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 data_to_csv(ticker): # Your key here - keep it key = '6LGG0QGAGBROB2M6' dire = 'DATA/'+ticker+'/' no_dir = True i = 0 while no_dir: if os.path.isdir(dire+str(i)): i += 1 else: dire += str(i) + '/' os.mkdir(dire) no_dir = False # break out of loop print(dire) ts = TimeSeries(key=key, output_format='pandas') ti = TechIndicators(key=key, output_format='pandas') ### PRICE ############################################################### intra, meta = ts.get_intraday(symbol=ticker, interval='5min', outputsize='full') intra.to_csv(dire+ticker+"_prices.csv") ### SMA ################################################################# sma8, meta = ti.get_sma(symbol=ticker, interval='5min', time_period=8) sma8.to_csv(dire+ticker+"_sma8.csv") ### EMA ################################################################# ema8, meta = ti.get_ema(symbol=ticker, interval='5min', time_period=8) ema8.to_csv(dire+ticker+"_ema8.csv") ### MACD ################################################################ macd, meta = ti.get_macd(symbol=ticker, interval='5min') macd.to_csv(dire+ticker+"_macd.csv")
def ema(self): #exponential moving average
d = TechIndicators(key=self.api_key, output_format='pandas')
data, meta_data = d.get_ema(symbol=self.stock_name,
interval='15min',
time_period=20)
return data
def save_all_indicators(stock_name):
'''
Saves everything to local .csv files with corresponding name
@DEV NOTE: can only process any 5 of them, the other 2 will encounter unexpected error
:param stock_name:
:return: None
'''
ti = TechIndicators(key=Env.alpha_vantage_api_key, output_format='pandas')
d, _ = ti.get_cci(symbol=stock_name, interval='daily', time_period=60)
d.to_csv(stock_name + '_CCI.csv', index=True, sep=',')
d, _ = ti.get_rsi(symbol=stock_name, interval='daily', series_type='close', time_period='60')
d.to_csv(stock_name + '_RSI.csv', index=True, sep=',')
d, _ = ti.get_stoch(symbol=stock_name, interval='daily')
d.to_csv(stock_name + '_stoch.csv', index=True, sep=',')
d, _ = ti.get_macd(symbol=stock_name, interval='daily', series_type='close')
d.to_csv(stock_name + '_MACD.csv', index=True, sep=',')
d, _ = ti.get_obv(symbol=stock_name, interval='daily')
d.to_csv(stock_name + '_OBV.csv', index=True, sep=',')
d, _ = ti.get_ema(symbol=stock_name, interval='daily', series_type='close')
d.to_csv(stock_name + '_EMA.csv', index=True, sep=',')
d, _ = ti.get_roc(symbol=stock_name, interval='daily', time_period=60, series_type='close')
d.to_csv(stock_name + '_ROC.csv', index=True, sep=',')
def ema(self, tp):
e = TechIndicators(key=self.api_key2, output_format='json')
data, meta_data = e.get_ema(symbol=self.stock_name,
interval='1min',
time_period=tp,
series_type='close')
return 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 EMA(stock_name):
'''
Exponential Moving Average (指数移动平均)
https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average
'''
ti = TechIndicators(key=Env.alpha_vantage_api_key, output_format='pandas')
data, meta_data = ti.get_ema(symbol=stock_name, interval='daily', series_type='close')
# data.to_csv(stock_name + '_EMA indicator.csv', index=True, sep=',')
return data.to_json(orient='split')
def getEMA200(symb):
whatever = None
while whatever is None:
try:
ti = TechIndicators(key='KU7YQYX4LFOFNQTU', output_format='pandas')
data, metadata = ti.get_ema(symbol=symb,
interval='1min',
time_period='200',
series_type='close')
whatever = data
return data
except KeyError:
pass
def ema_ratio(ticker_list, slow=10, fast=5):
ti = TechIndicators(key=alpha_key, output_format='pandas')
for ticker in ticker_list:
print(ticker)
slow_df, meta = ti.get_ema(ticker, interval='daily', time_period=slow)
fast_df, meta = ti.get_ema(ticker, interval='daily', time_period=fast)
ratio_df = slow_df.EMA / fast_df.EMA
ratio_df.fillna(1, inplace=True)
ratio_df = ratio_df.rename('{0}_{1}_mar'.format(str(slow), str(fast)))
# ratio_df = ratio_df.set_index(df.index)
ratio_df.to_csv('data/{0}_{1}_mar/{2}.csv'.format(
str(slow), str(fast), ticker),
header=True)
def get_indicators(ticker):
key = 'VDWEV1WBSQCQRZDF'
ti = TechIndicators(key=key, output_format='pandas')
ts = TimeSeries(key=key, output_format='pandas')
# RSI
data_rsi, meta_data_rsi = ti.get_rsi(symbol=ticker,
interval='30min',
time_period='14',
series_type='close')
rsi = float(data_rsi.tail(1)["RSI"].iloc[-1])
# MACD
data_macd, meta_data_macd = ti.get_macd(symbol=str(ticker),
interval='30min',
series_type='close',
fastperiod='12',
slowperiod='26',
signalperiod='9')
macd = float(data_macd["MACD_Hist"].head(1).iloc[-1])
prevMacd = float(data_macd["MACD_Hist"].head(2).iloc[-1])
# EMA
data_ema, meta_data_ema = ti.get_ema(ticker,
interval='30min',
time_period=9,
series_type='close')
ema = data_ema["EMA"].tail(1).iloc[-1]
# Stoch
# data_stoch, meta_data_stoch = ti.get_stoch(symbol = ticker, interval='30min')
# print(data_stoch.head(1))
# slowD = data_stoch.head(1)['SlowD'].iloc[-1]
# slowK = data_stoch.head(1)['SlowK'].iloc[-1]
# Intraday
data_intra, meta_data_intra = ts.get_intraday(ticker,
interval='30min',
outputsize='compact')
low = data_intra.head(1)['3. low'].iloc[-1]
prevLow = data_intra.head(2)['3. low'].iloc[-1]
# print(data_intra.head(3))
# lastLow = data_intra.head(3)['4. close'].iloc[-1]
# volume = data_intra.head(1)['5. volume'].iloc[-1]
# prevVol = data_intra.head(2)['5. volume'].iloc[-1]
return ((low > ema) and (macd > float(0.05)) and (rsi < 71)
and (macd - prevMacd > 0.0) and (low < float(100))
and ((low - prevLow) > 0.0) and ((low - lastLow) > 0.0))
def getEMA(message):
try:
ts = TechIndicators(key='ZBRM5PCHPIYQV8BY', output_format='pandas')
data, meta_data = ts.get_ema(tic, interval=interval, time_period=60)
data.plot()
plt.title('ema indicator for ' + tic + ' stock')
plt.savefig('ema.png')
plt.close()
photo = open("ema.png", 'rb')
bot.send_photo(message.chat.id, photo)
except Exception as e:
markup = types.ReplyKeyboardMarkup()
markup.row('get back')
bot.send_message(message.chat.id,
"ooops... something got wrong",
reply_markup=markup)
def getEMA26(self):
whatever = None
while whatever is None:
try:
ti = TechIndicators(key='JMCHDT9XJ90DOIQL',
output_format='pandas')
data, metadata = ti.get_ema(symbol=self.symbol,
interval='1min',
time_period='26',
series_type='close')
whatever = data
return [
float(data.iloc[data.shape[0] - 2]),
float(data.iloc[data.shape[0] - 1])
]
except KeyError:
pass
def fetch_stock_data(ticker, sma=False, ema=False): api_key = 'FP2YNP4PVWVSKZ4J' ts = TimeSeries(key=api_key, output_format='pandas') ts_data, ti_meta_data = ts.get_daily(symbol=ticker, outputsize='full') output_df = ts_data if sma or rsi or ema: ti = TechIndicators(key=api_key, output_format='pandas') if sma: sma_data, sma_meta_data = ti.get_sma(symbol=ticker, interval='daily', time_period=180, series_type='open') output_df = pd.merge(output_df, sma_data, left_index=True, right_index=True) if ema: ema_data, ema_meta_data = ti.get_ema(symbol=ticker, interval='daily', time_period=20, series_type='open') output_df = pd.merge(output_df, ema_data, left_index=True, right_index=True) print(output_df.head()) return output_df
def getIndicators(stocks, plot=False, write_to_csv=False):
for stock in stocks:
ti = TechIndicators(key='UOVDY8C1SRAQHY5D', output_format='pandas')
data, meta_data = ti.get_ema(symbol=stock,
interval='60min',
time_period=20)
for key, val in meta_data.items():
print(key[3:] + ': ' + str(val))
if plot:
data.plot()
plt.title('Ema Indicator for the ' + stock + ' stock (60 min)')
plt.xticks(
rotation=15) # Help prevent axis labels from overlapping
plt.show()
if write_to_csv:
csv_data = pd.DataFrame(data)
csv_data.to_csv("../indicators_data/EMA/" + stock + ".csv",
index=False)
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
# get PriceData
weekly_price, price_meta = ts.get_weekly_adjusted(symbol)
del weekly_price["7. dividend amount"]
weekly_price = weekly_price.sort_index() # sort to newst date last
# get MACD
macd_weekly, macd_meta = ti.get_macd(symbol,
interval="weekly",
fastperiod=12,
slowperiod=26,
signalperiod=9)
macd_weekly = macd_weekly.sort_index()
# get EMA
ema13_weekly, ema13_meta = ti.get_ema(symbol,
interval="weekly",
time_period=13)
ema13_weekly = ema13_weekly.sort_index()
# join everything
weekly_data = weekly_price.join(macd_weekly.join(ema13_weekly))
def elder_impulse(idx):
if idx == 0:
return 0
emaCur = weekly_data["EMA"].iat[idx]
emaLast = weekly_data["EMA"].iat[idx - 1]
mhCur = weekly_data["MACD_Hist"].iat[idx]
mhLast = weekly_data["MACD_Hist"].iat[idx - 1]
period = 60
symbol = 'MSFT'
interval = '60min' #1min, 15min, 30min, 60min
ts = TimeSeries(key=api_key, output_format='pandas')
data_ts, meta_data_ts = ts.get_intraday(symbol=symbol,
interval=interval,
outputsize='full')
ti_sma = TechIndicators(key=api_key, output_format='pandas')
data_ti_sma, meta_data_ti_sma = ti_sma.get_sma(symbol=symbol,
interval=interval,
time_period=period,
series_type='close')
ti_ema = TechIndicators(key=api_key, output_format='pandas')
data_ti_ema, meta_data_ti_ema = ti_ema.get_ema(symbol=symbol,
interval=interval,
time_period=period,
series_type='close')
ti_rsi = TechIndicators(key=api_key, output_format='pandas')
data_ti_rsi, meta_data_ti_rsi = ti_rsi.get_rsi(symbol=symbol,
interval=interval,
time_period=period,
series_type='close')
# In[5]:
df1 = data_ti_sma
df2 = data_ti_ema
df3 = data_ti_rsi
df4 = data_ts['4. close'].iloc[period - 1::]
#df2.index = df1.index
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 data_to_csv(ticker, dire):
# Your key here - keep it - not an issue, free
key = '6LGG0QGAGBROB2M6'
# make the directory if it doesn't exist
if os.path.isdir(dire) == False:
os.mkdir(ticker)
os.mkdir(dire)
ts = TimeSeries(key=key, output_format='pandas')
ti = TechIndicators(key=key, output_format='pandas')
### PRICE ###############################################################
intra, meta = ts.get_intraday(symbol=ticker,
interval='1min',
outputsize='full')
intra.to_csv(dire + ticker + "_prices.csv")
### SMA #################################################################
sma8, meta = ti.get_sma(symbol=ticker, interval='1min', time_period=8)
sma8.to_csv(dire + ticker + "_sma8.csv")
### EMA #################################################################
ema8, meta = ti.get_ema(symbol=ticker, interval='1min', time_period=8)
ema8.to_csv(dire + ticker + "_ema8.csv")
### VWAP ################################################################
vwap, meta = ti.get_vwap(symbol=ticker, interval='1min')
vwap.to_csv(dire + ticker + "_vwap.csv")
### MACD ################################################################
macd, meta = ti.get_macd(symbol=ticker, interval='1min')
macd.to_csv(dire + ticker + "_macd.csv")
# they limit user to 5 calls per minute and 500 per day for free access #
print("waiting so we don't overload API (60 seconds...)")
time.sleep(60)
### STOCH ###############################################################
stoch, meta = ti.get_stoch(symbol=ticker, interval='1min')
stoch.to_csv(dire + ticker + "_stoch.csv")
### RSI #################################################################
rsi, meta = ti.get_rsi(symbol=ticker, interval='1min', time_period=60)
rsi.to_csv(dire + ticker + "_rsi.csv")
### ADX #################################################################
adx, meta = ti.get_adx(symbol=ticker, interval='1min', time_period=60)
adx.to_csv(dire + ticker + "_adx.csv")
### CCI #################################################################
cci, meta = ti.get_cci(symbol=ticker, interval='1min', time_period=60)
cci.to_csv(dire + ticker + "_cci.csv")
# TODO: These are tricky, several lines, parse differently, separate indices really
### AROON ###############################################################
aroon, meta = ti.get_aroon(symbol=ticker, interval='1min', time_period=60)
aroon.to_csv(dire + ticker + "_aroon.csv")
# they limit user to 5 calls per minute and 500 per day for free access #
print("waiting so we don't overload API (60 seconds...)")
time.sleep(60)
### BBANDS ###############################################################
bbands, meta = ti.get_bbands(symbol=ticker,
interval='1min',
time_period=60)
bbands.to_csv(dire + ticker + "_bbands.csv")
'''
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
import pandas as pd
API_KEY = "your key"
listOfTickers = ["ASX:CBA"] # , "ASX:CBA", "ASX:MP1"
# Chose your output format, or default to JSON (python dict)
ts = TimeSeries(API_KEY, output_format='pandas')
ti = TechIndicators(API_KEY)
# Get the data, returns a tuple
# data is a pandas dataframe, meta_data is a dict
for ticker in listOfTickers:
df, df_meta_data = ts.get_daily(symbol=ticker, outputsize='full')
# aapl_sma is a dict, aapl_meta_sma also a dict
df_ema, df_meta_sma = ti.get_ema(symbol=ticker)
df = df.rename_axis(None)
# generate output file based on current ticker name. Removes "ASX:"
outfilename = "D:/00git/alphaAdvantageStocks/TestDeleteOutput" + ticker.replace(
'ASX:', '') + ".pkl"
test_df = pd.DataFrame.from_dict(df_ema, orient='index', columns=['EMA'])
print(test_df, df)
df = pd.concat([df, test_df], axis=1)
# df["Vol20dAvrg"] = map(is_hammer, df["Open"], df["Low"], df["Close"], df["High"])
print(df.tail())
# with open(outfilename, 'w') as outfile:
# aapl_data.to_pickle(outfilename)
# # Visualization
# figure(num=None, figsize=(15, 6), dpi=80, facecolor='w', edgecolor='k')
def get_daily_history(token_1, symbol, amount_of_entries=1000):
ts_1 = TimeSeries(key=token_1, output_format='pandas')
ti_1 = TechIndicators(key=token_1, output_format='pandas')
print('get_daily_history')
interval = 'daily'
series_type = 'open'
# Fetch dataset from api
# OHLC series #1
time_series, meta_time_series = ts_1.get_daily(symbol=symbol, outputsize='full')
# Bollinger Bands #2
bbands, meta = ti_1.get_bbands(symbol=symbol, interval=interval, time_period=60, series_type=series_type)
bbands = bbands.sort_index(ascending=False)
# SMA10 or MA10 #3
sma_10, meta = ti_1.get_sma(symbol=symbol, interval=interval, time_period=10, series_type=series_type)
sma_10 = sma_10.sort_index(ascending=False)
# SMA5 or MA5 #4
sma_5, meta = ti_1.get_sma(symbol=symbol, interval=interval, time_period=5, series_type=series_type)
sma_5 = sma_5.sort_index(ascending=False)
# ROC #5
roc_20, meta = ti_1.get_roc(symbol=symbol, interval=interval, time_period=20, series_type=series_type)
roc_20 = roc_20.sort_index(ascending=False)
# Wait 70 seconds
print('waiting 70 seconds');
sleep(70)
# MACD #1
macd, meta = ti_1.get_macd(symbol=symbol, interval=interval, series_type=series_type)
macd = macd.sort_index(ascending=False)
# CCI #2
cci_20, meta = ti_1.get_cci(symbol=symbol, interval=interval, time_period=20)
cci_20 = cci_20.sort_index(ascending=False)
# ATR #3
atr_20, meta = ti_1.get_atr(symbol=symbol, interval=interval, time_period=20)
atr_20 = atr_20.sort_index(ascending=False)
# EMA20 #4
ema_20, meta = ti_1.get_ema(symbol=symbol, interval=interval, time_period=20, series_type=series_type)
ema_20 = ema_20.sort_index(ascending=False)
# MTM6 #5
mtm_6, meta = ti_1.get_mom(symbol=symbol, interval=interval, time_period=180, series_type=series_type)
mtm_6 = mtm_6.sort_index(ascending=False)
# Wait 70 seconds
print('waiting 70 seconds');
sleep(70)
# MTM12 #1
mtm_12, meta = ti_1.get_mom(symbol=symbol, interval=interval, time_period=360, series_type=series_type)
mtm_12 = mtm_12.sort_index(ascending=False)
# Get last n data points in the dataset and set your respective column name
d1 = time_series[:amount_of_entries];
d1.columns = ['open', 'high', 'low', 'close', 'volume']
d2 = sma_10[:amount_of_entries]
d2.columns = ['sma10']
d3 = sma_5[:amount_of_entries]
d3.columns = ['sma5']
d4 = pd.DataFrame(bbands['Real Middle Band'][:amount_of_entries])
d4.columns = ['bbands']
d5 = roc_20[:amount_of_entries]
d5.columns = ['roc']
d6 = pd.DataFrame(macd['MACD'][:amount_of_entries])
d6.columns = ['macd']
d7 = cci_20[:amount_of_entries]
d7.columns = ['cci']
d8 = atr_20[:amount_of_entries]
d8.columns = ['atr']
d9 = ema_20[:amount_of_entries]
d9.columns = ['ema20']
d10 = mtm_6[:amount_of_entries]
d10.columns = ['mtm6']
d11 = mtm_12[:amount_of_entries]
d11.columns = ['mtm12']
# Merge elements
merged = d1.merge(d2, left_index=True, right_index=True)
merged = merged.merge(d3, left_index=True, right_index=True)
merged = merged.merge(d4, left_index=True, right_index=True)
merged = merged.merge(d5, left_index=True, right_index=True)
merged = merged.merge(d6, left_index=True, right_index=True)
merged = merged.merge(d7, left_index=True, right_index=True)
merged = merged.merge(d8, left_index=True, right_index=True)
merged = merged.merge(d9, left_index=True, right_index=True)
merged = merged.merge(d10, left_index=True, right_index=True)
merged = merged.merge(d11, left_index=True, right_index=True)
sep = os.path.sep
filename = 'datasets'+sep+'history_'+symbol+'.csv'
merged.to_csv(filename, index=True)
return merged, meta_time_series
'QCOM', 'SLAB', 'SIMO', 'SWKS', 'TSM', 'TER', 'TXN', 'XLNX', 'GOOG',
'SBUX', 'SNE', 'TSLA', 'MMM', 'AXP', 'AAPL', 'BA', 'CAT', 'CVX', 'CSCO',
'KO', 'DIS', 'DD', 'XOM', 'GE', 'GS', 'HD', 'IBM', 'INTC', 'JNJ', 'JPM',
'MCD', 'MRK', 'MSFT', 'NKE', 'PFE', 'PG', 'TRV', 'UTX', 'UNH', 'VZ', 'V',
'WMT', 'MU', 'MRNA', 'NIO', 'GILD', 'AMD', 'AMZN', 'T', 'COST', 'NFLX',
'PYPL'
]
watchlist = [
'TSLA', 'AAPL', 'AMZN', 'MSFT', 'GOOG', 'FB', 'AMD', 'NVDA', 'NIO', 'PLTR',
'SQ'
]
sybmL = ['NKLA']
for sym in sybmL:
data5, meta_data5 = ti.get_ema(symbol=sym,
interval='daily',
time_period=5,
series_type='close')
time.sleep(12)
pprint(data5.tail(1))
data20, meta_data20 = ti.get_ema(symbol=sym,
interval='daily',
time_period=20,
series_type='close')
time.sleep(12)
pprint(data20.tail(1))
fiveTwenty = data5.tail(1).values[0] > data20.tail(1).values[0]
print(sym + " " + str(fiveTwenty))
def main_job():
global data_EMA_10, data_EMA_20, data_MACD, data_BBands, data_Price
API_key = '5LDF8BV8UHC3F4Y7'
TI = TechIndicators(key=API_key, output_format='json')
TS = TimeSeries(key=API_key, output_format='json')
Stop_1 = True
while Stop_1:
try:
symbol = 'USDCHF'
interval = '5min'
slowperiod = '26'
signalperiod = '9'
fastperiod = '12'
series_type = 'close'
time_period = '20'
outputsize = "compact"
data_Price, meta_data_Price = TS.get_intraday(
symbol=symbol, interval=interval, outputsize=outputsize)
data_BBands, meta_data_BBands = TI.get_bbands(
symbol=symbol,
interval=interval,
time_period=time_period,
series_type=series_type)
time_period = '10'
data_EMA_10, meta_data_EMA_50 = TI.get_ema(symbol=symbol,
interval=interval,
time_period=time_period,
series_type=series_type)
time_period = '20'
data_EMA_20, meta_data_EMA_200 = TI.get_ema(
symbol=symbol,
interval=interval,
time_period=time_period,
series_type=series_type)
data_MACD, meta_data_MACD = TI.get_macd(symbol=symbol,
interval=interval,
series_type=series_type,
fastperiod=fastperiod,
slowperiod=slowperiod,
signalperiod=signalperiod)
Stop_1 = False
except (UnboundLocalError, ValueError, MaxRetryError, ConnectionError,
SSLError) as e:
print(e)
time.sleep(60)
pass
# print(data_EMA_10)
# print(data_EMA_20)
# Retrieve EMA 10 and EMA 20 data
"""EMA 10"""
EMA_10 = list(data_EMA_10.values())
"""Current ema 10"""
level = EMA_10[0].values()
current_ema_10 = (float([x for x in level][0]))
"""Previous ema 10"""
level = EMA_10[1].values()
previous_ema_10 = (float([x for x in level][0]))
"""EMA 20"""
EMA_20 = list(data_EMA_20.values())
"""Current ema 20"""
level = EMA_20[0].values()
current_ema_20 = (float([x for x in level][0]))
"""Previous ema 20"""
level = EMA_20[1].values()
previous_ema_20 = (float([x for x in level][0]))
# Check for crossover between EMA 10 and EMA 20
current_diff = current_ema_10 - current_ema_20
prev_diff = previous_ema_10 - previous_ema_20
file_name = 'Crossing_Flags_EMA.json'
with open(file_name) as f_obj:
data = f_obj.read()
retrieve = json.loads(data)
Prev_Cross_Up = retrieve['Cross_Up']
Prev_Cross_Down = retrieve['Cross_Down']
Crossing_Flag = {}
if current_diff >= 0 > prev_diff:
Crossing_Flag['Cross_Up'] = Prev_Cross_Up
Crossing_Flag['Cross_Down'] = True
elif current_diff <= 0 < prev_diff:
Crossing_Flag['Cross_Up'] = True
Crossing_Flag['Cross_Down'] = Prev_Cross_Down
else:
Crossing_Flag['Cross_Up'] = Prev_Cross_Up
Crossing_Flag['Cross_Down'] = Prev_Cross_Down
# Update the Crossing flag folder
file_name = "Crossing_Flags_EMA.json"
with open(file_name, "w") as f_obj:
json.dump(Crossing_Flag, f_obj)
# Retrieve MACD data
"""MACD"""
MACD = list(data_MACD.values())
current_Hist = float(MACD[0]['MACD_Hist'])
prev_Hist = float(MACD[1]['MACD_Hist'])
file_name = 'Crossing_Flags_MACD.json'
with open(file_name) as f_obj:
data = f_obj.read()
retrieve = json.loads(data)
Prev_Cross_Up = retrieve['Cross_Up']
Prev_Cross_Down = retrieve['Cross_Down']
Crossing_Flag = {}
if "-" not in str(prev_Hist) and "-" in str(current_Hist):
Crossing_Flag['Cross_Up'] = Prev_Cross_Up
Crossing_Flag['Cross_Down'] = True
elif "-" in str(prev_Hist) and "-" not in str(current_Hist):
Crossing_Flag['Cross_Up'] = True
Crossing_Flag['Cross_Down'] = Prev_Cross_Down
else:
Crossing_Flag['Cross_Up'] = Prev_Cross_Up
Crossing_Flag['Cross_Down'] = Prev_Cross_Down
file_name = "Crossing_Flags_MACD.json"
with open(file_name, "w") as f_obj:
json.dump(Crossing_Flag, f_obj)
# Retrieve crossing data for EMA and MACD
file_name = 'Crossing_Flags_MACD.json'
with open(file_name) as f_obj:
data = f_obj.read()
retrieve = json.loads(data)
MACD_Up = retrieve['Cross_Up']
MACD_Down = retrieve['Cross_Down']
file_name = 'Crossing_Flags_EMA.json'
with open(file_name) as f_obj:
data = f_obj.read()
retrieve = json.loads(data)
EMA_Up = retrieve['Cross_Up']
EMA_Down = retrieve['Cross_Down']
# Retrieve price and bollinger band data
BBands = list(data_BBands.values())
middle_band = float(BBands[0]['Real Middle Band'])
Price = list(data_Price.values())
Price = float(Price[0]['4. close'])
Trigger_Flags = {}
if MACD_Up and EMA_Up:
if Price < middle_band:
Buy_flag = True
Sell_flag = False
else:
Buy_flag = False
Sell_flag = False
elif MACD_Down and EMA_Down:
if Price > middle_band:
Buy_flag = False
Sell_flag = True
else:
Buy_flag = False
Sell_flag = False
else:
Buy_flag = False
Sell_flag = False
Trigger_Flags["Buy_flag"] = Buy_flag
Trigger_Flags["Sell_flag"] = Sell_flag
file_name = "Trigger_Flags.json"
with open(file_name, "w") as f_obj:
json.dump(Trigger_Flags, f_obj)
data, metadata =TS.get_intraday(symbol=Ticker, interval=datainterval,outputsize="full") #names the columns and gets intraday data for symbol MSFT (generalize this later) d1=data[::-1] #for some reason the time series data is list in reverse in the dataframe, this corrects that d1subset=d1[startdatetime:enddatetime] #creating technical indicators for buy and sell signals TI=TechIndicators(key=APIkey,output_format='pandas') #getting technical indicator and setting output to pandas dataframe dataSMA200, metadataSMA200 = TI.get_sma(symbol=Ticker, interval=datainterval, time_period=200) #200 periodSMA dataSMA200subset = dataSMA200[startdatetime:enddatetime] dataSMA, metadataSMA = TI.get_sma(symbol=Ticker, interval=datainterval, time_period=50) #50 periodSMA dataSMAsubset = dataSMA[startdatetime:enddatetime] dataEMA, metadataEMA = TI.get_ema(symbol=Ticker, interval=datainterval,time_period=10) #20 PeriodEMA dataEMAsubset = dataEMA[startdatetime:enddatetime] # dataRSI, metadataRSI = TI.get_rsi(symbol=Ticker, interval=datainterval,time_period=14) # dataRSIsubset = dataRSI[startdatetime:enddatetime] ### creating data frame with all of closing price and technical indicators total_df = pd.concat([d1subset["4. close"],dataSMAsubset,dataSMA200subset, dataEMAsubset], axis=1) total_df.columns=["Closing_Price","50_SMA","200_SMA","10_EMA"] #create buy and sell signals based off of moving average crossover. #genereates BUY signal when 10 EMA crosses 50SMA while above 200 SMA, SELL when crossing back below 50SMA #Also generates BUY signal when 10 EMA crosses above 200 SMA, signifying the the stock has strong momentum, sells if 10EMA crosses below 50SMA or 200SMA
# datetime have differrent format
for idx, el in enumerate(data.index._data):
data.index._data[idx] = el[:-3]
data["trend"] = data["4. close"].subtract(data["1. open"], fill_value=0)
del data["4. close"]
del data["1. open"]
print data.shape
ti = TechIndicators(key=apikey, output_format='pandas')
#1
bbands, meta_data_bbands = ti.get_bbands(symbol=stock, interval=t_period, series_type=series_type)
print bbands.shape
#2
ema, meta_data_ema = ti.get_ema(symbol=stock, interval=t_period, series_type= series_type)
print ema.shape
#3
wma, meta_data_wma = ti.get_wma(symbol=stock, interval=t_period, series_type= series_type)
print wma.shape
#4
sma, meta_data_sma = ti.get_sma(symbol=stock, interval=t_period, series_type= series_type)
print sma.shape
#5
rsi, meta_data_rsi = ti.get_rsi(symbol=stock, interval=t_period, series_type= series_type)
print rsi.shape
#6
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)
pass
# Get json object with the intraday data and another with the call's metadata
# data, meta_data = ts.get_intraday('GOOGL', interval='15min')
# print (data)
# this strategy takes account of ema4/8, sma20/50, bb20, macd12/26, rsi14,
# and volume.
# It detects abnomal activities, and suggests a potential trend base on
# analysises from multiple time-window
count = 10
trend_period = 5
ema4his = deque(maxlen=trend_period)
ema8his = deque(maxlen=trend_period)
his_len = 5
while True:
ema4, ema4_meta = ti.get_ema(symbol='GOOGL', interval='1min', time_period=4)
ema8, ema8_meta = ti.get_ema(symbol='GOOGL', interval='1min', time_period=8)
# sorted4ema = collections.OrderedDict(sorted(ema4.keys()))
keys = sorted(ema4)[-trend_period:]
print (keys[-1:])
v4 = []
v8 = []
t = []
for k in keys:
v4.append(float(ema4[k]['EMA']))
v8.append(float(ema8[k]['EMA']))
t.append(time.mktime(dateutil.parser.parse(k).timetuple()))
v4 = np.array(v4)
v8 = np.array(v8)
t = np.array(t)
outputsize = 'compact'
interval = input(
'Interval- 1min,5min,15min,30min,60min,daily,weekly,monthly : ')
time = input('Time Period : ')
tech_indi = input(
'Technical Indicator- SMA,EMA,VWAP,MACD,Stochastic Oscillator,RSI,Bollinger bands :'
)
ti = TechIndicators(key, output_format='pandas')
if tech_indi == 'SMA':
state = ti.get_sma(symbol,
interval=interval,
time_period=time,
series_type='close')[0]
elif tech_indi == 'EMA':
state = ti.get_ema(symbol,
interval=interval,
time_period=time,
series_type='close')[0]
elif tech_indi == 'VWAP':
state = ti.get_vwap(symbol, interval=interval)[0]
elif tech_indi == 'MACD':
state = ti.get_macd(symbol, interval=interval, series_type='close')[0]
elif tech_indi == 'Stochastic Oscillator':
state = ti.get_stoch(symbol, interval=interval)[0]
elif tech_indi == 'RSI':
state = ti.get_rsi(symbol,
interval=interval,
time_period=time,
series_type='close')[0]
elif tech_indi == 'Bollinger bands':
state = ti.get_bbands(symbol,
interval=interval,
#support.plot()
stonks.plot()
m, b = np.polyfit(highindexes, highpoints, 1)
# toppy line ^^
a, z = np.polyfit(lowindexes, lowpoints, 1)
# m, b = np.polyfit(stonks.index,stonks.values, 1)
# plt.plot(stonks.index, ((a + m) / 2) * stonks.index + ((b + z) / 2))
plt.plot(stonks.index, m * stonks.index + b)
plt.plot(stonks.index, a * stonks.index + z)
plt.ylabel("Price ($)")
plt.xlabel("Days")
t1 = TechIndicators(output_format='pandas', key=key)
ema200, meta = t1.get_ema(symbol=symbol,
interval='daily',
time_period=200,
series_type='close')
ema50, meta = t1.get_ema(symbol=symbol,
interval='daily',
time_period=50,
series_type='close')
ema200 = ema200.tail(100)
ema200.index = np.arange(100)
ema50 = ema50.tail(100)
ema50.index = np.arange(100)
ema50_m, ema50_b = np.polyfit(ema50.index, ema50, 1)
ema200_m, ema200_b = np.polyfit(ema200.index, ema200, 1)
plt.plot(ema50.index,
ema50_m * ema50.index + ema50_b,
label="50 Day Moving Average")
def init_data(comp, interval):
# Input all the technical indicators and the stock times series data
# Initialize the AlphaVantage API call functions
ts = TimeSeries(key='L2O2TYTG382ETN0N', output_format='pandas')
ti = TechIndicators(key='L2O2TYTG382ETN0N', output_format='pandas')
# Other keys: QB5APLD84E50TQAC, L2O2TYTG382ETN0N
# Fetch the simple moving average (SMA) values
main_data, meta_data = ti.get_sma(symbol=comp,
interval='60min',
time_period=20,
series_type='close')
time.sleep(3)
# Fetch the exponential moving average (EMA) values
ema_data, meta_data = ti.get_ema(symbol=comp,
interval='60min',
time_period=20,
series_type='close')
main_data['EMA'] = ema_data
time.sleep(3)
"""
# Fetch the weighted moving average (WMA) values
wma_data, meta_data = ti.get_wma(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['WMA'] = wma_data
# Fetch the double exponential moving agerage (DEMA) values
dema_data, meta_data = ti.get_dema(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['DEMA'] = dema_data
# Fetch the triple exponential moving average (TEMA) values
tema_data, meta_data = ti.get_tema(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['TEMA'] = tema_data
# Fetch the triangular moving average (TRIMA) values
trima_data, meta_data = ti.get_trima(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['TRIMA'] = trima_data
# Fetch the Kaufman adaptive moving average (KAMA) values
kama_data, meta_data = ti.get_kama(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['KAMA'] = kama_data
# Fetch the MESA adaptive moving average (MAMA) values
mama_data, meta_data = ti.get_mama(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['MAMA'] = mama_data['MAMA']
main_data['FAMA'] = mama_data['FAMA']
# Fetch the triple exponential moving average (T3) values
t3_data, meta_data = ti.get_t3(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['T3'] = t3_data
"""
# Fetch the moving average convergence / divergence (MACD) values
macd_data, meta_data = ti.get_macd(symbol=comp,
interval='60min',
series_type='close')
main_data['MACD'] = macd_data['MACD']
main_data['MACD_Hist'] = macd_data['MACD_Hist']
main_data['MACD_Signal'] = macd_data['MACD_Signal']
time.sleep(3)
"""
# Fetch the moving average convergence / divergence values with controllable moving average type
macdext_data, meta_data = ti.get_macdext(symbol=comp,interval='60min', series_type='close')
main_data['MACDEXT'] = macdext_data['MACD']
main_data['MACDEXT_Hist'] = macdext_data['MACD_Hist']
main_data['MACDEXT_Signal'] = macdext_data['MACD_Signal']
"""
# Fetch the stochastic oscillator (STOCH) values
stoch_data, meta_data = ti.get_stoch(symbol=comp, interval='60min')
main_data['SlowK'] = stoch_data['SlowK']
main_data['SlowD'] = stoch_data['SlowD']
time.sleep(3)
"""
# Fetch the stochastic fast (STOCHF) values
stochf_data, meta_data = ti.get_stochf(symbol=comp,interval='60min')
main_data['FastK'] = stochf_data['FastK']
main_data['FastD'] = stochf_data['FastD']
"""
# Fetch the relative strength index (RSI) values
rsi_data, meta_data = ti.get_rsi(symbol=comp,
interval='60min',
time_period=10,
series_type='close')
main_data['RSI'] = rsi_data
time.sleep(3)
"""
# Fetch the stochastic relative strength index (STOCHRSI) values
stochrsi_data, meta_data = ti.get_stochrsi(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['STOCHRSI_FastK'] = stochrsi_data['FastK']
main_data['STOCHRSI_FastD'] = stochrsi_data['FastD']
# Fetch the Williams' %R (WILLR) values
willr_data, meta_data = ti.get_willr(symbol=comp,interval='60min', time_period=10)
main_data['WILLR'] = willr_data
"""
# Fetch the average directional movement index (ADX) values
adx_data, meta_data = ti.get_adx(symbol=comp,
interval='60min',
time_period=20)
main_data['ADX'] = adx_data
time.sleep(3)
"""
# Fetch the average directional movement index rating (ADXR) values
adxr_data, meta_data = ti.get_adxr(symbol=comp,interval='60min', time_period=10)
main_data['ADXR'] = adxr_data
# Fetch the absolute price oscillator (APO) values
apo_data, meta_data = ti.get_apo(symbol=comp,interval='60min', series_type='close')
main_data['APO'] = apo_data
# Fetch the percentage price oscillator (PPO) values
ppo_data, meta_data = ti.get_ppo(symbol=comp,interval='60min', series_type='close')
main_data['PPO'] = ppo_data
# Fetch the momentum (MOM) values
mom_data, meta_data = ti.get_mom(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['MOM'] = mom_data
# Fetch the balance of power (BOP) values
bop_data, meta_data = ti.get_bop(symbol=comp,interval='60min')
main_data['BOP'] = bop_data
"""
# Fetch the commodity channel index (CCI) values
cci_data, meta_data = ti.get_cci(symbol=comp,
interval='60min',
time_period=20)
main_data['CCI'] = cci_data
time.sleep(3)
"""
# Fetch the Chande momentum oscillator (CMO) values
cmo_data, meta_data = ti.get_cmo(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['CMO'] = cmo_data
# Fetch the rate of change (ROC) values
roc_data, meta_data = ti.get_roc(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['ROC'] = roc_data
# Fetch the rate of change ratio (ROCR) values
rocr_data, meta_data = ti.get_rocr(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['ROCR'] = rocr_data
time.sleep(5)
"""
# Fetch the Aroon (AROON) values
aroon_data, meta_data = ti.get_aroon(symbol=comp,
interval='60min',
time_period=20)
main_data['Aroon Down'] = aroon_data['Aroon Down']
main_data['Aroon Up'] = aroon_data['Aroon Up']
time.sleep(3)
"""
# Fetch the Aroon oscillator (AROONOSC) values
aroonosc_data, meta_data = ti.get_aroonosc(symbol=comp,interval='60min', time_period=10)
main_data['AROONOSC'] = aroonosc_data
# Fetch the money flow index (MFI) values
mfi_data, meta_data = ti.get_mfi(symbol=comp,interval='60min', time_period=10)
main_data['MFI'] = mfi_data
# Fetch the 1-day rate of change of a triple smooth exponential moving average (TRIX) values
triX_train_data['AAPL'], meta_data = ti.get_trix(symbol=comp,interval='60min', time_period=10, series_type='close')
main_data['TRIX'] = triX_train_data['AAPL']
# Fetch the ultimate oscillator (ULTOSC) values
ultosc_data, meta_data = ti.get_ultsoc(symbol=comp,interval='60min', time_period=10)
main_data['ULTOSC'] = ultosc_data
# Fetch the directional movement index (DX) values
dX_train_data['AAPL'], meta_data = ti.get_dx(symbol=comp,interval='60min', time_period=10)
main_data['DX'] = dX_train_data['AAPL']
"""
"""
# Fetch the Chaikin A/D line (AD) value
ad_data, meta_data = ti.get_ad(symbol=comp,interval='60min')
main_data['AD'] = ad_data
"""
# Fetch the on balance volume (OBV) values
obv_data, meta_data = ti.get_obv(symbol=comp, interval='60min')
main_data['OBV'] = obv_data
intraday_data, meta_data = ts.get_intraday(symbol=comp,
interval='60min',
outputsize='full')
intraday_data.index = pd.Index(
[index[:-3] for index in intraday_data.index], name='date')
#intraday_data.set_index('date')
main_data = pd.concat([main_data, intraday_data], axis=1)
print(main_data.index)
print(main_data.head())
main_data = main_data.dropna()
main_data.index.name = 'date'
company = comp
main_data.to_csv(f'./rsc/{company}_AV_data.csv', sep=',')
time.sleep(1)
print(comp)
time.sleep(5)
