def get_fund_data(isin: str, start_date: np.datetime64,
end_date: np.datetime64) -> pd.DataFrame:
"""
Gets price data for fund for specified period
Args:
ticker (str): String ticker with ISIN
start_date (np.datetime64): Start date to get price data
end_date (np.datetime64): End date to get price data
Returns:
pd.DataFrame: Dataframe containing close, split, dividend data for ticker from start_date to end_date
"""
try:
fund_search = investpy.search_funds(by='isin', value=isin)
name = fund_search.at[0, 'name']
country = fund_search.at[0, 'country']
df = investpy.get_fund_historical_data(
fund=name,
country=country,
from_date=start_date.strftime('%d/%m/%Y'),
to_date=end_date.strftime('%d/%m/%Y'))
df.drop('Currency', axis=1, inplace=True)
df.reset_index(inplace=True)
except RuntimeError:
# if not in investpy database, check if there is a custom funds module, import and execute custom fund function
try:
from utils.custom_funds import get_custom_fund_data
df = get_custom_fund_data(isin, start_date, end_date)
except ImportError:
print('No custom funds module available')
df = None
except ValueError:
df = None
if isinstance(df, pd.DataFrame):
df['Stock Splits'] = 0
df['Dividends'] = 0
df.set_index(['Date'], inplace=True, drop=True)
return df
def get_prices_from_API(symbol_pair: str, start_date: np.datetime64,
end_date: np.datetime64) -> pd.DataFrame:
"""
Get crypto asset price history from Binance
Args:
symbol (str): String ticker of crypto asset (e.g ETH)
start_date (np.datetime64): start date for historical price data
end_date (np.datetime64): start date for historical price data
Returns:
pd.DataFrame: Dataframe containing binance data for crypto-currency pair from start_date to end_date
"""
client = Client(api_key=binance_api_key, api_secret=binance_api_secret)
try:
data = client.get_historical_klines(
symbol_pair, client.KLINE_INTERVAL_1DAY,
start_date.strftime("%d %b %Y %H:%M:%S"),
end_date.strftime("%d %b %Y %H:%M:%S"))
except BinanceAPIException as e:
print(f'symbol_pair is not available on Binance')
return None
else:
data_df = pd.DataFrame(data,
columns=[
'Date', 'Open', 'High', 'Low', 'Close',
'Volume', 'close_time', 'quote_av',
'trades', 'tb_base_av', 'tb_quote_av',
'ignore'
])
data_df['Date'] = pd.to_datetime(data_df['Date'], unit='ms')
data_df.set_index(['Date'], inplace=True, drop=True)
data_df[data_df.columns] = data_df[data_df.columns].apply(
pd.to_numeric, errors='coerce')
return data_df
def file_path(first_date: np.datetime64,
last_date: np.datetime64,
cycle_number: int,
pass_number: int,
parameters: settings.Parameters,
nadir: bool = False) -> str:
"""Get the absolute path of the file to be created.
Args:
first_date (numpy.datetime64): Date of the first simulated measurement.
last_date (numpy.datetime64): Date of the last simulated measurement.
cycle_number (int): Cycle number of the file to be created.
pass_number (int): Pass number of the file to be created.
parameters (settings.Parameters): Simulation parameters.
nadir (bool, optional): True if the product to be created contains the
nadir measurements, false if it is a product containing the swath
measurements.
Returns:
str: The path to the file to be created.
"""
first_date = first_date.astype(datetime.datetime)
last_date = last_date.astype(datetime.datetime)
product_type = "nadir" if nadir else "karin"
dirname = os.path.join(parameters.working_directory, product_type,
first_date.strftime("%Y"))
os.makedirs(dirname, exist_ok=True)
if nadir:
filename = (f"SWOT_GPN_2P1P{cycle_number:03d}_{pass_number:03d}_"
f"{first_date:%Y%m%d}_{first_date:%H%M%S}_"
f"{last_date:%Y%m%d}_{last_date:%H%M%S}.nc")
else:
product_type = "".join(
[item.capitalize() for item in parameters.product_type.split("_")])
filename = (f"SWOT_L2_LR_SSH_{product_type}_"
f"{cycle_number:03d}_{pass_number:03d}_"
f"{first_date:%Y%m%dT%H%M%S}_{last_date:%Y%m%dT%H%M%S}_"
"DG10_01.nc")
return os.path.join(dirname, filename)