def __init__(self, symbol):
self.symbol = symbol
self.exchange = bi.Binance()
self.df = self.exchange.GetSymbolData(symbol, '4h')
self.last_price = self.df['close'][len(self.df['close'])-1]
self.buy_signals = []
try:
self.df['fast_sma'] = sma(self.df['close'].tolist(), 10)
self.df['slow_sma'] = sma(self.df['close'].tolist(), 30)
self.df['low_boll'] = lbb(self.df['close'].tolist(), 14)
self.df['up_boll'] = ubb(self.df['close'].tolist(), 14)
self.df['vwap'] = (self.df['volume']*(self.df['close'])).cumsum() / self.df['volume'].cumsum()
#self.df['vwap'] = (self.df['volume']*(self.df['high']+self.df['low'])/2).cumsum() / self.df['volume'].cumsum()
self.df['vwma'] = self.vwma(14)
# From Indicators
self.df = ind.macd(self.df)
self.df = ind.money_flow_index(self.df)
self.df = ind.rsi(self.df)
#From Finta
vw_macd = TA.VW_MACD(self.df)
self.df['vw_macd'] = vw_macd['MACD']
self.df['vw_macd_sig'] = vw_macd['SIGNAL']
except Exception as e:
print(" Exception raised when trying to compute indicators on "+self.symbol)
print(e)
return None
def screen():
client = Client("api-key", "api-secret", {"verify": False, "timeout": 20})
positiveCoin = get_positive_coin.positiveCoin
bullishKScoin = []
for m in positiveCoin:
candles = client.get_klines(symbol=m, interval=client.KLINE_INTERVAL_30MINUTE)
if candles[-2][1] < candles[-2][4] <= candles[-1][1] < candles[-1][4]:
bullishKScoin.append(m)
macdCoin = []
for m in bullishKScoin:
candles = client.get_klines(symbol=m, interval=client.KLINE_INTERVAL_1HOUR)
close = []
for n in candles:
close.append(float(n[4]))
mac = macd(close, 12, 26)
macs = macds(mac)
mach = macdh(mac, macs)
# if mach[-1] > 0 and (macs[-1] - macs[-2]) > 0 and (mac[-1] - mac[-2]) > 0:
# macdCoin.append(m)
if mach[-1] > 0 and (macs[-1] - macs[-2]) > 0 and (mac[-1] - mac[-2]) > 0:
macdCoin.append(m)
bollCoin = []
for m in macdCoin:
candles = client.get_klines(symbol=m, interval=client.KLINE_INTERVAL_1HOUR)
close = []
for n in candles:
close.append(float(n[4]))
lb = lbb(close, 20, 2)
mb = mbb(close, 20, 2)
ub = ubb(close, 20, 2)
if lb[-1] < close[-1] < ((0.8 * ub[-1]) + (0.2 * mb[-1])):
bollCoin.append(m)
maxDemandRatio = 0
buyingCoin = ''
for m in bollCoin:
depth = client.get_order_book(symbol=m)
buyingVol = 0
sellingVol = 0
for n in depth['bids'][0:20]:
buyingVol = buyingVol + float(n[1])
for n in depth['asks'][0:20]:
sellingVol = sellingVol + float(n[1])
demandRatio = buyingVol / sellingVol
print(demandRatio)
print(maxDemandRatio)
if demandRatio > maxDemandRatio:
maxDemandRatio = demandRatio
buyingCoin = m
print(bullishKScoin)
print(macdCoin)
print(bollCoin)
print(buyingCoin)
return buyingCoin
def screen():
client = Client("api-key", "api-secret", {"verify": False, "timeout": 20})
positiveCoin = get_positive_coin.positiveCoin
bollCoin = []
for m in positiveCoin:
candles = client.get_klines(symbol=m,
interval=client.KLINE_INTERVAL_15MINUTE)
close = []
for n in candles:
close.append(float(n[4]))
pb = pbb(close, 20, 2.0, 2.0)
lb = lbb(close, 20, 2.0)
ub = ubb(close, 20, 0.2)
if pb[-1] > 50 and ((ub[-1] - lb[-1]) / lb[-1]) > 0.03:
if candles[-2][1] < candles[-2][4]:
if candles[-2][4] <= candles[-1][1]:
if candles[-1][1] < candles[-1][4]:
bollCoin.append(m)
buyingCoin = ''
maxDemandRatio = 0
for m in bollCoin:
depth = client.get_order_book(symbol=m)
buyingVol = 0
sellingVol = 0
for n in depth['bids'][0:20]:
buyingVol = buyingVol + float(n[1])
for n in depth['asks'][0:20]:
sellingVol = sellingVol + float(n[1])
demandRatio = buyingVol / sellingVol
print(demandRatio)
print(maxDemandRatio)
if demandRatio > maxDemandRatio:
maxDemandRatio = demandRatio
buyingCoin = m
if maxDemandRatio < 1.5:
buyingCoin = ''
print(bollCoin)
print(buyingCoin)
return buyingCoin
def ubb(self, df):
period = 20
if len(df) < period:
period = len(df) // 2
data = ubb(df, period)
return (data)
print (socket.get_instruments_for_candles()) # In[5]: data = socket.get_candles(instrument = 'GBP/USD', period = 'D1', start = dt.datetime(2016,1,1), end = dt.datetime(2018, 6, 10)) # In[6]: #Define useful variables data['upper_band'] = ubb(data['askclose'], period = 20) data['mid_band'] = mbb(data['askclose'], period = 20 ) data['lower_band'] = lbb(data['askclose'], period = 20 ) data['percent_b'] = percent_b(data['askclose'], period =20) data # In[7]: fig = plt.figure(figsize=(12,8)) ax1 = fig.add_subplot(111, xlabel = 'Date',ylabel='Close') data['askclose'].plot(ax=ax1, color='r', lw=1)
def upper_bollinger_band(data, period):
return ubb(data, period)