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 ROCIndicator(StockName):
ti = TechIndicators(key='QBGRPFYFV5WBTTCO', output_format='pandas')
data, meta_data = ti.get_roc(symbol=StockName,
interval='1min',
time_period=60,
series_type='close')
# realdata = data.to_json(orient='table')
# print(realdata)
# json_path = './' + StockName +'ROC.json'
# with open(json_path, "w") as f:
# json.dump(realdata, f)
# print("Complete...")
# print(type(data))
# # print(data)
# data.plot()
# plt.title('ROC indicator for '+ StockName+ ' stock (1 min)')
# fig = plt.gcf()
# plt.savefig("ROC.pdf")
# plt.show()
data.to_csv(StockName + 'ROC indicator.csv', index=True, sep=',')
print('Success')
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 sma(self): #simple moving average
d = TechIndicators(key=self.api_key, output_format='pandas')
data, meta_data = d.get_sma(symbol=self.stock_name,
interval='15min',
time_period=40)
return data
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 get_WMA(name, duration, sr_type):
ti = TechIndicators(key='YOUR_OWN_API', output_format='pandas')
data_wma, meta_data_wma = ti.get_wma(symbol=Company.comp[name],
interval=duration,
time_period='60',
series_type=sr_type)
return data_wma
def getInfo(self):
'''
Prints all relevant info about stock to a text file
Returns
------
none
'''
f = open("stockinfo.txt", "w")
ts = TimeSeries(key='GZR6JEGOB3LF6RCB')
tc = TechIndicators(key='GZR6JEGOB3LF6RCB')
data, meta_data = ts.get_daily(self.name)
data2, meta_data2 = tc.get_rsi(self.name)
data3, meta_data3 = tc.get_sma(self.name)
self.rsi = float(data2['2019-01-16']['RSI'])
self.sma = float(data3['2019-01-16']['SMA'])
teststring = data['2019-01-16']
self.value = float(teststring['4. close'])
f.write("The stock chosen is: " + self.name + "\n")
f.write("The most recent closing price is: " + str(self.value) + " more info in console")
if(self.rsi > 70):
print("this stock has a high RSI and is currently oversold. The price may fall soon")
elif(self.rsi < 30):
print("this stock has a low RSI and is currently overbought. The price may rise soon")
else:
print("the stock is trading at normal levels")
if(self.value > self.sma):
print("the price of this stock is currently higher than the moving average. it is trending upwards")
else:
print("the price of this stock is currently lower than the moving average. it is trending downwards")
f.close()
def get_alpha(ticker_list, alpha_key):
'''
Input:
ticker_list: list of strings of publicly traded companies symbols, 'XYZ'
alpha_key: get alpha vantage API key from alpha vantage website
Output:
build CSV files of stock price history, and technical indicators for
each company
'''
ts = TimeSeries(key=alpha_key, output_format='pandas')
ti = TechIndicators(key=alpha_key, output_format='pandas')
zero_time = time()
# Build CSV for Daily prices, RSI, CCI, OBV for each company
for ticker in ticker_list:
print('company: {}, time: {}'.format(ticker, str(time() - zero_time)))
df, meta = ts.get_daily(symbol=ticker, outputsize='full')
df.to_csv('data/daily/{}.csv'.format(ticker))
sleep(1)
df, meta = ti.get_rsi(ticker, interval='daily', time_period=5)
df.to_csv('data/rsi/{}.csv'.format(ticker))
sleep(1)
df, meta = ti.get_cci(ticker, interval='daily', time_period=5)
df.to_csv('data/cci/{}.csv'.format(ticker))
sleep(1)
df, meta = ti.get_obv(ticker, interval='daily')
df.to_csv('data/obv/{}.csv'.format(ticker))
def rsi_dataframe(stock=stock):
api_key = ''
period = 60
ts = TimeSeries(key=api_key, output_format='pandas')
data_ts = ts.get_intraday(stock.upper(),
interval='1min',
outputsize='full')
ti = TechIndicators(key=stock.upper(), output_format='pandas')
data_ti, meta_data_ti = ti.get_rsi(symbol=stock.upper(),
interval='1min',
time_period=period,
series_type='close')
df = data_ts[0][period::]
df.index = pd.Index(map(lambda x: str(x)[:-3], df.index))
df2 = data_ti
total_df = pd.concat([df, df2], axis=1, sort=True)
# for i in total_df['RSI']:
# print(i)
return print(total_df)
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 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 stock_comparison(symbol, interval):
import pandas as pd
from alpha_vantage.timeseries import TimeSeries
from alpha_vantage.techindicators import TechIndicators
import matplotlib.pyplot as plt
api_key = '67O5YS2MXMMVBHVL'
ts = TimeSeries(key=api_key, output_format='pandas')
data_ts, meta_data = ts.get_intraday(symbol, interval, outputsize='full')
#print(data_ts)
period = 60
ti = TechIndicators(key=api_key, output_format='pandas')
data_ti, meta_data_ti = ti.get_sma(symbol,
interval,
time_period=period,
series_type='close')
#print(data_ti)
df1 = data_ti
df2 = data_ts['4. close'].iloc[period - 1::]
df2.index = df1.index
total_df = pd.concat([df1, df2], axis=1)
#print(total_df)
total_df.plot()
r1 = total_df
return (r1)
def get_fifteen_minute_rsi(symbol):
time.sleep(15)
ti = TechIndicators(key=API_KEY, output_format='pandas')
data, meta_data = ti.get_rsi(symbol=symbol,
interval='15min',
time_period=14)
return int(data.tail(1).iloc[0]['RSI'])
def check_rsi(symbol='', key='', time=5, treshold=65, points=5):
ti = TechIndicators(key=key, output_format='pandas')
data, meta_data = ti.get_rsi(symbol=symbol, time_period=time)
rsidata = data.tail(points).to_dict()
for rsi in rsidata['RSI']:
if rsidata['RSI'][rsi] > treshold:
print('RSI ' + str(time), rsi, rsidata['RSI'][rsi])
def __init__(self, equity):
self.ti = TechIndicators(output_format='pandas')
self.current = None
self.info = None
self.equity = equity
self.rsi = self.rsi_data()
self.avg = self.avg_rsi()
def GetStockInformation(self, Ticker):
self.ticker = Ticker
ts = TimeSeries(key=self.alphaVantage_Appid, output_format='pandas')
ti = TechIndicators(key=self.alphaVantage_Appid,
output_format='pandas')
data, meta_data = ts.get_intraday(symbol=Ticker,
interval='1min',
outputsize='full')
indicators, indicator_metadata = ti.get_sma(symbol=Ticker,
interval='1min',
time_period=100,
series_type='close')
data['close'].plot()
#indicators.plot()
plt.show()
latest = len(data) - 1
iLatest = len(indicators) - 1
print(data['close'][latest])
print(indicators['SMA'][iLatest])
if data['close'][latest] > indicators['SMA'][iLatest]:
print('High')
else:
print('Low')
def get_ingest_ticker_price(inticker):
'''
Load AlphaVantage stock data into DB
parameter(s): stock (str)
returns: void
'''
conn = sqlite3.connect('../../data/sentiment.db')
cursor = conn.cursor()
try:
ts = TechIndicators(key='INSERTKEY', output_format='pandas')
data, meta_data = ts.get_sma(symbol=inticker,
interval='60min',
time_period=32,
series_type='close')
for row in data:
cursor.execute(
"INSERT INTO Tickerprices(ticker, date, close) VALUES(?,?,?)",
(inticker, timestamp, row))
return True
except:
se.send_ticker_error()
return False
def rsi_df(stock=stock):
api_key = key.API_key
period = 60
ts = TimeSeries(key=api_key, output_format='pandas')#This ts is helping us to connect to API
data_ts = ts.get_intraday(stock.upper(), interval='1min', outputsize='full')
#indicator
ti = TechIndicators(key=api_key, output_format='pandas')
#data_ti, meta_data_ti = ti.get_bbands(symbol = stock.upper(), interval='1min', time_period=period, series_type='close')
data_ti, meta_data_ti = ti.get_rsi(symbol=stock.upper(), interval='1min', time_period=period,
series_type='close')
df = data_ts[0]
df = df.iloc[::-1][period::]
df.index = pd.Index(map(lambda x: str(x)[:-3], df.index))
df.index.name = 'Date'
df2 = data_ti
df2.index = pd.Index(map(lambda x: str(x)[:-3], df2.index))
df2.index.name = 'Date'
total_df = pd.merge(df, df2, on="Date")
open = []
for i in total_df['1. open']:
open.append(float(i))
close = []
for c in total_df['4. close']:
close.append(float(c))
low = []
for l in total_df['3. low']:
low.append(float(l))
high = []
for h in total_df['2. high']:
high.append(float(h))
rsi = []
for r, l in zip(total_df['RSI'], low):
rsi.append(l-(l/r))
high_rsi_value = []
high_rsi_time = []
for value, time, l in zip(total_df['RSI'], total_df.index, low):
if value>60:
high_rsi_value.append(l-(l/value))
high_rsi_time.append(time)
low_rsi_value = []
low_rsi_time = []
for value, time, l in zip(total_df['RSI'], total_df.index, low):
if value<35:
low_rsi_value.append(l-(l/value))
low_rsi_time.append(time)
return print(total_df.columns)
def load_stock(stock):
a.clear()
a2.clear()
ts_data = None
rsi_data = None
ts = TimeSeries(key=API_KEY, output_format='pandas')
ti = TechIndicators(key=API_KEY, output_format='pandas')
while ts_data is None:
try:
ts_data, _ = ts.get_intraday(symbol=stock,
interval='1min',
outputsize='full')
except KeyError:
print("trying again...")
while rsi_data is None:
try:
rsi_data, _ = ti.get_rsi(symbol=stock,
interval='60min',
time_period=60)
except KeyError:
print("trying again...")
ts_data['4. close'].plot(ax=a)
fig.suptitle(f"{stock} Intraday")
rsi_data.plot(ax=a2)
fig2.suptitle(f"{stock} RSI")
canvas.draw()
canvas2.draw()
window.title(f"{stock} Info")
def ROCIndicator(StockName):
ti = TechIndicators(key='GKLC3DF23TMWX8LS', output_format='pandas')
time.sleep(15) # The alpha_ventage API limit 5 calls per minute
data, meta_data = ti.get_roc(symbol=StockName,
interval='daily',
series_type='close')
# realdata = data.to_json(orient='table')
# print(realdata)
# json_path = './' + StockName +'ROC.json'
# with open(json_path, "w") as f:
# json.dump(realdata, f)
# print("Complete...")
# print(type(data))
# # print(data)
# data.plot()
# plt.title('ROC indicator for '+ StockName+ ' stock (1 min)')
# fig = plt.gcf()
# plt.savefig("ROC.pdf")
# plt.show()
data.to_csv(StockName + '_ROC.csv', index=True, sep=',')
print(StockName + '_ROC saved successfully.')
insertDatabase(StockName + '_ROC')
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 technical_indicators(symbol, interval):
import pandas as pd
from alpha_vantage.techindicators import TechIndicators
import matplotlib.pyplot as plt
api_key = '67O5YS2MXMMVBHVL'
period = 60
ti = TechIndicators(key=api_key, output_format='pandas')
data_rsi, meta_data_rsi = ti.get_rsi(symbol,
interval,
time_period=period,
series_type='close')
data_sma, meta_data_sma = ti.get_sma(symbol,
interval,
time_period=period,
series_type='close')
#print(data_ti)
df1 = data_sma.iloc[1::]
df2 = data_rsi
df1.index = df2.index
fig, ax1 = plt.subplots()
ax1.plot(df1, 'b-')
ax2 = ax1.twinx()
ax2.plot(df2, 'r.')
plt.title("SMA & RSI graph")
# plt.show()
r2 = df1, df2
return (r2)
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 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 get_sma(symbol):
#Gera uma key aleatoria cada vez
ti = TechIndicators(key=str(randint(99994, 447389428)), output_format='pandas')
data7, metadata = ti.get_sma(symbol=symbol, interval='daily', time_period=7, series_type='close')
data21, metadata = ti.get_sma(symbol=symbol, interval='daily', time_period=21, series_type='close')
return data7, data21
def __init__(self, name, tag):
self.__name = name
self.__tag = tag
self.__timestamp = None
self.__sma7 = ''
self.__sma21 = ''
self.__ti = TechIndicators('IIB8DA6B2CRR6UL7')
self.updateData()
def GetMADay(ticker=None):
if ticker:
ti = TechIndicators(key=os.environ['AlphaKey'], output_format='json')
data, meta = ti.get_sma(symbol=ticker,
interval='daily',
time_period=480,
series_type='close')
return data
return None
def initialise(self) -> None:
logging.info("Initialising AVInterface...")
api_key = self._config.get_credentials()["av_api_key"]
self.TS = TimeSeries(key=api_key,
output_format="pandas",
treat_info_as_error=True)
self.TI = TechIndicators(key=api_key,
output_format="pandas",
treat_info_as_error=True)
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 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')
