# Sharpe ratio for dynamic portfolio

I want to test the performance of my strategy with different rebalancing period. I'm struggle with calculating the overall performance on backtest results and making final conclusions.

For example, by backtest window is 252 days. I want to measure performance with the set rebalancing_period = {12,36,63,126}
So after backtest for every rebalancing period t I have 252/rebalancing_period[t] statistics for every period.

------------
rebalancing_period = 12
------------
period: 1
p_mu = 0.895
p_std = 0.4
sharpe_ratio = 2.24

period: 2
p_mu = 0.675
p_std = 0.37
sharpe_ratio = 2.01

...
period: 20
p_mu = 0.679
p_std = 0.2
sharpe_ratio = 1.24

------------
rebalancing_period = 36
------------
period: 1
p_mu = 0.596
p_std = 0.21
sharpe_ratio = 1.24

period: 2
p_mu = 0.475
p_std = 0.27
sharpe_ratio = 2.0

...
period: 6
p_mu = 0.345
p_std = 0.15
sharpe_ratio = 1.27

....


But I can't understand how can interpret this results. Finding the mean of sharpe ratio over all periods seems weird.

I might be wrong with my initial idea, so I'll appreciate any suggestions and remarks.

• You're better off collecting all the days of returns under each of the 4 'treatments', each of which would then be a series of 252 days. Then just compute the Sharpe over each of them in the 'usual' way. – shabbychef May 17 '18 at 5:32
• @shabbychef yeah, I thought about that. But I don't understand how to consider the fact that my weight are changing on every rebalancing period. I thought to calculate like that np.sqrt(252)*np.mean(weighted_returns) / np.std(weighted_returns) where weighted_returns is daily portfolio returns. But not sure – dand1 May 17 '18 at 6:12