Here at Quant Science, we love using Python for financial market data analysis.

After all: it's the first step to building profitable trading strategies.

In today's issue of the QS Newsletter (get the code), we are going to build beautiful stock price charts with Python using the mplfinance package.

What You’ll Learn:

1. How to download and process historic stock price data (for free)

2. How to build OHLC stock price charts with volume

3. Bonus: How to add moving averages to the price charts

Our example stock:

• Assets: AAPL

• Date Range: January 2022 - June 2022

BONUS: Get the Python Code for EVERYTHING you see in this post

Disclaimer:

The information and educational material provided by Quant Science, LLC are for educational purposes only and should not be considered as financial advice or recommendations to purchase, hold, or sell any securities or other financial instruments. Before you proceed, please review our full disclaimer here.

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Now on to the show...

mplfinance for beautiful stock price charts

Ok, let's dive in and see how to use the mplfinance package to build beautiful stock price charts in a few lines of code.

First, make sure to sign up for our Newsletter to get all of the code you see today.

Load the Libraries, Data, and Get Price Data

In the first step, we'll import the necessary Python libraries and collect stock data for each asset using yfinance.

Run this code:

Sign up for our Newsletter to get all of the code you see today

The default chart type is a simple OHLC plot.

The result is a simple OHLC chart.

By adding the type argument, we can define the plot type.

Some people like simple line charts.

Other people like more complex charts like Renko charts.

We can also include moving averages to the plot. Use a single number for a single moving average. Use a tuple of numbers for several. The result is an OHLC chart with a 15 period moving average.

Let’s include three moving averages. The result is a candle chart with 7, 15, and 21 day moving averages.

We can also include volume. The result is a candle chart with multiple moving averages plotted with volume.

We can include non-trading days as well. The result is the same as above including non-trading days (note the gaps in the volume bars).

mplfinance handles intraday charting too. The result is a 1-minute chart showing the last 100 minutes of prices.

Congratulations!

You just learned how to create a comprehensive set of stock price charts in Python using the mplfinance package.

But, there's more to learn in algorithmic trading:

• Backtesting your portfolio construction algorithm to make sure the strategy will work in the future

• Executing the trades automatically

• Monthly rebalancing

• Tracking your actual Profit and Loss

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