Matplotlib：在图表上添加趋势线（连接标记点）的实现方法

Hey there! Let's figure out how to swap those scatter markers for colored connecting lines to visualize your trend and return signals more clearly. Here's a straightforward, step-by-step solution tailored to your data setup:

Step 1: Cleanly Filter Your Signal Data #

First, let's extract the subsets of data for each signal type—this makes the code easier to read and maintain:

import pandas as pd
import matplotlib.pyplot as plt

# Ensure time columns are recognized as datetime (skip if already done)
df['time'] = pd.to_datetime(df['time'])
df_trend['time'] = pd.to_datetime(df_trend['time'])
df_return['time'] = pd.to_datetime(df_return['time'])

# Filter trend events
crossing_up = df_trend[df_trend.event == 'crossing up']
crossing_down = df_trend[df_trend.event == 'crossing down']

# Filter return signals
return_positive = df_return[df_return.return12 > 0]
return_negative = df_return[df_return.return12 < 0]

Step 2: Plot Price Curve + Connecting Lines #

Instead of plt.scatter(), use plt.plot() to draw lines between your signal points. We'll assign unique colors and line styles to each signal type for clarity:

# First plot the original price curve
df.plot(y='price', x='time', label='Price', color='darkgray', linewidth=2)

# Plot crossing up signals: green solid line with downward triangle markers
plt.plot(crossing_up['time'], crossing_up['price'], color='forestgreen', linestyle='-', marker='v', label='Crossing Up')

# Plot crossing down signals: red dashed line with "1" markers
plt.plot(crossing_down['time'], crossing_down['price'], color='crimson', linestyle='--', marker='1', label='Crossing Down')

# Plot positive return signals: blue dotted line with "2" markers
plt.plot(return_positive['time'], return_positive['price'], color='royalblue', linestyle=':', marker='2', label='Return12 > 0')

# Plot negative return signals: orange dash-dot line with "3" markers
plt.plot(return_negative['time'], return_negative['price'], color='darkorange', linestyle='-.', marker='3', label='Return12 < 0')

# Add labels and legend for readability
plt.xlabel('Time')
plt.ylabel('Price')
plt.title('Price Trend with Signal Lines')
plt.legend()

# Adjust x-axis to fit your time range (optional but helpful)
plt.xlim(pd.to_datetime('2019-01-01 01:00:00.000Z'), pd.to_datetime('2019-01-15 23:59:59.999Z'))

plt.show()

Key Notes: #

• Line Style Customization: Use linestyle to tweak line appearance:
  • - = solid line
  • -- = dashed line
  • : = dotted line
  • -. = dash-dot line
• Markers (Optional): Keep the marker parameter if you want to highlight individual signal points alongside the connecting lines. Remove it if you only want the lines.
• Empty Data Handling: If any signal subset has no rows (e.g., no "crossing up" events), plt.plot() will skip it silently without throwing errors.

内容的提问来源于stack exchange，提问作者Viktor.w