def save_csv(stock, time_window, indicators):
api_key = av_key
print(stock, time_window)
ts = TimeSeries(key=api_key, output_format='pandas')
ti = TechIndicators(key=api_key, output_format='pandas')
if time_window.upper() == 'DAILY':
data, meta_data = ts.get_daily(symbol=stock, outputsize='full')
elif time_window.upper() == 'INTRADAILY':
data, meta_data = ts.get_intraday(symbol=stock,
interval='1min',
outputsize='full')
else:
print('Invalid Time Window.')
return
data = data.sort_values(by=['date'], ascending=True)
pprint(data.head(10))
data.to_csv(f'./charts/{stock}_{time_window.lower()}.csv')
for indicator in indicators:
# Daily values (throughout the past year)
if time_window.upper() == 'DAILY':
if indicator.upper() == 'SMA':
data, meta_data = ti.get_sma(symbol=stock,
interval='daily',
series_type='close')
elif indicator.upper() == 'RSI':
data, meta_data = ti.get_rsi(symbol=stock,
interval='daily',
series_type='close')
elif indicator.upper() == 'MACD':
data, meta_data = ti.get_macd(symbol=stock,
interval='daily',
series_type='close')
# Intradaily values (throughout the past week)
else:
if indicator.upper() == 'SMA':
data, meta_data = ti.get_sma(symbol=stock,
interval='1min',
series_type='close')
elif indicator.upper() == 'RSI':
data, meta_data = ti.get_rsi(symbol=stock,
interval='1min',
series_type='close')
elif indicator.upper() == 'MACD':
data, meta_data = ti.get_macd(symbol=stock,
interval='1min',
series_type='close')
data = data.sort_values(by=['date'], ascending=True)
pprint(data.head(10))
data.to_csv(
f'./charts/{stock}_{time_window.lower()}_{indicator.lower()}.csv')
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 getData(key, stock):
tS = TimeSeries(key=key, output_format='pandas', indexing_type='date')
tI = TechIndicators(key=key, output_format='pandas')
data, meta_data = tS.get_daily_adjusted(symbol=stock, outputsize='full')
macd, macd_meta = tI.get_macd(symbol=stock,
interval='daily',
series_type='close')
rsi, rsi_meta = tI.get_rsi(symbol=stock,
interval='daily',
time_period=14,
series_type='close')
willr, willr_meta = tI.get_willr(symbol=stock,
interval='daily',
time_period=14)
adx, adx_meta = tI.get_adx(symbol=stock, interval='daily', time_period=14)
mom, mom_meta = tI.get_mom(symbol=stock,
interval='daily',
time_period=10,
series_type='close')
all_vals = [data, macd, rsi, willr, adx, mom]
final_df = pd.concat(
all_vals, axis=1, sort=True
) # Sort arg may need to be False, leaving it blank raises Pandas error
final_df = final_df.dropna()
df = final_df.iloc[::-1]
df = df.reset_index()
df = df.drop(['6. volume', '7. dividend amount'], axis=1)
return df
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=',')
class ApiConnector:
"""
Responsability: To connecto to the alpha vantage api.
"""
def __init__(self, api_key):
"""Api objects instanciated with api_key"""
self.key = api_key
self.time_app = TimeSeries(api_key)
# can change the output_format to json which is default
self.tech_app = TechIndicators(api_key, output_format="json")
def get_macd(self, stock, time_interval, fastperiod, slowperiod, signalperiod):
"""
Returns macd_signal, macd, macd_hist
Interval: '1min', '5min', '15min', '30min', '60min', 'daily',
'weekly', 'monthly'
"""
return self.tech_app.get_macd(stock, interval=time_interval, fastperiod=fastperiod, slowperiod=slowperiod, signalperiod=signalperiod, series_type="close")
def get_days(self, stock):
"""Returns timeseries with close-value each day (20 years back)"""
return self.time_app.get_daily(stock)
def get_intraday(self, stock):
""""interval: '1min', '5min', '15min', '30min', '60min'"""
return self.time_app.get_intraday(stock, '1min', outputsize="full")
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 grabCompact(key, input_ticker, lookBack):
# Query ALPHA VANTAGE
try:
tS = TimeSeries(key=key, output_format='pandas', indexing_type='date')
tI = TechIndicators(key=key, output_format='pandas')
data, meta_data = tS.get_daily_adjusted(symbol=input_ticker, outputsize='compact')
macd, macd_meta = tI.get_macd(symbol=input_ticker, interval='daily', series_type='close')
rsi, rsi_meta = tI.get_rsi(symbol=input_ticker, interval='daily', time_period=14, series_type='close')
willr, willr_meta = tI.get_willr(symbol=input_ticker, interval='daily', time_period=14)
adx, adx_meta = tI.get_adx(symbol=input_ticker, interval='daily', time_period=14)
mom, mom_meta = tI.get_mom(symbol=input_ticker, interval='daily', time_period=10, series_type='close')
all_vals = [data, macd, rsi, willr, adx, mom]
final_df = pd.concat(all_vals, axis=1, sort=True) # Sort arg may need to be False, leaving it blank raises Pandas error
final_df = final_df.dropna()
df = final_df.iloc[::-1]
df = df.reset_index()
df = df.drop(['6. volume', '7. dividend amount'], axis=1)
df = df.drop(df.index[lookBack:])
return df
except:
print ("There was an error with %s" % input_ticker)
pass
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_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 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 MACD(stock_name):
'''
Moving Average Convergence / Divergence (指数平滑异同移动平均线)
https://zh.wikipedia.org/wiki/MACD
'''
ti = TechIndicators(key=Env.alpha_vantage_api_key, output_format='pandas')
data, meta_data = ti.get_macd(symbol=stock_name, interval='daily', series_type='close')
#data.to_csv(stock_name + '_MACD indicator.csv', index=True, sep=',')
return data.to_json(orient='split')
def collect_data(ticker: str, interval: str, key):
""" Collect data from alpha vantage. Currently collects current stock price
and 5 technical indicators """
print(f'Collecting data for {ticker}')
ts = TimeSeries(key)
ti = TechIndicators(key)
# get stock price
price, meta = ts.get_intraday(symbol=ticker,
interval=interval,
outputsize='full')
price = parse_ts(price).reset_index().rename(columns={'index': 'ts'})
price['ts'] = pd.to_datetime(price['ts'])
print(price.head())
# pause to avoid API throttling limits
time.sleep(45)
# get technical indicators
print('Collecting Technical Indicators')
rsi = parse_ts(ti.get_rsi(
symbol=ticker,
interval=interval)[0]).reset_index().rename(columns={'index': 'ts'})
rsi['ts'] = pd.to_datetime(rsi['ts'])
print(rsi.head())
macd = parse_ts(ti.get_macd(
symbol=ticker,
interval=interval)[0]).reset_index().rename(columns={'index': 'ts'})
macd['ts'] = pd.to_datetime(macd['ts'])
print(macd.head())
sar = parse_ts(ti.get_sar(
symbol=ticker,
interval=interval)[0]).reset_index().rename(columns={'index': 'ts'})
sar['ts'] = pd.to_datetime(sar['ts'])
print(sar.head())
time.sleep(45)
adx = parse_ts(ti.get_adx(
symbol=ticker,
interval=interval)[0]).reset_index().rename(columns={'index': 'ts'})
adx['ts'] = pd.to_datetime(adx['ts'])
print(adx.head())
stoch = parse_ts(ti.get_stoch(
symbol=ticker,
interval=interval)[0]).reset_index().rename(columns={'index': 'ts'})
stoch['ts'] = pd.to_datetime(stoch['ts'])
print(stoch.head())
# merge into single data set
dfs = [price, rsi, macd, sar, adx, stoch]
df_final = reduce(
lambda left, right: pd.merge(left, right, on='ts', how='outer'), dfs)
return df_final.sort_values('ts').dropna()
def get_data(symbol):
#get tech indicators for stock
ti = TechIndicators(key='5FWAPV1GCOE2WQLV', output_format='pandas')
sma_, _ = ti.get_sma(symbol=symbol, interval='daily')
macd_, _ = ti.get_macd(symbol=symbol, interval='daily')
rsi_, _ = ti.get_rsi(symbol=symbol, interval='daily')
adx_, _ = ti.get_adx(symbol=symbol, interval='daily')
ts = TimeSeries(key='5FWAPV1GCOE2WQLV', output_format='pandas')
data, _ = ts.get_daily(symbol=symbol, outputsize='full')
final_data = pd.concat([data, sma_, macd_, rsi_, adx_], axis=1, sort=True)
return final_data
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 checkMACD(message):
try:
ts = TechIndicators(key='ZBRM5PCHPIYQV8BY', output_format='pandas')
data, meta_data = ts.get_macd(tic, interval=interval)
data.plot()
plt.title('MACD indicator for ' + tic + ' stock')
plt.savefig('MACD.png')
plt.close()
photo = open("MACD.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)
class TechIndicatorClient(object):
def __init__(self, index=None):
self.ti = TechIndicators(constants.ALPHA_VANTAGE_API_KEY)
self.index = index
def get_full_code(self, stock_code):
if self.index:
return stock_code + "." + self.index
else:
return stock_code
def get_macd(self, stock_code):
stock_code = self.get_full_code(stock_code)
score = self.ti.get_macd(stock_code,
interval=constants.INTERVAL_60MIN,
series_type=constants.SERIES_TYPE_CLOSE)
return score
def get_rsi(self, stock_code):
stock_code = self.get_full_code(stock_code)
score = self.ti.get_rsi(stock_code,
interval=constants.INTERVAL_60MIN,
series_type=constants.SERIES_TYPE_CLOSE)
return score
def get_stoch(self, stock_code):
stock_code = self.get_full_code(stock_code)
score = self.ti.get_stoch(stock_code,
interval=constants.INTERVAL_60MIN)
return score
def get_stochrsi(self, stock_code):
stock_code = self.get_full_code(stock_code)
score = self.ti.get_stochrsi(stock_code,
interval=constants.INTERVAL_60MIN)
return score
def get_last_value(self, indicator_score):
last_key = list(indicator_score[0].keys())[0]
last_score = indicator_score[0][last_key]
return json.dumps(last_score)
def grabData(key, input_ticker, delta):
tS = TimeSeries(key=key, output_format='pandas', indexing_type='date')
tI = TechIndicators(key=key, output_format='pandas')
data, meta_data = tS.get_daily_adjusted(symbol=input_ticker, outputsize='full') #compact for last 100 or full for everything
macd, macd_meta = tI.get_macd(symbol=input_ticker, interval='daily', series_type='close')
rsi, rsi_meta = tI.get_rsi(symbol=input_ticker, interval='daily', time_period=14, series_type='close')
willr, willr_meta = tI.get_willr(symbol=input_ticker, interval='daily', time_period=14)
adx, adx_meta = tI.get_adx(symbol=input_ticker, interval='daily', time_period=14)
mom, mom_meta = tI.get_mom(symbol=input_ticker, interval='daily', time_period=10, series_type='close')
all_vals = [data, macd, rsi, willr, adx, mom]
final_df = pd.concat(all_vals, axis=1, sort=True) # Sort arg may need to be False, leaving it blank raises Pandas error
final_df = final_df.dropna()
df = final_df.iloc[::-1]
df = df.reset_index()
df = df.drop(['6. volume', '7. dividend amount'], axis=1)
# *************************************************************************
labels = []
priceDiffernces = []
for index, row in df.iterrows():
if 0 <= index < delta:
pass
else:
initPrice = row[5]
deltaPrice = df.iloc[index-delta][5]
priceDiffernces.append(round((deltaPrice-initPrice), 2))
if deltaPrice > initPrice:
labels.append(1)
else:
labels.append(0)
df = df.drop(list(range(delta)))
labelsPD = pd.Series(labels)
priceDiffsPD = pd.Series(priceDiffernces)
df['Labels'] = labelsPD.values
df['Price Diffs'] = priceDiffsPD.values
df.to_csv(input_ticker + '.csv')
def getMACD(sym):
global finalList
ti = TechIndicators(key='GSD3E3P11LSBZG5O', output_format='pandas')
try:
data, meta_data = ti.get_macd(symbol=sym, interval='monthly')
except Exception:
return
if len(data) == 0:
print("Misssing MACD Data!", sym)
return
if ((data["MACD_Hist"].iloc[-1]) < 0).all():
print("failed MACD", sym, (data["MACD_Hist"].iloc[-1]))
else:
finalList.put(sym)
print("passed MACD", sym)
def MACDIndicator(StockName):
ti = TechIndicators(key='QBGRPFYFV5WBTTCO', output_format='pandas')
data, meta_data= ti.get_macd(symbol=StockName, interval='1min', series_type ='close')
# realdata = data.to_json(orient='table')
# print(realdata)
# # data.plot()
# json_path = './' + StockName +'MACD.json'
# with open(json_path, "w") as f:
# json.dump(realdata, f)
# print("Complete...")
# plt.title('MACD indicator for '+ StockName+ ' stock (1 min)')
# fig = plt.gcf()
# plt.savefig("MACD.pdf")
# plt.show()
data.to_csv(StockName+'MACD indicator.csv',index=True,sep=',')
print('Success')
def loadMACD(symb, ival):
try:
apikey = random.randint(0, 1)
#print ("Key is {} ie {}".format(apikey,ALPHA_VANTAGE_API_KEY[apikey]))
ti = TechIndicators(key='ALPHA_VANTAGE_API_KEY[apikey]',
output_format='json')
data, meta_data = ti.get_macd(symbol=symb,
interval=ival,
series_type='close',
fastperiod=12,
slowperiod=26,
signalperiod=9)
systime.sleep(15)
return data
except Exception as e:
print(e)
systime.sleep(15)
return False
def MACD(stock_name, size, k):
ti = TechIndicators(key=k)
hist = []
signal = []
macd = []
temp_data, temp_meta = ti.get_macd(symbol=stock_name,
interval='5min',
fastperiod='12',
slowperiod='26',
signalperiod='9')
del temp_meta
for key in temp_data:
hist.append(float(temp_data[key]['MACD_Hist']))
signal.append(float(temp_data[key]['MACD_Signal']))
macd.append(float(temp_data[key]['MACD']))
del hist[size:], signal[size:], macd[size:]
hist.reverse()
signal.reverse()
macd.reverse()
return hist, signal, macd
def check_macd(symbol='', key='', interval='daily', points=5):
"""
https://mindspace.ru/abcinvest/shozhdenie-rashozhdenie-skolzyashhih-srednih-moving-average-convergence-divergence-macd/
https://mindspace.ru/abcinvest/aleksandr-elder-o-rashozhdeniyah-tseny-i-macd/
https://mindspace.ru/30305-kak-ispolzovat-divergentsii-macd-dlya-vyyavleniya-razvorota-na-rynke/
"""
ti = TechIndicators(key=key, output_format='pandas')
data, meta_data = ti.get_macd(symbol=symbol, interval=interval)
macddata = data.tail(points).to_dict()
last = None
for macd_date in macddata['MACD_Hist']:
macd_hist_value = macddata['MACD_Hist'][macd_date]
if not last:
last = macd_hist_value
continue
if last > 0 and macd_hist_value < 0:
print('MACD crossed on ' + interval, macd_date, last,
macd_hist_value)
last = macd_hist_value
def getMACD():
global finalList
smaList = list(finalList.queue)
for sym in smaList:
ti = TechIndicators(key='GSD3E3P11LSBZG5O', output_format='pandas')
try:
data, meta_data = ti.get_macd(symbol=sym, interval='monthly')
except Exception:
return
if len(data) == 0:
continue
if ((data["MACD_Hist"].iloc[-1]) < 0).all():
print("failed MACD", sym, (data["MACD_Hist"].iloc[-1]))
finalList.get(sym)
tempList = list(finalList.queue)
print ("MACD List:")
for item in tempList:
print item
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 MACDIndicator(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_macd(symbol=StockName,
interval='daily',
series_type='close')
# realdata = data.to_json(orient='table')
# print(realdata)
# # data.plot()
# json_path = './' + StockName +'MACD.json'
# with open(json_path, "w") as f:
# json.dump(realdata, f)
# print("Complete...")
# plt.title('MACD indicator for '+ StockName+ ' stock (1 min)')
# fig = plt.gcf()
# plt.savefig("MACD.pdf")
# plt.show()
data.to_csv(StockName + '_MACD.csv', index=True, sep=',')
print(StockName + '_MACD saved successfully.')
insertDatabase(StockName + '_MACD')
from alpha_vantage.techindicators import TechIndicators
import matplotlib.pyplot as plt
ti = TechIndicators(key='GSD3E3P11LSBZG5O', output_format='pandas')
data, meta_data = ti.get_macd(symbol='FB', interval='monthly')
data.plot()
plt.title('Simple Moving Average for FB stock (monthly)')
plt.show()
print("Fetch new data...")
oldData = False
if not oldData:
ts = TimeSeries(key=api_key, output_format="pandas")
ti = TechIndicators(key=api_key, output_format="pandas")
# 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:
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")
'''
from alpha_vantage.timeseries import TimeSeries
from alpha_vantage.techindicators import TechIndicators
import matplotlib.pyplot as plt
import Config
ApiKey = Config.config['alphaVantageApiKey']
ts = TimeSeries(key=ApiKey, output_format='pandas')
data, meta_data = ts.get_intraday(symbol='MSFT',interval='1min', outputsize='full')
data.plot()
plt.title('Intraday Times Series for the MSFT stock (1 min)')
plt.show()
ti = TechIndicators(key=ApiKey, output_format='pandas')
data, meta_data = ti.get_macd(symbol='MSFT',interval='1min')
data.plot()
plt.title('MACD for the MSFT stock (1 min)')
plt.show()
