Drawdown functions using Cython

Drawdown_cython

{
 "metadata": {
  "name": "Drawdown cython"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import numpy as np\nfrom numpy.lib.stride_tricks import as_strided\nimport matplotlib.pyplot as plt\n\n%matplotlib inline\n%load_ext cythonmagic",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%%cython\nimport numpy as np\ncimport numpy as np\nfrom libcpp cimport bool\ncimport cython\n\nDTYPE = np.float64\nctypedef np.float64_t DTYPE_t\n\[email protected](False)\[email protected](False)\[email protected](False)\ncpdef tuple cy_dd_custom(np.ndarray[DTYPE_t, ndim=1] ser):\n    cdef double running_global_peak = ser[0]\n    cdef double min_since_global_peak = ser[0]\n    cdef double running_max_dd = 0\n    \n    cdef long running_global_peak_id = 0\n    cdef long running_max_dd_peak_id = 0\n    cdef long running_max_dd_trough_id = 0\n    \n    cdef long i\n    cdef double val\n    for i in xrange(ser.shape[0]):\n        val = ser[i]\n        if val >= running_global_peak:\n            running_global_peak = val\n            running_global_peak_id = i\n            min_since_global_peak = val\n        if val < min_since_global_peak:\n            min_since_global_peak = val\n            if val - running_global_peak <= running_max_dd:\n                running_max_dd = val - running_global_peak\n                running_max_dd_peak_id = running_global_peak_id\n                running_max_dd_trough_id = i\n    return (running_max_dd, running_max_dd_peak_id, running_max_dd_trough_id, running_global_peak_id)\n\[email protected](False)\[email protected](False)\[email protected](False)\ncpdef np.ndarray[DTYPE_t, ndim=1] cy_rolling_dd_custom(np.ndarray[DTYPE_t, ndim=1] ser, long window):\n    cdef np.ndarray[DTYPE_t, ndim=2] result\n    result = np.zeros((ser.shape[0], 4))\n    \n    cdef double running_global_peak = ser[0]\n    cdef double min_since_global_peak = ser[0]\n    cdef double running_max_dd = 0\n    cdef long running_global_peak_id = 0\n    cdef long running_max_dd_peak_id = 0\n    cdef long running_max_dd_trough_id = 0\n    cdef long i\n    cdef double val\n    cdef int prob_1\n    cdef int prob_2\n    cdef tuple intermed\n    \n    for i in xrange(ser.shape[0]):\n        val = ser[i]\n        if i < window:\n            if val >= running_global_peak:\n                running_global_peak = val\n                running_global_peak_id = i\n                min_since_global_peak = val\n            if val < min_since_global_peak:\n                min_since_global_peak = val\n                if val - running_global_peak <= running_max_dd:\n                    running_max_dd = val - running_global_peak\n                    running_max_dd_peak_id = running_global_peak_id\n                    running_max_dd_trough_id = i\n                    \n            result[i, 0] = <double>running_max_dd\n            result[i, 1] = <double>running_max_dd_peak_id\n            result[i, 2] = <double>running_max_dd_trough_id\n            result[i, 3] = <double>running_global_peak_id\n            \n        else:\n            prob_1 = 1 if result[i-1, 3] <= float(i - window) else 0\n            prob_2 = 1 if result[i-1, 1] <= float(i - window) else 0\n            if prob_1 or prob_2:\n                intermed = cy_dd_custom(ser[i-window+1:i+1])\n                result[i, 0] = <double>intermed[0]\n                result[i, 1] = <double>(intermed[1] + i - window + 1)\n                result[i, 2] = <double>(intermed[2] + i - window + 1)\n                result[i, 3] = <double>(intermed[3] + i - window + 1)\n            else:\n                result[i, 3] = i if ser[i] >= ser[result[i-1, 3]] else result[i-1, 3]\n                if val - ser[result[i-1, 3]] <= result[i-1, 0]:\n                    result[i, 0] = <double>(val - ser[result[i-1, 3]])\n                    result[i, 1] = <double>result[i-1, 3]\n                    result[i, 2] = <double>i\n                else:\n                    result[i, 0] = <double>result[i-1, 0]\n                    result[i, 1] = <double>result[i-1, 1]\n                    result[i, 2] = <double>result[i-1, 2]\n    return result[:, 0]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def py_dd_custom(ser):\n    running_global_peak = ser[0]\n    min_since_global_peak = ser[0]\n    running_max_dd = 0\n    running_global_peak_id = np.nan\n    running_max_dd_peak_id = np.nan\n    running_max_dd_trough_id = np.nan\n\n    for i, val in enumerate(ser):\n        if val >= running_global_peak:\n            running_global_peak = val\n            running_global_peak_id = i\n            min_since_global_peak = val\n        if val < min_since_global_peak:\n            min_since_global_peak = val\n            if val - running_global_peak <= running_max_dd:\n                running_max_dd = val - running_global_peak\n                running_max_dd_peak_id = running_global_peak_id\n                running_max_dd_trough_id = i\n    return (running_max_dd, running_max_dd_peak_id, running_max_dd_trough_id, running_global_peak_id)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def windowed_view(x, window_size):\n    y = as_strided(x, shape=(x.size - window_size + 1, window_size),\n                   strides=(x.strides[0], x.strides[0]))\n    return y\n\ndef py_so_rolling_max_dd(x, window_size):\n    pad = np.empty(window_size - 1)\n    pad.fill(x[0])\n    x = np.concatenate((pad, x))\n    y = windowed_view(x, window_size)\n    running_max_y = np.maximum.accumulate(y, axis=1)\n    dd = y - running_max_y\n    return dd.min(axis=1)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "test = np.random.randn(100000).cumsum()\nmaxdd = cy_dd_custom(test)\n\nfig, ax = plt.subplots(1, 1)\nax.plot(test)\nax.plot(maxdd[1], test[maxdd[1]], 'go')\nax.plot(maxdd[2], test[maxdd[2]], 'ro')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": "[<matplotlib.lines.Line2D at 0x408e150>]"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ktJDypXHjZCuujh19f65Q1aWLup6eLp/oVqzwz7k59hSRj6WlyeEv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\nRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x4076190>"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "for serlen in [1e3, 1e4]:\n    ser = np.random.randn(serlen)\n    print 'Series length = %d' % serlen\n    print '  Python float drawdown'\n    %timeit py_dd_custom(ser)\n    print '  Cython float drawdown'\n    %timeit cy_dd_custom(ser)\n    for winfrac in [.01, .10, .25]:\n        print '    Window fraction = %.2f' % winfrac\n        print '      Numpy rolling drawdown'\n        %timeit py_so_rolling_max_dd(ser, int(serlen * winfrac))\n        print '      Cython rolling drawdown'\n        %timeit cy_rolling_dd_custom(ser, int(serlen * winfrac))       ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Series length = 1000\n  Python float drawdown\n1000 loops, best of 3: 291 \u00b5s per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n  Cython float drawdown\n100000 loops, best of 3: 1.76 \u00b5s per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n    Window fraction = 0.01\n      Numpy rolling drawdown\n1000 loops, best of 3: 221 \u00b5s per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n      Cython rolling drawdown\n100 loops, best of 3: 3.81 ms per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n    Window fraction = 0.10\n      Numpy rolling drawdown\n1000 loops, best of 3: 976 \u00b5s per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n      Cython rolling drawdown\n100 loops, best of 3: 2.76 ms per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n    Window fraction = 0.25\n      Numpy rolling drawdown\n100 loops, best of 3: 2.59 ms per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n      Cython rolling drawdown\n100 loops, best of 3: 3.15 ms per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\nSeries length = 10000\n  Python float drawdown\n100 loops, best of 3: 1.89 ms per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n  Cython float drawdown\n100000 loops, best of 3: 10.8 \u00b5s per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n    Window fraction = 0.01\n      Numpy rolling drawdown\n100 loops, best of 3: 16.2 ms per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n      Cython rolling drawdown\n10 loops, best of 3: 41.9 ms per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n    Window fraction = 0.10\n      Numpy rolling drawdown\n10 loops, best of 3: 146 ms per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n      Cython rolling drawdown\n10 loops, best of 3: 31.8 ms per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n    Window fraction = 0.25\n      Numpy rolling drawdown\n1 loops, best of 3: 340 ms per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n      Cython rolling drawdown\n10 loops, best of 3: 27.1 ms per loop"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n"
      }
     ],
     "prompt_number": 14
    }
   ],
   "metadata": {}
  }
 ]
}