def historico_index(self, indice, status='Close'):
"""
:param indice: índice que deseja analisar
:param status: Pode ser Close / Open / Low / High / Volume / Currency /
:return: Retorno dos dados
"""
if len(indice) > 1:
dados = pd.DataFrame()
for papel in indice:
dados[papel] = inv.get_index_historical_data(
papel,
self.pais,
from_date=self.data_inicial,
to_date=self.data_final,
as_json=False,
order=self.ordem,
interval=self.intervalo)[status]
if self.json:
data = dict()
for colunas in dados.columns:
paper = list()
for infos in range(len(dados)):
paper.append({
"data":
dados.index[infos].strftime("%Y-%m-%d"),
"preço":
dados[colunas][infos]
})
data[colunas] = paper
return data
else:
return dados
# to_json -> json.dumps(limpando, ensure_ascii=False).encode('utf8')
else:
dados = pd.DataFrame()
for papel in indice:
dados = inv.get_index_historical_data(
papel,
self.pais,
from_date=self.data_inicial,
to_date=self.data_final,
as_json=False,
order=self.ordem,
interval=self.intervalo)
if self.json:
stock_json = dict()
dados['Date'] = dados.index.strftime("%Y-%m-%d")
stock_json[indice[0]] = dados.to_dict('records')
return stock_json
else:
return dados
def __init__(self):
self.data_atual = datetime.today()
self.ifix = inv.get_index_historical_data(
'BM&FBOVESPA Real Estate IFIX',
country='brazil',
from_date=self.data_atual.strftime('%d/%m/2020'),
to_date=self.data_atual.strftime('%d/%m/%Y'))
def get_Index(countryname,assetname):
asset = investpy.get_index_historical_data(index=assetname,country=countryname,from_date='01/01/2010',to_date='01/01/2021')
filename = assetname.replace(".", "")
filename = filename.replace("/", "")
filename = filename.replace(" ", "")
filename = filename.replace("&", "")
asset.to_csv(wd+'\\toto_data\\'+'index-'+countryname+'-'+filename+'.csv')
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 idx_data(index, country):
df = investpy.get_index_historical_data(index=index,
country=country,
from_date=fromdate,
to_date=tdy)['Close']
df = pd.DataFrame(df)
df.columns = [index]
return df
def hist_data_india(name):
df = investpy.get_index_historical_data(index=name,
country='India',
from_date=oneyr,
to_date=tdy)['Close']
df = pd.DataFrame(df)
df.columns = [name]
return df
def ftse100(date1, date2):
df = ivpy.get_index_historical_data(index='FTSE 100',
country='united kingdom',
from_date=date1.strftime('%d/%m/%Y'),
to_date=date2.strftime('%d/%m/%Y'))
df = df.iloc[:, :4]
df = df.rename(lambda x: 'UK FTSE 100: ' + x, axis='columns')
return df
def getProfitThroughIndex(date, amount, Index):
df = investpy.get_index_historical_data(index = Index,
country='United States',
from_date = date,
to_date= datetime.today().strftime('%d/%m/%y%y'))
begin = df.first('1D')['Open'].item()
end = df.last('1D')['Close'].item()
quote = end/begin
return amount*quote-amount
def arca(date1, date2):
df = ivpy.get_index_historical_data(index='ARCA Major Markets',
country='united states',
from_date=date1.strftime('%d/%m/%Y'),
to_date=date2.strftime('%d/%m/%Y'))
df = df.iloc[:, :4]
df = df.rename(lambda x: 'US ARCA: ' + x, axis='columns')
return df
def nasdaq(date1, date2):
df = ivpy.get_index_historical_data(index='nasdaq',
country='united states',
from_date=date1.strftime('%d/%m/%Y'),
to_date=date2.strftime('%d/%m/%Y'))
df = df.iloc[:, :4]
df = df.rename(lambda x: 'US SP500: ' + x, axis='columns')
return df
def test_get_index_historical_data_dow_jones():
issue254 = investpy.get_index_historical_data(index='Dow Jones US',
country='united states',
interval='Daily',
from_date='01/01/1996',
to_date='17/12/2020',
order='descending')
assert issue254.columns[0] == 'Open'
assert len(issue254) == 2669
issue254a = investpy.get_index_historical_data(
index='dOw JoNeS UniTed StATEs',
country='united states',
interval='Daily',
from_date='01/01/1996',
to_date='17/12/2020',
order='descending')
assert issue254a.columns[0] == 'Open'
assert len(issue254a) == 2669
def BenchmarkDataInvesting(bench, country):
conn = sqlite3.connect('DatabaseVB.db')
df = investpy.get_index_historical_data(
index=f'{bench}',
country=f'{country}',
from_date='01/01/2000',
to_date=datetime.today().strftime("%d/%m/%Y"))
df.reset_index(inplace=True)
df.rename(columns={'Date': 'Datum'}, inplace=True)
df.to_sql(f'{bench}', if_exists="replace", con=conn)
def get_index_rates(data):
final_df = pd.DataFrame()
for _, row in data.iterrows():
for idx in row.Index:
df = investpy.get_index_historical_data(
index=idx,
country=row.Country,
from_date=config['earliest_date'],
to_date=config['latest_date'])
final_df = final_df.append(df.assign(Idx=idx))
final_df.to_csv("historical_index.csv")
def lessrisk_moregains(self,datei,datef,index_,qts):
country_='Brazil' ##I don't know how the symbols of stocks in other country works, so i will limit my code to Brazil
symbols = inv.get_stocks(country=country_).symbol
my_rank = pd.DataFrame({'symb':[],'abv_bench_mean':[],'beta':[],'ratio':[]})
bench_hist = inv.get_index_historical_data(index=index_,country=country_,from_date=datei,to_date=datef,interval="Monthly").Open
bench_hist_change = bench_hist[bench_hist!=0].pct_change()
bench_hist_change = bench_hist_change.dropna()
how_many_errors=0 ###counts how many unavailable assets
for symb in symbols:
if symb[-1]!='3':
continue
##There's some stocks names listed in inv.get_stocks that information is unavailable
##so i will do a test first
works=False
try:
asset_hist = inv.get_stock_historical_data(stock=symb,country=country_,from_date=datei,to_date=datef,interval="Monthly").Open
asset_hist_change = asset_hist[asset_hist!=0].pct_change()
asset_hist_change = asset_hist_change.dropna()
works=True
sort = pd.DataFrame({'benchmark':bench_hist_change,'asset':asset_hist_change}).dropna().reset_index()
except:
if(how_many_errors<30):
how_many_errors+=1
print("Sorry, but "+symb+" is not available, it will be excluded from the rank")
print("How many unavailable:"+str(how_many_errors))
if(how_many_errors==30):
print("More than 30 assets unavailable, it could be a connection problem")
how_many_errors+=1
pass
##sort = data sorted by common dates and delete dates not in common
if (works)and(len(sort.benchmark)!=0)and(len(sort.asset)!=0):
beta = line(sort.benchmark,sort.asset)[0]
if(beta<=0):
continue
abv_bench = sort.asset-sort.benchmark
abv_bench_mean = abv_bench.mean()
ratio = abv_bench_mean/beta
add={'symb':symb,'abv_bench_mean':(str(round(abv_bench_mean*100,2))+"%"),'beta':round(beta,4),'ratio':ratio}
my_rank = my_rank.append(add,ignore_index=True)
my_rank = my_rank.sort_values(by='ratio',ascending=False).head(qts)
my_rank = my_rank.drop('ratio',1)
fig,ax = plt.subplots(figsize=[10,5])
ax.set_title("Top "+str(qts)+" stocks with highest gains above "+index_+" index and lowest risks in "+country_)
period_string = ("Period: "+datei +" to "+datef)
ax.set_xlabel("Average monthly relative rentability(AMRR)="+" Average gains above Benchmark("+index_+" Index)\n"+period_string)
plt.xticks([])
plt.yticks([])
ax.table(bbox=[0,0.1,1,0.8],rowLabels=range(1,qts+1),colLabels=['Symbol','AMRR','Beta(risk)'],cellText=my_rank.values,loc='center',rowLoc='center',cellLoc='center')
fig.savefig('Rank.png')
plt.show()
def get_investing_price(self):
try:
instrument_nature = self.security.instrument_type
asset_country = self.security.country
investing_ticker = self.security.investing_ticker
start_date = '01/01/{}'.format(self.start_year)
end_date = '31/12/{}'.format(self.end_year)
if instrument_nature == "STOCK":
hist_prices = invest.get_stock_historical_data(
stock=investing_ticker,
country=asset_country,
from_date=start_date,
to_date=end_date).reset_index(drop=False)
elif instrument_nature == "ETF":
hist_prices = invest.get_etf_historical_data(
etf=investing_ticker,
country=asset_country,
from_date=start_date,
to_date=end_date).reset_index(drop=False)
elif instrument_nature == "index":
hist_prices = invest.get_index_historical_data(
index=investing_ticker,
country=asset_country,
from_date=start_date,
to_date=end_date).reset_index(drop=False)
elif instrument_nature == "Curr":
hist_prices = invest.currency_crosses.get_currency_cross_historical_data(
currency_cross=investing_ticker,
from_date=start_date,
to_date=end_date).reset_index(drop=False)
elif instrument_nature == 'BOND':
hist_prices = invest.bonds.get_bond_historical_data(
bond=investing_ticker,
from_date=start_date,
to_date=end_date).reset_index(drop=False)
elif instrument_nature == 'commodity':
hist_prices = invest.commodity.get_bond_historical_data(
commodity=investing_ticker,
from_date=start_date,
to_date=end_date).reset_index(drop=False)
self.investing_prices = hist_prices
except Exception as e:
#print(str(e))
#logger.error("Error in get_investing_price: {}".format(str(e)))
hist_prices = pd.DataFrame()
self.investing_prices = hist_prices
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 idx_data(index, country):
start1 = date(date.today().year - 1, mth, date.today().day)
bdates = pd.DataFrame(
index=pd.bdate_range(start=start1, end=date.today()))
bdates.index.name = 'Date'
tdy = str(date.today().day) + '/' + str(
date.today().month) + '/' + str(date.today().year)
start_ytd = '01/01/' + str(date.today().year)
start_1y = str(
date.today().day) + '/' + str(mth) + '/' + str(date.today().year -
1)
df = investpy.get_index_historical_data(index=index,
country=country,
from_date=start_1y,
to_date=tdy)['Close']
df = bdates.join(pd.DataFrame(df), on='Date').ffill()
df.columns = [index]
return df
def collect_data(self,
start_date='01/01/2000',
end_date=datetime.date.today().strftime('%d/%m/%Y')):
'''Downloads asset data for a given start and end time range. Stores the downloaded data.
Args
----
start_data: String
Start date for collecting asset data. format should be dd/mm/yyyy
end_date: String
End date for asset data collection period. Default value is set to today
'''
ticker_raw = 'ADANA-AEFES-ULKER-AKBNK-AKGRT-AKSA-ALARK-ALGYO-ALKIM-ANACM-ANHYT-ARCLK-ASELS-AYGAZ-BRISA-BRSAN-BTCIM-BUCIM-CEMTS-CIMSA-CLEBI-DEVA-DOHOL-ECILC-ECZYT-EGEEN-ENKAI-ERBOS-EREGL-FROTO-GARAN-GOODY-GSDHO-GUBRF-GUSGR-HEKTS-IPEKE-ISCTR-ISFIN-ISGYO-KARSN-KARTN-KCHOL-KERVT-KLMSN-KORDS-KRDMD-LOGO-MGROS-NETAS-NTHOL-OTKAR-PETKM-SAHOL-SASA-SISE-SKBNK-SODA-TATGD-TCELL-THYAO-TOASO-TRCAS-TRKCM-TSKB-TUPRS-VESTL-YATAS-AGHOL-YKBNK-ZOREN-KOZAA-TTRAK-DOAS-INDES-BIMAS-VAKBN-CCOLA-KAREL-TAVHL-HALKB-ISMEN-ALBRK-TKFEN-TTKOM-GOZDE-KOZAL-AKSEN-TRGYO-EKGYO-BIZIM-TMSN-HLGYO-PGSUS-ODAS-ISDMR-MAVI-ENJSA-MPARK-SOKM'
ticker_list = []
for tick in ticker_raw.split('-'):
ticker_list.append(tick)
if 'BIST 100' not in ticker_list:
ticker_list.insert(0, 'BIST 100')
Data._check_directory(self.save_directory)
k = 0
for i, ticker in enumerate(ticker_list):
if ticker == 'BIST 100':
data = investpy.get_index_historical_data(index=ticker,
country='turkey',
from_date=start_date,
to_date=end_date,
as_json=False,
order='ascending')
name = 'XU100'
else:
data = yf.download(
ticker + '.IS',
start=datetime.datetime.strptime(start_date, '%d/%m/%Y'),
end=datetime.datetime.strptime(end_date, '%d/%m/%Y'),
interval='1d',
threads=True,
progress=False)
name = ticker
data.to_csv(self.save_directory + '/' + name + '.csv')
k += 1
print('Downloaded: %d' % k)
def get_data_from_investing(tickers, countries, years=15, t_type="index"):
if t_type != "index" and t_type != "commodity":
print("Currently, only work with indexes&commodities from investing.com.")
return
if not os.path.exists('data/stock_dfs'):
os.makedirs('data/stock_dfs')
start = dt.datetime.now() - dt.timedelta(days=years * 365)
end = dt.datetime.now()
if t_type == "index":
for i in trange(len(tickers), desc="Downloading from investing.com"):
ticker = tickers[i]
if not os.path.exists('data/stock_dfs/{}.csv'.format(ticker)):
df = investpy.get_index_historical_data(index=ticker,
from_date=str(start.strftime("%d/%m/%Y")),
to_date=str(end.strftime("%d/%m/%Y")),
country=countries[i])
df.reset_index(inplace=True)
df.set_index("Date", inplace=True)
df.drop(['High', 'Low', 'Open', 'Volume', 'Currency'], axis=1, inplace=True)
df.to_csv('data/stock_dfs/{}.csv'.format(ticker))
elif t_type == "commodity":
for i in trange(len(tickers), desc="Downloading from investing.com"):
ticker = tickers[i]
df = investpy.get_commodity_historical_data(commodity=ticker,
from_date=str(start.strftime("%d/%m/%Y")),
to_date=str(end.strftime("%d/%m/%Y")),
country=countries[i])
df.reset_index(inplace=True)
df.set_index("Date", inplace=True)
df.drop(['High', 'Low', 'Open', 'Volume', 'Currency'], axis=1, inplace=True)
df.to_csv('data/stock_dfs/{}.csv'.format(ticker))
########################################################################################################################
investpy.get_indices(country=None)
list_index_us = investpy.get_indices(country="United States")
list_index_us_search = list_index_us[list_index_us['full_name'].str.contains(
"MSCI")]
list_index_kr = investpy.get_indices(country="south korea")
list_index_kr_search = list_index_kr[list_index_kr['full_name'].str.contains(
"MSCI")]
list_index_kr2 = list_index_kr.sort_values(by=['name'])
# 미국 S&P 500 지수 (1979.12.26 부터)
investpy_snp500 = investpy.get_index_historical_data(index="S&P 500",
country="United States",
from_date="30/01/1900",
to_date=DD_END_DATE)
investpy_snp500.to_pickle('./Market_Watch_Data/investpy_snp500.pkl')
# 미국 MSCI US REIT 지수 (2015.01.12 부터)
investpy_msci_us_reit = investpy.get_index_historical_data(
index="MSCI US REIT",
country="United States",
from_date="30/01/1900",
to_date=DD_END_DATE)
investpy_msci_us_reit.to_pickle(
'./Market_Watch_Data/investpy_msci_us_reit.pkl')
# KOSPI (1981.05.01 부터)
investpy_kospi = investpy.get_index_historical_data(index="KOSPI",
country="south korea",
df_msci1 = df_msci0[df_msci0["Date"] < pd.to_datetime(
"20210401", errors='coerce', format='%Y%m%d')]
# 필요한 변수만 추려내기
df_msci_em0 = df_msci1[[
"Date", "MXEF_Open", "MXEF_High", "MXEF_Low", "MXEF_Close"
]]
df_msci_world0 = df_msci1[[
"Date", "MXWO_Open", "MXWO_High", "MXWO_Low", "MXWO_Close"
]]
########################################################################################################################
# investpy 패키지를 사용하여 MSCI 자료 업데이트 받기
df_msci_em_update = investpy.get_index_historical_data(
index="MSCI Emerging Markets",
country="world",
from_date="01/04/2021",
to_date=DD_END_DATE)
df_msci_world_update = investpy.get_index_historical_data(
index="MSCI World",
country="world",
from_date="01/04/2021",
to_date=DD_END_DATE)
df_msci_em_update.reset_index(level=0, inplace=True)
df_msci_world_update.reset_index(level=0, inplace=True)
# 변수명 바꾸기
df_msci_em_update.rename(columns={
"Open": "MXEF_Open",
"High": "MXEF_High",
]
thread = 4
thread = thread if thread < cpu_count() else cpu_count()
pool = Pool(processes=thread)
start = dt.datetime.strptime('30/12/2009', '%d/%m/%Y')
end = dt.datetime.now() - dt.timedelta(days=1)
from_date = start.strftime('%d/%m/%Y')
to_date = end.strftime('%d/%m/%Y')
# Scrape SET Hist Data
for index in ['SET', 'SET TRI']:
df = investpy.get_index_historical_data(index=index,
country='thailand',
from_date=from_date,
to_date=to_date,
as_json=False,
order='ascending')
df.drop(labels='Currency', axis=1, inplace=True)
df[['Return']] = df[['Close']].pct_change()
df = df.round(6)
df.to_csv(dir_path + 'data/' + index + '.csv')
# Scrape investing.com
investing_filter = []
for symbol in df_stock.loc[:, 'Filename'].values.tolist():
if not os.path.isfile(dir_path +
'data/investing/{}.csv'.format(symbol)):
investing_filter.append(
df_stock[df_stock['Filename'] == symbol].index.values[0])
for f in os.listdir(dir_path + 'data/investing'):
def index(request):
n = ""
price = 0.0
pnl = 0.0
pnlper = 0.0
n2 = ""
price2 = 0.0
pnl2 = 0.0
pnlper2 = 0.0
# FX Live Data
res = requests.get('https://finance.yahoo.com/currencies')
soup = bs4.BeautifulSoup(res.text, "lxml")
tables = soup.select('table')
for row in tables[0].find_all('tr'):
for ch in row.find_all('td')[1:2]:
if ((ch.text == 'EUR/USD') | (ch.text == 'EUR/GBP') |
(ch.text == 'USD/JPY') | (ch.text == 'AUD/USD') |
(ch.text == 'USD/CNY') | (ch.text == 'GBP/USD')):
for cell in row.find_all('td')[1:2]:
n = cell.text
for cell in row.find_all('td')[2:3]:
price = cell.text
for cell in row.find_all('td')[3:4]:
pnl = cell.text
for cell in row.find_all('td')[4:5]:
pnlper = cell.text
pnlper = float(pnlper[:len(pnlper) - 1])
a = currency(name=n, price=price, pnl=pnl, pnlper=pnlper)
a.save()
query_results1 = currency.objects.all().order_by('-id')[:6]
# FX News
head3 = []
head4 = []
imglink3 = []
pairs = []
context = []
res2 = requests.get(
'https://www.fxstreet.com/news?q=&hPP=17&idx=FxsIndexPro&p=0&dFR%5BTags%5D%5B0%5D=EURUSD&dFR%5BTags%5D%5B1%5D=GBPUSD'
)
soup = bs4.BeautifulSoup(res2.text, "lxml")
link = soup.find_all(
"div",
{"class": "fxs_col editorialhighlight editorialhighlight_medium"})
if ((link[0].h3.a.text).find('/') > 0):
head3.append(link[0].h3.a.text)
context.append(link[0].div.p.text)
imglink3.append(link[0].div.a.img['data-src'])
if ((link[1].h3.a.text).find('/') > 0):
head3.append(link[1].h3.a.text)
context.append(link[1].div.p.text)
imglink3.append(link[1].div.a.img['data-src'])
#Other headlines
res5 = requests.get('https://www.dailyfx.com/market-news')
soup5 = bs4.BeautifulSoup(res5.text, "lxml")
link5 = soup5.find_all(
"a",
{"class": "dfx-articleListItem jsdfx-articleListItem d-flex mb-3"})
for i in range(len(link5)):
if ((len(link5[i].span.text) < 73) &
((link5[i].span.text).find('/') >= 0) &
((link5[i].span.text).find('?') < 0) &
((link5[i].span.text).find('Charts') < 0)):
head4.append(link5[i].span.text)
# Removing duplicates
head4 = list(dict.fromkeys(head4))
head4 = head4[len(head4) - 4:len(head4)]
img_url_fx = [
'https://currencylive.com/news/wp-content/uploads/2019/08/100859-gbp-usd-pound-rises-may-brussels.jpg',
'https://responsive.fxempire.com/cover/1845x1230/webp-lossy-70.q50/_fxempire_/2020/01/Pounds-British.jpg',
'https://images.newindianexpress.com/uploads/user/imagelibrary/2019/9/25/w900X450/australian-2874029_960_720.jpg',
'https://responsive.fxempire.com/cover/1845x1230/webp-lossy-70.q50/_fxempire_/2019/11/US-Dollars-Yen-Notes.jpg'
]
for i in range(len(head4)):
g = fxnews(headline=head4[i], imgurl=img_url_fx[i])
g.save()
query_results6 = fxnews.objects.all().order_by('-id')[:4]
for i in range(len(head3)):
if (head3[i].find('/') > 0):
arr = head3[i].split('/')
first = (arr[0])[len(arr[0]) - 3:len(arr[0])]
second = arr[1][0:3]
pairs.append(first + '/' + second)
for i in range(len(head4)):
if (head4[i].find('/') > 0):
arr = head4[i].split('/')
first = (arr[0])[len(arr[0]) - 3:len(arr[0])]
second = arr[1][0:3]
pairs.append(first + '/' + second)
pairs[0].split('/')
first = ((pairs[0].split('/'))[0])[:3]
second = ((pairs[0].split('/'))[1])[0:3]
pair1 = first + '/' + second
pairs[0].split('/')
third = ((pairs[1].split('/'))[0])[:3]
fourth = ((pairs[1].split('/'))[1])[0:3]
pair2 = third + '/' + fourth
imglink3 = imglink3[0]
fxcenter = head3[0]
context = context[0]
#Currency Graph
start_date = date.today()
end_date = start_date - timedelta(days=21)
rates = []
forexdates = []
rates2 = []
forexdates2 = []
before = 0.0
before2 = 0.0
c = CurrencyRates()
for i in range(0, 21):
new = c.get_rate(first, second, end_date)
if (before != new):
rates.append(new)
forexdates.append(end_date.strftime("%Y-%m-%d"))
before = new
end_date = end_date + timedelta(days=1)
end_date = start_date - timedelta(days=21)
for i in range(0, 21):
new = c.get_rate(third, fourth, end_date)
if (before2 != new):
rates2.append(new)
forexdates2.append(end_date.strftime("%Y-%m-%d"))
before2 = new
end_date = end_date + timedelta(days=1)
#Index data
res3 = requests.get('https://finance.yahoo.com/world-indices')
soup = bs4.BeautifulSoup(res3.text, "lxml")
tables2 = soup.select('table')
for row in tables2[0].find_all('tr'):
for ch in row.find_all('td')[1:2]:
if ((ch.text == 'S&P 500') |
(ch.text == 'Dow Jones Industrial Average') |
(ch.text == 'NASDAQ Composite') |
(ch.text == 'HANG SENG INDEX') | (ch.text == 'Nikkei 225') |
(ch.text == 'S&P BSE SENSEX')):
for cell in row.find_all('td')[1:2]:
if (ch.text == 'Dow Jones Industrial Average'):
n2 = 'Dow Jones'
elif (ch.text == 'S&P BSE SENSEX'):
n2 = 'BSE SENSEX'
else:
n2 = cell.text
for cell in row.find_all('td')[2:3]:
price2 = cell.text
for cell in row.find_all('td')[3:4]:
pnl2 = cell.text
for cell in row.find_all('td')[4:5]:
pnlper2 = cell.text
pnlper2 = float(pnlper2[:len(pnlper2) - 1])
b = indexes(name=n2, price=price2, pnl=pnl2, pnlper=pnlper2)
b.save()
query_results2 = indexes.objects.all().order_by('-id')[:6]
# Index Graph
#Dow Graph
end = date.today()
start = (end - timedelta(days=21))
end = end.strftime('%d/%m/%Y')
start = start.strftime('%d/%m/%Y')
dataframe2 = investpy.get_index_historical_data(index='Dow 30',
country='united states',
from_date=start,
to_date=end)
dprice = (round(dataframe2['Close'], 3))
dow = list((round(dataframe2['Close'], 3)))
dumvar2 = dprice.index
dates = []
for i in range(len(dprice)):
dates.append(dumvar2[i].strftime('%d-%m-%Y'))
#BSE Sensex graph
end2 = date.today()
start2 = (end2 - timedelta(days=21))
end2 = end2.strftime('%d/%m/%Y')
start2 = start2.strftime('%d/%m/%Y')
dataframe = investpy.get_index_historical_data(index='BSE Sensex',
country='india',
from_date=start2,
to_date=end2)
dprice2 = (round(dataframe['Close'], 3))
bse = list((round(dataframe['Close'], 3)))
dumvar = dprice2.index
bsedates = []
for i in range(len(dprice2)):
bsedates.append(dumvar[i].strftime('%d-%m-%Y'))
#Index News
index_title = []
index_description = []
index_imgurl = []
newsapi = NewsApiClient(api_key='3de8090563454aadbd116bb099718ded')
string = ['Nifty', 'Sensex', 'S&P', 'Dow']
y = re.compile('<[^>]+>')
x = date.today()
z = x
x = x - timedelta(days=1)
for i in string:
if i == 'Sensex' or i == 'Nifty':
start_date = z
else:
start_date = x
all_articles = newsapi.get_everything(
q=i,
from_param=start_date.strftime("%Y-%m-%d"),
language="en",
sort_by="relevancy",
page_size=5)
articles = all_articles['articles']
for j in range(2):
if ((articles[j]['title'] not in index_title) &
(articles[j]['title'].find('?') == -1)):
index_title.append(articles[j]['title'])
index_description.append(y.sub('', articles[j]['description']))
index_imgurl.append(articles[j]['urlToImage'])
else:
pass
for i in range(1, 5):
if index_imgurl[i] != None and index_title[
i] != None and index_description[i] != None:
f = stocknews(headline=index_title[i], imgurl=index_imgurl[i])
f.save()
index_centertitle = index_title[0]
index_centerdescription = index_description[0]
index_centerimgurl = index_imgurl[0]
query_results3 = stocknews.objects.all().order_by('-id')[:4]
#Equities Graph
end = date.today()
start = end - timedelta(days=7)
diff = []
aapl = yf.download("AAPL", start, end)
nflx = yf.download("NFLX", start, end)
msft = yf.download("MSFT", start, end)
amzn = yf.download("AMZN", start, end)
tsla = yf.download("TSLA", start, end)
fb = yf.download("FB", start, end)
comp = [aapl, nflx, msft, amzn, tsla, fb]
for i in comp:
dprice = (round(i['Close'], 3))
diff.append(round(dprice[-1] - dprice[-2], 3))
# Indian Equities
end3 = date.today()
start3 = end3 - timedelta(days=7)
diff3 = []
end3 = end3.strftime('%d/%m/%Y')
start3 = start3.strftime('%d/%m/%Y')
tcs = investpy.get_stock_historical_data(stock='TCS',
country='india',
from_date=start3,
to_date=end3)
icbk = investpy.get_stock_historical_data(stock='ICBK',
country='india',
from_date=start3,
to_date=end3)
hdbk = investpy.get_stock_historical_data(stock='HDBK',
country='india',
from_date=start3,
to_date=end3)
infy = investpy.get_stock_historical_data(stock='INFY',
country='india',
from_date=start3,
to_date=end3)
lart = investpy.get_stock_historical_data(stock='LART',
country='india',
from_date=start3,
to_date=end3)
reli = investpy.get_stock_historical_data(stock='RELI',
country='india',
from_date=start3,
to_date=end3)
comp2 = [tcs, icbk, hdbk, infy, lart, reli]
for i in comp2:
ghe = (round(i['Close'], 3))
diff3.append(round(ghe[-1] - ghe[-2], 3))
#Equities data
res4 = requests.get(
'https://www.tradingview.com/markets/stocks-usa/market-movers-large-cap/'
)
soup4 = bs4.BeautifulSoup(res4.text, "lxml")
tables4 = soup4.select('table')
rows4 = tables4[0].find_all('tr')
for i in range(1, 50):
cells4 = rows4[i].find_all('td')
name4 = cells4[0].div.a.text
price4 = cells4[1].text
pnlper4 = cells4[2].text
pnl4 = cells4[3].text
pnlper4 = float(pnlper4[:len(pnlper4) - 1])
if ((name4 == 'AAPL') | (name4 == 'MSFT') | (name4 == 'NFLX') |
(name4 == 'AMZN') | (name4 == 'FB') | (name4 == 'TSLA')):
d = equities(name=name4, price=price4, pnl=pnl4, pnlper=pnlper4)
d.save()
query_results4 = equities.objects.all().order_by('-id')[:6]
streq = ["Trending stocks"]
headeq = []
imgurleq = []
for j in range(len(streq)):
top_headlineseq = newsapi.get_everything(
q=streq[j],
from_param=(date.today() - timedelta(days=1)).strftime('%Y-%m-%d'),
sort_by='relevancy',
language='en',
)
articleseq = top_headlineseq['articles']
for i in range(len(articleseq)):
if (articleseq[i]['title'].find('Trending stocks') != -1):
e = eqnews(headline=articleseq[i]['title'],
imgurl=articleseq[i]['urlToImage'])
e.save()
query_results5 = eqnews.objects.all().order_by('-id')[:4]
# newsapi = NewsApiClient(api_key='082157f2d57c4560878f51cc05ace5ea')
streq2 = ["Trending stocks"]
for j in range(len(streq2)):
top_headlineseq2 = newsapi.get_everything(
q=streq2[j],
from_param=(date.today() - timedelta(days=1)).strftime('%Y-%m-%d'),
sort_by='relevancy',
language='en',
)
articleseq2 = top_headlineseq2['articles']
title_center_eq = articleseq2[1]['title']
imgURL_center_eq = articleseq2[1]['urlToImage']
desc_center_eq = articleseq2[1]['description']
desc_center_eq = desc_center_eq.split(".")
desc_center_eq = desc_center_eq[0]
return render(
request, 'news/index.html', {
'query_results1': query_results1,
'query_results2': query_results2,
'query_results3': query_results3,
'query_results4': query_results4,
'query_results5': query_results5,
'query_results6': query_results6,
'fxcenter': fxcenter,
'head3': head3,
'head4': head4,
'context': context,
'imglink3': imglink3,
'pairs': pairs,
'dow': dow,
'dates': dates,
'rates': rates,
'forexdates': forexdates,
'pair1': pair1,
'rates2': rates2,
'forexdates2': forexdates2,
'pair2': pair2,
'diff': diff,
'bse': bse,
'bsedates': bsedates,
'diff3': diff3,
'headeq': headeq,
'imgurleq': imgurleq,
'index_centertitle': index_centertitle,
'index_centerdescription': index_centerdescription,
'index_centerimgurl': index_centerimgurl,
'title_center_eq': title_center_eq,
'imgURL_center_eq': imgURL_center_eq,
'desc_center_eq': desc_center_eq,
'img_url_fx': img_url_fx
})
def test_investpy_indices():
"""
This function checks that index data retrieval functions listed in investpy work properly.
"""
params = [
{
'country': 'spain',
},
{
'country': None,
},
]
for param in params:
investpy.get_indices(country=param['country'])
investpy.get_indices_list(country=param['country'])
params = [
{
'country': None,
'columns': ['name', 'currency'],
'as_json': True
},
{
'country': None,
'columns': ['name', 'currency'],
'as_json': False
},
{
'country': 'spain',
'columns': ['name', 'currency'],
'as_json': True
},
{
'country': 'spain',
'columns': ['name', 'currency'],
'as_json': False
},
{
'country': 'spain',
'columns': None,
'as_json': False
},
]
for param in params:
investpy.get_indices_dict(country=param['country'],
columns=param['columns'],
as_json=param['as_json'])
investpy.get_index_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_index_recent_data(index='ibex 35',
country='spain',
as_json=param['as_json'],
order=param['order'],
interval='Daily')
investpy.get_index_historical_data(index='ibex 35',
country='spain',
from_date='01/01/2018',
to_date='01/01/2019',
as_json=param['as_json'],
order=param['order'],
interval='Daily')
params = [
{
'index': 'ibex 35',
'country': 'spain',
'as_json': False
},
{
'index': 'ibex 35',
'country': 'spain',
'as_json': True
}
]
for param in params:
investpy.get_index_information(index=param['index'], country=param['country'], as_json=param['as_json'])
params = [
{
'country': 'united states',
'as_json': False,
'n_results': 10
},
{
'country': 'united kingdom',
'as_json': True,
'n_results': 10
}
]
for param in params:
investpy.get_indices_overview(country=param['country'], as_json=param['as_json'], n_results=param['n_results'])
investpy.search_indices(by='name', value='ibex')
#k1=k[k.Symbol==k.Symbol.unique()[pos]].iloc[-lb:]
automl = AutoML(mode='Compete', total_time_limit=600)
k1 = investpy.search_quotes(text='Kotak NIFTY ETF',
products=['etfs'],
countries=['India'],
n_results=2)[0]
k1 = investpy.get_etf_historical_data('KOTAKNIFTY',
country='India',
from_date='01/01/2010',
to_date='20/03/2021')
#k1=investpy.search_quotes(text='AARTIIND',products=['stocks'],countries=['India'],n_results=2)[0].retrieve_historical_data(from_date='01/01/2019',to_date='07/12/2020')
k2 = investpy.get_index_historical_data(index='India VIX',
country='India',
from_date='01/01/2010',
to_date='20/03/2021')
def slret(o, h, l, c, sl):
hp = ((h - o) / o) * 100
if -hp < sl:
return sl
else:
return (((o - c) / o) * 100)
def slret_long(x):
if x > 0:
return 1
else:
connection = pymysql.connect(host="fumire.moe",
user="******",
password=f.readline().strip(),
db="fumiremo_StockDB",
charset="utf8",
port=3306)
cursor = connection.cursor(pymysql.cursors.DictCursor)
for country in ["south korea", "japan", "united states"]:
print(country)
for _, row1 in investpy.get_indices(country=country).iterrows():
print("-", row1["name"])
try:
for date, row2 in investpy.get_index_historical_data(
index=row1["name"],
country=country,
from_date="01/01/1900",
to_date=datetime.datetime.today().strftime("%d/%m/%Y"),
order="descending").iterrows():
query = "SELECT * FROM `IndexData` WHERE `country` LIKE '%s' AND `Name` LIKE '%s' AND `Symbol` LIKE '%s' AND `Date` = '%s'" % (
country, row1["name"], row1["symbol"], date.date())
cursor.execute(query)
if cursor.fetchall():
break
query = "INSERT INTO `IndexData` (`IndexColumn`, `Country`, `Name`, `Symbol`, `Date`, `Open`, `High`, `Low`, `Close`, `Volume`, `Currency`) VALUES (NULL, '%s', '%s', '%s', '%s', '%f', '%f', '%f', '%f', '%d', '%s');" % (
country, row1["name"], row1["symbol"], date.date(),
row2["Open"], row2["High"], row2["Low"], row2["Close"],
row2["Volume"], row2["Currency"])
cursor.execute(query)
def test_investpy_indices():
"""
This function checks that index data retrieval functions listed in investpy work properly.
"""
params = [
{
'country': 'spain',
},
{
'country': None,
},
]
for param in params:
investpy.get_indices(country=param['country'])
investpy.get_indices_list(country=param['country'])
params = [
{
'country': None,
'columns': ['name', 'currency'],
'as_json': True
},
{
'country': None,
'columns': ['name', 'currency'],
'as_json': False
},
{
'country': 'spain',
'columns': ['name', 'currency'],
'as_json': True
},
{
'country': 'spain',
'columns': ['name', 'currency'],
'as_json': False
},
{
'country': 'spain',
'columns': None,
'as_json': False
},
]
for param in params:
investpy.get_indices_dict(country=param['country'],
columns=param['columns'],
as_json=param['as_json'])
investpy.get_index_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_index_recent_data(index='ibex 35',
country='spain',
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
investpy.get_index_historical_data(index='ibex 35',
country='spain',
from_date='01/01/2018',
to_date='01/01/2019',
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
investpy.search_indices(by='name', value='ibex')
def get_indices(index_name, country, start, end):
index = investpy.get_index_historical_data(index_name,
country=country,
from_date=start,
to_date=end)
return index
def test_indices_errors():
"""
This function raises errors on index retrieval functions
"""
try:
retrieve_indices(test_mode=None)
except:
pass
try:
retrieve_index_countries(test_mode=None)
except:
pass
try:
retrieve_global_indices_countries(test_mode=None)
except:
pass
params = [
{
'country': ['error']
},
{
'country': 'error'
},
]
for param in params:
try:
investpy.get_indices(country=param['country'])
except:
pass
try:
investpy.get_indices_list(country=param['country'])
except:
pass
params = [
{
'country': ['error'],
'columns': None,
'as_json': False
},
{
'country': 'spain',
'columns': None,
'as_json': 'error'
},
{
'country': 'spain',
'columns': 0,
'as_json': True
},
{
'country': 'spain',
'columns': ['error'],
'as_json': False
},
]
for param in params:
try:
investpy.get_indices_dict(country=param['country'],
columns=param['columns'],
as_json=param['as_json'])
except:
pass
params = [
{
'index': None,
'country': 'spain',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': ['error'],
'country': 'spain',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': None,
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'error',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'netherlands',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': ['error'],
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'spain',
'as_json': 'error',
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'spain',
'as_json': True,
'order': 'error',
'debug': True
},
{
'index': 'error',
'country': 'spain',
'as_json': True,
'order': 'ascending',
'debug': True
},
{
'index': ['error'],
'country': 'spain',
'as_json': True,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'spain',
'as_json': True,
'order': 'ascending',
'debug': 'error'
},
]
for param in params:
try:
investpy.get_index_recent_data(index=param['index'],
country=param['country'],
as_json=param['as_json'],
order=param['order'],
debug=param['debug'])
except:
pass
params = [
{
'index': None,
'country': 'spain',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'error',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'netherlands',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': None,
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': ['error'],
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'spain',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': 'error',
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'spain',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'error',
'debug': True
},
{
'index': 'ibex 35',
'country': 'spain',
'from_date': 'error',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'spain',
'from_date': '01/01/2019',
'to_date': 'error',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'error',
'country': 'spain',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': ['error'],
'country': 'spain',
'from_date': '01/01/2018',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'spain',
'from_date': '01/01/1998',
'to_date': '01/01/2019',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'spain',
'from_date': '01/01/2019',
'to_date': '01/01/1998',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'spain',
'from_date': '01/01/1900',
'to_date': '01/01/1950',
'as_json': False,
'order': 'ascending',
'debug': True
},
{
'index': 'ibex 35',
'country': 'spain',
'from_date': '01/01/2019',
'to_date': '01/03/2019',
'as_json': True,
'order': 'ascending',
'debug': 'error'
},
]
for param in params:
try:
investpy.get_index_historical_data(index=param['index'],
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 = [
{
'by': None,
'value': 'ibex',
},
{
'by': ['error'],
'value': 'ibex',
},
{
'by': 'error',
'value': 'ibex',
},
{
'by': 'name',
'value': None,
},
{
'by': 'name',
'value': ['error'],
},
{
'by': 'name',
'value': 'error',
},
]
for param in params:
try:
investpy.search_indices(by=param['by'], value=param['value'])
except:
pass
#investing.com
#pip install investpy==0.9.7
import investpy
df = investpy.get_stock_recent_data(stock='BBVA',
country='spain')
print(df.head())
df = investpy.get_stock_historical_data(stock='BBVA',
country='spain',
from_date='01/01/2010',
to_date='01/01/2019')
print(df.head())
df = investpy.get_indices_historical_data(indices='ise-100',
country='turkey',
from_date='01/01/2010',
to_date='01/01/2019')
print(df.head())
BIST100=investpy.get_index_historical_data(index='BIST 100', country='turkey', from_date='01/01/2001', to_date='27/10/2019',order='ascending')
writer = pd.ExcelWriter('BIST100.xlsx', engine='xlsxwriter')
BIST100.to_excel(writer, 'Sheet1')
writer.save()
