def test_update_fill(self):
events_queue = queue.Queue(100)
bars = HistoricCSVDataHandler(events_queue, './tests/datasets/',
['BTC_ETC'],
['open', 'high', 'low', 'close'])
p = NaivePortfolio(bars, events_queue, datetime(2017, 4, 1, 0, 0, 0),
1000.0)
fill_event = FillEvent(datetime.utcnow(), 'BTC_ETC', 'BACKTEST', 1.0,
'BUY', None)
bars.update_bars()
p.update_fill(fill_event)
self.assertEqual(
p.current_holdings, {
'BTC_ETC': 0.0025947399999999999,
'cash': 999.99540525999998,
'commission': 0.002,
'total': 999.99540525999998
})
self.assertEqual(p.current_positions, {'BTC_ETC': 1.0})
bars.update_bars()
p.update_timeindex(None)
self.assertEqual(p.all_positions,
[{
'BTC_ETC': 0,
'datetime': datetime(2017, 4, 1, 0, 0)
}, {
'BTC_ETC': 1.0,
'datetime': datetime(2017, 4, 22, 18, 35)
}])
print(p.all_holdings)
self.assertEqual(p.all_holdings,
[{
'commission': 0.0,
'BTC_ETC': 0.0,
'total': 1000.0,
'datetime': datetime(2017, 4, 1, 0, 0),
'cash': 1000.0
}, {
'commission': 0.002,
'BTC_ETC': 0.00259552,
'total': 999.99800077999998,
'datetime': datetime(2017, 4, 22, 18, 35),
'cash': 999.99540525999998
}])
def test():
events=Queue()
csv_dir='/Users/weileizhang/Downloads/'
symbol_list=['XOM']
#start_date=datetime.datetime(2013,1,1)
#end_date=datetime.datetime(2015,1,5)
#bars=HistoricCSVDataHandler(events,csv_dir,symbol_list,start_date,end_date)
start_date='2010-1-1'
end_date='2015-1-1'
bars=DataBaseDataHandler(events,symbol_list,start_date,end_date)
# strategy=BuyAndHoldStrategy(bars,events)
strategy=MovingAverageCrossStrategy(bars,events,short_window=100,long_window=400)
port=NaivePortfolio(bars,events,start_date,initial_capital=100000.0)
broker=SimulatedExecutionHandler(events)
while True:
if bars.continue_backtest==True:
bars.update_bars()
else:
break
while True:
try:
event=events.get(False)
except:
break
else:
if event is not None:
if event.type=='MARKET':
strategy.calculate_signals(event)
port.update_timeindex(event)
elif event.type=='SIGNAL':
port.update_signal(event)
elif event.type=='ORDER':
broker.execute_order(event)
elif event.type=='FILL':
port.update_fill(event)
port.create_equity_curve_dataframe()
stats=port.output_summary_stats()
print(stats)
def test_execution_stop_order(self):
csv_dir = './tests/datasets/'
symbol_list = [
'BTC_ETC',
]
events_queue = queue.Queue(100)
bars = HistoricCSVDataHandler(events_queue, csv_dir, symbol_list,
['open', 'high', 'low', 'close'])
p = NaivePortfolio(bars, events_queue, datetime(2017, 4, 1, 0, 0, 0),
1000.0)
e = SimulatedExecutionHandler(events_queue, portfolio=p)
bars.update_bars()
oe = OrderEvent('BTC_ETC', 'STP', 300000.0, 'BUY', 0.00259)
e.execute_order(oe)
self.assertTrue(len(e.stop_orders) > 0)
bars.update_bars()
ee = events_queue.get(False)
e.check_stop_orders(ee)
p.update_timeindex(ee)
p.create_equity_curve_dataframe()
self.assertEqual(p.equity_curve['equity_curve'][-1],
1.0001020000000001)
input()
while bars.continue_backtest == True:
# print ("Updating Bars.")
bars.update_bars()
while True:
if events.empty():
break
else:
event = events.get(False)
if event is not None:
if event.type == 'MARKET':
strategy.calculate_signals(event)
port.update_timeindex(event)
elif event.type == 'SIGNAL':
port.update_signal(event)
elif event.type == 'ORDER':
broker.execute_order(event)
elif event.type == 'FILL':
port.update_fill(event)
time.sleep(0.1)
result_stats = port.output_summary_stats()
print('==========================================')
print(result_stats)
def test_update_fill_all_up(self):
events_queue = queue.Queue(100)
bars = HistoricCSVDataHandler(events_queue, './tests/datasets/',
['ALL_UP'],
['open', 'high', 'low', 'close'])
p = NaivePortfolio(bars, events_queue, datetime(2017, 4, 1, 0, 0, 0),
1000.0)
fill_event = FillEvent(datetime.utcnow(), 'ALL_UP', 'BACKTEST', 1.0,
'BUY', None)
bars.update_bars()
p.update_fill(fill_event)
self.assertEqual(
p.current_holdings, {
'ALL_UP': 10.0,
'cash': 989.99800000000005,
'commission': 0.002,
'total': 989.99800000000005
})
self.assertEqual(p.current_positions, {'ALL_UP': 1.0})
bars.update_bars()
p.update_timeindex(None)
self.assertEqual(p.all_positions,
[{
'ALL_UP': 0,
'datetime': datetime(2017, 4, 1, 0, 0)
}, {
'ALL_UP': 1.0,
'datetime': datetime(2017, 4, 22, 18, 35)
}])
self.assertEqual(p.all_holdings,
[{
'commission': 0.0,
'ALL_UP': 0.0,
'total': 1000.0,
'datetime': datetime(2017, 4, 1, 0, 0),
'cash': 1000.0
}, {
'commission': 0.002,
'ALL_UP': 20.0,
'total': 1009.998,
'datetime': datetime(2017, 4, 22, 18, 35),
'cash': 989.99800000000005
}])
bars.update_bars()
p.update_timeindex(None)
self.assertEqual(p.all_positions, [
{
'ALL_UP': 0,
'datetime': datetime(2017, 4, 1, 0, 0)
},
{
'ALL_UP': 1.0,
'datetime': datetime(2017, 4, 22, 18, 35)
},
{
'ALL_UP': 1.0,
'datetime': datetime(2017, 4, 22, 18, 40)
},
])
self.assertEqual(p.all_holdings, [
{
'commission': 0.0,
'ALL_UP': 0.0,
'total': 1000.0,
'datetime': datetime(2017, 4, 1, 0, 0),
'cash': 1000.0
},
{
'commission': 0.002,
'ALL_UP': 20.0,
'total': 1009.998,
'datetime': datetime(2017, 4, 22, 18, 35),
'cash': 989.99800000000005
},
{
'commission': 0.002,
'ALL_UP': 30.0,
'total': 1019.998,
'datetime': datetime(2017, 4, 22, 18, 40),
'cash': 989.99800000000005
},
])
bars.update_bars()
p.update_timeindex(None)
self.assertEqual(p.all_positions, [
{
'ALL_UP': 0,
'datetime': datetime(2017, 4, 1, 0, 0)
},
{
'ALL_UP': 1.0,
'datetime': datetime(2017, 4, 22, 18, 35)
},
{
'ALL_UP': 1.0,
'datetime': datetime(2017, 4, 22, 18, 40)
},
{
'ALL_UP': 1.0,
'datetime': datetime(2017, 4, 22, 18, 45)
},
])
self.assertEqual(p.all_holdings, [
{
'commission': 0.0,
'ALL_UP': 0.0,
'total': 1000.0,
'datetime': datetime(2017, 4, 1, 0, 0),
'cash': 1000.0
},
{
'commission': 0.002,
'ALL_UP': 20.0,
'total': 1009.998,
'datetime': datetime(2017, 4, 22, 18, 35),
'cash': 989.99800000000005
},
{
'commission': 0.002,
'ALL_UP': 30.0,
'total': 1019.998,
'datetime': datetime(2017, 4, 22, 18, 40),
'cash': 989.99800000000005
},
{
'commission': 0.002,
'ALL_UP': 5.0,
'total': 994.99800000000005,
'datetime': datetime(2017, 4, 22, 18, 45),
'cash': 989.99800000000005
},
])
bars.update_bars()
p.update_timeindex(None)
self.assertEqual(p.all_positions, [
{
'ALL_UP': 0,
'datetime': datetime(2017, 4, 1, 0, 0)
},
{
'ALL_UP': 1.0,
'datetime': datetime(2017, 4, 22, 18, 35)
},
{
'ALL_UP': 1.0,
'datetime': datetime(2017, 4, 22, 18, 40)
},
{
'ALL_UP': 1.0,
'datetime': datetime(2017, 4, 22, 18, 45)
},
{
'ALL_UP': 1.0,
'datetime': datetime(2017, 4, 22, 18, 50)
},
])
self.assertEqual(p.all_holdings, [
{
'commission': 0.0,
'ALL_UP': 0.0,
'total': 1000.0,
'datetime': datetime(2017, 4, 1, 0, 0),
'cash': 1000.0
},
{
'commission': 0.002,
'ALL_UP': 20.0,
'total': 1009.998,
'datetime': datetime(2017, 4, 22, 18, 35),
'cash': 989.99800000000005
},
{
'commission': 0.002,
'ALL_UP': 30.0,
'total': 1019.998,
'datetime': datetime(2017, 4, 22, 18, 40),
'cash': 989.99800000000005
},
{
'commission': 0.002,
'ALL_UP': 5.0,
'total': 994.99800000000005,
'datetime': datetime(2017, 4, 22, 18, 45),
'cash': 989.99800000000005
},
{
'commission': 0.002,
'ALL_UP': 50.0,
'total': 1039.998,
'datetime': datetime(2017, 4, 22, 18, 50),
'cash': 989.99800000000005
},
])
def simulate():
events = queue.Queue()
##Set this directory as the one where you will store the .csv files by ticker symbol ex. FB.csv etc it will be named from root
##so a directory in your home folder might be /home/data where /data is the folder with the files
directory = '/twoterradata'
ab_path = os.path.abspath(directory)
##Below symbol list for stocks
##Could be modified for Futures
symbol_list = ['FB', 'AAPL', 'GOOG']
##list of thresholds to be set initially for each stock/futures symbols and passed in to the strategy class
##May need to think through whether these can work better with Order types of LMT, Trailing Orders etc for better execution
# g_sell_gain_thresh = 0
# g_sell_loss_thresh = 0
# g_buy_thresh = 0
# g_buy_again_thresh = 0
# g_incr is the
global_thresholds = {
'g_sell_gain_thresh': 0,
'g_sell_loss_thresh': 0,
'g_buy_thresh': 0,
'g_buy_again_thresh': 0,
'g_incr': 0
}
for s in symbol_list:
global_symbol_thresholds[s] = global_thresholds
##Futures_list --would have to update code or use the current symbol_list variable modified for Futures
##Define these global thresholds for each value in the symbol
##Ensures person executes this tester on a Linux or Mac or uses a VM
if platform.system() not in ['Linux', 'Darwin']:
print "Program must run on Linux/Unix system or on Virtual Machine running such a system, please run again"
quit()
##ensure you are logged into session at quandl or set the api key, but for WIKI dataset not necessary
##Below URL will be modifed to obtain futures dataset and most likely will be modified with database queries
quandl_url = "\'https://www.quandl.com/api/v3/datasets/WIKI/"
for s in symbol_list:
if os.path.exists(ab_path + '/' + s + '.csv'):
days_modified = (calendar.timegm(time.gmtime()) -
os.path.getmtime(ab_path + '/' + s + '.csv'))
if days_modified > 86400:
cmd = "curl " + quandl_url + s + "/data.csv\'" + "> \'" + ab_path + '/' + s + ".csv\'"
os.system(cmd)
else:
cmd = "curl " + quandl_url + s + "/data.csv\'" + "> \'" + ab_path + '/' + s + ".csv\'"
os.system(cmd)
print global_symbol_thresholds
bars = HistoricCSVDataHandler(events, ab_path + '/', symbol_list)
##strategy = BuyAndHoldStrategy(bars, events)
##strategy for simple trends, this variable must be set as a list to test multiple strategies etc.
## Set strategy by modifying here
strategy = SimpleTrendsStrategy(bars, events)
port = NaivePortfolio(bars, events, "2015-11-18")
broker = SimulatedExecutionHandler(events)
while True:
# Update the bars (specific backtest code, as opposed to live trading)
if bars.continue_backtest == True:
bars.update_bars()
else:
break
# Handle the events
while True:
try:
event = events.get(False)
except queue.Empty:
break
else:
if event is not None:
if event.type == 'MARKET':
strategy.calculate_signals(event)
port.update_timeindex(event)
elif event.type == 'SIGNAL':
port.update_signal(event)
elif event.type == 'ORDER':
#event.print_order()
broker.execute_order(event)
elif event.type == 'FILL':
port.update_fill(event)
print port.output_summary_stats()
print port.all_holdings[-1]
data.update_data() #drip-feed new line of data
else:
break
#inner loop: handles events in the queue. Breaks when the queue is empty to get new data
while True:
try:
event = event_queue.get_nowait() #gets new event but does not wait for queue to fill again if it is empty
except: #raises exception if queue is empty
break
if event is not None:
if isinstance(event, MarketEvent):
print('market event')
portfolio.update_timeindex(event)
strategy.calculate_signals(event) #strategy generates signals from market data
elif isinstance(event, SignalEvent): #portfolio takes signals as advice on how to trade and generates orders based on these
print('signal event')
portfolio.update_signal(event)
elif isinstance(event, OrderEvent): #executes orders
print('order event')
broker.execute_order(event)
elif isinstance(event, FillEvent): #broker tells the portfolio how much of the order was filled
print('fill event')
portfolio.update_fill(event)
portfolio.update_timeindex(event)
bars.update_bars()
else:
break
# Handle the events
while True:
try:
event = events.get(False)
except Queue.Empty:
break
else:
if event is not None:
if event.type == 'MARKET':
print 'MARKET!'
strategy.calculate_signals(event)
port.update_timeindex(event)
elif event.type == 'SIGNAL':
print 'SIGNAL!'
port.update_signal(event)
elif event.type == 'ORDER':
print 'ORDER!'
broker.execute_order(event) #problem
elif event.type == 'FILL':
print 'FILL!'
port.update_fill(event)
#time.sleep(.1)