def update_graph(selected_dropdown_value):
global stockpricedf # Needed to modify global copy of stockpricedf
ticker_fb = Fetcher(selected_dropdown_value, [yr, mo, dy])
#ticker_fb = Fetcher('fb', [yr,mo,dy])
stockpricedf = ticker_fb.get_historical()
stockpricedf['Date'] = pd.to_datetime(stockpricedf['Date'])
stockpricedf['Close'] = stockpricedf['Close']
return {
'data': [{
'x': stockpricedf.Date,
'y': stockpricedf.Close,
'line': {
'color': 'green'
}
}]
}
"""
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
import oauth_info as auth
import quandl
from yahoo_historical import Fetcher
from fbprophet import Prophet
import statsmodels.api as sm
import statsmodels.formula.api as smf
quandl.ApiConfig.api_key = auth.QUANDL_KEY
ticker_googl = Fetcher("NFLX", [2018, 1, 1])
googl_df = ticker_googl.get_historical()
print(googl_df.head())
# The adjusted close accounts for stock splits, so that is what we should graph
plt.plot(googl_df.Date, googl_df['Adj Close'])
plt.title('Google Stock Price')
plt.ylabel('Price ($)')
plt.xticks(rotation=45)
plt.show()
# Keep only the adj. Close
googl_adj_close = googl_df[['Date', 'Adj Close']]
# Rename the columns
googl_adj_close.columns = ['ds', 'y']
def downloadTicker(stock: str, startyear: int) -> pd.DataFrame:
stockFetcher = Fetcher(stock, [startyear, 1, 1], [2020, 12, 10])
stock_df = stockFetcher.get_historical()
return stock_df
from yahoo_historical import Fetcher
from indicators import simpleMovingAverage, exponentialMovingAverage
import matplotlib.pyplot as plt
data_link = Fetcher("EDP.LS", [2007, 1, 1])
data = data_link.get_historical()
ts_date = data['Date']
ts_price = data['Close']
ts_sma = simpleMovingAverage(ts_price, periods=30)
ts_ema = exponentialMovingAverage(ts_price, periods=30)
print(ts_sma[200])
print(ts_ema[200])
print(data.iloc[200, :])
plt.plot(ts_price)
plt.plot(ts_sma)
plt.plot(ts_ema)
plt.ylabel('price')
plt.show()
from yahoo_historical import Fetcher
import pandas as pd
companies = pd.read_csv('companies-abbreviations.csv')
for co in companies['Stock Abbreviation']:
data = Fetcher(co, [2018, 12, 31], [2020, 1, 1])
filename = co + "_financial.csv"
historical = data.get_historical()
historical.to_csv("./financial/" + filename)
