def fundamentalData(API_key, ticker):
from alpha_vantage.fundamentaldata import FundamentalData
import matplotlib.pyplot as plt
fd=FundamentalData(key=API_key, output_format='pandas')
option=input('1. Company Overview\n2. Earnings\n3. Income Statement\n4. Balance Sheet\n5. Cash Flow\n6. Listing Status\n7. Earnings Calendar\n8. IPO calandar\n').lower()
if option=='company overview' or option=='1':
data=fd.get_company_overview(symbol=ticker)[0]
return data
elif option=='earnings' or option=='2':
data=fd.get_earnings(symbol=ticker)
return data
elif option=='income statement' or option=='3':
period=input("1. Annual\n2. Quaterly\n").lower()
if period=='annual' or period=='1':
data=fd.get_income_statement_annual(symbol=ticker)[0].T
return data
elif period=='quaterly' or period=='2':
data=fd.get_income_statement_quaterly(symbol=ticker)[0].T
return data
else:
print("No data available")
elif option=='balance sheet' or option=='4':
period=input("1. Annual\n2. Quaterly\n").lower()
if period=='annual' or period=='1':
data=fd.get_balance_sheet_annual(symbol=ticker)[0].T
return data
elif period=='quaterly' or period=='2':
data=fd.get_balance_sheet_quaterly(symbol=ticker)[0].T
return data
else:
print("No data available")
elif option=='cash flow' or option=='5':
period=input("1. Annual\n2. Quaterly\n").lower()
if period=='annual' or period=='1':
data=fd.get_cash_flow_annual(symbol=ticker)[0].T
return data
elif period=='quaterly' or period=='2':
data=fd.get_cash_flow_quaterly(symbol=ticker)[0].T
return data
else:
print("No data available")
elif option=='listing status' or option=='6':
data=fd.get_listing_status()
return data
elif option=='earnings calendar' or option=='7':
data=fd.get_earnings_calendar()
elif option=='ipo calendar' or option=='8':
data=fd.get_ipo_calendar()
else:
print("CANNOT RECOGNIZE")
def cash_flow(l_args, s_ticker):
parser = argparse.ArgumentParser(
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, l_unknown_args) = parser.parse_known_args(l_args)
if l_unknown_args:
print(
f"The following args couldn't be interpreted: {l_unknown_args}\n"
)
return
if ns_parser.n_num == 1:
pd.set_option('display.max_colwidth', -1)
else:
pd.options.display.max_colwidth = 40
fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE,
output_format='pandas')
if ns_parser.b_quarter:
df_fa, d_fd_metadata = fd.get_cash_flow_quarterly(symbol=s_ticker)
else:
df_fa, d_fd_metadata = fd.get_cash_flow_annual(symbol=s_ticker)
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_fa)
print("")
except:
print("")
return
def cash_flow(other_args: List[str], ticker: str):
"""Alpha Vantage cash flow
Parameters
----------
other_args : List[str]
argparse other args
ticker : str
Fundamental analysis ticker symbol
"""
parser = argparse.ArgumentParser(
add_help=False,
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)
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_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)
print("")
return
def cashflow_init_annual(request):
if request.method == 'GET':
symbol = request.GET.get('symbol')
data = FundamentalData(key='I7WB8M63PERU90OY', output_format='pandas')
cashflows, vasymbol = data.get_cash_flow_annual(symbol=symbol)
for fical in cashflows['fiscalDateEnding']:
cashflow = cashflows[cashflows['fiscalDateEnding'] == fical]
for col in cashflow.columns:
if col not in ['fiscalDateEnding', 'reportedCurrency']:
if cashflow[col].values[0] == 'None':
cashflow[col] = 0.0
else:
pass
else:
pass
cash = CashFlow(
symbol=symbol,
report_type='annualReport',
fiscal_date_ending=datetime.strptime(
cashflow['fiscalDateEnding'].values[0], '%Y-%m-%d'),
reported_currency=cashflow['reportedCurrency'].values[0],
investments=cashflow['investments'].values[0],
change_in_liabilities=cashflow['changeInLiabilities'].
values[0],
cashflow_from_investment=cashflow['cashflowFromInvestment'].
values[0],
cashflow_from_financing=cashflow['cashflowFromFinancing'].
values[0],
other_cashflow_from_financing=cashflow[
'otherCashflowFromFinancing'].values[0],
change_in_operating_activities=cashflow[
'changeInOperatingActivities'].values[0],
net_income=cashflow['netIncome'].values[0],
change_in_cash=cashflow['changeInCash'].values[0],
operating_cashflow=cashflow['operatingCashflow'].values[0],
other_operating_cashflow=cashflow['otherOperatingCashflow'].
values[0],
depreciation=cashflow['depreciation'].values[0],
dividend_payout=cashflow['dividendPayout'].values[0],
stock_sale_and_purchase=cashflow['stockSaleAndPurchase'].
values[0],
change_in_inventory=cashflow['changeInInventory'].values[0],
change_in_account_receivables=cashflow[
'changeInAccountReceivables'].values[0],
change_in_net_income=cashflow['changeInNetIncome'].values[0],
capital_expenditures=cashflow['capitalExpenditures'].values[0],
change_in_receivables=cashflow['changeInReceivables'].
values[0],
change_in_exchange_rate=cashflow['changeInExchangeRate'].
values[0],
change_in_cash_and_cash_equivalents=cashflow[
'changeInCashAndCashEquivalents'].values[0])
cash.save()
return JsonResponse({'message': 'Annualy Data Save successlly'},
status=status.HTTP_200_OK)
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: 0 if x else int(x)).values
sales = df_is["totalRevenue"].apply(lambda x: 0 if x else int(x)).values
cogs = (
df_is["costofGoodsAndServicesSold"].apply(lambda x: 0 if x else int(x)).values
)
ni = df_is["netIncome"].apply(lambda x: 0 if x else int(x)).values
ca = df_bs["totalCurrentAssets"].apply(lambda x: 0 if x else int(x)).values
cl = df_bs["totalCurrentLiabilities"].apply(lambda x: 0 if x else int(x)).values
ppe = df_bs["propertyPlantEquipment"].apply(lambda x: 0 if x else int(x)).values
cash = (
df_bs["cashAndCashEquivalentsAtCarryingValue"]
.apply(lambda x: 0 if x else int(x))
.values
)
cash_and_sec = (
df_bs["cashAndShortTermInvestments"].apply(lambda x: 0 if x else int(x)).values
)
sec = [y - x for (x, y) in zip(cash, cash_and_sec)]
ta = df_bs["totalAssets"].apply(lambda x: 0 if x else int(x)).values
dep = (
df_bs["accumulatedDepreciationAmortizationPPE"]
.apply(lambda x: 0 if x else int(x))
.values
)
sga = (
df_is["sellingGeneralAndAdministrative"]
.apply(lambda x: 0 if x else int(x))
.values
)
tl = df_bs["totalLiabilities"].apply(lambda x: 0 if x else int(x)).values
icfo = (
df_is["netIncomeFromContinuingOperations"]
.apply(lambda x: 0 if x else int(x))
.values
)
cfo = df_cf["operatingCashflow"].apply(lambda x: 0 if x else 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
def get_fraud_ratios(
ticker: str,
) -> Tuple[Optional[Dict[str, float]], Optional[float], Optional[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
"""
try:
fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE,
output_format="pandas")
# pylint: disable=unbalanced-tuple-unpacking
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)
except Exception as e:
console.print(e)
return None, None, None
# pylint: disable=no-member
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_values(df_bs, "currentNetReceivables")
sales = df_values(df_is, "totalRevenue")
cogs = df_values(df_is, "costofGoodsAndServicesSold")
ni = df_values(df_is, "netIncome")
ca = df_values(df_bs, "totalCurrentAssets")
cl = df_values(df_bs, "totalCurrentLiabilities")
ppe = df_values(df_bs, "propertyPlantEquipment")
cash = df_values(df_bs, "cashAndCashEquivalentsAtCarryingValue")
cash_and_sec = df_values(df_bs, "cashAndShortTermInvestments")
sec = [y - x for (x, y) in zip(cash, cash_and_sec)]
ta = df_values(df_bs, "totalAssets")
dep = df_values(df_bs, "accumulatedDepreciationAmortizationPPE")
sga = df_values(df_is, "sellingGeneralAndAdministrative")
tl = df_values(df_bs, "totalLiabilities")
icfo = df_values(df_is, "netIncomeFromContinuingOperations")
cfo = df_values(df_cf, "operatingCashflow")
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])))
v1 = np.log(ta[0] / 1000)
v2 = ni[0] / ta[0]
v3 = cash[0] / cl[0]
x = ((v1 + 0.85) * v2) - 0.85
y = 1 + v3
mckee = x**2 / (x**2 + y**2)
return ratios, zscore, mckee
def fraud(other_args: List[str], 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]"),
)
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")
from alpha_vantage.fundamentaldata import FundamentalData
key = 'M4JP31006H3PKZ8T'
symbol = input('Ticker : ')
period = input('Period- annual, quarterly : ')
statement = input('Statement- balance sheet, income statement, cash flow : ')
fd = FundamentalData(key,output_format = 'pandas')
if period == 'annual':
if statement == 'balance sheet':
state = fd.get_balance_sheet_annual(symbol)[0].T[2:]
state.columns = list(fd.get_balance_sheet_annual(symbol)[0].T.iloc[0])
elif statement == 'income statement':
state = fd.get_income_statement_annual(symbol)[0].T[2:]
state.columns = list(fd.get_income_statement_annual(symbol)[0].T.iloc[0])
elif statement == 'cash flow':
state = fd.get_cash_flow_annual(symbol)[0].T[2:]
state.columns = list(fd.get_cash_flow_annual(symbol)[0].T.iloc[0])
else:
print("wrong input given by the user ")
elif period == 'quarterly':
if statement == 'balance sheet':
state = fd.get_balance_sheet_quarterly(symbol)[0].T[2:]
state.columns = list(fd.get_balance_sheet_quarterly(symbol)[0].T.iloc[0])
elif statement == 'income statement':
state = fd.get_income_statement_quarterly(symbol)[0].T[2:]
state.columns = list(fd.get_income_statement_quarterly(symbol)[0].T.iloc[0])
elif statement == 'cash flow':
state = fd.get_cash_flow_quarterly(symbol)[0].T[2:]
state.columns = list(fd.get_cash_flow_quarterly(symbol)[0].T.iloc[0])
else:
print('Wrong Entry')