def run():
key = '1TKWNTNJE9BNAP7K'
ts = TimeSeries(key)
daily_peloton, daily_meta = ts.get_daily(symbol='PTON')
monthly_peloton, monthly_meta = ts.get_monthly(symbol='PTON')
create_file()
today = datetime.today().strftime('%Y-%m-%d')
daily_stock_data = daily_peloton.get(today)
highest_price = 0
for month in monthly_peloton:
month_stock_data = monthly_peloton.get(month)
high = month_stock_data.get('2. high')
if highest_price < float(high):
highest_price = float(high)
if daily_stock_data:
close = daily_stock_data.get('4. close')[:-2]
high = daily_stock_data.get('2. high')[:-2]
open_amt = daily_stock_data.get('1. open')[:-2]
low = daily_stock_data.get('3. low')[:-2]
content = "Peloton stock for %s:" % today + "\n"
content += "\tOpened at: $" + open_amt + "\n"
content += "\tClosed at: $" + close + "\n"
content += "\tLow: $" + low + "\n"
content += "\tHigh: $" + high + "\n"
content += "Historical High: $" + str(highest_price) + "\n"
content += "\n\n"
if float(close) > 50.0:
content += "SO RICH"
else:
content += "Not rich yet =("
write_to_file(content)
def export_data(request, api_detail, parent_symbol, symbol, data_type):
# print('Request Data: ', api_detail, parent_symbol, symbol, data_type)
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment;filename=av_data.csv'
# opts = queryset.model._meta
field_names = ['Time Series', 'Open', 'High', 'Low', 'Close', 'Volume']
writer = csv.writer(response)
writer.writerow(field_names)
if api_detail == '1':
ts = TimeSeries(key=settings.API_KEY, output_format='json')
if data_type == 'Intraday':
api_data, meta_data = ts.get_intraday(symbol=symbol,
interval='1min',
outputsize='full')
elif data_type == 'Weekly':
api_data, meta_data = ts.get_weekly(symbol=symbol)
elif data_type == 'Daily':
api_data, meta_data = ts.get_daily(symbol=symbol)
else:
api_data, meta_data = ts.get_monthly(symbol=symbol)
for key, value in api_data.items():
csv_list = [key]
for k, v in value.items():
csv_list.append(v)
writer.writerow(csv_list)
return response
def Time_series(name, time, output, type):
#ts = TimeSeries(key=API_key, output_format='pandas')
ts = TimeSeries(key=API_key)
if (type == 'Intraday'):
data, meta_data = ts.get_intraday(symbol=name,
interval=time,
outputsize=output)
#print_time_series(data,name,time,type)
return data
elif (type == 'Day Adjusted'):
data, meta_data = ts.get_daily_adjusted(symbol=name, outputsize=output)
print_time_series(data, name, time, type)
elif (type == 'Weekly'):
data, meta_data = ts.get_weekly(symbol=name)
print_time_series(data, name, time, type)
elif (type == 'Weekly Adjusted'):
data, meta_data = ts.get_weekly_adjusted(symbol=name)
print_time_series(data, name, time, type)
elif (type == 'Monthly'):
data, meta_data = ts.get_monthly(symbol=name)
#print_time_series(data,name,time,type)
description(data, name, time, type)
elif (type == 'Monthly Adjusted'):
data, meta_data = ts.get_monthly_adjusted(symbol=name)
print_time_series(data, name, time, type)
def getAVdata(stock, interval, akey):
while True:
try:
ts = TimeSeries(key=alphakeys[akey], output_format='pandas')
if interval == 'daily':
tdata, meta_data = ts.get_daily(symbol=stock,
outputsize='full')
elif interval == 'weekly':
tdata, meta_data = ts.get_weekly(symbol=stock,
outputsize='full')
elif interval == 'monthly':
tdata, meta_data = ts.get_monthly(symbol=stock,
outputsize='full')
else:
tdata, meta_data = ts.get_intraday(symbol=stock,
interval=interval,
outputsize='full')
break
except Exception as e:
print('Exception!!!: Stock %s Alphakey: %s Interval: %s' %
(stock, alphakeys[akey], interval))
akey = (akey + 1) % len(alphakeys)
print(e, '\n')
continue
print(stock, alphakeys[akey], interval)
tdata = tdata.rename(columns={'1. open': 'Open', '2. high': 'High', '3. low': 'Low', \
'4. close': 'Close', '5. volume': 'Volume'})
tdata = tdata.dropna()
return tdata, akey
def monthlyPivotInfo(symbol):
ts = TimeSeries(key=key, output_format='pandas')
data, meta_data = ts.get_monthly(symbol=symbol)
high = data.iloc[
1, 1] # ['1. open', '2. high', '3. low', '4. close', '5. volume']
low = data.iloc[1, 2]
close = data.iloc[1, 3]
pivotPointCalculation = (high + low + close) / 3
pivotPoint = round(pivotPointCalculation, 2)
print(pivotPoint)
return pivotPoint
def pull_tick_monthly(self, tick, adjusted=True):
self.tracker.wait()
ts = TimeSeries(key=self.key, output_format='pandas')
if adjusted:
df, meta_data = ts.get_monthly_adjusted(tick)
else:
df, meta_data = ts.get_monthly(tick)
df = df.reset_index()
df['ticker'] = tick
self.tracker.update(1)
return df
def Data(Ticker):
API_key = os.environ['key']
allData = TimeSeries(API_key, output_format='pandas')
data, meta_data= allData.get_monthly(Ticker)
data.drop(['1. open', '2. high','3. low'], axis=1, inplace=True)
data.rename(columns={'4. close' : 'price', '5. volume': 'volume'}, inplace=True)
data.reset_index(level=data.index.names, inplace=True)
data = data.sort_index(ascending=False)
data['year'] = data['date'].dt.year
data['date'] = data['date'].dt.strftime('%b-%Y')
return data
def stockData(self):
ts = TimeSeries(key=self.apiKey, output_format='pandas')
if (self.timePeriod == "interday"):
data, meta_data = ts.get_intraday(symbol=self.stockName)
elif (self.timePeriod == "daily"):
data, meta_data = ts.get_daily(symbol=self.stockName)
elif (self.timePeriod == "weekly"):
data, meta_data = ts.get_weekly(symbol=self.stockName)
elif (self.timePeriod == "monthly"):
data, meta_data = ts.get_monthly(symbol=self.stockName)
return data
def get_data_monthly(symbol, savingtoCsv=True):
# gets data over of a week
# loading necessary modules
from alpha_vantage.timeseries import TimeSeries
# getting api key
API_KEY, waiting_times = api_key_finder()
# creating object
ts = TimeSeries(key=API_KEY, output_format='pandas')
# reading data into pandas
data, meta_data = ts.get_monthly(symbol=symbol)
# writing data to database and csv
write_to_database(data, 'Monthly', symbol, 'monthly', savingtoCsv)
return data, meta_data
def Feed_Monthly(self):
collectionname = 'Monthly'
for com in self.Company:
try:
ts = TimeSeries(key=self.APIKEYS[3], output_format='pandas')
data, meta_data = ts.get_monthly(com)
data = pd.DataFrame(data)
data.reset_index(inplace=True)
mongodb.UpdateValue(collectionname, com, data.to_dict(orient='list'))
# mongodb.WriteValue(collectionname, com, data.to_dict(orient='list'))
# print(data)
except Exception as e:
print('Company Ignore due to high service call' + '\nError : ' + str(e))
def stockchart_5year(symbol1, symbol2):
title = '5 Years'
ts = TimeSeries(key='QBGZM8IV1P2X2VJQ', output_format='pandas')
data1, meta_data = ts.get_monthly(symbol=symbol1)
data2, meta_data = ts.get_monthly(symbol=symbol2)
newdata1 = data1.drop(data1.index[0:len(data1.index) - 62])
newdata2 = data2.drop(data2.index[0:len(data2.index) - 62])
newdata1 = modify_data(newdata1)
newdata2 = modify_data(newdata2)
return graph(symbol1, symbol2, newdata1, newdata2, meta_data, ts, title)
def ogmonthly(name, k):
key = k
ts = TimeSeries(key, output_format='pandas')
#ti = TechIndicators(key)
#sym = input('Enter stock symbol: ')
global datam
sym = name
datam, meta_data = ts.get_monthly(symbol=sym)
print("model for month of data")
#print(data)
fn = name + ".month.csv"
with open(fn, 'w') as ff:
datam.to_csv(ff)
def TimeSeriesMonthly(request, pk, format=None):
try:
symbol = Stock.objects.get(pk=pk).symbol
except symbol.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
from alpha_vantage.timeseries import TimeSeries
ts = TimeSeries(key=key)
try:
data, meta_data = ts.get_monthly(symbol=symbol)
except Exception:
return Response(status=status.HTTP_404_NOT_FOUND)
return Response(data)
def output(self):
"""
Retrieve stock data
@return TableWrapper of stock data
"""
stock_symbols = self.ap_paramList[0]()
timeseries_retriever = TimeSeries(key=DataFetcher.getConfigItem(
'stocks', 'api_key'),
output_format='pandas',
indexing_type='date')
data_dict = OrderedDict()
metadata_dict = OrderedDict()
for symbol in stock_symbols:
# Extract data
if self.data_type == 'daily':
data, metadata = timeseries_retriever.get_daily(
symbol, outputsize='full')
elif self.data_type == 'daily_adjusted':
data, metadata = timeseries_retriever.get_daily_adjusted(
symbol, outputsize='full')
elif self.data_type == 'monthly':
data, metadata = timeseries_retriever.get_monthly(symbol)
elif self.data_type == 'monthly_adjusted':
data, metadata = timeseries_retriever.get_monthly_adjusted(
symbol)
elif self.data_type == 'weekly':
data, metadata = timeseries_retriever.get_weekly(symbol)
elif self.data_type == 'weekly_adjusted':
data, metadata = timeseries_retriever.get_weekly_adjusted(
symbol)
elif self.data_type == 'intraday':
data, metadata = timeseries_retriever.get_weekly_adjusted(
symbol, self.interval, outputsize='full')
# Convert index to pandas datetime
if self.data_type == 'intraday':
data.index = pd.to_datetime(data.index).tz_localize(
metadata['6. Time Zone'])
else:
data.index = pd.to_datetime(data.index)
data_dict[symbol] = data[self.start_date:self.end_date]
metadata_dict[symbol] = metadata
return TableWrapper(data_dict, meta_data=metadata_dict)
def home():
form = Submissionform()
if form.validate_on_submit():
flash('Ticker Submited : {}'.format(form.text.data))
try:
if form.submit.data == True:
ts = TimeSeries(key='FSFW64CYZLUB5O4C', output_format='pandas')
data, meta_data = ts.get_intraday(symbol=form.text.data,
interval='1min',
outputsize='full')
data = data.reindex(index=data.index[::-1])
values = data['4. close']
labels = list(data.index)
legend = form.text.data
return render_template('chart.html',
values=values,
labels=labels,
legend=legend)
if form.monthly.data == True:
ts = TimeSeries(key='FSFW64CYZLUB5O4C', output_format='pandas')
data, meta_data = ts.get_monthly(symbol=form.text.data)
print(data)
data = data.reindex(index=data.index[::-1])
values = data['4. close']
labels = list(data.index)
legend = form.text.data
return render_template('chart.html',
values=values,
labels=labels,
legend=legend)
if form.download.data == True:
ts = TimeSeries(key='FSFW64CYZLUB5O4C', output_format='pandas')
data, meta_data = ts.get_intraday(symbol=form.text.data,
interval='1min',
outputsize='full')
csv = data.to_csv()
#print(csv)
return Response(csv,
mimetype="text/csv",
headers={
"Content-disposition":
"attachment; filename=data.csv"
})
except:
flash('Enter Valid Ticker!')
else:
flash_errors(form)
return render_template('home.html', form=form)
def getTimeSeries(self,
function,
symbols,
interval='1min',
outputsize="compact",
datatype="pandas"):
#check if datatype is valid and create instance of time series
if datatype != 'pandas' and datatype != 'json' and datatype != 'csv':
print(
"Invalid Datatype: Vaible options are 'pandas', 'json', 'csv'")
return -1
else:
ts = TimeSeries(key=self.key, output_format=datatype)
#check if outputsize is a valid input
if outputsize != 'compact' and outputsize != 'full':
print(
"Invalide Output Size: Viable options are 'compact' and 'full'"
)
return -1
#determine what time series the user wants and build request
if function == 'intraday':
valid_intervals = ['1min', '5min', '15min', '30min', '60min']
if interval not in valid_intervals:
print(
"Invalid Interval: Viable options are '1min', '5min', '15min', '30min', '60min'"
)
return -1
else:
data, meta_data = ts.get_intraday(symbol=symbols,
interval=interval,
outputsize=outputsize)
elif function == 'daily':
data, meta_data = ts.get_daily(symbol=symbols,
outputsize=outputsize)
elif function == 'weekly':
data, meta_data = ts.get_weekly(symbol=symbols)
elif function == 'monthly':
data, meta_data = ts.get_monthly(symbol=symbols)
else:
print(
"Invalid Function: Viable options are 'intraday', 'daily', 'weekly', and 'monthly'"
)
return -1
return data
def get_monthly_price(self):
'''
object -> None
This method gets a json file with prices
'''
ts = TimeSeries(key='WYXA08Z6LYUO5HL1', retries=2)
# Get json object with the intraday data and another with the call's metadata
price = ts.get_monthly(self.name)[0]
self.history = {}
for i in price:
if len(self.history) < 25:
self.history[i] = price[i]
else:
break
def getData(symbol, timeSeries, chartType, startDate, endDate):
ts = TimeSeries(key=API_KEY, output_format='pandas')
if timeSeries == '1':
data, meta_data = ts.get_intraday(symbol=symbol,
interval='60min',
outputsize='full')
f = 'H'
if timeSeries == '2':
data, meta_data = ts.get_daily(symbol=symbol, outputsize='compact')
f = 'D'
if timeSeries == '3':
data, meta_data = ts.get_weekly(symbol=symbol)
f = 'W'
if timeSeries == '4':
data, meta_data = ts.get_monthly(symbol=symbol)
f = 'M'
data_date_changed = data[endDate:startDate]
if chartType == "1":
line_chart = pygal.Bar(x_label_rotation=20, width=1000, height=400)
line_chart.title = 'Stock Data for {}: {} to {}'.format(
symbol, startDate, endDate)
labels = data_date_changed.index.to_list()
line_chart.x_labels = reversed(labels)
line_chart.add("Open", data_date_changed['1. open'])
line_chart.add("High", data_date_changed['2. high'])
line_chart.add("Low", data_date_changed['3. low'])
line_chart.add("Close", data_date_changed['4. close'])
line_chart.render_in_browser()
if chartType == "2":
line_chart = pygal.Line(x_label_rotation=20, spacing=80)
line_chart.title = 'Stock Data for {}: {} to {}'.format(
symbol, startDate, endDate)
labels = data_date_changed.index.to_list()
line_chart.x_labels = reversed(labels)
line_chart.add("Open", data_date_changed['1. open'])
line_chart.add("High", data_date_changed['2. high'])
line_chart.add("Low", data_date_changed['3. low'])
line_chart.add("Close", data_date_changed['4. close'])
line_chart.render_in_browser()
def load_data_history(ticker='SPY', freq='15min', start='2014', end='2016'):
""" Loads data from Yahoo. After loading it renames columns to shorter
format, which is what Backtest expects.
Set `adjust close` to True to correct all fields with with divident info
provided by Yahoo via Adj Close field.
Defaults are in place for convenience. """
if isinstance(ticker, list):
return pd.Panel({
t: load_data_history(ticker=t,
start=start,
adjust_close=adjust_close)
for t in ticker
})
ts = TimeSeries(key='8YLDYU6Z9IAZNIS4')
if freq == 'daily':
data, meta_data = ts.get_daily(ticker, outputsize='full')
elif freq == 'weekly':
data, meta_data = ts.get_weekly(ticker, outputsize='full')
elif freq == 'monthly':
data, meta_data = ts.get_monthly(ticker, outputsize='full')
else:
data, meta_data = ts.get_intraday(
ticker, interval=freq,
outputsize='full') #supported 1min, 15min, 30min, 60min
#r = data['Close']
ohlc_cols = ['Open', 'High', 'Low', 'Close']
#data[ohlc_cols] = data[ohlc_cols].mul(r, axis=0)
data = data.rename(
columns={
'Open': 'O',
'High': 'H',
'Low': 'L',
'Close': 'C',
'Adj Close': 'AC',
'Volume': 'V'
})
return data
def get_historical_data(key, time_slice, symbol):
"""Wrapper function to source historical EOD stock data
Args:
key (str): alphavantage api key
time_slice (str): Aggregate level to fetch. Options are:
- daily
- weekly
- monthly
symbol (str): Symbol used, including the exchange
Returns:
DataFrame: EOD dataframe
"""
# Instantiate Session
ts = TimeSeries(key=key, output_format="pandas", indexing_type="integer")
# Retrieve Data
if time_slice == "daily":
df, metadata = ts.get_daily(symbol, outputsize="full")
elif time_slice == "weekly":
df, metadata = ts.get_weekly(symbol)
elif time_slice == "monthly":
df, metadata = ts.get_monthly(symbol)
# Replace 0's with NA's because they're almost certainly false
df.replace(0, np.nan, inplace=True)
# Fix crappy column header that contain numbers & periods
df.columns = [
remove_numbers(x).replace(". ", "") for x in df.columns.tolist()
]
# Fix Date type
df["date"] = pd.to_datetime(df["date"])
return df
class AlphaVantage:
def __init__(self, last_date = None):
self.key = config.get_alphavantage_key()
self.last_date = last_date
self.ts = TimeSeries(key=self.key, retries=5, output_format='pandas', indexing_type='date')
def compact_quotes(self, sym):
try:
df, m = self.ts.get_daily(sym, outputsize='compact')
except:
print 'Compact quote get caused exception'
return None
if df is not None:
df = df.rename(columns = {'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'close', '5. volume': 'volume'})
df = df[['open', 'high', 'low', 'close', 'volume']]
return df
def full_quotes(self, sym):
try:
df, m = self.ts.get_daily(sym, outputsize='full')
except:
print 'Full quote get caused exception'
return None
if df is not None:
df = df.rename(columns = {'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'close', '5. volume': 'volume'})
df = df[['open', 'high', 'low', 'close', 'volume']]
return df
def compact_quotes_intraday(self, sym):
try:
df, m = self.ts.get_intraday(sym, interval='30min', outputsize='compact')
except:
print 'Compact quote intraday get caused exception'
return None
if df is not None:
df = df.rename(columns = {'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'close', '5. volume': 'volume'})
df = df[['open', 'high', 'low', 'close', 'volume']]
return df
def full_quotes_intraday(self, sym):
try:
df, m = self.ts.get_intraday(sym, interval='30min', outputsize='full')
except:
print 'Full quote intraday get caused exception'
return None
if df is not None:
df = df.rename(columns = {'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'close', '5. volume': 'volume'})
df = df[['open', 'high', 'low', 'close', 'volume']]
return df
def quotes_weekly(self, sym):
try:
df, m = self.ts.get_weekly(sym)
except:
print 'Weekly quote get caused exception'
return None
if df is not None:
df = df.rename(columns = {'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'close', '5. volume': 'volume'})
df = df[['open', 'high', 'low', 'close', 'volume']]
return df
def quotes_monthly(self, sym):
try:
df, m = self.ts.get_monthly(sym)
except:
print 'Weekly quote get caused exception'
return None
if df is not None:
df = df.rename(columns = {'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'close', '5. volume': 'volume'})
df = df[['open', 'high', 'low', 'close', 'volume']]
return df
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
# default is localhost:27017
connection = MongoClient('localhost:27017')
# Switching to a specific Database within MongoDB
db = connection.StockMarketDB
# Switching to a specific Collection(table) within the DB
col = db.monthlyStatistics
####################################################################
# Personal issued key from alpha_vantage
ts = TimeSeries(key='II0VU3FTX7AAEU99')
# Calling an individual company's monthly stock data
data, meta_data = ts.get_monthly(symbol='MSFT')
scope = monthly
# Will display that data
# pprint(meta_data)
parsed_data = {}
# formatting nested dictionary keys for mongoDB
for date, stock in data.items():
parsed_data[date] = {}
for price in stock.keys():
parsed_data[date][price[3:]] = data[date][price]
#adding 'date' dictionary key as a value to
import pandas as pd
from pandas import ExcelWriter
from alpha_vantage.timeseries import TimeSeries
import time
from credentials import *
import matplotlib.pyplot as plt
ts = TimeSeries(key=api_key, output_format='pandas')
a = 0
call_limit = [4, 8, 12]
writer = ExcelWriter('output.xlsx')
for i in stock_list:
stonk_data, meta_data = ts.get_monthly(symbol=i)
index=stonk_data.index
index.name= "Stock: " + i
close_data = stonk_data['4. close']
percentage_change = close_data.pct_change() * 100
stonks = "STOCK:", i, close_data, percentage_change
print(stonk_data['4. close'])
a += 1
print("a =", a)
file_name = i + " - Sheet - " + str(a)
print(file_name)
#Matplotlib will print a plot of each stock, how do i print them all in one view?
#df = pd.DataFrame(stonk_data, columns=['4. close'])
#df.plot(kind = 'line')
#plt.show()
stonk_data['4. close'].to_excel(writer, sheet_name=file_name)
def plot(symbol):
data = TimeSeries(key=key, output_format='pandas')
data, meta_data = data.get_monthly(symbol)
data['close'].plot()
pyplot.title(symbol)
pyplot.show()
def get_monthly(symbol: str):
ts = TimeSeries(key=config.AV_KEY, output_format="pandas")
data, _ = ts.get_monthly(symbol)
return data
'SXR8'
]
#%%
from alpha_vantage.timeseries import TimeSeries
ts = TimeSeries(key='F5DIRSGL3KXINNCM', output_format='pandas')
# Get json object with the intraday data and another with the call's metadata
#data, meta_data = ts.get_intraday('SPY')
dfs = []
metas = []
for ticker in finax_tickers:
try:
df, meta = ts.get_monthly(ticker)
dfs.append(df)
metas.append(meta)
except Exception as e:
print(f'ticker {ticker} data retreive error...')
print(e)
else:
print(f'ticker {ticker} SUCCESS.....')
time.sleep(13)
#%%
import investpy
# https://investpy.readthedocs.io/_info/introduction.html
# Retrieve all the available stocks as a Python list
from alpha_vantage.timeseries import TimeSeries
import matplotlib.pyplot as plt
ts = TimeSeries(key='GSD3E3P11LSBZG5O', output_format='pandas')
data, meta_data = ts.get_monthly(symbol='FB')
data['close'].plot()
plt.title('Intraday Time Series for the FB4 stock (monthly)')
plt.show()
def collectData(self, outputsize='compact') -> pd.DataFrame:
ts = TimeSeries(key=key, output_format='pandas')
data, metadata = ts.get_monthly(self.symbol)
return pd.DataFrame(data)
def stockchart_5year(symbol1):
title = '4 Years'
ts = TimeSeries(key='QBGZM8IV1P2X2VJQ', output_format='pandas')
data1, meta_data = ts.get_monthly(symbol=symbol1)
return graph(symbol1, data1, title)
