def test_get_next_earnings(self, mock_get_earnings_html, pages,
mock_next_earnings_rets, expected_result):
mock_get_earnings_html.side_effect = mock_next_earnings_rets
ret = get_next_earnings(pages)
assertions.assertEqual(ret.to_csv().replace("\r\n", "\n"),
expected_result.replace("\r\n", "\n"))
def earnings_command():
"""Display Upcoming Earnings. [Source: Seeking Alpha]"""
# Debug
if imps.DEBUG:
logger.debug("earnings")
df_earnings = seeking_alpha_model.get_next_earnings(2)
for n_days, earning_date in enumerate(df_earnings.index.unique()):
df_earn = df_earnings[earning_date == df_earnings.index][[
"Ticker", "Name"
]].dropna()
df_earn.index = df_earn["Ticker"].values
df_earn.drop(columns=["Ticker"], inplace=True)
title = f"Earnings on {earning_date.date()}"
dindex = len(df_earn.index)
if dindex > 15:
embeds: list = []
# Output
i, i2, end = 0, 0, 15
df_pg, embeds_img, images_list = [], [], []
while i < dindex:
df_pg = df_earn.iloc[i:end]
df_pg.append(df_pg)
fig = imps.plot_df(
df_pg,
fig_size=(800, (40 + (40 * dindex))),
col_width=[1, 5],
tbl_header=imps.PLT_TBL_HEADER,
tbl_cells=imps.PLT_TBL_CELLS,
font=imps.PLT_TBL_FONT,
row_fill_color=imps.PLT_TBL_ROW_COLORS,
paper_bgcolor="rgba(0, 0, 0, 0)",
)
fig.update_traces(cells=(dict(align=["center", "left"])))
imagefile = "disc-upcoming.png"
imagefile = imps.save_image(imagefile, fig)
if imps.IMAGES_URL or not imps.IMG_HOST_ACTIVE:
image_link = imps.multi_image(imagefile)
images_list.append(imagefile)
else:
image_link = imps.multi_image(imagefile)
embeds_img.append(f"{image_link}", )
embeds.append(disnake.Embed(
title=title,
colour=imps.COLOR,
), )
i2 += 1
i += 15
end += 15
# Author/Footer
for i in range(0, i2):
embeds[i].set_author(
name=imps.AUTHOR_NAME,
url=imps.AUTHOR_URL,
icon_url=imps.AUTHOR_ICON_URL,
)
embeds[i].set_footer(
text=imps.AUTHOR_NAME,
icon_url=imps.AUTHOR_ICON_URL,
)
i = 0
for i in range(0, i2):
embeds[i].set_image(url=embeds_img[i])
i += 1
embeds[0].set_footer(text=f"Page 1 of {len(embeds)}")
choices = [
disnake.SelectOption(label="Home", value="0", emoji="🟢"),
]
output = {
"view": imps.Menu,
"title": title,
"embed": embeds,
"choices": choices,
"embeds_img": embeds_img,
"images_list": images_list,
}
else:
fig = imps.plot_df(
df_earn,
fig_size=(800, (40 + (40 * dindex))),
col_width=[1, 5],
tbl_header=imps.PLT_TBL_HEADER,
tbl_cells=imps.PLT_TBL_CELLS,
font=imps.PLT_TBL_FONT,
row_fill_color=imps.PLT_TBL_ROW_COLORS,
paper_bgcolor="rgba(0, 0, 0, 0)",
)
fig.update_traces(cells=(dict(align=["center", "left"])))
imagefile = imps.save_image("disc-upcoming.png", fig)
output = {
"title": title,
"imagefile": imagefile,
}
return output
def test_get_next_earnings(recorder):
df_earnings = seeking_alpha_model.get_next_earnings(1)
recorder.capture(df_earnings)
def upcoming_earning_release_dates(num_pages: int, num_earnings: int,
export: str):
"""Displays upcoming earnings release dates
Parameters
----------
num_pages: int
Number of pages to scrap
num_earnings: int
Number of upcoming earnings release dates
export : str
Export dataframe data to csv,json,xlsx file
"""
# TODO: Check why there are repeated companies
# TODO: Create a similar command that returns not only upcoming, but antecipated earnings
# i.e. companies where expectation on their returns are high
df_earnings = seeking_alpha_model.get_next_earnings(num_pages)
pd.set_option("display.max_colwidth", None)
if export:
l_earnings = []
l_earnings_dates = []
for n_days, earning_date in enumerate(df_earnings.index.unique()):
if n_days > (num_earnings - 1):
break
# TODO: Potentially extract Market Cap for each Ticker, and sort
# by Market Cap. Then cut the number of tickers shown to 10 with
# bigger market cap. Didier attempted this with yfinance, but
# the computational time involved wasn't worth pursuing that solution.
df_earn = df_earnings[earning_date == df_earnings.index][[
"Ticker", "Name"
]].dropna()
if export:
l_earnings_dates.append(earning_date.date())
l_earnings.append(df_earn)
df_earn.index = df_earn["Ticker"].values
df_earn.drop(columns=["Ticker"], inplace=True)
print(
tabulate(
df_earn,
showindex=True,
headers=[f"Earnings on {earning_date.date()}"],
tablefmt="fancy_grid",
),
"\n",
)
if export:
for i, _ in enumerate(l_earnings):
l_earnings[i].reset_index(drop=True, inplace=True)
df_data = pd.concat(l_earnings, axis=1, ignore_index=True)
df_data.columns = l_earnings_dates
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"upcoming",
df_data,
)