--', alpha=0.5) axes[0].set_ylabel("Price") axes[0].set_title("Original Signal with Segment Boundaries") # Detrended signal axes[1].plot(seg.x, seg.y_de_trended, 'g-', alpha=0.7) axes[1].axhline(y=0, color='black', linestyle='-', alpha=0.3) for segment in seg.segments: axes[1].axvline(x=segment.start, color='gray', linestyle='--', alpha=0.5) axes[1].set_xlabel("Index") axes[1].set_ylabel("Detrended Value") axes[1].set_title("Detrended Signal (Residuals)") plt.tight_layout() plt.show()

Figure Size Reference¶

Use Case	Recommended Size
Notebook inline	(10, 5) default
Wide presentation	(16, 5) or (18, 5)
Compact/thumbnail	(8, 3)
Detailed analysis	(14, 6)
Side-by-side comparison	(7, 4) per subplot

Next Steps¶

• 04_configuration.py - Tune parameters to improve segmentation
• 05_classification.py - Classify segments by trend direction