def test_bbwidth():
"""test TA.BBWIDTH"""
bb = TA.BBWIDTH(ohlc)
assert isinstance(bb, series.Series)
assert 0 < bb.values[-1] < 1
def prep_data(data_1m, t, ma, ma_type, vol_type):
data = resample(data_1m, t)
# limit data to latest 10,500 periods for indicator calculations
data = data[-10500:]
if ma_type == 'sma':
data['ma'] = data['close'].rolling(window=ma).mean()
elif ma_type == 'hma':
data['ma'] = TA.HMA(data, ma)
data['roc'] = data['close'].pct_change(periods=roc_p)
data['ma_roc'] = data['ma'].pct_change(
) # is the ma higher or lower than previous period
if vol_type == 'roc':
data['vol'] = data['roc']
if vol_type == 'bbw':
data['bbw'] = TA.BBWIDTH(data, ma, data['ma'])
# print('roc', data['roc'].tail())
# print('atr', data['bbw'].tail())
data['vol'] = data['roc'] / data['bbw']
# print('vol min', data['vol'].min(), 'vol max', data['vol'].max())
elif vol_type == 'atr':
data['atr'] = TA.ATR(data, 9)
# print('roc', data['roc'].tail())
# print('atr', data['atr'].tail())
data['vol'] = (data['roc'] / data['atr']) * data[
'close'] # dividing by close price makes it proportional
# print(t, 'vol min', data['vol'].min(), 'vol max', data['vol'].max())
# limit data to latest 10,000 periods to trim off NaNs and focus on recent data
data = data[-10000:]
return data
def get_bbandwidth(data):
"""Calculate the Bollinger bandwidth for values of given dataframe.
:param data: a dataframe in OHLC format
:return: a Pandas series
"""
if data is None:
raise EmptyDataError("[!] Invalid data value")
result = TA.BBWIDTH(data)
if result is None:
raise IndicatorException
return result
def get_bbo(self, contract): # Get best b/o excluding own orders
j = self.ohlcv[contract].json()
fut2 = contract
#print(contract)
best_bids = []
best_asks = []
o = []
h = []
l = []
c = []
v = []
for b in j['result']['open']:
o.append(b)
for b in j['result']['high']:
h.append(b)
for b in j['result']['low']:
l.append(b)
for b in j['result']['close']:
c.append(b)
for b in j['result']['volume']:
v.append(b)
abc = 0
ohlcv2 = []
for b in j['result']['open']:
ohlcv2.append([o[abc], h[abc], l[abc], c[abc], v[abc]])
abc = abc + 1
ddf = pd.DataFrame(ohlcv2,
columns=['open', 'high', 'low', 'close', 'volume'])
if 1 in self.directional:
sleep(0)
try:
self.dsrsi = TA.STOCHRSI(ddf).iloc[-1] * 100
except:
self.dsrsi = 50
##print(self.dsrsi)
# Get orderbook
if 2 in self.volatility or 3 in self.price or 4 in self.quantity_switch:
self.bands[fut2] = TA.BBANDS(ddf).iloc[-1]
self.bbw[fut2] = (TA.BBWIDTH(ddf).iloc[-1])
#print(float(self.bands[fut2]['BB_UPPER'] - self.bands[fut2]['BB_LOWER']))
if (float(self.bands[fut2]['BB_UPPER'] -
self.bands[fut2]['BB_LOWER'])) > 0:
deltab = (self.get_spot() - self.bands[fut2]['BB_LOWER']) / (
self.bands[fut2]['BB_UPPER'] -
self.bands[fut2]['BB_LOWER'])
if deltab > 50:
self.diffdeltab[fut2] = (deltab - 50) / 100 + 1
if deltab < 50:
self.diffdeltab[fut2] = (50 - deltab) / 100 + 1
else:
self.diffdeltab[fut2] = 25 / 100 + 1
if 3 in self.volatility:
self.atr[fut2] = TA.ATR(ddf).iloc[-1]
if 0 in self.price:
ob = self.client.getorderbook(contract)
bids = ob['bids']
asks = ob['asks']
ords = self.client.getopenorders(contract)
bid_ords = [o for o in ords if o['direction'] == 'buy']
ask_ords = [o for o in ords if o['direction'] == 'sell']
best_bid = None
best_ask = None
err = 10**-(self.get_precision(contract) + 1)
for b in bids:
match_qty = sum([
o['quantity'] for o in bid_ords
if math.fabs(b['price'] - o['price']) < err
])
if match_qty < b['quantity']:
best_bid = b['price']
break
for a in asks:
match_qty = sum([
o['quantity'] for o in ask_ords
if math.fabs(a['price'] - o['price']) < err
])
if match_qty < a['quantity']:
best_ask = a['price']
break
best_asks.append(best_ask)
best_bids.append(best_bid)
if 1 in self.price:
dvwap = TA.VWAP(ddf)
##print(dvwap)
tsz = self.get_ticksize(contract)
try:
bid = ticksize_floor(dvwap.iloc[-1], tsz)
ask = ticksize_ceil(dvwap.iloc[-1], tsz)
except:
bid = ticksize_floor(self.get_spot(), tsz)
ask = ticksize_ceil(self.get_spot(), tsz)
#print( { 'bid': bid, 'ask': ask })
best_asks.append(best_ask)
best_bids.append(best_bid)
if 2 in self.quantity_switch:
dppo = TA.PPO(ddf)
self.buysellsignal[fut2] = 1
try:
if (dppo.iloc[-1].PPO > 0):
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 + PRICE_MOD)
else:
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 - PRICE_MOD)
if (dppo.iloc[-1].HISTO > 0):
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 + PRICE_MOD)
else:
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 - PRICE_MOD)
if (dppo.iloc[-1].SIGNAL > 0):
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 + PRICE_MOD)
else:
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 - PRICE_MOD)
except:
self.buysellsignal[fut2] = 1
##print({ 'bid': best_bid, 'ask': best_ask })
return {
'bid': self.cal_average(best_bids),
'ask': self.cal_average(best_asks)
}
def get_bbo(self, contract): # Get best b/o excluding own orders
vwap = {}
ohlcv2 = {}
fut2 = contract
if contract is 'XBTUSD':
fut2 = 'BTC/USD'
if contract is 'ETHUSD':
fut2 = 'ETH/USD'
now = datetime.now()
format_iso_now = now.isoformat()
then = now - timedelta(minutes=100)
format_later_iso = then.isoformat()
thetime = then.strftime('%Y-%m-%dT%H:%M:%S')
ohlcv = self.client.fetchOHLCV(fut2, '1m',
self.client.parse8601(thetime))
ohlcv2 = []
for o in ohlcv:
ohlcv2.append([o[1], o[2], o[3], o[4], o[5]])
df = pd.DataFrame(ohlcv2,
columns=['open', 'high', 'low', 'close', 'volume'])
best_bids = []
best_asks = []
if 1 in self.directional:
#print(df)
try:
self.dsrsi = TA.STOCHRSI(df).iloc[-1] * 100
except:
self.dsrsi = 50
#print(self.dsrsi)
# Get orderbook
if 2 in self.volatility or 3 in self.price or 4 in self.quantity_switch:
self.bands[fut2] = TA.BBANDS(df).iloc[-1]
self.bbw[fut2] = (TA.BBWIDTH(df).iloc[-1])
print(
float(self.bands[fut2]['BB_UPPER'] -
self.bands[fut2]['BB_LOWER']))
if (float(self.bands[fut2]['BB_UPPER'] -
self.bands[fut2]['BB_LOWER'])) > 0:
deltab = (self.get_spot() - self.bands[fut2]['BB_LOWER']) / (
self.bands[fut2]['BB_UPPER'] -
self.bands[fut2]['BB_LOWER'])
if deltab > 50:
self.diffdeltab[fut2] = (deltab - 50) / 100 + 1
if deltab < 50:
self.diffdeltab[fut2] = (50 - deltab) / 100 + 1
else:
self.diffdeltab[fut2] = 25 / 100 + 1
if 3 in self.volatility:
self.atr[fut2] = TA.ATR(df).iloc[-1]
if 0 in self.price:
# Get orderbook
if contract == 'BTC/USD':
ob = self.ws['XBTUSD'].market_depth()
else:
ob = self.ws[contract].market_depth()
#ob = self.client.fetchOrderBook( contract )
#print(ob)
bids = []
asks = []
for o in ob:
if o['side'] == 'Sell':
bids.append([o['price'], o['size']])
else:
asks.append([o['price'], o['size']])
if contract == 'BTC/USD':
ords = self.ws['XBTUSD'].open_orders('')
else:
ords = self.ws[contract].open_orders('')
#print(ords)
bid_ords = [o for o in ords if o['side'] == 'Buy']
ask_ords = [o for o in ords if o['side'] == 'Sell']
best_bid = None
best_ask = None
err = 10**-(self.get_precision(contract) + 1)
best_bid = 9999999999999999999
for a in bids:
if a[0] < best_bid:
best_bid = a[0]
best_ask = 0
for a in asks:
if a[0] > best_ask:
best_ask = a[0]
print({'bid': best_bid, 'ask': best_ask})
best_asks.append(best_ask)
best_bids.append(best_bid)
if 1 in self.price:
dvwap = TA.VWAP(df)
#print(dvwap)
tsz = self.get_ticksize(contract)
try:
bid = ticksize_floor(dvwap.iloc[-1], tsz)
ask = ticksize_ceil(dvwap.iloc[-1], tsz)
except:
bid = ticksize_floor(self.get_spot(), tsz)
ask = ticksize_ceil(self.get_spot(), tsz)
print({'bid': bid, 'ask': ask})
best_asks.append(best_ask)
best_bids.append(best_bid)
if 2 in self.quantity_switch:
dppo = TA.PPO(df)
self.buysellsignal[fut2] = 1
try:
if (dppo.iloc[-1].PPO > 0):
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 + PRICE_MOD)
else:
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 - PRICE_MOD)
if (dppo.iloc[-1].HISTO > 0):
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 + PRICE_MOD)
else:
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 - PRICE_MOD)
if (dppo.iloc[-1].SIGNAL > 0):
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 + PRICE_MOD)
else:
self.buysellsignal[fut2] = self.buysellsignal[fut2] * (
1 - PRICE_MOD)
except:
self.buysellsignal[fut2] = 1
#print({ 'bid': best_bid, 'ask': best_ask })
return {
'bid': self.cal_average(best_bids),
'ask': self.cal_average(best_asks)
}
