Merge pull request #69 from quantopian/prof_yappi

Prof yappi

etc/requirements_dev.txt

@@ -33,3 +33,6 @@ wsgiref==0.1.2
 # misc
 pycleaner==1.1.1
 -e git://github.com/pydata/vbench.git#egg=vbench
+
+# profilers
+yappi==0.62

zipline/optimize/factory.py

@@ -4,6 +4,7 @@ Factory functions to prepare useful data for optimize tests.
 Author: Thomas V. Wiecki (thomas.wiecki@gmail.com), 2012
 """
 from datetime import timedelta
+import pandas as pd
 
 import zipline.protocol as zp
 
@@ -43,7 +44,7 @@ def create_updown_trade_source(sid, trade_count, trading_environment, base_price
     price = base_price-amplitude/2.
 
     cur = trading_environment.first_open
-    one_day = timedelta(days = 1)
+    one_day = timedelta(minutes = 1)#days = 1)
 
     #create iterator to cycle through up and down phases
     change = cycle([1,-1])

zipline/profile/prof_yappi.py

@@ -0,0 +1,93 @@
+from __future__ import division
+
+import logging
+from zipline.core.devsimulator import AddressAllocator
+import zipline.finance
+from zipline.optimize.factory import create_predictable_zipline
+import pandas as pd
+import numpy as np
+import os.path
+
+def convert_ystats(ystats):
+    """Convert yappi.get_stats().func_stats object to pandas
+    DataFrame.
+
+    """
+    func_names = [os.path.split(item[0])[-1] for item in ystats]
+    ncall = [float(item[1]) for item in ystats]
+    ttot = [float(item[2]) for item in ystats]
+    tsub = [float(item[3]) for item in ystats]
+    tavg = [float(item[4]) for item in ystats]
+    stats = pd.DataFrame({'ncall': ncall, 'ttot': ttot, 'tsub': tsub, 'tavg': tavg}, index=func_names)
+
+    return stats
+
+
+allocator = AddressAllocator(1000)
+
+config = {  'allocator'   :allocator,
+            'sid'         :133,
+            'trade_count' :5000,
+            'amplitude'   :30,
+            'base_price'  :50
+         }
+
+LOGGER = logging.getLogger('ZiplineLogger')
+
+import yappi
+
+def gen_single_stats(func, *args, **kwargs):
+    """Profile func(*args, **kwargs) with yappi.
+
+    Returns DataFrame of statistics.
+    """
+    yappi.start()
+    func(*args, **kwargs)
+    yappi.stop()
+    return convert_ystats(yappi.get_stats().func_stats)
+
+def gen_avg_stats(func, runs=1, *args, **kwargs):
+    """Profile func(*args, **kwargs) with yappi. Runs multiple times at computes the average.
+
+    Returns DataFrame of average statistics.
+    """
+
+    avg_stats = pd.concat([gen_single_stats() for i in range(runs)], keys=range(runs))
+    grouped = avg_stats.groupby(level=1)
+
+    return grouped.aggregate(np.mean)
+
+def run_updown(fname='before_stats.csv'):
+    """Profile a zipline with the UpDown tradesource (does not require
+    DB access) and the buy/sell algorithm (requires no
+    computation).
+
+    Saves output statics under fname.
+
+    Returns Dataframe of statistics.
+    """
+    zp, _ = create_predictable_zipline(config, simulate=False)
+    stats = gen_single_stats(zp.simulate, blocking=True)
+    stats.to_csv(fname)
+
+    return stats
+
+def calc_speedup(before='before_stats.csv', after='after_stats.csv'):
+    """Calculate speed-up between two previously run and saved
+    statistics under filename before and after.
+
+    Prints DataFrame of top 30 speed-ups and top 30 slow-downs.
+
+    """
+    old = pd.DataFrame.from_csv(before)
+    new = pd.DataFrame.from_csv(after)
+    speed_up = old / new
+    speed_up = speed_up.fillna(1)
+    speed_up = speed_up.sort(column='ttot', ascending=False)
+    slow_down = speed_up.sort(column='ttot', ascending=True)
+    print speed_up[:30]
+    print slow_down[:30]
+
+if __name__ == '__main__':
+    run_updown()
+    yappi.print_stats(sort_type=yappi.SORTTYPE_TTOT)