*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 = [0] 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 loopHmm 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 loopWell, 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 loopWow, this version runs in 122

*micro*seconds, 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.

You can boost performance with PyPy instead of Cython. It works with pure Python code i.e. does not require code modifications for boosting. A disadvantage is that there are issues with numpy and scipy packages using PyPy.

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