Некоторое время назад я создал программу для фундаментального анализа акций. В то время я вводил все значения вручную, что занимало много времени и очень раздражало, если значение вводилось неправильно. Теперь скрипт сам позаботится об этом. Иногда, когда API не завершен, скрипт выдает ошибку KeyError, которую я еще не нашел хорошего способа без попытки / за исключением каждого значения.
Однако я уверен, что этот сценарий можно значительно улучшить, и буду благодарен за любые отзывы. Спасибо Вам и хорошего дня 🙂
from time import strftime
from pyfiglet import figlet_format
import json, locale, requests
# Reference: https://rapidapi.com/apidojo/api/yahoo-finance1?endpoint=apiendpoint_2e0b16d4-a66b-469e-bc18-b60cec60661b
# https://finance.yahoo.com/quote/AMRN/financials?p=AMRN
print(figlet_format("Stock Fundamental Analysisn"))
print("Note: The Stock's numbers will be interpreted as Euro for all but american stocks")
print("Ticker symbol:")
ticker = input("> ")
url_base = "https://apidojo-yahoo-finance-v1.p.rapidapi.com/stock/v2/"
url_summary = url_base + "get-summary"
url_financials = url_base + "get-financials"
date = strftime("%Y-%m-%d")
time = strftime(" %H:%M:%S")
def get_json(url):
"""
:param url: url to the API endpoint
:return: json_data
"""
querystring = {f"symbol": {ticker}}
headers = {
'x-rapidapi-key': "0c8263e001msh672e94fe4861c32p1e61f0jsn0746fab04762",
'x-rapidapi-host': "apidojo-yahoo-finance-v1.p.rapidapi.com"
}
response = requests.request("GET", url, headers=headers, params=querystring)
j_data = json.loads(response.text)
return j_data
def get_currency(currency):
if currency == "USD":
locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')
else:
locale.setlocale(locale.LC_ALL, '')
def ask_yes_no(prompt):
"""
:param prompt: The question to be asked
:return: Value to check
"""
while True:
answer = input(prompt + " (y/n): ")
if answer == "y":
return True
elif answer == "n":
return False
print("Please only enter y or n.")
def check_partners_trends():
# Check Partners
if ask_yes_no("Does it have big partners?"):
partners = input("Who? ")
else:
partners = "none"
# Check Trend
if ask_yes_no("Does it participate in any current trends?"):
trends = input("Which? ")
else:
trends = "none"
return partners, trends
def check_age_forecast_commodity():
# Check age
if ask_yes_no("Is the company older than 10 years?"):
old = True
else:
old = False
# Check forecast
if ask_yes_no("Do you still see the company around in 10 years?"):
future = True
else:
future = False
# Check commodity reliance
if ask_yes_no("Is the company distinguishable from others/ Does it have an economic moat?"):
distinguish = True
else:
distinguish = False
return old, future, distinguish
partner, trend = check_partners_trends()
category = input("Category according to Peter Lynch: ")
age, forecast, moat = check_age_forecast_commodity()
print("Receiving data from Yahoo...")
###############################
""" --- Get-Summary --- """ ##
###############################
json_data = get_json(url_summary)
defaultKeyStatistics = json_data['defaultKeyStatistics']
profit_margin = defaultKeyStatistics['profitMargins']['fmt']
eps = defaultKeyStatistics['forwardEps']['raw']
book_value = defaultKeyStatistics['bookValue']['raw']
try:
price_to_book = round(defaultKeyStatistics['priceToBook']['raw'], 2)
except KeyError:
price_to_book = 0
peg_ratio = defaultKeyStatistics['pegRatio']['fmt']
shares_outstanding = defaultKeyStatistics['sharesOutstanding']['raw']
shares_short = defaultKeyStatistics['sharesShort']['raw']
short_ratio = defaultKeyStatistics['shortRatio']['fmt'] + "%"
summary_profile = json_data['summaryProfile']
sector = summary_profile['sector']
country = summary_profile['country']
city = summary_profile['city']
website = summary_profile['website']
industry = summary_profile['industry']
price = json_data['price']
market_cap = price['marketCap']['raw']
current_price = price['regularMarketPrice']['raw']
name = price['longName']
holders = json_data['majorHoldersBreakdown']
insiders_percentage = holders['insidersPercentHeld']['fmt']
institutions_percentage = holders['institutionsPercentHeld']['fmt']
institutions_float_percentage = holders['institutionsFloatPercentHeld']['fmt']
institutions_count = holders['institutionsCount']['fmt']
financialData = json_data['financialData']
currency = financialData['financialCurrency']
get_currency(currency)
gross_margin = financialData['grossMargins']['raw']
revenue_growth = financialData['revenueGrowth']['fmt']
operating_margins = financialData['operatingMargins']['fmt']
ebitda = financialData['ebitda']['fmt']
current_ratio = financialData['currentRatio']['raw']
return_on_assets = financialData['returnOnAssets']['raw']
debt_to_equity = financialData['debtToEquity']['fmt']
return_on_equity = financialData['returnOnEquity']['fmt']
total_cash_per_share = financialData['totalCashPerShare']['fmt']
quick_ratio = financialData['quickRatio']['fmt']
total_debt = financialData['totalDebt']['fmt']
##################################
""" --- Get-Financials --- """ ##
##################################
json_data = get_json(url_financials)
# Income Statement
def special_income():
try:
research = json_data['incomeStatementHistory']['incomeStatementHistory'][0]['researchDevelopment']['fmt']
except KeyError:
research = 0
return research
income = json_data['incomeStatementHistory']['incomeStatementHistory']
total_rev = income[0]['totalRevenue']['raw']
last_rev = income[1]['totalRevenue']['raw']
gross_profit = income[0]['grossProfit']['raw']
last_profit = income[1]['grossProfit']['raw']
operating_income = income[0]['operatingIncome']['raw']
operating_expenses = income[0]['totalOperatingExpenses']['raw']
interest_expenses = income[0]['interestExpense']['fmt']
cost_of_revenue = income[0]['costOfRevenue']['raw']
net_income = income[0]['netIncome']['raw']
last_income = income[1]['netIncome']['raw']
ebit = income[0]['ebit']['raw']
research = special_income()
# Balance Sheet
def special_balance():
try:
intangible_assets = json_data['balanceSheetHistory']['balanceSheetStatements'][0]['intangibleAssets']['fmt']
except KeyError:
intangible_assets = 0
try:
capital_surplus = json_data['balanceSheetHistory']['balanceSheetStatements'][0]['capitalSurplus']['fmt']
except KeyError:
capital_surplus = 0
return intangible_assets, capital_surplus
balance = json_data['balanceSheetHistory']['balanceSheetStatements']
total_liablities = balance[0]['totalLiab']['raw']
last_liabilities = balance[1]['totalLiab']['raw']
current_liabilities = balance[0]['totalCurrentLiabilities']['raw']
stockholders_equity = balance[0]['totalStockholderEquity']['raw']
last_stockholders = balance[1]['totalStockholderEquity']['raw']
total_assets = balance[0]['totalAssets']['raw']
last_assets = balance[1]['totalAssets']['raw']
current_assets = balance[0]['totalCurrentAssets']['raw']
long_term_debt = balance[0]['longTermDebt']['raw']
last_long_term = balance[1]['longTermDebt']['raw']
short_term_debt = balance[0]['shortLongTermDebt']['raw']
cash = balance[0]['cash']['raw']
inventory = balance[0]['inventory']['fmt']
net_ppe = balance[0]['propertyPlantEquipment']['fmt']
intangible_assets, capital_surplus = special_balance()
# Cash Flow Statement
def special_cash():
try:
repurchase_stock = json_data['cashflowStatementHistory']['cashflowStatements'][0]['repurchaseOfStock']['fmt']
except KeyError:
repurchase_stock = 0
try:
capital_expenditure = json_data['cashflowStatementHistory']['cashflowStatements'][0]['capitalExpenditures']['raw']
except KeyError:
capital_expenditure = 0
return repurchase_stock, capital_expenditure
cashflow = json_data['cashflowStatementHistory']['cashflowStatements']
investing_cash = cashflow[0]['totalCashflowsFromInvestingActivities']['fmt']
financing_cash = cashflow[0]['totalCashFromFinancingActivities']['fmt']
operating_cash = cashflow[0]['totalCashFromOperatingActivities']['raw']
shares_issued = cashflow[0]['issuanceOfStock']['raw']
last_shares = cashflow[1]['issuanceOfStock']['raw']
depreciation = cashflow[0]['depreciation']['raw']
change_cash = cashflow[0]['changeInCash']['raw']
repurchase_stock, capital_expenditure = special_cash()
shares_increased = True if (shares_issued > last_shares) else False
more_cash = True if (cash > long_term_debt) else False
debt_decrease = True if (long_term_debt < last_long_term) else False
asset_increase = True if (total_assets > last_assets) else False
liabilities_increase = True if (total_liablities > last_liabilities) else False
income_increase = True if (net_income > last_income) else False
last_margin = (last_profit / last_rev * 100)
gross_increase = True if (gross_margin > last_margin) else False
net_cash = operating_cash - capital_expenditure
working_capital = current_assets - current_liabilities
return_on_capital_employed = ebit / (total_assets - current_liabilities)
rcoe_to_price = return_on_capital_employed * current_price
company_valuation = round((market_cap / total_rev), 2)
last_current = last_assets / last_liabilities
last_return_on_assets = last_income / last_assets
total_debt_to_total_assets="{0:.2f}%".format((current_liabilities + long_term_debt) / total_assets)
print("Analyzing data...")
def analyze():
# Income analysis
income_red_list = ["n[+] Income statement red flags: n"]
income_green_list = ["n[+] Income statement green flags: n"]
income_red = 0
if income_increase: income_green_list.append("n[->] Income increased year over year.n")
if gross_increase: income_green_list.append(['n[->] The gross margin increased year over year.n'])
if net_income < last_income:
income_red += 1
income_red_list.append("n[!] Smaller net income than last year!")
if operating_income < 0:
income_red += 1
income_red_list.append(
"n[!] The company doesn't generate cash from operations! Negative operative income.")
if operating_expenses > total_rev:
income_red += 1
income_red_list.append(
"n[!] Operating expenses are more expensive than the produced revenue!")
if cost_of_revenue > gross_profit:
income_red += 1
income_red_list.append(
"n[!] Cost of revenue higher than gross profit! The product costs more than it produces.")
# Balance analysis
balance_list = ["[+] Balance sheet red flags: n"]
balance_red = 0
if not asset_increase:
balance_red += 1
balance_list.append("n[!] Assets are decreasing!")
if not debt_decrease:
balance_red += 1
balance_list.append("n[!] Long term debt is increasing!")
if not more_cash:
balance_red += 1
balance_list.append("n[!] The company has less cash than debt!")
if liabilities_increase:
balance_red += 1
balance_list.append("n[!] The liabilities are increasing!")
if shares_increased:
balance_red += 1
balance_list.append("n[!] Number of issued shares increased! Company may finance itself via public offerings.")
if stockholders_equity < 0:
balance_red += 1
balance_list.append("n[!] Stockholders equity is negative! Liabilities are growing faster than assets.")
# Cash flow analysis
cash_list = ["[+] Cash flow red flags: n"]
cash_red = 0
if net_cash < 0:
cash_red += 1
cash_list.append("n[!] Negative Net Cash Position.")
if operating_cash < 0:
cash_red += 1
cash_list.append("n[!] Negative operating cash flow.")
if working_capital < 0:
cash_red += 1
cash_list.append(
"n[!] Working capital negative! The company took on more debt or sold something to generate more money")
if change_cash < 0:
cash_red += 1
cash_list.append(
"n[!] Negative net change in cash! Find out why, did the company buy something big?")
if operating_cash < 0:
cash_red += 1
cash_list.append(
"n[!] Negative operating cash flow! Company is operating at a loss.")
# Intrinsic value
intrinsic_value = 0
total_red = income_red + balance_red + cash_red
if price_to_book < 1.5: intrinsic_value += 1
if book_value > 0: intrinsic_value += 1
if age: intrinsic_value += 1
if forecast: intrinsic_value += 1
if moat: intrinsic_value += 1
if (net_income / stockholders_equity * 100) > 0.1: intrinsic_value += 1
if (total_liablities / stockholders_equity * 100) < 1: intrinsic_value += 1
if net_cash > 0: intrinsic_value += 1
if total_red < 1: intrinsic_value += 1
# Piotroski-F score -> max 8
piotroski = 0
# Profitability criteria
if int(return_on_assets) > 0: piotroski += 1
if operating_cash > 0: piotroski += 1
if return_on_assets > last_return_on_assets: piotroski += 1
if operating_cash > return_on_assets: piotroski += 1
# Leverage, Luquidity and Source of Funds Criteria
if debt_decrease: piotroski += 1
if current_ratio > last_current: piotroski += 1
if not shares_increased: piotroski += 1
if stockholders_equity > last_stockholders: piotroski += 1
# Operating efficiency criteria
if gross_increase: piotroski += 1
return income_red, income_red_list, income_green_list, balance_list, balance_red, cash_list, cash_red, total_red, intrinsic_value, piotroski
income_red, income_red_list, income_green_list, balance_list, balance_red, cash_list, cash_red, total_red, intrinsic_value, piotroski = analyze()
print("Creating report...")
def create_report():
def convert_numbers():
json_data = get_json(url_financials)
mc = json_data['summaryDetail']['marketCap']['fmt']
tr = json_data['incomeStatementHistory']['incomeStatementHistory'][0]['totalRevenue']['fmt']
cr = json_data['incomeStatementHistory']['incomeStatementHistory'][0]['costOfRevenue']['fmt']
ni = json_data['incomeStatementHistory']['incomeStatementHistory'][0]['netIncome']['fmt']
gp = json_data['incomeStatementHistory']['incomeStatementHistory'][0]['grossProfit']['fmt']
oi = json_data['incomeStatementHistory']['incomeStatementHistory'][0]['operatingIncome']['fmt']
oe = json_data['incomeStatementHistory']['incomeStatementHistory'][0]['totalOperatingExpenses']['fmt']
ebi = json_data['incomeStatementHistory']['incomeStatementHistory'][0]['ebit']['fmt']
ta = json_data['balanceSheetHistory']['balanceSheetStatements'][0]['totalAssets']['fmt']
ca = json_data['balanceSheetHistory']['balanceSheetStatements'][0]['totalCurrentAssets']['fmt']
dep = json_data['cashflowStatementHistory']['cashflowStatements'][0]['depreciation']['fmt']
tl = json_data['balanceSheetHistory']['balanceSheetStatements'][0]['totalLiab']['fmt']
cl = json_data['balanceSheetHistory']['balanceSheetStatements'][0]['totalCurrentLiabilities']['fmt']
ltd = json_data['balanceSheetHistory']['balanceSheetStatements'][0]['longTermDebt']['fmt']
oc = json_data['cashflowStatementHistory']['cashflowStatements'][0]['totalCashFromOperatingActivities']['fmt']
se = json_data['balanceSheetHistory']['balanceSheetStatements'][0]['totalStockholderEquity']['fmt']
ce = json_data['cashflowStatementHistory']['cashflowStatements'][0]['capitalExpenditures']['fmt']
return mc, tr, cr, ni, gp, oi, oe, ebi, ta, ca, dep, tl, cl, ltd, oc, se, ce
market_cap, total_rev, cost_of_revenue, net_income, gross_profit,
operating_income, operating_expenses, ebit, total_assets, current_assets,
depreciation, total_liablities, current_liabilities, long_term_debt,
operating_cash, stockholders_equity, capital_expenditure = convert_numbers()
with open(f"{name}.txt", "a") as file:
file.write(figlet_format(name) + "nn")
file.write(f"Date of evaluation: {date}nTime: {time}n")
file.write("nn________________________________________ntt######################ntt## General Overview ##ntt######################n________________________________________nn")
def general_overview():
file.write("# General informationnn")
file.write(
f"{name} is situated in: {country} in {city}n- Sector: {sector}n- Industry: {industry}n- Company website: {website}n- Company partners: {partner}n- Participating in the trend: {trend}nn")
file.write("# Valuationn")
file.write(f"- Current price: %sn- EPS: {eps}tt- PE/G: {peg_ratio}n" % locale.currency(current_price,
grouping=True))
file.write(
f"- Market capitalization: {market_cap}n- [+] Valued at: %sx of the total revenuenn" % company_valuation)
file.write("# Stockholdersn")
file.write(
f"- Insiders holding: {insiders_percentage}n- Institutions holding: {institutions_percentage}n- Float owned by institutions: {institutions_float_percentage}n- Number of holding institutions: {institutions_count}n- Shares held short: {short_ratio}nn")
file.write("# Financial Datan")
file.write(f"- Profit margin: {profit_margin}n")
file.write(f"- Book value: %sn- Price to book value: {price_to_book}n" % locale.currency(book_value,
grouping=True))
file.write("- Gross margin: " + '{0:.2f}%'.format(gross_margin) + "n")
file.write(f"- Revenue growth: {revenue_growth}n- Operating margins: {operating_margins}n")
file.write("- Return on assets: " + '{0:.2f}%'.format(return_on_assets) + "n")
file.write(f"- Return on equity: {return_on_equity}n- Debt to equity ratio: {debt_to_equity}" + "%n")
file.write(f"- Total debt to total assets ratio: {total_debt_to_total_assets}n- Total cash per share: {total_cash_per_share} {currency}n")
file.write(f"- Quick ratio: {quick_ratio}n")
file.write("- Return on capital employed: " + '{0:.2f}%'.format(return_on_capital_employed) + "n")
file.write("- Return on capital employed to price: %sn" % locale.currency(rcoe_to_price, grouping=True))
def income_statement():
file.write(
"nn________________________________________ntt######################ntt## Income Statement ##ntt######################n________________________________________nn")
file.write(
f"- Total revenue: {total_rev}tt- Cost of revenue: {cost_of_revenue}n- Net income: {net_income}ttt- Gross profit: {gross_profit}n")
file.write(f"- Operating income: {operating_income}tt- Operating expenses: {operating_expenses}n")
file.write(f"- EBIT: {ebit}ttttt- EBITDA: {ebitda}n")
file.write(f"- Money spent on research: {research}n- Interest expenses: {interest_expenses}nnn")
file.write("# Income analysis")
for item in income_green_list:
file.write(item)
for item in income_red_list:
file.write(item)
file.write(f"nn[+] Total income red flags: {income_red}nnn")
def balance_sheet():
file.write(
"_____________________________________ntt#####################ntt### Balance sheet ###ntt#####################n_____________________________________nn")
file.write("# Assets that be liquidated within one yearn")
file.write(
f"- Total assets: {total_assets}n- Current assets: {current_assets}n- Inventory value: {inventory}nn")
file.write("# Non-current assets -> require more time for liquidationn")
file.write(
f"- Net PPE: {net_ppe}n- Depreciation: {depreciation}n- Intangible assets: {intangible_assets}nn")
file.write("# Liabilitiesn")
file.write(
f"- Total liabilities: {total_liablities}n- Current liabilities/short term debt: {current_liabilities}n- Long term debt: {long_term_debt}n- Short long term debt: {short_term_debt}n- Total debt: {total_debt}nn")
file.write("# Additional informationn")
file.write(f"- Stockholders equity: {stockholders_equity}n- Current ratio: {current_ratio}nn")
file.write("# Balance analysisn")
for item in balance_list:
file.write(item)
file.write(f"nn[+] Total balance red flags: {balance_red}nnn")
def cash_flow():
file.write("_____________________________________ntt###################ntt#### Cash flow ####ntt###################n_____________________________________nn")
file.write(f"- Operating cash flow: {operating_cash}n- Investing cash flow: {investing_cash}n- Financing cash flow: {financing_cash}n")
file.write(f"- Free cash: %sn- Capital expenditure: {capital_expenditure}n- Stock repurchased: {repurchase_stock}nn" % locale.currency(net_cash, grouping=True))
file.write("# Cash analysisn")
for item in cash_list:
file.write(item)
file.write(f"nn[+] Total cash red flags: {cash_red}nnn")
def evaluation():
file.write(
"_____________________________________ntt####################ntt# Final Evaluation #ntt####################n_____________________________________nn")
file.write("# Intrinsic valuen")
file.write(f"- Total red flags: {total_red}n- Intrinsic value score: {intrinsic_value}/8n- Piotroski score: {piotroski}/8n")
def analysis():
if intrinsic_value <= 3:
file.write(
"nn[+] Fundamental Analysis: High risk!nt[=>] Be careful investing into this company and make sure to check the "
"financial statements andnttcompany story again properly. Further research recommended!")
elif intrinsic_value == 4 or 5 or 6:
file.write(
"nn[+] Fundamental Analysis: Medium risk.nt[=>] This company could be turning a profit but for safety reasons, "
"please check the financial statements,nttred flags and other facts again, to be sure that nothing is "
"inadvertently overlooked")
elif intrinsic_value >= 7:
file.write(
"nn[+] Fundamental Analysis: Low risk.nt[=>] It's unlikely that the company will go bankrupt in the foreseeable "
"future.")
def glossary():
file.write("nnn_____________________________________ntt###################ntt#### Glossary ####ntt###################n_____________________________________n")
file.write("[+] EPS:n- indicates how much money a company makes for each share of its stock, and is a widely used metric to estimate corporate valuen- A higher EPS indicates greater value because investors will pay more for a company's shares if they think the company has higher profits relative to its share pricenn")
file.write("[+] PE/G:n- considered to be an indicator of a stock's true value, and similar to the P/E ratio, a lower PEG may indicate that a stock is undervalued.nn")
file.write("[+] Market capitalization:n- defined as the total market value of all outstanding shares.n Companies are typically divided according to market capitalization: large-cap ($10 billion or more), mid-cap ($2 billion to $10 billion), and small-cap ($300 million to $2 billion.nn")
file.write("[+] Holdings:n- Who owns the company. The more insiders are holding shares of their own company, the more interested they are in a good performance.nn")
file.write("[+] Profit margin:n- indicates how many cents of profit has been generated for each dollar of sale.n- As typical profit margins vary by industry sector, care should be taken when comparing the figures for different businesses.nn")
file.write("[+] Book value:n- the difference in value between that company's total assets and total liabilities on its balance sheet.n- Traditionally, a P/B less than 1.0 is considered a good value, but it can be difficult to pinpoint a 'good' P/B ratio since it can vary by industry and any particular company may have underlying financial troubles.nn")
file.write("[+] Price to book value:n- Investors use the price-to-book value to gauge whether a company's stock price is valued properly.n- A price-to-book ratio of one means that the stock price is trading in line with the book value of the company.n- A P/B ratio with lower values, particularly those below one, are a signal to investors that a stock may be undervalued.nn")
file.write("[+] Gross margins:n- shows the amount of profit made before deducting selling, general, and administrative costs.nn")
file.write("[+] Revenue growth:n- measures the increase in a firm's sales from one year to another.nFor an accurate picture of growth, investors should look at the growth of several quarters and how consistent it is.nn")
file.write("[+] Operating margins:n- the profit a company makes on a dollar of sales after paying for variable costs but before paying any interest or taxes.n- Operating margin is a profitability ratio that shows how much profit a company makes from its core operations in relation to the total revenues it brings in.n- An increasing operating margin over a period of time indicates a company whose profitability is improving.nn")
file.write("[+] Return on assets:n- an indicator of how well a company utilizes its assets, by determining how profitable a company is relative to its total assets.n- best used when comparing similar companies or comparing a company to its previous performance.n- takes a company's debt into account.nn")
file.write("[+] Return on equity:n- measures how the profitability of a corporation in relation to stockholders’ equity.n- As a shortcut, investors can consider an ROE near the long-term average of the S&P 500 (14%) as an acceptable ratio and anything less than 10% as poor.nn")
file.write("[+] Debt to equity ratio:n- compares a company’s total liabilities to its shareholder equity and can be used to evaluate how much leverage a company is using.n- Higher leverage ratios tend to indicate a company or stock with higher risk to shareholders.nn")
file.write("[+] Total debt to total assets ratio:n- shows the degree to which a company has used debt to finance its assets.n- If a company has a total-debt-to-total-assets ratio of 0.4, 40% of its assets are financed by creditors, and 60% are financed by owners (shareholders) equity.nn")
file.write("[+] Total cash per share:n- tells us the percentage of a company’s share price available to spend on strengthening the business, paying down debt, returning money to shareholders, and other positive campaigns.n- Paradoxically, too much cash per share can be a negative indicator of a company's health, because it may suggest an unwillingness by management to nurture forward-thinking measures.nn")
file.write("[+] Quick ratio:n- indicates a company's capacity to pay its current liabilities without needing to sell its inventory or get additional financing.n- The higher the ratio result, the better a company's liquidity and financial health; the lower the ratio, the more likely the company will struggle with paying debts.nn")
file.write("[+] Return on capital employed:n- measures a company’s profitability in terms of all of its capital.n- can be used when analyzing a company’s financials for profitability performance.n- reflects a company's ability to earn a return on all of the capital it employs.nn")
file.write("[+] Return on capital employed to price:n- Amount of money the company is generating per one share at the current price.nn")
file.write("[+] EBIT:n- earnings before interest and taxesn- a company's net income before income tax expense and interest expenses are deducted.n- used to analyze the performance of a company's core operations without the costs of the capital structure and tax expenses impacting profit.n- takes a company's cost of manufacturing including raw materials and total operating expenses, which include employee wages, into consideration.nn")
file.write("[+] EBITDA:n- a good measure of core profit trends because it eliminates some extraneous factors and allows a more 'apples-to-apples' comparisons.n- can be used as a shortcut to estimate the cash flow available to pay the debt of long-term assets.nn")
file.write("[+] Net PPE:n- Property, plant, and equipment are also called fixed assets, meaning they are physical assets that a company cannot easily liquidate.n- long-term assets vital to business operations and the long-term financial health of a company.n- Purchases of PP&E are a signal that management has faith in the long-term outlook and profitability of its company.nn")
file.write("[+] Depreciation:n- Wear and Tear on the assets.n- represents how much of an asset's value has been used up.n- Depreciating assets helps companies earn revenue from an asset while expensing a portion of its cost each year the asset is in use.nn")
file.write("[+] Intangible assets:n- an asset that is not physical in nature. Goodwill, brand recognition and intellectual property, such as patents, trademarks, and copyrights.n- Intangible assets exist in opposition to tangible assets, which include land, vehicles, equipment, and inventory.nn")
file.write("[+] Long term debt:n- A loan that the company took to finance its operations.n- debt that matures in more than one year and is often treated differently from short-term debt.n- Entities choose to issue long-term debt with various considerations, primarily focusing on the timeframe for repayment and interest to be paid.nn")
file.write("[+] Stockholder's Equity:n- refers to the assets remaining in a business once all liabilities have been settled.n- A negative stockholders' equity may indicate an impending bankruptcy.nn")
file.write("[+] Current ratio:n- compares all of a company’s current assets to its current liabilities. These are usually defined as assets that are cash or will be turned into cash in a year or less, and liabilities that will be paid in a year or less.n- sometimes referred to as the “working capital” ratio and helps investors understand more about a company’s ability to cover its short-term debt with its current assets.n- A ratio under 1 indicates that the company’s debts due in a year or less are greater than its assets nn")
file.write("[+] Free Cash Flow:n- the money a company has left over after paying its operating expenses and capital expenditures.n- The more free cash flow a company has, the more it can allocate to dividends, paying down debt, and growth opportunities.n- If a company has a decreasing free cash flow, that is not necessarily bad if the company is investing in its growth.nn")
file.write("[+] Capital expenditure:n- funds used by a company to acquire, upgrade, and maintain physical assets such as property, plants, buildings, technology, or equipment.n- can include repairing a roof, purchasing a piece of equipment, or building a new factory.nn")
file.write("[+] Intrinsic value:n- Evaluates some key fundamental data to determine the company's health.n")
file.write("[+] Piotroski Score:n- incorporates eight factors that speak to a firm's financial strength.n- aspects are based on accounting results over a number of years; a point is awarded each time a standard is met, resulting in an overall score.n- A favorite metric used to judge value stocks.n- Used to determine the strength of a firms financial positionnn")
general_overview()
income_statement()
balance_sheet()
cash_flow()
evaluation()
analysis()
glossary()
create_report()
print("Done.n")
print("The intrinsic value score is: " + str(intrinsic_value) + "/9n")
print("The Piotroski F score is: " + str(piotroski) + "/8n")
print("A report has been generated. Please check the same directory this program is located innThank you for using the Stock analysis tool.")
1 ответ
Небольшие заметки:
Вы уверены, что это правильно:
querystring = {f"symbol": {ticker}}? Похоже, ты либо хотелquerystring = {"symbol": ticker}илиquerystring = f"symbol={ticker}", но не эту комбинацию двух.get_currencyможно упростить с помощью тернарного выражения:def get_currency(currency): locale.setlocale(locale.LC_ALL, 'en_US.UTF-8' if currency == "USD" else '')Ваш
check_*функции можно упростить, напрямую используя логический результат:def check_age_forecast_commodity(): # Check age old = ask_yes_no("Is the company older than 10 years?") # Check forecast future = ask_yes_no("Do you still see the company around in 10 years?") # Check commodity reliance distinguish = ask_yes_no("Is the company distinguishable from others/ Does it have an economic moat?") return old, future, distinguishТочно так же здесь вам не нужны тернарные выражения, вы можете напрямую использовать логические результаты:
shares_increased = shares_issued > last_sharesУ Python есть официальное руководство по стилю, PEP8. Он рекомендует всегда помещать код в новую строку при запуске нового блока, поэтому не используйте это:
if price_to_book < 1.5: intrinsic_value += 1Но это:
if price_to_book < 1.5: intrinsic_value += 1Вместо того, чтобы иметь много
file.writeвызовы, сначала рассмотрите возможность создания списка (или любого итеративного) строк, а затем просто используйтеfile.writelines(lines)вместо. Обратите внимание, что строки все еще нуждаются в финальномn, иначе они фактически не превратятся в несколько строк. Это также позволяет вам отделить получение содержимого от фактической записи файла, позволяя вместо этого распечатать его на терминале, отправить по электронной почте или делать с ним все, что вы можете захотеть сделать в будущем.