def get_data(symbol):
print 'Getting data'
# Technical Indicators
ti = TechIndicators(key='4BTFICZGTPWZRRQS', output_format='pandas')
sma, _ = ti.get_sma(symbol=symbol, interval='daily')
wma, _ = ti.get_wma(symbol='SPX', interval='daily')
ema, _ = ti.get_ema(symbol=symbol, interval='daily')
macd, _ = ti.get_macd(symbol=symbol, interval='daily')
stoch, _ = ti.get_stoch(symbol=symbol, interval='daily')
rsi, _ = ti.get_rsi(symbol=symbol, interval='daily')
adx, _ = ti.get_adx(symbol=symbol, interval='daily')
cci, _ = ti.get_cci(symbol=symbol, interval='daily')
aroon, _ = ti.get_aroon(symbol=symbol, interval='daily')
bbands, _ = ti.get_bbands(symbol='SPX', interval='daily')
ad, _ = ti.get_ad(symbol='SPX', interval='daily')
obv, _ = ti.get_obv(symbol='SPX', interval='daily')
mom, _ = ti.get_mom(symbol='SPX', interval='daily')
willr, _ = ti.get_willr(symbol='SPX', interval='daily')
tech_ind = pd.concat([sma, ema, macd, stoch, rsi, adx, cci, aroon, bbands, ad, obv, wma, mom, willr], axis=1)
print 'Getting time series'
ts = TimeSeries(key='4BTFICZGTPWZRRQS', output_format='pandas')
close = ts.get_daily(symbol=symbol, outputsize='full')[0].rename(columns={'4. close': 'close'})['close']
direction = (close > close.shift()).astype(int)
target = direction.shift(-1).fillna(0).astype(int)
target.name = 'target'
data = pd.concat([tech_ind, close, target], axis=1)
return data
def get_daily_technical(stock, indicator, API_key, period=-1):
ti = TechIndicators(key=API_key, output_format='pandas')
if indicator == "bband":
if (period <= 0):
period = 20
data, meta_data = ti.get_bbands(symbol=stock, interval='daily', time_period=period)
elif indicator == "macd":
data, meta_data = ti.get_macd(symbol=stock, interval='daily')
elif indicator == "rsi":
if (period <= 0):
period = 14
data, meta_data = ti.get_rsi(symbol=stock, interval='daily', time_period=period)
elif indicator == "cci":
if (period <= 0):
period = 20
data, meta_data = ti.get_cci(symbol=stock, interval='daily', time_period=period)
elif indicator == "aroon":
if (period <= 0):
period = 14
data, meta_data = ti.get_aroon(symbol=stock, interval='daily', time_period=period)
elif indicator == "ad":
data, meta_data = ti.get_ad(symbol=stock, interval='daily')
elif indicator == "adx":
if (period <= 0):
period = 20
data, meta_data = ti.get_adx(symbol=stock, interval='daily', time_period=period)
elif indicator == "sma":
if (period <= 0):
period = 40
data, meta_data = ti.get_sma(symbol=stock, interval='daily', time_period=period)
else:
sys.exit('Failed to input a valid indicator')
return data, meta_data
def get_data(symbol):
# Technical Indicators
ti = TechIndicators(key='YOUR_API_KEY', output_format='pandas')
sma, _ = ti.get_sma(symbol=symbol, interval='daily')
wma, _ = ti.get_wma(symbol=symbol, interval='daily')
ema, _ = ti.get_ema(symbol=symbol, interval='daily')
macd, _ = ti.get_macd(symbol=symbol, interval='daily')
stoch, _ = ti.get_stoch(symbol=symbol, interval='daily')
rsi, _ = ti.get_rsi(symbol=symbol, interval='daily')
adx, _ = ti.get_adx(symbol=symbol, interval='daily')
cci, _ = ti.get_cci(symbol=symbol, interval='daily')
aroon, _ = ti.get_aroon(symbol=symbol, interval='daily')
bbands, _ = ti.get_bbands(symbol=symbol, interval='daily')
ad, _ = ti.get_ad(symbol=symbol, interval='daily')
obv, _ = ti.get_obv(symbol=symbol, interval='daily')
mom, _ = ti.get_mom(symbol=symbol, interval='daily')
willr, _ = ti.get_willr(symbol=symbol, interval='daily')
tech_ind = pd.concat([sma, ema, macd, stoch, rsi, adx, cci, aroon, bbands, ad, obv, wma, mom, willr], axis=1)
ts = TimeSeries(key='YOUR_API_KEY', output_format='pandas')
close = ts.get_daily(symbol=symbol, outputsize='full')[0]['close'] # compact/full
direction = (close > close.shift()).astype(int)
target = direction.shift(-1).fillna(0).astype(int)
target.name = 'target'
data = pd.concat([tech_ind, close, target], axis=1)
return data
def get_data(symbol):
# API KEY: 51N1JMO66W56AYA3
ti = TechIndicators(key='51N1JMO66W56AYA3', output_format='pandas')
sma, _ = ti.get_sma(symbol=symbol, interval='daily')
wma, _ = ti.get_wma(symbol=symbol, interval='daily')
ema, _ = ti.get_ema(symbol=symbol, interval='daily')
macd, _ = ti.get_macd(symbol=symbol, interval='daily')
stoch, _ = ti.get_stoch(symbol=symbol, interval='daily')
# Alpha Vantage Times out for more than 5 request
time.sleep(65)
rsi, _ = ti.get_rsi(symbol=symbol, interval='daily')
adx, _ = ti.get_adx(symbol=symbol, interval='daily')
cci, _ = ti.get_cci(symbol=symbol, interval='daily')
aroon, _ = ti.get_aroon(symbol=symbol, interval='daily')
bbands, _ = ti.get_bbands(symbol=symbol, interval='daily')
time.sleep(65)
ad, _ = ti.get_ad(symbol=symbol, interval='daily')
obv, _ = ti.get_obv(symbol=symbol, interval='daily')
mom, _ = ti.get_mom(symbol=symbol, interval='daily')
willr, _ = ti.get_willr(symbol=symbol, interval='daily')
time.sleep(65)
tech_ind = pd.concat([
sma, ema, macd, rsi, adx, cci, aroon, bbands, ad, obv, wma, mom, willr,
stoch
],
axis=1)
ts = TimeSeries(key='51N1JMO66W56AYA3', output_format='pandas')
close2 = ts.get_daily(symbol=symbol, outputsize='full')
close = ts.get_daily(symbol=symbol, outputsize='full')[0]['4. close']
direction = (close > close.shift()).astype(int)
target = direction.shift(-1).fillna(0).astype(int)
target.name = 'target'
data = pd.concat([tech_ind, close, target], axis=1)
return data
def stockIndicators(self):
ti = TechIndicators(key=self.apiKey, output_format='pandas')
if (self.stockIndi == "sma"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_sma(symbol=self.stockName,
time_period=30)
elif (self.stockIndi == "ema"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_ema(symbol=self.stockName,
time_period=30)
elif (self.stockIndi == "vwap"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_vwap(symbol=self.stockName,
time_period=30)
elif (self.stockIndi == "macd"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_macd(symbol=self.stockName,
time_period=30)
elif (self.stockIndi == "obv"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_obv(symbol=self.stockName,
time_period=30)
elif (self.stockIndi == "ad"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_ad(symbol=self.stockName,
time_period=30)
elif (self.stockIndi == "bbands"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_bbands(symbol=self.stockName,
time_period=30)
elif (self.stockIndi == "aroon"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_aroon(symbol=self.stockName,
time_period=30)
elif (self.stockIndi == "cci"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_cci(symbol=self.stockName,
time_period=30)
elif (self.stockIndi == "adx"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_adx(symbol=self.stockName,
time_period=30)
elif (self.stockIndi == "rsi"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_rsi(symbol=self.stockName,
time_period=30)
elif (self.stockIndi == "stoch"):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=self.stockName)
dataIndi, meta_dataIndi = ti.get_stoch(symbol=self.stockName,
time_period=30)
return data, dataIndi
macd, meta_data_macd = ti.get_macd(symbol=stock,interval=t_period, series_type= series_type) print macd.shape #7 stoch, meta_data_stoch = ti.get_stoch(symbol=stock, interval=t_period) print stoch.shape #8 adx, meta_data_adx = ti.get_adx(symbol=stock, interval=t_period) print adx.shape #9 cci, meta_data_cci = ti.get_cci(symbol=stock, interval=t_period) print cci.shape #10 aroon, meta_data_aroon = ti.get_aroon(symbol=stock, interval=t_period, series_type=series_type) print aroon.shape #11 ad, meta_data_ad = ti.get_ad(symbol=stock, interval=t_period) print ad.shape #12 obv, meta_data_obv = ti.get_obv(symbol=stock, interval=t_period) print obv.shape #13 mom, meta_data_mom = ti.get_mom(symbol=stock, interval=t_period, series_type= series_type) #14 willr, meta_data_willr = ti.get_willr(symbol=stock, interval=t_period) result = data.join(bbands, how='left').join(ema , how='left').join( wma, how='left').join(sma , how='left').join(rsi , how='left').join(macd , how='left').join(stoch , how='left').join( adx, how='left').join(cci , how='left').join(aroon , how='left').join(ad, how='left').join(obv,how='left').join(mom,how='left').join(willr,how='left') # join at the end result['label'] = np.where(result['trend']>= 0, 1, 0) # 1 price-up and 0 price-down del result["trend"] #print result print result.columns.values
def get_technical(symbol, is_save=False):
"""
Esta función es para descargar todos los indicadores técnicos.
Si usamos una API_KEY gratuita de ALPHA VANTAGE tenemos que
descomentar los time.sleep() de la función para que no de un
problema de peticiones; en el caso de una API_KEY premium no haría
falta descomentarlo.
Parámetros:
----------
symbol str:
Nombre de la acción en bolsa.
is_save bool:
Booleano para decidir si guardar o no los datos
descargados. Por defecto False no guarda los
datos descargados
"""
try:
# Comprueba si ya existe o no el archivo y en el caso de que
# si exista guarda solo los días que no estén descargados
if os.path.isfile(f"{c.PATH_SAVE_TECH}{symbol}.csv"):
df = pd.read_csv(f"{c.PATH_SAVE_TECH}{symbol}.csv",
names=c.COLUMNS)
df = df[df['symbol'] == symbol]
df['date'] = pd.to_datetime(df['date'])
ld = df['date'].tail(1)
last = datetime(ld.dt.year, ld.dt.month, ld.dt.day)
else:
last = None
techindc = list()
# Descarga los datos de indicadores técnicos.
ti = TechIndicators(key=c.ALPHA_VAN_KEY, output_format='pandas')
init = time.time()
macd = ti.get_macd(symbol, interval='daily')[0]
techindc.append(macd)
stoch = ti.get_stoch(symbol, interval='daily')[0]
techindc.append(stoch)
ad = ti.get_ad(symbol, interval='daily')[0]
techindc.append(ad)
obv = ti.get_obv(symbol, interval='daily')[0]
techindc.append(obv)
# time.sleep(60)
sma = ti.get_sma(symbol, interval='daily', time_period=50)[0]
sma.columns = [f"{c}50" for c in sma.columns]
techindc.append(sma)
bbands = ti.get_bbands(symbol, interval='daily', time_period=28)[0]
bbands.columns = [f"{c}28" for c in bbands.columns]
techindc.append(bbands)
for tp in c.TIME_PERIOD:
rsi = ti.get_rsi(symbol, interval='daily', time_period=tp)[0]
rsi.columns = [f"{c}{tp}" for c in rsi.columns]
techindc.append(rsi)
adx = ti.get_adx(symbol, interval='daily', time_period=tp)[0]
adx.columns = [f"{c}{tp}" for c in adx.columns]
techindc.append(adx)
# time.sleep(60)
cci = ti.get_cci(symbol, interval='daily', time_period=tp)[0]
cci.columns = [f"{c}{tp}" for c in cci.columns]
techindc.append(cci)
aroon = ti.get_aroon(symbol, interval='daily', time_period=tp)[0]
aroon.columns = [f"{c}{tp}" for c in aroon.columns]
techindc.append(aroon)
df_techindc = pd.concat(techindc, axis=1, join='inner')
df_techindc.reset_index(inplace=True)
df_techindc['symbol'] = symbol
if last is not None:
df_techindc = df_techindc[df_techindc['date'] > last]
# Guardar los datos
if is_save:
df_techindc[c.COLUMNS_TECH].to_csv(
f"{c.PATH_SAVE_TECH}{symbol}.csv",
mode='a',
index=False,
header=False)
except:
LOGGER.warning(f"Ticker {symbol} ha fallado.")
def technicalIndicators(API_key,ticker):
from alpha_vantage.techindicators import TechIndicators
import matplotlib.pyplot as plt
import mplfinance as mpf
ti=TechIndicators(key=API_key, output_format='pandas')
option=input('1. SMA\n2. EMA\n3. VWAP\n4. MACD\n5. STOCH\n6. RSI\n7. ADX\n8. CCI\n9. AROON\n10. BBANDS\n11. AD\n12. OBV\n').lower()
if option=='sma' or option=='1':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n\t6. daily \n\t7. weekly\n\t8. monthly\n"))
intervalList=['','1min','5min','15min','30min','60min','daily','weekly','monthly']
timeperiod=int(input('Enter Time Period\n'))
ser=int(input('Enter the series:\n\t1. close\n\t2. open\n\t3. low\n\t4. high\n'))
series=['','close','open','low','high']
data=ti.get_sma(symbol=ticker, interval=intervalList[interval], time_period=timeperiod, series_type=series[ser])[0]
data.columns = ['SMA']
data.index.name ="Date"
mpf.plot(data,
type='candle',
title=f'SMA for the {ticker} stock',
mav=(20),
volume=True,
tight_layout=True,
style='yahoo')
mpf.plot(data,type='line', volume=True)
return data
elif option=='ema' or option=='2':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n\t6. daily \n\t7. weekly\n\t8. monthly\n"))
intervalList=['','1min','5min','15min','30min','60min','daily','weekly','monthly']
timeperiod=int(input('Enter Time Period\n'))
ser=int(input('Enter the series:\n\t1. close\n\t2. open\n\t3. low\n\t4. high\n'))
series=['','close','open','low','high']
data=ti.get_ema(symbol=ticker, interval=intervalList[interval], time_period=timeperiod, series_type=series[ser])[0]
data.plot()
plt.title(f'EMA for the {ticker} stock')
plt.tight_layout()
plt.grid()
plt.show()
return data
elif option=='vwap' or option=='3':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n"))
intervalList=['','1min','5min','15min','30min','60min']
data=ti.get_vwap(symbol=ticker, interval=intervalList[interval])[0]
data.plot()
plt.title(f'VWAP for the {ticker} stock')
plt.tight_layout()
plt.grid()
plt.show()
return data
elif option=='macd' or option=='4':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n\t6. daily \n\t7. weekly\n\t8. monthly\n"))
intervalList=['','1min','5min','15min','30min','60min','daily','weekly','monthly']
ser=int(input('Enter the series:\n\t1. close\n\t2. open\n\t3. low\n\t4. high\n'))
series=['','close','open','low','high']
data=ti.get_macd(symbol=ticker, interval=intervalList[interval], series_type=series[ser])[0]
data.plot()
plt.title(f'MACD for the {ticker} stock')
plt.tight_layout()
plt.grid()
plt.show()
return data
elif option=='stoch' or option=='5':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n\t6. daily \n\t7. weekly\n\t8. monthly\n"))
intervalList=['','1min','5min','15min','30min','60min','daily','weekly','monthly']
data=ti.get_stoch(symbol=ticker, interval=intervalList[interval])[0]
data.plot()
plt.title(f'STOCH for the {ticker} stock')
plt.tight_layout()
plt.grid()
plt.show()
return data
elif option=='rsi' or option=='6':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n\t6. daily \n\t7. weekly\n\t8. monthly\n"))
intervalList=['','1min','5min','15min','30min','60min','daily','weekly','monthly']
timeperiod=int(input('Enter Time Period\n'))
ser=int(input('Enter the series:\n\t1. close\n\t2. open\n\t3. low\n\t4. high\n'))
series=['','close','open','low','high']
data=ti.get_rsi(symbol=ticker, interval=intervalList[interval], time_period=timeperiod, series_type=series[ser])[0]
data.plot()
plt.title(f'RSI for the {ticker} stock')
plt.tight_layout()
plt.grid()
plt.show()
return data
elif option=='adx' or option=='7':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n\t6. daily \n\t7. weekly\n\t8. monthly\n"))
intervalList=['','1min','5min','15min','30min','60min','daily','weekly','monthly']
timeperiod=int(input('Enter Time Period\n'))
data=ti.get_adx(symbol=ticker, interval=intervalList[interval], time_period=timeperiod)[0]
data.plot()
plt.title(f'ADX for the {ticker} stock')
plt.tight_layout()
plt.grid()
plt.show()
return data
elif option=='cci' or option=='8':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n\t6. daily \n\t7. weekly\n\t8. monthly\n"))
intervalList=['','1min','5min','15min','30min','60min','daily','weekly','monthly']
timeperiod=int(input('Enter Time Period\n'))
data=ti.get_cci(symbol=ticker, interval=intervalList[interval], time_period=timeperiod)[0]
data.plot()
plt.title(f'CCI for the {ticker} stock')
plt.tight_layout()
plt.grid()
plt.show()
return data
elif option=='aroon' or option=='9':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n\t6. daily \n\t7. weekly\n\t8. monthly\n"))
intervalList=['','1min','5min','15min','30min','60min','daily','weekly','monthly']
timeperiod=int(input('Enter Time Period'))
data=ti.get_aroon(symbol=ticker, interval=intervalList[interval], time_period=timeperiod)[0]
data.plot()
plt.title(f'AROON for the {ticker} stock')
plt.tight_layout()
plt.grid()
plt.show()
return data
elif option=='bbands' or option=='10':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n\t6. daily \n\t7. weekly\n\t8. monthly\n"))
intervalList=['','1min','5min','15min','30min','60min','daily','weekly','monthly']
timeperiod=int(input('Enter Time Period\n'))
ser=int(input('Enter the series:\n\t1. close\n\t2. open\n\t3. low\n\t4. high\n'))
series=['','close','open','low','high']
data=ti.get_bbands(symbol=ticker, interval=intervalList[interval], time_period=timeperiod, series_type=series[ser])[0]
data.plot()
plt.title(f'BBANDS for the {ticker} stock')
plt.tight_layout()
plt.grid()
plt.show()
return data
elif option=='ad' or option=='11':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n\t6. daily \n\t7. weekly\n\t8. monthly\n"))
intervalList=['','1min','5min','15min','30min','60min','daily','weekly','monthly']
data=ti.get_ad(symbol=ticker, interval=intervalList[interval])[0]
data.plot()
plt.title(f'AD for the {ticker} stock')
plt.tight_layout()
plt.grid()
plt.show()
return data
elif option=='obv' or option=='12':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n\t6. daily \n\t7. weekly\n\t8. monthly\n"))
intervalList=['','1min','5min','15min','30min','60min','daily','weekly','monthly']
data=ti.get_obv(symbol=ticker, interval=intervalList[interval])[0]
data.plot()
plt.title(f'OBV for the {ticker} stock')
plt.tight_layout()
plt.grid()
plt.show()
return data
else:
print("CANNOT RECOGNIZE")
def preds(api_key):
df = pd.read_csv('GOOGL.csv', index_col='Date')
preds = pd.DataFrame()
for i in np.arange(start=1, stop=6):
if preds.empty:
preds = pd.DataFrame(data=df['close'].pct_change(1))
preds.columns = ['today']
else:
lags = pd.DataFrame(df['close'].pct_change(i))
lags.columns = ['lag{}'.format(i)]
preds = preds.join(lags)
preds = preds.join(df['volume'])
preds = preds.join(df['close'])
preds['dir'] = (preds['close'] > preds['close'].shift(1)).shift(-1)
filename = 'GOOGL-lags.csv'
try:
preds.to_csv('{}'.format(filename))
print('GOOGL lags saved successfully as "{}"'.format(filename))
except:
print('Unable to save "{}"'.format(filename))
ti = TechIndicators(key=api_key, output_format='pandas')
sma, _ = ti.get_sma(symbol='GOOGL', interval='daily', series_type='close')
ema, _ = ti.get_ema(symbol='GOOGL', interval='daily', series_type='close')
wma, _ = ti.get_wma(symbol='GOOGL', interval='daily', series_type='close')
trima, _ = ti.get_trima(symbol='GOOGL',
interval='daily',
series_type='close')
kama, _ = ti.get_kama(symbol='GOOGL',
interval='daily',
series_type='close')
macd, _ = ti.get_macd(symbol='GOOGL',
interval='daily',
series_type='close')
rsi, _ = ti.get_rsi(symbol='GOOGL', interval='daily', series_type='close')
adx, _ = ti.get_adx(symbol='GOOGL', interval='daily')
apo, _ = ti.get_apo(symbol='GOOGL', interval='daily', series_type='close')
stoch, _ = ti.get_stoch(symbol='GOOGL', interval='daily')
ppo, _ = ti.get_ppo(symbol='GOOGL', interval='daily')
mom, _ = ti.get_mom(symbol='GOOGL', interval='daily', series_type='close')
cci, _ = ti.get_cci(symbol='GOOGL', interval='daily')
aroon, _ = ti.get_aroon(symbol='GOOGL',
interval='daily',
series_type='close')
mfi, _ = ti.get_mfi(symbol='GOOGL', interval='daily', series_type='close')
dx, _ = ti.get_dx(symbol='GOOGL', interval='daily', series_type='close')
ad, _ = ti.get_ad(symbol='GOOGL', interval='daily')
obv, _ = ti.get_obv(symbol='GOOGL', interval='daily')
filename2 = 'GOOGL-techs.csv'
try:
preds_test = pd.concat([
sma, ema, wma, trima, kama, macd, rsi, adx, apo, stoch, ppo, mom,
cci, aroon, mfi, dx, ad, obv
],
axis=1)
pd.DataFrame(preds_test).to_csv('{}'.format(filename2))
print('Google techs saved as successfully as "{}"'.format(filename2))
except:
print('Unable to save "{}"'.format(filename2))
class RealTimeApi(object):
def __init__(self, symbol, api_key='KHMFAMB5CA0XGKXO'):
self.api_key = api_key
self.ticker = symbol
self.ts = TimeSeries(api_key)
self.ti = TechIndicators(api_key)
self.sp = SectorPerformances(api_key)
self.counter = 0
self.MAX_API_CALL = 500
def create_real_time_stock_dataframe(self):
"""
Take the return of the real time stock api call and converts the return into a dataframe
:return: Dataframe of new stock data
"""
real_time_data = self.get_time_series_daily()[0]
column_list = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
new_stock_data = []
for sd in real_time_data:
date = sd.split(' ')[0]
new_stock_data.append(
(date, real_time_data[sd]['1. open'],
real_time_data[sd]['2. high'], real_time_data[sd]['3. low'],
real_time_data[sd]['4. close'],
real_time_data[sd]['5. volume']))
return pd.DataFrame(new_stock_data, columns=column_list)
def get_intra_day_data(self, interval='1min'):
"""
Take the return of the real time stock api call and converts the return into a dataframe
:return: Dataframe of new stock data
"""
real_time_data = self.get_time_series_intraday(interval='1min')[0]
column_list = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
new_stock_data = []
for sd in real_time_data:
date = sd.split(' ')[0]
new_stock_data.append(
(date, real_time_data[sd]['1. open'],
real_time_data[sd]['2. high'], real_time_data[sd]['3. low'],
real_time_data[sd]['4. close'],
real_time_data[sd]['5. volume']))
return pd.DataFrame(new_stock_data, columns=column_list)
def _counter(self):
"""
Utility function that keeps track of how many API calls are made. Limit is
5 per minute and no more then 500 per day per api_key. This function only
will track api calls and raise an exception when the count reaches the max
count of 500.
"""
self.counter += 1
if self.counter <= self.MAX_API_CALL:
string = 'API call has been made'
# print(string)
pass
else:
string = 'You have reached the limit of the API calls. '.format(
x=self.MAX_API_CALL)
# print(string)
raise ValueError('Too many API calls have been made today.')
def _get_counter(self):
return 'Current API call counter is at: {}'.format(self.counter)
def get_time_series_intraday(self,
interval='1min',
outputsize='compact',
datatype='json'):
"""
This API returns intraday time series (timestamp, open, high, low, close,
volume) of the equity specified. API Parameters
Required: symbol
The name of the equity of your choice. For example: symbol=MSFT
Required: interval
Time interval between two consecutive data points in the time series.
The following values are supported: 1min, 5min, 15min, 30min, 60min
Optional: outputsize
By default, outputsize=compact. Strings compact and full are accepted
with the following specifications: compact returns only the latest 100
data points in the intraday time series; full returns the full-length
intraday time series. The "compact" option is recommended if you would
like to reduce the data size of each API call.
Optional: datatype
By default, datatype=json. Strings json and csv are accepted with the
following specifications: json returns the intraday time series in JSON
format; csv returns the time series as a CSV (comma separated value)
file.
"""
self._counter()
try:
data, meta_data = self.ts.get_intraday(symbol=self.ticker,
interval=interval,
outputsize=outputsize)
except:
raise Exception('API call get_intraday failed.')
return data, meta_data
def get_time_series_daily(self, outputsize='compact', datatype='json'):
"""
This API returns daily time series (date, daily open, daily high, daily low,
daily close, daily volume) of the global equity specified, covering 20+
years of historical data. The most recent data point is the prices and
volume information of the current trading day, updated realtime.
API Parameters
Required: symbol
The name of the equity of your choice. For example: symbol=MSFT
Optional: outputsize
By default, outputsize=compact. Strings compact and full are accepted
with the following specifications: compact returns only the latest 100
data points; full returns the full-length time series of 20+ years of
historical data. The "compact" option is recommended if you would like
to reduce the data size of each API call.
Optional: datatype
By default, datatype=json. Strings json and csv are accepted with the
following specifications: json returns the daily time series in JSON
format; csv returns the time series as a CSV (comma separated value)
file.
"""
self._counter()
try:
data, meta_data = self.ts.get_daily(symbol=self.ticker,
outputsize=outputsize)
except:
raise Exception('API call get_daily failed')
return data, meta_data
def get_time_series_daily_adjusted(
self,
outputsize='compact',
datatype='json',
):
"""
This API returns daily time series (date, daily open, daily high, daily low,
daily close, daily volume, daily adjusted close, and split/dividend events)
of the global equity specified, covering 20+ years of historical data.
The most recent data point is the prices and volume information of the
current trading day, updated realtime.
API Parameters
Required: symbol
The name of the equity of your choice. For example: symbol=MSFT
Optional: outputsize
By default, outputsize=compact. Strings compact and full are accepted
with the following specifications: compact returns only the latest 100
data points; full returns the full-length time series of 20+ years of
historical data. The "compact" option is recommended if you would
like to reduce the data size of each API call.
Optional: datatype
By default, datatype=json. Strings json and csv are accepted with the
following specifications: json returns the daily time series in JSON
format; csv returns the time series as a CSV (comma separated value)
file.
"""
self._counter()
try:
data, meta_data = self.ts.get_daily_adjusted(symbol=self.ticker,
outputsize=outputsize,
datatype=datatype)
except:
raise Exception('API call get_daily_adjusted failed')
return data, meta_data
def get_simple_moving_average(self,
interval='daily',
time_period=20,
series_type='close',
datatype='json'):
"""
This API returns the simple moving average (SMA) values. See also:
Investopedia article and mathematical reference.
API Parameters
Required: symbol
The name of the security of your choice. For example: symbol=MSFT
Required: interval
Time interval between two consecutive data points in the time series.
The following values are supported: 1min, 5min, 15min, 30min, 60min,
daily, weekly, monthly
Required:time_period
Number of data points used to calculate each moving average value.
Positive integers are accepted (e.g., time_period=60, time_period=200)
Required: series_type
The desired price type in the time series. Four types are supported:
close, open, high, low
Optional: datatype
By default, datatype=json. Strings json and csv are accepted with the
following specifications: json returns the daily time series in JSON
format; csv returns the time series as a CSV (comma separated value)
file.
"""
self._counter()
try:
data, meta_data = self.ti.get_sma(symbol=self.ticker,
interval=interval,
time_period=time_period,
series_type=series_type,
datatype=datatype)
except:
raise Exception('API call simple_moving_average failed')
return data, meta_data
def get_exponential_moving_average(self,
interval='daily',
time_period=20,
series_type='close',
datatype='json'):
"""
This API returns the exponential moving average (EMA) values.
API Parameters
Required: symbol
The name of the security of your choice. For example: symbol=MSFT
Required: interval
Time interval between two consecutive data points in the time series.
The following values are supported: 1min, 5min, 15min, 30min, 60min,
daily, weekly, monthly
Required:time_period
Number of data points used to calculate each moving average value.
Positive integers are accepted (e.g., time_period=60, time_period=200)
Required: series_type
The desired price type in the time series. Four types are supported:
close, open, high, low
Optional: datatype
By default, datatype=json. Strings json and csv are accepted with the
following specifications: json returns the daily time series in JSON
format; csv returns the time series as a CSV (comma separated value)
file.
"""
self._counter()
try:
data, meta_data = self.ti.get_ema(symbol=self.ticker,
interval=interval,
time_period=time_period,
series_type=series_type,
datatype=datatype)
except:
raise Exception('API call exponential_moving_average failed')
return data, meta_data
def get_moving_average_conv_div(self,
interval='daily',
series_type='close',
fastperiod=12,
slowperiod=26,
signalperiod=9,
datatype='json'):
"""
This API returns the moving average convergence / divergence (MACD) values.
API Parameters
Required: symbol
The name of the security of your choice. For example: symbol=MSFT
Required: interval
Time interval between two consecutive data points in the time series.
The following values are supported: 1min, 5min, 15min, 30min, 60min,
daily, weekly, monthly
Required: series_type
The desired price type in the time series. Four types are supported:
close, open, high, low
Optional: fastperiod
Positive integers are accepted. By default, fastperiod=12.
Optional: slowperiod
Positive integers are accepted. By default, slowperiod=26.
Optional: signalperiod
Positive integers are accepted. By default, signalperiod=9.
Optional: datatype
By default, datatype=json. Strings json and csv are accepted with the
following specifications: json returns the daily time series in JSON
format; csv returns the time series as a CSV (comma separated value)
file.
"""
self._counter()
try:
data, meta_data = self.ti.get_macd(symbol=self.ticker,
interval=interval,
series_type=series_type,
fastperiod=fastperiod,
slowperiod=slowperiod,
signalperiod=signalperiod,
datatype=datatype)
except:
raise Exception('API call get_moving_average_conv_div failed')
return data, meta_data
def get_stochastic_oscillator(self,
interval='daily',
fastkperiod=5,
slowkperiod=3,
slowdperiod=3,
slowkmatype=0,
slowdmatype=0,
datatype='json'):
"""
This API returns the stochastic oscillator (STOCH) values.
API Parameters
Required: symbol
The name of the security of your choice. For example: symbol=MSFT
Required: interval
Time interval between two consecutive data points in the time series.
The following values are supported: 1min, 5min, 15min, 30min, 60min,
daily, weekly, monthly
Optional: fastkperiod
The time period of the fastk moving average. Positive integers are
accepted. By default, fastkperiod=5.
Optional: slowkperiod
The time period of the slowk moving average. Positive integers are
accepted. By default, slowkperiod=3.
Optional: slowdperiod
The time period of the slowd moving average. Positive integers are
accepted. By default, slowdperiod=3.
Optional: slowkmatype
Moving average type for the slowk moving average. By default,
slowkmatype=0. Integers 0 - 8 are accepted with the following mappings.
0 = Simple Moving Average (SMA),
1 = Exponential Moving Average (EMA),
2 = Weighted Moving Average (WMA),
3 = Double Exponential Moving Average (DEMA),
4 = Triple Exponential Moving Average (TEMA),
5 = Triangular Moving Average (TRIMA),
6 = T3 Moving Average,
7 = Kaufman Adaptive Moving Average (KAMA),
8 = MESA Adaptive Moving Average (MAMA).
Optional: slowdmatype
Moving average type for the slowd moving average. By default, slowdmatype=0.
Integers 0 - 8 are accepted with the following mappings.
0 = Simple Moving Average (SMA),
1 = Exponential Moving Average (EMA),
2 = Weighted Moving Average (WMA),
3 = Double Exponential Moving Average (DEMA),
4 = Triple Exponential Moving Average (TEMA),
5 = Triangular Moving Average (TRIMA),
6 = T3 Moving Average,
7 = Kaufman Adaptive Moving Average (KAMA),
8 = MESA Adaptive Moving Average (MAMA).
Optional: datatype
By default, datatype=json. Strings json and csv are accepted with the
following specifications: json returns the daily time series in JSON
format; csv returns the time series as a CSV (comma separated value)
file.
"""
self._counter()
try:
data, meta_data = self.ti.get_stoch(symbol=self.ticker,
interval=interval,
fastkperiod=fastkperiod,
slowkperiod=slowkperiod,
slowdperiod=slowdperiod,
slowkmatype=slowkmatype,
slowdmatype=slowdmatype,
datatype=datatype)
except:
raise Exception('API call get_moving_average_conv_div failed')
return data, meta_data
def get_bollinger_bands(self,
interval='daily',
time_period=20,
series_type='close',
nbdevup=2,
nbdevdn=2,
matype=0,
datatype='json'):
"""
This API returns the Bollinger bands (BBANDS) values.
API Parameters
Required: symbol
The name of the security of your choice. For example: symbol=MSFT
Required: interval
Time interval between two consecutive data points in the time series.
The following values are supported: 1min, 5min, 15min, 30min, 60min,
daily, weekly, monthly
Required:time_period
Number of data points used to calculate each BBANDS value. Positive
integers are accepted (e.g., time_period=60, time_period=200)
Required: series_type
The desired price type in the time series. Four types are supported:
close, open, high, low
Optional: nbdevup
The standard deviation multiplier of the upper band. Positive integers
are accepted. By default, nbdevup=2.
Optional: nbdevdn
The standard deviation multiplier of the lower band. Positive integers
are accepted. By default, nbdevdn=2.
Optional: matype
Moving average type of the time series. By default, matype=0.
Integers 0 - 8 are accepted with the following mappings.
0 = Simple Moving Average (SMA),
1 = Exponential Moving Average (EMA),
2 = Weighted Moving Average (WMA),
3 = Double Exponential Moving Average (DEMA),
4 = Triple Exponential Moving Average (TEMA),
5 = Triangular Moving Average (TRIMA),
6 = T3 Moving Average,
7 = Kaufman Adaptive Moving Average (KAMA),
8 = MESA Adaptive Moving Average (MAMA).
Optional: datatype
By default, datatype=json. Strings json and csv are accepted with the
following specifications: json returns the daily time series in JSON
format; csv returns the time series as a CSV (comma separated value)
file.
"""
self._counter()
try:
data, meta_data = self.ti.get_bbands(symbol=self.ticker,
interval=interval,
time_period=time_period,
series_type=series_type,
nbdevup=nbdevup,
nbdevdn=nbdevdn,
matype=matype,
datatype=datatype)
except:
raise Exception('API call get_bollinger_bands failed')
return data, meta_data
def get_Chaikin_A_D_line(self, interval='daily', datatype='json'):
"""
This API returns the Chaikin A/D line (AD) values.
API Parameters
Required: symbol
The name of the security of your choice. For example: symbol=MSFT
Required: interval
Time interval between two consecutive data points in the time series.
The following values are supported: 1min, 5min, 15min, 30min, 60min,
daily, weekly, monthly
Optional: datatype
By default, datatype=json. Strings json and csv are accepted with the
following specifications: json returns the daily time series in JSON
format; csv returns the time series as a CSV (comma separated value)
file.
"""
self._counter()
try:
data, meta_data = self.ti.get_ad(symbol=self.ticker,
interval=interval,
datatype=datatype)
except:
raise Exception('API call get_Chaikin_A_D_line failed')
return data, meta_data
def get_balance_volume(self, interval='daily', datatype='json'):
"""
This API returns the on balance volume (OBV) values.
API Parameters
Required: symbol
The name of the security of your choice. For example: symbol=MSFT
Required: interval
Time interval between two consecutive data points in the time series.
The following values are supported: 1min, 5min, 15min, 30min, 60min,
daily, weekly, monthly
Optional: datatype
By default, datatype=json. Strings json and csv are accepted with the
following specifications: json returns the daily time series in JSON
format; csv returns the time series as a CSV (comma separated value)
file.
"""
self._counter()
try:
data, meta_data = self.ti.get_obv(symbol=self.ticker,
interval=interval,
datatype=datatype)
except:
raise Exception('API call get_balance_volume failed')
return data, meta_data
def get_sector_performance(self):
"""
This API returns the realtime and historical sector performances calculated
from S&P500 incumbents.
"""
self._counter()
try:
data, meta_data = self.sp.get_sector()
except:
raise Exception('API call get_sector_performance failed')
return data, meta_data
def alpha_collect_technical_indicators():
ti = TechIndicators(key='YOUR_API_KEY', output_format='pandas')
data, meta_data = ti.get_ad(symbol='MSFT', interval='60min', time_period=60)
df['ema'], _ = ti.get_ema(symbol=symbol, time_period=ema_time_period)
df['rsi'], _ = ti.get_rsi(symbol=symbol, time_period=rsi_time_period)
df['obv'], _ = ti.get_obv(symbol=symbol)
macd = ti.get_macd(symbol=symbol)[0].sort_index(axis=0, ascending=True)
for column in macd.columns:
df[column] = macd[column]
stoch = ti.get_stoch(symbol=symbol)[0].sort_index(axis=0, ascending=True)
for column in stoch.columns:
df[column] = stoch[column]
df['adx'], _ = ti.get_adx(symbol=symbol, time_period=adx_time_period)
df['cci'], _ = ti.get_cci(symbol=symbol, time_period=cci_time_period)
aroon = ti.get_aroon(
symbol=symbol, time_period=aroon_time_period)[0].sort_index(axis=0,
ascending=True)
for column in aroon.columns:
df[column] = aroon[column]
bbands = ti.get_bbands(symbol=symbol,
time_period=bbands_time_period)[0].sort_index(
axis=0, ascending=True)
for column in bbands.columns:
df[column] = bbands[column]
df['ad'], _ = ti.get_ad(symbol=symbol)
df['shifted_ROC_BOOL'] = (df['roc'].shift(-1) >= 0).astype(int)
df.to_csv(filename)
def techIndicatorPull(symbol):
interval = "1min"
print("Calculating Tech Indicators ...")
ti = TechIndicators(key='1HAYLUHGCB6E0VXC', output_format='pandas')
print("Progress: 0% complete...")
ad_data, meta_data = ti.get_ad(symbol=symbol, interval=interval)
adosc_data, meta_data = ti.get_adosc(symbol=symbol, interval=interval)
adx_data, meta_data = ti.get_adx(symbol=symbol, interval=interval)
adxr_data, meta_data = ti.get_adxr(symbol=symbol, interval=interval)
apo_data, meta_data = ti.get_apo(symbol=symbol, interval=interval)
aroon_data, meta_data = ti.get_aroon(symbol=symbol, interval=interval)
aroonosc_data, meta_data = ti.get_aroonosc(symbol=symbol,
interval=interval)
atr_data, meta_data = ti.get_atr(symbol=symbol, interval=interval)
bop_data, meta_data = ti.get_bop(symbol=symbol, interval=interval)
cci_data, meta_data = ti.get_cci(symbol=symbol, interval=interval)
print("Progress: 20% complete...")
cmo_data, meta_data = ti.get_cmo(symbol=symbol, interval=interval)
dema_data, meta_data = ti.get_dema(symbol=symbol, interval=interval)
dx_data, meta_data = ti.get_dx(symbol=symbol, interval=interval)
ema_data, meta_data = ti.get_ema(symbol=symbol, interval=interval)
ht_dcperiod_data, meta_data = ti.get_ht_dcperiod(symbol=symbol,
interval=interval)
ht_dcphase_data, meta_data = ti.get_ht_dcphase(symbol=symbol,
interval=interval)
ht_phasor_data, meta_data = ti.get_ht_phasor(symbol=symbol,
interval=interval)
ht_sine_data, meta_data = ti.get_ht_sine(symbol=symbol, interval=interval)
ht_trendline_data, meta_data = ti.get_ht_trendline(symbol=symbol,
interval=interval)
ht_trendmode_data, meta_data = ti.get_ht_trendmode(symbol=symbol,
interval=interval)
print("Progress: 40% complete...")
kama_data, meta_data = ti.get_kama(symbol=symbol, interval=interval)
macd_data, meta_data = ti.get_macd(symbol=symbol, interval=interval)
macdext_data, meta_data = ti.get_macdext(symbol=symbol, interval=interval)
mama_data, meta_data = ti.get_mama(symbol=symbol, interval=interval)
mfi_data, meta_data = ti.get_mfi(symbol=symbol, interval=interval)
midpoint_data, meta_data = ti.get_midpoint(symbol=symbol,
interval=interval)
midprice_data, meta_data = ti.get_midprice(symbol=symbol,
interval=interval)
minus_di_data, meta_data = ti.get_minus_di(symbol=symbol,
interval=interval)
mom_data, meta_data = ti.get_mom(symbol=symbol, interval=interval)
natr_data, meta_data = ti.get_natr(symbol=symbol, interval=interval)
print("Progress: 60% complete...")
obv_data, meta_data = ti.get_obv(symbol=symbol, interval=interval)
plus_di_data, meta_data = ti.get_plus_di(symbol=symbol, interval=interval)
plus_dm_data, meta_data = ti.get_plus_dm(symbol=symbol, interval=interval)
ppo_data, meta_data = ti.get_ppo(symbol=symbol, interval=interval)
roc_data, meta_data = ti.get_roc(symbol=symbol, interval=interval)
rocr_data, meta_data = ti.get_rocr(symbol=symbol, interval=interval)
rsi_data, meta_data = ti.get_rsi(symbol=symbol, interval=interval)
sar_data, meta_data = ti.get_sar(symbol=symbol, interval=interval)
sma_data, meta_data = ti.get_sma(symbol=symbol, interval=interval)
stoch_data, meta_data = ti.get_stoch(symbol=symbol, interval=interval)
print("Progress: 80% complete...")
stochf_data, meta_data = ti.get_stochf(symbol=symbol, interval=interval)
stochrsi_data, meta_data = ti.get_stochrsi(symbol=symbol,
interval=interval)
t3_data, meta_data = ti.get_t3(symbol=symbol, interval=interval)
tema_data, meta_data = ti.get_tema(symbol=symbol, interval=interval)
trange_data, meta_data = ti.get_trange(symbol=symbol, interval=interval)
trima_data, meta_data = ti.get_trima(symbol=symbol, interval=interval)
trix_data, meta_data = ti.get_trix(symbol=symbol, interval=interval)
ultsoc_data, meta_data = ti.get_ultsoc(symbol=symbol, interval=interval)
willr_data, meta_data = ti.get_willr(symbol=symbol, interval=interval)
wma_data, meta_data = ti.get_wma(symbol=symbol, interval=interval)
bbands_data, meta_data = ti.get_bbands(symbol=symbol, interval=interval)
print("Progress: 90% complete...")
result = pd.concat([
ad_data, adosc_data, adx_data, adxr_data, apo_data, aroon_data,
aroonosc_data, atr_data, bop_data, cci_data, cmo_data, dema_data,
dx_data, ema_data, ht_dcperiod_data, ht_dcphase_data, ht_phasor_data,
ht_sine_data, ht_trendline_data, ht_trendmode_data, kama_data,
macd_data, macdext_data, mama_data, mfi_data, midpoint_data,
midprice_data, minus_di_data, mom_data, natr_data, obv_data,
plus_di_data, plus_dm_data, ppo_data, roc_data, rocr_data, rsi_data,
sar_data, sma_data, stoch_data, stochf_data, stochrsi_data, t3_data,
tema_data, trange_data, trima_data, trix_data, ultsoc_data, willr_data,
wma_data, bbands_data
],
axis=1)
result["stockticker"] = symbol
result = result.reindex(result.index.rename('timestamp'))
result = result.dropna(axis=0, how='any')
result = result.round(2)
result = result.rename(
columns={
'Chaikin A/D': 'ChaikinAD',
'Aroon Up': 'AroonUp',
'Aroon Down': 'AroonDown',
'LEAD SINE': 'LeadSine',
'Real Upper Band': 'RealUpperBand',
'Real Middle Band': 'RealMiddleBand',
'Real Lower Band': 'RealLowerBand'
})
fd = open('results/rawTechIndicatorData.csv', 'w')
result.to_csv(fd, index=True, encoding='utf-8')
fd.close()
print("Progress: 99% complete...")
df = pd.read_csv('results/rawTechIndicatorData.csv')
insertToOracleDatabase(
df,
'''insert /*+ ignore_row_on_dupkey_index(stocktechindicator, stocktechindicator_pk) */
into stocktechindicator
(timestamp,ChaikinAD,ADOSC,ADX,ADXR,
APO,AroonUp,AroonDown,AROONOSC,ATR,
BOP,CCI,CMO,DEMA,DX,
EMA,DCPERIOD,HT_DCPHASE,QUADRATURE, PHASE,
LEADSINE,SINE,HT_TRENDLINE,TRENDMODE,KAMA,
MACD,MACD_Hist,MACD_Signal,MACD_Signal2,MACD2,
MACD_Hist2,FAMA,MAMA,MFI,MIDPOINT,
MIDPRICE,MINUS_DI,MOM,NATR,OBV,
PLUS_DI,PLUS_DM,PPO,ROC,ROCR,
RSI,SAR,SMA,SlowK,SlowD,
FastK,FastD,FastK2,FastD2,T3,
TEMA,TRANGE,TRIMA,TRIX,ULTOSC,
WILLR,WMA,RealLowerBand,RealUpperBand,RealMiddleBand,
stockticker)
values (:1, :2, :3, :4, :5,
:6, :7, :8, :9, :10,
:11, :12, :13, :14, :15,
:16, :17, :18, :19, :20,
:21, :22, :23, :24, :25,
:26, :27, :28, :29, :30,
:31, :32, :33, :34, :35,
:36, :37, :38, :39, :40,
:41, :42, :43, :44, :45,
:46, :47, :48, :49, :50,
:51, :52, :53, :54, :55,
:56, :57, :58, :59, :60,
:61, :62, :63, :64, :65,
:66)''')
def getData(tker,alg,time,tDF,tI,incP,typeClass,return_list):
print("Getting Data")
dataDict= []
symb = tker
#Do not use 60min since that does not work.
#Cannot figure out how, but the keys for date and time are different.
tInterval = time
ourKey = '2ONP54KRQ2G73D3K'
#Using the wrapper class to get the different data for the inputted ticker
#In a try-catch because sometime the API fails
try:
#Making the retry number high so the API call should not fail
ts = TimeSeries(key = ourKey, output_format = 'json',retries=10000)
#tInterval has first letter capitalized when getting input for neatness, it is then lowered here for the API call
#Here we are getting the price data for the requested time interval
if tInterval == 'Daily':
priceData, meta_priceData = ts.get_daily(symbol= symb, outputsize='full')
tInterval = 'daily'
elif tInterval == 'Weekly':
priceData, meta_priceData = ts.get_weekly(symbol = symb)
tInterval = 'weekly'
elif tInterval =='Monthly':
priceData, meta_priceData = ts.get_monthly(symbol = symb)
tInterval = 'monthly'
else:
priceData, meta_priceData = ts.get_intraday(symbol = symb, interval = tInterval, outputsize='full')
#Intraday date and time key includes seconds, while it does not for technicial indicators
for day in priceData.copy():
priceData[day[:-3]] = priceData.pop(day)
ti = TechIndicators(key = ourKey, output_format ='json')
#Here we get the technical indicators the user chose from the menu, unless they chose to do a tDF.
if tDF == "No":
if 0 in tI:
macD, meta_macD = ti.get_macd(symbol = symb, interval = tInterval, series_type = 'close')
dataDict.append(macD)
if 1 in tI:
stoch, meta_stoch = ti.get_stoch(symbol = symb, interval = tInterval, fastkperiod = '5')
dataDict.append(stoch)
if 2 in tI:
ema, meta_ema = ti.get_ema(symbol = symb, interval = tInterval, time_period = '8', series_type = 'close')
dataDict.append(ema)
if 3 in tI:
rsi, meta_rsi = ti.get_rsi(symbol = symb, interval = tInterval, time_period = '14', series_type='close')
dataDict.append(rsi)
if 4 in tI:
plusDI, meta_plusDI = ti.get_plus_di(symbol = symb, interval = tInterval, time_period = '14')
minusDI, meta_minusDI = ti.get_minus_di(symbol = symb, interval = tInterval, time_period = '14')
dataDict.append(plusDI)
dataDict.append(minusDI)
if 5 in tI:
cci, meta_cci = ti.get_cci(symbol = symb, interval = tInterval, time_period = '14')
dataDict.append(cci)
if 6 in tI:
willR, meta_willR = ti.get_willr(symbol = symb, interval = tInterval, time_period = '14')
dataDict.append(willR)
if 7 in tI:
oBV, meta_OBV = ti.get_obv(symbol = symb, interval = tInterval)
dataDict.append(oBV)
if 8 in tI:
aD, meta_AD = ti.get_ad(symbol = symb, interval = tInterval)
dataDict.append(aD)
if 9 in tI:
bBands, meta_bBands = ti.get_bbands(symbol = symb, interval = tInterval, time_period = '14', series_type = 'close')
dataDict.append(bBands)
if 10 in tI:
aroon, meta_aroon = ti.get_aroon(symbol = symb, interval = tInterval, time_period = '14')
dataDict.append(aroon)
if 11 in tI:
adx, meta_adx = ti.get_adx(symbol = symb, interval = tInterval, time_period = '14')
dataDict.append(adx)
if(incP == "Yes"):
dataDict.append(priceData)
#Since a tDF requires a manual transformation, we choose the technical indicators for it, which are retrieved here
else:
macD, meta_macD = ti.get_macd(symbol = symb, interval = tInterval, series_type = 'close')
stoch, meta_stoch = ti.get_stoch(symbol = symb, interval = tInterval, fastkperiod = '5')
ema, meta_ema = ti.get_ema(symbol = symb, interval = tInterval, time_period = '8', series_type = 'close')
rsi, meta_rsi = ti.get_rsi(symbol = symb, interval = tInterval, time_period = '14', series_type='close')
plusDI, meta_plusDI = ti.get_plus_di(symbol = symb, interval = tInterval, time_period = '14')
minusDI, meta_minusDI = ti.get_minus_di(symbol = symb, interval = tInterval, time_period = '14')
cci, meta_cci = ti.get_cci(symbol = symb, interval = tInterval, time_period = '14')
willR, meta_willR = ti.get_willr(symbol = symb, interval = tInterval, time_period = '14')
dataDict.append(macD)
dataDict.append(stoch)
dataDict.append(ema)
dataDict.append(rsi)
dataDict.append(plusDI)
dataDict.append(minusDI)
dataDict.append(cci)
dataDict.append(willR)
#Returning with nothing tells our main process that there was an error
except ValueError:
return
#Sometimes the API may return a empty list which will cause an error for the training
for list in dataDict:
if not list:
return
#Need atleast one of the dictionaries to be ordered by date, so we can organize all our data.
priceData = collections.OrderedDict(sorted(priceData.items()))
#dates used for drawing graph afterwards
dates = []
for day in priceData:
dates.append(day)
#Parsing the data
#All the data for each day is put into an array, which is put into trainingData, sorted by dates
trainingData = []
#Putting the ordered price data into priceList, instead of using a Dictionary
#So if the user doesn't want to include price in training, we still have a list to train
#for the actual outputs/
priceList = []
#If the user is using this in the middle of the day/week/month, the data will have a partial time interval up to the current time
#Here we remove the last day if it is the same date as today, so we can get a guess for the timer interval asked.
todaysDate = str(datetime.datetime.now())[:10]
if todaysDate in priceData.keys():
del priceData[todaysDate]
for day in priceData:
#Making sure the day is in the priceData and each of the technical indicator lists.
#The API doesn't necessarily give the data for every data for the different requests
if all(day in indic for indic in dataDict):
vector = []
priceData[day] = collections.OrderedDict(sorted(priceData[day].items()))
for stat in priceData[day]:
vector.append(float(priceData[day][stat]))
priceList.append(vector)
vector = []
for x in range(0,len(dataDict)):
#Order the different stats within a data, so the order is consistent across vectors
dataDict[x][day] = collections.OrderedDict(sorted(dataDict[x][day].items()))
#Combine all the different data into a vector
for x in range(0,len(dataDict)):
for stat in dataDict[x][day]:
vector.append(float(dataDict[x][day][stat]))
trainingData.append(vector)
#If the user wanted to do a tDF, the training data is transformed, otherwise the trainingData goes straight to PredictAndTest
trendDeter = False if tDF == "No" else True
#delete first element of priceList because trendDeter, remove first element form training data
if trendDeter:
del priceList[0]
predictAndTest(trendDeterTransform(trainingData), priceList, trendDeter, alg, tInterval, dates, typeClass, return_list)
else:
predictAndTest(trainingData, priceList, trendDeter, alg, tInterval, dates, typeClass, return_list)
cursor.executemany(query, rows)
connection.commit()
print("Begining")
symbol = "ORCL"
interval = "1min"
ti = TechIndicators(key='1HAYLUHGCB6E0VXC', output_format='pandas')
print("Progress: 0% complete...")
ad_data, meta_data = ti.get_ad(symbol=symbol, interval=interval)
adosc_data, meta_data = ti.get_adosc(symbol=symbol, interval=interval)
adx_data, meta_data = ti.get_adx(symbol=symbol, interval=interval)
adxr_data, meta_data = ti.get_adxr(symbol=symbol, interval=interval)
apo_data, meta_data = ti.get_apo(symbol=symbol, interval=interval)
aroon_data, meta_data = ti.get_aroon(symbol=symbol, interval=interval)
aroonosc_data, meta_data = ti.get_aroonosc(symbol=symbol, interval=interval)
atr_data, meta_data = ti.get_atr(symbol=symbol, interval=interval)
bop_data, meta_data = ti.get_bop(symbol=symbol, interval=interval)
cci_data, meta_data = ti.get_cci(symbol=symbol, interval=interval)
print("Progress: 20% complete...")
cmo_data, meta_data = ti.get_cmo(symbol=symbol, interval=interval)
dema_data, meta_data = ti.get_dema(symbol=symbol, interval=interval)
dx_data, meta_data = ti.get_dx(symbol=symbol, interval=interval)
def save_ti(ticker, interval='daily'):
ti = TechIndicators(key='WS8K5VM8FZL52BTH', output_format='pandas')
ti.get_sma(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_sma.csv')
call_check()
ti.get_ema(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_ema.csv')
call_check()
ti.get_wma(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_wma.csv')
call_check()
ti.get_dema(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_dema.csv')
call_check()
ti.get_tema(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_tema.csv')
call_check()
ti.get_trima(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_trima.csv')
call_check()
ti.get_kama(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_kama.csv')
call_check()
ti.get_mama(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_mama.csv')
call_check()
ti.get_t3(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_t3.csv')
call_check()
ti.get_macd(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_macd.csv')
call_check()
ti.get_macdext(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_macdext.csv')
call_check()
ti.get_stoch(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_stoch.csv')
call_check()
ti.get_stochf(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_stochf.csv')
call_check()
ti.get_rsi(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_rsi.csv')
call_check()
ti.get_stochrsi(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_stochris.csv')
call_check()
ti.get_willr(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_willr.csv')
call_check()
ti.get_adx(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_adx.csv')
call_check()
ti.get_adxr(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_adxr.csv')
call_check()
ti.get_apo(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_apo.csv')
call_check()
ti.get_ppo(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_ppo.csv')
call_check()
ti.get_mom(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_mom.csv')
call_check()
ti.get_bop(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_bop.csv')
call_check()
ti.get_cci(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_cci.csv')
call_check()
ti.get_cmo(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_cmo.csv')
call_check()
ti.get_roc(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_roc.csv')
call_check()
ti.get_rocr(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_rocr.csv')
call_check()
ti.get_aroon(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_aroon.csv')
call_check()
ti.get_aroonosc(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_aroonosc.csv')
call_check()
ti.get_mfi(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_mfi.csv')
call_check()
ti.get_trix(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_trix.csv')
call_check()
ti.get_ultosc(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_ultosc.csv')
call_check()
ti.get_dx(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_dx.csv')
call_check()
ti.get_minus_di(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_minus_di.csv')
call_check()
ti.get_plus_di(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_plus_di.csv')
call_check()
ti.get_minus_dm(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_minus_dm.csv')
call_check()
ti.get_plus_dm(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_plus_dm.csv')
call_check()
ti.get_bbands(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_bbands.csv')
call_check()
ti.get_midpoint(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_midpoint.csv')
call_check()
ti.get_midprice(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_midprice.csv')
call_check()
ti.get_sar(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_sar.csv')
call_check()
ti.get_trange(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_trange.csv')
call_check()
ti.get_atr(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_atr.csv')
call_check()
ti.get_natr(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_natr.csv')
call_check()
ti.get_ad(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_ad.csv')
call_check()
ti.get_adosc(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_adosc.csv')
call_check()
ti.get_obv(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_obv.csv')
call_check()
ti.get_ht_trendline(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_ht_trendline.csv')
call_check()
ti.get_ht_sine(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_ht_sine.csv')
call_check()
ti.get_ht_trendmode(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_ht_trendmode.csv')
call_check()
ti.get_ht_dcperiod(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_dcperiod.csv')
call_check()
ti.get_ht_dcphase(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_dcphase.csv')
call_check()
ti.get_ht_phasor(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_ht_phasor.csv')
