def add_quick_summary(self):
quick_summary_md = ""
quick_summary_md += f"# Quick summary about {self.name}\n\n"
quick_summary_md += "This is the quick summary:\n\n"
try:
status, messages, _ = self.is_good_business()
quick_summary_md += f"{self.name} is a good business? \nAnswer: {status}\n\n"
if status == False:
quick_summary_md += f"Reason:, {messages}\n\n"
except:
pass
for item in [
"sector", "longBusinessSummary", "country", "city",
"trailingPE", "priceToSalesTrailing12Months",
"fiftyTwoWeekHigh", "fiftyTwoWeekLow", "pegRatio",
"shortPercentOfFloat", "next_earnings_date"
]:
try:
tmp_str = f"- {item}: {self.attribute[item]}\n\n"
quick_summary_md += tmp_str
except:
pass
info = si.get_stats_valuation(self.name)
quick_summary_md += info.to_markdown() + "\n\n"
info = si.get_analysts_info(self.name)
quick_summary_md += info["Growth Estimates"].to_markdown() + "\n\n"
#quick_summary_md += info["Revenue Estimate"].to_markdown() + "\n\n"
# finally, add it into markdown_notes
self.markdown_notes += quick_summary_md
return quick_summary_md
def fair_value(ticker, years=1, wish_return=0.4):
netincome = Decimal(si.get_income_statement(ticker)[si.get_income_statement(ticker).columns[0]].netIncome)
shares_outstanding = number_encoder(str(si.get_stats(ticker)["Value"][9]))
current_eps = netincome / shares_outstanding
all_growth_rates = [Decimal(x[:-1]) for x in si.get_analysts_info(ticker)["Revenue Estimate"][
si.get_analysts_info(ticker)["Revenue Estimate"].columns[
:-2]].iloc[5][1:].dropna()]
growth_rate = sum(all_growth_rates) / len(all_growth_rates) / 100
projected_eps = current_eps * (1 + growth_rate) ** years
projected_pe_ratio = Decimal(si.get_live_price(ticker)) / projected_eps
forwarded_pe_ratio = Decimal(si.get_stats_valuation(ticker)[si.get_stats_valuation(ticker).columns[1]][2]) * (
1 + growth_rate) ** (years - 1)
return (forwarded_pe_ratio * projected_eps) / Decimal(1 + wish_return)
def plot_valuation(self):
stock_name = self.name
valuation = si.get_stats_valuation(stock_name)
valuation = valuation.set_index('Unnamed: 0')
valuation = valuation.applymap(algotrading.stock._convert_to_numeric)
valuation = valuation.T
valuation = valuation.sort_index()
tmp = plt.rcParams["figure.figsize"]
plt.rcParams["figure.figsize"] = (20, 20)
valuation[[
'Trailing P/E', 'Forward P/E 1', 'PEG Ratio (5 yr expected) 1',
'Price/Sales (ttm)', 'Price/Book (mrq)'
]].plot(subplots=True, grid=True)
plt.rcParams["figure.figsize"] = tmp
return valuation
def __init__(self, a_ticker: str = 'CNI'):
self.__ticker = a_ticker
self.__quote = si.get_quote_table(a_ticker)
self.__val = si.get_stats_valuation(a_ticker)
self.__val = self.__val.iloc[:, :2]
self.__val.columns = ['Attribute', 'Recent']
self.__pe_ratio = float(self.__val[self.__val.Attribute.str.contains(
'Trailing P/E')].iloc[0, 1])
self.__fpe_ratio = float(self.__val[self.__val.Attribute.str.contains(
'Forward P/E')].iloc[0, 1])
self.__peg_ratio = float(
self.__val[self.__val.Attribute.str.contains('PEG')].iloc[0, 1])
self.__price_to_book = float(self.__val[
self.__val.Attribute.str.contains('Price/Book')].iloc[0, 1])
self.__price_to_earn = float(self.__val[
self.__val.Attribute.str.contains('Trailing P/E')].iloc[0, 1])
self.__price_to_sales = float(self.__val[
self.__val.Attribute.str.contains('Price/Sales')].iloc[0, 1])
def get_valuation_stats(self):
selected = {
'Market Cap (intraday) 5': 'Market Cap',
'Price/Sales (ttm)': 'Price/Sales (ttm)',
'Trailing P/E': 'P/E (trailing)',
'PEG Ratio (5 yr expected) 1': 'PEG (5 yr expected)',
'Price/Book (mrq)': 'Price/Book',
}
df = si.get_stats_valuation(self.tinker)
df = df.set_index(df.columns[0])
new_df = pd.DataFrame(index=selected.keys()).fillna('na')
for idx in new_df.index:
try:
value = df.loc[idx, df.columns[0]]
except Exception as e:
value = 'N/A'
new_df.loc[idx, self.tinker] = value
# print(df.index)
return new_df
table.index = ticker_index
table1 = table[ratio_stat]
time.sleep(60 * 20)
table = pd.DataFrame()
tickers = ticker_index.copy()
new_index = []
retry_ticker = []
count = 0
for p in tickers:
# print(p)
try:
extra_ratio = si.get_stats_valuation(p)
extra_ratio = extra_ratio.iloc[:, 0:2]
extra_ratio.index = extra_ratio['Unnamed: 0']
extra_ratio = extra_ratio.drop(labels='Unnamed: 0', axis=1)
new_table = extra_ratio.T
table = table.append(new_table) #Table having Data about the company
new_index.append(p)
time.sleep(10)
except:
count = count + 1
print('Bad Ticker {}: {}'.format(count, p))
retry_ticker.append(p)
time.sleep(60 * 5)
if len(retry_ticker) > 0:
time.sleep(60 * 20)
def Stock():
page_bg_img = '''
<style>
body {
background-image: url("https://images.pexels.com/photos/1353938/pexels-photo-1353938.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=1000");
background-size: cover;
}
</style>
'''
st.markdown(page_bg_img, unsafe_allow_html=True)
symbols = 'https://raw.githubusercontent.com/Moly-malibu/Stocks/main/bxo_lmmS1.csv'
df = pd.read_csv(symbols)
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Financial Information</h1>",
unsafe_allow_html=True)
st.write("""
Financial information from the companies and Stocks by years!
""")
start = st.sidebar.date_input("Date to Analysis")
st.sidebar.subheader("Index")
tickerSymbol2 = st.sidebar.selectbox('Stocks by Company', (df['Symbol']))
tickerData = yf.Ticker(tickerSymbol2)
tickerDf = tickerData.history(period='id', start=start, end=None)
def get_symbol(symbol):
url = "http://d.yimg.com/autoc.finance.yahoo.com/autoc?query={}®ion=1&lang=en".format(
symbol)
result = requests.get(url).json()
for x in result['ResultSet']['Result']:
if x['symbol'] == symbol:
return x['name']
company_name = get_symbol(tickerSymbol2.upper())
st.write("""# Analysis of """, company_name)
company = yf.Ticker(tickerSymbol2)
# st.write(company.info)
company_general = st.sidebar.checkbox("Company Information")
if company_general:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>General Information</h1>",
unsafe_allow_html=True)
st.write('***Sector:*** ', company.info["sector"])
st.write('***Industry:*** ', company.info["industry"])
st.write('***Phone:*** ', company.info["phone"])
st.write('***Address: ***', company.info["address1"])
st.write('***City: ***', company.info["city"])
st.write('***Country: ***', company.info["country"])
st.write('***Web: ***', company.info["website"])
st.write('***Business Summary:*** ', '\n',
company.info["longBusinessSummary"])
st.write('***Job Generator***', company.info["fullTimeEmployees"])
company_hist = st.sidebar.checkbox("Company Shares Asigned")
if company_hist:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Company Shares Asigned </h1>",
unsafe_allow_html=True)
display_histo = company.major_holders
display_mh = company.history(period='max')
if display_histo.empty == True:
st.write("No data available")
else:
st.write(display_histo)
company_recomend = st.sidebar.checkbox("Stocks Recommendations")
if company_recomend:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Stocks Recommendatios</h1>",
unsafe_allow_html=True)
display_recomend = company.recommendations
if display_recomend.empty == True:
st.write("No data available")
else:
st.write(display_recomend)
company_job = st.sidebar.checkbox("Action and Split")
if company_job:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>History Actions and Split</h1>",
unsafe_allow_html=True)
data = {}
list = [(tickerSymbol2)]
for ticker in list:
ticker_object = yf.Ticker(ticker)
temp = pd.DataFrame.from_dict(ticker_object.info, orient='index')
temp.reset_index(inplace=True)
temp.columns = ['Attribute', 'Recent']
data[ticker] = temp
merge = pd.concat(data)
merge = merge.reset_index()
del merge['level_1']
merge.columns = ['Ticker', 'Attribute', 'Recent']
split = company.history(period='max', interval='1wk')
st.sidebar.checkbox("Stock level")
st.write('Company History', split)
company_stadistic = st.sidebar.checkbox("Statistics")
if company_stadistic:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Statistics</h1>",
unsafe_allow_html=True)
data = yf.download((tickerSymbol2),
start=start,
end=None,
group_by='tickers')
table = st.table(data.describe())
company_hist = st.sidebar.checkbox("Status of Evaluation")
if company_hist:
st.markdown(
"<h1 style='text-align: center; color: #002966;'>Status of Evaluation</h1>",
unsafe_allow_html=True)
display_evaluation = si.get_stats_valuation(tickerSymbol2)
if display_evaluation.empty == True:
st.write("No data available")
else:
st.write(display_evaluation)
import asyncio
import pandas
import yahoo_fin.stock_info as si
async def get_fundamental_indicators_for_company(config, company):
company.fundmantal_indicators = {}
# Statistics Valuation
keys = {
'Market Cap (intraday) 5': 'MarketCap',
'Price/Sales (ttm)': 'PS',
'Trailing P/E': 'PE',
'PEG Ratio (5 yr expected) 1': 'PEG',
'Price/Book (mrq)': 'PB'
}
data = si.get_stats_valuation(company.symbol)
get_data_item(company.fundmantal_indicators, data, keys)
# Income statement and Balance sheet
data = get_statatistics(company.symbol)
get_data_item(company.fundmantal_indicators, data,
{
'Profit Margin': 'ProfitMargin',
'Operating Margin (ttm)': 'OperMargin',
'Current Ratio (mrq)': 'CurrentRatio',
'Payout Ratio 4': 'DivPayoutRatio'
})
get_last_data_item(company.fundmantal_indicators, data,
{
'Return on assets': 'ROA',
def get_stats(self, ticker):
stats = yahoo.get_stats_valuation(ticker)
return (stats)
def my_valuation(ticker):
# Quote table
try:
key_attributes_quote = [
'PE Ratio (TTM)', 'Beta (5Y Monthly)', '1y Target Est',
'EPS (TTM)', 'Previous Close', 'Market Cap'
]
quote = si.get_quote_table(ticker)
attr_results = []
for attribute in key_attributes_quote:
attr_results.append(quote[attribute])
attr_results_df = pd.DataFrame([attr_results],
columns=key_attributes_quote)
attr_results_df['Ticker'] = ticker
# Stats table
stats_table = si.get_stats(ticker)
stats_table = stats_table.iloc[[22, 24, 46, 47, 48], :].transpose()
stats_table.columns = stats_table.iloc[0, :]
stats_table = stats_table.iloc[1, :]
stats_table = pd.DataFrame(stats_table)
stats_table = stats_table.transpose().reset_index(drop=True)
# Stats valuation
val = si.get_stats_valuation(ticker).transpose()
val.columns = val.iloc[0, :]
val = val.iloc[1, :]
val = pd.DataFrame(val)
val = val.transpose().reset_index(drop=True)
# Analysts recommendation
lhs_url = 'https://query2.finance.yahoo.com/v10/finance/quoteSummary/'
rhs_url = '?formatted=true&crumb=swg7qs5y9UP&lang=en-US®ion=US&' 'modules=upgradeDowngradeHistory,recommendationTrend,' 'financialData,earningsHistory,earningsTrend,industryTrend&' 'corsDomain=finance.yahoo.com'
url = lhs_url + ticker + rhs_url
headers = {
'User-Agent':
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
}
r = requests.get(url, headers=headers)
if not r.ok:
recommendation = 6
try:
result = r.json()['quoteSummary']['result'][0]
recommendation = result['financialData']['recommendationMean'][
'fmt']
except:
recommendation = 6
stats_table['Recommendation'] = recommendation
# Adding business summary
comp_ticker = yf.Ticker(ticker)
stats_table['Company Summary'] = comp_ticker.info[
'longBusinessSummary']
# join
horizontal_stack = pd.concat([stats_table, val, attr_results_df],
axis=1)
# Changing columns order
cols = horizontal_stack.columns.tolist()
cols = cols[-1:] + cols[:-1]
horizontal_stack = horizontal_stack[cols]
return horizontal_stack
except:
return pd.DataFrame()
import yahoo_fin.stock_info as si
import pandas as pd
quote = si.get_quote_table("aapl")
df = pd.DataFrame([quote])
print (df.T)
# get list of Dow tickers
dow_list = si.tickers_dow()
# Get data in the current column for each stock's valuation table
dow_stats = {}
for ticker in dow_list:
temp = si.get_stats_valuation(ticker)
temp = temp.iloc[:,:2]
temp.columns = ["Attribute", "Value"]
dow_stats[ticker] = temp
# combine all the stats valuation tables into a single data frame
combined_stats = pd.concat(dow_stats)
combined_stats = combined_stats.reset_index()
del combined_stats["level_1"]
# update column names
combined_stats.columns = ["Ticker", "Attribute", "Value"]
print(combined_stats)
dow_extra_stats = {}
for ticker in dow_list:
dow_extra_stats[ticker] = si.get_stats(ticker)
def getStatsValuation(ticker):
return si.get_stats_valuation(ticker)
import yahoo_fin.stock_info as si
import pandas as pd
import pprint
quote = si.get_quote_table("aapl")
quote["PE Ratio (TTM)"]
val = si.get_stats_valuation("aapl")
val = val.iloc[:,:2]
val.columns = ["Attribute", "Recent"]
val = float(val[val.Attribute.str.contains("Trailing P/E")].iloc[0,1])
pprint.pprint(quote)
if Company_name(symbol) == None:
print('Company not found')
print('')
col += 1
continue
print('Company: ' + Company_name(symbol))
print('Collecting Data: ', end = '')
start_time = time.time()
Balance_Sheet = si.get_balance_sheet(symbol)
Income_Statement = si.get_income_statement(symbol)
Cash_Flow = si.get_cash_flow(symbol)
Quote = si.get_quote_table(symbol)
Valuation_Stats = si.get_stats_valuation(symbol)
print(round(time.time() - start_time,2), 'seconds')
##column 2
print('Insert Data: ', end = '')
start_time = time.time()
total_assets = int(Balance_Sheet.loc[list(Balance_Sheet.iloc[:,0]).index('Total Assets')][1])
total_debt = int(Balance_Sheet.loc[list(Balance_Sheet.iloc[:,0]).index('Total Debt')][1])
try: ebitda_anual = int(Income_Statement.loc[list(Income_Statement.iloc[:,0]).index('Normalized EBITDA')][2])
except: ebitda_anual = 'Fail'
if ebitda_anual == 'Fail': Debt_Ebitda = 'Fail'
else: Debt_Ebitda = total_debt / ebitda_anual
try: operating_income = int(Income_Statement.loc[list(Income_Statement.iloc[:,0]).index('Operating Income')][1])
'What Percentage of Total Revenue Comes From that Country? (1=100%, and if its a developed country write 1):'
))
WAM = int(
input(
'What is the Weighted Average Maturity of Debt Found in 10k Report (if unsure write 5):'
))
print(int(input('Stock Based Compensation:')))
Ticker = input("Insert Ticker:")
quote = yf.get_quote_table(Ticker)
# indexing market cap
MarketCap = quote["Market Cap"]
# print market cap
beta = quote["Beta (5Y Monthly)"]
print('Market Cap:', "{:,}".format(conv_mrktcap(MarketCap)), '$')
print('Beta:', beta)
stats = yf.get_stats_valuation(Ticker)
Data = yf.get_data(Ticker)
Balance_Sheet = yf.get_balance_sheet(Ticker)
financials = yf.get_financials(Ticker)
analyst = yf.get_analysts_info(Ticker)
# import company's valuations as stats
income = yf.get_income_statement(Ticker)
Cash = yf.get_cash_flow(Ticker)
# import comapny's income statement as income
ebit = income.loc["ebit"]
# indexing ebit in icnome statement
ebit2020 = int(ebit["2020"])
# indexing latest ebit in income statement
print('Latest Calender Ebit:', "{:,}".format(ebit2020), "$")
def update_stats_valuation(self, symbol):
stats_valuation = si.get_stats_valuation(symbol).to_dict(orient="dict")
self.update(symbol, stats_valuation, 'Stocks', 'Stats_Valuation')
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import yahoo_fin.stock_info as si
df = pd.read_csv(r'C:/Users/91855/Desktop/oil and gas/oil.csv')
df
# In[ ]:
count = -1
for company in df['compsymbol']:
count = count + 1
Market_Cap = []
Enterprise_Value = []
EBITDA_LIST_VALUES = []
data_stats_valuation = si.get_stats_valuation(company)
market_cap = data_stats_valuation.iloc[0]
market_cap_final = market_cap[1]
Market_Cap.append(market_cap_final)
print('-----', company)
print('marktcp', Market_Cap)
enterprice_value = data_stats_valuation.iloc[1]
enterprice_value_final = enterprice_value[1]
Enterprise_Value.append(enterprice_value_final)
print('enterpval', Enterprise_Value)
mydata_stats = si.get_stats(company)
row_touse = mydata_stats.loc[mydata_stats['Attribute'] == 'EBITDA']
ebitda_row = row_touse.iloc[0]
final_ebitda = ebitda_row[1]
EBITDA_LIST_VALUES.append(final_ebitda)
print('eb', EBITDA_LIST_VALUES)
df = pd.read_csv(f'stock_data/{ticker}.csv',
index_col=0) # Load dataframe from csv file
moving_averages = [
150, 200
] #these numbers can be added more, 100 days, 150 days ....
for ma in moving_averages:
df['SMA_' + str(ma)] = round(
df['Adj Close'].rolling(window=ma).mean(), 2
) #Aggregate the values over a timeframe and apply mean function and round to 2 decimal places.
latest_price = df['Adj Close'][-1]
pe_ratio = float(si.get_quote_table(ticker)
['PE Ratio (TTM)']) # Trailing 12 Months (TTM)
# To add your own criteria use print(si.get_quote_table(ticker)) which will lead to a dictionary for all the available parameters.
peg_ratio = float(si.get_stats_valuation(ticker)[1][4])
# To add your own criteria use print(si.get_stats_valuation(ticker)) which will lead to all the available parameters.
moving_average_150 = df['SMA_150'][-1]
moving_average_200 = df['SMA_200'][-1]
low_52week = round(
min(df['Low'][-(52 * 5):]),
2) # 5 days in a week for 52 weeks, so get the lowest out of all.
high_52week = round(max(df['High'][-(52 * 5):]), 2)
score = round(best_performers[best_performers['Ticker'] == ticker]
['Score'].tolist()[0])
# get instance where the ticker symbol is ticker get the score value and then turn it to a list and pick the first value
condition_1 = latest_price > moving_average_150 > moving_average_200
condition_2 = latest_price >= (
1.1 * low_52week
