def add_macd(df_or_lst, short_period, long_period):
if isinstance(df_or_lst, list) == True:
return macd(df_or_lst, short_period, long_period)
else:
Indicator.stand_alone.append('macd ' + str(short_period) +
str(long_period))
data_frame['macd ' + str(short_period) + str(long_period)] = macd(
df_or_lst['close'].tolist(), short_period, long_period)
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 next_calculation(self, candle):
if self.get_datawindow() is not None:
md = macd(self.get_close(), self.params['short_window'],
self.periods)[-1]
self.macd.append(md)
signal = ema(self.macd, self.params['signal_window'])
self.value = {
'macd': md,
'signal': signal[-1],
'crossover': md - signal[-1]
}
def macd_func(indices, presences, data):
presence = np.mean([presences])
if presence > 52.5:
if indices[0] > indices[1]:
indices[0], indices[1] = indices[1], indices[0]
print("indices: " + str(indices[0]) + " " + str(indices[1]))
values = "(" + str(indices[0]) + "," + str(indices[1]) + ")"
data['macd' + values] = macd(data['Close'].values, indices[0],
indices[1])
data['macd_sign'] = ema(data['macd' + values].values, 9)
data['macd_hist'] = data['macd' +
values].values - data['macd_sign'].values
return data
def prepare_data(symbol):
coin = symbol
coin_1 = crypto_data(coin)
df = coin_1
df['SMA_20'] = sma(df['Close'], 20)
df['SMA_50'] = sma(df['Close'], 50)
df['EMA_20'] = ema(df['Close'], 20)
df['EMA_50'] = ema(df['Close'], 50)
df['MACD'] = macd(df['Close'], 26, 12)
df['per_k_stoch_10'] = percent_k(df['Close'], 10)
df['per_d_stoch_10'] = percent_d(df['Close'], 10)
df['OBV'] = obv(df['Close'], df['Volume'])
fp = []
for price in df['Close']:
fp.append(price)
fp.pop(0)
fp.append(df['Close'].mean())
df['FP'] = fp
df_predict = df.tail(1)
df.drop(df.tail(1).index, inplace=True)
label = []
for i in range(len(df)):
if df['FP'][i] > df['Close'][i]:
label.append(1)
else:
label.append(0)
df['goes_up'] = label
df = df.drop(['FP'], axis=1)
df = df.fillna(df.mean())
df_predict = df_predict.drop(['FP'], axis=1)
return df, df_predict
def screen():
client = Client("api-key", "api-secret", {"verify": False, "timeout": 20})
positiveCoin = get_positive_coin.coin
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)
if pb[-1] < 10:
bollCoin.append(m)
macdCoin = []
for m in bollCoin:
candles = client.get_klines(symbol=m,
interval=client.KLINE_INTERVAL_5MINUTE)
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)
rsiCoin = []
for m in macdCoin:
candles = client.get_klines(symbol=m,
interval=client.KLINE_INTERVAL_15MINUTE)
close = []
for n in candles:
close.append(float(n[4]))
rs = rsi(close, 14)
if rs[-1] < 30:
rsiCoin.append(m)
buyingCoin = ''
maxDemandRatio = 0
for m in rsiCoin:
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(macdCoin)
print(rsiCoin)
print(buyingCoin)
return buyingCoin
def moving_average_convergence_divergence(data, short_period, long_period):
return macd(data, short_period, long_period)