def get_earnings_history_(tick):
ern_hist = yf.get_earnings_history(tick)
epsactual = [eh['epsactual'] for eh in ern_hist][::-1]
epsestimate = [eh['epsestimate'] for eh in ern_hist][::-1]
date = [eh['startdatetime'][0:10] for eh in ern_hist][::-1]
return {'epsactual': epsactual, 'epsestimate': epsestimate, 'date': date}
def get_data_on_symbols(symbols, type_="info"):
data = []
symbols_with_data = []
for symbol in symbols:
if type_ == "info":
try:
data.append(yf_info.Ticker(symbol).info)
symbols_with_data.append(symbol)
except:
continue
elif type_ == "earnings":
try:
data.append(
pd.DataFrame.from_dict(
yf_earn.get_earnings_history(symbol)))
symbols_with_data.append(symbol)
except:
continue
else:
raise Exception("ERROR", "Unrecognised type_ value.")
return dict(zip(symbols_with_data, data))
pred_high = reg_high.predict(time)
reg_low = LinearRegression().fit(time, sklow)
pred_low = reg_low.predict(time)
plt.plot(time, pred_high)
plt.plot(time, pred_low)
plt.plot(time, skclose)
plt.legend(['High', 'Low', 'Close'])
plt.show()
##################### historical PE ratio #####################
## eps
ticker = 'adbe'
stock = yf.Ticker(ticker)
earning_tmp1 = si.get_earnings_history(ticker)
earning_tmp2 = pd.DataFrame(earning_tmp1)
earning = earning_tmp2[earning_tmp2['startdatetimetype'] != 'TAS']
# actual earning
earn_act = earning.dropna()
# actual eps date
earntime = list(earn_act['startdatetime'].str[:10])
# price on actual earning date
price_cls = []
for i in range(len(earntime)):
skprice = stock.history(start=dt.date.fromisoformat(earntime[i]) -
dt.timedelta(days=30),
end=earntime[i])
def plot_earning(self, **kwargs):
plt.figure(figsize=(12, 9))
fig, axes = plt.subplots(2)
# add earning history
earnings_history = si.get_earnings_history(self.name)
earnings_history = pd.DataFrame(earnings_history)
earnings_history.dropna(inplace=True)
earnings_history = earnings_history.set_index("startdatetime")
earnings_history = earnings_history.sort_index()
earnings_history.index = pd.to_datetime(earnings_history.index)
if len(earnings_history) > 10:
earnings_history = earnings_history.iloc[-10:]
result_dict = {}
result_dict["epsactual"] = list(earnings_history["epsactual"])
result_dict["epsestimate"] = list(earnings_history["epsestimate"])
earnings = si.get_earnings(self.name)
info = si.get_analysts_info(self.name)
result_dict["epsactual"].extend([None, None])
result_dict["epsestimate"].append(
info["Earnings Estimate"].T.iloc[1].loc[1])
result_dict["epsestimate"].append(
info["Earnings Estimate"].T.iloc[2].loc[1])
result_df = pd.DataFrame(result_dict)
this_year = dt.datetime.now().year
next_year = this_year + 1
new_row = {
"date":
this_year,
"revenue":
_convert_to_numeric(info["Revenue Estimate"].T.iloc[3].loc[1]),
#"earnings": _convert_to_numeric(info["Earnings Estimate"].T.iloc[3].loc[1])
}
earnings["yearly_revenue_earnings"] = earnings[
"yearly_revenue_earnings"].append(new_row, ignore_index=True)
new_row = {
"date":
next_year,
"revenue":
_convert_to_numeric(info["Revenue Estimate"].T.iloc[4].loc[1]),
#"earnings": _convert_to_numeric(info["Earnings Estimate"].T.iloc[4].loc[1])
}
earnings["yearly_revenue_earnings"] = earnings[
"yearly_revenue_earnings"].append(new_row, ignore_index=True)
earnings["yearly_revenue_earnings"]["revenue"] = earnings[
"yearly_revenue_earnings"]["revenue"] / 1000000000
#earnings["yearly_revenue_earnings"] = earnings["yearly_revenue_earnings"]/1000000000
earnings["yearly_revenue_earnings"].set_index('date', inplace=True)
earnings["yearly_revenue_earnings"]["revenue"].plot(
ax=axes[0], marker='o', legend=["revenue(B)"])
#print(result_df)
result_df.plot(ax=axes[1], marker='o')
# save to file if possible
#fig = plt.gcf()
image_name = self.name
if "image_name" in kwargs:
image_name = kwargs['image_name']
result_dir = kwargs['result_dir'] if 'result_dir' in kwargs else None
if result_dir is not None:
file_name = image_name + "_earnings.png"
fig.savefig(os.path.join(result_dir, file_name), dpi=300)
plt.close(fig)
self.markdown_notes += "\n\n \pagebreak\n\n"
self.markdown_notes += f"\n\n\n"
return file_name
else:
return fig, axes
import yahoo_fin.stock_info as si
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import dates
from datetime import datetime as dt
import datetime
codelist = ['msft']
for i in range(len(codelist)):
ticker=codelist[i]
ticker_earnings_hist = si.get_earnings_history(ticker)
df =pd.DataFrame.from_dict(ticker_earnings_hist).loc[:,["startdatetime","epsestimate","epsactual"]]
df.rename(columns={'epsestimate':'EPS Estimate','epsactual':'EPS Actual'},inplace=True)
df['day'] = pd.to_datetime(df["startdatetime"], format='%Y-%m-%d')
df['startdatetime'] = df['day'].dt.strftime('%Y-%m-%d')
df = df.sort_index(axis='index',ascending=False)[["startdatetime","EPS Estimate", "EPS Actual"]]
df[(df['startdatetime'] > '2006-01-01')].plot.bar(title=ticker , x="startdatetime",color=['lightgray', 'red'], figsize=(14,6),fontsize=10)
plt.title(ticker, fontsize=50)
plt.xlabel(xlabel="", fontsize=20)
plt.ylabel(ylabel="EPS", fontsize=20)
plt.legend(fontsize=25)
plt.show()
import pandas as pd
from yahoo_fin import options
from yahoo_fin import stock_info as si
"""
nflx_dates = options.get_expiration_dates('nflx')
chain = options.get_options_chain("nflx")
calls = chain["calls"]
puts = chain["puts"]
nflx_dates= options.get_expiration_dates("nflx")
info = {}
for date in nflx_dates:
info[date] = options.get_options_chain("nflx")
"""
# dow_tickers = si.tickers_dow()
# nasdaq = si.tickers_nasdaq()
# sp = si.tickers_sp500()
aapl_earnings = si.get_next_earnings_date("aapl")
aapl_earnings_hist = si.get_earnings_history("aapl")
data = pd.DataFrame.from_dict(aapl_earnings_hist)
def getEarningsHistory(ticker):
return si.get_earnings_history(ticker)