Python is wild for quantitative finance and algorithmic trading! In this QS Newsletter (get the code), we are showing how to do financial performance analysis in Python with the ffn package.

What You’ll Learn:

1. How to retrieve and analyze financial data using ffn.

2. Performance analysis, drawdowns, and returns visualization.

3. Bonus: Extracting and displaying key statistics as a DataFrame.

Sample Portfolio:

• Assets: AAPL, GOOGL, MSFT, JPM, NVDA

• Date Range: January 2023 - September 2024

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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Financial Functions for Python with the ffn Package

Ok, let's dive in and see how to use the ffn package to perform quantitative finance analysis in Python. First, make sure to sign up for our Newsletter to get all of the code you see today.

Step 1: Load the Libraries, Data, and Get Price Data

In the first step, we'll import the necessary Python libraries, collect stock data for each asset using yfinance, and then convert them to returns. Run this code:

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This returns the daily closing prices for each stock symbol:

Step 2: Performance Analysis at a Glance

Next, let's complete a quick performance analysis. We'll use calc_stats(), which gives a complete performance breakdown of your assets, summarizing returns, volatility, Sharpe ratios, and more.

Performance Breakdown:

• Sharpe Ratio: Measures risk-adjusted return.

• Max Drawdown: Largest peak-to-trough decline during the time period.

• Total Return: The overall percentage gain or loss. Run this code:

Run this code:

Step 3: Lookback Returns

Visualize and analyze lookback returns over different time periods such as MTD, 3 Month, 6 Month, YTD, 1Y, 3Y, 5Y, and 10Y. Perfect for making client reports. Run this code:

Step 4: Monthly Returns by Asset

Another thing I love about ffn is how easy it is to make performance reports by month. Run this code:

Step 5: Get Performance, Asset Return Correlations, and Drawdowns

• Performance Plots can help you understand which assets or portfolios are growing the fastest compared to benchmarks and other assets. ffn makes it easy to make performance plots.

• Correlations can help you understand the interrelationships between different assets in the portfolio. ffn makes it easy to visualize these with heatmaps.

• Drawdowns are a critic metric in risk management. ffn makes it easy to visualize drawdown plots.

Run this code:

Step 6 (BONUS): Get all of the stats as a data frame

One thing that bugged me is that I want the key performance stats as a data frame. This is useful when I want to store information about trades in a database or post-process performance analysis. I can get the data by running this code:

Conclusion: ffn makes it easy to financial performance analysis

Congrats! You just learned how to create a comprehensive financial performance analysis in Python using the ffn package. But, there's more to learn in algorithmic trading:

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• Executing the trades automatically

• Monthly rebalancing

• Tracking your actual Profit and Loss

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