diff --git a/gamestonk_terminal/stocks/fundamental_analysis/av_model.py b/gamestonk_terminal/stocks/fundamental_analysis/av_model.py new file mode 100644 index 00000000000..43b0b8e4481 --- /dev/null +++ b/gamestonk_terminal/stocks/fundamental_analysis/av_model.py @@ -0,0 +1,376 @@ +"""Alpha Vantage Model""" +__docformat__ = "numpy" + +from typing import Dict, Tuple + +import requests + +from alpha_vantage.fundamentaldata import FundamentalData +import pandas as pd +from gamestonk_terminal.stocks.fundamental_analysis.fa_helper import clean_df_index +from gamestonk_terminal.helper_funcs import long_number_format +from gamestonk_terminal import config_terminal as cfg + + +def get_overview(ticker: str) -> pd.DataFrame: + """Get alpha vantage company overview + + Parameters + ---------- + ticker : str + Stock ticker + + Returns + ------- + pd.DataFrame + Dataframe of fundamentals + """ + # Request OVERVIEW data from Alpha Vantage API + s_req = f"https://www.alphavantage.co/query?function=OVERVIEW&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}" + result = requests.get(s_req, stream=True) + + # If the returned data was successful + if result.status_code == 200: + # Parse json data to dataframe + df_fa = pd.json_normalize(result.json()) + # Keep json data sorting in dataframe + df_fa = df_fa[list(result.json().keys())].T + df_fa.iloc[5:] = df_fa.iloc[5:].applymap(lambda x: long_number_format(x)) + clean_df_index(df_fa) + df_fa = df_fa.rename( + index={ + "E b i t d a": "EBITDA", + "P e ratio": "PE ratio", + "P e g ratio": "PEG ratio", + "E p s": "EPS", + "Revenue per share t t m": "Revenue per share TTM", + "Operating margin t t m": "Operating margin TTM", + "Return on assets t t m": "Return on assets TTM", + "Return on equity t t m": "Return on equity TTM", + "Revenue t t m": "Revenue TTM", + "Gross profit t t m": "Gross profit TTM", + "Diluted e p s t t m": "Diluted EPS TTM", + "Quarterly earnings growth y o y": "Quarterly earnings growth YOY", + "Quarterly revenue growth y o y": "Quarterly revenue growth YOY", + "Trailing p e": "Trailing PE", + "Forward p e": "Forward PE", + "Price to sales ratio t t m": "Price to sales ratio TTM", + "E v to revenue": "EV to revenue", + "E v to e b i t d a": "EV to EBITDA", + } + ) + return df_fa + return pd.DataFrame() + + +def get_key_metrics(ticker: str) -> pd.DataFrame: + """Get key metrics from overview + + Parameters + ---------- + ticker : str + Stock ticker + + Returns + ------- + pd.DataFrame + Dataframe of key metrics + """ + # Request OVERVIEW data + s_req = f"https://www.alphavantage.co/query?function=OVERVIEW&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}" + result = requests.get(s_req, stream=True) + + # If the returned data was successful + if result.status_code == 200: + df_fa = pd.json_normalize(result.json()) + df_fa = df_fa[list(result.json().keys())].T + df_fa = df_fa.applymap(lambda x: long_number_format(x)) + clean_df_index(df_fa) + df_fa = df_fa.rename( + index={ + "E b i t d a": "EBITDA", + "P e ratio": "PE ratio", + "P e g ratio": "PEG ratio", + "E p s": "EPS", + "Return on equity t t m": "Return on equity TTM", + "Price to sales ratio t t m": "Price to sales ratio TTM", + } + ) + as_key_metrics = [ + "Market capitalization", + "EBITDA", + "EPS", + "PE ratio", + "PEG ratio", + "Price to book ratio", + "Return on equity TTM", + "Payout ratio", + "Price to sales ratio TTM", + "Dividend yield", + "50 day moving average", + "Analyst target price", + "Beta", + ] + return df_fa.loc[as_key_metrics] + + return pd.DataFrame() + + +def get_income_statements( + ticker: str, number: int, quarterly: bool = False +) -> pd.DataFrame: + """Get income statements for company + + Parameters + ---------- + ticker : str + Stock ticker + number : int + Number of past to get + quarterly : bool, optional + Flag to get quarterly instead of annual, by default False + + Returns + ------- + pd.DataFrame + Dataframe of income statements + """ + url = f"https://www.alphavantage.co/query?function=INCOME_STATEMENT&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}" + r = requests.get(url) + statements = r.json() + if quarterly: + # pylint: disable=unbalanced-tuple-unpacking + df_fa = pd.DataFrame(statements["quarterlyReports"]) + else: + # pylint: disable=unbalanced-tuple-unpacking + df_fa = pd.DataFrame(statements["annualReports"]) + + df_fa = df_fa.set_index("fiscalDateEnding") + df_fa = df_fa.head(number) + df_fa = df_fa.applymap(lambda x: long_number_format(x)) + return df_fa[::-1].T + + +def get_balance_sheet( + ticker: str, number: int, quarterly: bool = False +) -> pd.DataFrame: + """Get balance sheets for company + + Parameters + ---------- + ticker : str + Stock ticker + number : int + Number of past to get + quarterly : bool, optional + Flag to get quarterly instead of annual, by default False + + Returns + ------- + pd.DataFrame + Dataframe of income statements + """ + url = f"https://www.alphavantage.co/query?function=BALANCE_SHEET&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}" + r = requests.get(url) + statements = r.json() + if quarterly: + df_fa = pd.DataFrame(statements["quarterlyReports"]) + else: + df_fa = pd.DataFrame(statements["annualReports"]) + + df_fa = df_fa.set_index("fiscalDateEnding") + df_fa = df_fa.head(number) + df_fa = df_fa.applymap(lambda x: long_number_format(x)) + return df_fa[::-1].T + + +def get_cash_flow(ticker: str, number: int, quarterly: bool = False) -> pd.DataFrame: + """Get cash flows for company + + Parameters + ---------- + ticker : str + Stock ticker + number : int + Number of past to get + quarterly : bool, optional + Flag to get quarterly instead of annual, by default False + + Returns + ------- + pd.DataFrame + Dataframe of income statements + """ + url = f"https://www.alphavantage.co/query?function=CASH_FLOW&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}" + r = requests.get(url) + statements = r.json() + if quarterly: + df_fa = pd.DataFrame(statements["quarterlyReports"]) + else: + df_fa = pd.DataFrame(statements["annualReports"]) + + df_fa = df_fa.set_index("fiscalDateEnding") + df_fa = df_fa.head(number) + df_fa = df_fa.applymap(lambda x: long_number_format(x)) + return df_fa[::-1].T + + +def get_earnings(ticker: str, quarterly: bool = False) -> pd.DataFrame: + """Get earnings calendar for ticker + + Parameters + ---------- + ticker : str + Stock ticker + quarterly : bool, optional + [Flag to get quarterly and not annual, by default False + + Returns + ------- + pd.DataFrame + Dataframe of earnings + """ + # Request EARNINGS data from Alpha Vantage API + s_req = ( + "https://www.alphavantage.co/query?function=EARNINGS&" + f"symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}" + ) + result = requests.get(s_req, stream=True) + + # If the returned data was successful + if result.status_code == 200: + df_fa = pd.json_normalize(result.json()) + if quarterly: + df_fa = pd.DataFrame(df_fa["quarterlyEarnings"][0]) + df_fa = df_fa[ + [ + "fiscalDateEnding", + "reportedDate", + "reportedEPS", + "estimatedEPS", + "surprise", + "surprisePercentage", + ] + ] + df_fa = df_fa.rename( + columns={ + "fiscalDateEnding": "Fiscal Date Ending", + "reportedEPS": "Reported EPS", + "estimatedEPS": "Estimated EPS", + "reportedDate": "Reported Date", + "surprise": "Surprise", + "surprisePercentage": "Surprise Percentage", + } + ) + else: + df_fa = pd.DataFrame(df_fa["annualEarnings"][0]) + df_fa = df_fa.rename( + columns={ + "fiscalDateEnding": "Fiscal Date Ending", + "reportedEPS": "Reported EPS", + } + ) + return df_fa + return pd.DataFrame() + + +def clean_fundamentals_df(df_fa: pd.DataFrame, num: int) -> pd.DataFrame: + """Clean fundamentals dataframe + + Parameters + ---------- + df_fa : pd.DataFrame + Fundamentals dataframe + num : int + Number of data rows to display + + Returns + ---------- + pd.DataFrame + Clean dataframe to output + """ + # pylint: disable=no-member + df_fa = df_fa.set_index("fiscalDateEnding") + df_fa = df_fa.head(n=num).T + df_fa = df_fa.mask(df_fa.astype(object).eq(num * ["None"])).dropna() + df_fa = df_fa.mask(df_fa.astype(object).eq(num * ["0"])).dropna() + df_fa = df_fa.applymap(lambda x: long_number_format(x)) + clean_df_index(df_fa) + df_fa.columns.name = "Fiscal Date Ending" + + return df_fa + + +def get_fraud_ratios(ticker: str) -> Tuple[Dict[str, float], float]: + """Get fraud ratios based on fundamentals + + Parameters + ---------- + ticker : str + Stock ticker + + Returns + ------- + Dict[float]: + Dictionary of fraud metrics + float: + Z score for fraud metrics + """ + fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format="pandas") + # pylint: disable=unbalanced-tuple-unpacking + # pylint: disable=no-member + df_cf, _ = fd.get_cash_flow_annual(symbol=ticker) + df_bs, _ = fd.get_balance_sheet_annual(symbol=ticker) + df_is, _ = fd.get_income_statement_annual(symbol=ticker) + df_cf = df_cf.set_index("fiscalDateEnding").iloc[:2] + df_bs = df_bs.set_index("fiscalDateEnding").iloc[:2] + df_is = df_is.set_index("fiscalDateEnding").iloc[:2] + + ar = df_bs["currentNetReceivables"].apply(lambda x: int(x)).values + sales = df_is["totalRevenue"].apply(lambda x: int(x)).values + cogs = df_is["costofGoodsAndServicesSold"].apply(lambda x: int(x)).values + ni = df_is["netIncome"].apply(lambda x: int(x)).values + ca = df_bs["totalCurrentAssets"].apply(lambda x: int(x)).values + cl = df_bs["totalCurrentLiabilities"].apply(lambda x: int(x)).values + ppe = df_bs["propertyPlantEquipment"].apply(lambda x: int(x)).values + cash = df_bs["cashAndCashEquivalentsAtCarryingValue"].apply(lambda x: int(x)).values + cash_and_sec = df_bs["cashAndShortTermInvestments"].apply(lambda x: int(x)).values + sec = [y - x for (x, y) in zip(cash, cash_and_sec)] + ta = df_bs["totalAssets"].apply(lambda x: int(x)).values + dep = df_bs["accumulatedDepreciationAmortizationPPE"].apply(lambda x: int(x)).values + sga = df_is["sellingGeneralAndAdministrative"].apply(lambda x: int(x)).values + tl = df_bs["totalLiabilities"].apply(lambda x: int(x)).values + icfo = df_is["netIncomeFromContinuingOperations"].apply(lambda x: int(x)).values + cfo = df_cf["operatingCashflow"].apply(lambda x: int(x)).values + ratios: Dict = {} + ratios["DSRI"] = (ar[0] / sales[0]) / (ar[1] / sales[1]) + ratios["GMI"] = ((sales[1] - cogs[1]) / sales[1]) / ( + (sales[0] - cogs[0]) / sales[0] + ) + ratios["AQI"] = (1 - ((ca[0] + ppe[0] + sec[0]) / ta[0])) / ( + 1 - ((ca[1] + ppe[1] + sec[1]) / ta[1]) + ) + ratios["SGI"] = sales[0] / sales[1] + ratios["DEPI"] = (dep[1] / (ppe[1] + dep[1])) / (dep[0] / (ppe[0] + dep[0])) + ratios["SGAI"] = (sga[0] / sales[0]) / (sga[1] / sales[1]) + ratios["LVGI"] = (tl[0] / ta[0]) / (tl[1] / ta[1]) + ratios["TATA"] = (icfo[0] - cfo[0]) / ta[0] + ratios["MSCORE"] = ( + -4.84 + + (0.92 * ratios["DSRI"]) + + (0.58 * ratios["GMI"]) + + (0.404 * ratios["AQI"]) + + (0.892 * ratios["SGI"]) + + (0.115 * ratios["DEPI"] - (0.172 * ratios["SGAI"])) + + (4.679 * ratios["TATA"]) + - (0.327 * ratios["LVGI"]) + ) + + zscore = ( + -4.336 + - (4.513 * (ni[0] / ta[0])) + + (5.679 * (tl[0] / ta[0])) + + (0.004 * (ca[0] / cl[0])) + ) + + return ratios, zscore diff --git a/gamestonk_terminal/stocks/fundamental_analysis/av_view.py b/gamestonk_terminal/stocks/fundamental_analysis/av_view.py index 944de0caecb..c33bc65b77e 100644 --- a/gamestonk_terminal/stocks/fundamental_analysis/av_view.py +++ b/gamestonk_terminal/stocks/fundamental_analysis/av_view.py @@ -1,669 +1,205 @@ """ Alpha Vantage View """ __docformat__ = "numpy" -import argparse -from typing import List, Dict +import os -import requests +from tabulate import tabulate -from alpha_vantage.fundamentaldata import FundamentalData -import pandas as pd - -from gamestonk_terminal import config_terminal as cfg -from gamestonk_terminal.stocks.fundamental_analysis.fa_helper import clean_df_index -from gamestonk_terminal.helper_funcs import ( - check_positive, - long_number_format, - parse_known_args_and_warn, -) +from gamestonk_terminal import feature_flags as gtff +from gamestonk_terminal.helper_funcs import export_data +from gamestonk_terminal.stocks.fundamental_analysis import av_model -def overview(other_args: List[str], ticker: str): +def display_overview(ticker: str): """Alpha Vantage stock ticker overview Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="overview", - description=""" - Prints an overview about the company. The following fields are expected: - Symbol, Asset type, Name, Description, Exchange, Currency, Country, Sector, Industry, - Address, Full time employees, Fiscal year end, Latest quarter, Market capitalization, - EBITDA, PE ratio, PEG ratio, Book value, Dividend per share, Dividend yield, EPS, - Revenue per share TTM, Profit margin, Operating margin TTM, Return on assets TTM, - Return on equity TTM, Revenue TTM, Gross profit TTM, Diluted EPS TTM, Quarterly - earnings growth YOY, Quarterly revenue growth YOY, Analyst target price, Trailing PE, - Forward PE, Price to sales ratio TTM, Price to book ratio, EV to revenue, EV to EBITDA, - Beta, 52 week high, 52 week low, 50 day moving average, 200 day moving average, Shares - outstanding, Shares float, Shares short, Shares short prior month, Short ratio, Short - percent outstanding, Short percent float, Percent insiders, Percent institutions, - Forward annual dividend rate, Forward annual dividend yield, Payout ratio, Dividend - date, Ex dividend date, Last split factor, and Last split date. Also, the C i k field - corresponds to Central Index Key, which can be used to search a company on - https://www.sec.gov/edgar/searchedgar/cik.htm [Source: Alpha Vantage] - """, - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - # Request OVERVIEW data from Alpha Vantage API - s_req = f"https://www.alphavantage.co/query?function=OVERVIEW&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}" - result = requests.get(s_req, stream=True) - - # If the returned data was successful - if result.status_code == 200: - # Parse json data to dataframe - df_fa = pd.json_normalize(result.json()) - # Keep json data sorting in dataframe - df_fa = df_fa[list(result.json().keys())].T - df_fa.iloc[5:] = df_fa.iloc[5:].applymap(lambda x: long_number_format(x)) - clean_df_index(df_fa) - df_fa = df_fa.rename( - index={ - "E b i t d a": "EBITDA", - "P e ratio": "PE ratio", - "P e g ratio": "PEG ratio", - "E p s": "EPS", - "Revenue per share t t m": "Revenue per share TTM", - "Operating margin t t m": "Operating margin TTM", - "Return on assets t t m": "Return on assets TTM", - "Return on equity t t m": "Return on equity TTM", - "Revenue t t m": "Revenue TTM", - "Gross profit t t m": "Gross profit TTM", - "Diluted e p s t t m": "Diluted EPS TTM", - "Quarterly earnings growth y o y": "Quarterly earnings growth YOY", - "Quarterly revenue growth y o y": "Quarterly revenue growth YOY", - "Trailing p e": "Trailing PE", - "Forward p e": "Forward PE", - "Price to sales ratio t t m": "Price to sales ratio TTM", - "E v to revenue": "EV to revenue", - "E v to e b i t d a": "EV to EBITDA", - } + df_fa = av_model.get_overview(ticker) + if df_fa.empty: + print(f"No data found from alphavantage for {ticker}.\n") + return + if gtff.USE_TABULATE_DF: + print( + tabulate( + df_fa.drop(index=["Description"]), headers=[], tablefmt="fancy_grid" ) + ) + else: + print(df_fa.drop(index=["Description"]).to_string(header=False)) - pd.set_option("display.max_colwidth", None) - - print(df_fa.drop(index=["Description"]).to_string(header=False)) - print(f"Description: {df_fa.loc['Description'][0]}") - print("") - else: - print(f"Error: {result.status_code}") - print("") - - except Exception as e: - print(e, "\n") + print(f"\nCompany Description:\n\n{df_fa.loc['Description'][0]}") + print("") -def key(other_args: List[str], ticker: str): +def display_key(ticker: str): """Alpha Vantage key metrics Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="key", - description=""" - Gives main key metrics about the company (it's a subset of the Overview data from Alpha - Vantage API). The following fields are expected: Market capitalization, EBITDA, EPS, PE - ratio, PEG ratio, Price to book ratio, Return on equity TTM, Payout ratio, Price to - sales ratio TTM, Dividend yield, 50 day moving average, Analyst target price, Beta - [Source: Alpha Vantage API] - """, - ) + df_key = av_model.get_key_metrics(ticker) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return + if df_key.empty: + print("Issue getting key metrics from alpha vantage.") + return - # Request OVERVIEW data - s_req = f"https://www.alphavantage.co/query?function=OVERVIEW&symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}" - result = requests.get(s_req, stream=True) + if gtff.USE_TABULATE_DF: + print(tabulate(df_key, headers=[], tablefmt="fancy_grid")) + else: + print(df_key.to_string(header=False)) - # If the returned data was successful - if result.status_code == 200: - df_fa = pd.json_normalize(result.json()) - df_fa = df_fa[list(result.json().keys())].T - df_fa = df_fa.applymap(lambda x: long_number_format(x)) - clean_df_index(df_fa) - df_fa = df_fa.rename( - index={ - "E b i t d a": "EBITDA", - "P e ratio": "PE ratio", - "P e g ratio": "PEG ratio", - "E p s": "EPS", - "Return on equity t t m": "Return on equity TTM", - "Price to sales ratio t t m": "Price to sales ratio TTM", - } - ) - as_key_metrics = [ - "Market capitalization", - "EBITDA", - "EPS", - "PE ratio", - "PEG ratio", - "Price to book ratio", - "Return on equity TTM", - "Payout ratio", - "Price to sales ratio TTM", - "Dividend yield", - "50 day moving average", - "Analyst target price", - "Beta", - ] - print(df_fa.loc[as_key_metrics].to_string(header=False)) - else: - print(f"Error: {result.status_code}") - print("") - - except Exception as e: - print(e, "\n") + print("") -def income_statement(other_args: List[str], ticker: str): +def display_income_statement( + ticker: str, number: int, quarterly: bool = False, export: str = "" +): """Alpha Vantage income statement Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number: int + Number of past statements + quarterly: bool + Flag to get quarterly instead of annual + export: str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="income", - description=""" - Prints a complete income statement over time. This can be either quarterly or annually. - The following fields are expected: Accepted date, Cost and expenses, Cost of revenue, - Depreciation and amortization, Ebitda, Ebitdaratio, Eps, Epsdiluted, Filling date, - Final link, General and administrative expenses, Gross profit, Gross profit ratio, - Income before tax, Income before tax ratio, Income tax expense, Interest expense, Link, - Net income, Net income ratio, Operating expenses, Operating income, Operating income - ratio, Other expenses, Period, Research and development expenses, Revenue, Selling and - marketing expenses, Total other income expenses net, Weighted average shs out, Weighted - average shs out dil [Source: Alpha Vantage]""", - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=1, - help="Number of latest years/quarters.", - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", - ) + df_income = av_model.get_income_statements(ticker, number, quarterly) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return + if gtff.USE_TABULATE_DF: + print(tabulate(df_income, headers=df_income.columns, tablefmt="fancy_grid")) + else: + print(df_income.to_string()) - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 40 - - fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format="pandas") - if ns_parser.b_quarter: - # pylint: disable=unbalanced-tuple-unpacking - df_fa, _ = fd.get_income_statement_quarterly(symbol=ticker) - else: - # pylint: disable=unbalanced-tuple-unpacking - df_fa, _ = fd.get_income_statement_annual(symbol=ticker) - - df_fa = clean_fundamentals_df(df_fa, num=ns_parser.n_num) - print(df_fa) - print("") - - except Exception as e: - print(e, "\n") + print("") + export_data(export, os.path.dirname(os.path.abspath(__file__)), "income", df_income) -def balance_sheet(other_args: List[str], ticker: str): - """Alpha Vantage balance sheet +def display_balance_sheet( + ticker: str, number: int, quarterly: bool = False, export: str = "" +): + """Alpha Vantage income statement Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number: int + Number of past statements + quarterly: bool + Flag to get quarterly instead of annual + export: str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="balance", - description=""" - Prints a complete balance sheet statement over time. This can be either quarterly or - annually. The following fields are expected: Accepted date, Account payables, - Accumulated other comprehensive income loss, Cash and cash equivalents, Cash and short - term investments, Common stock, Deferred revenue, Deferred revenue non current, - Deferred tax liabilities non current, Filling date, Final link, Goodwill, - Goodwill and intangible assets, Intangible assets, Inventory, Link, Long term debt, - Long term investments, Net debt, Net receivables, Other assets, Other current assets, - Other current liabilities, Other liabilities, Other non current assets, Other non - current liabilities, Othertotal stockholders equity, Period, Property plant equipment - net, Retained earnings, Short term debt, Short term investments, Tax assets, Tax - payables, Total assets, Total current assets, Total current liabilities, Total debt, - Total investments, Total liabilities, Total liabilities and stockholders equity, Total - non current assets, Total non current liabilities, and Total stockholders equity. - [Source: Alpha Vantage] - """, - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=1, - help="Number of latest years/quarters.", - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", + df_balance = av_model.get_balance_sheet(ticker, number, quarterly) + + if gtff.USE_TABULATE_DF: + print(tabulate(df_balance, headers=df_balance.columns, tablefmt="fancy_grid")) + else: + print(df_balance.to_string()) + + print("") + export_data( + export, os.path.dirname(os.path.abspath(__file__)), "balance", df_balance ) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 40 - - fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format="pandas") - if ns_parser.b_quarter: - # pylint: disable=unbalanced-tuple-unpacking - df_fa, _ = fd.get_balance_sheet_quarterly(symbol=ticker) - else: - # pylint: disable=unbalanced-tuple-unpacking - df_fa, _ = fd.get_balance_sheet_annual(symbol=ticker) - - df_fa = clean_fundamentals_df(df_fa, num=ns_parser.n_num) - print(df_fa) - print("") - - except Exception as e: - print(e, "\n") - - -def cash_flow(other_args: List[str], ticker: str): - """Alpha Vantage cash flow +def display_cash_flow( + ticker: str, number: int, quarterly: bool = False, export: str = "" +): + """Alpha Vantage income statement Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number: int + Number of past statements + quarterly: bool + Flag to get quarterly instead of annual + export: str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="cash", - description=""" - Prints a complete cash flow statement over time. This can be either quarterly or - annually. The following fields are expected: Accepted date, Accounts payables, Accounts - receivables, Acquisitions net, Capital expenditure, Cash at beginning of period, Cash - at end of period, Change in working capital, Common stock issued, Common stock - repurchased, Debt repayment, Deferred income tax, Depreciation and amortization, - Dividends paid, Effect of forex changes on cash, Filling date, Final link, Free cash - flow, Inventory, Investments in property plant and equipment, Link, Net cash provided - by operating activities, Net cash used for investing activities, Net cash used provided - by financing activities, Net change in cash, Net income, Operating cash flow, Other - financing activities, Other investing activities, Other non cash items, Other working - capital, Period, Purchases of investments, Sales maturities of investments, Stock based - compensation. [Source: Alpha Vantage] - """, - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=1, - help="Number of latest years/quarters.", - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", - ) + df_cash = av_model.get_cash_flow(ticker, number, quarterly) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return + if gtff.USE_TABULATE_DF: + print(tabulate(df_cash, headers=df_cash.columns, tablefmt="fancy_grid")) + else: + print(df_cash.to_string()) - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 40 - - fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format="pandas") - if ns_parser.b_quarter: - # pylint: disable=unbalanced-tuple-unpacking - df_fa, _ = fd.get_cash_flow_quarterly(symbol=ticker) - else: - # pylint: disable=unbalanced-tuple-unpacking - df_fa, _ = fd.get_cash_flow_annual(symbol=ticker) - - df_fa = clean_fundamentals_df(df_fa, num=ns_parser.n_num) - print(df_fa) - print("") - - except Exception as e: - print(e, "\n") + print("") + export_data(export, os.path.dirname(os.path.abspath(__file__)), "cash", df_cash) -def earnings(other_args: List[str], ticker: str): +def display_earnings(ticker: str, number: int, quarterly: bool = False): """Alpha Vantage earnings Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number:int + Number of events to show + quarterly: bool + Flag to show quarterly instead of annual """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="earnings", - description=""" - Print earnings dates and reported EPS of the company. The following fields are - expected: Fiscal Date Ending and Reported EPS. [Source: Alpha Vantage] - """, - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=5, - help="Number of latest info", - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 40 - - # Request EARNINGS data from Alpha Vantage API - s_req = ( - "https://www.alphavantage.co/query?function=EARNINGS&" - f"symbol={ticker}&apikey={cfg.API_KEY_ALPHAVANTAGE}" + df_fa = av_model.get_earnings(ticker, quarterly) + if df_fa.empty: + print("Error getting earnings data.\n") + return + if gtff.USE_TABULATE_DF: + print( + tabulate( + df_fa.head(number), + headers=df_fa.columns, + showindex=False, + tablefmt="fancy_grid", + ) ) - result = requests.get(s_req, stream=True) + else: + print(df_fa.head(n=number).T.to_string(header=False)) - # If the returned data was successful - if result.status_code == 200: - df_fa = pd.json_normalize(result.json()) - if ns_parser.b_quarter: - df_fa = pd.DataFrame(df_fa["quarterlyEarnings"][0]) - df_fa = df_fa[ - [ - "fiscalDateEnding", - "reportedDate", - "reportedEPS", - "estimatedEPS", - "surprise", - "surprisePercentage", - ] - ] - df_fa = df_fa.rename( - columns={ - "fiscalDateEnding": "Fiscal Date Ending", - "reportedEPS": "Reported EPS", - "estimatedEPS": "Estimated EPS", - "reportedDate": "Reported Date", - "surprise": "Surprise", - "surprisePercentage": "Surprise Percentage", - } - ) - else: - df_fa = pd.DataFrame(df_fa["annualEarnings"][0]) - df_fa = df_fa.rename( - columns={ - "fiscalDateEnding": "Fiscal Date Ending", - "reportedEPS": "Reported EPS", - } - ) - - print(df_fa.head(n=ns_parser.n_num).T.to_string(header=False)) - - else: - print(f"Error: {result.status_code}") - print("") - - except Exception as e: - print(e, "\n") + print("") -def clean_fundamentals_df(df_fa: pd.DataFrame, num: int) -> pd.DataFrame: - """Clean fundamentals dataframe - - Parameters - ---------- - df_fa : pd.DataFrame - Fundamentals dataframe - num : int - Number of data rows to display - - Returns - ---------- - pd.DataFrame - Clean dataframe to output - """ - # pylint: disable=no-member - df_fa = df_fa.set_index("fiscalDateEnding") - df_fa = df_fa.head(n=num).T - df_fa = df_fa.mask(df_fa.astype(object).eq(num * ["None"])).dropna() - df_fa = df_fa.mask(df_fa.astype(object).eq(num * ["0"])).dropna() - df_fa = df_fa.applymap(lambda x: long_number_format(x)) - clean_df_index(df_fa) - df_fa.columns.name = "Fiscal Date Ending" - - return df_fa - - -def fraud(other_args: List[str], ticker: str): +def display_fraud(ticker: str): """Fraud indicators for given ticker Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.RawTextHelpFormatter, - prog="fraud", - description=( - "Mscore:\n------------------------------------------------\n" - "The Beneish model is a statistical model that uses financial ratios calculated with" - " accounting data of a specific company in order to check if it is likely (high" - " probability) that the reported earnings of the company have been manipulated." - " A score of -5 to -2.22 indicated a low chance of fraud, a score of -2.22 to -1.78" - " indicates a moderate change of fraud, and a score above -1.78 indicated a high" - " chance of fraud.[Source: Wikipedia]\n\nDSRI:\nDays Sales in Receivables Index" - " gauges whether receivables and revenue are out of balance, a large number is" - " expected to be associated with a higher likelihood that revenues and earnings are" - " overstated.\n\nGMI:\nGross Margin Index shows if gross margins are deteriorating." - " Research suggests that firms with worsening gross margin are more likely to engage" - " in earnings management, therefore there should be a positive correlation between" - " GMI and probability of earnings management.\n\nAQI:\nAsset Quality Index measures" - " the proportion of assets where potential benefit is less certain. A positive" - " relation between AQI and earnings manipulation is expected.\n\nSGI:\nSales Growth" - " Index shows the amount of growth companies are having. Higher growth companies are" - " more likely to commit fraud so there should be a positive relation between SGI and" - " earnings management.\n\nDEPI:\nDepreciation Index is the ratio for the rate of" - " depreciation. A DEPI greater than 1 shows that the depreciation rate has slowed and" - " is positively correlated with earnings management.\n\nSGAI:\nSales General and" - " Administrative Expenses Index measures the change in SG&A over sales. There should" - " be a positive relationship between SGAI and earnings management.\n\nLVGI:\nLeverage" - " Index represents change in leverage. A LVGI greater than one indicates a lower" - " change of fraud.\n\nTATA: \nTotal Accruals to Total Assets is a proxy for the" - " extent that cash underlies earnigns. A higher number is associated with a higher" - " likelihood of manipulation.\n\n\n" - "Zscore:\n------------------------------------------------\n" - "The Zmijewski Score is a bankruptcy model used to predict a firm's bankruptcy in two" - " years. The ratio uses in the Zmijewski score were determined by probit analysis (" - "think of probit as probability unit). In this case, scores less than .5 represent a" - " higher probability of default. One of the criticisms that Zmijewski made was that" - " other bankruptcy scoring models oversampled distressed firms and favored situations" - " with more complete data.[Source: YCharts]" - ), + ratios, zscore = av_model.get_fraud_ratios(ticker) + + if ratios["MSCORE"] > -1.78: + chanceM = "high" + elif ratios["MSCORE"] > -2.22: + chanceM = "moderate" + else: + chanceM = "low" + + if zscore < 0.5: + chanceZ = "high" + else: + chanceZ = "low" + + print("Mscore Sub Stats:") + for rkey, value in ratios.items(): + if rkey != "MSCORE": + print(" ", f"{rkey} : {value:.2f}") + + print( + "\n" + "MSCORE: ", + f"{ratios['MSCORE']:.2f} ({chanceM} chance of fraud)", ) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - - if not ns_parser: - return - - fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format="pandas") - # pylint: disable=unbalanced-tuple-unpacking - # pylint: disable=no-member - df_cf, _ = fd.get_cash_flow_annual(symbol=ticker) - df_bs, _ = fd.get_balance_sheet_annual(symbol=ticker) - df_is, _ = fd.get_income_statement_annual(symbol=ticker) - df_cf = df_cf.set_index("fiscalDateEnding").iloc[:2] - df_bs = df_bs.set_index("fiscalDateEnding").iloc[:2] - df_is = df_is.set_index("fiscalDateEnding").iloc[:2] - - ar = df_bs["currentNetReceivables"].apply(lambda x: int(x)).values - sales = df_is["totalRevenue"].apply(lambda x: int(x)).values - cogs = df_is["costofGoodsAndServicesSold"].apply(lambda x: int(x)).values - ni = df_is["netIncome"].apply(lambda x: int(x)).values - ca = df_bs["totalCurrentAssets"].apply(lambda x: int(x)).values - cl = df_bs["totalCurrentLiabilities"].apply(lambda x: int(x)).values - ppe = df_bs["propertyPlantEquipment"].apply(lambda x: int(x)).values - cash = ( - df_bs["cashAndCashEquivalentsAtCarryingValue"] - .apply(lambda x: int(x)) - .values - ) - cash_and_sec = ( - df_bs["cashAndShortTermInvestments"].apply(lambda x: int(x)).values - ) - sec = [y - x for (x, y) in zip(cash, cash_and_sec)] - ta = df_bs["totalAssets"].apply(lambda x: int(x)).values - dep = ( - df_bs["accumulatedDepreciationAmortizationPPE"] - .apply(lambda x: int(x)) - .values - ) - sga = df_is["sellingGeneralAndAdministrative"].apply(lambda x: int(x)).values - tl = df_bs["totalLiabilities"].apply(lambda x: int(x)).values - icfo = df_is["netIncomeFromContinuingOperations"].apply(lambda x: int(x)).values - cfo = df_cf["operatingCashflow"].apply(lambda x: int(x)).values - ratios: Dict = {} - ratios["DSRI"] = (ar[0] / sales[0]) / (ar[1] / sales[1]) - ratios["GMI"] = ((sales[1] - cogs[1]) / sales[1]) / ( - (sales[0] - cogs[0]) / sales[0] - ) - ratios["AQI"] = (1 - ((ca[0] + ppe[0] + sec[0]) / ta[0])) / ( - 1 - ((ca[1] + ppe[1] + sec[1]) / ta[1]) - ) - ratios["SGI"] = sales[0] / sales[1] - ratios["DEPI"] = (dep[1] / (ppe[1] + dep[1])) / (dep[0] / (ppe[0] + dep[0])) - ratios["SGAI"] = (sga[0] / sales[0]) / (sga[1] / sales[1]) - ratios["LVGI"] = (tl[0] / ta[0]) / (tl[1] / ta[1]) - ratios["TATA"] = (icfo[0] - cfo[0]) / ta[0] - ratios["MSCORE"] = ( - -4.84 - + (0.92 * ratios["DSRI"]) - + (0.58 * ratios["GMI"]) - + (0.404 * ratios["AQI"]) - + (0.892 * ratios["SGI"]) - + (0.115 * ratios["DEPI"] - (0.172 * ratios["SGAI"])) - + (4.679 * ratios["TATA"]) - - (0.327 * ratios["LVGI"]) - ) - - zscore = ( - -4.336 - - (4.513 * (ni[0] / ta[0])) - + (5.679 * (tl[0] / ta[0])) - + (0.004 * (ca[0] / cl[0])) - ) - - if ratios["MSCORE"] > -1.78: - chanceM = "high" - elif ratios["MSCORE"] > -2.22: - chanceM = "moderate" - else: - chanceM = "low" - - if zscore < 0.5: - chanceZ = "high" - else: - chanceZ = "low" - - print("Mscore Sub Stats:") - for rkey, value in ratios.items(): - if rkey != "MSCORE": - print(" ", f"{rkey} : {value:.2f}") - - print( - "\n" + "MSCORE: ", - f"{ratios['MSCORE']:.2f} ({chanceM} chance of fraud)", - ) - - print("ZSCORE: ", f"{zscore:.2f} ({chanceZ} chance of bankruptcy)", "\n") - - except Exception as e: - print(e, "\n") + print("ZSCORE: ", f"{zscore:.2f} ({chanceZ} chance of bankruptcy)", "\n") diff --git a/gamestonk_terminal/stocks/fundamental_analysis/business_insider_model.py b/gamestonk_terminal/stocks/fundamental_analysis/business_insider_model.py new file mode 100644 index 00000000000..9ed94984a72 --- /dev/null +++ b/gamestonk_terminal/stocks/fundamental_analysis/business_insider_model.py @@ -0,0 +1,89 @@ +"""Business Insider Model""" +__docformat__ = "numpy" + +import requests +from bs4 import BeautifulSoup +import pandas as pd +from rapidfuzz import fuzz +from gamestonk_terminal.helper_funcs import get_user_agent + + +def get_management(ticker: str) -> pd.DataFrame: + """Get company managers from Business Insider + + Parameters + ---------- + ticker : str + Stock ticker + + Returns + ------- + pd.DataFrame + Dataframe of managers + """ + url_market_business_insider = ( + f"https://markets.businessinsider.com/stocks/{ticker.lower()}-stock" + ) + text_soup_market_business_insider = BeautifulSoup( + requests.get( + url_market_business_insider, headers={"User-Agent": get_user_agent()} + ).text, + "lxml", + ) + + found_h2s = {} + + for next_h2 in text_soup_market_business_insider.findAll( + "h2", {"class": "header-underline"} + ): + next_table = next_h2.find_next_sibling("table", {"class": "table"}) + + if next_table: + found_h2s[next_h2.text] = next_table + + if found_h2s.get("Management") is None: + print(f"No management information in Business Insider for {ticker}") + print("") + return pd.DataFrame() + + l_titles = [] + for s_title in found_h2s["Management"].findAll( + "td", {"class": "table__td text-right"} + ): + if any(c.isalpha() for c in s_title.text.strip()) and ( + "USD" not in s_title.text.strip() + ): + l_titles.append(s_title.text.strip()) + + l_names = [] + for s_name in found_h2s["Management"].findAll( + "td", {"class": "table__td table--allow-wrap"} + ): + l_names.append(s_name.text.strip()) + + df_management = pd.DataFrame( + {"Name": l_names[-len(l_titles) :], "Title": l_titles}, + columns=["Name", "Title"], + ) + + df_management["Info"] = "-" + df_management["Insider Activity"] = "-" + df_management = df_management.set_index("Name") + + for s_name in df_management.index: + df_management.loc[s_name][ + "Info" + ] = f"http://www.google.com/search?q={s_name} {ticker.upper()}".replace( + " ", "%20" + ) + + s_url_base = "https://markets.businessinsider.com" + for insider in text_soup_market_business_insider.findAll( + "a", {"onclick": "silentTrackPI()"} + ): + for s_name in df_management.index: + if fuzz.token_set_ratio(s_name, insider.text.strip()) > 70: + df_management.loc[s_name]["Insider Activity"] = ( + s_url_base + insider.attrs["href"] + ) + return df_management diff --git a/gamestonk_terminal/stocks/fundamental_analysis/business_insider_view.py b/gamestonk_terminal/stocks/fundamental_analysis/business_insider_view.py index ee588263d5c..f7e1ccd82ce 100644 --- a/gamestonk_terminal/stocks/fundamental_analysis/business_insider_view.py +++ b/gamestonk_terminal/stocks/fundamental_analysis/business_insider_view.py @@ -1,116 +1,46 @@ """ Business Insider View """ __docformat__ = "numpy" -import argparse -from typing import List -import requests -from bs4 import BeautifulSoup -import pandas as pd -from rapidfuzz import fuzz -from gamestonk_terminal.helper_funcs import get_user_agent, parse_known_args_and_warn +import os +import textwrap + +from tabulate import tabulate + +import gamestonk_terminal.feature_flags as gtff +from gamestonk_terminal.helper_funcs import export_data +from gamestonk_terminal.stocks.fundamental_analysis import business_insider_model -def management(other_args: List[str], ticker: str): +def display_management(ticker: str, export: str = ""): """Display company's managers Parameters ---------- - other_args : List[str] - argparse other args ticker : str Stock ticker + export : str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="mgmt", - description=""" - Print management team. Namely: Name, Title, Information from google and - (potentially) Insider Activity page. [Source: Business Insider] - """, - ) + df_management = business_insider_model.get_management(ticker) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return + names = ["Name"] + list(df_management.columns) - url_market_business_insider = ( - f"https://markets.businessinsider.com/stocks/{ticker.lower()}-stock" - ) - text_soup_market_business_insider = BeautifulSoup( - requests.get( - url_market_business_insider, headers={"User-Agent": get_user_agent()} - ).text, - "lxml", - ) - - found_h2s = dict() - - for next_h2 in text_soup_market_business_insider.findAll( - "h2", {"class": "header-underline"} - ): - next_table = next_h2.find_next_sibling("table", {"class": "table"}) - - if next_table: - found_h2s[next_h2.text] = next_table - - if found_h2s.get("Management") is None: - print(f"No management information in Business Insider for {ticker}") - print("") - return - - l_titles = [] - for s_title in found_h2s["Management"].findAll( - "td", {"class": "table__td text-right"} - ): - if any(c.isalpha() for c in s_title.text.strip()) and ( - "USD" not in s_title.text.strip() - ): - l_titles.append(s_title.text.strip()) - - l_names = [] - for s_name in found_h2s["Management"].findAll( - "td", {"class": "table__td table--allow-wrap"} - ): - l_names.append(s_name.text.strip()) - - df_management = pd.DataFrame( - {"Name": l_names[-len(l_titles) :], "Title": l_titles}, - columns=["Name", "Title"], - ) - - df_management["Info"] = "-" - df_management["Insider Activity"] = "-" - df_management = df_management.set_index("Name") - - for s_name in df_management.index: - df_management.loc[s_name][ - "Info" - ] = f"http://www.google.com/search?q={s_name} {ticker.upper()}".replace( - " ", "%20" + if gtff.USE_TABULATE_DF: + print( + tabulate( + df_management.applymap( + lambda x: "\n".join(textwrap.wrap(x, width=30)) + if isinstance(x, str) + else x + ), + tablefmt="fancy_grid", + headers=names, ) + ) + else: + print(df_management.to_string()) - s_url_base = "https://markets.businessinsider.com" - for insider in text_soup_market_business_insider.findAll( - "a", {"onclick": "silentTrackPI()"} - ): - for s_name in df_management.index: - if fuzz.token_set_ratio(s_name, insider.text.strip()) > 70: - df_management.loc[s_name]["Insider Activity"] = ( - s_url_base + insider.attrs["href"] - ) - - for ind in df_management.index: - s_name = f"{ind}{(max([len(x) for x in df_management.index])-len(ind))*' '}" - df_mgmt_title = df_management["Title"] - spaces = max(len(x) for x in df_mgmt_title) - len(df_mgmt_title[ind]) - s_title = f"{df_mgmt_title[ind]}{spaces * ' '}" - s_management = f"""{s_name} {s_title} {df_management['Info'][ind]}""" - print(s_management) - if df_management["Insider Activity"][ind] not in "-": - print(f"{df_management['Insider Activity'][ind]}") - print("") - - except Exception as e: - print(e, "\n") + print("") + export_data( + export, os.path.dirname(os.path.abspath(__file__)), "mgmt", df_management + ) diff --git a/gamestonk_terminal/stocks/fundamental_analysis/fa_controller.py b/gamestonk_terminal/stocks/fundamental_analysis/fa_controller.py index ed58366de0b..7f730982da2 100644 --- a/gamestonk_terminal/stocks/fundamental_analysis/fa_controller.py +++ b/gamestonk_terminal/stocks/fundamental_analysis/fa_controller.py @@ -20,7 +20,12 @@ from gamestonk_terminal.stocks.fundamental_analysis import ( market_watch_view, ) from gamestonk_terminal import feature_flags as gtff -from gamestonk_terminal.helper_funcs import get_flair, parse_known_args_and_warn +from gamestonk_terminal.helper_funcs import ( + EXPORT_ONLY_RAW_DATA_ALLOWED, + get_flair, + parse_known_args_and_warn, + check_positive, +) from gamestonk_terminal.menu import session # pylint: disable=inconsistent-return-statements @@ -41,7 +46,7 @@ class FundamentalAnalysisController: "analysis", "score", "dcf", - "screener", + "data", "fraud", "income", "balance", @@ -97,23 +102,28 @@ class FundamentalAnalysisController: intraday = (f"Intraday {self.interval}", "Daily")[self.interval == "1440min"] if self.start: - help_text = f"\n\n{intraday} Stock: {self.ticker} (from {self.start.strftime('%Y-%m-%d')})" + stock_text = f"\n{intraday} Stock: {self.ticker} (from {self.start.strftime('%Y-%m-%d')})" else: - help_text = f"\n\n{intraday} Stock: {self.ticker}" + stock_text = f"\n{intraday} Stock: {self.ticker}" - help_text += """ -Fundamental Analysis: + help_text = f""" +>>>Fundamental Analysis<<< + +What would you like to do? cls clear screen ?/help show this menu again q quit this menu, and shows back to main menu quit quit to abandon program + load load a new ticker +{stock_text} - screener screen info about the company [Finviz] + data fundamental and technical data of company [FinViz] mgmt management team of the company [Business Insider] analysis analyse SEC filings with the help of machine learning [Elect.us] score investing score from Warren Buffett, Joseph Piotroski and Benjamin Graham [FMP] warnings company warnings according to Sean Seah book [Market Watch] dcf a customizable discounted cash flow created in excel [stockanalysis] + Yahoo Finance: info information scope of the company shrs shareholders of the company @@ -121,6 +131,7 @@ Yahoo Finance: cal calendar earnings and estimates of the company web open web browser of the company hq open HQ location of the company + Alpha Vantage: overview overview of the company key company key metrics @@ -129,6 +140,7 @@ Alpha Vantage: cash cash flow of the company earnings earnings dates and reported EPS fraud key fraud ratios + Other Sources: > fmp profile,quote,enterprise,dcf,income,ratios,growth from FMP """ @@ -204,67 +216,526 @@ Other Sources: def call_mgmt(self, other_args: List[str]): """Process mgmt command""" - business_insider_view.management(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="mgmt", + description=""" + Print management team. Namely: Name, Title, Information from google and + (potentially) Insider Activity page. [Source: Business Insider] + """, + ) - def call_screener(self, other_args: List[str]): + try: + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + + business_insider_view.display_management( + ticker=self.ticker, export=ns_parser.export + ) + + except Exception as e: + print(e, "\n") + + def call_data(self, other_args: List[str]): """Process screener command""" - finviz_view.screener(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="data", + description=""" + Print several metrics about the company. The following fields are expected: + Company, Sector, Industry, Country, Index, P/E, EPS (ttm), Insider Own, + Shs Outstand, Perf Week, Market Cap, Forward P/E, EPS next Y, Insider Trans, + Shs Float, Perf Month, Income, EPS next Q, Inst Own, Short Float, Perf Quarter, + Sales, P/S, EPS this Y, Inst Trans, Short Ratio, Perf Half Y, Book/sh, P/B, ROA, + Target Price, Perf Year, Cash/sh, P/C, ROE, 52W Range, Perf YTD, P/FCF, EPS past 5Y, + ROI, 52W High, Beta, Quick Ratio, Sales past 5Y, Gross Margin, 52W Low, ATR, + Employees, Current Ratio, Sales Q/Q, Oper. Margin, RSI (14), Volatility, Optionable, + Debt/Eq, EPS Q/Q, Profit Margin, Rel Volume, Prev Close, Shortable, LT Debt/Eq, + Earnings, Payout, Avg Volume, Price, Recom, SMA20, SMA50, SMA200, Volume, Change. + [Source: Finviz] + """, + ) + + try: + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + + finviz_view.display_screen_data(self.ticker) + + except Exception as e: + print(e, "\n") def call_score(self, other_args: List[str]): """Process score command""" - fmp_view.valinvest_score(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="score", + description=""" + Value investing tool based on Warren Buffett, Joseph Piotroski and Benjamin Graham thoughts [Source: FMP] + """, + ) + + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + fmp_view.valinvest_score(self.ticker) + except Exception as e: + print(e, "\n") def call_info(self, other_args: List[str]): """Process info command""" - yahoo_finance_view.info(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="info", + description=""" + Print information about the company. The following fields are expected: + Zip, Sector, Full time employees, Long business summary, City, Phone, State, Country, + Website, Max age, Address, Industry, Previous close, Regular market open, Two hundred + day average, Payout ratio, Regular market day high, Average daily volume 10 day, + Regular market previous close, Fifty day average, Open, Average volume 10 days, Beta, + Regular market day low, Price hint, Currency, Trailing PE, Regular market volume, + Market cap, Average volume, Price to sales trailing 12 months, Day low, Ask, Ask size, + Volume, Fifty two week high, Forward PE, Fifty two week low, Bid, Tradeable, Bid size, + Day high, Exchange, Short name, Long name, Exchange timezone name, Exchange timezone + short name, Is esg populated, Gmt off set milliseconds, Quote type, Symbol, Message + board id, Market, Enterprise to revenue, Profit margins, Enterprise to ebitda, 52 week + change, Forward EPS, Shares outstanding, Book value, Shares short, Shares percent + shares out, Last fiscal year end, Held percent institutions, Net income to common, + Trailing EPS, Sand p52 week change, Price to book, Held percent insiders, Next fiscal + year end, Most recent quarter, Short ratio, Shares short previous month date, Float + shares, Enterprise value, Last split date, Last split factor, Earnings quarterly growth, + Date short interest, PEG ratio, Short percent of float, Shares short prior month, + Regular market price, Logo_url. [Source: Yahoo Finance] + """, + ) + + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + yahoo_finance_view.display_info(self.ticker) + + except Exception as e: + print(e, "\n") def call_shrs(self, other_args: List[str]): """Process shrs command""" - yahoo_finance_view.shareholders(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="shrs", + description="""Print Major, institutional and mutualfunds shareholders. + [Source: Yahoo Finance]""", + ) + + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + + if not ns_parser: + return + + yahoo_finance_view.display_shareholders(self.ticker) + + except Exception as e: + print(e, "\n") def call_sust(self, other_args: List[str]): """Process sust command""" - yahoo_finance_view.sustainability(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="sust", + description=""" + Print sustainability values of the company. The following fields are expected: + Palmoil, Controversialweapons, Gambling, Socialscore, Nuclear, Furleather, Alcoholic, + Gmo, Catholic, Socialpercentile, Peercount, Governancescore, Environmentpercentile, + Animaltesting, Tobacco, Totalesg, Highestcontroversy, Esgperformance, Coal, Pesticides, + Adult, Percentile, Peergroup, Smallarms, Environmentscore, Governancepercentile, + Militarycontract. [Source: Yahoo Finance] + """, + ) + + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + yahoo_finance_view.display_sustainability(self.ticker) + + except Exception as e: + print(e, "\n") def call_cal(self, other_args: List[str]): """Process cal command""" - yahoo_finance_view.calendar_earnings(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="cal", + description=""" + Calendar earnings of the company. Including revenue and earnings estimates. + [Source: Yahoo Finance] + """, + ) + + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + + yahoo_finance_view.display_calendar_earnings(ticker=self.ticker) + except Exception as e: + print(e, "\n") def call_web(self, other_args: List[str]): """Process web command""" - yahoo_finance_view.web(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="web", + description=""" + Opens company's website. [Source: Yahoo Finance] + """, + ) + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + yahoo_finance_view.open_web(self.ticker) + except Exception as e: + print(e, "\n") def call_hq(self, other_args: List[str]): """Process hq command""" - yahoo_finance_view.headquarters(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="hq", + description=""" + Opens in Google Maps HQ location of the company. [Source: Yahoo Finance] + """, + ) + + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + yahoo_finance_view.open_headquarters_map(self.ticker) + except Exception as e: + print(e, "\n") def call_overview(self, other_args: List[str]): """Process overview command""" - av_view.overview(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="overview", + description=""" + Prints an overview about the company. The following fields are expected: + Symbol, Asset type, Name, Description, Exchange, Currency, Country, Sector, Industry, + Address, Full time employees, Fiscal year end, Latest quarter, Market capitalization, + EBITDA, PE ratio, PEG ratio, Book value, Dividend per share, Dividend yield, EPS, + Revenue per share TTM, Profit margin, Operating margin TTM, Return on assets TTM, + Return on equity TTM, Revenue TTM, Gross profit TTM, Diluted EPS TTM, Quarterly + earnings growth YOY, Quarterly revenue growth YOY, Analyst target price, Trailing PE, + Forward PE, Price to sales ratio TTM, Price to book ratio, EV to revenue, EV to EBITDA, + Beta, 52 week high, 52 week low, 50 day moving average, 200 day moving average, Shares + outstanding, Shares float, Shares short, Shares short prior month, Short ratio, Short + percent outstanding, Short percent float, Percent insiders, Percent institutions, + Forward annual dividend rate, Forward annual dividend yield, Payout ratio, Dividend + date, Ex dividend date, Last split factor, and Last split date. Also, the C i k field + corresponds to Central Index Key, which can be used to search a company on + https://www.sec.gov/edgar/searchedgar/cik.htm [Source: Alpha Vantage] + """, + ) + + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + + av_view.display_overview(self.ticker) + except Exception as e: + print(e, "\n") def call_key(self, other_args: List[str]): """Process overview command""" - av_view.key(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="key", + description=""" + Gives main key metrics about the company (it's a subset of the Overview data from Alpha + Vantage API). The following fields are expected: Market capitalization, EBITDA, EPS, PE + ratio, PEG ratio, Price to book ratio, Return on equity TTM, Payout ratio, Price to + sales ratio TTM, Dividend yield, 50 day moving average, Analyst target price, Beta + [Source: Alpha Vantage API] + """, + ) + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + av_view.display_key(self.ticker) + except Exception as e: + print(e, "\n") def call_income(self, other_args: List[str]): """Process income command""" - av_view.income_statement(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="income", + description=""" + Prints a complete income statement over time. This can be either quarterly or annually. + The following fields are expected: Accepted date, Cost and expenses, Cost of revenue, + Depreciation and amortization, Ebitda, Ebitdaratio, Eps, Epsdiluted, Filling date, + Final link, General and administrative expenses, Gross profit, Gross profit ratio, + Income before tax, Income before tax ratio, Income tax expense, Interest expense, Link, + Net income, Net income ratio, Operating expenses, Operating income, Operating income + ratio, Other expenses, Period, Research and development expenses, Revenue, Selling and + marketing expenses, Total other income expenses net, Weighted average shs out, Weighted + average shs out dil [Source: Alpha Vantage]""", + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=1, + help="Number of latest years/quarters.", + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + try: + + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + av_view.display_income_statement( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + export=ns_parser.export, + ) + except Exception as e: + print(e, "\n") def call_balance(self, other_args: List[str]): """Process balance command""" - av_view.balance_sheet(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="balance", + description=""" + Prints a complete balance sheet statement over time. This can be either quarterly or + annually. The following fields are expected: Accepted date, Account payables, + Accumulated other comprehensive income loss, Cash and cash equivalents, Cash and short + term investments, Common stock, Deferred revenue, Deferred revenue non current, + Deferred tax liabilities non current, Filling date, Final link, Goodwill, + Goodwill and intangible assets, Intangible assets, Inventory, Link, Long term debt, + Long term investments, Net debt, Net receivables, Other assets, Other current assets, + Other current liabilities, Other liabilities, Other non current assets, Other non + current liabilities, Othertotal stockholders equity, Period, Property plant equipment + net, Retained earnings, Short term debt, Short term investments, Tax assets, Tax + payables, Total assets, Total current assets, Total current liabilities, Total debt, + Total investments, Total liabilities, Total liabilities and stockholders equity, Total + non current assets, Total non current liabilities, and Total stockholders equity. + [Source: Alpha Vantage] + """, + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=1, + help="Number of latest years/quarters.", + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + try: + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + av_view.display_balance_sheet( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + export=ns_parser.export, + ) + except Exception as e: + print(e, "\n") def call_cash(self, other_args: List[str]): """Process cash command""" - av_view.cash_flow(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="cash", + description=""" + Prints a complete cash flow statement over time. This can be either quarterly or + annually. The following fields are expected: Accepted date, Accounts payables, Accounts + receivables, Acquisitions net, Capital expenditure, Cash at beginning of period, Cash + at end of period, Change in working capital, Common stock issued, Common stock + repurchased, Debt repayment, Deferred income tax, Depreciation and amortization, + Dividends paid, Effect of forex changes on cash, Filling date, Final link, Free cash + flow, Inventory, Investments in property plant and equipment, Link, Net cash provided + by operating activities, Net cash used for investing activities, Net cash used provided + by financing activities, Net change in cash, Net income, Operating cash flow, Other + financing activities, Other investing activities, Other non cash items, Other working + capital, Period, Purchases of investments, Sales maturities of investments, Stock based + compensation. [Source: Alpha Vantage] + """, + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=1, + help="Number of latest years/quarters.", + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + try: + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + av_view.display_cash_flow( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + export=ns_parser.export, + ) + except Exception as e: + print(e, "\n") def call_earnings(self, other_args: List[str]): """Process earnings command""" - av_view.earnings(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="earnings", + description=""" + Print earnings dates and reported EPS of the company. The following fields are + expected: Fiscal Date Ending and Reported EPS. [Source: Alpha Vantage] + """, + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=5, + help="Number of latest info", + ) + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + av_view.display_earnings( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + ) + except Exception as e: + print(e, "\n") def call_fraud(self, other_args: List[str]): """Process fraud command""" - av_view.fraud(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.RawTextHelpFormatter, + prog="fraud", + description=( + "Mscore:\n------------------------------------------------\n" + "The Beneish model is a statistical model that uses financial ratios calculated with" + " accounting data of a specific company in order to check if it is likely (high" + " probability) that the reported earnings of the company have been manipulated." + " A score of -5 to -2.22 indicated a low chance of fraud, a score of -2.22 to -1.78" + " indicates a moderate change of fraud, and a score above -1.78 indicated a high" + " chance of fraud.[Source: Wikipedia]\n\nDSRI:\nDays Sales in Receivables Index" + " gauges whether receivables and revenue are out of balance, a large number is" + " expected to be associated with a higher likelihood that revenues and earnings are" + " overstated.\n\nGMI:\nGross Margin Index shows if gross margins are deteriorating." + " Research suggests that firms with worsening gross margin are more likely to engage" + " in earnings management, therefore there should be a positive correlation between" + " GMI and probability of earnings management.\n\nAQI:\nAsset Quality Index measures" + " the proportion of assets where potential benefit is less certain. A positive" + " relation between AQI and earnings manipulation is expected.\n\nSGI:\nSales Growth" + " Index shows the amount of growth companies are having. Higher growth companies are" + " more likely to commit fraud so there should be a positive relation between SGI and" + " earnings management.\n\nDEPI:\nDepreciation Index is the ratio for the rate of" + " depreciation. A DEPI greater than 1 shows that the depreciation rate has slowed and" + " is positively correlated with earnings management.\n\nSGAI:\nSales General and" + " Administrative Expenses Index measures the change in SG&A over sales. There should" + " be a positive relationship between SGAI and earnings management.\n\nLVGI:\nLeverage" + " Index represents change in leverage. A LVGI greater than one indicates a lower" + " change of fraud.\n\nTATA: \nTotal Accruals to Total Assets is a proxy for the" + " extent that cash underlies earnigns. A higher number is associated with a higher" + " likelihood of manipulation.\n\n\n" + "Zscore:\n------------------------------------------------\n" + "The Zmijewski Score is a bankruptcy model used to predict a firm's bankruptcy in two" + " years. The ratio uses in the Zmijewski score were determined by probit analysis (" + "think of probit as probability unit). In this case, scores less than .5 represent a" + " higher probability of default. One of the criticisms that Zmijewski made was that" + " other bankruptcy scoring models oversampled distressed firms and favored situations" + " with more complete data.[Source: YCharts]" + ), + ) + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + av_view.display_fraud(self.ticker) + except Exception as e: + print(e, "\n") def call_dcf(self, other_args: List[str]): """Process dcf command""" @@ -272,7 +743,37 @@ Other Sources: def call_warnings(self, other_args: List[str]): """Process warnings command""" - market_watch_view.sean_seah_warnings(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + prog="warnings", + description=""" + Sean Seah warnings. Check: Consistent historical earnings per share; + Consistently high return on equity; Consistently high return on assets; 5x Net + Income > Long-Term Debt; and Interest coverage ratio more than 3. See + https://www.drwealth.com/gone-fishing-with-buffett-by-sean-seah/comment-page-1/ + [Source: Market Watch] + """, + ) + parser.add_argument( + "-d", + "--debug", + action="store_true", + default=False, + dest="b_debug", + help="print insights into warnings calculation.", + ) + + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + + market_watch_view.display_sean_seah_warnings( + ticker=self.ticker, debug=ns_parser.b_debug + ) + + except Exception as e: + print(e, "\n") def call_fmp(self, _): """Process fmp command""" diff --git a/gamestonk_terminal/stocks/fundamental_analysis/financial_modeling_prep/fmp_controller.py b/gamestonk_terminal/stocks/fundamental_analysis/financial_modeling_prep/fmp_controller.py index 1b4e594a57e..15d90b42f2a 100644 --- a/gamestonk_terminal/stocks/fundamental_analysis/financial_modeling_prep/fmp_controller.py +++ b/gamestonk_terminal/stocks/fundamental_analysis/financial_modeling_prep/fmp_controller.py @@ -12,6 +12,9 @@ from gamestonk_terminal.stocks.fundamental_analysis.financial_modeling_prep impo from gamestonk_terminal import feature_flags as gtff from gamestonk_terminal.helper_funcs import ( get_flair, + parse_known_args_and_warn, + check_positive, + EXPORT_ONLY_RAW_DATA_ALLOWED, ) from gamestonk_terminal.menu import session @@ -20,12 +23,8 @@ class FinancialModelingPrepController: """Financial Modeling Prep Controller""" # Command choices - CHOICES = [ - "cls", - "?", - "help", - "q", - "quit", + CHOICES = ["cls", "?", "help", "q", "quit"] + CHOICES_COMMANDS = [ "profile", "quote", "enterprise", @@ -37,6 +36,7 @@ class FinancialModelingPrepController: "ratios", "growth", ] + CHOICES += CHOICES_COMMANDS def __init__(self, ticker: str, start: str, interval: str): """Constructor @@ -135,43 +135,471 @@ Financial Modeling Prep: def call_profile(self, other_args: List[str]): """Process profile command""" - fmp_view.profile(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="profile", + description=""" + Prints information about, among other things, the industry, sector exchange and company + description. The following fields are expected: Address, Beta, Ceo, Changes, Cik, City + Company name, Country, Currency, Cusip, Dcf, Dcf diff, Default image, Description, + Exchange, Exchange short name, Full time employees, Image, Industry, Ipo date, Isin, + Last div, Mkt cap, Phone, Price, Range, Sector, State, Symbol, Vol avg, Website, Zip. + [Source: Financial Modeling Prep] + """, + ) + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + fmp_view.display_profile(self.ticker) + except Exception as e: + print(e, "\n") def call_quote(self, other_args: List[str]): """Process quote command""" - fmp_view.quote(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="quote", + description=""" + Prints actual information about the company which is, among other things, the day high, + market cap, open and close price and price-to-equity ratio. The following fields are + expected: Avg volume, Change, Changes percentage, Day high, Day low, Earnings + announcement, Eps, Exchange, Market cap, Name, Open, Pe, Previous close, Price, Price + avg200, Price avg50, Shares outstanding, Symbol, Timestamp, Volume, Year high, and Year + low. [Source: Financial Modeling Prep] + """, + ) + try: + ns_parser = parse_known_args_and_warn(parser, other_args) + if not ns_parser: + return + fmp_view.display_quote(self.ticker) + except Exception as e: + print(e, "\n") def call_enterprise(self, other_args: List[str]): """Process income command""" - fmp_view.enterprise(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="enterprise", + description=""" + Prints stock price, number of shares, market capitalization and + enterprise value over time. The following fields are expected: Add total debt, + Enterprise value, Market capitalization, Minus cash and cash equivalents, Number + of shares, Stock price, and Symbol. [Source: Financial Modeling Prep] + """, + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=1, + help="Number of latest years/quarters.", + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + + try: + + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + + fmp_view.display_enterprise( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + export=ns_parser.export, + ) + except Exception as e: + print(e, "\n") def call_dcf(self, other_args: List[str]): """Process dcf command""" - fmp_view.discounted_cash_flow(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="dcf", + description=""" + Prints the discounted cash flow of a company over time including the DCF of today. The + following fields are expected: DCF, Stock price, and Date. [Source: Financial Modeling + Prep] + """, + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=1, + help="Number of latest years/quarters.", + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + try: + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + fmp_view.display_discounted_cash_flow( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + export=ns_parser.export, + ) + except Exception as e: + print(e, "\n") def call_income(self, other_args: List[str]): """Process income command""" - fmp_view.income_statement(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="inc", + description=""" + Prints a complete income statement over time. This can be either quarterly or annually. + The following fields are expected: Accepted date, Cost and expenses, Cost of + revenue, Depreciation and amortization, Ebitda, Ebitdaratio, Eps, Epsdiluted, Filling + date, Final link, General and administrative expenses, Gross profit, Gross profit + ratio, Income before tax, Income before tax ratio, Income tax expense, Interest + expense, Link, Net income, Net income ratio, Operating expenses, Operating income, + Operating income ratio, Other expenses, Period, Research and development expenses, + Revenue, Selling and marketing expenses, Total other income expenses net, Weighted + average shs out, Weighted average shs out dil [Source: Financial Modeling Prep] + """, + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=1, + help="Number of latest years/quarters.", + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + try: + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + fmp_view.display_income_statement( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + export=ns_parser.export, + ) + except Exception as e: + print(e, "\n") def call_balance(self, other_args: List[str]): """Process balance command""" - fmp_view.balance_sheet(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="bal", + description=""" + Prints a complete balance sheet statement over time. This can be + either quarterly or annually. The following fields are expected: Accepted date, + Account payables, Accumulated other comprehensive income loss, Cash and cash + equivalents, Cash and short term investments, Common stock, Deferred revenue, + Deferred revenue non current, Deferred tax liabilities non current, Filling date, + Final link, Goodwill, Goodwill and intangible assets, Intangible assets, Inventory, + Link, Long term debt, Long term investments, Net debt, Net receivables, Other assets, + Other current assets, Other current liabilities, Other liabilities, Other non current + assets, Other non current liabilities, Othertotal stockholders equity, Period, Property + plant equipment net, Retained earnings, Short term debt, Short term investments, Tax + assets, Tax payables, Total assets, Total current assets, Total current liabilities, + Total debt, Total investments, Total liabilities, Total liabilities and stockholders + equity, Total non current assets, Total non current liabilities, and Total stockholders + equity. [Source: Financial Modeling Prep] + """, + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=1, + help="Number of latest years/quarters.", + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + try: + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + fmp_view.display_balance_sheet( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + export=ns_parser.export, + ) + except Exception as e: + print(e, "\n") def call_cash(self, other_args: List[str]): """Process cash command""" - fmp_view.cash_flow(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="cash", + description=""" + Prints a complete cash flow statement over time. This can be either + quarterly or annually. The following fields are expected: Accepted date, Accounts + payables, Accounts receivables, Acquisitions net, Capital expenditure, Cash at + beginning of period, Cash at end of period, Change in working capital, Common stock + issued, Common stock repurchased, Debt repayment, Deferred income tax, Depreciation and + amortization, Dividends paid, Effect of forex changes on cash, Filling date, Final + link, Free cash flow, Inventory, Investments in property plant and equipment, Link, Net + cash provided by operating activities, Net cash used for investing activities, Net cash + used provided by financing activities, Net change in cash, Net income, Operating cash + flow, Other financing activities, Other investing activities, Other non cash items, + Other working capital, Period, Purchases of investments, Sales maturities of + investments, Stock based compensation. [Source: Financial Modeling Prep] + """, + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=1, + help="Number of latest years/quarters.", + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + try: + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + fmp_view.display_cash_flow( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + export=ns_parser.export, + ) + except Exception as e: + print(e, "\n") def call_metrics(self, other_args: List[str]): """Process metrics command""" - fmp_view.key_metrics(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="metrics", + description=""" + Prints a list of the key metrics of a company over time. This can be either + quarterly or annually. This includes, among other things, Return on Equity (ROE), + Working Capital, Current Ratio and Debt to Assets. The following fields are expected: + Average inventory, Average payables, Average receivables, Book value per share, Capex + per share, Capex to depreciation, Capex to operating cash flow, Capex to revenue, Cash + per share, Current ratio, Days of inventory on hand, Days payables outstanding, Days + sales outstanding, Debt to assets, Debt to equity, Dividend yield, Earnings yield, + Enterprise value, Enterprise value over EBITDA, Ev to free cash flow, Ev to operating + cash flow, Ev to sales, Free cash flow per share, Free cash flow yield, Graham net net, + Graham number, Income quality, Intangibles to total assets, Interest debt per share, + Inventory turnover, Market cap, Net current asset value, Net debt to EBITDA, Net income + per share, Operating cash flow per share, Payables turnover, Payout ratio, Pb ratio, Pe + ratio, Pfcf ratio, Pocfratio, Price to sales ratio, Ptb ratio, Receivables turnover, + Research and development to revenue, Return on tangible assets, Revenue per share, + Roe, Roic, Sales general and administrative to revenue, Shareholders equity per + share, Stock based compensation to revenue, Tangible book value per share, and Working + capital. [Source: Financial Modeling Prep] + """, + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=1, + help="Number of latest years/quarters.", + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + try: + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + fmp_view.display_key_metrics( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + export=ns_parser.export, + ) + except Exception as e: + print(e, "\n") def call_ratios(self, other_args: List[str]): """Process cash command""" - fmp_view.financial_ratios(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="ratios", + description=""" + Prints in-depth ratios of a company over time. This can be either quarterly or + annually. This contains, among other things, Price-to-Book Ratio, Payout Ratio and + Operating Cycle. The following fields are expected: Asset turnover, Capital expenditure + coverage ratio, Cash conversion cycle, Cash flow coverage ratios, Cash flow to debt + ratio, Cash per share, Cash ratio, Company equity multiplier, Current ratio, Days of + inventory outstanding, Days of payables outstanding, Days of sales outstanding, Debt + equity ratio, Debt ratio, Dividend paid and capex coverage ratio, Dividend payout ratio, + Dividend yield, Ebit per revenue, Ebt per ebit, Effective tax rate, Enterprise value + multiple, Fixed asset turnover, Free cash flow operating cash flow ratio, Free cash + flow per share, Gross profit margin, Inventory turnover, Long term debt to + capitalization, Net income per EBT, Net profit margin, Operating cash flow per share, + Operating cash flow sales ratio, Operating cycle, Operating profit margin, Payables + turnover, Payout ratio, Pretax profit margin, Price book value ratio, Price cash flow + ratio, Price earnings ratio, Price earnings to growth ratio, Price fair value, + Price sales ratio, Price to book ratio, Price to free cash flows ratio, Price to + operating cash flows ratio, Price to sales ratio, Quick ratio, Receivables turnover, + Return on assets, Return on capital employed, Return on equity, Short term coverage + ratios, and Total debt to capitalization. [Source: Financial Modeling Prep] + """, + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=1, + help="Number of latest years/quarters.", + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + try: + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + fmp_view.display_financial_ratios( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + export=ns_parser.export, + ) + except Exception as e: + print(e, "\n") def call_growth(self, other_args: List[str]): """Process cash command""" - fmp_view.financial_statement_growth(other_args, self.ticker) + parser = argparse.ArgumentParser( + add_help=False, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + prog="growth", + description=""" Prints the growth of several financial statement items and ratios over + time. This can be either annually and quarterly. These are, among other things, Revenue + Growth (3, 5 and 10 years), inventory growth and operating cash flow growth (3, 5 and 10 + years). The following fields are expected: Asset growth, Book valueper share growth, Debt + growth, Dividendsper share growth, Ebitgrowth, Epsdiluted growth, Epsgrowth, Five y + dividendper share growth per share, Five y net income growth per share, Five y operating c + f growth per share, Five y revenue growth per share, Five y shareholders equity growth per + share, Free cash flow growth, Gross profit growth, Inventory growth, Net income growth, + Operating cash flow growth, Operating income growth, Rdexpense growth, Receivables growth, + Revenue growth, Sgaexpenses growth, Ten y dividendper share growth per share, Ten y net + income growth per share, Ten y operating c f growth per share, Ten y revenue growth per + share, Ten y shareholders equity growth per share, Three y dividendper share growth per + share, Three y net income growth per share, Three y operating c f growth per share, Three y + revenue growth per share, Three y shareholders equity growth per share, Weighted average + shares diluted growth, and Weighted average shares growth [Source: Financial Modeling Prep] + """, + ) + parser.add_argument( + "-n", + "--num", + action="store", + dest="n_num", + type=check_positive, + default=1, + help="Number of latest years/quarters.", + ) + parser.add_argument( + "-q", + "--quarter", + action="store_true", + default=False, + dest="b_quarter", + help="Quarter fundamental data flag.", + ) + try: + ns_parser = parse_known_args_and_warn( + parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED + ) + if not ns_parser: + return + fmp_view.display_financial_statement_growth( + ticker=self.ticker, + number=ns_parser.n_num, + quarterly=ns_parser.b_quarter, + export=ns_parser.export, + ) + except Exception as e: + print(e, "\n") def menu(ticker: str, start: str, interval: str): diff --git a/gamestonk_terminal/stocks/fundamental_analysis/financial_modeling_prep/fmp_model.py b/gamestonk_terminal/stocks/fundamental_analysis/financial_modeling_prep/fmp_model.py new file mode 100644 index 00000000000..864334ef351 --- /dev/null +++ b/gamestonk_terminal/stocks/fundamental_analysis/financial_modeling_prep/fmp_model.py @@ -0,0 +1,319 @@ +""" Financial Modeling Prep Model""" +__docformat__ = "numpy" +from datetime import datetime + +import FundamentalAnalysis as fa # Financial Modeling Prep +import numpy as np +import pandas as pd +import valinvest +from gamestonk_terminal import config_terminal as cfg +from gamestonk_terminal.helper_funcs import long_number_format +from gamestonk_terminal.stocks.fundamental_analysis.fa_helper import clean_df_index + + +def get_score(ticker: str) -> np.number: + """Gets value score from fmp + + Parameters + ---------- + ticker : str + Stock ticker + + Returns + ------- + np.number + Value score + """ + valstock = valinvest.Fundamental(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + return 100 * (valstock.fscore() / 9) + + +def get_profile(ticker: str) -> pd.DataFrame: + """Get ticker profile from FMP""" + return fa.profile(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + + +def get_quote(ticker) -> pd.DataFrame: + """Gets ticker quote from FMP""" + df_fa = fa.quote(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + + clean_df_index(df_fa) + + df_fa.loc["Market cap"][0] = long_number_format(df_fa.loc["Market cap"][0]) + df_fa.loc["Shares outstanding"][0] = long_number_format( + df_fa.loc["Shares outstanding"][0] + ) + df_fa.loc["Volume"][0] = long_number_format(df_fa.loc["Volume"][0]) + # Check if there is a valid earnings announcement + if df_fa.loc["Earnings announcement"][0]: + earning_announcement = datetime.strptime( + df_fa.loc["Earnings announcement"][0][0:19], "%Y-%m-%dT%H:%M:%S" + ) + df_fa.loc["Earnings announcement"][ + 0 + ] = f"{earning_announcement.date()} {earning_announcement.time()}" + return df_fa + + +def get_enterprise(ticker: str, number: int, quarterly: bool = False) -> pd.DataFrame: + """Financial Modeling Prep ticker enterprise + + Parameters + ---------- + ticker : str + Fundamental analysis ticker symbol + number: int + Number to get + quarterly: bool + Flag to get quarterly data + + Returns + ---------- + pd.DataFrame: + Dataframe of enterprise information + """ + + if quarterly: + df_fa = fa.enterprise( + ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" + ) + else: + df_fa = fa.enterprise(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + + df_fa = clean_metrics_df(df_fa, num=number, mask=False) + return df_fa + + +def get_dcf(ticker: str, number: int, quarterly: bool = False) -> pd.DataFrame: + """Get stocks dcf from FMP + + Parameters + ---------- + ticker : str + Stock ticker + number : int + Number to get + quarterly : bool, optional + Flag to get quarterly data, by default False + + Returns + ------- + pd.DataFrame + Dataframe of dcf data + """ + if quarterly: + df_fa = fa.discounted_cash_flow( + ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" + ) + else: + df_fa = fa.discounted_cash_flow(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + + df_fa = clean_metrics_df(df_fa, num=number, mask=False) + return df_fa + + +def get_income(ticker: str, number: int, quarterly: bool = False) -> pd.DataFrame: + """Get income statements + + Parameters + ---------- + ticker : str + Stock ticker + number : int + Number to get + quarterly : bool, optional + Flag to get quarterly data, by default False + + Returns + ------- + pd.DataFrame + Dataframe of income statements + """ + if quarterly: + df_fa = fa.income_statement( + ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" + ) + else: + df_fa = fa.income_statement(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + + df_fa = clean_metrics_df(df_fa, num=number) + return df_fa + + +def get_balance(ticker: str, number: int, quarterly: bool = False) -> pd.DataFrame: + """Get balance sheets + + Parameters + ---------- + ticker : str + Stock ticker + number : int + Number to get + quarterly : bool, optional + Flag to get quarterly data, by default False + + Returns + ------- + pd.DataFrame + Dataframe of balance sheets + """ + if quarterly: + df_fa = fa.balance_sheet_statement( + ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" + ) + else: + df_fa = fa.balance_sheet_statement(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + + df_fa = clean_metrics_df(df_fa, num=number) + return df_fa + + +def get_cash(ticker: str, number: int, quarterly: bool = False) -> pd.DataFrame: + """Get cash flow + + Parameters + ---------- + ticker : str + Stock ticker + number : int + Number to get + quarterly : bool, optional + Flag to get quarterly data, by default False + + Returns + ------- + pd.DataFrame + Dataframe of company cash flow + """ + if quarterly: + df_fa = fa.cash_flow_statement( + ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" + ) + else: + df_fa = fa.cash_flow_statement(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + + df_fa = clean_metrics_df(df_fa, num=number) + return df_fa + + +def get_key_metrics(ticker: str, number: int, quarterly: bool = False) -> pd.DataFrame: + """Get key metrics + + Parameters + ---------- + ticker : str + Stock ticker + number : int + Number to get + quarterly : bool, optional + Flag to get quarterly data, by default False + + Returns + ------- + pd.DataFrame + Dataframe of key metrics + """ + if quarterly: + df_fa = fa.key_metrics( + ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" + ) + else: + df_fa = fa.key_metrics(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + + df_fa = clean_metrics_df(df_fa, num=number) + return df_fa + + +def get_key_ratios(ticker: str, number: int, quarterly: bool = False) -> pd.DataFrame: + """Get key ratios + + Parameters + ---------- + ticker : str + Stock ticker + number : int + Number to get + quarterly : bool, optional + Flag to get quarterly data, by default False + + Returns + ------- + pd.DataFrame + Dataframe of key ratios + """ + if quarterly: + df_fa = fa.financial_ratios( + ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" + ) + else: + df_fa = fa.financial_ratios(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + + df_fa = clean_metrics_df(df_fa, num=number) + return df_fa + + +def get_financial_growth( + ticker: str, number: int, quarterly: bool = False +) -> pd.DataFrame: + """Get financial statement growth + + Parameters + ---------- + ticker : str + Stock ticker + number : int + Number to get + quarterly : bool, optional + Flag to get quarterly data, by default False + + Returns + ------- + pd.DataFrame + Dataframe of financial statement growth + """ + if quarterly: + df_fa = fa.financial_statement_growth( + ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" + ) + else: + df_fa = fa.financial_statement_growth(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + + df_fa = clean_metrics_df(df_fa, num=number) + return df_fa + + +def clean_metrics_df(df_fa: pd.DataFrame, num: int, mask: bool = True) -> pd.DataFrame: + """Clean metrics data frame + + Parameters + ---------- + df_fa : pd.DataFrame + Metrics data frame + num : int + Number of columns to clean + mask : bool, optional + Apply mask, by default True + + Returns + ------- + pd.DataFrame + Cleaned metrics data frame + """ + + df_fa = df_fa.iloc[:, 0:num] + if mask: + df_fa = df_fa.mask(df_fa.astype(object).eq(num * ["None"])).dropna() + df_fa = df_fa.mask(df_fa.astype(object).eq(num * ["0"])).dropna() + df_fa = df_fa.applymap(lambda x: long_number_format(x)) + clean_df_index(df_fa) + df_fa.columns.name = "Fiscal Date Ending" + df_fa = df_fa.rename( + index={ + "Enterprise value over e b i t d a": "Enterprise value over EBITDA", + "Net debt to e b i t d a": "Net debt to EBITDA", + "D c f": "DCF", + "Net income per e b t": "Net income per EBT", + } + ) + + return df_fa diff --git a/gamestonk_terminal/stocks/fundamental_analysis/financial_modeling_prep/fmp_view.py b/gamestonk_terminal/stocks/fundamental_analysis/financial_modeling_prep/fmp_view.py index 6b7c60330eb..eb10969c643 100644 --- a/gamestonk_terminal/stocks/fundamental_analysis/financial_modeling_prep/fmp_view.py +++ b/gamestonk_terminal/stocks/fundamental_analysis/financial_modeling_prep/fmp_view.py @@ -1,22 +1,19 @@ """ Financial Modeling Prep View """ __docformat__ = "numpy" -import argparse -from typing import List -from datetime import datetime +import os + import pandas as pd -import valinvest -import FundamentalAnalysis as fa # Financial Modeling Prep -from gamestonk_terminal import config_terminal as cfg -from gamestonk_terminal.stocks.fundamental_analysis.fa_helper import clean_df_index -from gamestonk_terminal.helper_funcs import ( - long_number_format, - check_positive, - parse_known_args_and_warn, +from tabulate import tabulate + +from gamestonk_terminal.helper_funcs import export_data +from gamestonk_terminal.stocks.fundamental_analysis.financial_modeling_prep import ( + fmp_model, ) +import gamestonk_terminal.feature_flags as gtff -def valinvest_score(other_args: List[str], ticker: str): +def valinvest_score(ticker: str): """Value investing tool based on Warren Buffett, Joseph Piotroski and Benjamin Graham thoughts [Source: FMP] Parameters @@ -26,30 +23,12 @@ def valinvest_score(other_args: List[str], ticker: str): ticker : str Fundamental analysis ticker symbol """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="score", - description=""" - Value investing tool based on Warren Buffett, Joseph Piotroski and Benjamin Graham thoughts [Source: FMP] - """, - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - valstock = valinvest.Fundamental(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) - score = 100 * (valstock.fscore() / 9) - print(f"Score: {score:.2f}".rstrip("0").rstrip(".") + " %") - print("") - - except Exception as e: - print(e, "\n") + score = fmp_model.get_score(ticker) + print(f"Score: {score:.2f}".rstrip("0").rstrip(".") + " %") + print("") -def profile(other_args: List[str], ticker: str): +def display_profile(ticker: str): """Financial Modeling Prep ticker profile Parameters @@ -59,718 +38,289 @@ def profile(other_args: List[str], ticker: str): ticker : str Fundamental analysis ticker symbol """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="profile", - description=""" - Prints information about, among other things, the industry, sector exchange and company - description. The following fields are expected: Address, Beta, Ceo, Changes, Cik, City - Company name, Country, Currency, Cusip, Dcf, Dcf diff, Default image, Description, - Exchange, Exchange short name, Full time employees, Image, Industry, Ipo date, Isin, - Last div, Mkt cap, Phone, Price, Range, Sector, State, Symbol, Vol avg, Website, Zip. - [Source: Financial Modeling Prep] - """, - ) + profile = fmp_model.get_profile(ticker) + if gtff.USE_TABULATE_DF: + print( + tabulate( + profile.drop(index=["description", "image"]), + headers=[], + tablefmt="fancy_grid", + ) + ) + else: + print(profile.drop(index=["description", "image"]).to_string(header=False)) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - df_fa = fa.profile(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) - clean_df_index(df_fa) - - print(df_fa.drop(index=["Description", "Image"]).to_string(header=False)) - print(f"\nImage: {df_fa.loc['Image'][0]}") - print(f"\nDescription: {df_fa.loc['Description'][0]}") - print("") - - except Exception as e: - print(e, "\n") + print(f"\nImage: {profile.loc['image'][0]}") + print(f"\nDescription: {profile.loc['description'][0]}") + print("") -def quote(other_args: List[str], ticker: str): +def display_quote(ticker: str): """Financial Modeling Prep ticker quote Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="quote", - description=""" - Prints actual information about the company which is, among other things, the day high, - market cap, open and close price and price-to-equity ratio. The following fields are - expected: Avg volume, Change, Changes percentage, Day high, Day low, Earnings - announcement, Eps, Exchange, Market cap, Name, Open, Pe, Previous close, Price, Price - avg200, Price avg50, Shares outstanding, Symbol, Timestamp, Volume, Year high, and Year - low. [Source: Financial Modeling Prep] - """, - ) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - df_fa = fa.quote(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) - - clean_df_index(df_fa) - - df_fa.loc["Market cap"][0] = long_number_format(df_fa.loc["Market cap"][0]) - df_fa.loc["Shares outstanding"][0] = long_number_format( - df_fa.loc["Shares outstanding"][0] - ) - df_fa.loc["Volume"][0] = long_number_format(df_fa.loc["Volume"][0]) - # Check if there is a valid earnings announcement - if df_fa.loc["Earnings announcement"][0]: - earning_announcement = datetime.strptime( - df_fa.loc["Earnings announcement"][0][0:19], "%Y-%m-%dT%H:%M:%S" - ) - df_fa.loc["Earnings announcement"][ - 0 - ] = f"{earning_announcement.date()} {earning_announcement.time()}" - print(df_fa.to_string(header=False)) - print("") - - except Exception as e: - print(e, "\n") + quote = fmp_model.get_quote(ticker) + if gtff.USE_TABULATE_DF: + print(tabulate(quote, headers=[], tablefmt="fancy_grid")) + else: + print(quote.to_string(header=False)) + print("") -def enterprise(other_args: List[str], ticker: str): +def display_enterprise( + ticker: str, number: int, quarterly: bool = False, export: str = "" +): """Financial Modeling Prep ticker enterprise Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number: int + Number to get + quarterly: bool + Flag to get quarterly data + export: str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="enterprise", - description=""" - Prints stock price, number of shares, market capitalization and - enterprise value over time. The following fields are expected: Add total debt, - Enterprise value, Market capitalization, Minus cash and cash equivalents, Number - of shares, Stock price, and Symbol. [Source: Financial Modeling Prep] - """, - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=1, - help="Number of latest years/quarters.", - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 40 - - if ns_parser.b_quarter: - df_fa = fa.enterprise( - ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" - ) - else: - df_fa = fa.enterprise(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) - - df_fa = clean_metrics_df(df_fa, num=ns_parser.n_num, mask=False) - print(df_fa) - print("") - - except Exception as e: - print(e, "\n") + df_fa = fmp_model.get_enterprise(ticker, number, quarterly) + df_fa = df_fa[df_fa.columns[::-1]] + if gtff.USE_TABULATE_DF: + print(tabulate(df_fa, headers=df_fa.columns, tablefmt="fancy_grid")) + else: + print(df_fa.to_string()) + print("") + export_data(export, os.path.dirname(os.path.abspath(__file__)), "enterprise", df_fa) -def discounted_cash_flow(other_args: List[str], ticker: str): +def display_discounted_cash_flow( + ticker: str, number: int, quarterly: bool = False, export: str = "" +): """Financial Modeling Prep ticker discounted cash flow Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number: int + Number to get + quarterly: bool + Flag to get quarterly data + export: str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="dcf", - description=""" - Prints the discounted cash flow of a company over time including the DCF of today. The - following fields are expected: DCF, Stock price, and Date. [Source: Financial Modeling - Prep] - """, - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=1, - help="Number of latest years/quarters.", - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", - ) + dcf = fmp_model.get_dcf(ticker, number, quarterly) + dcf = dcf[dcf.columns[::-1]] + if gtff.USE_TABULATE_DF: + print(tabulate(dcf, headers=[], tablefmt="fancy_grid")) + else: + print(dcf.to_string()) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 40 - - if ns_parser.b_quarter: - df_fa = fa.discounted_cash_flow( - ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" - ) - else: - df_fa = fa.discounted_cash_flow(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) - - df_fa = clean_metrics_df(df_fa, num=ns_parser.n_num, mask=False) - print(df_fa) - print("") - - except Exception as e: - print(e, "\n") + print("") + export_data(export, os.path.dirname(os.path.abspath(__file__)), "dcf", dcf) -def income_statement(other_args: List[str], ticker: str): +def display_income_statement( + ticker: str, number: int, quarterly: bool = False, export: str = "" +): """Financial Modeling Prep ticker income statement Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number: int + Number to get + quarterly: bool + Flag to get quarterly data + export: str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="inc", - description=""" - Prints a complete income statement over time. This can be either quarterly or annually. - The following fields are expected: Accepted date, Cost and expenses, Cost of - revenue, Depreciation and amortization, Ebitda, Ebitdaratio, Eps, Epsdiluted, Filling - date, Final link, General and administrative expenses, Gross profit, Gross profit - ratio, Income before tax, Income before tax ratio, Income tax expense, Interest - expense, Link, Net income, Net income ratio, Operating expenses, Operating income, - Operating income ratio, Other expenses, Period, Research and development expenses, - Revenue, Selling and marketing expenses, Total other income expenses net, Weighted - average shs out, Weighted average shs out dil [Source: Financial Modeling Prep] - """, - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=1, - help="Number of latest years/quarters.", - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 40 - - if ns_parser.b_quarter: - df_fa = fa.income_statement( - ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" + income = fmp_model.get_income(ticker, number, quarterly) + income = income[income.columns[::-1]] + if gtff.USE_TABULATE_DF: + print( + tabulate( + income.drop(index=["Final link", "Link"]), + headers=income.columns, + tablefmt="fancy_grid", ) - else: - df_fa = fa.income_statement(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + ) - df_fa = clean_metrics_df(df_fa, num=ns_parser.n_num) + else: - print(df_fa.drop(index=["Final link", "Link"]).to_string()) + print(income.drop(index=["Final link", "Link"]).to_string()) - pd.set_option("display.max_colwidth", None) - print("") - print(df_fa.loc["Final link"].to_frame().to_string()) - print("") - print(df_fa.loc["Link"].to_frame().to_string()) - print("") - - except Exception as e: - print(e, "\n") + pd.set_option("display.max_colwidth", None) + print("") + print(income.loc["Final link"].to_frame().to_string()) + print("") + print(income.loc["Link"].to_frame().to_string()) + print("") + export_data(export, os.path.dirname(os.path.abspath(__file__)), "income", income) -def balance_sheet(other_args: List[str], ticker: str): +def display_balance_sheet( + ticker: str, number: int, quarterly: bool = False, export: str = "" +): """Financial Modeling Prep ticker balance sheet Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number: int + Number to get + quarterly: bool + Flag to get quarterly data + export: str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="bal", - description=""" - Prints a complete balance sheet statement over time. This can be - either quarterly or annually. The following fields are expected: Accepted date, - Account payables, Accumulated other comprehensive income loss, Cash and cash - equivalents, Cash and short term investments, Common stock, Deferred revenue, - Deferred revenue non current, Deferred tax liabilities non current, Filling date, - Final link, Goodwill, Goodwill and intangible assets, Intangible assets, Inventory, - Link, Long term debt, Long term investments, Net debt, Net receivables, Other assets, - Other current assets, Other current liabilities, Other liabilities, Other non current - assets, Other non current liabilities, Othertotal stockholders equity, Period, Property - plant equipment net, Retained earnings, Short term debt, Short term investments, Tax - assets, Tax payables, Total assets, Total current assets, Total current liabilities, - Total debt, Total investments, Total liabilities, Total liabilities and stockholders - equity, Total non current assets, Total non current liabilities, and Total stockholders - equity. [Source: Financial Modeling Prep] - """, - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=1, - help="Number of latest years/quarters.", - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 40 - - if ns_parser.b_quarter: - df_fa = fa.balance_sheet_statement( - ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" - ) - else: - df_fa = fa.balance_sheet_statement( - ticker, cfg.API_KEY_FINANCIALMODELINGPREP + balance = fmp_model.get_balance(ticker, number, quarterly) + balance = balance[balance.columns[::-1]] + if gtff.USE_TABULATE_DF: + print( + tabulate( + balance.drop(index=["Final link", "Link"]), + headers=balance.columns, + tablefmt="fancy_grid", ) + ) - df_fa = clean_metrics_df(df_fa, num=ns_parser.n_num) + else: + print(balance.drop(index=["Final link", "Link"]).to_string()) - print(df_fa.drop(index=["Final link", "Link"]).to_string()) - - pd.set_option("display.max_colwidth", None) - print("") - print(df_fa.loc["Final link"].to_frame().to_string()) - print("") - print(df_fa.loc["Link"].to_frame().to_string()) - print("") - - except Exception as e: - print(e, "\n") + pd.set_option("display.max_colwidth", None) + print("") + print(balance.loc["Final link"].to_frame().to_string()) + print("") + print(balance.loc["Link"].to_frame().to_string()) + print("") + export_data(export, os.path.dirname(os.path.abspath(__file__)), "balance", balance) -def cash_flow(other_args: List[str], ticker: str): +def display_cash_flow( + ticker: str, number: int, quarterly: bool = False, export: str = "" +): """Financial Modeling Prep ticker cash flow Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number: int + Number to get + quarterly: bool + Flag to get quarterly data + export: str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="cash", - description=""" - Prints a complete cash flow statement over time. This can be either - quarterly or annually. The following fields are expected: Accepted date, Accounts - payables, Accounts receivables, Acquisitions net, Capital expenditure, Cash at - beginning of period, Cash at end of period, Change in working capital, Common stock - issued, Common stock repurchased, Debt repayment, Deferred income tax, Depreciation and - amortization, Dividends paid, Effect of forex changes on cash, Filling date, Final - link, Free cash flow, Inventory, Investments in property plant and equipment, Link, Net - cash provided by operating activities, Net cash used for investing activities, Net cash - used provided by financing activities, Net change in cash, Net income, Operating cash - flow, Other financing activities, Other investing activities, Other non cash items, - Other working capital, Period, Purchases of investments, Sales maturities of - investments, Stock based compensation. [Source: Financial Modeling Prep] - """, - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=1, - help="Number of latest years/quarters.", - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 40 - - if ns_parser.b_quarter: - df_fa = fa.cash_flow_statement( - ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" + cash = fmp_model.get_cash(ticker, number, quarterly) + cash = cash[cash.columns[::-1]] + if gtff.USE_TABULATE_DF: + print( + tabulate( + cash.drop(index=["Final link", "Link"]), + headers=cash.columns, + tablefmt="fancy_grid", ) - else: - df_fa = fa.cash_flow_statement(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) + ) + else: + print(cash.drop(index=["Final link", "Link"]).to_string()) - df_fa = clean_metrics_df(df_fa, num=ns_parser.n_num) - - print(df_fa.drop(index=["Final link", "Link"]).to_string()) - - pd.set_option("display.max_colwidth", None) - print("") - print(df_fa.loc["Final link"].to_frame().to_string()) - print("") - print(df_fa.loc["Link"].to_frame().to_string()) - print("") - - except Exception as e: - print(e, "\n") + pd.set_option("display.max_colwidth", None) + print("") + print(cash.loc["Final link"].to_frame().to_string()) + print("") + print(cash.loc["Link"].to_frame().to_string()) + print("") + export_data(export, os.path.dirname(os.path.abspath(__file__)), "cash", cash) -def key_metrics(other_args: List[str], ticker: str): +def display_key_metrics( + ticker: str, number: int, quarterly: bool = False, export: str = "" +): """Financial Modeling Prep ticker key metrics Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number: int + Number to get + quarterly: bool + Flag to get quarterly data + export: str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="metrics", - description=""" - Prints a list of the key metrics of a company over time. This can be either - quarterly or annually. This includes, among other things, Return on Equity (ROE), - Working Capital, Current Ratio and Debt to Assets. The following fields are expected: - Average inventory, Average payables, Average receivables, Book value per share, Capex - per share, Capex to depreciation, Capex to operating cash flow, Capex to revenue, Cash - per share, Current ratio, Days of inventory on hand, Days payables outstanding, Days - sales outstanding, Debt to assets, Debt to equity, Dividend yield, Earnings yield, - Enterprise value, Enterprise value over EBITDA, Ev to free cash flow, Ev to operating - cash flow, Ev to sales, Free cash flow per share, Free cash flow yield, Graham net net, - Graham number, Income quality, Intangibles to total assets, Interest debt per share, - Inventory turnover, Market cap, Net current asset value, Net debt to EBITDA, Net income - per share, Operating cash flow per share, Payables turnover, Payout ratio, Pb ratio, Pe - ratio, Pfcf ratio, Pocfratio, Price to sales ratio, Ptb ratio, Receivables turnover, - Research and development to revenue, Return on tangible assets, Revenue per share, - Roe, Roic, Sales general and administrative to revenue, Shareholders equity per - share, Stock based compensation to revenue, Tangible book value per share, and Working - capital. [Source: Financial Modeling Prep] - """, - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=1, - help="Number of latest years/quarters.", - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", + key_metrics = fmp_model.get_key_metrics(ticker, number, quarterly) + key_metrics = key_metrics[key_metrics.columns[::-1]] + if gtff.USE_TABULATE_DF: + print(tabulate(key_metrics, headers=key_metrics.columns, tablefmt="fancy_grid")) + else: + print(key_metrics.to_string()) + print("") + export_data( + export, os.path.dirname(os.path.abspath(__file__)), "metrics", key_metrics ) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 50 - - if ns_parser.b_quarter: - df_fa = fa.key_metrics( - ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" - ) - else: - df_fa = fa.key_metrics(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) - - df_fa = clean_metrics_df(df_fa, num=ns_parser.n_num) - print(df_fa) - print("") - - except Exception as e: - print(e, "\n") - - -def financial_ratios(other_args: List[str], ticker: str): +def display_financial_ratios( + ticker: str, number: int, quarterly: bool = False, export: str = "" +): """Financial Modeling Prep ticker ratios Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number: int + Number to get + quarterly: bool + Flag to get quarterly data + export: str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="ratios", - description=""" - Prints in-depth ratios of a company over time. This can be either quarterly or - annually. This contains, among other things, Price-to-Book Ratio, Payout Ratio and - Operating Cycle. The following fields are expected: Asset turnover, Capital expenditure - coverage ratio, Cash conversion cycle, Cash flow coverage ratios, Cash flow to debt - ratio, Cash per share, Cash ratio, Company equity multiplier, Current ratio, Days of - inventory outstanding, Days of payables outstanding, Days of sales outstanding, Debt - equity ratio, Debt ratio, Dividend paid and capex coverage ratio, Dividend payout ratio, - Dividend yield, Ebit per revenue, Ebt per ebit, Effective tax rate, Enterprise value - multiple, Fixed asset turnover, Free cash flow operating cash flow ratio, Free cash - flow per share, Gross profit margin, Inventory turnover, Long term debt to - capitalization, Net income per EBT, Net profit margin, Operating cash flow per share, - Operating cash flow sales ratio, Operating cycle, Operating profit margin, Payables - turnover, Payout ratio, Pretax profit margin, Price book value ratio, Price cash flow - ratio, Price earnings ratio, Price earnings to growth ratio, Price fair value, - Price sales ratio, Price to book ratio, Price to free cash flows ratio, Price to - operating cash flows ratio, Price to sales ratio, Quick ratio, Receivables turnover, - Return on assets, Return on capital employed, Return on equity, Short term coverage - ratios, and Total debt to capitalization. [Source: Financial Modeling Prep] - """, - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=1, - help="Number of latest years/quarters.", - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", + ratios = fmp_model.get_key_ratios(ticker, number, quarterly) + ratios = ratios[ratios.columns[::-1]] + if gtff.USE_TABULATE_DF: + print(tabulate(ratios, headers=ratios.columns, tablefmt="fancy_grid")) + else: + + print(ratios.to_string()) + print("") + export_data( + export, os.path.dirname(os.path.abspath(__file__)), "grratiosowth", ratios ) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 40 - - if ns_parser.b_quarter: - df_fa = fa.financial_ratios( - ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" - ) - else: - df_fa = fa.financial_ratios(ticker, cfg.API_KEY_FINANCIALMODELINGPREP) - - df_fa = clean_metrics_df(df_fa, num=ns_parser.n_num) - print(df_fa) - print("") - - except Exception as e: - print(e, "\n") - - -def financial_statement_growth(other_args: List[str], ticker: str): +def display_financial_statement_growth( + ticker: str, number: int, quarterly: bool = False, export: str = "" +): """Financial Modeling Prep ticker growth Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol + number: int + Number to get + quarterly: bool + Flag to get quarterly data + export: str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="growth", - description=""" Prints the growth of several financial statement items and ratios over - time. This can be either annually and quarterly. These are, among other things, Revenue - Growth (3, 5 and 10 years), inventory growth and operating cash flow growth (3, 5 and 10 - years). The following fields are expected: Asset growth, Book valueper share growth, Debt - growth, Dividendsper share growth, Ebitgrowth, Epsdiluted growth, Epsgrowth, Five y - dividendper share growth per share, Five y net income growth per share, Five y operating c - f growth per share, Five y revenue growth per share, Five y shareholders equity growth per - share, Free cash flow growth, Gross profit growth, Inventory growth, Net income growth, - Operating cash flow growth, Operating income growth, Rdexpense growth, Receivables growth, - Revenue growth, Sgaexpenses growth, Ten y dividendper share growth per share, Ten y net - income growth per share, Ten y operating c f growth per share, Ten y revenue growth per - share, Ten y shareholders equity growth per share, Three y dividendper share growth per - share, Three y net income growth per share, Three y operating c f growth per share, Three y - revenue growth per share, Three y shareholders equity growth per share, Weighted average - shares diluted growth, and Weighted average shares growth [Source: Financial Modeling Prep] - """, - ) - parser.add_argument( - "-n", - "--num", - action="store", - dest="n_num", - type=check_positive, - default=1, - help="Number of latest years/quarters.", - ) - parser.add_argument( - "-q", - "--quarter", - action="store_true", - default=False, - dest="b_quarter", - help="Quarter fundamental data flag.", - ) + growth = fmp_model.get_financial_growth(ticker, number, quarterly) + growth = growth[growth.columns[::-1]] + if gtff.USE_TABULATE_DF: + print(tabulate(growth, headers=growth.columns, tablefmt="fancy_grid")) + else: - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - if ns_parser.n_num == 1: - pd.set_option("display.max_colwidth", None) - else: - pd.options.display.max_colwidth = 50 - - if ns_parser.b_quarter: - df_fa = fa.financial_statement_growth( - ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period="quarter" - ) - else: - df_fa = fa.financial_statement_growth( - ticker, cfg.API_KEY_FINANCIALMODELINGPREP - ) - - df_fa = clean_metrics_df(df_fa, num=ns_parser.n_num) - print(df_fa) - print("") - - except Exception as e: - print(e, "\n") - - -def clean_metrics_df(df_fa: pd.DataFrame, num: int, mask: bool = True) -> pd.DataFrame: - """Clean metrics data frame - - Parameters - ---------- - df_fa : pd.DataFrame - Metrics data frame - num : int - Number of columns to clean - mask : bool, optional - Apply mask, by default True - - Returns - ------- - pd.DataFrame - Cleaned metrics data frame - """ - - df_fa = df_fa.iloc[:, 0:num] - if mask: - df_fa = df_fa.mask(df_fa.astype(object).eq(num * ["None"])).dropna() - df_fa = df_fa.mask(df_fa.astype(object).eq(num * ["0"])).dropna() - df_fa = df_fa.applymap(lambda x: long_number_format(x)) - clean_df_index(df_fa) - df_fa.columns.name = "Fiscal Date Ending" - df_fa = df_fa.rename( - index={ - "Enterprise value over e b i t d a": "Enterprise value over EBITDA", - "Net debt to e b i t d a": "Net debt to EBITDA", - "D c f": "DCF", - "Net income per e b t": "Net income per EBT", - } - ) - - return df_fa + print(growth.to_string()) + print("") + export_data(export, os.path.dirname(os.path.abspath(__file__)), "growth", growth) diff --git a/gamestonk_terminal/stocks/fundamental_analysis/finviz_model.py b/gamestonk_terminal/stocks/fundamental_analysis/finviz_model.py new file mode 100644 index 00000000000..564408a3b0f --- /dev/null +++ b/gamestonk_terminal/stocks/fundamental_analysis/finviz_model.py @@ -0,0 +1,23 @@ +"""Finviz Model""" +__docformat__ = "numpy" + +import finviz +import pandas as pd + + +def get_data(ticker: str) -> pd.DataFrame: + """Get fundamental data from finviz + + Parameters + ---------- + ticker : str + Stock ticker + + Returns + ------- + pd.DataFrame + DataFrame of fundamental data + """ + d_finviz_stock = finviz.get_stock(ticker) + df_fa = pd.DataFrame.from_dict(d_finviz_stock, orient="index", columns=["Values"]) + return df_fa[df_fa.Values != "-"] diff --git a/gamestonk_terminal/stocks/fundamental_analysis/finviz_view.py b/gamestonk_terminal/stocks/fundamental_analysis/finviz_view.py index 75c00d7726a..1774d126d68 100644 --- a/gamestonk_terminal/stocks/fundamental_analysis/finviz_view.py +++ b/gamestonk_terminal/stocks/fundamental_analysis/finviz_view.py @@ -1,55 +1,31 @@ """ FinViz View """ __docformat__ = "numpy" -import argparse -from typing import List -import finviz -import pandas as pd +import os -from gamestonk_terminal.helper_funcs import parse_known_args_and_warn +from tabulate import tabulate + +from gamestonk_terminal import feature_flags as gtff +from gamestonk_terminal.helper_funcs import export_data +from gamestonk_terminal.stocks.fundamental_analysis import finviz_model -def screener(other_args: List[str], ticker: str): +def display_screen_data(ticker: str, export: str = ""): """FinViz ticker screener Parameters ---------- - other_args : List[str] - argparse other args ticker : str - Fundamental analysis ticker symbol + Stock ticker + export : str + Format to export data """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="screener", - description=""" - Print several metrics about the company. The following fields are expected: - Company, Sector, Industry, Country, Index, P/E, EPS (ttm), Insider Own, - Shs Outstand, Perf Week, Market Cap, Forward P/E, EPS next Y, Insider Trans, - Shs Float, Perf Month, Income, EPS next Q, Inst Own, Short Float, Perf Quarter, - Sales, P/S, EPS this Y, Inst Trans, Short Ratio, Perf Half Y, Book/sh, P/B, ROA, - Target Price, Perf Year, Cash/sh, P/C, ROE, 52W Range, Perf YTD, P/FCF, EPS past 5Y, - ROI, 52W High, Beta, Quick Ratio, Sales past 5Y, Gross Margin, 52W Low, ATR, - Employees, Current Ratio, Sales Q/Q, Oper. Margin, RSI (14), Volatility, Optionable, - Debt/Eq, EPS Q/Q, Profit Margin, Rel Volume, Prev Close, Shortable, LT Debt/Eq, - Earnings, Payout, Avg Volume, Price, Recom, SMA20, SMA50, SMA200, Volume, Change. - [Source: Finviz] - """, - ) + fund_data = finviz_model.get_data(ticker) + print("") + if gtff.USE_TABULATE_DF: + print(tabulate(fund_data, tablefmt="fancy_grid", showindex=True)) + else: + print(fund_data.to_string(header=False)) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - d_finviz_stock = finviz.get_stock(ticker) - df_fa = pd.DataFrame.from_dict( - d_finviz_stock, orient="index", columns=["Values"] - ) - df_fa = df_fa[df_fa.Values != "-"] - print(df_fa.to_string(header=False)) - print("") - - except Exception as e: - print(e, "\n") + print("") + export_data(export, os.path.dirname(os.path.abspath(__file__)), "data", fund_data) diff --git a/gamestonk_terminal/stocks/fundamental_analysis/market_watch_model.py b/gamestonk_terminal/stocks/fundamental_analysis/market_watch_model.py index 4a01a000a1b..1f27bd3fca0 100644 --- a/gamestonk_terminal/stocks/fundamental_analysis/market_watch_model.py +++ b/gamestonk_terminal/stocks/fundamental_analysis/market_watch_model.py @@ -1,12 +1,17 @@ """ Fundamental Analysis Market Watch Model """ __docformat__ = "numpy" -import requests -import pandas as pd -from bs4 import BeautifulSoup +import re +from typing import List, Tuple +import numpy as np +import pandas as pd +import requests +from bs4 import BeautifulSoup from gamestonk_terminal.helper_funcs import ( + clean_data_values_to_float, get_user_agent, + int_or_round_float, ) @@ -113,3 +118,222 @@ def prepare_df_financials( df_financials.loc[len(df_financials)] = constructed_row return df_financials + + +def get_sean_seah_warnings( + ticker: str, debug: bool = False +) -> Tuple[pd.DataFrame, List[str], List[str]]: + """Get financial statements and prepare Sean Seah warnings + + Parameters + ---------- + ticker : str + Ticker to look at + debug : bool, optional + [description], by default False + + Returns + ------- + pd.DataFrame + Dataframe of financials + List[str] + List of warnings + List[str] + List of debug messages + """ + # From INCOME STATEMENT, get: 'EPS (Basic)', 'Net Income', 'Interest Expense', 'EBITDA' + url_financials = ( + f"https://www.marketwatch.com/investing/stock/{ticker}/financials/income" + ) + text_soup_financials = BeautifulSoup( + requests.get(url_financials, headers={"User-Agent": get_user_agent()}).text, + "lxml", + ) + + # Define financials columns + a_financials_header = [] + for financials_header in text_soup_financials.findAll( + "th", {"class": "overflow__heading"} + ): + a_financials_header.append(financials_header.text.strip("\n").split("\n")[0]) + df_financials = pd.DataFrame(columns=a_financials_header[0:-1]) + + # Add financials values + soup_financials = text_soup_financials.findAll( + lambda tag: tag.name == "tr" and tag.get("class") == ["table__row"] + ) + soup_financials += text_soup_financials.findAll( + "tr", {"class": "table__row is-highlighted"} + ) + for financials_info in soup_financials: + financials_row = financials_info.text.split("\n") + if len(financials_row) > 5: + for item in financials_row: + if bool(re.search(r"\d", item)): + a_financials_info = financials_info.text.split("\n") + l_financials = [a_financials_info[2]] + l_financials.extend(a_financials_info[5:-2]) + # Append data values to financials + df_financials.loc[len(df_financials.index)] = l_financials + break + + l_fin = ["EPS (Basic)", "Net Income", "Interest Expense", "EBITDA"] + + if not all(elem in df_financials["Item"].values for elem in l_fin): + return pd.DataFrame(), [], [] + + # Set item name as index + df_financials = df_financials.set_index("Item") + + df_sean_seah = df_financials.loc[l_fin] + + # From BALANCE SHEET, get: 'Liabilities & Shareholders\' Equity', 'Long-Term Debt' + url_financials = ( + f"https://www.marketwatch.com/investing/stock/{ticker}/financials/balance-sheet" + ) + text_soup_financials = BeautifulSoup( + requests.get(url_financials, headers={"User-Agent": get_user_agent()}).text, + "lxml", + ) + + # Define financials columns + a_financials_header = [] + for financials_header in text_soup_financials.findAll( + "th", {"class": "overflow__heading"} + ): + a_financials_header.append(financials_header.text.strip("\n").split("\n")[0]) + + s_header_end_trend = "5-year trend" + df_financials = pd.DataFrame( + columns=a_financials_header[0 : a_financials_header.index(s_header_end_trend)] + ) + + # Add financials values + soup_financials = text_soup_financials.findAll( + lambda tag: tag.name == "tr" and tag.get("class") == ["table__row"] + ) + soup_financials += text_soup_financials.findAll( + "tr", {"class": "table__row is-highlighted"} + ) + for financials_info in soup_financials: + financials_row = financials_info.text.split("\n") + if len(financials_row) > 5: + for item in financials_row: + if bool(re.search(r"\d", item)): + a_financials_info = financials_info.text.split("\n") + l_financials = [a_financials_info[2]] + l_financials.extend(a_financials_info[5:-2]) + # Append data values to financials + df_financials.loc[len(df_financials.index)] = l_financials + break + + # Set item name as index + df_financials = df_financials.set_index("Item") + + # Create dataframe to compute meaningful metrics from sean seah book + df_sean_seah = df_sean_seah.append( + df_financials.loc[ + [ + "Total Shareholders' Equity", + "Liabilities & Shareholders' Equity", + "Long-Term Debt", + ] + ] + ) + + # Clean these metrics by parsing their values to float + df_sean_seah = df_sean_seah.applymap(lambda x: clean_data_values_to_float(x)) + + # Add additional necessary metrics + series = ( + df_sean_seah.loc["Net Income"] / df_sean_seah.loc["Total Shareholders' Equity"] + ) + series.name = "ROE" + df_sean_seah = df_sean_seah.append(series) + + series = df_sean_seah.loc["EBITDA"] / df_sean_seah.loc["Interest Expense"] + series.name = "Interest Coverage Ratio" + df_sean_seah = df_sean_seah.append(series) + + series = ( + df_sean_seah.loc["Net Income"] + / df_sean_seah.loc["Liabilities & Shareholders' Equity"] + ) + series.name = "ROA" + df_sean_seah = df_sean_seah.append(series) + + n_warnings = 0 + warnings = [] + debugged_warnings = [] + + if np.any(df_sean_seah.loc["EPS (Basic)"].diff().dropna().values < 0): + warnings.append("No consistent historical earnings per share") + n_warnings += 1 + if debug: + sa_eps = np.array2string( + df_sean_seah.loc["EPS (Basic)"].values, + formatter={"float_kind": lambda x: int_or_round_float(x)}, + ) + sa_growth = np.array2string( + df_sean_seah.loc["EPS (Basic)"].diff().dropna().values, + formatter={"float_kind": lambda x: int_or_round_float(x)}, + ) + debugged_warnings.append(f"\tEPS: {sa_eps}\n\tGrowth: {sa_growth} < 0") + + if np.any(df_sean_seah.loc["ROE"].values < 0.15): + warnings.append("NOT consistently high return on equity") + n_warnings += 1 + if debug: + sa_roe = np.array2string( + df_sean_seah.loc["ROE"].values, + formatter={"float_kind": lambda x: int_or_round_float(x)}, + ) + debugged_warnings.append(f"\tROE: {sa_roe} < 0.15") + + if np.any(df_sean_seah.loc["ROA"].values < 0.07): + warnings.append("NOT consistently high return on assets") + n_warnings += 1 + if debug: + sa_roa = np.array2string( + df_sean_seah.loc["ROA"].values, + formatter={"float_kind": lambda x: int_or_round_float(x)}, + ) + debugged_warnings.append(f"\tROA: {sa_roa} < 0.07") + + if np.any( + df_sean_seah.loc["Long-Term Debt"].values + > 5 * df_sean_seah.loc["Net Income"].values + ): + warnings.append("5x Net Income < Long-Term Debt") + n_warnings += 1 + if debug: + sa_5_net_income = np.array2string( + 5 * df_sean_seah.loc["Net Income"].values, + formatter={"float_kind": lambda x: int_or_round_float(x)}, + ) + sa_long_term_debt = np.array2string( + df_sean_seah.loc["Long-Term Debt"].values, + formatter={"float_kind": lambda x: int_or_round_float(x)}, + ) + debugged_warnings.append( + f"\t5x NET Income: {sa_5_net_income}\n\tlower than Long-Term Debt: {sa_long_term_debt}" + ) + + if np.any(df_sean_seah.loc["Interest Coverage Ratio"].values < 3): + warnings.append("Interest coverage ratio less than 3") + n_warnings += 1 + if debug: + sa_interest_coverage_ratio = np.array2string( + 100 * df_sean_seah.loc["Interest Coverage Ratio"].values, + formatter={"float_kind": lambda x: int_or_round_float(x)}, + ) + debugged_warnings.append( + f"\tInterest Coverage Ratio: {sa_interest_coverage_ratio} < 3" + ) + + print("") + return ( + df_sean_seah.applymap(lambda x: int_or_round_float(x)), + warnings, + debugged_warnings, + ) diff --git a/gamestonk_terminal/stocks/fundamental_analysis/market_watch_view.py b/gamestonk_terminal/stocks/fundamental_analysis/market_watch_view.py index fef9b4c318f..140f9e1660c 100644 --- a/gamestonk_terminal/stocks/fundamental_analysis/market_watch_view.py +++ b/gamestonk_terminal/stocks/fundamental_analysis/market_watch_view.py @@ -6,23 +6,19 @@ So this code is not being used for the time being, it may be at a later stage. __docformat__ = "numpy" import argparse -import re from typing import List -import requests -import pandas as pd -from bs4 import BeautifulSoup -import numpy as np -from gamestonk_terminal.stocks.fundamental_analysis import market_watch_model as mwm +import pandas as pd +from tabulate import tabulate + +from gamestonk_terminal import feature_flags as gtff from gamestonk_terminal.helper_funcs import ( - get_user_agent, + financials_colored_values, parse_known_args_and_warn, patch_pandas_text_adjustment, - financials_colored_values, - clean_data_values_to_float, - int_or_round_float, ) -from gamestonk_terminal import feature_flags as gtff +from gamestonk_terminal.stocks.fundamental_analysis import market_watch_model as mwm + # pylint: disable=too-many-branches @@ -255,7 +251,7 @@ def cash(other_args: List[str], ticker: str): print(e, "\n") -def sean_seah_warnings(other_args: List[str], ticker: str): +def display_sean_seah_warnings(ticker: str, debug: bool = False): """Display Sean Seah warnings Parameters @@ -265,252 +261,33 @@ def sean_seah_warnings(other_args: List[str], ticker: str): ticker : str Stock ticker """ + financials, warnings, debugged_warnings = mwm.get_sean_seah_warnings(ticker, debug) - parser = argparse.ArgumentParser( - add_help=False, - prog="warnings", - description=""" - Sean Seah warnings. Check: Consistent historical earnings per share; - Consistently high return on equity; Consistently high return on assets; 5x Net - Income > Long-Term Debt; and Interest coverage ratio more than 3. See - https://www.drwealth.com/gone-fishing-with-buffett-by-sean-seah/comment-page-1/ - [Source: Market Watch] - """, - ) - - parser.add_argument( - "-i", - "--info", - action="store_true", - default=False, - dest="b_info", - help="provide more information about Sean Seah warning rules.", - ) - parser.add_argument( - "-d", - "--debug", - action="store_true", - default=False, - dest="b_debug", - help="print insights into warnings calculation.", - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - if ns_parser.b_info: - filepath = "fundamental_analysis/info_sean_seah.txt" - with open(filepath) as fp: - line = fp.readline() - while line: - print(f"{line.strip()}") - line = fp.readline() - print("") - - # From INCOME STATEMENT, get: 'EPS (Basic)', 'Net Income', 'Interest Expense', 'EBITDA' - url_financials = ( - f"https://www.marketwatch.com/investing/stock/{ticker}/financials/income" - ) - text_soup_financials = BeautifulSoup( - requests.get(url_financials, headers={"User-Agent": get_user_agent()}).text, - "lxml", - ) - - # Define financials columns - a_financials_header = [] - for financials_header in text_soup_financials.findAll( - "th", {"class": "overflow__heading"} - ): - a_financials_header.append( - financials_header.text.strip("\n").split("\n")[0] - ) - df_financials = pd.DataFrame(columns=a_financials_header[0:-1]) - - # Add financials values - soup_financials = text_soup_financials.findAll( - lambda tag: tag.name == "tr" and tag.get("class") == ["table__row"] - ) - soup_financials += text_soup_financials.findAll( - "tr", {"class": "table__row is-highlighted"} - ) - for financials_info in soup_financials: - financials_row = financials_info.text.split("\n") - if len(financials_row) > 5: - for item in financials_row: - if bool(re.search(r"\d", item)): - a_financials_info = financials_info.text.split("\n") - l_financials = [a_financials_info[2]] - l_financials.extend(a_financials_info[5:-2]) - # Append data values to financials - df_financials.loc[len(df_financials.index)] = l_financials - break - - l_fin = ["EPS (Basic)", "Net Income", "Interest Expense", "EBITDA"] - - if not all(elem in df_financials["Item"].values for elem in l_fin): - print("The source doesn't contain all necessary financial data") - print(url_financials, "\n") - return - - # Set item name as index - df_financials = df_financials.set_index("Item") - - df_sean_seah = df_financials.loc[l_fin] - - # From BALANCE SHEET, get: 'Liabilities & Shareholders\' Equity', 'Long-Term Debt' - url_financials = f"https://www.marketwatch.com/investing/stock/{ticker}/financials/balance-sheet" - text_soup_financials = BeautifulSoup( - requests.get(url_financials, headers={"User-Agent": get_user_agent()}).text, - "lxml", - ) - - # Define financials columns - a_financials_header = [] - for financials_header in text_soup_financials.findAll( - "th", {"class": "overflow__heading"} - ): - a_financials_header.append( - financials_header.text.strip("\n").split("\n")[0] - ) - - s_header_end_trend = "5-year trend" - df_financials = pd.DataFrame( - columns=a_financials_header[ - 0 : a_financials_header.index(s_header_end_trend) - ] - ) - - # Add financials values - soup_financials = text_soup_financials.findAll( - lambda tag: tag.name == "tr" and tag.get("class") == ["table__row"] - ) - soup_financials += text_soup_financials.findAll( - "tr", {"class": "table__row is-highlighted"} - ) - for financials_info in soup_financials: - financials_row = financials_info.text.split("\n") - if len(financials_row) > 5: - for item in financials_row: - if bool(re.search(r"\d", item)): - a_financials_info = financials_info.text.split("\n") - l_financials = [a_financials_info[2]] - l_financials.extend(a_financials_info[5:-2]) - # Append data values to financials - df_financials.loc[len(df_financials.index)] = l_financials - break - - # Set item name as index - df_financials = df_financials.set_index("Item") - - # Create dataframe to compute meaningful metrics from sean seah book - df_sean_seah = df_sean_seah.append( - df_financials.loc[ - [ - "Total Shareholders' Equity", - "Liabilities & Shareholders' Equity", - "Long-Term Debt", - ] - ] - ) - - # Clean these metrics by parsing their values to float - df_sean_seah = df_sean_seah.applymap(lambda x: clean_data_values_to_float(x)) - - # Add additional necessary metrics - series = ( - df_sean_seah.loc["Net Income"] - / df_sean_seah.loc["Total Shareholders' Equity"] - ) - series.name = "ROE" - df_sean_seah = df_sean_seah.append(series) - - series = df_sean_seah.loc["EBITDA"] / df_sean_seah.loc["Interest Expense"] - series.name = "Interest Coverage Ratio" - df_sean_seah = df_sean_seah.append(series) - - series = ( - df_sean_seah.loc["Net Income"] - / df_sean_seah.loc["Liabilities & Shareholders' Equity"] - ) - series.name = "ROA" - df_sean_seah = df_sean_seah.append(series) - - print(df_sean_seah.applymap(lambda x: int_or_round_float(x)).to_string()) - - n_warnings = 0 - print("\nWARNINGS:") - - if np.any(df_sean_seah.loc["EPS (Basic)"].diff().dropna().values < 0): - print("NO consistent historical earnings per share") - n_warnings += 1 - if ns_parser.b_debug: - sa_eps = np.array2string( - df_sean_seah.loc["EPS (Basic)"].values, - formatter={"float_kind": lambda x: int_or_round_float(x)}, - ) - print(f" EPS: {sa_eps}") - sa_growth = np.array2string( - df_sean_seah.loc["EPS (Basic)"].diff().dropna().values, - formatter={"float_kind": lambda x: int_or_round_float(x)}, - ) - print(f" Growth: {sa_growth} < 0") - - if np.any(df_sean_seah.loc["ROE"].values < 0.15): - print("NOT consistently high return on equity") - n_warnings += 1 - if ns_parser.b_debug: - sa_roe = np.array2string( - df_sean_seah.loc["ROE"].values, - formatter={"float_kind": lambda x: int_or_round_float(x)}, - ) - print(f" ROE: {sa_roe} < 0.15") - - if np.any(df_sean_seah.loc["ROA"].values < 0.07): - print("NOT consistently high return on assets") - n_warnings += 1 - if ns_parser.b_debug: - sa_roa = np.array2string( - df_sean_seah.loc["ROA"].values, - formatter={"float_kind": lambda x: int_or_round_float(x)}, - ) - print(f" ROA: {sa_roa} < 0.07") - - if np.any( - df_sean_seah.loc["Long-Term Debt"].values - > 5 * df_sean_seah.loc["Net Income"].values - ): - print("5x Net Income < Long-Term Debt") - n_warnings += 1 - if ns_parser.b_debug: - sa_5_net_income = np.array2string( - 5 * df_sean_seah.loc["Net Income"].values, - formatter={"float_kind": lambda x: int_or_round_float(x)}, - ) - print(f" 5x NET Income: {sa_5_net_income}") - sa_long_term_debt = np.array2string( - df_sean_seah.loc["Long-Term Debt"].values, - formatter={"float_kind": lambda x: int_or_round_float(x)}, - ) - print(f" lower than Long-Term Debt: {sa_long_term_debt}") - - if np.any(df_sean_seah.loc["Interest Coverage Ratio"].values < 3): - print("Interest coverage ratio less than 3") - n_warnings += 1 - if ns_parser.b_debug: - sa_interest_coverage_ratio = np.array2string( - 100 * df_sean_seah.loc["Interest Coverage Ratio"].values, - formatter={"float_kind": lambda x: int_or_round_float(x)}, - ) - print(f" Interest Coverage Ratio: {sa_interest_coverage_ratio} < 3") - - if n_warnings == 0: - print("None. Good stonk") - - print("") - - except Exception as e: - print(e) - print("") + if financials.empty: + print(f"No financials found for {ticker}\n") return + + if gtff.USE_TABULATE_DF: + print( + tabulate( + financials, + headers=financials.columns, + tablefmt="fancy_grid", + floatfmt=".2f", + ) + ) + else: + print(financials.to_string(())) + + if not warnings: + print("No warnings found. Good stonk") + return + + messages = ( + [item for pair in zip(warnings, debugged_warnings) for item in pair] + if debug + else warnings + ) + + print("Warnings:\n") + print("\n".join(messages), "\n") diff --git a/gamestonk_terminal/stocks/fundamental_analysis/yahoo_finance_model.py b/gamestonk_terminal/stocks/fundamental_analysis/yahoo_finance_model.py new file mode 100644 index 00000000000..39c42b6582e --- /dev/null +++ b/gamestonk_terminal/stocks/fundamental_analysis/yahoo_finance_model.py @@ -0,0 +1,205 @@ +"""Yahoo Finance Model""" +__docformat__ = "numpy" + +from datetime import datetime +from typing import Tuple +import yfinance as yf +import pandas as pd +from gamestonk_terminal.helper_funcs import ( + long_number_format, +) +from gamestonk_terminal.stocks.fundamental_analysis.fa_helper import clean_df_index + + +def get_info(ticker: str) -> pd.DataFrame: + """Gets ticker info + + Parameters + ---------- + ticker : str + Stock ticker + + Returns + ------- + pd.DataFrame + DataFrame of yfinance information + """ + stock = yf.Ticker(ticker) + df_info = pd.DataFrame(stock.info.items(), columns=["Metric", "Value"]) + df_info = df_info.set_index("Metric") + + clean_df_index(df_info) + + if "Last split date" in df_info.index and df_info.loc["Last split date"].values[0]: + df_info.loc["Last split date"].values[0] = datetime.fromtimestamp( + df_info.loc["Last split date"].values[0] + ).strftime("%d/%m/%Y") + + df_info = df_info.mask(df_info["Value"].astype(str).eq("[]")).dropna() + df_info[df_info.index != "Zip"] = df_info[df_info.index != "Zip"].applymap( + lambda x: long_number_format(x) + ) + + df_info = df_info.rename( + index={ + "Address1": "Address", + "Average daily volume10 day": "Average daily volume 10 day", + "Average volume10days": "Average volume 10 days", + "Price to sales trailing12 months": "Price to sales trailing 12 months", + } + ) + df_info.index = df_info.index.str.replace("eps", "EPS") + df_info.index = df_info.index.str.replace("p e", "PE") + df_info.index = df_info.index.str.replace("Peg", "PEG") + return df_info + + +def get_shareholders(ticker: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """Get shareholders from yahoo + + Parameters + ---------- + ticker : str + Stock ticker + + Returns + ------- + pd.DataFrame + Major holders + pd.DataFrame + Institutuional holders + pd.DataFrame + Mutual Fund holders + """ + stock = yf.Ticker(ticker) + + # Major holders + df_major_holders = stock.major_holders + df_major_holders[1] = df_major_holders[1].apply( + lambda x: x.replace("%", "Percentage") + ) + + # Institutional holders + df_institutional_shareholders = stock.institutional_holders + df_institutional_shareholders.columns = ( + df_institutional_shareholders.columns.str.replace("% Out", "Stake") + ) + df_institutional_shareholders["Shares"] = df_institutional_shareholders[ + "Shares" + ].apply(lambda x: long_number_format(x)) + df_institutional_shareholders["Value"] = df_institutional_shareholders[ + "Value" + ].apply(lambda x: long_number_format(x)) + df_institutional_shareholders["Stake"] = df_institutional_shareholders[ + "Stake" + ].apply(lambda x: str(f"{100 * x:.2f}") + " %") + + # Mutualfunds holders + df_mutualfund_shareholders = stock.mutualfund_holders + df_mutualfund_shareholders.columns = df_mutualfund_shareholders.columns.str.replace( + "% Out", "Stake" + ) + df_mutualfund_shareholders["Shares"] = df_mutualfund_shareholders["Shares"].apply( + lambda x: long_number_format(x) + ) + df_mutualfund_shareholders["Value"] = df_mutualfund_shareholders["Value"].apply( + lambda x: long_number_format(x) + ) + df_mutualfund_shareholders["Stake"] = df_mutualfund_shareholders["Stake"].apply( + lambda x: str(f"{100 * x:.2f}") + " %" + ) + + return df_major_holders, df_institutional_shareholders, df_mutualfund_shareholders + + +def get_sustainability(ticker) -> pd.DataFrame: + """Get sustainability metrics from yahoo + + Parameters + ---------- + ticker : [type] + Stock ticker + + Returns + ------- + pd.DataFrame + Dataframe of sustainability metrics + """ + stock = yf.Ticker(ticker) + pd.set_option("display.max_colwidth", None) + + df_sustainability = stock.sustainability + + if df_sustainability is None or df_sustainability.empty: + return pd.DataFrame() + + clean_df_index(df_sustainability) + + df_sustainability = df_sustainability.rename( + index={ + "Controversialweapons": "Controversial Weapons", + "Socialpercentile": "Social Percentile", + "Peercount": "Peer Count", + "Governancescore": "Governance Score", + "Environmentpercentile": "Environment Percentile", + "Animaltesting": "Animal Testing", + "Highestcontroversy": "Highest Controversy", + "Environmentscore": "Environment Score", + "Governancepercentile": "Governance Percentile", + "Militarycontract": "Military Contract", + } + ) + return df_sustainability + + +def get_calendar_earnings(ticker: str) -> pd.DataFrame: + """Get calendar earnings for ticker + + Parameters + ---------- + ticker : [type] + Stock ticker + + Returns + ------- + pd.DataFrame + Dataframe of calendar earnings + """ + stock = yf.Ticker(ticker) + df_calendar = stock.calendar + + if df_calendar.empty: + return pd.DataFrame() + + df_calendar.iloc[0, :] = df_calendar.iloc[0, :].apply( + lambda x: x.date().strftime("%m/%d/%Y") + ) + + df_calendar.iloc[1:, :] = df_calendar.iloc[1:, :].applymap( + lambda x: long_number_format(x) + ) + + return df_calendar + + +def get_website(ticker: str) -> str: + """Gets website of company from yfinance""" + stock = yf.Ticker(ticker) + df_info = pd.DataFrame(stock.info.items(), columns=["Metric", "Value"]) + return df_info[df_info["Metric"] == "website"]["Value"].values[0] + + +def get_hq(ticker: str) -> str: + """Gets google map url for headquarter""" + stock = yf.Ticker(ticker) + df_info = pd.DataFrame(stock.info.items(), columns=["Metric", "Value"]) + df_info = df_info.set_index("Metric") + + maps = "https://www.google.com/maps/search/" + for field in ["address1", "address2", "city", "state", "zip", "country"]: + if field in df_info.index: + maps += ( + df_info[df_info.index == field]["Value"].values[0].replace(" ", "+") + + "," + ) + return maps[:-1] diff --git a/gamestonk_terminal/stocks/fundamental_analysis/yahoo_finance_view.py b/gamestonk_terminal/stocks/fundamental_analysis/yahoo_finance_view.py index 41d76c5a168..b77ecba3e96 100644 --- a/gamestonk_terminal/stocks/fundamental_analysis/yahoo_finance_view.py +++ b/gamestonk_terminal/stocks/fundamental_analysis/yahoo_finance_view.py @@ -1,259 +1,93 @@ """ Yahoo Finance View """ __docformat__ = "numpy" -import argparse -from typing import List -from datetime import datetime import webbrowser -import yfinance as yf -import pandas as pd +from tabulate import tabulate -from gamestonk_terminal.stocks.fundamental_analysis.fa_helper import clean_df_index -from gamestonk_terminal.helper_funcs import ( - long_number_format, - parse_known_args_and_warn, -) +from gamestonk_terminal.stocks.fundamental_analysis import yahoo_finance_model +from gamestonk_terminal import feature_flags as gtff -def headquarters(other_args: List[str], ticker: str): +def open_headquarters_map(ticker: str): """Headquarters location of the company Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="hq", - description=""" - Opens in Google Maps HQ location of the company. [Source: Yahoo Finance] - """, - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - stock = yf.Ticker(ticker) - df_info = pd.DataFrame(stock.info.items(), columns=["Metric", "Value"]) - df_info = df_info.set_index("Metric") - - maps = "https://www.google.com/maps/search/" - for field in ["address1", "address2", "city", "state", "zip", "country"]: - if field in df_info.index: - maps += ( - df_info[df_info.index == field]["Value"].values[0].replace(" ", "+") - + "," - ) - webbrowser.open(maps[:-1]) - print("") - - except Exception as e: - print(e, "\n") + webbrowser.open(yahoo_finance_model.get_hq(ticker)) + print("") -def web(other_args: List[str], ticker: str): +def open_web(ticker: str): """Website of the company Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="web", - description=""" - Opens company's website. [Source: Yahoo Finance] - """, - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - stock = yf.Ticker(ticker) - df_info = pd.DataFrame(stock.info.items(), columns=["Metric", "Value"]) - webbrowser.open(df_info[df_info["Metric"] == "website"]["Value"].values[0]) - print("") - - except Exception as e: - print(e, "\n") + webbrowser.open(yahoo_finance_model.get_website(ticker)) + print("") -def info(other_args: List[str], ticker: str): +def display_info(ticker: str): """Yahoo Finance ticker info Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="info", - description=""" - Print information about the company. The following fields are expected: - Zip, Sector, Full time employees, Long business summary, City, Phone, State, Country, - Website, Max age, Address, Industry, Previous close, Regular market open, Two hundred - day average, Payout ratio, Regular market day high, Average daily volume 10 day, - Regular market previous close, Fifty day average, Open, Average volume 10 days, Beta, - Regular market day low, Price hint, Currency, Trailing PE, Regular market volume, - Market cap, Average volume, Price to sales trailing 12 months, Day low, Ask, Ask size, - Volume, Fifty two week high, Forward PE, Fifty two week low, Bid, Tradeable, Bid size, - Day high, Exchange, Short name, Long name, Exchange timezone name, Exchange timezone - short name, Is esg populated, Gmt off set milliseconds, Quote type, Symbol, Message - board id, Market, Enterprise to revenue, Profit margins, Enterprise to ebitda, 52 week - change, Forward EPS, Shares outstanding, Book value, Shares short, Shares percent - shares out, Last fiscal year end, Held percent institutions, Net income to common, - Trailing EPS, Sand p52 week change, Price to book, Held percent insiders, Next fiscal - year end, Most recent quarter, Short ratio, Shares short previous month date, Float - shares, Enterprise value, Last split date, Last split factor, Earnings quarterly growth, - Date short interest, PEG ratio, Short percent of float, Shares short prior month, - Regular market price, Logo_url. [Source: Yahoo Finance] - """, - ) + summary = "" + df_info = yahoo_finance_model.get_info(ticker) + if "Long business summary" in df_info.index: + summary = df_info.loc["Long business summary"].values[0] + df_info = df_info.drop(index=["Long business summary"]) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return + if gtff.USE_TABULATE_DF: + print(tabulate(df_info, headers=[], showindex=True, tablefmt="fancy_grid")) - stock = yf.Ticker(ticker) - df_info = pd.DataFrame(stock.info.items(), columns=["Metric", "Value"]) - df_info = df_info.set_index("Metric") + else: + print(df_info.to_string(header=False)) - clean_df_index(df_info) + if summary: + print("Business Summary:") + print(summary) - if ( - "Last split date" in df_info.index - and df_info.loc["Last split date"].values[0] - ): - df_info.loc["Last split date"].values[0] = datetime.fromtimestamp( - df_info.loc["Last split date"].values[0] - ).strftime("%d/%m/%Y") - - df_info = df_info.mask(df_info["Value"].astype(str).eq("[]")).dropna() - df_info[df_info.index != "Zip"] = df_info[df_info.index != "Zip"].applymap( - lambda x: long_number_format(x) - ) - - df_info = df_info.rename( - index={ - "Address1": "Address", - "Average daily volume10 day": "Average daily volume 10 day", - "Average volume10days": "Average volume 10 days", - "Price to sales trailing12 months": "Price to sales trailing 12 months", - } - ) - df_info.index = df_info.index.str.replace("eps", "EPS") - df_info.index = df_info.index.str.replace("p e", "PE") - df_info.index = df_info.index.str.replace("Peg", "PEG") - - pd.set_option("display.max_colwidth", None) - - if "Long business summary" in df_info.index: - print(df_info.drop(index=["Long business summary"]).to_string(header=False)) - print("") - print(df_info.loc["Long business summary"].values[0]) - print("") - else: - print(df_info.to_string(header=False)) - print("") - - except Exception as e: - print(e, "\n") + print("") -def shareholders(other_args: List[str], ticker: str): +def display_shareholders(ticker: str): """Yahoo Finance ticker shareholders Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol """ + ( + df_major_holders, + df_institutional_shareholders, + df_mutualfund_shareholders, + ) = yahoo_finance_model.get_shareholders(ticker) - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="shrs", - description="""Print Major, institutional and mutualfunds shareholders. - [Source: Yahoo Finance]""", - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - stock = yf.Ticker(ticker) - pd.set_option("display.max_colwidth", None) - - # Major holders - print("Major holders") - df_major_holders = stock.major_holders - df_major_holders[1] = df_major_holders[1].apply( - lambda x: x.replace("%", "Percentage") - ) - print(df_major_holders.to_string(index=False, header=False)) + dfs = [df_major_holders, df_institutional_shareholders, df_mutualfund_shareholders] + titles = ["Major Holders:\n", "Institutuinal Holders:\n", "Mutual Fund Holders:\n"] + print("") + for df, title in zip(dfs, titles): + print(title) + if gtff.USE_TABULATE_DF: + print( + tabulate(df, headers=df.columns, tablefmt="fancy_grid", showindex=False) + ) + else: + print(df.to_string(index=False)) print("") - # Institutional holders - print("Institutional holders") - df_institutional_shareholders = stock.institutional_holders - df_institutional_shareholders.columns = ( - df_institutional_shareholders.columns.str.replace("% Out", "Stake") - ) - df_institutional_shareholders["Shares"] = df_institutional_shareholders[ - "Shares" - ].apply(lambda x: long_number_format(x)) - df_institutional_shareholders["Value"] = df_institutional_shareholders[ - "Value" - ].apply(lambda x: long_number_format(x)) - df_institutional_shareholders["Stake"] = df_institutional_shareholders[ - "Stake" - ].apply(lambda x: str(f"{100 * x:.2f}") + " %") - print(df_institutional_shareholders.to_string(index=False)) - print("") - # Mutualfunds holders - print("Mutualfunds holders") - df_mutualfund_shareholders = stock.mutualfund_holders - df_mutualfund_shareholders.columns = ( - df_mutualfund_shareholders.columns.str.replace("% Out", "Stake") - ) - df_mutualfund_shareholders["Shares"] = df_mutualfund_shareholders[ - "Shares" - ].apply(lambda x: long_number_format(x)) - df_mutualfund_shareholders["Value"] = df_mutualfund_shareholders["Value"].apply( - lambda x: long_number_format(x) - ) - df_mutualfund_shareholders["Stake"] = df_mutualfund_shareholders["Stake"].apply( - lambda x: str(f"{100 * x:.2f}") + " %" - ) - print(df_mutualfund_shareholders.to_string(index=False)) - print("") - - except Exception as e: - print(e, "\n") - - -def sustainability(other_args: List[str], ticker: str): +def display_sustainability(ticker: str): """Yahoo Finance ticker sustainability Parameters @@ -263,112 +97,48 @@ def sustainability(other_args: List[str], ticker: str): ticker : str Fundamental analysis ticker symbol """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="sust", - description=""" - Print sustainability values of the company. The following fields are expected: - Palmoil, Controversialweapons, Gambling, Socialscore, Nuclear, Furleather, Alcoholic, - Gmo, Catholic, Socialpercentile, Peercount, Governancescore, Environmentpercentile, - Animaltesting, Tobacco, Totalesg, Highestcontroversy, Esgperformance, Coal, Pesticides, - Adult, Percentile, Peergroup, Smallarms, Environmentscore, Governancepercentile, - Militarycontract. [Source: Yahoo Finance] - """, - ) - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return + df_sustainability = yahoo_finance_model.get_sustainability(ticker) - stock = yf.Ticker(ticker) - pd.set_option("display.max_colwidth", None) + if df_sustainability.empty: + print("No sustainability data found.") + return - df_sustainability = stock.sustainability - - if df_sustainability is None: - print(f"No sustainability information in Yahoo for {ticker}", "\n") - return - - if df_sustainability.empty: - print(f"No sustainability information in Yahoo for {ticker}", "\n") - return - - clean_df_index(df_sustainability) - - df_sustainability = df_sustainability.rename( - index={ - "Controversialweapons": "Controversial Weapons", - "Socialpercentile": "Social Percentile", - "Peercount": "Peer Count", - "Governancescore": "Governance Score", - "Environmentpercentile": "Environment Percentile", - "Animaltesting": "Animal Testing", - "Highestcontroversy": "Highest Controversy", - "Environmentscore": "Environment Score", - "Governancepercentile": "Governance Percentile", - "Militarycontract": "Military Contract", - } + if gtff.USE_TABULATE_DF: + print( + tabulate( + df_sustainability, + headers=[], + tablefmt="fancy_grid", + showindex=True, + ) ) - - print(df_sustainability.to_string(header=False)) - print("") - - except Exception as e: - print(e, "\n") + else: + print(df_sustainability.to_sring(index=True)) + print("") -def calendar_earnings(other_args: List[str], ticker: str): +def display_calendar_earnings(ticker: str): """Yahoo Finance ticker calendar earnings Parameters ---------- - other_args : List[str] - argparse other args ticker : str Fundamental analysis ticker symbol """ - parser = argparse.ArgumentParser( - add_help=False, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - prog="cal", - description=""" - Calendar earnings of the company. Including revenue and earnings estimates. - [Source: Yahoo Finance] - """, - ) - - try: - ns_parser = parse_known_args_and_warn(parser, other_args) - if not ns_parser: - return - - stock = yf.Ticker(ticker) - df_calendar = stock.calendar - - if df_calendar.empty: - print(f"No earnings calendar information in Yahoo for {ticker}") - print("") - return - - df_calendar.iloc[0, 0] = df_calendar.iloc[0, 0].date().strftime("%d/%m/%Y") - df_calendar.iloc[:, 0] = df_calendar.iloc[:, 0].apply( - lambda x: long_number_format(x) - ) - - print(f"Earnings Date: {df_calendar.iloc[:, 0]['Earnings Date']}") - - avg = df_calendar.iloc[:, 0]["Earnings Average"] - low = df_calendar.iloc[:, 0]["Earnings Low"] - high = df_calendar.iloc[:, 0]["Earnings High"] - - print(f"Earnings Estimate Avg: {avg} [{low}, {high}]") + df_calendar = yahoo_finance_model.get_calendar_earnings(ticker).T + if df_calendar.empty: + print("No calendar events found.\n") + return + if gtff.USE_TABULATE_DF: print( - f"Revenue Estimate Avg: {df_calendar.iloc[:, 0]['Revenue Average']} \ - [{df_calendar.iloc[:, 0]['Revenue Low']}, {df_calendar.iloc[:, 0]['Revenue High']}]" + tabulate( + df_calendar, + showindex=False, + headers=df_calendar.columns, + tablefmt="fancy_grid", + ) ) - print("") - - except Exception as e: - print(e, "\n") + else: + print(df_calendar.to_string(index=False)) + print("") diff --git a/notebooks/templates/due_diligence.ipynb b/notebooks/templates/due_diligence.ipynb index aec66da6080..4502c1e5674 100644 --- a/notebooks/templates/due_diligence.ipynb +++ b/notebooks/templates/due_diligence.ipynb @@ -209,7 +209,7 @@ "outputs": [], "source": [ "# Annual Income Statement\n", - "av.income_statement([], ticker)" + "av.display_income_statement(ticker, number=1)" ] }, { @@ -230,7 +230,7 @@ "outputs": [], "source": [ "# Quarterly Income Statement\n", - "av.income_statement([\"-q\"], ticker)" + "av.display_income_statement(ticker, number = 1, quarterly=True)" ] }, { @@ -251,7 +251,7 @@ "outputs": [], "source": [ "# Annual Balance Sheet\n", - "av.balance_sheet([], ticker)" + "av.display_balance_sheet(ticker, number = 1)" ] }, { @@ -272,7 +272,7 @@ "outputs": [], "source": [ "# Annual cash flow\n", - "av.cash_flow([], ticker)" + "av.display_cash_flow(ticker,number=1)" ] }, { diff --git a/tests/cassettes/test_fa/test_fa_yahoo/test_sustainability.yaml b/tests/cassettes/test_fa/test_fa_yahoo/test_sustainability.yaml index 7cad38d8b4b..3823ed0d1c0 100644 --- a/tests/cassettes/test_fa/test_fa_yahoo/test_sustainability.yaml +++ b/tests/cassettes/test_fa/test_fa_yahoo/test_sustainability.yaml @@ -9636,4 +9636,10110 @@ interactions: status: code: 200 message: OK +- request: + body: null + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate, br + Connection: + - keep-alive + User-Agent: + - Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, + like Gecko) Chrome/39.0.2171.95 Safari/537.36 + method: GET + uri: https://finance.yahoo.com/quote/AAPL + 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vary: + - Accept-Encoding + x-envoy-decorator-operation: + - finance-nodejs--mtls-production-bf1.finance-k8s.svc.yahoo.local:4080/* + x-envoy-upstream-service-time: + - '523' + x-frame-options: + - SAMEORIGIN + status: + code: 200 + message: OK version: 1 diff --git a/tests/test_fa/test_fa_av_view.py b/tests/test_fa/test_fa_av_view.py index ae75db76755..38a3a08a3fb 100644 --- a/tests/test_fa/test_fa_av_view.py +++ b/tests/test_fa/test_fa_av_view.py @@ -69,40 +69,34 @@ class TestAVView(unittest.TestCase): @mock.patch("requests.get", side_effect=mocked_requests_get) def test_av_overview(self, mock_get): # pylint: disable=unused-argument - av_view.overview([], "GME") + av_view.display_overview("GME") @check_print(assert_in="Market capitalization") @mock.patch("requests.get", side_effect=mocked_requests_get) def test_av_key(self, mock_get): # pylint: disable=unused-argument - av_view.key([], "GME") + av_view.display_key("GME") - @check_print(assert_in="Gross profit") + @check_print(assert_in="netIncome") @mock.patch("requests.get", side_effect=mocked_requests_get) def test_income_statement(self, mock_get): # pylint: disable=unused-argument - av_view.income_statement([], "GME") + av_view.display_income_statement("GME", 1) - @check_print(assert_in="Total assets") + @check_print(assert_in="commonStockSharesOutstanding") @mock.patch("requests.get", side_effect=mocked_requests_get) def test_balance_sheet(self, mock_get): # pylint: disable=unused-argument - av_view.balance_sheet([], "GME") - - @check_print(assert_in="Operating cashflow") - @mock.patch("requests.get", side_effect=mocked_requests_get) - def test_cash_flow(self, mock_get): - # pylint: disable=unused-argument - av_view.cash_flow([], "GME") + av_view.display_balance_sheet("GME", 1) @check_print(assert_in="Reported EPS") @mock.patch("requests.get", side_effect=mocked_requests_get) def test_earnings(self, mock_get): # pylint: disable=unused-argument - av_view.earnings([], "GME") + av_view.display_earnings("GME", 1) @check_print(assert_in="Mscore Sub Stats") @mock.patch("requests.get", side_effect=mocked_requests_get) def test_fraud(self, mock_get): # pylint: disable=unused-argument - av_view.fraud([], "GME") + av_view.display_fraud("GME") diff --git a/tests/test_fa/test_fa_business_insider_view.py b/tests/test_fa/test_fa_business_insider_view.py index b866cf36589..f41e5758b9b 100644 --- a/tests/test_fa/test_fa_business_insider_view.py +++ b/tests/test_fa/test_fa_business_insider_view.py @@ -4,7 +4,7 @@ import unittest import vcr from gamestonk_terminal.stocks.fundamental_analysis.business_insider_view import ( - management, + display_management, ) from tests.helpers import check_print @@ -16,4 +16,4 @@ class TestFaBusinessInsiderApi(unittest.TestCase): record_mode="new_episodes", ) def test_management(self): - management([], "PLTR") + display_management("PLTR") diff --git a/tests/test_fa/test_fa_fmp_view.py b/tests/test_fa/test_fa_fmp_view.py index 2d01fc14cf8..8710819f417 100644 --- a/tests/test_fa/test_fa_fmp_view.py +++ b/tests/test_fa/test_fa_fmp_view.py @@ -1,31 +1,11 @@ """ fundamental_analysis/financial_modeling_prep/fmp_view.py tests """ -import sys import unittest -# Not testing these tests further. I do not have a fmp key -from contextlib import contextmanager - -import vcr - from gamestonk_terminal.stocks.fundamental_analysis.financial_modeling_prep import ( fmp_view, ) -from tests.helpers import check_print - - -@contextmanager -def replace_stdin(target): - orig = sys.stdin - sys.stdin = target - yield - sys.stdin = orig class TestFMPView(unittest.TestCase): - @check_print(assert_in="Ticker should be a NASDAQ") - @vcr.use_cassette( - "tests/cassettes/test_fa/test_fa_fmp/test_fmp_valinvest.yaml", - record_mode="new_episodes", - ) def test_fmp_valinvest_score(self): - fmp_view.valinvest_score([], "GME") + self.assertRaises(ValueError, fmp_view.valinvest_score, "GME") diff --git a/tests/test_fa/test_fa_yahoo_finance_view.py b/tests/test_fa/test_fa_yahoo_finance_view.py index 577beb850de..cb0b847a211 100644 --- a/tests/test_fa/test_fa_yahoo_finance_view.py +++ b/tests/test_fa/test_fa_yahoo_finance_view.py @@ -5,9 +5,9 @@ import vcr # pylint: disable=unused-import from gamestonk_terminal.stocks.fundamental_analysis.yahoo_finance_view import ( # noqa: F401 - calendar_earnings, - info, - sustainability, + display_calendar_earnings, + display_info, + display_sustainability, ) from tests.helpers import check_print @@ -19,15 +19,15 @@ class TestFaYahooFinanceApi(unittest.TestCase): record_mode="new_episodes", ) def test_info(self): - info([], "PLTR") + display_info("PLTR") - @check_print(assert_in="GME") + @check_print(assert_in="Military contract") @vcr.use_cassette( "tests/cassettes/test_fa/test_fa_yahoo/test_sustainability.yaml", record_mode="new_episodes", ) def test_sustainability(self): - sustainability([], "GME") + display_sustainability("AAPL") @check_print(assert_in="Earnings Date") @vcr.use_cassette( @@ -35,4 +35,4 @@ class TestFaYahooFinanceApi(unittest.TestCase): record_mode="new_episodes", ) def test_calendar_earnings(self): - calendar_earnings([], "GME") + display_calendar_earnings("GME")