In [18]:
%load_ext zipline
In [33]:
%%zipline --start=2010-1-1 --end=2013-12-31 --bundle=quantopian-quandl -o perf.pickle --capital-base 250 --data-frequency daily

# when the algorithm is run it generates the perf.pickle file with information about the performance of the 
# strategy, this datastructure is used later to analyze the performance

import zipline
import pandas as pd
from zipline.api import order_target, record, symbol

def initialize(context):
    context.sym = symbol('AAPL')
    context.i = 0

def handle_data(context, data):
    # Skip first 100 days to get full windows
    context.i += 1
    if context.i < 100:
        return

    # Compute averages
    # history() has to be called with the same params
    # from above and returns a pandas dataframe.
    short_mavg = data.history(context.sym, 'price', 50, '1d').mean()
    long_mavg = data.history(context.sym, 'price', 100, '1d').mean()

    # Trading logic
    if short_mavg > long_mavg:
        # order_target orders as many shares as needed to
        # achieve the desired number of shares.
        order_target(context.sym, 1)
    elif short_mavg < long_mavg:
        order_target(context.sym, 0)

    # Save values for later inspection
    record(AAPL=data.current(context.sym, "price"),
           short_mavg=short_mavg,
           long_mavg=long_mavg)

[2018-01-08 20:29:58.547872] WARNING: Loader: Refusing to download new benchmark data because a download succeeded at 2018-01-08 20:11:46.849974+00:00.
[2018-01-08 20:30:06.810146] INFO: Performance: Simulated 1006 trading days out of 1006.
[2018-01-08 20:30:06.811322] INFO: Performance: first open: 2010-01-04 14:31:00+00:00
[2018-01-08 20:30:06.812132] INFO: Performance: last close: 2013-12-31 21:00:00+00:00
Out[33]:
AAPL algo_volatility algorithm_period_return alpha benchmark_period_return benchmark_volatility beta capital_used ending_cash ending_exposure ... short_mavg short_value shorts_count sortino starting_cash starting_exposure starting_value trading_days transactions treasury_period_return
2010-01-04 21:00:00+00:00 NaN NaN 0.000000 NaN 0.016960 NaN NaN 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 1 [] 0.0385
2010-01-05 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.019652 0.160660 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 2 [] 0.0377
2010-01-06 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.020370 0.140929 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 3 [] 0.0385
2010-01-07 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.024677 0.116833 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 4 [] 0.0385
2010-01-08 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.028087 0.103122 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 5 [] 0.0383
2010-01-11 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.029523 0.096122 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 6 [] 0.0385
2010-01-12 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.019921 0.122315 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 7 [] 0.0374
2010-01-13 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.028536 0.117521 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 8 [] 0.0380
2010-01-14 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.031317 0.110021 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 9 [] 0.0376
2010-01-15 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.019742 0.127235 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 10 [] 0.0370
2010-01-19 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.032484 0.130768 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 11 [] 0.0373
2010-01-20 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.021985 0.138401 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 12 [] 0.0368
2010-01-21 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 0.002333 0.161774 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 13 [] 0.0362
2010-01-22 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.020011 0.182443 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 14 [] 0.0362
2010-01-25 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.014986 0.177820 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 15 [] 0.0366
2010-01-26 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.019113 0.172271 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 16 [] 0.0365
2010-01-27 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.014447 0.168345 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 17 [] 0.0366
2010-01-28 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.025754 0.168127 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 18 [] 0.0368
2010-01-29 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.036342 0.166993 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 19 [] 0.0363
2010-02-01 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.021357 0.173935 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 20 [] 0.0368
2010-02-02 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.009512 0.175521 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 21 [] 0.0367
2010-02-03 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.014447 0.171993 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 22 [] 0.0373
2010-02-04 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.044867 0.195627 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 23 [] 0.0362
2010-02-05 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.042893 0.191763 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 24 [] 0.0359
2010-02-08 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.049803 0.188526 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 25 [] 0.0362
2010-02-09 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.037868 0.190177 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 26 [] 0.0367
2010-02-10 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.039752 0.186492 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 27 [] 0.0372
2010-02-11 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.029702 0.186456 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 28 [] 0.0373
2010-02-12 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.030510 0.183097 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 29 [] 0.0369
2010-02-16 21:00:00+00:00 NaN 0.000000 0.000000 0.000000 -0.015255 0.186338 0.000000 0.0 250.00 0.000 ... NaN 0 0 NaN 250.00 0.000 0.000 30 [] 0.0366
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2013-11-18 21:00:00+00:00 518.629 0.254569 0.985996 0.104758 0.610014 0.170587 0.759821 0.0 -22.13 518.629 ... 497.31452 0 0 1.268847 -22.13 524.991 524.991 977 [] 0.0267
2013-11-19 21:00:00+00:00 519.549 0.254439 0.989676 0.105564 0.606515 0.170506 0.759747 0.0 -22.13 519.549 ... 497.87012 0 0 1.271096 -22.13 518.629 518.629 978 [] 0.0271
2013-11-20 21:00:00+00:00 515.000 0.254359 0.971480 0.103682 0.601490 0.170428 0.759980 0.0 -22.13 515.000 ... 498.87022 0 0 1.255655 -22.13 519.549 519.549 979 [] 0.0280
2013-11-21 21:00:00+00:00 521.135 0.254298 0.996020 0.105146 0.614411 0.170384 0.760374 0.0 -22.13 521.135 ... 499.89400 0 0 1.274451 -22.13 515.000 515.000 980 [] 0.0279
2013-11-22 21:00:00+00:00 519.799 0.254174 0.990676 0.103412 0.622487 0.170312 0.760102 0.0 -22.13 519.799 ... 501.04596 0 0 1.269580 -22.13 521.135 521.135 981 [] 0.0275
2013-11-25 21:00:00+00:00 523.740 0.254070 1.006440 0.105549 0.620872 0.170227 0.759989 0.0 -22.13 523.740 ... 502.57062 0 0 1.281286 -22.13 519.799 519.799 982 [] 0.0274
2013-11-26 21:00:00+00:00 533.400 0.254112 1.045080 0.110331 0.621321 0.170141 0.759944 0.0 -22.13 533.400 ... 504.18508 0 0 1.310658 -22.13 523.740 523.740 983 [] 0.0271
2013-11-27 21:00:00+00:00 545.960 0.254266 1.095320 0.115984 0.625269 0.170057 0.760316 0.0 -22.13 545.960 ... 505.86462 0 0 1.348273 -22.13 533.400 533.400 984 [] 0.0274
2013-11-29 18:00:00+00:00 556.070 0.254307 1.135760 0.120962 0.624192 0.169971 0.760108 0.0 -22.13 556.070 ... 507.59484 0 0 1.377648 -22.13 545.960 545.960 985 [] 0.0275
2013-12-02 21:00:00+00:00 551.230 0.254228 1.116400 0.118998 0.619975 0.169893 0.760319 0.0 -22.13 551.230 ... 509.32550 0 0 1.362308 -22.13 556.070 556.070 986 [] 0.0281
2013-12-03 21:00:00+00:00 566.322 0.254483 1.176768 0.127183 0.612976 0.169824 0.758969 0.0 -22.13 566.322 ... 510.89610 0 0 1.405980 -22.13 551.230 551.230 987 [] 0.0279
2013-12-04 21:00:00+00:00 565.000 0.254359 1.171480 0.126454 0.612796 0.169738 0.758985 0.0 -22.13 565.000 ... 512.47088 0 0 1.401453 -22.13 566.322 566.322 988 [] 0.0284
2013-12-05 21:00:00+00:00 567.901 0.254240 1.183084 0.128586 0.605707 0.169671 0.758628 0.0 -22.13 567.901 ... 514.25420 0 0 1.409047 -22.13 565.000 565.000 989 [] 0.0288
2013-12-06 21:00:00+00:00 560.020 0.254230 1.151560 0.122926 0.623654 0.169670 0.756423 0.0 -22.13 560.020 ... 515.78664 0 0 1.384544 -22.13 567.901 567.901 990 [] 0.0288
2013-12-09 21:00:00+00:00 566.430 0.254162 1.177200 0.125320 0.627782 0.169587 0.756591 0.0 -22.13 566.430 ... 517.51628 0 0 1.402324 -22.13 560.020 560.020 991 [] 0.0286
2013-12-10 21:00:00+00:00 565.550 0.254037 1.173680 0.125475 0.621949 0.169514 0.756569 0.0 -22.13 565.550 ... 519.34762 0 0 1.399094 -22.13 566.430 566.430 992 [] 0.0281
2013-12-11 21:00:00+00:00 561.360 0.253946 1.156920 0.125552 0.603733 0.169533 0.756539 0.0 -22.13 561.360 ... 520.87226 0 0 1.386056 -22.13 565.550 565.550 993 [] 0.0286
2013-12-12 21:00:00+00:00 560.540 0.253821 1.153640 0.125675 0.598439 0.169458 0.756523 0.0 -22.13 560.540 ... 522.34870 0 0 1.382990 -22.13 561.360 561.360 994 [] 0.0289
2013-12-13 21:00:00+00:00 554.430 0.253768 1.129200 0.122688 0.598259 0.169373 0.756590 0.0 -22.13 554.430 ... 523.82522 0 0 1.363910 -22.13 560.540 560.540 995 [] 0.0288
2013-12-16 21:00:00+00:00 557.500 0.253653 1.141480 0.122827 0.608220 0.169313 0.756618 0.0 -22.13 557.500 ... 525.37070 0 0 1.372137 -22.13 554.430 554.430 996 [] 0.0289
2013-12-17 21:00:00+00:00 554.990 0.253541 1.131440 0.122115 0.603105 0.169238 0.756709 0.0 -22.13 554.990 ... 526.77212 0 0 1.364066 -22.13 557.500 557.500 997 [] 0.0285
2013-12-18 21:00:00+00:00 550.770 0.253452 1.114560 0.117159 0.630474 0.169357 0.753604 0.0 -22.13 550.770 ... 528.22456 0 0 1.350759 -22.13 554.990 554.990 998 [] 0.0289
2013-12-19 21:00:00+00:00 544.460 0.253407 1.089320 0.114226 0.628589 0.169274 0.753776 0.0 -22.13 544.460 ... 529.43848 0 0 1.330779 -22.13 550.770 550.770 999 [] 0.0294
2013-12-20 21:00:00+00:00 549.020 0.253311 1.107560 0.116240 0.629217 0.169190 0.753765 0.0 -22.13 549.020 ... 530.68296 0 0 1.343563 -22.13 544.460 544.460 1000 [] 0.0289
2013-12-23 21:00:00+00:00 570.090 0.253944 1.191840 0.124970 0.637922 0.169122 0.755264 0.0 -22.13 570.090 ... 532.28576 0 0 1.404465 -22.13 549.020 549.020 1001 [] 0.0294
2013-12-24 18:00:00+00:00 567.670 0.253831 1.182160 0.123332 0.641511 0.169040 0.755169 0.0 -22.13 567.670 ... 533.77594 0 0 1.396839 -22.13 570.090 570.090 1002 [] 0.0299
2013-12-26 21:00:00+00:00 563.900 0.253735 1.167080 0.120571 0.649856 0.168971 0.754721 0.0 -22.13 563.900 ... 535.13824 0 0 1.385202 -22.13 567.670 567.670 1003 [] 0.0300
2013-12-27 21:00:00+00:00 560.090 0.253639 1.151840 0.118690 0.649767 0.168887 0.754761 0.0 -22.13 560.090 ... 536.37594 0 0 1.373384 -22.13 563.900 563.900 1004 [] 0.0302
2013-12-30 21:00:00+00:00 554.520 0.253575 1.129560 0.115997 0.649498 0.168803 0.754829 0.0 -22.13 554.520 ... 537.43490 0 0 1.356115 -22.13 560.090 560.090 1005 [] 0.0299
2013-12-31 21:00:00+00:00 561.020 0.253513 1.155560 0.118003 0.657304 0.168732 0.755130 0.0 -22.13 561.020 ... 538.53658 0 0 1.374173 -22.13 554.520 554.520 1006 [] 0.0304

1006 rows × 40 columns

In [34]:
%matplotlib inline
import pyfolio as pf
import pyfolio.plotting as plotting
import matplotlib.pyplot as plt
import pickle
import os

# because pyfolio can't download data from google / yahoo we change it's cache directory
# to fetch our presaved data
os.environ['PYFOLIO_CACHE_DIR'] = "/root/notebooks/.cache/"

results = pd.read_pickle("perf.pickle")

# use pyfolio to extract data
returns, positions, transactions, gross_lev = pf.utils.extract_rets_pos_txn_from_zipline(results)
benchmark_rets = pf.utils.get_symbol_rets('SPY', returns.index[1], returns.index[-1])

fig = plt.figure(figsize=(20, 20))
plotting.plot_rolling_returns(returns,factor_returns=benchmark_rets)
Out[34]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f0904060588>
/root/miniconda3/envs/zipline/lib/python3.4/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
In [29]:
import numpy

def vol(returns):
    # Return the standard deviation of returns
    return numpy.std(returns)

def information_ratio(returns, benchmark):
    diff = returns - benchmark
    return numpy.mean(diff) / vol(diff)
In [35]:
import empyrical as ep
import matplotlib.pyplot as plt
import numpy as np

yearly_returns = ep.aggregate_returns(returns, 'yearly')
yearly_bench = ep.aggregate_returns(benchmark_rets, 'yearly')

years = np.arange(yearly_returns.index.size)
bar_width = 0.35

# plot returns 
fig, ax = plt.subplots()    

plt.bar(years, yearly_returns, bar_width, alpha=0.5, label='Strategy')
plt.bar(years+bar_width, yearly_bench, bar_width, alpha=0.5, label='Benchmark')
plt.legend(loc='upper right')
plt.xticks(years + bar_width/2, yearly_returns.index.values) 
vals = ax.get_yticks()
ax.set_yticklabels(['{0:.0f}%'.format(x*100) for x in vals])

for i, v in enumerate(yearly_returns):
    ax.text(i-0.18, v+0.01, "{0:.1f}%".format(v * 100), color='blue')
    
for i, v in enumerate(yearly_bench):
    ax.text(i+0.18, v+0.01, "{0:.1f}%".format(v * 100), color='green')
plt.show()

# plot years history
monthly_returns = ep.aggregate_returns(returns, 'monthly').values[-12:]
monthly_bench = ep.aggregate_returns(benchmark_rets, 'monthly').values[-12:]
months = np.arange(12)
fig, ax = plt.subplots()

plt.bar(months, monthly_returns, bar_width, alpha=0.5, label='Strategy')
plt.bar(months+bar_width, monthly_bench, bar_width, alpha=0.5, label='Benchmark')
plt.legend(loc='upper right')
vals = ax.get_yticks()
ax.set_yticklabels(['{0:.0f}%'.format(x*100) for x in vals])

plt.show()

# plot return / risk plots
fig, ax = plt.subplots()
ax.set_xlabel('Daily Standard Deviation')
ax.set_ylabel('Daily Return')
plt.plot(returns.std(),        returns.mean(),        'o', alpha=0.5, label='Strategy')
plt.plot(benchmark_rets.std(), benchmark_rets.mean(), 'o', alpha=0.5, label='Benchmark')
plt.legend(loc='upper left')

plt.show()


# numbers for table
lastDayOfBenchmark = returns.index[-1]
firstDayOfLastYearOfBenchmark = lastDayOfBenchmark.date().replace(month=1, day=1)    

returnsYTD = returns[firstDayOfLastYearOfBenchmark:lastDayOfBenchmark]
benchmarkYTD = benchmark_rets[firstDayOfLastYearOfBenchmark:lastDayOfBenchmark]

absReturnStrategy = ep.cum_returns_final(returns)
absReturnBenchmark = ep.cum_returns_final(benchmark_rets)

absReturnYTD = ep.cum_returns_final(returnsYTD)
absBenchmarkYTD = ep.cum_returns_final(benchmarkYTD)

relReturn = absReturnStrategy - absReturnBenchmark
relReturnYTD = absReturnYTD - absBenchmarkYTD

beta = ep.beta(returns, benchmark_rets)
betaYTD = ep.beta(returnsYTD, benchmark_rets)

stdStrategy = returns.std()
stdBenchmark = benchmark_rets.std()

stdStrategyYTD = returnsYTD.std()
stdBenchmarkYTD = benchmarkYTD.std()

sharpeRatioStrategy = ep.sharpe_ratio(returns)
sharpeRatioBenchmark = ep.sharpe_ratio(benchmark_rets)
sharpeRatioStrategyYTD = ep.sharpe_ratio(returnsYTD)
sharpeRatioBenchmarkYTD = ep.sharpe_ratio(benchmarkYTD)


maxDrawdownStrategy = ep.max_drawdown(returns)
maxDrawdownBenchmark = ep.max_drawdown(benchmark_rets)
maxDrawdownStrategyYTD = ep.max_drawdown(returnsYTD)
maxDrawdownBenchmarkYTD = ep.max_drawdown(benchmarkYTD)

irStrategy = information_ratio(returns, benchmark_rets)
irStrategyYTD = information_ratio(returnsYTD, benchmarkYTD)

from sklearn.metrics import r2_score
r2Strategy = r2_score(benchmark_rets, returns[0:benchmark_rets.size])
r2StrategyYTD = r2_score(benchmarkYTD, returnsYTD)

# display data table
from IPython.display import HTML, display

data = [
    ['',            'Strategy Overall',                    'Bench Overall',                        'Strategy YTD',                           'Bench YTD'],
    ['Abs. Return', "{:.2%}".format(absReturnStrategy),    "{:.2%}".format(absReturnBenchmark),    "{:.2%}".format(absReturnYTD),            "{:.2%}".format(absBenchmarkYTD)],
    ['Rel. Return', "{:.2%}".format(relReturn),            'N/A',                                  "{:.2%}".format(relReturnYTD),            'N/A'],
    ['Beta',        "{0:.2f}".format(beta),                'N/A',                                  "{0:.2f}".format(betaYTD),                'N/A'],
    ['Std. Dev',    "{:.2%}".format(stdStrategy),          "{:.2%}".format(stdBenchmark),          "{:.2%}".format(stdStrategyYTD),          "{:.2%}".format(stdBenchmarkYTD)],
    ['Sharpe',      "{0:.2f}".format(sharpeRatioStrategy), "{0:.2f}".format(sharpeRatioBenchmark), "{0:.2f}".format(sharpeRatioStrategyYTD), "{0:.2f}".format(sharpeRatioBenchmarkYTD)],
    ['Draw Down',   "{:.2%}".format(maxDrawdownStrategy),  "{:.2%}".format(maxDrawdownBenchmark),  "{:.2%}".format(maxDrawdownStrategyYTD),  "{:.2%}".format(maxDrawdownBenchmarkYTD)],
    ['IR',          "{0:.2f}".format(irStrategy),          'N/A',                                  "{0:.2f}".format(irStrategyYTD),          'N/A'],
    ['R2',          "{0:.2f}".format(r2Strategy),          'N/A',                                  "{0:.2f}".format(r2StrategyYTD),          'N/A'],
]

display(HTML(
    '<table><tr>{}</tr></table>'.format(
        '</tr><tr>'.join(
            '<td>{}</td>'.format('</td><td>'.join(str(_) for _ in row)) for row in data)
        )
 ))

data = [
    ['']            + list(range(1,13)),
    ['Strategy']    + ["{:.2%}".format(ret) for ret in list(monthly_returns)],
    ['Benchmark']   + ["{:.2%}".format(ret) for ret in list(monthly_bench)],
    ['Rel. Return'] + ["{:.2%}".format(ret) for ret in list(monthly_returns - monthly_bench)],
]

display(HTML(
    '<table><tr>{}</tr></table>'.format(
        '</tr><tr>'.join(
            '<td>{}</td>'.format('</td><td>'.join(str(_) for _ in row)) for row in data)
        )
 ))

/root/miniconda3/envs/zipline/lib/python3.4/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
Strategy OverallBench OverallStrategy YTDBench YTD
Abs. Return115.56%76.92%11.63%32.31%
Rel. Return38.64%N/A-20.67%N/A
Beta0.76N/A0.14N/A
Std. Dev1.60%1.06%0.96%0.70%
Sharpe0.880.930.802.58
Draw Down-29.60%-18.61%-12.86%-5.55%
IR0.02N/A-0.06N/A
R2-2.45N/A-1.63N/A
123456789101112
Strategy0.00%0.00%0.00%0.00%0.00%0.00%-1.11%-2.57%-2.25%10.11%6.67%0.93%
Benchmark5.12%1.28%3.80%1.92%2.36%-1.33%5.17%-3.00%3.16%4.63%2.96%2.59%
Rel. Return-5.12%-1.28%-3.80%-1.92%-2.36%1.33%-6.28%0.43%-5.41%5.48%3.70%-1.67%