piRSquared piRSquared - 8 months ago 281
Python Question

Calculate max draw down with a vectorized solution in python

Maximum Drawdown is a common risk metric used in quantitative finance to assess the largest negative return that has been experienced.

Recently, I became impatient with the time to calculate max drawdown using my looped approach.

def max_dd_loop(returns):
"""returns is assumed to be a pandas series"""
max_so_far = None
start, end = None, None
r = returns.add(1).cumprod()
for r_start in r.index:
for r_end in r.index:
if r_start < r_end:
current = r.ix[r_end] / r.ix[r_start] - 1
if (max_so_far is None) or (current < max_so_far):
max_so_far = current
start, end = r_start, r_end
return max_so_far, start, end

I'm familiar with the common perception that a vectorized solution would be better.

The questions are:

  • can I vectorize this problem?

  • What does this solution look like?

  • How beneficial is it?


I modified Alexander's answer into the following function:

def max_dd(returns):
"""Assumes returns is a pandas Series"""
r = returns.add(1).cumprod()
dd = r.div(r.cummax()).sub(1)
mdd = dd.min()
end = dd.argmin()
start = r.loc[:end].argmax()
return mdd, start, end


df_returns is assumed to be a dataframe of returns, where each column is a seperate strategy/manager/security, and each row is a new date (e.g. monthly or daily).

cum_returns = (1 + df_returns).cumprod()
drawdown =  1 - cum_returns.div(cum_returns.cummax())