Automating Daily and Intraday Chart Generation using mplfinance

When it comes to visualizing stock market data, generating daily and intraday charts can be a crucial part of a trader's toolkit. Mplfinance, a library for creating financial charts in Python, makes this easier by providing an intuitive interface and powerful set of charting capabilities. In this article, I'll walk you through the process of automating the creation of these charts using mplfinance.

What is mplfinance?

mplfinance is a library built around matplotlib, explicitly geared for financial data plotting. Leveraging the capabilities of Python’s pandas to manipulate data, it allows you to create high-quality candlestick, moving averages, and other market analyses charts effortlessly.

Setting Up Your Environment

Before diving into chart creation, ensure you have Python and the necessary libraries installed. If you haven't already, you can set up your environment by installing the dependencies via pip:

pip install mplfinance pandas

These two packages are key components needed to facilitate data handling and chart generation.

Loading Your Data

To start creating your charts, you'll first need to gather your market data. Usually, this can be in the form of a CSV file that includes historical stock data. For this example, let's assume we have a CSV file named data.csv:

import pandas as pd

data = pd.read_csv('data.csv', index_col=0, parse_dates=True)
print(data.head())

This piece of code loads the data into a DataFrame, which mplfinance can use.

Generating Daily Charts

Once the data is loaded, generating a daily candle chart is straightforward:

import mplfinance as mpf

mpf.plot(data, type='candle', title='Daily Candlestick Chart', style='yahoo', mav=(5,10,20))

Here, the code generates a candlestick chart of your data with moving averages of 5, 10, and 20 days. The style is set to 'yahoo', which is one amongst various available styles in mplfinance that modifies the aesthetics of the chart.

Creating Intraday Charts

Generating intraday charts is not much different than daily charts. However, they offer fine-grained insights by focusing on time intervals shorter than the daily spectrum:

intraday_data = data.between_time('09:30', '16:00')
mpf.plot(intraday_data, type='ohlc', title='Intraday 5-Minute OHLC', style='charles', volume=True)

This code snippet specifically filters the data to include only regular market hours and plots the chart with OHLC bars.

Automation Techniques

To automate these chart generation tasks, you can use Python scripts that run at scheduled intervals using utilities like cron jobs on Unix or Task Scheduler on Windows.

# Automate using schedule library
import schedule
import time

def job():
    mpf.plot(data, type='candle', title='Automated Daily Candle Chart', style='mike')

# Setup a schedule to run every day at market close
schedule.every().day.at("16:30").do(job)

while True:
    schedule.run_pending()
    time.sleep(60)

This script showcases automation using the schedule library that runs the chart generation function every day after market close, maintaining your insights upto date.

Conclusion

Using the mplfinance library simplifies the complex task of financial chart plotting. By automating these tasks, you not only save on substantial manual workload but also ensure consistency and reliability in your trading insights. Whether you’d like to visualize daily or intraday data, mplfinance streamlines it through just a few lines of Python, critical for any data-driven traders building strategic insights.