Evaluate strategy performance by Sharpe ratio
The Sharpe ratio is a risk-adjusted return measure developed by Nobel laureate William F. Sharpe. It is calculated as the average return over the risk-free rate divided by the standard deviation of the excess return.
You will calculate and review the Sharpe ratio of a signal-based strategy. The strategy generates long or short signals using two moving average indicators, and has been backtested using the 3-year historical price data of the Google stock. The backtest statistics has been provided in resInfo.
This exercise is part of the course
Financial Trading in Python
Exercise instructions
- Get annual return and volatility from
resInfoand save them inyearly_meanandyearly_volrespectively. - Calculate the Sharpe ratio manually using
yearly_returnandyearly_vol. - Print the annualized Sharpe ratio directly obtained from
resInfo.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Get annual return and volatility
yearly_return = ____
print('Annual return: %.2f'% yearly_return)
yearly_vol = ____
print('Annual volatility: %.2f'% yearly_vol)
# Calculate the Sharpe ratio manually
sharpe_ratio = ____ / ____
print('Sharpe ratio calculated: %.2f'% sharpe_ratio)
# Print the Sharpe ratio
print('Sharpe ratio %.2f'% ____)