## Saturday, June 7, 2014

### Boosting performance with Cython

Even with my old pc (AMD Athlon II, 3GB ram), I seldom run into performance issues when running vectorized code. But unfortunately there are plenty of cases where that can not be easily vectorized, for example the drawdown function. My implementation of such was extremely slow, so I decided to use it as a test case for speeding things up. I'll be using the SPY timeseries with ~5k samples as test data. Here comes the original version of my drawdown function (as it is now implemented in the TradingWithPython library)
```def drawdown(pnl):
"""
calculate max drawdown and duration

Returns:
drawdown : vector of drawdwon values
duration : vector of drawdown duration
"""
cumret = pnl

highwatermark = 

idx = pnl.index
drawdown = pd.Series(index = idx)
drawdowndur = pd.Series(index = idx)

for t in range(1, len(idx)) :
highwatermark.append(max(highwatermark[t-1], cumret[t]))
drawdown[t]= (highwatermark[t]-cumret[t])
drawdowndur[t]= (0 if drawdown[t] == 0 else drawdowndur[t-1]+1)

return drawdown, drawdowndur

%timeit drawdown(spy)
1 loops, best of 3: 1.21 s per loop
```
Hmm 1.2 seconds is not too speedy for such a simple function. There are some things here that could be a great drag to performance, such as a list *highwatermark* that is being appended on each loop iteration. Accessing Series by their index should also involve some processing that is not strictly necesarry. Let's take a look at what happens when this function is rewritten to work with numpy data
```def dd(s):
#    ''' simple drawdown function '''

highwatermark = np.zeros(len(s))
drawdown = np.zeros(len(s))
drawdowndur = np.zeros(len(s))

for t in range(1,len(s)):
highwatermark[t] = max(highwatermark[t-1], s[t])
drawdown[t] = (highwatermark[t]-s[t])
drawdowndur[t]= (0 if drawdown[t] == 0 else drawdowndur[t-1]+1)

return drawdown , drawdowndur

%timeit dd(spy.values)
10 loops, best of 3: 27.9 ms per loop
```
Well, this is much faster than the original function, approximately 40x speed increase. Still there is much room for improvement by moving to compiled code with cython Now I rewrite the dd function from above, but using optimisation tips that I've found on the cython tutorial . Note that this is my first try ever at optimizing functions with Cython.
```%%cython
import numpy as np
cimport numpy as np

DTYPE = np.float64
ctypedef np.float64_t DTYPE_t

cimport cython
@cython.boundscheck(False) # turn of bounds-checking for entire function
def dd_c(np.ndarray[DTYPE_t] s):
#    ''' simple drawdown function '''
cdef np.ndarray[DTYPE_t] highwatermark = np.zeros(len(s),dtype=DTYPE)
cdef np.ndarray[DTYPE_t] drawdown = np.zeros(len(s),dtype=DTYPE)
cdef np.ndarray[DTYPE_t] drawdowndur = np.zeros(len(s),dtype=DTYPE)

cdef int t
for t in range(1,len(s)):
highwatermark[t] = max(highwatermark[t-1], s[t])
drawdown[t] = (highwatermark[t]-s[t])
drawdowndur[t]= (0 if drawdown[t] == 0 else drawdowndur[t-1]+1)

return drawdown , drawdowndur

%timeit dd_c(spy.values)
10000 loops, best of 3: 121 µs per loop

```
Wow, this version runs in 122 microseconds, making it ten thousand times faster than my original version! I must say that I'm very impressed by what the Cython and IPython teams have achieved! The speed compared with ease of use is just awesome!
P.S. I used to do code optimisations in Matlab using pure C and .mex wrapping, it was all just pain in the ass compared to this.