Evaluating Your Strategy’s Performance Using Python and the ffn Library

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In this discussion, we’ll explore how to use the Python library ffn to evaluate the performance of an investment strategy. Specifically, we’ll analyze the returns generated by the momentum strategy described earlier, leveraging the capabilities of the ffn library.

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Step 1: Compute Strategy Returns

As outlined in the previous article, we calculated the strategy’s monthly returns. We’ll now use those returns as a base for assessing performance.

Step 2: Install and Import the ffn Library

Start by installing the required library and importing it into your Python script:

!pip install ffnimport ffn

Step 3: Input Returns into ffn

Use the strategy returns (e.g., momentum_portfolio_returns from the prior article) with the ffn library. Additionally, specify a risk-free rate. In this example, we assume a 6% annualized risk-free rate, which is factored into the calculation of key performance metrics like the Sharpe ratio.

perf = ffn.core.PerformanceStats(momentum_portfolio_returns, rf=0.06)

Step 4: Display Performance Metrics

Generate a detailed summary of your strategy’s performance:

perf.display()

Here’s an example output:

Stats for None from 2022-06-30 00:00:00 - 2024-06-30 00:00:00
Annual risk-free rate considered: 6.00%

Summary:
Total Return Sharpe CAGR Max Drawdown
-------------- -------- ------ --------------
192.69% - 71.02% -4.08%
Annualized Returns:
mtd 3m 6m ytd 1y 3y 5y 10y incep.
----- ------ ------ ------ ------ ------ ---- ----- --------
4.02% 14.47% 52.71% 37.01% 95.07% 71.02% - - 71.02%
Periodic:
daily monthly yearly
------ ------- --------- --------
sharpe - 3.17 2.10
mean - 56.07% 52.73%
vol - 15.86% 22.23%
skew - 0.07 -
kurt - -1.31 -
best - 12.60% 68.45%
worst - -1.67% 37.01%
Drawdowns:
max avg # days
------ ------ --------
-4.08% -2.30% 60.33
Misc:
--------------- -------
avg. up month 6.62%
avg. down month -1.16%
up year % 100.00%
12m up % 100.00%
--------------- -------

The summary provides an extensive overview. For instance, the strategy generated an impressive 192.69% total return with a CAGR of 71.02%. Equally remarkable, the maximum drawdown was only 4.08%, meaning your portfolio’s steepest decline never exceeded 4.08%.

The Annualized Returns section shows performance over various time frames, from the last 3 months to year-to-date and since inception.

Step 5: Monthly Return Analysis

Analyze the monthly returns for a deeper understanding:

perf.display_monthly_returns()

Sample output:

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD
------ ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- -----
2022 0 0 0 0 0 0 8.68 9.31 -1.13 7.98 1.46 -1.45 26.82
2023 -1.19 -1.24 -0.26 8.45 4.13 7.65 4.69 -1.67 8.89 1.21 12.6 11.46 68.45
2024 10.85 7.98 3.58 2.67 3.48 4.02 0 0 0 0 0 0 37.01

From this table, you can observe how the strategy performed every month. For instance, 2024 year-to-date performance is 37.01%, showcasing the strategy’s consistent growth trajectory.

The Python library ffn serves as a powerful tool for assessing investment or trading strategies. While this article showcased the library's fundamental features, ffn offers numerous other functionalities worth exploring. Dive deeper into its documentation to maximize its potential for your strategies.

Wishing you successful investing endeavors!

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