雅虎股票分析漏报数据

Question

不久前，我创建了一个对股票进行基本分析的程序。那时，我经常用手输入所有的值，这花费了很多时间，如果输入的值不正确，这是非常恼人的。现在，脚本自己处理了其中的大部分问题。有时，当API不完整时，脚本抛出一个KeyError，除了每个值之外，我还没有找到一个很好的方法。

不过，我确信，这个脚本可以得到很大的改进，并将感谢任何反馈。谢谢你，祝你今天愉快:)

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 Analysis\n"))
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': "API-KEY",
        '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) + "\n\n")
        file.write(f"Date of evaluation: {date}\nTime: {time}\n")
        file.write("\n\n________________________________________\n\t\t######################\n\t\t## General Overview ##\n\t\t######################\n________________________________________\n\n")

        def general_overview():
            file.write("# General information\n\n")
            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}\n\n")

            file.write("# Valuation\n")
            file.write(f"- Current price: %s\n- EPS: {eps}\t\t- 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 revenue\n\n" % company_valuation)

            file.write("# Stockholders\n")
            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}\n\n")

            file.write("# Financial Data\n")
            file.write(f"- Profit margin: {profit_margin}\n")
            file.write(f"- Book value: %s\n- 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: %s\n" % locale.currency(rcoe_to_price, grouping=True))

        def income_statement():
            file.write(
                "\n\n________________________________________\n\t\t######################\n\t\t## Income Statement ##\n\t\t######################\n________________________________________\n\n")
            file.write(
                f"- Total revenue: {total_rev}\t\t- Cost of revenue: {cost_of_revenue}\n- Net income: {net_income}\t\t\t- Gross profit: {gross_profit}\n")
            file.write(f"- Operating income: {operating_income}\t\t- Operating expenses: {operating_expenses}\n")
            file.write(f"- EBIT: {ebit}\t\t\t\t\t- EBITDA: {ebitda}\n")
            file.write(f"- Money spent on research: {research}\n- Interest expenses: {interest_expenses}\n\n\n")

            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"\n\n[+] Total income red flags: {income_red}\n\n\n")

        def balance_sheet():
            file.write(
                "_____________________________________\n\t\t#####################\n\t\t### Balance sheet ###\n\t\t#####################\n_____________________________________\n\n")
            file.write("# Assets that be liquidated within one year\n")
            file.write(
                f"- Total assets: {total_assets}\n- Current assets: {current_assets}\n- Inventory value: {inventory}\n\n")

            file.write("# Non-current assets -> require more time for liquidation\n")
            file.write(
                f"- Net PPE: {net_ppe}\n- Depreciation: {depreciation}\n- Intangible assets: {intangible_assets}\n\n")

            file.write("# Liabilities\n")
            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}\n\n")

            file.write("# Additional information\n")
            file.write(f"- Stockholders equity: {stockholders_equity}\n- Current ratio: {current_ratio}\n\n")

            file.write("# Balance analysis\n")
            for item in balance_list:
                file.write(item)
            file.write(f"\n\n[+] Total balance red flags: {balance_red}\n\n\n")

        def cash_flow():
            file.write("_____________________________________\n\t\t###################\n\t\t#### Cash flow ####\n\t\t###################\n_____________________________________\n\n")
            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: %s\n- Capital expenditure: {capital_expenditure}\n- Stock repurchased: {repurchase_stock}\n\n" % locale.currency(net_cash, grouping=True))

            file.write("# Cash analysis\n")
            for item in cash_list:
                file.write(item)
            file.write(f"\n\n[+] Total cash red flags: {cash_red}\n\n\n")

        def evaluation():
            file.write(
                "_____________________________________\n\t\t####################\n\t\t# Final Evaluation #\n\t\t####################\n_____________________________________\n\n")
            file.write("# Intrinsic value\n")
            file.write(f"- Total red flags: {total_red}\n- Intrinsic value score: {intrinsic_value}/8\n- Piotroski score: {piotroski}/8\n")

        def analysis():
            if intrinsic_value <= 3:
                file.write(
                    "\n\n[+] Fundamental Analysis: High risk!\n\t[=>] Be careful investing into this company and make sure to check the "
                    "financial statements and\n\t\tcompany story again properly. Further research recommended!")
            elif intrinsic_value == 4 or 5 or 6:
                file.write(
                    "\n\n[+] Fundamental Analysis: Medium risk.\n\t[=>] This company could be turning a profit but for safety reasons, "
                    "please check the financial statements,\n\t\tred flags and other facts again, to be sure that nothing is "
                    "inadvertently overlooked")
            elif intrinsic_value >= 7:
                file.write(
                    "\n\n[+] Fundamental Analysis: Low risk.\n\t[=>] It's unlikely that the company will go bankrupt in the foreseeable "
                    "future.")

        def glossary():
            file.write("\n\n\n_____________________________________\n\t\t###################\n\t\t#### Glossary ####\n\t\t###################\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 value\n- 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 price\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            file.write("[+] Gross margins:\n- shows the amount of profit made before deducting selling, general, and administrative costs.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            file.write("[+] Return on capital employed to price:\n- Amount of money the company is generating per one share at the current price.\n\n")
            file.write("[+] EBIT:\n- earnings before interest and taxes\n- 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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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.\n\n")
            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 \n\n")
            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.\n\n")
            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.\n\n")

            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 position\n\n")

        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) + "/9\n")
print("The Piotroski F score is: " + str(piotroski) + "/8\n")
print("A report has been generated. Please check the same directory this program is located in\nThank you for using the Stock analysis tool.")

编辑:我尝试使用for循环解决KeyError：

item_list = ['forwardEPS', 'bookValue']

for item in item_list:
    try:
        item = defaultKeyStatistics[item]['raw']
    except KeyError:
        item = 0

但我想不出如何用相应的值(例如forwardEps = 10，bookValue = 5)返回变量，因为它总是返回最后一个项，以项作为变量名(例如item = 5)。

Answer 1

只是一些简短的笔记：

• 你确定这是正确的：querystring = {f"symbol": {ticker}}吗？看起来你要么想要querystring = {"symbol": ticker}，要么想要querystring = f"symbol={ticker}"，而不是两者的结合。
• 可以使用三元表达式对get_currency进行简化: def get_currency(货币)：locale.setlocale(locale.LC_ALL，'en_US.UTF-8‘，如果货币==为“locale.LC_ALL”)
• 您的check_*函数可以通过直接使用布尔结果来简化: def check_age_forecast_commodity()：# Check age old =ask_yes_no(“公司的年龄在10年以上吗？”)# Check预测未来=ask_yes_no(“您在10年内还能看到公司吗？”)#检查商品依赖区分=ask_yes_no(“该公司与其他公司有区别吗/它有经济护城河吗？”)回归旧的、未来的、与众不同的
• 类似地，这里不需要三元表达式，可以直接使用布尔结果: shares_increased = shares_issued > last_shares
• Python有一个正式的风格指南，PEP8。它建议在启动新块时始终将代码放在新行上，因此不要使用以下代码: if price_to_book < 1.5: intrinsic_value += 1，但这个: if price_to_book < 1.5: intrinsic_value += 1
• 与其有许多file.write调用，不如先构建一个行列表(或任何可迭代的，真正的)，然后使用file.writelines(lines)。请注意，这些行仍然需要一个最终的\n，否则它们实际上不会变成多行。这还允许您将获取内容与实际编写文件分开，允许您将其打印到终端，通过电子邮件发送，或在将来做任何您可能想做的事情。

Answer 2

我设法处理了我问题的第二部分，关于试/除部分：

item_list = ['forwardEps', 'bookValue']

def get_data(value, url, query_data):
    """
    Query the items of query_data, avoiding KeyErrors
    -> returns a dictionary, e.g.: {'forwardEps': 5.42, 'bookValue': 23.37}
    """

    json_data = get_json(url)
    value = json_data['defaultKeyStatistics']

    results = {}

    for item in query_data:
        try:
            results[item] = value[item]['raw']
        except KeyError:
            results[item] = 0

    return results


results = get_data("defaultKeyStatistics", url_summary, item_list)

eps = results['forwardEps']
book_value = results['bookValue']