def test_fund_errors():
params = [
{'columns': None, 'as_json': 'error'},
{'columns': 0, 'as_json': True},
{'columns': ['error'], 'as_json': False},
]
for param in params:
try:
investpy.get_funds_dict(columns=param['columns'], as_json=param['as_json'])
except:
pass
params = [
{'fund': 'quality inversion conservadora fi', 'as_json': 'error', 'order': 'ascending'},
{'fund': 'quality inversion conservadora fi', 'as_json': True, 'order': 'error'},
{'fund': 'error', 'as_json': True, 'order': 'ascending'},
]
for param in params:
try:
investpy.get_fund_recent_data(fund=param['fund'], as_json=param['as_json'], order=param['order'])
except:
pass
params = [
{'fund': 'quality inversion conservadora fi', 'start': '01/01/2019', 'end': '01/01/2019', 'as_json': 'error', 'order': 'ascending'},
{'fund': 'quality inversion conservadora fi', 'start': '01/01/2019', 'end': '01/01/2019', 'as_json': False, 'order': 'error'},
{'fund': 'quality inversion conservadora fi', 'start': 'error', 'end': '01/01/2019', 'as_json': False, 'order': 'ascending'},
{'fund': 'quality inversion conservadora fi', 'start': '01/01/2019', 'end': 'error', 'as_json': False, 'order': 'ascending'},
{'fund': 'error', 'start': '01/01/2019', 'end': '01/01/2019', 'as_json': False, 'order': 'ascending'},
{'fund': 'quality inversion conservadora fi', 'start': '01/01/1998', 'end': '01/01/2019', 'as_json': False, 'order': 'ascending'},
]
for param in params:
try:
print(param)
investpy.get_fund_historical_data(fund=param['fund'], start=param['start'], end=param['end'], as_json=param['as_json'], order=param['order'])
except:
pass
params = [
{'fund': 'quality inversion conservadora fi', 'as_json': 'error'},
{'fund': 'error', 'as_json': True},
]
for param in params:
try:
print('hola')
investpy.get_fund_information(fund=param['fund'], as_json=param['as_json'])
except:
pass
def get_asset_data(asset_list: list, from_date: str, to_date: str,
asset_type: str,
asset_df: pd.DataFrame) -> Tuple[list, pd.DataFrame]:
# commodity, bond, currency
# etfs and funds need country
if asset_type == "Bonds":
func = lambda a, s, e: investpy.get_bond_historical_data(a, s, e)
elif asset_type == "Currencies":
func = lambda a, s, e: investpy.get_currency_cross_historical_data(
a, s, e)
elif asset_type == "ETFs":
func = lambda a, s, e, c: investpy.get_etf_historical_data(a, c, s, e)
elif asset_type == "Funds":
func = lambda a, s, e, c: investpy.get_fund_historical_data(a, c, s, e)
elif asset_type == "Commodities":
func = lambda a, s, e: investpy.get_commodity_historical_data(a, s, e)
elif asset_type == "Indices":
func = lambda a, s, e, c: investpy.get_index_historical_data(
a, c, s, e)
elif asset_type == "Crypto":
func = lambda a, s, e: investpy.get_crypto_historical_data(a, s, e)
df_list = []
for asset in asset_list:
if asset_type != "ETFs" and asset_type != "Funds" and asset_type != "Indices":
df = func(asset, from_date, to_date)
df_list.append(df)
else:
country = get_attribute_investing(asset_df, asset, 'country')
df = func(asset, from_date, to_date, country)
df_list.append(df)
close_list = [df.Close for df in df_list]
# print(close_list)
close_df = pd.concat(close_list, axis=1)
close_df.columns = asset_list
return df_list, close_df
def get_investiments_returns(pct_change_window=1):
stocks_name = get_assets_env_var('STOCKS')
funds_name = get_assets_env_var('FUNDS')
today = date.today()
today = today.strftime("%d/%m/%Y")
close_prices = []
for stock in stocks_name:
prices = inv.get_stock_historical_data(stock=stock,
country='brazil',
from_date='01/01/2015',
to_date=today)
close_prices.append(prices.Close)
for fund in funds_name:
prices = inv.get_fund_historical_data(fund,
country='brazil',
from_date='01/01/2015',
to_date=today)
close_prices.append(prices.Close)
prices = pd.concat(close_prices, axis=1)
prices = prices.dropna()
columns_name = []
for column_name in (stocks_name + funds_name):
columns_name.append(column_name[:10])
prices.columns = columns_name
returns = prices.pct_change(pct_change_window).dropna()
return returns
def ApiGetAllByIsin(self, isin, tipologia_strumento, periodicita, start): #restituisce il nome di corrispondenza all'isin inserito
country_iniziali = isin[:2]
country = GLOBE.country_isin.get(country_iniziali)
endus = datetime.now()
endeu = endus.strftime("%d/%m/%Y")
# print(inv.stocks.get_stock_countries())
if tipologia_strumento == GLOBE.mappa_strumenti.get("stock"):
stock = inv.stocks.get_stocks(country = country)
info_gen = stock.loc[stock["isin"] == isin]
df = inv.get_stock_historical_data(stock = info_gen["symbol"].values[0], country = country, from_date = start, to_date = endeu, as_json=False, order='ascending', interval = GLOBE.mappa_periodicita[periodicita])
elif tipologia_strumento == GLOBE.mappa_strumenti.get("etf"):
etf = inv.etfs.get_etfs(country = country)
info_gen = etf.loc[etf["isin"] == isin]
df = inv.get_etf_historical_data(etf = info_gen["name"].values[0], country = country, from_date = start, to_date = endeu, as_json=False, order='ascending', interval = GLOBE.mappa_periodicita[periodicita])
elif tipologia_strumento == GLOBE.mappa_strumenti.get("fund"):
fund = inv.funds.get_funds(country = country)
info_gen = fund.loc[fund["isin"] == isin]
df = inv.get_fund_historical_data(fund = info_gen["name"].values[0], country = country, from_date = start, to_date = endeu, as_json=False, order='ascending', interval = GLOBE.mappa_periodicita[periodicita])
all_info = { #dict con tutte le informazioni
"datafetch" : df,
"info_gen" : info_gen,
"tipo_strumento" : tipologia_strumento
}
return all_info
def get_fund_info(ticker):
price_now = get_fund_price(ticker)
now = datetime.datetime.now()
price_year_ago = investpy.get_fund_historical_data(
fund=get_fund_from_ticker(ticker),
country='united states',
from_date=(now - datetime.timedelta(days=365)).strftime('%d/%m/%Y'),
to_date=(now - datetime.timedelta(days=350)).strftime(
'%d/%m/%Y'))['Close'][-1] * get_currency_price('USD')
return price_now, price_year_ago
def loadData(self):
print('Loading ', self.name)
try:
self.df = investpy.get_fund_historical_data(
fund=self.name,
country=self.country,
from_date=self.sampling_from_str,
to_date=self.end_date_str)
self.parseDF()
except ConnectionError:
print('Internet connection Error')
except ValueError:
print('No data found')
def evolucion_fondo(isin):
import pandas as pd
import investpy
from datetime import date
isin = isin
nombre = investpy.search_funds(by='isin', value=isin).loc[0, 'name']
pais = investpy.search_funds(by='isin', value=isin).loc[0, 'country']
df = investpy.get_fund_historical_data(
fund=nombre,
from_date="01/01/2000",
to_date=date.today().strftime("%d/%m/%Y"),
country=pais)
df = df.drop(['Open', 'High', 'Low'], axis=1)
df['returns'] = df['Close'].pct_change()
df = df.dropna()
return df
def _download_data(self, asset_type, symbol, name, country, from_date,
max_date):
if asset_type == "BOND":
# name == symbol
df = ip.get_bond_historical_data(symbol,
from_date=from_date,
to_date=max_date)
elif asset_type == "CERT":
df = ip.get_certificate_historical_data(name,
country=country,
from_date=from_date,
to_date=max_date)
elif asset_type == "CRYPTO":
df = ip.get_crypto_historical_data(name,
from_date=from_date,
to_date=max_date)
elif asset_type == "COMM":
df = ip.get_commodity_historical_data(symbol,
from_date=from_date,
to_date=max_date)
elif asset_type == "ETF":
df = ip.get_etf_historical_data(name,
country=country,
from_date=from_date,
to_date=max_date)
elif asset_type == "FUND":
df = ip.get_fund_historical_data(name,
country=country,
from_date=from_date,
to_date=max_date)
elif asset_type == "FX":
df = ip.get_currency_cross_historical_data(symbol,
from_date=from_date,
to_date=max_date)
elif asset_type == "INDEX":
df = ip.get_index_historical_data(name,
country=country,
from_date=from_date,
to_date=max_date)
elif asset_type == "STOCK":
df = ip.get_stock_historical_data(symbol,
country=country,
from_date=from_date,
to_date=max_date)
return df
def ApiGetAllByIsinPortafoglio(self, isin, tipologia_strumento):
years_obs = timedelta(days=365.24) * 5
endus = datetime.now()
endeu = endus.strftime("%d/%m/%Y") #conversioni date US a EU
startus = endus - years_obs
starteu = startus.strftime("%d/%m/%Y")
country_iniziali = isin[:2] #convertitore ISIN to country
country = GLOBE.country_isin.get(country_iniziali)
df = 0
info_gen = 0
info_tech = 0
if tipologia_strumento == GLOBE.mappa_strumenti.get("stock"):
stock = inv.stocks.get_stocks(country = country)
info_gen = stock.loc[stock["isin"] == isin]
info_tech = inv.stocks.get_stock_information(info_gen["symbol"].values[0], country, as_json=False)
df = inv.get_stock_historical_data(stock = info_gen["symbol"].values[0], country = country, from_date = starteu, to_date = endeu, as_json=False, order='ascending', interval = GLOBE.mappa_periodicita.get("Monthly"))
elif tipologia_strumento == GLOBE.mappa_strumenti.get("etf"):
etf = inv.etfs.get_etfs(country = country)
info_gen = etf.loc[etf["isin"] == isin]
info_tech = inv.etfs.get_etf_information(info_gen["name"].values[0], country, as_json=False)
df = inv.get_etf_historical_data(etf = info_gen["name"].values[0], country = country, from_date = starteu, to_date = endeu, as_json=False, order='ascending', interval = GLOBE.mappa_periodicita.get("Monthly"))
elif tipologia_strumento == GLOBE.mappa_strumenti.get("fund"):
fund = inv.funds.get_funds(country = country)
info_gen = fund.loc[fund["isin"] == isin]
info_tech = inv.funds.get_fund_information(info_gen["name"].values[0], country, as_json=False)
df = inv.get_fund_historical_data(fund = info_gen["name"].values[0], country = country, from_date = starteu, to_date = endeu, as_json=False, order='ascending', interval = GLOBE.mappa_periodicita.get("Monthly"))
all_info_portafoglio = { #dict con tutte le informazioni
"datafetch" : df,
"info_gen" : info_gen,
"info_tech" : info_tech,
"tipo_strumento" : tipologia_strumento
}
return all_info_portafoglio
def import_funds(successfulPulls):
# imports funds in the US
search_results = investpy.search_funds(by='country', value='united states')
list_of_fund_names = search_results["name"]
firstIndex = datetime.datetime.strptime(_configKeys.STARTPULL, '%d/%m/%Y')
lastIndex = datetime.datetime.strptime(_configKeys.ENDPULL, '%d/%m/%Y')
for name in list_of_fund_names[:2500]:
try:
# Have an if statement in place in case if we don't want to pull every fund because there are a lot of funds
# Program takes a long time to run if we have to webscrape every fund each time we run
fundData = []
fundData = investpy.get_fund_historical_data(
fund=name,
country='united states',
from_date=_configKeys.STARTPULL,
to_date=_configKeys.ENDPULL)
newIndex = []
for index in fundData.index:
newIndex.append(
datetime.datetime.strptime(
datetime.datetime.strftime((index + timedelta(days=1)),
'%Y-%m-%d'), '%Y-%m-%d'))
fundData['Date'] = newIndex
fundData.set_index('Date', inplace=True)
# If there's something that's been loaded into stockData, then the length is no longer 0
# if the differences is under 2~3 days, then it is ok to take this data since there is still enough data in the week to be usable
# this timedelta fixes the problem of trying to pull during a long weekend
name = str(name) + "Fund"
if fundData.empty == False and fundData.index[
0] - firstIndex <= timedelta(
days=2
) and lastIndex - fundData.index[-1] <= timedelta(days=3):
successfulPulls["Symbol"].append(name.replace("/", ""))
successfulPulls["Type"].append("Fund")
fundData.to_csv(
os.path.join(Path(_configKeys.DATA_FOLDER),
name.replace("/", "") + '.csv'))
except:
print("Something went wrong when importing: " + name)
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 test_investpy_funds():
"""
This function checks that fund data retrieval functions listed in investpy work properly.
"""
params = [
{
'country': 'spain',
},
{
'country': None,
},
]
for param in params:
investpy.get_funds(country=param['country'])
investpy.get_funds_list(country=param['country'])
params = [
{
'country': None,
'columns': ['id', 'name'],
'as_json': True
},
{
'country': None,
'columns': ['id', 'name'],
'as_json': False
},
{
'country': 'spain',
'columns': ['id', 'name'],
'as_json': True
},
{
'country': 'spain',
'columns': ['id', 'name'],
'as_json': False
},
{
'country': 'spain',
'columns': None,
'as_json': False
},
]
for param in params:
investpy.get_funds_dict(country=param['country'],
columns=param['columns'],
as_json=param['as_json'])
investpy.get_fund_countries()
params = [
{
'as_json': True,
'order': 'ascending',
'debug': False
},
{
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'as_json': True,
'order': 'descending',
'debug': False
},
{
'as_json': False,
'order': 'descending',
'debug': False
},
]
for param in params:
investpy.get_fund_recent_data(fund='bbva multiactivo conservador pp',
country='spain',
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
investpy.get_fund_historical_data(
fund='bbva multiactivo conservador pp',
country='spain',
from_date='01/01/2010',
to_date='01/01/2019',
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
params = [
{
'fund': 'bbva multiactivo conservador pp',
'country': 'spain',
'as_json': True,
},
{
'fund': 'bbva multiactivo conservador pp',
'country': 'spain',
'as_json': False,
},
]
for param in params:
investpy.get_fund_information(fund=param['fund'],
country=param['country'],
as_json=param['as_json'])
investpy.search_funds(by='name', value='bbva')
retrieve_funds(test_mode=True)
retrieve_fund_countries(test_mode=True)
def test_investpy():
"""
This function checks that main functions of investpy work properly.
"""
print(investpy.__author__, investpy.__version__)
for value in ['spain', None]:
investpy.get_equities(country=value)
investpy.get_equities_list(country=value)
params = [
{
'country': None,
'columns': ['id', 'name'],
'as_json': True
},
{
'country': 'spain',
'columns': ['id', 'name'],
'as_json': True
},
{
'country': None,
'columns': ['id', 'name'],
'as_json': False
},
{
'country': 'spain',
'columns': None,
'as_json': False
},
{
'country': 'spain',
'columns': ['id', 'name'],
'as_json': False
},
]
for param in params:
investpy.get_equities_dict(country=param['country'],
columns=param['columns'],
as_json=param['as_json'])
investpy.get_equity_countries()
params = [
{
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'as_json': True,
'order': 'descending',
'debug': False
},
{
'as_json': True,
'order': 'ascending',
'debug': False
},
{
'as_json': False,
'order': 'descending',
'debug': False
},
]
for param in params:
investpy.get_recent_data(equity='enagás',
country='spain',
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
investpy.get_historical_data(equity='enagás',
country='spain',
from_date='01/01/1990',
to_date='01/01/2019',
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
for value in ['spanish', 'english']:
investpy.get_equity_company_profile(equity='enagás',
country='spain',
language=value)
retrieve_equities(test_mode=True)
retrieve_equity_countries(test_mode=True)
params = [
{
'country': 'spain',
},
{
'country': None,
},
]
for param in params:
investpy.get_funds(country=param['country'])
investpy.get_funds_list(country=param['country'])
params = [
{
'country': None,
'columns': ['id', 'name'],
'as_json': True
},
{
'country': 'spain',
'columns': ['id', 'name'],
'as_json': True
},
{
'country': None,
'columns': ['id', 'name'],
'as_json': False
},
{
'country': 'spain',
'columns': None,
'as_json': False
},
{
'country': 'spain',
'columns': ['id', 'name'],
'as_json': False
},
]
for param in params:
investpy.get_funds_dict(country=param['country'],
columns=param['columns'],
as_json=param['as_json'])
params = [
{
'fund': 'bbva multiactivo conservador pp',
'country': 'spain',
'as_json': True,
},
{
'fund': 'bbva multiactivo conservador pp',
'country': 'spain',
'as_json': False,
},
]
for param in params:
investpy.get_fund_information(fund=param['fund'],
country=param['country'],
as_json=param['as_json'])
params = [
{
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'as_json': True,
'order': 'descending',
'debug': False
},
{
'as_json': True,
'order': 'ascending',
'debug': False
},
{
'as_json': False,
'order': 'descending',
'debug': False
},
]
for param in params:
investpy.get_fund_recent_data(fund='bbva multiactivo conservador pp',
country='spain',
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
investpy.get_fund_historical_data(
fund='bbva multiactivo conservador pp',
country='spain',
from_date='01/01/2010',
to_date='01/01/2019',
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
investpy.get_fund_countries()
investpy.get_funds()
retrieve_funds(test_mode=True)
retrieve_fund_countries(test_mode=True)
investpy.get_etf_countries()
for value in ['spain', None]:
investpy.get_etfs(country=value)
investpy.get_etfs_list(country=value)
params = [
{
'country': None,
'columns': ['id', 'name'],
'as_json': True
},
{
'country': 'spain',
'columns': ['id', 'name'],
'as_json': True
},
{
'country': None,
'columns': ['id', 'name'],
'as_json': False
},
{
'country': 'spain',
'columns': None,
'as_json': False
},
{
'country': 'spain',
'columns': ['id', 'name'],
'as_json': False
},
]
for param in params:
investpy.get_etfs_dict(country=param['country'],
columns=param['columns'],
as_json=param['as_json'])
params = [
{
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'as_json': True,
'order': 'descending',
'debug': False
},
{
'as_json': True,
'order': 'ascending',
'debug': False
},
{
'as_json': False,
'order': 'descending',
'debug': False
},
]
for param in params:
investpy.get_etf_recent_data(etf='bbva accion dj eurostoxx 50',
country='spain',
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
investpy.get_etf_historical_data(etf='bbva accion dj eurostoxx 50',
country='spain',
from_date='01/01/2010',
to_date='01/01/2019',
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
params = [
{
'country': 'france',
'as_json': True,
},
{
'country': 'usa',
'as_json': False,
},
]
for param in params:
investpy.get_etfs_overview(country=param['country'],
as_json=param['as_json'])
retrieve_etfs(test_mode=True)
def fetch_prices():
consecutive_trades = '' # Variable for checking consecutive trades (i.e. where two different purchases were sold together)
consecutive_trades_I = '' # Another variable for checking consecutive trades (but in second part of code)
for equity in enumerate(equities_DF.index):
# Add fund/share as dict key, and add sub-dict with key as 'Investpy fund name' / 'ISIN' / 'Ticker' and search name as value.
if equity[1] not in prices:
prices[equity[1]] = equities_DF.iloc[equity[0],
7:10].dropna().to_dict()
consecutive_trades = equity[1]
elif equity[
1] == consecutive_trades: # If a consecutive buy exists, add the date of that buy.
prices[equity[1]]['Additional buy'] = equities_DF.iloc[equity[0],
1]
consecutive_trades = equity[1]
# Set default search country as UK, unless 'USD' found.
country = 'United Kingdom'
if 'USD' in equity[1]:
country = 'United States'
elif 'CAD' in equity[1]:
country = 'Canada'
# Retrieve historic fund/share prices
# First check what type of search we need to do: using Fund Name, ISIN, or ticker
if equity[
1] == consecutive_trades_I: # Skip the additional buys if they are part of same sell transaction.
print(f'{equity[0]}. Additional buy for {equity[1]} - skipped.')
continue
elif 'InvestPy Fund Name' in prices[equity[1]]:
search_name = prices[equity[1]][
'InvestPy Fund Name'] # Get value that we use to search InvestPy or Yahoo.
try: # Add a df of historical price data to 'Price History' key
prices[equity[1]][
'Price History'] = investpy.get_fund_historical_data(
fund=search_name,
country=
country, # Below converts datetime to string format for searching
from_date=equities_DF.iloc[equity[0],
-4].strftime('%d/%m/%Y'),
to_date=equities_DF.iloc[equity[0],
-3].strftime('%d/%m/%Y'),
interval='Daily')
print(
f'{equity[0]}. Retrieved fund price data for {equity[1]}.')
except RuntimeError:
print(
RuntimeError,
f'CHECK! InvestPy did not find price data for {equity[1]}')
elif 'Stock Ticker' in prices[equity[1]]:
search_name = prices[equity[1]]['Stock Ticker']
try:
prices[equity[1]][
'Price History'] = investpy.get_stock_historical_data(
stock=search_name,
country=country,
from_date=equities_DF.iloc[equity[0],
-4].strftime('%d/%m/%Y'),
to_date=equities_DF.iloc[equity[0],
-3].strftime('%d/%m/%Y'),
interval='Daily')
print(
f'{equity[0]}. Retrieved stock price data for {equity[1]}.'
)
except RuntimeError: # If InvestPy fails, try Yahoo Finance.
prices[equity[1]]['Price History'] = pd.read_csv(
f'https://query1.finance.yahoo.com/v7/finance/download/{search_name}?period1={equities_DF.iloc[equity[0], -2]}&period2={equities_DF.iloc[equity[0], -1]}&interval=1d&events=history',
index_col='Date')
# Yahoo Finance data not downloaded as datetime objects - convert these:
prices[equity[1]]['Price History'].index = pd.to_datetime(
prices[equity[1]]['Price History'].index,
format='%Y-%m-%d')
print(
f'{equity[0]}. Retrieved stock price data for {equity[1]} from YF.'
)
sleep(1) # Ensures we don't overload Yahoo with requests.
except HTTPError:
print('CHECK! Yahoo Finance request failed for', equity[1])
elif 'ISIN for Yahoo Finance' in prices[equity[1]]:
search_name = prices[equity[1]]['ISIN for Yahoo Finance']
try:
prices[equity[1]]['Price History'] = pd.read_csv(
f'https://query1.finance.yahoo.com/v7/finance/download/{search_name}?period1={equities_DF.iloc[equity[0], -2]}&period2={equities_DF.iloc[equity[0], -1]}&interval=1d&events=history',
index_col='Date')
prices[equity[1]]['Price History'].index = pd.to_datetime(
prices[equity[1]]['Price History'].index,
format='%Y-%m-%d') # Convert index to datetime
print(
f'{equity[0]}. Retrieved fund price data for {equity[1]} using ISIN.'
)
sleep(1)
except HTTPError:
try: # Some ISIN numbers require a '.L' on the end to be found on Yahoo for some reason.
prices[equity[1]]['Price History'] = pd.read_csv(
f'https://query1.finance.yahoo.com/v7/finance/download/{search_name}.L?period1={equities_DF.iloc[equity[0], -2]}&period2={equities_DF.iloc[equity[0], -1]}&interval=1d&events=history',
index_col='Date')
prices[equity[1]]['Price History'].index = pd.to_datetime(
prices[equity[1]]['Price History'].index,
format='%Y-%m-%d') # Convert index to datetime
print(
f'{equity[0]}. Retrieved fund price data for {equity[1]} using ISIN.'
)
sleep(1)
except HTTPError:
print('CHECK! Yahoo Finance request failed for', equity[1])
except Exception as UnknownError:
print('Unknown error for', equity[1], UnknownError)
else: # I couldn't find this equity on Investing.com or Yahoo Finance so we just skip it.
print(f'{equity[0]}. No price data for this equity - skipped.')
consecutive_trades_I = equity[
1] # Overwrite this var to check for consecutives.
# Now correct price data which is in £ not pennies: Some funds randomly change from £s to pennies midway through dataset.
try: # Correct values which are < max value divided by 100.
prices[equity[1]]['Price History'].loc[
prices[equity[1]]['Price History']['Close'] <
prices[equity[1]]['Price History']['Close'].max() / 100,
['Open', 'High', 'Low', 'Close']] *= 100
except KeyError:
print(KeyError, 'This equity had no price data')
def my_fund_historical_data(fund, country, from_date, to_date):
return investpy.get_fund_historical_data(fund=fund,
country=country,
from_date=from_date,
to_date=to_date)
print(fundo) # In[77]: # pega os dados recentes do fundo nome_fundo = 'Fundo De Investimento Caixa Master Personalizado 50 Renda Fixa Longo Prazo' investpy.get_fund_recent_data(fund=nome_fundo, country='brazil') # In[78]: # Retrieves historical data of 'Bankia Cauto Pp', which is a fund from 'Spain', on the specified date range as a pandas.DataFrame investpy.get_fund_historical_data(fund=nome_fundo, country='brazil', from_date='01/01/2018', to_date='25/09/2020') # In[91]: # LBMA - Quandl url = 'https://www.quandl.com/api/v3/datasets/LBMA/GOLD.json?api_key=x1dVtsgtRJvLJ1tyNh8S' df = pd.read_json(url) df = pd.DataFrame(df['dataset']['data'], columns=df['dataset']['column_names']) df # In[ ]:
def test_investpy_funds():
"""
This function checks that fund data retrieval functions listed in investpy work properly.
"""
params = [
{
'country': 'spain',
},
{
'country': None,
},
]
for param in params:
investpy.get_funds(country=param['country'])
investpy.get_funds_list(country=param['country'])
params = [
{
'country': None,
'columns': ['name'],
'as_json': True
},
{
'country': None,
'columns': ['name'],
'as_json': False
},
{
'country': 'spain',
'columns': ['name'],
'as_json': True
},
{
'country': 'spain',
'columns': ['name'],
'as_json': False
},
{
'country': 'spain',
'columns': None,
'as_json': False
},
]
for param in params:
investpy.get_funds_dict(country=param['country'],
columns=param['columns'],
as_json=param['as_json'])
investpy.get_fund_countries()
params = [
{
'as_json': True,
'order': 'ascending',
},
{
'as_json': False,
'order': 'ascending',
},
{
'as_json': True,
'order': 'descending',
},
{
'as_json': False,
'order': 'descending',
},
]
for param in params:
investpy.get_fund_recent_data(fund='bbva multiactivo conservador pp',
country='spain',
as_json=param['as_json'],
order=param['order'],
interval='Daily')
investpy.get_fund_historical_data(fund='bbva multiactivo conservador pp',
country='spain',
from_date='01/01/2010',
to_date='01/01/2019',
as_json=param['as_json'],
order=param['order'],
interval='Daily')
params = [
{
'fund': 'bbva multiactivo conservador pp',
'country': 'spain',
'as_json': True,
},
{
'fund': 'bbva multiactivo conservador pp',
'country': 'spain',
'as_json': False,
},
]
for param in params:
investpy.get_fund_information(fund=param['fund'],
country=param['country'],
as_json=param['as_json'])
params = [
{
'country': 'andorra',
'as_json': True,
'n_results': 2
},
{
'country': 'andorra',
'as_json': False,
'n_results': 2
},
{
'country': 'united states',
'as_json': False,
'n_results': 2
},
{
'country': 'united kingdom',
'as_json': False,
'n_results': 2
}
]
for param in params:
investpy.get_funds_overview(country=param['country'], as_json=param['as_json'], n_results=param['n_results'])
investpy.search_funds(by='name', value='bbva')
as_json=False,
order='ascending')
# Retrieve the company profile of the introduced stock on english
profile = investpy.get_stock_company_profile(stock='bbva',
country='spain',
language='english')
df_aapl = investpy.get_stock_historical_data(stock='AAPL',
country='United States',
from_date='01/01/2010',
to_date='01/01/2020')
df_fund = investpy.get_fund_historical_data(
fund='bbva plan multiactivo moderado pp',
country='spain',
from_date='01/01/2010',
to_date='01/01/2019')
df_bbva_etf = investpy.get_etf_recent_data(etf='bbva accion dj eurostoxx 50',
country='spain')
etfs_ger = investpy.get_etfs_list(country='germany')
investpy.get_stock_countries()
#%% get finax tickers
from_date = '01/01/1990'
to_date = '21/03/2021'
search = investpy.search_quotes('FRCK')
df_frck = search[0].retrieve_historical_data(from_date=from_date,
def test_investpy():
"""
This function checks that main functions of investpy work properly.
"""
investpy.get_equities()
investpy.get_equities_list()
params = [
{'as_json': False, 'order': 'ascending'},
{'as_json': True, 'order': 'descending'},
{'as_json': True, 'order': 'ascending'},
{'as_json': False, 'order': 'descending'},
]
for param in params:
investpy.get_recent_data(equity='enagás', as_json=param['as_json'], order=param['order'])
investpy.get_historical_data(equity='enagás', start='01/01/1990', end='01/01/2019', as_json=param['as_json'], order=param['order'])
for value in ['spanish', 'english']:
investpy.get_equity_company_profile(equity='enagás', language=value)
get_equity_names()
investpy.get_funds()
investpy.get_funds_list()
for value in [True, False]:
investpy.get_funds_dict(columns=['id', 'name'], as_json=value)
investpy.get_fund_information(fund='bbva multiactivo conservador pp', as_json=value)
params = [
{'as_json': False, 'order': 'ascending'},
{'as_json': True, 'order': 'descending'},
{'as_json': True, 'order': 'ascending'},
{'as_json': False, 'order': 'descending'},
]
for param in params:
investpy.get_fund_recent_data(fund='bbva multiactivo conservador pp', as_json=param['as_json'], order=param['order'])
investpy.get_fund_historical_data(fund='bbva multiactivo conservador pp', start='01/01/2010', end='01/01/2019', as_json=param['as_json'], order=param['order'])
get_fund_names()
# investpy.get_etfs()
# # investpy.get_etfs_list()
# #
# # for value in [True, False]:
# # investpy.get_etfs_dict(columns=['id', 'name'], as_json=value)
params = [
{'as_json': False, 'order': 'ascending'},
{'as_json': True, 'order': 'descending'},
{'as_json': True, 'order': 'ascending'},
{'as_json': False, 'order': 'descending'},
]
for param in params:
investpy.get_etf_recent_data(etf='bbva accion dj eurostoxx 50', as_json=param['as_json'], order=param['order'])
investpy.get_etf_historical_data(etf='bbva accion dj eurostoxx 50', start='01/01/2010', end='01/01/2019', as_json=param['as_json'], order=param['order'])
get_etfs()
def funds(request):
price = 0.0
m = money.objects.get(pk=1)
amount = m.mymoney
amount = round(amount, 2)
rem = 1000000 - amount
rem = round(rem, 2)
close = []
close1 = []
close2 = []
no = []
no1 = []
no2 = []
n = "Funds name"
for i in range(len(close)):
no.append(i + 1)
for i in range(len(close1)):
no1.append(i + 1)
for i in range(len(close2)):
no2.append(i + 1)
s = ""
labels = []
datap = []
DataFrame3 = "Table for Particular Stock"
indianfunds = [
'Icici Prudential Life - Balancer Fund',
'Hdfc Standard Life - Balanced Managed Investment Pension'
]
#fetch recent 10 investments
query_results = investmentsinfunds.objects.all().order_by('-id')[:10]
last_stock = 0
v1 = 0
if request.method == 'POST':
#for buy stock,available assets,investments and pushing purcahses into investment table
if ('stockname' in request.POST):
n = request.POST['stockname']
#api_key = 'BJQZ9I2H012Q7FDD'
#code for profit loss returns none if no query found so used if condition
last_stock = investmentsinfunds.objects.filter(name=n).values(
'id', 'price').last()
#ts = TimeSeries(key=api_key, output_format='json')
#data, meta_data = ts.get_quote_endpoint(n)
#get price real-time
if (n in indianfunds):
v1 = investpy.funds.get_fund_recent_data(
fund=n,
country='india',
as_json=False,
order='descending',
interval='Daily').iloc[0, :]['Close']
v1 = float(v1)
c = CurrencyRates()
inrtousd = c.get_rate('INR', 'USD')
v1 = v1 * inrtousd
price = round(v1, 2)
else:
v1 = float(
investpy.funds.get_fund_recent_data(
fund=n,
country='united states',
as_json=False,
order='descending',
interval='Daily').iloc[0, :]['Close'])
price = round(v1, 2)
if (last_stock != None):
last_stock = float(last_stock['price'])
last_stock = round(last_stock, 2)
else: #assigned price because profit or loss=0 if we are adding it to table first time(assumption)
last_stock = price
if (price != 0.0):
if (amount > price):
amount = amount - price
amount = round(amount, 2)
m.mymoney = amount
m.save()
t = time.localtime()
current_time = str(time.strftime("%H:%M:%S", t))
last_stock = price - last_stock
last_stock = round(last_stock, 2)
a = investmentsinfunds(name=n.upper(),
price=price,
date_created=str(date.today()),
current_time=current_time,
gain_loss=last_stock)
a.save()
rem = 1000000 - amount
rem = round(rem, 2)
#for table start to end
elif ('sn' in request.POST):
a11 = request.POST['sdate1']
import datetime
darevy = str(datetime.datetime.strptime(a11, "%Y-%m-%d").year)
darevd = str(datetime.datetime.strptime(a11, "%Y-%m-%d").day)
darevm = str(datetime.datetime.strptime(a11, "%Y-%m-%d").month)
a11 = request.POST['edate1']
edarevy = str(datetime.datetime.strptime(a11, "%Y-%m-%d").year)
edarevd = str(datetime.datetime.strptime(a11, "%Y-%m-%d").day)
edarevm = str(datetime.datetime.strptime(a11, "%Y-%m-%d").month)
if (request.POST['sn'] in indianfunds):
DataFrame3 = investpy.get_fund_historical_data(
fund=request.POST['sn'],
country='india',
from_date=darevd + '/' + darevm + '/' + darevy,
to_date=edarevd + '/' + edarevm + '/' + edarevy)
else:
DataFrame3 = investpy.get_fund_historical_data(
fund=request.POST['sn'],
country='united states',
from_date=darevd + '/' + darevm + '/' + darevy,
to_date=edarevd + '/' + edarevm + '/' + edarevy)
DataFrame3 = DataFrame3.to_html()
#for graphs start to end
else:
a11 = request.POST['sdate']
import datetime
darevy = str(datetime.datetime.strptime(a11, "%Y-%m-%d").year)
darevd = str(datetime.datetime.strptime(a11, "%Y-%m-%d").day)
darevm = str(datetime.datetime.strptime(a11, "%Y-%m-%d").month)
a11 = request.POST['edate']
edarevy = str(datetime.datetime.strptime(a11, "%Y-%m-%d").year)
edarevd = str(datetime.datetime.strptime(a11, "%Y-%m-%d").day)
edarevm = str(datetime.datetime.strptime(a11, "%Y-%m-%d").month)
if (request.POST['s'] in indianfunds):
DataFrame = investpy.get_fund_historical_data(
fund=request.POST['s'],
country='india',
from_date=darevd + '/' + darevm + '/' + darevy,
to_date=edarevd + '/' + edarevm + '/' + edarevy)
else:
DataFrame = investpy.get_fund_historical_data(
fund=request.POST['s'],
country='united states',
from_date=darevd + '/' + darevm + '/' + darevy,
to_date=edarevd + '/' + edarevm + '/' + edarevy)
#DataFrame = yf.download(request.POST['s'],request.POST['sdate'],request.POST['edate'])
close = DataFrame['Close'].tolist()
s = str(request.POST['s'])
sdate2 = date(int(darevy), int(darevm), int(darevd))
edate2 = date(int(edarevy), int(edarevm), int(edarevd))
delta2 = edate2 - sdate2
for i in range(delta2.days + 1):
day = sdate2 + timedelta(days=i)
no.append(str(day))
diff = len(no) - len(close)
for i in range(1, diff + 1):
no.remove(no[i])
d = dict()
#for pie chart getting all investemnts summing up the stock by their labels
queryset = investmentsinfunds.objects.order_by('name')
for stock in queryset:
labels.append(stock.name)
datap.append(stock.price)
for i in range(len(labels)):
if (labels[i] in d.keys()):
d[labels[i]] += datap[i]
else:
d[labels[i]] = datap[i]
labels = list(d.keys())
datap = list(d.values())
#returning all the calculates values to html
return render(
request, 'portfolio/funds.html', {
'n': n,
'labels': labels,
'datap': datap,
'price': price,
'amount': amount,
'query_results': query_results,
'rem': rem,
'close': close,
'no': no,
's': s,
'close1': close1,
'close2': close2,
'no1': no1,
'no2': no2,
'DataFrame3': DataFrame3
})
def test_funds_errors():
"""
This function raises errors on fund retrieval functions
"""
try:
retrieve_funds(test_mode=None)
except:
pass
try:
retrieve_fund_countries(test_mode=None)
except:
pass
params = [
{
'country': ['error'],
},
]
for param in params:
try:
investpy.get_funds(country=param['country'])
except:
pass
try:
investpy.get_funds_list(country=param['country'])
except:
pass
params = [
{
'country': ['error'],
'columns': None,
'as_json': False
},
{
'country': None,
'columns': None,
'as_json': 'error'
},
{
'country': None,
'columns': 0,
'as_json': True
},
{
'country': None,
'columns': ['error'],
'as_json': False
},
]
for param in params:
try:
investpy.get_funds_dict(country=param['country'],
columns=param['columns'],
as_json=param['as_json'])
except:
pass
params = [
{
'fund': None,
'country': 'spain',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': None,
'as_json': False,
'order': 'ascending',
'debug': False
},
{
'fund': 'quality inversion conservadora fi',
'country': ['error'],
'as_json': False,
'order': 'ascending',
'debug': False
},
{
'fund': 'quality inversion conservadora fi',
'country': 'error',
'as_json': False,
'order': 'ascending',
'debug': False
},
{
'fund': 'quality inversion conservadora fi',
'country': 'germany',
'as_json': False,
'order': 'ascending',
'debug': False
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'as_json': 'error',
'order': 'ascending',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'as_json': True,
'order': 'error',
'debug': True
},
{
'fund': 'error',
'country': 'spain',
'as_json': True,
'order': 'ascending',
'debug': True
},
{
'fund': ['error'],
'country': 'spain',
'as_json': True,
'order': 'ascending',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'as_json': True,
'order': 'ascending',
'debug': 'error'
},
]
for param in params:
try:
investpy.get_fund_recent_data(fund=param['fund'],
country=param['country'],
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
except:
pass
params = [
{
'fund': None,
'country': 'spain',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': None,
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': False
},
{
'fund': 'quality inversion conservadora fi',
'country': ['error'],
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': False
},
{
'fund': 'quality inversion conservadora fi',
'country': 'error',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': False
},
{
'fund': 'quality inversion conservadora fi',
'country': 'germany',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': False
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': 'error',
'order': 'ascending',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': 'error',
'order': 'ascending',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'error',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'from_date': 'error',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'from_date': '01/01/2019',
'to_date': 'error',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'fund': 'error',
'country': 'spain',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'fund': ['error'],
'country': 'spain',
'from_date': '01/01/1998',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'from_date': '01/01/1998',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'from_date': '01/01/2019',
'to_date': '01/01/1998',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'from_date': '01/01/1900',
'to_date': '01/01/1950',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'from_date': '01/01/2019',
'to_date': '01/03/2019',
'as_json': False,
'order': 'ascending',
'debug': 'error'
},
]
for param in params:
try:
investpy.get_fund_historical_data(fund=param['fund'],
country=param['country'],
from_date=param['from_date'],
to_date=param['to_date'],
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
except:
pass
params = [
{
'fund': None,
'country': 'spain',
'as_json': False
},
{
'fund': 'quality inversion conservadora fi',
'country': None,
'as_json': False
},
{
'fund': 'quality inversion conservadora fi',
'country': ['error'],
'as_json': False
},
{
'fund': 'quality inversion conservadora fi',
'country': 'error',
'as_json': False
},
{
'fund': 'quality inversion conservadora fi',
'country': 'germany',
'as_json': False
},
{
'fund': 'quality inversion conservadora fi',
'country': 'spain',
'as_json': 'error'
},
{
'fund': 'error',
'country': 'spain',
'as_json': True
},
{
'fund': ['error'],
'country': 'spain',
'as_json': True
},
]
for param in params:
try:
investpy.get_fund_information(fund=param['fund'],
country=param['country'],
as_json=param['as_json'])
except:
pass
params = [
{
'by': None,
'value': 'bbva',
},
{
'by': ['error'],
'value': 'bbva',
},
{
'by': 'error',
'value': 'bbva',
},
{
'by': 'name',
'value': None,
},
{
'by': 'name',
'value': ['error'],
},
{
'by': 'isin',
'value': 'error',
},
]
for param in params:
try:
investpy.search_funds(by=param['by'], value=param['value'])
except:
pass
stock_from_date = xw.sheets[home_sheet].range('H5').value
stock_from_date = stock_from_date.strftime("%d/%m/%Y")
stock_to_date = xw.sheets[home_sheet].range('I5').value
stock_to_date = stock_to_date.strftime("%d/%m/%Y")
bond_from_date = xw.sheets[home_sheet].range('H6').value
bond_from_date = bond_from_date.strftime("%d/%m/%Y")
bond_to_date = xw.sheets[home_sheet].range('I6').value
bond_to_date = bond_to_date.strftime("%d/%m/%Y")
if fund and fund_country and fund_from_date and fund_to_date != "":
fund_data_historical = investpy.get_fund_historical_data(
fund=fund,
country=fund_country,
from_date=fund_from_date,
to_date=fund_to_date)
if index and index_country and index_from_date and index_to_date != "":
index_data_historical = investpy.get_index_historical_data(
index=index,
country=index_country,
from_date=index_from_date,
to_date=index_to_date)
if stock and stock_country and stock_from_date and stock_to_date != "":
stock_data_historical = investpy.get_stock_historical_data(
stock=stock,
country=stock_country,
from_date=stock_from_date,
