def create_sector_classification_file(**kwargs):
file_name = directory_name + '\sector_classification.pkl'
if os.path.isfile(file_name):
return pd.read_pickle(file_name)
if 'report_date' in kwargs.keys():
report_date = kwargs['report_date']
else:
report_date = exp.doubledate_shift_bus_days()
symbol_list = ssd.get_symbol_list_4date(settle_date=report_date)
for i in range(len(symbol_list)):
eqty = Equity(symbol_list[i])
try:
sector_list.append(eqty.sector)
industry_list.append(eqty.industry)
except:
sector_list.append(None)
industry_list.append(None)
sector_classification = pd.DataFrame.from_dict({'ticker': symbol_list, 'sector': sector_list, 'industry': industry_list})
sector_classification.to_pickle(file_name)
return sector_classification
def get_vg_ticker_info(equity_file, days):
with open(equity_file, 'r') as f:
excluded_industry = ['Oil & Gas', 'REIT', 'Biotechnology']
for line in f:
# Skip if line has 'ETF', 'FUND', 'TRUST', 'THERA', 'PHARMA'
ticker = re.search(r'\((.*?)\)',line).group(1)
if ticker:
#rb_client = robinhood.Equity(ticker)
yahoo_ticker = Equity(ticker)
yahoo_equity_summary = yahoo_info.get_summary(ticker)
profile = yahoo_ticker.profile
price = float(yahoo_equity_summary['Open'])
if profile is None or price >= 50:
continue
market_cap = yahoo_equity_summary.get('Market Cap', None)
if market_cap:
if yahoo_equity_summary['Market Cap'].endswith('M'):
data = re.split(r'(\d+)', yahoo_equity_summary['Market Cap'])[1]
if int(data) < 50:
print "Skipping {} because of mkt cap below 50M".format(ticker)
continue
else:
continue
# Skip REIT
industry = yahoo_ticker.industry
if industry in excluded_industry:
continue
stocks.get_stock_info(ticker, days)
click.pause(info="Press any key to continue...", err=False)
def __init__(self, ticker='AAPL'):
self.ticker = ticker
self.YF = yf(ticker)
self.PF = Equity(ticker)
self.date_str = dt.date.today().strftime('%Y-%m-%d')
self.prices_dir = 'prices/prices_{}'.format(self.date_str)
self.info_dir = 'info/info_{}'.format(self.date_str)
def store_company():
df = pd.read_csv('constituents.csv')
aapl = Equity('AAPL')
print(aapl.profile)
for index, row in df.head(50).iterrows():
company_check = CompanyInfo.objects.filter(
symbol=row['Symbol']).count()
if company_check == 0:
company = Equity(row['Symbol'])
try:
print(company.profile)
newCompany = CompanyInfo(name=row['Name'],
symbol=row['Symbol'],
sector=company.profile['Sector'],
website=company.profile['Website'],
country=company.profile['Country'])
newCompany.save()
except:
pass
def setUpClass(cls):
cls.aapl = Equity('AAPL')
cls.options = OptionChain(cls.aapl)
def setUpClass(cls):
cls.aapl = Equity('AAPL')
cls.date = datetime.date(2013, 1, 25)
def get_stock_info(ticker, name=None, days=None):
""" Returns ticker info """
yahoo_ticker_obj = Equity(ticker)
ticker_obj = etrade_api.Equity(etrade_acct_obj, ticker)
if ticker_obj.quote_all is None:
return None
rb_client = robinhood.Equity(rh_acct_obj, ticker)
if rb_client.equity_ticker is None:
return None
yahoo_summary_info = yahoo_info.get_summary(ticker)
yahoo_equity_info = yahoo_info.get_stats(ticker)
trade_date, end_date = get_end_date(days)
trade_date = datetime.strptime(trade_date, '%Y-%m-%d').date()
sma_10 = ticker_10_sma(ticker)
#sma_10_p1 = multiprocessing.Process(target=ticker_10_sma, args=(ticker,))
#sma_10 = sma_10_p1.start()
#sma_10_p1.join()
sma_21 = ticker_21_sma(ticker)
#sma_21_p1 = multiprocessing.Process(target=ticker_21_sma, args=(ticker,))
#sma_21 = sma_21_p1.start()
#sma_21_p1.join()
sma_50 = ticker_50_sma(ticker)
#sma_50_p1 = multiprocessing.Process(target=ticker_50_sma, args=(ticker,))
#sma_50 = sma_50_p1.start()
#sma_50_p1.join()
sma_200 = ticker_200_sma(ticker)
#sma_200_p1 = multiprocessing.Process(target=ticker_200_sma, args=(ticker,))
#sma_200 = sma_200_p1.start()
#sma_200_p1.join()
rsi = get_rsi(ticker)
url = '{}/query?function=TIME_SERIES_DAILY&symbol={}&apikey={}'.\
format(host, ticker, AV_API_KEY)
resp = session.get(url)
try:
resp.raise_for_status()
except requests.HTTPError as e:
raise StockHTTPError(e)
data = resp.json()
symbol_table = PrettyTable([
'Name', 'Ticker', 'Sector', 'Industry', 'Date', 'Company Founded',
'CEO', 'IPO Date', 'Total Employee', 'Market Cap'
])
if name is not None:
symbol_table.add_row([
name, data['Meta Data']['2. Symbol'], yahoo_ticker_obj.sector,
yahoo_ticker_obj.industry, today, rb_client.company_founded,
rb_client.company_ceo, rb_client.company_ipo_date,
rb_client.company_employees_total,
market_cap(rb_client.market_cap)
])
#click.echo("Name: {}".format(name))
else:
symbol_table.add_row([
'None', data['Meta Data']['2. Symbol'], yahoo_ticker_obj.sector,
yahoo_ticker_obj.industry, today, rb_client.company_founded,
rb_client.company_ceo, rb_client.company_ipo_date,
rb_client.company_employees_total,
market_cap(rb_client.market_cap)
])
#symbol_table.add_row(['None', data['Meta Data']['2. Symbol'], yahoo_ticker_obj.sector, yahoo_ticker_obj.industry, today])
#click.echo("Ticker: {}, Sector: {}, Industry:{}, Date: {}".\
# format(click.style(data['Meta Data']['2. Symbol'], fg='red'),
# yahoo_ticker_obj.sector, yahoo_ticker_obj.industry,
# today))
click.echo(symbol_table)
click.echo("Company Summary: {}".format(rb_client.company_info))
price_data = data['Time Series (Daily)']
close_price_list = []
sorted_price_data = collections.OrderedDict(
sorted(price_data.items(), reverse=True))
ticker_table = PrettyTable([
'Date', 'Open Price', 'Close price', 'High ($)', 'Low ($)',
'Fluctuate ($)', 'Fluctuate (%)', 'Change ($)', 'Change (%)',
'Volume(k)'
])
for k, v in sorted_price_data.items():
if ':' in k:
k = datetime.strptime(k, '%Y-%m-%d %H:%M:%S').date()
else:
k = datetime.strptime(k, '%Y-%m-%d').date()
prev_date = get_prev_close_date(k)
prev_date_str = datetime.strftime(prev_date, '%Y-%m-%d')
prev_close_date = price_data.get(prev_date_str, None)
if prev_close_date is None:
prev_date = get_prev_close_date(k - timedelta(days=1))
prev_date = datetime.strftime(prev_date, '%Y-%m-%d')
if k < end_date:
click.echo(ticker_table)
#company_info_table = PrettyTable(['Company Founded', 'CEO', 'IPO Date', 'Total Employee', 'Market Cap'])
#company_info_table.add_row([rb_client.company_founded, rb_client.company_ceo, rb_client.company_ipo_date,
# rb_client.company_employees_total, market_cap(rb_client.market_cap)])
#click.echo(company_info_table)
#click.echo("Company Founded: {}\t\tCEO: {}\tIPO date: {}\t\t"
# "Total Employee: {}".format(click.style(str(rb_client.company_founded), fg='red'),
# rb_client.company_ceo, click.style(rb_client.company_ipo_date, fg='red'),
# rb_client.company_employees_total))
snap_ratio_table = PrettyTable([
'PE', 'EPS', 'Estimated EPS', 'Trailing PE', 'Forward PE',
'P/S (ttm)', 'P/B (mrq)', 'Current Ratio (mrq)',
'Gross Profit(ttm)', 'Profit Margin', 'Operating Margin(ttm)',
'ROE', 'ROA'
])
snap_ratio_table.add_row([
rb_client.company_pe_ratio, ticker_obj.eps,
ticker_obj.estEarnings, yahoo_equity_info['Trailing P/E'],
yahoo_equity_info['Forward P/E 1'],
yahoo_equity_info['Price/Sales (ttm)'],
yahoo_equity_info['Price/Book (mrq)'],
yahoo_equity_info['Current Ratio (mrq)'],
yahoo_equity_info['Gross Profit (ttm)'],
yahoo_equity_info['Profit Margin'],
yahoo_equity_info['Operating Margin (ttm)'],
yahoo_equity_info['Return on Equity (ttm)'],
yahoo_equity_info['Return on Assets (ttm)']
])
click.echo(snap_ratio_table)
#click.echo("Market Cap: {}\tPE: {}\t\tEPS: {}\t\t\t"
# "Dividend Yield: {}\t\tDividend Per Share: {}\t"
# "Estimated EPS: {}".\
# format(market_cap(rb_client.market_cap),
# click.style(str(rb_client.company_pe_ratio), fg='red'),
# ticker_obj.eps,
# ticker_obj.dividend,
# ticker_obj.annualDividend,
# ticker_obj.estEarnings))
snap_financial_table = PrettyTable([
'Revenue per Share', 'Qtr Revenue Growth (yoy)',
'Qtr Earnings Growth (yoy)', 'Total Debt/Equity (mrq)',
'Book Value/Share (mrq)', 'Total Cash/Share (mrq)'
])
snap_financial_table.add_row([
yahoo_equity_info['Revenue Per Share (ttm)'],
yahoo_equity_info['Quarterly Revenue Growth (yoy)'],
yahoo_equity_info['Quarterly Earnings Growth (yoy)'],
yahoo_equity_info['Total Debt/Equity (mrq)'],
yahoo_equity_info['Book Value Per Share (mrq)'],
yahoo_equity_info['Total Cash Per Share (mrq)']
])
click.echo(snap_financial_table)
#click.echo("Trailing PE: {}\t\tForward PE: {}\tP/S (ttm): {}\t\t\t"
# "P/B (mrq): {}\t\t\tBeta: {}\t\t\tProfit Margin: {}\n"
# "Operating Margin(ttm): {}\tROE: {}\t\tROA: {}\t\t\tRevenue per Share: {}\t"
# "Gross Profit(ttm): {}\tCurrent Ratio (mrq): {}\nQuarterly Revenue Growth (yoy): {}\t\t\t"
# "Quarterly Earnings Growth (yoy): {}\t\t\t\tTotal Debt/Equity (mrq): {}\n"
# "Book Value Per Share (mrq): {}\t\t\tTotal Cash Per Share (mrq): {}\t\t\t\t 1 yr target: {}".
# format(yahoo_equity_info['Trailing P/E'], yahoo_equity_info['Forward P/E 1'],
# yahoo_equity_info['Price/Sales (ttm)'], yahoo_equity_info['Price/Book (mrq)'],
# yahoo_equity_info['Beta'], yahoo_equity_info['Profit Margin'],
# yahoo_equity_info['Operating Margin (ttm)'],
# yahoo_equity_info['Return on Equity (ttm)'],
# yahoo_equity_info['Return on Assets (ttm)'],
# yahoo_equity_info['Revenue Per Share (ttm)'],
# yahoo_equity_info['Gross Profit (ttm)'],
# yahoo_equity_info['Current Ratio (mrq)'],
# yahoo_equity_info['Quarterly Revenue Growth (yoy)'],
# yahoo_equity_info['Quarterly Earnings Growth (yoy)'],
#yahoo_equity_info['Total Cash (mrq)'],
# yahoo_equity_info['Total Debt/Equity (mrq)'],
# yahoo_equity_info['Book Value Per Share (mrq)'],
# yahoo_equity_info['Total Cash Per Share (mrq)'],
# yahoo_summary_info['1y Target Est']))
# click.echo("Market Cap: {}, PE: {},".\
# format(market_cap(rb_client.market_cap),
# click.style(str(rb_client.company_pe_ratio), fg='red')))
snap_low_high_table = PrettyTable([
'1 yr target', 'RSI', 'Beta', 'Dividend Yield',
'Dividend/Share', 'Highest', 'Lowest', '52 week high',
'52 week low', 'SMA10', 'SMA21', 'SMA50', 'SMA200'
])
snap_low_high_table.add_row([
yahoo_summary_info['1y Target Est'],
str(rsi), yahoo_equity_info['Beta'], ticker_obj.dividend,
ticker_obj.annualDividend,
str(max(close_price_list)),
str(min(close_price_list)),
str(rb_client.high_52_weeks),
str(rb_client.low_52_weeks), sma_10, sma_21, sma_50, sma_200
])
click.echo(snap_low_high_table)
#click.echo("RSI: {}\tHighest: {}\tLowest: {}\t52 week high: {}\t"
# "52 week low: {}\tSMA10: {}\tSMA21: {}\tSMA50: {}\t"
# "SMA200: {}".format(click.style(str(rsi), fg='red'),
# click.style(str(max(close_price_list)), fg='red'),
# click.style(str(min(close_price_list)), fg='red'),
# click.style(str(rb_client.high_52_weeks), fg='red'),
# click.style(str(rb_client.low_52_weeks), fg='red'),
# sma_10, sma_21, sma_50, sma_200))
return True
fluctuate = get_volatility(float(v['2. high']), float(v['3. low']))
fluctuate_percent = get_volatility_percent(fluctuate,
float(v['4. close']))
try:
change = get_price_change(float(v['4. close']),
float(price_data[prev_date]['4. close']))
except KeyError:
continue
change_percent = get_price_change_percent(
change, float(price_data[prev_date]['4. close']))
vol = int(v['5. volume']) / 1000
close_price_list.append(float(v['4. close']))
ticker_table.add_row([
k,
round(float(v['1. open']), 3),
round(float(v['4. close']), 3),
round(float(v['2. high']), 3),
round(float(v['3. low']), 3),
round(fluctuate, 3),
round(fluctuate_percent, 2),
round(change, 2),
round(change_percent, 2), vol
])
def setUpClass(cls):
pytest.skip('Skip option tests due to broken yahoo api')
cls.aapl = Equity('AAPL')
cls.options = OptionChain(cls.aapl)