def test_alpaca_intraday(self):
provider = JSONDataProvider(['data/alpaca'],
['t', 'o', 'h', 'l', 'c', 'v'],
epoch=True,
verbose=2)
symbol_datas = [
SymbolData('SPY',
'15min',
'15min',
'2020-05-11',
'2020-05-13',
rth_only=False),
SymbolData('SPY',
'5min',
'5min',
'2020-05-11',
'2020-05-13',
rth_only=False)
]
datas = provider.get_datas(symbol_datas)
assert datas[0].timeframe == '15min'
assert datas[1].timeframe == '5min'
assert datas[0].df.loc['2020-05-12 16:15']['Close'] == 285.48
assert datas[1].df.loc['2020-05-12 16:35']['Close'] == 284.88
def test_alpaca_week(self):
provider = JSONDataProvider(['data/alpaca'], [
'startEpochTime', 'openPrice', 'highPrice', 'lowPrice',
'closePrice', 'volume'
],
epoch=True,
verbose=2)
symbol_datas = [
SymbolData('XONE',
'day',
'day',
'2020-01-11',
'2020-05-17',
rth_only=True),
SymbolData('XONE',
'day',
'week',
'2020-01-11',
'2020-05-17',
rth_only=True)
]
datas = provider.get_datas(symbol_datas)
daily = datas[0].df
weekly = datas[1].df
#assert d.timeframe == 'week'
print(daily)
print(weekly)
def test_weekly_volume(self):
provider = CsvFileDataProvider(["data"])
spy_daily, spy_weekly = provider.get_dataframes([
SymbolData('SPY', 'day', 'day', '2010-01-01', '2017-01-01'),
SymbolData('SPY', 'day', 'week', '2010-01-01', '2017-01-01')
])
assert spy_daily.loc['20160104':'20160108', 'Volume'].sum() == \
spy_weekly.loc['20160104']['Volume']
def test_resample_timeframe_metadata(self):
provider = CsvFileDataProvider(["data"], verbose=2)
datas = provider.get_datas([
SymbolData('SPY', 'day', 'day', '2016-01-01', '2016-12-31'),
SymbolData('SPY', 'day', 'week', '2016-01-01', '2016-12-31'),
SymbolData('SPY', 'day', 'month', '2016-01-01', '2016-12-31')
])
assert datas[0].timeframe == 'day'
assert datas[1].timeframe == 'week'
assert datas[2].timeframe == 'month'
def test_resample_timeframe(self):
provider = CsvFileDataProvider(["data"], verbose=2)
#df = provider.get_data(['AAPL_2018-01-06'], '2017-12-10', '2017-12-31', timeframe='5min', transform='60min', rth=True)[0]
df = provider.get_dataframes([
SymbolData('AAPL_2018-01-06',
'5min',
'60min',
'2017-12-10',
'2017-12-31',
rth_only=True)
])[0]
# Assert 2017-12-29 09:30: O=170.71, H=170.71, L=169.71, C=169.93
assert str(
df.loc[pd.to_datetime('2017-12-29 09:00:00')]['Open']) == "170.71"
assert str(
df.loc[pd.to_datetime('2017-12-29 09:00:00')]['High']) == "170.71"
assert str(
df.loc[pd.to_datetime('2017-12-29 09:00:00')]['Low']) == "169.71"
assert str(
df.loc[pd.to_datetime('2017-12-29 09:00:00')]['Close']) == "169.93"
# Assert 2017-12-29 10:00: O=169.92, H=169.95, L=169.31, C=169.64
assert str(
df.loc[pd.to_datetime('2017-12-29 10:00:00')]['Open']) == "169.92"
assert str(
df.loc[pd.to_datetime('2017-12-29 10:00:00')]['High']) == "169.95"
assert str(
df.loc[pd.to_datetime('2017-12-29 10:00:00')]['Low']) == "169.31"
assert str(
df.loc[pd.to_datetime('2017-12-29 10:00:00')]['Close']) == "169.64"
# Assert 2017-12-29 16:00: O=169.23
assert str(
df.loc[pd.to_datetime('2017-12-29 16:00:00')]['Open']) == "169.23"
def test_filter_days(self):
provider = CsvFileDataProvider(["data"])
nyse = mcal.get_calendar('NYSE')
days = nyse.valid_days(start_date='2000-01-01', end_date='2017-01-01')
daily = provider.get_dataframes(
[SymbolData('DIS', 'day', 'day', '2000-01-01', '2017-01-01')])[0]
assert len(days) == len(daily)
def test_resample_multi_timeframes(self):
provider = CsvFileDataProvider(["data"], verbose=2)
(min60, daily, weekly) = provider.get_datas([
SymbolData('VOLV.B_5min', '5min', '60min', '', '', rth_only=False),
SymbolData('VOLV.B_5min', '5min', 'day', '', '', rth_only=False),
SymbolData('VOLV.B_5min', '5min', 'week', '', '', rth_only=False)
])
assert str(min60.df.loc[pd.to_datetime('2020-04-01 10:00:00')]
['Open']) == "115.45"
assert str(
daily.df.loc[pd.to_datetime('2020-04-01')]['Open']) == "116.0"
assert str(
round(daily.df.loc[pd.to_datetime('2020-04-01')]['Close'],
2)) == "112.83"
assert str(
weekly.df.loc[pd.to_datetime('2020-04-14')]['Open']) == "130.0"
def test_daily_trading_days(self):
provider = CsvFileDataProvider(["data"])
spy_daily = provider.get_dataframes(
[SymbolData('SPY', 'day', 'day', '2010-01-01', '2017-01-01')])[0]
assert spy_daily.loc['20160104']['Day'] == 1 # Monday Jan. 4
assert spy_daily.loc['20160108']['Day'] == 5 # Friday Jan. 8
assert spy_daily.loc['20160111']['Day'] == 6 # Monday Jan. 11
assert spy_daily.loc['20161230']['Day'] == 252 # 240? Friday Dec. 30
def test_infront_daily(self):
provider = CsvFileDataProvider(["data"])
nyse = mcal.get_calendar('NYSE')
days = nyse.valid_days(start_date='2008-01-01', end_date='2015-12-31')
daily_xlp = provider.get_dataframes(
[SymbolData('NYSF_XLP', 'day', 'day', '2008-01-01',
'2015-12-31')])[0]
assert str(daily_xlp.loc['2010-11-05']['High']) == '29.2301'
def test_filter_rth(self):
provider = CsvFileDataProvider(["data"])
df = provider.get_dataframes([
SymbolData('AAPL_2017-11-03',
'5min',
'5min',
'2017-10-10',
'2017-10-31',
rth_only=True)
])[0]
assert len(df.at_time('09:25:00')) == 0
assert len(df.at_time('09:30:00')) > 0
assert len(df.at_time('16:05:00')) == 0
def test_daily_with_snapshots(self):
provider = JSONDataProvider(
['data/alpaca-v2'],
['DateTime', 'Open', 'High', 'Low', 'Close', 'Volume'],
verbose=2)
sym_datas = [
SymbolData('AAPL', 'day', 'day', '2021-01-01', '2021-12-02')
]
df = provider.get_datas(sym_datas, snapshots=True)[0].df
assert df.loc['2021-01-05']['Close'] == 130.20761
assert df.loc['2021-11-30'][
'Close'] == 165.3 # Last bar from daily file
assert df.loc['2021-12-01'][
'Close'] == 164.77 # Single bar from snapshots file
def test_alpaca_intraday_v2(self):
provider = JSONDataProvider(
['data/alpaca-v2'],
['DateTime', 'Open', 'High', 'Low', 'Close', 'Volume'],
verbose=2)
sym_datas = [
SymbolData('AAPL',
'1min',
'1min',
'2021-12-01',
'2021-12-03',
rth_only=False)
]
df = provider.get_datas(sym_datas)[0].df
assert df.loc['2021-12-03 07:59']['Close'] == 163.76
self.logger.warning(
f"Could not find or open {symbol_data.symbol} in {self.paths}")
return pd.DataFrame()
else:
raise Exception("{} not found in {}".format(
symbol_data.symbol, self.paths))
def add_quotes(self, data, ticker):
"""
Quotes are not available...
"""
return data
if __name__ == '__main__':
paths = [f"/Users/{os.environ['USER']}/OneDrive/Data/tradingview"]
tradingview = CsvFileDataProvider(
paths,
col_names=['time', 'open', 'high', 'low', 'close', 'Volume'],
epoch=True)
# alphavantage = CsvFileDataProvider(paths, col_names=['timestamp', 'open', 'high', 'low', 'close', 'Volume'], epoch=False)
# data = provider.get_data(['OMXS30 F18-OMF_1 Minute'], '2017-12-01', '2017-12-31', timeframe='1min', transform='1h')
symbols_data = [
SymbolData('BIDU', '5min', '5min', '2021-02-24 09:30',
'2021-02-26 16:00')
# SymbolData('SKA_B_1H', '60min', '60min', '2020-04-02 10:00:00', '2020-04-03 15:00:00')
]
datas = asyncio.run(tradingview.get_datas_async(symbols_data))
df = datas[0].df
print(df.tail())