class AVInterface:
"""
AlphaVantage interface class, provides methods to call AlphaVantage API
and return the result in useful format handling possible errors.
"""
def __init__(self, apiKey, config):
self.read_configuration(config)
self._last_call_ts = dt.datetime.now()
self.TS = TimeSeries(
key=apiKey,
output_format="pandas",
indexing_type="integer",
treat_info_as_error=True,
retries=0,
)
self.TI = TechIndicators(key=apiKey, output_format="pandas", retries=0)
logging.info("AlphaVantage initialised.")
def read_configuration(self, config):
self.enable = config["alpha_vantage"]["enable"]
self.api_timeout = config["alpha_vantage"]["api_timeout"]
def get_prices(self, market_id, interval):
"""
Return the price time series of the requested market with the interval
granularity. Return None if the interval is invalid
"""
if (
interval == AVInterval.MIN_1
or interval == AVInterval.MIN_5
or interval == AVInterval.MIN_15
or interval == AVInterval.MIN_30
or interval == AVInterval.MIN_60
):
return self.intraday(market_id, interval)
elif interval == AVInterval.DAILY:
return self.daily(market_id)
elif interval == AVInterval.WEEKLY:
return self.weekly(market_id)
# TODO implement monthly call
else:
return None
def daily(self, marketId):
"""
Calls AlphaVantage API and return the Daily time series for the given market
- **marketId**: string representing an AlphaVantage compatible market id
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call()
market = self._format_market_id(marketId)
try:
data, meta_data = self.TS.get_daily(symbol=market, outputsize="full")
return data
except Exception as e:
logging.error("AlphaVantage wrong api call for {}".format(market))
logging.debug(e)
logging.debug(traceback.format_exc())
logging.debug(sys.exc_info()[0])
return None
def intraday(self, marketId, interval):
"""
Calls AlphaVantage API and return the Intraday time series for the given market
- **marketId**: string representing an AlphaVantage compatible market id
- **interval**: string representing an AlphaVantage interval type
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call()
if interval is AVIntervals.DAILY:
logging.error("AlphaVantage Intraday does not support DAILY interval")
return None
market = self._format_market_id(marketId)
try:
data, meta_data = self.TS.get_intraday(
symbol=market, interval=interval.value, outputsize="full"
)
return data
except Exception as e:
logging.error("AlphaVantage wrong api call for {}".format(market))
logging.debug(e)
logging.debug(traceback.format_exc())
logging.debug(sys.exc_info()[0])
return None
def weekly(self, marketId):
"""
Calls AlphaVantage API and return the Weekly time series for the given market
- **marketId**: string representing an AlphaVantage compatible market id
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call()
market = self._format_market_id(marketId)
try:
data, meta_data = self.TS.get_weekly(symbol=market)
return data
except Exception as e:
logging.error("AlphaVantage wrong api call for {}".format(market))
logging.debug(e)
logging.debug(traceback.format_exc())
logging.debug(sys.exc_info()[0])
return None
def quote_endpoint(self, market_id):
"""
Calls AlphaVantage API and return the Quote Endpoint data for the given market
- **market_id**: string representing the market id to fetch data of
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call()
market = self._format_market_id(market_id)
try:
data, meta_data = self.TS.get_quote_endpoint(
symbol=market, outputsize="full"
)
return data
except:
logging.error("AlphaVantage wrong api call for {}".format(market))
return None
# Technical indicators
def macdext(self, marketId, interval):
"""
Calls AlphaVantage API and return the MACDEXT tech indicator series for the given market
- **marketId**: string representing an AlphaVantage compatible market id
- **interval**: string representing an AlphaVantage interval type
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call()
market = self._format_market_id(marketId)
try:
data, meta_data = self.TI.get_macdext(
market,
interval=interval.value,
series_type="close",
fastperiod=12,
slowperiod=26,
signalperiod=9,
fastmatype=2,
slowmatype=1,
signalmatype=0,
)
if data is None:
return None
data.index = range(len(data))
return data
except Exception as e:
logging.error("AlphaVantage wrong api call for {}".format(market))
logging.debug(e)
logging.debug(traceback.format_exc())
logging.debug(sys.exc_info()[0])
return None
def macd(self, marketId, interval):
"""
Calls AlphaVantage API and return the MACDEXT tech indicator series for the given market
- **marketId**: string representing an AlphaVantage compatible market id
- **interval**: string representing an AlphaVantage interval type
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call()
market = self._format_market_id(marketId)
try:
data, meta_data = self.TI.get_macd(
market,
interval=interval.value,
series_type="close",
fastperiod=12,
slowperiod=26,
signalperiod=9,
)
return data
except Exception as e:
logging.error("AlphaVantage wrong api call for {}".format(market))
logging.debug(e)
logging.debug(traceback.format_exc())
logging.debug(sys.exc_info()[0])
return None
# Utils functions
def _format_market_id(self, marketId):
"""
Convert a standard market id to be compatible with AlphaVantage API.
Adds the market exchange prefix (i.e. London is LON:)
"""
return "{}:{}".format("LON", marketId.split("-")[0])
def _wait_before_call(self):
"""
Wait between API calls to not overload the server
"""
while (dt.datetime.now() - self._last_call_ts) <= dt.timedelta(
seconds=self.api_timeout
):
time.sleep(0.5)
self._last_call_ts = dt.datetime.now()
class AVInterface(StocksInterface):
"""
AlphaVantage interface class, provides methods to call AlphaVantage API
and return the result in useful format handling possible errors.
"""
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 _to_av_interval(self, interval: Interval) -> AVInterval:
"""
Convert the Broker Interval to AlphaVantage compatible intervals.
Return the converted interval or None if a conversion is not available
"""
if interval == Interval.MINUTE_1:
return AVInterval.MIN_1
elif interval == Interval.MINUTE_5:
return AVInterval.MIN_5
elif interval == Interval.MINUTE_15:
return AVInterval.MIN_15
elif interval == Interval.MINUTE_30:
return AVInterval.MIN_30
elif interval == Interval.HOUR:
return AVInterval.MIN_60
elif interval == Interval.DAY:
return AVInterval.DAILY
elif interval == Interval.WEEK:
return AVInterval.WEEKLY
elif interval == Interval.MONTH:
return AVInterval.MONTHLY
else:
logging.error(
"Unable to convert interval {} to AlphaVantage equivalent".
format(interval.value))
raise ValueError("Unsupported Interval value: {}".format(interval))
def get_prices(self, market: Market, interval: Interval,
data_range: int) -> MarketHistory:
data = None
av_interval = self._to_av_interval(interval)
if (av_interval == AVInterval.MIN_1 or av_interval == AVInterval.MIN_5
or av_interval == AVInterval.MIN_15
or av_interval == AVInterval.MIN_30
or av_interval == AVInterval.MIN_60):
data = self.intraday(market.id, av_interval)
elif av_interval == AVInterval.DAILY:
data = self.daily(market.id)
elif av_interval == AVInterval.WEEKLY:
data = self.weekly(market.id)
# TODO implement monthly call
else:
raise ValueError("Unsupported Interval.{}".format(interval.name))
history = MarketHistory(
market,
data.index,
data["2. high"].values,
data["3. low"].values,
data["4. close"].values,
data["5. volume"].values,
)
return history
def daily(self, marketId: str) -> pandas.DataFrame:
"""
Calls AlphaVantage API and return the Daily time series for the given market
- **marketId**: string representing an AlphaVantage compatible market id
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call(self._config.get_alphavantage_api_timeout())
market = self._format_market_id(marketId)
try:
data, meta_data = self.TS.get_daily(symbol=market,
outputsize="full")
return data
except Exception as e:
print(e)
logging.error("AlphaVantage wrong api call for {}".format(market))
logging.debug(e)
logging.debug(traceback.format_exc())
logging.debug(sys.exc_info()[0])
return None
def intraday(self, marketId: str,
interval: AVInterval) -> pandas.DataFrame:
"""
Calls AlphaVantage API and return the Intraday time series for the given market
- **marketId**: string representing an AlphaVantage compatible market id
- **interval**: string representing an AlphaVantage interval type
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call(self._config.get_alphavantage_api_timeout())
market = self._format_market_id(marketId)
try:
data, meta_data = self.TS.get_intraday(symbol=market,
interval=interval.value,
outputsize="full")
return data
except Exception as e:
logging.error("AlphaVantage wrong api call for {}".format(market))
logging.debug(e)
logging.debug(traceback.format_exc())
logging.debug(sys.exc_info()[0])
return None
def weekly(self, marketId: str) -> pandas.DataFrame:
"""
Calls AlphaVantage API and return the Weekly time series for the given market
- **marketId**: string representing an AlphaVantage compatible market id
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call(self._config.get_alphavantage_api_timeout())
market = self._format_market_id(marketId)
try:
data, meta_data = self.TS.get_weekly(symbol=market)
return data
except Exception as e:
logging.error("AlphaVantage wrong api call for {}".format(market))
logging.debug(e)
logging.debug(traceback.format_exc())
logging.debug(sys.exc_info()[0])
return None
def quote_endpoint(self, market_id: str) -> pandas.DataFrame:
"""
Calls AlphaVantage API and return the Quote Endpoint data for the given market
- **market_id**: string representing the market id to fetch data of
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call(self._config.get_alphavantage_api_timeout())
market = self._format_market_id(market_id)
try:
data, meta_data = self.TS.get_quote_endpoint(symbol=market,
outputsize="full")
return data
except Exception:
logging.error("AlphaVantage wrong api call for {}".format(market))
return None
# Technical indicators
def get_macd(self, market: Market, interval: Interval,
datapoints_range: int) -> MarketMACD:
av_interval = self._to_av_interval(interval)
data = self.macdext(market.id, av_interval)
macd = MarketMACD(
market,
data.index,
data["MACD"].values,
data["MACD_Signal"].values,
data["MACD_Hist"].values,
)
return macd
def macdext(self, marketId: str, interval: AVInterval) -> pandas.DataFrame:
"""
Calls AlphaVantage API and return the MACDEXT tech indicator series for the given market
- **marketId**: string representing an AlphaVantage compatible market id
- **interval**: string representing an AlphaVantage interval type
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call(self._config.get_alphavantage_api_timeout())
market = self._format_market_id(marketId)
data, meta_data = self.TI.get_macdext(
market,
interval=interval.value,
series_type="close",
fastperiod=12,
slowperiod=26,
signalperiod=9,
fastmatype=2,
slowmatype=1,
signalmatype=0,
)
return data
def macd(self, marketId: str, interval: AVInterval) -> pandas.DataFrame:
"""
Calls AlphaVantage API and return the MACDEXT tech indicator series for the given market
- **marketId**: string representing an AlphaVantage compatible market id
- **interval**: string representing an AlphaVantage interval type
- Returns **None** if an error occurs otherwise the pandas dataframe
"""
self._wait_before_call(self._config.get_alphavantage_api_timeout())
market = self._format_market_id(marketId)
data, meta_data = self.TI.get_macd(
market,
interval=interval.value,
series_type="close",
fastperiod=12,
slowperiod=26,
signalperiod=9,
)
return data
# Utils functions
def _format_market_id(self, marketId: str) -> str:
"""
Convert a standard market id to be compatible with AlphaVantage API.
Adds the market exchange prefix (i.e. London is LON:)
"""
# TODO MarketProvider/IGInterface should return marketId without "-UK"
return "{}:{}".format("LON", marketId.split("-")[0])
cmo_data, meta_data = ti.get_cmo(symbol=symbol, interval=interval)
dema_data, meta_data = ti.get_dema(symbol=symbol, interval=interval)
dx_data, meta_data = ti.get_dx(symbol=symbol, interval=interval)
ema_data, meta_data = ti.get_ema(symbol=symbol, interval=interval)
ht_dcperiod_data, meta_data = ti.get_ht_dcperiod(symbol=symbol, interval=interval)
ht_dcphase_data, meta_data = ti.get_ht_dcphase(symbol=symbol, interval=interval)
ht_phasor_data, meta_data = ti.get_ht_phasor(symbol=symbol, interval=interval)
ht_sine_data, meta_data = ti.get_ht_sine(symbol=symbol, interval=interval)
ht_trendline_data, meta_data = ti.get_ht_trendline(symbol=symbol, interval=interval)
ht_trendmode_data, meta_data = ti.get_ht_trendmode(symbol=symbol, interval=interval)
print("Progress: 40% complete...")
kama_data, meta_data = ti.get_kama(symbol=symbol, interval=interval)
macd_data, meta_data = ti.get_macd(symbol=symbol, interval=interval)
macdext_data, meta_data = ti.get_macdext(symbol=symbol, interval=interval)
mama_data, meta_data = ti.get_mama(symbol=symbol, interval=interval)
mfi_data, meta_data = ti.get_mfi(symbol=symbol, interval=interval)
midpoint_data, meta_data = ti.get_midpoint(symbol=symbol, interval=interval)
midprice_data, meta_data = ti.get_midprice(symbol=symbol, interval=interval)
minus_di_data, meta_data = ti.get_minus_di(symbol=symbol, interval=interval)
mom_data, meta_data = ti.get_mom(symbol=symbol, interval=interval)
natr_data, meta_data = ti.get_natr(symbol=symbol, interval=interval)
print("Progress: 60% complete...")
obv_data, meta_data = ti.get_obv(symbol=symbol, interval=interval)
plus_di_data, meta_data = ti.get_plus_di(symbol=symbol, interval=interval)
plus_dm_data, meta_data = ti.get_plus_dm(symbol=symbol, interval=interval)
ppo_data, meta_data = ti.get_ppo(symbol=symbol, interval=interval)
roc_data, meta_data = ti.get_roc(symbol=symbol, interval=interval)
def techIndicatorPull(symbol):
interval = "1min"
print("Calculating Tech Indicators ...")
ti = TechIndicators(key='1HAYLUHGCB6E0VXC', output_format='pandas')
print("Progress: 0% complete...")
ad_data, meta_data = ti.get_ad(symbol=symbol, interval=interval)
adosc_data, meta_data = ti.get_adosc(symbol=symbol, interval=interval)
adx_data, meta_data = ti.get_adx(symbol=symbol, interval=interval)
adxr_data, meta_data = ti.get_adxr(symbol=symbol, interval=interval)
apo_data, meta_data = ti.get_apo(symbol=symbol, interval=interval)
aroon_data, meta_data = ti.get_aroon(symbol=symbol, interval=interval)
aroonosc_data, meta_data = ti.get_aroonosc(symbol=symbol,
interval=interval)
atr_data, meta_data = ti.get_atr(symbol=symbol, interval=interval)
bop_data, meta_data = ti.get_bop(symbol=symbol, interval=interval)
cci_data, meta_data = ti.get_cci(symbol=symbol, interval=interval)
print("Progress: 20% complete...")
cmo_data, meta_data = ti.get_cmo(symbol=symbol, interval=interval)
dema_data, meta_data = ti.get_dema(symbol=symbol, interval=interval)
dx_data, meta_data = ti.get_dx(symbol=symbol, interval=interval)
ema_data, meta_data = ti.get_ema(symbol=symbol, interval=interval)
ht_dcperiod_data, meta_data = ti.get_ht_dcperiod(symbol=symbol,
interval=interval)
ht_dcphase_data, meta_data = ti.get_ht_dcphase(symbol=symbol,
interval=interval)
ht_phasor_data, meta_data = ti.get_ht_phasor(symbol=symbol,
interval=interval)
ht_sine_data, meta_data = ti.get_ht_sine(symbol=symbol, interval=interval)
ht_trendline_data, meta_data = ti.get_ht_trendline(symbol=symbol,
interval=interval)
ht_trendmode_data, meta_data = ti.get_ht_trendmode(symbol=symbol,
interval=interval)
print("Progress: 40% complete...")
kama_data, meta_data = ti.get_kama(symbol=symbol, interval=interval)
macd_data, meta_data = ti.get_macd(symbol=symbol, interval=interval)
macdext_data, meta_data = ti.get_macdext(symbol=symbol, interval=interval)
mama_data, meta_data = ti.get_mama(symbol=symbol, interval=interval)
mfi_data, meta_data = ti.get_mfi(symbol=symbol, interval=interval)
midpoint_data, meta_data = ti.get_midpoint(symbol=symbol,
interval=interval)
midprice_data, meta_data = ti.get_midprice(symbol=symbol,
interval=interval)
minus_di_data, meta_data = ti.get_minus_di(symbol=symbol,
interval=interval)
mom_data, meta_data = ti.get_mom(symbol=symbol, interval=interval)
natr_data, meta_data = ti.get_natr(symbol=symbol, interval=interval)
print("Progress: 60% complete...")
obv_data, meta_data = ti.get_obv(symbol=symbol, interval=interval)
plus_di_data, meta_data = ti.get_plus_di(symbol=symbol, interval=interval)
plus_dm_data, meta_data = ti.get_plus_dm(symbol=symbol, interval=interval)
ppo_data, meta_data = ti.get_ppo(symbol=symbol, interval=interval)
roc_data, meta_data = ti.get_roc(symbol=symbol, interval=interval)
rocr_data, meta_data = ti.get_rocr(symbol=symbol, interval=interval)
rsi_data, meta_data = ti.get_rsi(symbol=symbol, interval=interval)
sar_data, meta_data = ti.get_sar(symbol=symbol, interval=interval)
sma_data, meta_data = ti.get_sma(symbol=symbol, interval=interval)
stoch_data, meta_data = ti.get_stoch(symbol=symbol, interval=interval)
print("Progress: 80% complete...")
stochf_data, meta_data = ti.get_stochf(symbol=symbol, interval=interval)
stochrsi_data, meta_data = ti.get_stochrsi(symbol=symbol,
interval=interval)
t3_data, meta_data = ti.get_t3(symbol=symbol, interval=interval)
tema_data, meta_data = ti.get_tema(symbol=symbol, interval=interval)
trange_data, meta_data = ti.get_trange(symbol=symbol, interval=interval)
trima_data, meta_data = ti.get_trima(symbol=symbol, interval=interval)
trix_data, meta_data = ti.get_trix(symbol=symbol, interval=interval)
ultsoc_data, meta_data = ti.get_ultsoc(symbol=symbol, interval=interval)
willr_data, meta_data = ti.get_willr(symbol=symbol, interval=interval)
wma_data, meta_data = ti.get_wma(symbol=symbol, interval=interval)
bbands_data, meta_data = ti.get_bbands(symbol=symbol, interval=interval)
print("Progress: 90% complete...")
result = pd.concat([
ad_data, adosc_data, adx_data, adxr_data, apo_data, aroon_data,
aroonosc_data, atr_data, bop_data, cci_data, cmo_data, dema_data,
dx_data, ema_data, ht_dcperiod_data, ht_dcphase_data, ht_phasor_data,
ht_sine_data, ht_trendline_data, ht_trendmode_data, kama_data,
macd_data, macdext_data, mama_data, mfi_data, midpoint_data,
midprice_data, minus_di_data, mom_data, natr_data, obv_data,
plus_di_data, plus_dm_data, ppo_data, roc_data, rocr_data, rsi_data,
sar_data, sma_data, stoch_data, stochf_data, stochrsi_data, t3_data,
tema_data, trange_data, trima_data, trix_data, ultsoc_data, willr_data,
wma_data, bbands_data
],
axis=1)
result["stockticker"] = symbol
result = result.reindex(result.index.rename('timestamp'))
result = result.dropna(axis=0, how='any')
result = result.round(2)
result = result.rename(
columns={
'Chaikin A/D': 'ChaikinAD',
'Aroon Up': 'AroonUp',
'Aroon Down': 'AroonDown',
'LEAD SINE': 'LeadSine',
'Real Upper Band': 'RealUpperBand',
'Real Middle Band': 'RealMiddleBand',
'Real Lower Band': 'RealLowerBand'
})
fd = open('results/rawTechIndicatorData.csv', 'w')
result.to_csv(fd, index=True, encoding='utf-8')
fd.close()
print("Progress: 99% complete...")
df = pd.read_csv('results/rawTechIndicatorData.csv')
insertToOracleDatabase(
df,
'''insert /*+ ignore_row_on_dupkey_index(stocktechindicator, stocktechindicator_pk) */
into stocktechindicator
(timestamp,ChaikinAD,ADOSC,ADX,ADXR,
APO,AroonUp,AroonDown,AROONOSC,ATR,
BOP,CCI,CMO,DEMA,DX,
EMA,DCPERIOD,HT_DCPHASE,QUADRATURE, PHASE,
LEADSINE,SINE,HT_TRENDLINE,TRENDMODE,KAMA,
MACD,MACD_Hist,MACD_Signal,MACD_Signal2,MACD2,
MACD_Hist2,FAMA,MAMA,MFI,MIDPOINT,
MIDPRICE,MINUS_DI,MOM,NATR,OBV,
PLUS_DI,PLUS_DM,PPO,ROC,ROCR,
RSI,SAR,SMA,SlowK,SlowD,
FastK,FastD,FastK2,FastD2,T3,
TEMA,TRANGE,TRIMA,TRIX,ULTOSC,
WILLR,WMA,RealLowerBand,RealUpperBand,RealMiddleBand,
stockticker)
values (:1, :2, :3, :4, :5,
:6, :7, :8, :9, :10,
:11, :12, :13, :14, :15,
:16, :17, :18, :19, :20,
:21, :22, :23, :24, :25,
:26, :27, :28, :29, :30,
:31, :32, :33, :34, :35,
:36, :37, :38, :39, :40,
:41, :42, :43, :44, :45,
:46, :47, :48, :49, :50,
:51, :52, :53, :54, :55,
:56, :57, :58, :59, :60,
:61, :62, :63, :64, :65,
:66)''')
def save_ti(ticker, interval='daily'):
ti = TechIndicators(key='WS8K5VM8FZL52BTH', output_format='pandas')
ti.get_sma(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_sma.csv')
call_check()
ti.get_ema(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_ema.csv')
call_check()
ti.get_wma(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_wma.csv')
call_check()
ti.get_dema(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_dema.csv')
call_check()
ti.get_tema(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_tema.csv')
call_check()
ti.get_trima(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_trima.csv')
call_check()
ti.get_kama(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_kama.csv')
call_check()
ti.get_mama(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_mama.csv')
call_check()
ti.get_t3(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_t3.csv')
call_check()
ti.get_macd(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_macd.csv')
call_check()
ti.get_macdext(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_macdext.csv')
call_check()
ti.get_stoch(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_stoch.csv')
call_check()
ti.get_stochf(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_stochf.csv')
call_check()
ti.get_rsi(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_rsi.csv')
call_check()
ti.get_stochrsi(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_stochris.csv')
call_check()
ti.get_willr(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_willr.csv')
call_check()
ti.get_adx(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_adx.csv')
call_check()
ti.get_adxr(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_adxr.csv')
call_check()
ti.get_apo(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_apo.csv')
call_check()
ti.get_ppo(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_ppo.csv')
call_check()
ti.get_mom(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_mom.csv')
call_check()
ti.get_bop(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_bop.csv')
call_check()
ti.get_cci(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_cci.csv')
call_check()
ti.get_cmo(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_cmo.csv')
call_check()
ti.get_roc(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_roc.csv')
call_check()
ti.get_rocr(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_rocr.csv')
call_check()
ti.get_aroon(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_aroon.csv')
call_check()
ti.get_aroonosc(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_aroonosc.csv')
call_check()
ti.get_mfi(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_mfi.csv')
call_check()
ti.get_trix(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_trix.csv')
call_check()
ti.get_ultosc(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_ultosc.csv')
call_check()
ti.get_dx(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_dx.csv')
call_check()
ti.get_minus_di(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_minus_di.csv')
call_check()
ti.get_plus_di(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_plus_di.csv')
call_check()
ti.get_minus_dm(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_minus_dm.csv')
call_check()
ti.get_plus_dm(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_plus_dm.csv')
call_check()
ti.get_bbands(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_bbands.csv')
call_check()
ti.get_midpoint(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_midpoint.csv')
call_check()
ti.get_midprice(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_midprice.csv')
call_check()
ti.get_sar(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_sar.csv')
call_check()
ti.get_trange(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_trange.csv')
call_check()
ti.get_atr(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_atr.csv')
call_check()
ti.get_natr(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_natr.csv')
call_check()
ti.get_ad(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_ad.csv')
call_check()
ti.get_adosc(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_adosc.csv')
call_check()
ti.get_obv(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_obv.csv')
call_check()
ti.get_ht_trendline(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_ht_trendline.csv')
call_check()
ti.get_ht_sine(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_ht_sine.csv')
call_check()
ti.get_ht_trendmode(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_ht_trendmode.csv')
call_check()
ti.get_ht_dcperiod(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_dcperiod.csv')
call_check()
ti.get_ht_dcphase(
ticker,
interval)[0].to_csv(f'./Stock Data/TI/{ticker}_{interval}_dcphase.csv')
call_check()
ti.get_ht_phasor(ticker, interval)[0].to_csv(
f'./Stock Data/TI/{ticker}_{interval}_ht_phasor.csv')
