def run_ensemble():
global ds
conf = p.conf
# All In (from 0.5)
# Strategy Return: 128611.19
# Sortino Ratio: 8.35
# Position Sizing (from 0)
# Strategy Return: 71163.77
# Sortino Ratio: 8.73
d1 = runModel('ETHUSDNN1')
d2 = runModel('ETHUSDNN1S')
d3 = runModel('ETHUSDNN2')
# Reloading config after previous models
p.load_config(conf)
d1 = d1[['date', 'open', 'high', 'low', 'close',
'signal']].rename(columns={'signal': 'signal_1'})
d2 = d2[['date', 'signal']].rename(columns={'signal': 'signal_2'})
d3 = d3[['date', 'signal']].rename(columns={'signal': 'signal_3'})
ds = pd.merge(d1, d2, on='date', how='left')
ds = pd.merge(ds, d3, on='date', how='left')
y_pred_val = (np.where(ds.signal_1 == 'Buy', 1, 0) + np.where(
ds.signal_2 == 'Buy', 1, 0) + np.where(ds.signal_3 == 'Buy', 1, 0)) / 3
ds = gen_signal(ds, y_pred_val)
ds['size'] = ds['y_pred_val']
td = bt.run_backtest(ds, conf)
return td
def runModel(conf):
global td
p.load_config(conf)
td = globals()[p.model_type]()
print(str(get_signal_str(td=td)))
return td
def init(conf):
global actions
global tl
global qt
p.load_config(conf)
qt = init_q() # Initialise Model
actions = pd.DataFrame(np.linspace(-1 if p.short else 0, 1, p.actions))
if os.path.isfile(p.tl):
tl = pickle.load(open(p.tl, "rb"))
else:
tl = t.TradeLog()
def test_execute():
conf = 'ETHUSDNN'
p.load_config(conf)
s = get_signal(conf)
p.order_size = 0.02
s = {}
s['action'] = 'Buy'
s['new_trade'] = False
s['sl'] = False
s['tp'] = False
s['sl_price'] = 100
s['tp_price'] = 200
execute(s)
def test_order2():
p.load_config('ETHUSDNN')
# Create Market Order
ex.createOrder('ETH/USD', 'market', 'buy', 0.02)
ex.createOrder('ETH/USD', 'market', 'sell', 0.02)
ex.createOrder('ETH/USD', 'market', 'buy', 0.02, 0) # Price is ignored
# Create Limit Order for fixed price
ex.createOrder('ETH/USD', 'limit', 'buy', 0.02, 100)
# Create Limit Order for -1% to market price
ex.createOrder('ETH/USD', 'limit', 'buy', 0.02, 0, {'price': '-1%'})
# Fetch Open Orders
orders = ex.fetchOpenOrders()
# Order Size
orders[0]['amount']
ex.fetchBalance()['ETH']
def test_order1():
p.load_config('ETHUSDNN')
p.order_size = 0.02
# Print available API methods
print(dir(ex))
# Buy
ex.fetch_balance()['free']
# Close SL Order
cancel_orders()
ex.fetchOpenOrders()
ex.fetchClosedOrders('ETH/USD')
# Get Open Positions
ex.privatePostOpenPositions()
# Limit Order with current price
create_order('Buy', 'limit', 0.02, {'price': '+0%'})
ex.createOrder('ETH/USD', 'market', 'buy', 0.02)
def test_order3():
p.load_config('ETHUSDNN')
p.order_size = 0.02
p.order_wait = 10
open_position('Buy')
print(get_balance())
res = take_profit('Buy', 200)
res = stop_loss('Buy', 100)
res = close_position('Buy', wait=False)
get_balance('ETH')
ex.fetchOpenOrders()
cancel_sl()
cancel_tp()
cancel_orders()
get_price()
res = ex.privatePostOpenPositions()
len(res['result'])
open_position('Sell')
close_position('Sell', wait=False)
ex.fetchOpenOrders()
get_price()
create_order('buy', 10, 215.19, 'stop-loss', 1, False)
def runModel(conf):
p.load_config(conf)
globals()[p.model_type]()
def load_ticker(ticker):
p.load_config('ETHUSDNN')
p.ticker = ticker
ds = load_data_cc()
ds.to_csv('./data/' + ticker + '.csv')
def loglog():
p.load_config('ETHUSDNN')
p.datasource = 'cc'
ds = load_data()
ds['days'] = ds.date - ds.date.iloc[0]
ds.plot(x='days', y='close', loglog=True)
tsret.loc[tsret['Today'].abs() < 0.0001, ['Today']] = 0.0001
# Create the lagged percentage returns columns
for i in range(0, lags):
tsret['Lag%s' % str(i+1)] = \
tslag['Lag%s' % str(i+1)].pct_change() * 100.0
# Create the "Direction" column (+1 or -1) indicating an up/down day
tsret['Direction'] = np.sign(tsret['Today'])
tsret = tsret.dropna()
return tsret
if __name__ == '__main__':
p.load_config('ETHUSDNN')
p.datasource = 'cc'
p.signal_threshold = 0
ts = dl.load_data().set_index('date')
ts['date'] = ts.index
ds = create_lagged_series(ts, lags=5)
# Use the prior two days of returns as predictor
# values, with direction as the response
X = ds[['Lag1', 'Lag2']]
y = ds['Direction']
# The test data is split into two parts: Before and after 1st Jan 2017.
start_test = dt(2019, 1, 1)
