PyFolio Portfolio Analysis


In [24]:
import pyfolio as pf
import matplotlib.pyplot as plt
import empyrical

Set A Benchmark Algo for SPY


In [ ]:
def initialize(context):
    context.spy = sid(8554)

    
    set_max_leverage(1.01)
    
    schedule_function(rebalance,date_rules.every_day(),time_rules.market_open())
    
def rebalance(context,data):
    order_target_percent(context.spy,1)

In [41]:
# Get benchmark returns
benchmark_rets = get_backtest('5986c511c94d014fc81acf7b')


100% Time: 0:00:00|###########################################################|

In [42]:
bm_returns = benchmark_rets.daily_performance['returns']
bm_positions = benchmark_rets.pyfolio_positions
bm_transactions = benchmark_rets.pyfolio_transactions

Use Algo from Leverage Lecture


In [43]:
# Use same algo as in the Leverage Lecture!
bt = get_backtest('5986b969dbab994fa4264696')


100% Time: 0:00:00|###########################################################|

In [44]:
bt_returns = bt.daily_performance['returns']
bt_positions = bt.pyfolio_positions
bt_transactions = bt.pyfolio_transactions

In [45]:
bt_returns.plot()


Out[45]:
<matplotlib.axes._subplots.AxesSubplot at 0x7eff86d1c410>

In [46]:
empyrical.beta(bt_returns,bm_returns)


Out[46]:
0.14608838992380041

PyFolio Plots


In [47]:
benchmark_rets = bm_returns

In [48]:
# Cumulative Returns
plt.subplot(2,1,1)
pf.plotting.plot_rolling_returns(bt_returns, benchmark_rets)

# Daily, Non-Cumulative Returns
plt.subplot(2,1,2)
pf.plotting.plot_returns(bt_returns)
plt.tight_layout()



In [49]:
fig = plt.figure(1)
plt.subplot(1,3,1)
pf.plot_annual_returns(bt_returns)
plt.subplot(1,3,2)
pf.plot_monthly_returns_dist(bt_returns)
plt.subplot(1,3,3)
pf.plot_monthly_returns_heatmap(bt_returns)
plt.tight_layout()
fig.set_size_inches(15,5)



In [50]:
pf.plot_return_quantiles(bt_returns);



In [51]:
pf.plot_rolling_beta(bt_returns, benchmark_rets);



In [52]:
pf.plot_rolling_sharpe(bt_returns);



In [53]:
pf.plot_rolling_fama_french(bt_returns);



In [54]:
pf.plot_drawdown_periods(bt_returns);



In [55]:
pf.plot_drawdown_underwater(bt_returns);



In [56]:
pf.plot_gross_leverage(bt_returns, bt_positions);



In [57]:
pos_percent = pf.pos.get_percent_alloc(bt_positions)
pf.plotting.show_and_plot_top_positions(bt_returns, pos_percent);


Top 10 long positions of all time max
AMZN-16841 50.93%
Top 10 short positions of all time max
IBM-3766 -52.03%
Top 10 positions of all time max
IBM-3766 52.03%
AMZN-16841 50.93%
All positions ever held max
IBM-3766 52.03%
AMZN-16841 50.93%

In [58]:
pf.plot_turnover(bt_returns, bt_transactions, bt_positions);



In [59]:
pf.plotting.plot_daily_turnover_hist(bt_transactions, bt_positions);



In [60]:
pf.plotting.plot_daily_volume(bt_returns, bt_transactions);



In [61]:
pf.create_round_trip_tear_sheet(bt_returns, bt_positions, bt_transactions);


Summary stats All trades Short trades Long trades
Total number of round_trips 231.00 105.00 126.00
Percent profitable 0.81 0.59 0.99
Winning round_trips 187.00 62.00 125.00
Losing round_trips 44.00 43.00 1.00
Even round_trips 0.00 0.00 0.00
PnL stats All trades Short trades Long trades
Total profit $259314.80 $60829.40 $198485.40
Gross profit $272619.52 $74125.78 $198493.74
Gross loss $-13304.72 $-13296.38 $-8.34
Profit factor $20.49 $5.57 $23797.26
Avg. trade net profit $1122.57 $579.33 $1575.28
Avg. winning trade $1457.86 $1195.58 $1587.95
Avg. losing trade $-302.38 $-309.22 $-8.34
Ratio Avg. Win:Avg. Loss $4.82 $3.87 $190.38
Largest winning trade $151212.26 $61139.59 $151212.26
Largest losing trade $-1626.13 $-1626.13 $-8.34
Duration stats All trades Short trades Long trades
Avg duration 178 days 01:22:27.536796 174 days 20:37:58.866666 180 days 17:19:31.428571
Median duration 182 days 01:00:00 170 days 01:00:00 184 days 13:00:00
Return stats All trades Short trades Long trades
Avg returns all round_trips 0.09% 0.05% 0.13%
Avg returns winning 0.12% 0.10% 0.13%
Avg returns losing -0.03% -0.03% -0.00%
Median returns all round_trips 0.01% 0.00% 0.02%
Median returns winning 0.02% 0.01% 0.02%
Median returns losing -0.02% -0.02% -0.00%
Largest winning trade 12.16% 4.92% 12.16%
Largest losing trade -0.16% -0.16% -0.00%
Symbol stats AMZN-16841 IBM-3766
Avg returns all round_trips 0.13% 0.05%
Avg returns winning 0.13% 0.10%
Avg returns losing -0.00% -0.03%
Median returns all round_trips 0.02% 0.00%
Median returns winning 0.02% 0.01%
Median returns losing -0.00% -0.02%
Largest winning trade 12.16% 4.92%
Largest losing trade -0.00% -0.16%
Profitability (PnL / PnL total) per name pnl
AMZN-16841 0.77%
IBM-3766 0.23%
<matplotlib.figure.Figure at 0x7effa57e1550>