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 techIndicator_get_bbands(self, timePeriod, format):
#outputting data in JSON format in the below line
ti = TechIndicators(key=settings.API_KEY, output_format=format)
data, meta_data = ti.get_bbands(symbol=self.symbol,
interval='daily',
time_period=timePeriod)
return data, meta_data
def getData(equity, MY_API_KEY):
"""
parse data for a given stock symbol.
:param equity (panda series): TimeSeries data and TechIndicators pandaframe.
"""
# Pandaframe for TimeSeries
ts = TimeSeries(key=f"{MY_API_KEY}",
output_format='pandas',
indexing_type='date')
tsdata, tsmeta_data = ts.get_intraday(symbol=equity,
interval='60min',
outputsize='full')
TS = tsdata.head(1000)
path = "data/TimeSeries/"
path += equity + ".csv"
tsdata.to_csv(path_or_buf=path)
# Pandaframe for TechIndicators
ti = TechIndicators(key=f"{MY_API_KEY}",
output_format='pandas',
indexing_type='data')
tidata, timeta_data = ti.get_bbands(symbol=equity,
interval='60min',
time_period=60)
TI = tidata.head(1000)
path = "data/TechIndicators/"
path += equity + ".csv"
tidata.to_csv(path_or_buf=path)
def get_raw_input_data(stock="DJI"):
ts = TimeSeries(key=AV_API_KEY, output_format='pandas')
d, md = ts.get_daily(symbol=stock, outputsize='compact')
d = d.loc['2020-01-01':'2020-03-27']
d[CLOSE] = d[CLOSE].pct_change()
d = d[d[CLOSE] <= 100]
d = d.dropna(axis=0)
TI = TechIndicators(key=AV_API_KEY, output_format='pandas')
rsi_data, meta_data = TI.get_rsi(symbol=stock,
interval="daily",
time_period=14)
macd_data, meta_data = TI.get_macd(symbol=stock, interval="daily")
sma_data, meta_data = TI.get_sma(symbol=stock, time_period=30)
bbands_data, meta_data = TI.get_bbands(symbol=stock)
input_data = pd.DataFrame(columns=["Volume", "Price_Change"],
index=d.index)
input_data["Volume"] = d[VOLUME]
input_data["Price_Change"] = d[CLOSE]
input_data = input_data.merge(rsi_data, left_index=True, right_index=True)
input_data = input_data.merge(sma_data, left_index=True, right_index=True)
input_data = input_data.merge(macd_data, left_index=True, right_index=True)
input_data = input_data.merge(bbands_data,
left_index=True,
right_index=True)
input_data = input_data.merge(load_covid(),
left_index=True,
right_index=True)
input_data = input_data.merge(gen_sentiment_df(stock),
left_index=True,
right_index=True)
return input_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_company_techindicators(company_symbol, interval):
ts = TechIndicators(key=apiKey, output_format='json')
# Get json object with the techindicator data and another with the call's metadata
data, meta_data = ts.get_bbands(symbol=company_symbol, interval=interval)
return data, meta_data
def checkSymbol(symbol, interval, msgInterval, mustCandleBullish = False):
bullish = False
rate = 0
gMsg = '#'+symbol + ' '+msgInterval
ts = TimeSeries(key=cfg.APIKey, output_format=cfg.format)
if interval == cfg.intervalDaily: #daily
dataTS, meta_data = ts.get_daily(symbol=symbol)
else:
dataTS, meta_data = ts.get_intraday(symbol=symbol,interval=interval)
sample = [dict() for x in range(3)]
for i in range(1,4):
sample[i-1] = {'close':dataTS['close'][-i],'high':dataTS['high'][-i],'low':dataTS['low'][-i],'open':dataTS['open'][-i]}
csp = CandleStickPattern(sample[2],sample[1],sample[0])
if csp.bullishPattern():
bullish = True
rate += 1
gMsg += '\nCandle Bull'
else:
if mustCandleBullish:
return False, gMsg, rate
ti = TechIndicators(key=cfg.APIKey, output_format=cfg.format)
##### bband
data, meta_data = ti.get_bbands(symbol=symbol, interval=interval)
# data.plot()
bbLower = data['Real Lower Band'][-1]
if sample[0]['low'] < bbLower:
bullish = True
rate += 1
gMsg += '\nBB lower band tested'
##### MACD
data, meta_data = ti.get_macd(symbol=symbol, interval=interval, fastperiod=12, slowperiod = 26, signalperiod = 9)
#print data.keys()
if data['MACD_Hist'][-1] > 0:
for i in range(2,6):
if data['MACD_Hist'][-i] < 0:
gMsg += '\nMACD cross'
bullish = True
rate += 1
break
if bullish:
data, meta_data = ti.get_rsi(symbol=symbol, interval = interval)
if data['RSI'][-1] < 50:
rate += 1
gMsg += '\nRSI ' + round(data['RSI'][-1]).__str__()
gMsg += '\nVol ' + printInK(dataTS['volume'][-1]) +" "+ printInK(dataTS['volume'][-2]) +" "+ printInK(dataTS['volume'][-3])
return bullish, gMsg, rate
def technical_indicators():
if request.method == 'POST':
ticker = request.form['ticker']
ti = TechIndicators(key='SS66OZ9EH3PLN7HS', output_format='pandas')
data, meta_data = ti.get_bbands(symbol=ticker, interval='60min', time_period=60)
plt.title('BBbands indicator')
plt.savefig("static\\images\\technical_indicators\\" + ticker)
filename = 'images/technical_indicators/'+ ticker + '.png'
print(filename)
return render_template('technical_indicators.html', technical_indicator_image = filename)
def bollinger_bands(key, symbol):
from alpha_vantage.techindicators import TechIndicators
import matplotlib.pyplot as plt
ti = TechIndicators(key=key, output_format="pandas")
data, meta_data = ti.get_bbands(symbol=symbol,
interval="60min",
time_period=60)
data.plot()
plt.title("BBbands indicator for MSFT stock (60 min)")
plt.show()
def bands(self):
try:
ti = TechIndicators(key='MY_API_KEY', output_format='pandas')
self.BBdata, meta_data = ti.get_bbands(symbol=self.entry.get(),
interval='60min',
time_period=60)
self.table_head = 'BBbands indicator for {} stock (60 min)'.format(
self.entry.get())
self.more_info.configure(state='normal')
self.BBdata.plot()
plt.title(self.table_head)
self.display_content = self.BBdata
plt.show()
except:
messagebox.showwarning("ERROR", "Información no disponible")
def filterPriceThree(self): ti = TechIndicators(key='XP9KDY0X1E13B4HN',output_format='pandas') self.ctrtwo = 0 for i in range(0, len(self.symbols)-(self.ctrtwo),1): if i > len(self.symbols): break try: print(self.symbols[i]) data, meta_data = ti.get_bbands(symbol= self.symbols[i], interval='60min',time_period= 5) except ValueError: self.symbols.pop(i) self.price.pop(i) self.ctrtwo += 1 print(self.symbols)
def pull(self): # Load Indicator ti = TechIndicators(key='XP9KDY0X1E13B4HN',output_format='pandas') # Pulls Bollinger Bands - symbol = current symbol, interval = Time between data points, time_period = number of data points data, meta_data = ti.get_bbands(symbol= self.stocks[self.temp], interval='daily',time_period= 60) # Not used atm ################# self.middle.append(data['Real Middle Band'].tolist()) ################## # Appends data points of current stock to self.upper.append(data['Real Upper Band'].tolist()) self.lower.append(data['Real Lower Band'].tolist())
def bands():
global table_head, display_content
try:
ti = TechIndicators(key='MY_API_KEY', output_format='pandas')
print(ti)
BBdata, meta_data = ti.get_bbands(symbol=entry.get(), interval='60min', time_period=60)
print(type(BBdata))
table_head = 'BBbands indicator for {} stock (60 min)'.format(entry.get())
more_info.configure(state='normal')
BBdata.plot()
plt.title(table_head)
plt.grid()
display_content = BBdata
plt.show()
except Exception as e:
print(str(e))
messagebox.showwarning("ERROR","Información no disponible.")
def bbands(update, context):
ti = TechIndicators(key=keys, output_format='pandas')
word = str(umt)
word_refined = word.replace('/indicator ', '')
data_ti, meta_data_ti = ti.get_bbands(symbol=str(word_refined),
interval='60min',
time_period=60) # pylint: disable=W0632, W0612
data_refined = pandas.DataFrame(data_ti)
query = update.callback_query
query.answer()
data_refined.plot()
plt.title('Bollinger Bands indicator for ' + word_refined +
' stock (60min)')
plt.savefig('indicator.png')
context.bot.send_photo(chat_id=update.effective_chat.id,
photo=open('indicator.png', 'rb'))
plt.close()
def bbands(symbol, interval):
# Parameters: stock symbol and time interval
# Calculates three Bollinger Bands for the given stock
# Returns the lower, upper, middle band, and standard deviation data points
ti = TechIndicators(key=api_key, output_format="pandas")
data_ti, meta_data_ti = ti.get_bbands(symbol=symbol,
interval=interval,
time_period=20,
series_type="close")
bbands = data_ti
bbands.reset_index(inplace=True)
bbands["date"] = bbands["date"].map(mdates.date2num)
lower_band = bbands["Real Lower Band"]
middle_band = bbands["Real Middle Band"]
upper_band = bbands["Real Upper Band"]
sd = middle_band - lower_band
return lower_band, middle_band, upper_band, sd
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 check_bb(self):
# get bollinger band data
ti = TechIndicators(key='YOUR_API_KEY', output_format='pandas')
bbdata, meta_data = ti.get_bbands(symbol='MSFT',
interval='60min',
time_period=60)
bbdata.reset_index(inplace=True)
bbdata['date'] = pd.to_datetime(bbdata['date'], utc=True)
bbdata = bbdata.resample('D',
on='date').mean().sort_values(by='date',
ascending=False)
bb2days_middle = bbdata.iloc[2, 0]
bb2days_upper = bbdata.iloc[2, 1]
bb2days_lower = bbdata.iloc[2, 2]
df = pd.read_csv(self.csv_dir)
df = df.sort_values(by='Datetime', ascending=False)
df['Datetime'] = pd.to_datetime(df['Datetime'], utc=True)
df_day = df.resample('D', on='Datetime').mean().sort_values(
by='Datetime', ascending=False).head()
# get price day from 2 days and 1 day ago
price2days = df_day.iloc[2, 3]
price1day = df_day.iloc[1, 3]
currentprice = df_day.iloc[0, 3]
# if price2days > bb2days_upper
# if price2days > price1day AND price1day > current, SELL
# if price2days < price1day AND price1day < current, BUY
if price2days > bb2days_upper or price2days < bb2days_lower or price2days == bb2days_middle:
if price2days > price1day and price1day > currentprice:
self.bb_condition_sell = True
if price2days < price1day and price1day < currentprice:
self.bb_condition_buy = True
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 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
plt.show()
# Plot Volume Data
data1['5. volume'].plot()
plt.title('Intraday Time Series for %s stock at (%s) intervals' %
(ticker, interval))
plt.show()
# ------------------------------
# Part Two: Technical Indicators
# ------------------------------
ti = TechIndicators(key='API_KEY', output_format='pandas')
interval = '60min'
data, metadata = ti.get_bbands(symbol=ticker,
interval=interval,
time_period=60)
data.plot()
plt.title('BBands indicator for %s stock, at %s interval' % (ticker, interval))
plt.show()
# ------------------------------
# Part Three: Sector Performance
# ------------------------------
sp = SectorPerformances(key='API_KEY', output_format='pandas')
data, metadata = sp.get_sector()
data['Rank A: Real-Time Performance'].plot(kind='bar')
plt.title('Real Time Performance (%) per Sector')
plt.tight_layout()
plt.grid()
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
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)
from click._compat import raw_input
from alpha_vantage.timeseries import TimeSeries
import pandas as pd
key = 'PH2SVH5EIR56RTXD'
ti = TechIndicators(key, output_format='pandas')
ts = TimeSeries(key, output_format='pandas')
AAPL_MACD, meta_data = ti.get_macd(symbol='AAPL',
interval='daily',
series_type="close")
AAPL_RSI, meta_data = ti.get_rsi(symbol='AAPL',
interval='daily',
series_type="close")
AAPL_BBANDS, meta_data = ti.get_bbands(symbol="AAPL",
interval='daily',
series_type="close")
AAPL_PRICE, meta_data = ts.get_daily(symbol="AAPL", outputsize='full')
AAPL = AAPL_MACD.join(AAPL_RSI["RSI"])
AAPL = AAPL.join(AAPL_BBANDS["Real Lower Band"])
AAPL = AAPL.join(AAPL_BBANDS['Real Middle Band'])
AAPL = AAPL.join(AAPL_BBANDS['Real Upper Band'])
AAPL = AAPL.join(AAPL_PRICE['4. close'])
AAPL.to_csv('C:/Users/akeats97/Desktop/AAPL.csv')
'''
#print(AAPL_MACD)
data, meta_data = ts.get_intraday(symbol=stock,interval=t_period)
#print data.shape
#print data.columns.values
# 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)
def bbands(self):
c = TechIndicators(key=self.api_key, output_format='pandas')
data, meta_data = c.get_bbands(symbol=self.stock_name)
return data
)
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,
time_period=time,
series_type='close')[0]
else:
print('Wrong Entry')
print(state)
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")
'''
import os
from alpha_vantage.timeseries import TimeSeries, TimeSeriesDaily
from alpha_vantage.techindicators import TechIndicators
key = os.environ.get('ALPHAVANTAGE_KEY')
print(key)
ts = TimeSeries(key=key)
import matplotlib.pyplot as plt
symbol = 'MSFT'
symbol = 'NSE:RELIANCE'
ti = TechIndicators(key=key, output_format='pandas')
data, meta_data = ti.get_bbands(symbol=symbol, interval='60min', time_period=60)
data.plot()
plt.title('BBbands indicator for MSFT stock (60 min)')
plt.show()
print(meta_data)
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.")
