mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 14:09:44 +08:00
Thomas Wiecki
93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
from __future__ import division
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import logging
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from zipline.core.devsimulator import AddressAllocator
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import zipline.finance
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from zipline.optimize.factory import create_predictable_zipline
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import pandas as pd
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import numpy as np
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import os.path
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def convert_ystats(ystats):
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"""Convert yappi.get_stats().func_stats object to pandas
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DataFrame.
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"""
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func_names = [os.path.split(item[0])[-1] for item in ystats]
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ncall = [float(item[1]) for item in ystats]
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ttot = [float(item[2]) for item in ystats]
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tsub = [float(item[3]) for item in ystats]
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tavg = [float(item[4]) for item in ystats]
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stats = pd.DataFrame({'ncall': ncall, 'ttot': ttot, 'tsub': tsub, 'tavg': tavg}, index=func_names)
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return stats
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allocator = AddressAllocator(1000)
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config = { 'allocator' :allocator,
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'sid' :133,
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'trade_count' :5000,
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'amplitude' :30,
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'base_price' :50
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}
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LOGGER = logging.getLogger('ZiplineLogger')
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import yappi
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def gen_single_stats(func, *args, **kwargs):
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"""Profile func(*args, **kwargs) with yappi.
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Returns DataFrame of statistics.
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"""
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yappi.start()
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func(*args, **kwargs)
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yappi.stop()
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return convert_ystats(yappi.get_stats().func_stats)
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def gen_avg_stats(func, runs=1, *args, **kwargs):
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"""Profile func(*args, **kwargs) with yappi. Runs multiple times at computes the average.
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Returns DataFrame of average statistics.
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"""
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avg_stats = pd.concat([gen_single_stats() for i in range(runs)], keys=range(runs))
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grouped = avg_stats.groupby(level=1)
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return grouped.aggregate(np.mean)
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def run_updown(fname='before_stats.csv'):
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"""Profile a zipline with the UpDown tradesource (does not require
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DB access) and the buy/sell algorithm (requires no
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computation).
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Saves output statics under fname.
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Returns Dataframe of statistics.
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"""
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zp, _ = create_predictable_zipline(config, simulate=False)
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stats = gen_single_stats(zp.simulate, blocking=True)
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stats.to_csv(fname)
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return stats
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def calc_speedup(before='before_stats.csv', after='after_stats.csv'):
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"""Calculate speed-up between two previously run and saved
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statistics under filename before and after.
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Prints DataFrame of top 30 speed-ups and top 30 slow-downs.
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"""
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old = pd.DataFrame.from_csv(before)
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new = pd.DataFrame.from_csv(after)
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speed_up = old / new
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speed_up = speed_up.fillna(1)
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speed_up = speed_up.sort(column='ttot', ascending=False)
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slow_down = speed_up.sort(column='ttot', ascending=True)
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print speed_up[:30]
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print slow_down[:30]
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if __name__ == '__main__':
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run_updown()
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yappi.print_stats(sort_type=yappi.SORTTYPE_TTOT) |