def ts_stock_data(s_symbol, func_key, s_interval):
if func_key == 'i':
ts = timeseries.TimeSeries(key=apikey)
tsdata, meta_data = ts.get_intraday(symbol=s_symbol,
interval=s_interval)
pprint(tsdata)
pprint(meta_data)
elif func_key == 'd':
ts = timeseries.TimeSeries(key=apikey)
tsdata, meta_data = ts.get_daily(symbol=s_symbol)
pprint(tsdata)
pprint(meta_data)
elif func_key == 'da':
ts = timeseries.TimeSeries(key=apikey)
tsdata, meta_data = ts.get_daily_adjusted(symbol=s_symbol)
pprint(tsdata)
pprint(meta_data)
elif func_key == 'w':
ts = timeseries.TimeSeries(key=apikey)
tsdata, meta_data = ts.get_weekly(symbol=s_symbol)
pprint(tsdata)
pprint(meta_data)
elif func_key == 'wa':
ts = timeseries.TimeSeries(key=apikey)
tsdata, meta_data = ts.get_weekly_adjusted(symbol=s_symbol)
pprint(tsdata)
pprint(meta_data)
def _get_data(self, alpha_vantage_APIkey):
# determine output size. Might be time critical, maybe not.
if self.start >= self.retrieve_day - dt.timedelta(
days=140) and self.end >= self.retrieve_day - dt.timedelta(
days=140):
outputsize = 'compact'
else:
outputsize = 'full'
# get data according to specified times
timeseries = ts.TimeSeries(key=alpha_vantage_APIkey,
output_format='pandas')
data, metadata = timeseries.get_daily(symbol=self.ticker,
outputsize=outputsize)
data.Date = pd.to_datetime(data.index, format='%Y-%m-%d')
data.set_index(data.Date, inplace=True)
data = data[self.start:self.end]
return data, metadata
def get_ts(ticker, tick='batch_daily_adjusted', dropna=True):
# tick can be 'intraday', 'daily_adjusted', 'batch_daily_adjusted'
ts = alpha.TimeSeries(key='VVLO0LVEKD59PF4Y', output_format='pandas')
if tick == 'intraday':
data, _ = ts.get_intraday(symbol=ticker,
interval='1min',
outputsize='full')
data = data[['4. close']]
data = data.rename(columns={"4. close": ticker})
elif tick == 'daily_adjusted':
data, _ = ts.get_daily_adjusted(symbol=ticker, outputsize='full')
data = data[['5. adjusted close']]
data = data.rename(columns={"5. adjusted close": ticker})
elif tick == 'batch_daily_adjusted':
data = []
for s in ticker:
df, _ = ts.get_daily_adjusted(symbol=s, outputsize='full')
df = df[['5. adjusted close']]
df = df.rename(columns={"5. adjusted close": s})
data.append(df)
data = pd.concat(data, axis=1, sort=True)
if dropna:
data.dropna(inplace=True)
return data
@author: vmaaaaan
'''
"""data format: open, high, low, close, volume
"""
import alpha_vantage.timeseries as avts
def is_number(s):
try:
float(s)
return True
except ValueError:
return False
ts = avts.TimeSeries(key='UQV2D45VFXJGX00j')
print("Fetching Data...")
raw_data, meta_data = ts.get_daily('ge', outputsize="full")
#print(raw_data)
data_p1 = str(raw_data).split("': '")
data_p2 = []
for d in data_p1:
for ds in d.split("', '"):
data_p2.append(ds)
data_p2b = []
for d in data_p2:
for ds in d.split("'},"):
data_p2b.append(ds)
data_rev = []
new_seg = []
label=x,
linewidth=1)
leg = plt.legend()
for x in leg.get_lines():
x.set_linewidth(4)
to_plot = [[[], []]]
style.use('fivethirtyeight')
fig = plt.figure()
ax_data = {}
ani = matplotlib.animation.FuncAnimation(fig, animate, interval=5000)
plt.show()
app = QApplication(sys.argv)
window = simple()
holder = timeseries.TimeSeries(key=KEY)
# handling data updating... DaemonThread used so that the GUI will trigger closing
data_update_thread = thread.Timer(refresh_rate, data_thread_handler)
data_update_thread.setDaemon(True)
data_update_thread.start()
thread.Thread(target=handle_graph, daemon=True).start()
print('made it')
# launching application
sys.exit(app.exec_())
def get_stock_data(strticker):
"""request to get the data for stocks"""
ts = avt.TimeSeries(key=hidden.alpha_api)
data, meta = ts.get_daily(strticker)
return data
start_date=start_date,
end_date=end_date,
api_key=quandl_api_key)['Adj. Close']
print(data_multiples.head())
data_multiples.plot(figsize=(17, 6))
plt.title('Adjusted closes prices')
plt.ylabel('Price', fontsize=14)
plt.xlabel('Year', fontsize=14)
plt.grid(color='k', linestyle='-.', linewidth=0.5)
plt.show()
#----------------------------
#alpha vantage => get intraday data
ts = avts.TimeSeries(alpha_vantage_api_key, output_format='pandas')
data_intraday, data_intraday_info = ts.get_intraday(tickers[0],
outputsize='full',
interval='1min')
print(data_intraday_info)
print(data_intraday.head())
#get custome frecuency data
#During strategy modelling, you are required to work
# with a custom frequency of stock market data such as 7 minutes or 35 minutes.
# This custom frequency candles are not provided by data vendors or web sources, but
# you can always use pandas => 'resample'
#converting 1 min to 10 min frecuency sample data
# define dictionary for convention logic
ohlcv_dict = {