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Add FMP to Fundamental Analysis income, balance and cash

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This commit is contained in:
didier
2020-12-27 20:17:37 +00:00
parent ea13cdb3c6
commit d342da5401
3 changed files with 116 additions and 42 deletions
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9
stock_market_cli.py
View File

@@ -59,10 +59,13 @@ 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(" income income statements of the company")
print(" balance balance sheet of the company")
print(" cash cash flow of the company")
print("- financial statement -")
print(" income income statements of the company (default: AV)")
print(" balance balance sheet of the company (default: AV)")
print(" cash cash flow of the company (default: AV)")
print("- ratios -")
print("\nTechnical Analysis:")
print(" sma simple moving average")

144
stock_market_fundamental_analysis.py
View File

@@ -3,6 +3,7 @@ from alpha_vantage.fundamentaldata import FundamentalData
import config_bot as cfg
import argparse
from stock_market_helper_funcs import *
import pandas as pd
# ---------------------------------------------------- RATINGS ----------------------------------------------------
def ratings(l_args, s_ticker):
@@ -33,8 +34,9 @@ def income_statement(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='income',
description=""" """)
parser.add_argument('-n', "--num", action="store", dest="n_num", type=check_positive, default=1, help='Number of informations')
parser.add_argument('-n', "--num", action="store", dest="n_num", type=check_positive, default=1, help='Number of latest info')
parser.add_argument('-q', action="store_true", default=False, dest="b_quarter", help='Quarter fundamental data')
parser.add_argument('--fmp', action="store_true", default=False, dest="b_fmp", help='Use Financial Modeling Prep instead of Alpha Vantage')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
@@ -46,26 +48,48 @@ def income_statement(l_args, s_ticker):
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
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)
if ns_parser.n_num == 1:
pd.set_option('display.max_colwidth', -1)
else:
df_fd, d_fd_metadata = fd.get_income_statement_annual(symbol=s_ticker)
pd.options.display.max_colwidth = 40
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]
# 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')
else:
df_fd = 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"
# 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)
else:
df_fd, 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"
print(df_fd)
print("")
except:
print("")
return
# ---------------------------------------------------- BALANCE_SHEET ----------------------------------------------------
def balance_sheet(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='balance',
@@ -73,6 +97,7 @@ def balance_sheet(l_args, s_ticker):
parser.add_argument('-n', "--num", action="store", dest="n_num", type=check_positive, default=1, help='Number of informations')
parser.add_argument('-q', action="store_true", default=False, dest="b_quarter", help='Quarter fundamental data')
parser.add_argument('--fmp', action="store_true", default=False, dest="b_fmp", help='Use Financial Modeling Prep instead of Alpha Vantage')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
@@ -84,25 +109,44 @@ def balance_sheet(l_args, s_ticker):
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
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)
else:
df_fd, d_fd_metadata = fd.get_balance_sheet_annual(symbol=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')
else:
df_fd = 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"
# 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)
else:
df_fd, 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_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]
print(df_fd)
print("")
except:
print("")
return
# ---------------------------------------------------- CASH_FLOW ----------------------------------------------------
def cash_flow(l_args, s_ticker):
@@ -111,6 +155,7 @@ def cash_flow(l_args, s_ticker):
parser.add_argument('-n', "--num", action="store", dest="n_num", type=check_positive, default=1, help='Number of informations')
parser.add_argument('-q', action="store_true", default=False, dest="b_quarter", help='Quarter fundamental data')
parser.add_argument('--fmp', action="store_true", default=False, dest="b_fmp", help='Use Financial Modeling Prep instead of Alpha Vantage')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
@@ -122,21 +167,44 @@ def cash_flow(l_args, s_ticker):
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
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)
if ns_parser.n_num == 1:
pd.set_option('display.max_colwidth', -1)
else:
df_fd, d_fd_metadata = fd.get_cash_flow_annual(symbol=s_ticker)
pd.options.display.max_colwidth = 40
# 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')
else:
df_fd = 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"
# 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)
else:
df_fd, 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_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]
print(df_fd)
print("")
except:
print("")
return
return

5
stock_market_helper_funcs.py
View File

@@ -129,8 +129,11 @@ def b_is_stock_market_open():
# -----------------------------------------------------------------------------------------------------------------------
def long_number_format(s_num):
if not isinstance(s_num, str):
s_num = str(s_num)
if s_num.lstrip("-").isdigit():
n_num = int(s_num)
n_num /= 1.0
magnitude = 0
while abs(n_num) >= 1000:
magnitude += 1
@@ -138,5 +141,5 @@ def long_number_format(s_num):
if n_num.is_integer():
return '%d%s' % (n_num, ['', ' K', ' M', ' B', ' T', ' P'][magnitude])
else:
return '%.2f%s' % (n_num, ['', ' K', ' M', ' B', ' T', ' P'][magnitude])
return '%.3f%s' % (n_num, ['', ' K', ' M', ' B', ' T', ' P'][magnitude])
return s_num