This risk metric measures the traded volume, which can also have an interval-based variant that measures volume per period. This is another risk measure that is meant to detect and prevent overtrading. For example, some of the catastrophic software implementation bugs we discussed in this chapter could've been prevented if they had a tight volume limit in place that warned operators about risk violations and possibly a volume limit that shut down trading strategies. Let's observe the traded volume for our strategy, which is shown in the following code:

traded_volume = 0
for i in range(0, num_days):
  if results['Trades'].iloc[i] != 0:
    traded_volume += abs(results['Position'].iloc[i] - results['Position'].iloc[i-1])

print('Total traded volume:', traded_volume)

The preceding code will return the following output:

Total traded volume: 4050

In this case, the strategy behavior is as expected, that is, no overtrading is detected. We can use this to calibrate what total traded volume to expect from this strategy when it is deployed to live markets. If it ever trades significantly more than what is expected, we can detect that to be an over-trading condition.