The Pandas GroupBy function is an essential tool in data analysis and algorithmic trading. Whether you are building cryptocurrency trading strategies, performing quantitative analysis in the stock market, or backtesting — properly grouping and aggregating data is an essential skill.
Role of GroupBy in Algo Trading
In algorithmic trading, we often need to group large amounts of time series data based on various parameters (e.g. stock ticker, date, volume, price, etc.). Pandas GroupBy is a great help in this task. With this, we can extract different metrics like mean, sum, std deviation, etc. for a particular group.
In-depth use of Aggregation
The blog explains how we can extract multiple metrics using the aggregate() method with GroupBy. For example:
- For a particular stock, we can calculate the sum, mean and standard deviation of volume.
- For price, we can calculate max, min and mean.
Here, the aggregate() method uses a combination of dictionary and list. Example:
grouped_data.aggregate({
‘volume’: [‘sum’, ‘mean’, ‘std’],
‘price’: [‘max’, ‘min’, ‘mean’]
})
Advanced Analysis: VWAP (Volume Weighted Average Price)
The blog also discusses an advanced topic called VWAP (Volume Weighted Average Price), which is an important indicator in the trading industry. To calculate VWAP, we multiply the price with the volume for each ticker, get the sum, and then divide it by the total volume.
The syntax to calculate VWAP is something like this:
df[‘vwap’] = (df[‘price’] * df[‘volume’]).groupby(df[‘ticker’]).transform(‘sum’) / df[‘volume’].groupby(df[‘ticker’]).transform(‘sum’)
Next step towards algo trading
The blog also points out that as we move forward in the journey of 100 Days of Algo Trading, the topics are gradually getting deeper. Concepts like VWAP show that we are now entering an even deeper level of quantitative trading.
Things to learn
In-depth understanding of GroupBy Object.
- Combination of both dictionary and list in Aggregate method.
- Extracting multiple matrices for each ticker.
- Calculation of advanced indicators like VWAP.
Watch this Day 28 video tutorial
Day 28 :Pandas GroupBy
