Working with Different Time Intervals in mplfinance

mplfinance is a popular Python library used for creating high-quality financial charts. Whether you are dealing with stocks, forex, or any other financial instrument, visualization can significantly boost your analysis. A common requirement is to handle data at various time intervals or timeframes. In this guide, we will delve into the process of plotting data at different time intervals using mplfinance.

Getting Started

Before we begin plotting, ensure you have mplfinance installed. If not, you can easily install it via pip:

pip install mplfinance

We'll also need some sample data. For illustration purposes, pandas is a great tool for handling date and time data in the form of DataFrames. You can fetch financial data using a library like yfinance:

import yfinance as yf

ticker = 'AAPL'
data = yf.download(ticker, start='2023-01-01', end='2023-10-01')
print(data.head())

Plotting Daily Data

The default option for mplfinance is to plot daily data. Here's how you can quickly plot the daily variations:

import mplfinance as mpf

mpf.plot(data, type='candle', volume=True, title='AAPL Daily Prices')  

In this snippet, type='candle' specifies that we're using candlestick charts, which are common in financial analyses. The volume=True option includes the trading volume shown beneath the price chart.

Changing Time Intervals

For many financial analysts, moving from daily to other time intervals such as weekly, monthly, or even intraday (like minute-wise) charts is crucial for granular analysis.

Weekly Data

Plotting weekly data requires resampling the daily data to a weekly timeframe. Here's how to do it:

weekly_data = data.resample('W').agg({'Open': 'first', 
                                      'High': 'max', 
                                      'Low': 'min', 
                                      'Close': 'last', 
                                      'Volume': 'sum'})

mpf.plot(weekly_data, type='candle', volume=True, title='AAPL Weekly Prices')

In this example, we're using the resample method to aggregate our original data from daily to weekly intervals. The argument 'W' stands for weekly.

Intraday Data

To plot intraday data, you might need to fetch specific intraday data points since most sources like yfinance provide only end-of-day data for free. However, the process remains similar with aggregation, provided you have minutely or hourly data:

intraday_data = data.loc[data.index.strftime('%Y-%m-%d') == '2023-09-15']
# Assume `data` includes minutely data

mpf.plot(intraday_data.set_index(data.index), 
         type='line', title='AAPL Intraday Prices - Sept 15, 2023')  

Here, we've isolated a day's worth of data for detailed analysis.

Monthly Data

For monthly trends, you can similarly aggregate data to a monthly frequency:

monthly_data = data.resample('M').agg({'Open': 'first', 
                                        'High': 'max', 
                                        'Low': 'min', 
                                        'Close': 'last', 
                                        'Volume': 'sum'})

mpf.plot(monthly_data, type='candle', volume=True, title='AAPL Monthly Prices')

The shorthand 'M' is used for monthly resampling. Notice how versatile the mplfinance library becomes once you grasp resampling and time periods.

Conclusion

mplfinance offers a robust API for financial visualization. Understanding how to manipulate time intervals opens myriad applications, be it daily updates, intraday scalping details, weekly trend analysis, or long-term monthly overviews. With Python and its ecosystem, your ability to articulate financial narratives using data is practically limitless.