Animation funcanimation mplfinance candlestick and line togheter python

When it comes to creating animated visualizations in Python, there are several options available. One popular choice is to use the funcanimation module from the matplotlib.animation package. This module allows you to create animations by repeatedly calling a function to update the plot. In this article, we will explore different ways to combine candlestick and line plots using mplfinance library and funcanimation module in Python.

Option 1: Using Subplots

The first option is to use subplots to create separate axes for the candlestick and line plots. This approach allows us to have more control over the layout and styling of the plots. Here’s how you can do it:

import matplotlib.pyplot as plt
import mplfinance as mpf
from matplotlib.animation import FuncAnimation

# Create a figure and subplots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))

# Define the update function
def update(frame):
    # Clear the axes
    ax1.clear()
    ax2.clear()
    
    # Update the candlestick plot
    mpf.plot(candlestick_data[frame], type='candle', ax=ax1)
    
    # Update the line plot
    ax2.plot(line_data[frame])
    
    # Set the title and labels
    ax1.set_title('Candlestick Plot')
    ax2.set_title('Line Plot')
    ax1.set_xlabel('Date')
    ax1.set_ylabel('Price')
    ax2.set_xlabel('Date')
    ax2.set_ylabel('Value')

# Create the animation
animation = FuncAnimation(fig, update, frames=len(candlestick_data), interval=100)

# Show the plot
plt.show()

In this code, we first create a figure and two subplots using the subplots function. We then define the update function, which is called repeatedly by the animation. Inside the update function, we clear the axes, update the candlestick plot using mplfinance.plot, and update the line plot using ax2.plot. Finally, we set the titles and labels for the plots and create the animation using FuncAnimation.

Option 2: Using Overlay

The second option is to use the overlay feature provided by the mplfinance library. This allows us to overlay the line plot on top of the candlestick plot. Here’s how you can do it:

import mplfinance as mpf
from matplotlib.animation import FuncAnimation

# Create a figure and axis
fig, ax = mpf.plot(candlestick_data[0], type='candle', returnfig=True)

# Define the update function
def update(frame):
    # Clear the axis
    ax.clear()
    
    # Update the candlestick plot
    mpf.plot(candlestick_data[frame], type='candle', ax=ax)
    
    # Overlay the line plot
    ax.plot(line_data[frame])
    
    # Set the title and labels
    ax.set_title('Candlestick and Line Plot')
    ax.set_xlabel('Date')
    ax.set_ylabel('Price')

# Create the animation
animation = FuncAnimation(fig, update, frames=len(candlestick_data), interval=100)

# Show the plot
mpf.show()

In this code, we first create a figure and axis using the mpf.plot function. We then define the update function, which is called repeatedly by the animation. Inside the update function, we clear the axis, update the candlestick plot using mplfinance.plot, and overlay the line plot using ax.plot. Finally, we set the title and labels for the plot and create the animation using FuncAnimation.

Option 3: Using Subplots and Overlay

The third option is to combine the previous two options by using subplots and overlaying the line plot on top of the candlestick plot. This approach provides the most flexibility in terms of layout and styling. Here’s how you can do it:

import matplotlib.pyplot as plt
import mplfinance as mpf
from matplotlib.animation import FuncAnimation

# Create a figure and subplots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))

# Define the update function
def update(frame):
    # Clear the axes
    ax1.clear()
    ax2.clear()
    
    # Update the candlestick plot
    mpf.plot(candlestick_data[frame], type='candle', ax=ax1)
    
    # Overlay the line plot
    ax1.plot(line_data[frame])
    
    # Set the title and labels
    ax1.set_title('Candlestick and Line Plot')
    ax1.set_xlabel('Date')
    ax1.set_ylabel('Price')
    ax2.set_xlabel('Date')
    ax2.set_ylabel('Value')

# Create the animation
animation = FuncAnimation(fig, update, frames=len(candlestick_data), interval=100)

# Show the plot
plt.show()

In this code, we create a figure and two subplots using the subplots function. We then define the update function, which is called repeatedly by the animation. Inside the update function, we clear the axes, update the candlestick plot using mplfinance.plot, and overlay the line plot using ax1.plot. Finally, we set the titles and labels for the plots and create the animation using FuncAnimation.

Among the three options, the best choice depends on your specific requirements and preferences. Option 1 provides more control over the layout and styling of the plots, while Option 2 offers a simpler approach by overlaying the line plot on top of the candlestick plot. Option 3 combines the advantages of both options, allowing for more flexibility in customization. Consider your needs and choose the option that suits you best.

Rate this post

6 Responses

    1. I couldnt disagree more. Option 2 looks like a cluttered mess to me. I prefer a clean and simple approach. Different strokes for different folks, I guess. #lessismore

  1. Option 3 sounds like a spicy combo! Cant wait to see those candlesticks and lines mingling together in Python! 🕺

Leave a Reply

Your email address will not be published. Required fields are marked *

Table of Contents