1+ import os
2+ import pandas as pd
3+ from datetime import datetime
4+ import backtrader as bt
5+
6+ class BuyAndHold (bt .Strategy ):
7+ def __init__ (self ):
8+ # To keep track of pending orders and buy price/commission
9+ self .order = None
10+
11+ def log (self , txt , dt = None ):
12+ ''' Logging function fot this strategy'''
13+ dt = dt or self .datas [0 ].datetime .date (0 )
14+ print ('%s, %s' % (dt .isoformat (), txt ))
15+
16+ def start (self ):
17+ self .val_start = self .broker .get_cash () # keep the starting cash
18+
19+ def notify_trade (self , trade ):
20+ if not trade .isclosed :
21+ return
22+ self .log ('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade .pnl , trade .pnlcomm ))
23+
24+ def notify_order (self , order ):
25+ if order .status in [order .Submitted , order .Accepted ]:
26+ # Buy/Sell order submitted/accepted to/by broker - Nothing to do
27+ return
28+
29+ # Check if an order has been completed
30+ # Attention: broker could reject order if not enough cash
31+ if order .status in [order .Completed ]:
32+ if order .isbuy ():
33+ self .log (
34+ 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
35+ (order .executed .price ,
36+ order .executed .value ,
37+ order .executed .comm ))
38+
39+ self .buyprice = order .executed .price
40+ self .buycomm = order .executed .comm
41+ else : # Sell
42+ self .log ('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
43+ (order .executed .price ,
44+ order .executed .value ,
45+ order .executed .comm ))
46+
47+ self .bar_executed = len (self )
48+
49+ def next (self ):
50+ # Simply log the closing price of the series from the reference
51+ # self.log('Close, %.2f' % self.data.close[0])
52+ # Buy all the available cash
53+ # self.order_target_value(target=self.broker.get_cash())
54+ #size = int(self.broker.get_cash() / self.data)
55+ #self.order = self.buy(size=size)
56+ self .buy ()
57+
58+ def stop (self ):
59+ # calculate the actual returns
60+ self .roi = (self .broker .get_value () / self .val_start ) - 1.0
61+ print ('ROI: {:.2f}%' .format (100.0 * self .roi ))
62+
63+
64+ if __name__ == '__main__' :
65+ # Create a cerebro entitysetsizing
66+ cerebro = bt .Cerebro ()
67+
68+ datapath = os .path .join ('../data/' , 'SPX.csv' )
69+
70+ # Create a Data Feed
71+ data = bt .feeds .YahooFinanceCSVData (
72+ dataname = datapath ,
73+ # Do not pass values before this date
74+ fromdate = datetime (2010 , 1 , 1 ),
75+ # Do not pass values before this date
76+ todate = datetime (2019 , 12 , 31 ),
77+ # Do not pass values after this date
78+ reverse = False )
79+
80+ # Add the Data Feed to Cerebro
81+ cerebro .adddata (data )
82+
83+ # Set our desired cash start
84+ cerebro .broker .setcash (100000.0 )
85+
86+ # Add a FixedSize sizer according to the stake
87+ # cerebro.addsizer(bt.sizers.FixedSize, stake=10)
88+ cerebro .addsizer (bt .sizers .PercentSizer , percents = 95 )
89+
90+ # Set the commission - 0.1% ... divide by 100 to remove the %
91+ cerebro .broker .setcommission (commission = 0.001 )
92+ # cheat-on-close
93+ cerebro .broker .set_coc (True ) # doesn't seems to be working
94+
95+ # Print out the starting conditions
96+ print ('Starting Portfolio Value: %.2f' % cerebro .broker .getvalue ())
97+
98+ # Add Analyzer
99+ cerebro .addanalyzer (bt .analyzers .PyFolio , _name = 'pyfolio' )
100+
101+ # Add a strategy
102+ cerebro .addstrategy (BuyAndHold )
103+
104+ # Run over everything
105+ results = cerebro .run ()
106+
107+ # Print out the final result
108+ print ('Final Portfolio Value: %.2f' % cerebro .broker .getvalue ())
109+ cerebro .plot (style = 'candlestick' )
110+
111+ strat = results [0 ]
112+ pyfoliozer = strat .analyzers .getbyname ('pyfolio' )
113+ returns , positions , transactions , gross_lev = pyfoliozer .get_pf_items ()
114+ print ('-------------- RETURNS ----------------' )
115+ print (returns )
116+ print ('-------------- POSITIONS ----------------' )
117+ print (positions )
118+ print ('-------------- TRANSACTIONS ----------------' )
119+ print (transactions )
120+ print ('-------------- GROSS LEVERAGE ----------------' )
121+ print (gross_lev )
122+
123+ import empyrical as ep
124+ import pyfolio as pf
125+ import matplotlib .pyplot as plt
126+ perf_stats_strat = pf .timeseries .perf_stats (returns )
127+ drawdown_table = pf .timeseries .gen_drawdown_table (returns , 5 )
128+ monthly_ret_table = ep .aggregate_returns (returns , 'monthly' )
129+ monthly_ret_table = monthly_ret_table .unstack ().round (3 )
130+ ann_ret_df = pd .DataFrame (ep .aggregate_returns (returns , 'yearly' ))
131+ ann_ret_df = ann_ret_df .unstack ().round (3 )
132+ print ('-------------- PERFORMANCE ----------------' )
133+ print (perf_stats_strat )
134+ print ('-------------- DRAWDOWN ----------------' )
135+ print (drawdown_table )
136+ print ('-------------- MONTHLY RETURN ----------------' )
137+ print (monthly_ret_table )
138+ print ('-------------- ANNUAL RETURN ----------------' )
139+ print (ann_ret_df )
140+
141+ pf .create_full_tear_sheet (
142+ returns ,
143+ positions = positions ,
144+ transactions = transactions ,
145+ #live_start_date='2005-05-01',
146+ round_trips = False )
147+ plt .show ()
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