def test_dividend(self):
"""
Test if it can correctly return the dividends in the correct order and format(parsed correctly)
"""
output1 = pd.DataFrame(columns=["Dividends"])
output1 = pd.DataFrame(data=[{
'amount': 0.448,
'date': 1615987800
}, {
'amount': 0.442,
'date': 1608215400
}])
output1.set_index("date", inplace=True)
output1.index = pd.to_datetime(output1.index, unit="s")
output1.sort_index(inplace=True)
#dividends.index = dividends.index.tz_localize(tz)
output1.columns = ["Dividends"]
#case1: when Data that contains two different dividends and contains no splits.
result = utils.parse_actions(data1)
self.assertNotEqual(result,
None) #check if the returned result is None
self.assertFalse(
result[0].empty) #check if the returned dividents is not empty
self.assertTrue(
result[1].empty) #check if the returned splits returned is empty
assert_frame_equal(
result[0], output1
) #check if the data frame returned match with expected output
#case2: when data has no dividends and splits events
result2 = utils.parse_actions(data2)
self.assertTrue(result2[0].empty)
self.assertTrue(result2[1].empty)
def test_split(self):
"""
Test if it can correctly parsr the data with splits.
"""
#mock dataframe containing a stock info with splits
output = pd.DataFrame(data=[{
'date': 1616482800,
'numerator': 1,
'denominator': 50,
'splitRatio': '1:50'
}])
output.set_index("date", inplace=True)
output.index = pd.to_datetime(output.index, unit="s")
output.sort_index(inplace=True)
output["Stock Splits"] = output["numerator"] / \
output["denominator"]
output = output["Stock Splits"]
#case3: data has only split event
result = utils.parse_actions(
data3) #call parse action function with the above data
self.assertNotEqual(result, None) #Check if the result is None
self.assertFalse(result[1].empty) #Check if the result contains splits
self.assertTrue(
result[0].empty) #Check if the result does not contain dividends
assert_series_equal(result[1],
output) #Check if the result matches the mock data
def test_dividend(self):
"""
Test if it can correctly return the dividends in the correct order and format(parsed correctly)
"""
#case1: when Data that contains two different dividends and contains no splits.
result = utils.parse_actions(data1)
self.assertNotEqual(result,
None) #check if the returned result is None
self.assertFalse(
result[0].empty) #check if the returned dividents is not empty
self.assertTrue(
result[1].empty) #check if the returned splits returned is empty
assert_frame_equal(
result[0], output1
) #check if the data frame returned match with expected output
#case2: when data has no dividends and splits events
result2 = utils.parse_actions(data2)
self.assertTrue(result2[0].empty)
self.assertTrue(result2[1].empty)
def test_emptyInput(self):
"""
Test if data can handle wrong input argument
"""
#output = pd.DataFrame(columns=["Dividends"])
#output = pd.DataFrame(data=[{'amount': 0.205, 'date': 1604673000}, {'amount': 0.205, 'date': 1612535400}])
output = pd.DataFrame(columns=["Dividends"])
output = pd.DataFrame(data=[''])
output.index = pd.to_datetime(output.index, unit="s")
result = utils.parse_actions(data5)
self.assertTrue(result[0].empty)
self.assertTrue(result[1].empty)
def test_wrongDate(self):
"""
Test the date beyond the electronic stock market was created
"""
output = pd.DataFrame(columns=["Dividends"])
output = pd.DataFrame(data=[{
'amount': 0.205,
'date': 1604673000
}, {
'amount': 0.205,
'date': 3308400
}])
output.set_index("date", inplace=True)
output.index = pd.to_datetime(output.index, unit="s")
dividends, splits = utils.parse_actions(data6)
self.assertEqual(dividends.index[1], output.index[1])
def test_dateTime(self):
"""
Test if date and time are in correct format
"""
output = pd.DataFrame(columns=["Dividends"])
output = pd.DataFrame(data=[{
'amount': 0.205,
'date': 1604673000
}, {
'amount': 0.205,
'date': 1612535400
}])
output.set_index("date", inplace=True)
output.index = pd.to_datetime(output.index, unit="s")
dividends, splits = utils.parse_actions(data4)
self.assertTrue(splits.empty)
self.assertFalse(dividends.empty)
self.assertEqual(dividends.index[0], output.index[0])
def history(self,
period="1mo",
interval="1d",
start=None,
end=None,
prepost=False,
actions=True,
auto_adjust=True,
back_adjust=False,
proxy=None,
rounding=False,
tz=None,
**kwargs):
"""
:Parameters:
period : str
Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
Either Use period parameter or use start and end
interval : str
Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
Intraday data cannot extend last 60 days
start: str
Download start date string (YYYY-MM-DD) or _datetime.
Default is 1900-01-01
end: str
Download end date string (YYYY-MM-DD) or _datetime.
Default is now
prepost : bool
Include Pre and Post market data in results?
Default is False
auto_adjust: bool
Adjust all OHLC automatically? Default is True
back_adjust: bool
Back-adjusted data to mimic true historical prices
proxy: str
Optional. Proxy server URL scheme. Default is None
rounding: bool
Round values to 2 decimal places?
Optional. Default is False = precision suggested by Yahoo!
tz: str
Optional timezone locale for dates.
(default data is returned as non-localized dates)
**kwargs: dict
debug: bool
Optional. If passed as False, will suppress
error message printing to console.
"""
if start or period is None or period.lower() == "max":
if start is None:
start = -2208988800
elif isinstance(start, _datetime.datetime):
start = int(_time.mktime(start.timetuple()))
else:
start = int(
_time.mktime(_time.strptime(str(start), '%Y-%m-%d')))
if end is None:
end = int(_time.time())
elif isinstance(end, _datetime.datetime):
end = int(_time.mktime(end.timetuple()))
else:
end = int(_time.mktime(_time.strptime(str(end), '%Y-%m-%d')))
params = {"period1": start, "period2": end}
else:
period = period.lower()
params = {"range": period}
params["interval"] = interval.lower()
params["includePrePost"] = prepost
params["events"] = "div,splits"
# 1) fix weired bug with Yahoo! - returning 60m for 30m bars
if params["interval"] == "30m":
params["interval"] = "15m"
# setup proxy in requests format
if proxy is not None:
if isinstance(proxy, dict) and "https" in proxy:
proxy = proxy["https"]
proxy = {"https": proxy}
# Getting data from json
url = "{}/v8/finance/chart/{}".format(self._base_url, self.ticker)
data = self.session.get(url=url, params=params, proxies=proxy)
if "Will be right back" in data.text:
raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
"Our engineers are working quickly to resolve "
"the issue. Thank you for your patience.")
data = data.json()
# Work with errors
debug_mode = True
if "debug" in kwargs and isinstance(kwargs["debug"], bool):
debug_mode = kwargs["debug"]
err_msg = "No data found for this date range, symbol may be delisted"
if "chart" in data and data["chart"]["error"]:
err_msg = data["chart"]["error"]["description"]
shared._DFS[self.ticker] = utils.empty_df()
shared._ERRORS[self.ticker] = err_msg
if "many" not in kwargs and debug_mode:
print('- %s: %s' % (self.ticker, err_msg))
return shared._DFS[self.ticker]
elif "chart" not in data or data["chart"]["result"] is None or \
not data["chart"]["result"]:
shared._DFS[self.ticker] = utils.empty_df()
shared._ERRORS[self.ticker] = err_msg
if "many" not in kwargs and debug_mode:
print('- %s: %s' % (self.ticker, err_msg))
return shared._DFS[self.ticker]
# parse quotes
try:
quotes = utils.parse_quotes(data["chart"]["result"][0], tz)
except Exception:
shared._DFS[self.ticker] = utils.empty_df()
shared._ERRORS[self.ticker] = err_msg
if "many" not in kwargs and debug_mode:
print('- %s: %s' % (self.ticker, err_msg))
return shared._DFS[self.ticker]
# 2) fix weired bug with Yahoo! - returning 60m for 30m bars
if interval.lower() == "30m":
quotes2 = quotes.resample('30T')
quotes = _pd.DataFrame(index=quotes2.last().index,
data={
'Open': quotes2['Open'].first(),
'High': quotes2['High'].max(),
'Low': quotes2['Low'].min(),
'Close': quotes2['Close'].last(),
'Adj Close':
quotes2['Adj Close'].last(),
'Volume': quotes2['Volume'].sum()
})
try:
quotes['Dividends'] = quotes2['Dividends'].max()
except Exception:
pass
try:
quotes['Stock Splits'] = quotes2['Dividends'].max()
except Exception:
pass
if auto_adjust:
quotes = utils.auto_adjust(quotes)
elif back_adjust:
quotes = utils.back_adjust(quotes)
if rounding:
quotes = _np.round(quotes,
data["chart"]["result"][0]["meta"]["priceHint"])
quotes['Volume'] = quotes['Volume'].fillna(0).astype(_np.int64)
quotes.dropna(inplace=True)
# actions
dividends, splits = utils.parse_actions(data["chart"]["result"][0], tz)
# combine
df = _pd.concat([quotes, dividends, splits], axis=1, sort=True)
df["Dividends"].fillna(0, inplace=True)
df["Stock Splits"].fillna(0, inplace=True)
# index eod/intraday
df.index = df.index.tz_localize("UTC").tz_convert(
data["chart"]["result"][0]["meta"]["exchangeTimezoneName"])
if params["interval"][-1] == "m":
df.index.name = "Datetime"
elif params["interval"] == "1h":
pass
else:
df.index = _pd.to_datetime(df.index.date)
if tz is not None:
df.index = df.index.tz_localize(tz)
df.index.name = "Date"
self._history = df.copy()
if not actions:
df.drop(columns=["Dividends", "Stock Splits"], inplace=True)
return df