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Add profile to fundamental analysis

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This commit is contained in:
didier
2020-12-27 23:27:36 +00:00
parent ff9e5c9be3
commit d31e39a039
3 changed files with 136 additions and 102 deletions
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19
stock_market_cli.py
View File

@@ -42,6 +42,7 @@ def print_help(s_ticker, s_start, s_interval, b_is_market_open):
print("\nMenu:")
print(" gainers show latest top gainers")
print(" sectors show sectors performance")
print(" view view and load a specific stock ticker for technical analysis")
print(" clear clear a specific stock ticker from analysis")
print(" load load a specific stock ticker for analysis")
@@ -60,7 +61,8 @@ def print_help(s_ticker, s_start, s_interval, b_is_market_open):
if s_ticker:
print("\nFundamental Analysis:")
#print("- details - ")
print(" ratings company ratings from strong sell to strong buy")
print(" profile profile of the company")
print(" rating rating of the company from strong sell to strong buy")
# print("- financial statement -")
print(" income income statements of the company (default: AV)")
print(" balance balance sheet of the company (default: AV)")
@@ -124,7 +126,7 @@ def main():
# Add list of arguments that the main parser accepts
main_parser.add_argument('cmd', choices=['quit', 'help', 'gainers', 'sectors', 'view', 'load', 'clear',
'sma', 'ema', 'macd', 'vwap', 'stoch', 'rsi', 'adx',
'cci', 'aroon', 'bbands', 'ad', 'obv', 'ratings',
'cci', 'aroon', 'bbands', 'ad', 'obv', 'rating', 'profile',
'income', 'balance', 'cash', 'metrics', 'ratios', 'growth'])
# Print first welcome message and help
@@ -153,7 +155,7 @@ def main():
smm.gainers(l_args)
continue
# --------------------------------------------------- GAINERS --------------------------------------------------
# --------------------------------------------------- SECTORS --------------------------------------------------
if ns_known_args.cmd == 'sectors':
smm.sectors(l_args)
continue
@@ -187,9 +189,14 @@ def main():
# ------------------------------------------- FUNDAMENTAL ANALYSIS ---------------------------------------------
# --------------------------------------------------------------------------------------------------------------
# --------------------------------------------------- RATINGS --------------------------------------------------
elif ns_known_args.cmd == 'ratings':
smfa.ratings(l_args, s_ticker)
# -------------------------------------------------- PROFILE --------------------------------------------------
elif ns_known_args.cmd == 'profile':
smfa.profile(l_args, s_ticker)
continue
# --------------------------------------------------- RATING --------------------------------------------------
elif ns_known_args.cmd == 'rating':
smfa.rating(l_args, s_ticker)
continue
# ----------------------------------------------- INCOME_STATEMENT -----------------------------------------------

217
stock_market_fundamental_analysis.py
View File

@@ -5,9 +5,36 @@ import argparse
from stock_market_helper_funcs import *
import pandas as pd
# ---------------------------------------------------- RATINGS ----------------------------------------------------
def ratings(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='ratings',
# ---------------------------------------------------- PROFILE ----------------------------------------------------
def profile(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='profile',
description=""" """)
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
except SystemExit:
print("")
return
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}")
pd.options.display.max_colwidth = 100
try:
df_fa = fa.profile(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
print(df_fa.to_string(header=False))
print("")
except:
print("")
# ---------------------------------------------------- RATING ----------------------------------------------------
def rating(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='rating',
description=""" Gives information about the rating of a company which includes
i.a. the company rating and recommendation as well as ratings based
on a variety of ratios.""")
@@ -22,8 +49,8 @@ def ratings(l_args, s_ticker):
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
df_ratings = fa.rating(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
print(df_ratings)
df_fa = fa.rating(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
print(df_fa)
print("")
except:
print("")
@@ -56,35 +83,35 @@ def income_statement(l_args, s_ticker):
# Use Financial Modeling Prep API
if ns_parser.b_fmp:
if ns_parser.b_quarter:
df_fd = fa.income_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
df_fa = fa.income_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
else:
df_fd = fa.income_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fa = fa.income_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fd = df_fd.iloc[:, 0:ns_parser.n_num]
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fd = df_fd.applymap(lambda x: long_number_format(x))
df_fd.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fd.index.tolist()]
df_fd.index = [s_val.capitalize() for s_val in df_fd.index]
df_fd.columns.name = "Fiscal Date Ending"
df_fa = df_fa.iloc[:, 0:ns_parser.n_num]
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fa = df_fa.applymap(lambda x: long_number_format(x))
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
# Use Alpha Vantage API
else:
fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format='pandas')
if ns_parser.b_quarter:
df_fd, d_fd_metadata = fd.get_income_statement_quarterly(symbol=s_ticker)
df_fa, d_fd_metadata = fd.get_income_statement_quarterly(symbol=s_ticker)
else:
df_fd, d_fd_metadata = fd.get_income_statement_annual(symbol=s_ticker)
df_fa, d_fd_metadata = fd.get_income_statement_annual(symbol=s_ticker)
df_fd = df_fd.set_index('fiscalDateEnding')
df_fd = df_fd.head(n=ns_parser.n_num).T
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fd = df_fd.applymap(lambda x: long_number_format(x))
df_fd.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fd.index.tolist()]
df_fd.index = [s_val.capitalize() for s_val in df_fd.index]
df_fd.columns.name = "Fiscal Date Ending"
df_fa = df_fa.set_index('fiscalDateEnding')
df_fa = df_fa.head(n=ns_parser.n_num).T
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fa = df_fa.applymap(lambda x: long_number_format(x))
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fd)
print(df_fa)
print("")
except:
print("")
@@ -112,36 +139,36 @@ def balance_sheet(l_args, s_ticker):
# Use Financial Modeling Prep API
if ns_parser.b_fmp:
if ns_parser.b_quarter:
df_fd = fa.balance_sheet_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
df_fa = fa.balance_sheet_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
else:
df_fd = fa.balance_sheet_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fa = fa.balance_sheet_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fd = df_fd.iloc[:, 0:ns_parser.n_num]
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fd = df_fd.applymap(lambda x: long_number_format(x))
df_fd.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fd.index.tolist()]
df_fd.index = [s_val.capitalize() for s_val in df_fd.index]
df_fd.columns.name = "Fiscal Date Ending"
df_fa = df_fa.iloc[:, 0:ns_parser.n_num]
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fa = df_fa.applymap(lambda x: long_number_format(x))
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
# Use Alpha Vantage API
else:
fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format='pandas')
if ns_parser.b_quarter:
df_fd, d_fd_metadata = fd.get_balance_sheet_quarterly(symbol=s_ticker)
df_fa, d_fd_metadata = fd.get_balance_sheet_quarterly(symbol=s_ticker)
else:
df_fd, d_fd_metadata = fd.get_balance_sheet_annual(symbol=s_ticker)
df_fa, d_fd_metadata = fd.get_balance_sheet_annual(symbol=s_ticker)
df_fd = df_fd.set_index('fiscalDateEnding')
df_fd = df_fd.head(n=ns_parser.n_num).T
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fd = df_fd.applymap(lambda x: long_number_format(x))
df_fd.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fd.index.tolist()]
df_fd.index = [s_val.capitalize() for s_val in df_fd.index]
df_fd.columns.name = "Fiscal Date Ending"
df_fa = df_fa.set_index('fiscalDateEnding')
df_fa = df_fa.head(n=ns_parser.n_num).T
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fa = df_fa.applymap(lambda x: long_number_format(x))
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fd)
print(df_fa)
print("")
except:
print("")
@@ -175,35 +202,35 @@ def cash_flow(l_args, s_ticker):
# Use Financial Modeling Prep API
if ns_parser.b_fmp:
if ns_parser.b_quarter:
df_fd = fa.cash_flow_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
df_fa = fa.cash_flow_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
else:
df_fd = fa.cash_flow_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fa = fa.cash_flow_statement(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fd = df_fd.iloc[:, 0:ns_parser.n_num]
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fd = df_fd.applymap(lambda x: long_number_format(x))
df_fd.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fd.index.tolist()]
df_fd.index = [s_val.capitalize() for s_val in df_fd.index]
df_fd.columns.name = "Fiscal Date Ending"
df_fa = df_fa.iloc[:, 0:ns_parser.n_num]
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fa = df_fa.applymap(lambda x: long_number_format(x))
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
# Use Alpha Vantage API
else:
fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format='pandas')
if ns_parser.b_quarter:
df_fd, d_fd_metadata = fd.get_cash_flow_quarterly(symbol=s_ticker)
df_fa, d_fd_metadata = fd.get_cash_flow_quarterly(symbol=s_ticker)
else:
df_fd, d_fd_metadata = fd.get_cash_flow_annual(symbol=s_ticker)
df_fa, d_fd_metadata = fd.get_cash_flow_annual(symbol=s_ticker)
df_fd = df_fd.set_index('fiscalDateEnding')
df_fd = df_fd.head(n=ns_parser.n_num).T
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fd = df_fd.applymap(lambda x: long_number_format(x))
df_fd.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fd.index.tolist()]
df_fd.index = [s_val.capitalize() for s_val in df_fd.index]
df_fd.columns.name = "Fiscal Date Ending"
df_fa = df_fa.set_index('fiscalDateEnding')
df_fa = df_fa.head(n=ns_parser.n_num).T
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fa = df_fa.applymap(lambda x: long_number_format(x))
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fd)
print(df_fa)
print("")
except:
print("")
@@ -234,19 +261,19 @@ def key_metrics(l_args, s_ticker):
pd.options.display.max_colwidth = 40
if ns_parser.b_quarter:
df_fd = fa.key_metrics(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
df_fa = fa.key_metrics(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
else:
df_fd = fa.key_metrics(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fa = fa.key_metrics(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fd = df_fd.iloc[:, 0:ns_parser.n_num]
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fd = df_fd.applymap(lambda x: long_number_format(x))
df_fd.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fd.index.tolist()]
df_fd.index = [s_val.capitalize() for s_val in df_fd.index]
df_fd.columns.name = "Fiscal Date Ending"
df_fa = df_fa.iloc[:, 0:ns_parser.n_num]
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fa = df_fa.applymap(lambda x: long_number_format(x))
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fd)
print(df_fa)
print("")
except:
print("")
@@ -277,19 +304,19 @@ def financial_ratios(l_args, s_ticker):
pd.options.display.max_colwidth = 40
if ns_parser.b_quarter:
df_fd = fa.financial_ratios(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
df_fa = fa.financial_ratios(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
else:
df_fd = fa.financial_ratios(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fa = fa.financial_ratios(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fd = df_fd.iloc[:, 0:ns_parser.n_num]
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fd = df_fd.applymap(lambda x: long_number_format(x))
df_fd.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fd.index.tolist()]
df_fd.index = [s_val.capitalize() for s_val in df_fd.index]
df_fd.columns.name = "Fiscal Date Ending"
df_fa = df_fa.iloc[:, 0:ns_parser.n_num]
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fa = df_fa.applymap(lambda x: long_number_format(x))
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fd)
print(df_fa)
print("")
except:
print("")
@@ -320,19 +347,19 @@ def financial_statement_growth(l_args, s_ticker):
pd.options.display.max_colwidth = 40
if ns_parser.b_quarter:
df_fd = fa.financial_statement_growth(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
df_fa = fa.financial_statement_growth(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
else:
df_fd = fa.financial_statement_growth(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fa = fa.financial_statement_growth(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP)
df_fd = df_fd.iloc[:, 0:ns_parser.n_num]
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fd = df_fd.mask(df_fd.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fd = df_fd.applymap(lambda x: long_number_format(x))
df_fd.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fd.index.tolist()]
df_fd.index = [s_val.capitalize() for s_val in df_fd.index]
df_fd.columns.name = "Fiscal Date Ending"
df_fa = df_fa.iloc[:, 0:ns_parser.n_num]
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['None'])).dropna()
df_fa = df_fa.mask(df_fa.astype(object).eq(ns_parser.n_num*['0'])).dropna()
df_fa = df_fa.applymap(lambda x: long_number_format(x))
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fd)
print(df_fa)
print("")
except:
print("")

2
stock_market_helper_funcs.py
View File

@@ -151,4 +151,4 @@ def long_number_format(num):
return '%d%s' % (num, ['', ' K', ' M', ' B', ' T', ' P'][magnitude])
else:
return '%.3f%s' % (num, ['', ' K', ' M', ' B', ' T', ' P'][magnitude])
return num
return num