def test_load_cashflow():
"""Test simfin.bulk.load_cashflow()"""
for dataset, variant, market in iter_all_datasets(datasets='cashflow'):
kwargs = _create_kwargs(variant=variant, market=market)
sf.load_cashflow(**kwargs)
sf.load_cashflow_banks(**kwargs)
sf.load_cashflow_insurance(**kwargs)
def get_cashflow() -> pd.DataFrame:
"""Gets the bulk cashflow statements from SimFin API
Downloads the data if you don't already have it
"""
setup_simfin()
df = sf.load_cashflow(variant='quarterly', market='us')
return df
def SF_cashflow_statment(self,
sec_id: str = 'AAPL',
period: str = 'quarterly',
market: str = 'us') -> pd.DataFrame:
"""[summary] Load cash flow statement.
Args:
sec_id (str, optional): [stock ticker]. Defaults to 'AAPL'.
period (str, optional): [annual, quarterly]. Defaults to 'quarterly'.
market (str, optional): [us, de, etc]. Defaults to 'us'.
Returns:
pd.DataFrame:
"""
df_cashflow = simfin.load_cashflow(variant=period, market=market)
df_cashflow = df_cashflow.loc[sec_id]
return df_cashflow
def load_financial_data(path: str):
sf.set_api_key('free')
sf.set_data_dir(path)
# Load the full list of companies in the selected market (United States).
df_companies = sf.load_companies(market='us')
# Load all the industries that are available.
df_industries = sf.load_industries()
# Load the quarterly Income Statements for all companies in the selected market.
df_income = sf.load_income(variant='quarterly', market='us')
# Load the quarterly Balance Sheet data for all companies in the selected market.
df_balance = sf.load_balance(variant='quarterly', market='us')
# Load the quarterly Balance Sheet data for all companies in the selected market.
df_cashflow = sf.load_cashflow(variant='quarterly', market='us')
return df_companies, df_industries, df_income, df_balance, df_cashflow
import yfinance as yf
# Set your API-key for downloading data. This key gets the free data.
sf.set_api_key('free')
# Set the local directory where data-files are stored.
# The directory will be created if it does not already exist.
sf.set_data_dir('~/simfin_data/')
# NOMBRE EN LA BOLSA
company = 'AAPL'
# Download the data from the SimFin server and load into a Pandas DataFrame.
# annual/quarterly/ttm
BALANCE = sf.load_balance(variant='annual', market='us').loc[company, ]
CASH_FLOW = sf.load_cashflow(variant='annual', market='us').loc[company, ]
INCOME = sf.load_income(variant='annual', market='us').loc[company, ]
#PRICE = sf.load_shareprices(variant='daily', market='us').loc[company, ]
PRICE = yf.download(tickers=f'{company}', period='10y', interval='1mo')
PRICE.reset_index(inplace=True)
PRICE = PRICE[PRICE['Date'].dt.month == 12][['Close', 'Date']]
INCOME['Date'] = INCOME.index.strftime('%m-%Y')
BALANCE['Date'] = BALANCE.index.strftime('%m-%Y')
CASH_FLOW['Date'] = CASH_FLOW.index.strftime('%m-%Y')
PRICE['Date'] = PRICE['Date'].dt.strftime('%m-%Y')
PRICE = PRICE.set_index('Date')
INCOME = INCOME.set_index('Date')
BALANCE = BALANCE.set_index('Date')
CASH_FLOW = CASH_FLOW.set_index('Date')
def load_dataset(refresh_days=1,
dataset='general',
thresh=0.7,
simfin_api_key='free',
simfin_directory='simfin_data/',
data_directory=DATA_DIR,
shareprices_df=''):
# Set Simfin Settings
sf.set_api_key(simfin_api_key)
sf.set_data_dir(simfin_directory)
derived_shareprice_df = sf.load_derived_shareprices(variant='latest',
market='us')
derived_shareprice_df.to_csv(data_directory / 'stock_derived.csv')
company_df = sf.load_companies(market='us', refresh_days=1)
company_df.to_csv(data_directory / 'company.csv')
industry_df = sf.load_industries(refresh_days=1)
industry_df.to_csv(data_directory / 'industry.csv')
if dataset == 'general':
# Load Data from Simfin
income_df = sf.load_income(variant='ttm',
market='us',
refresh_days=refresh_days)
income_df = income_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
income_quarterly_df = sf.load_income(variant='quarterly',
market='us',
refresh_days=refresh_days)
income_quarterly_df = income_quarterly_df.sort_index(
level=['Ticker', 'Report Date'], ascending=[1, 1])
income_df.groupby('Ticker').last().to_csv(data_directory /
'general_income.csv')
balance_df = sf.load_balance(variant='ttm',
market='us',
refresh_days=refresh_days)
balance_df = balance_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
balance_quarterly_df = sf.load_balance(variant='quarterly',
market='us',
refresh_days=refresh_days)
balance_quarterly_df = balance_quarterly_df.sort_index(
level=['Ticker', 'Report Date'], ascending=[1, 1])
balance_df.groupby('Ticker').last().to_csv(data_directory /
'general_balance.csv')
cashflow_df = sf.load_cashflow(variant='ttm',
market='us',
refresh_days=refresh_days)
cashflow_df = cashflow_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
cashflow_quarterlay_df = sf.load_cashflow(variant='quarterly',
market='us',
refresh_days=refresh_days)
cashflow_quarterlay_df = cashflow_quarterlay_df.sort_index(
level=['Ticker', 'Report Date'], ascending=[1, 1])
cashflow_df.groupby('Ticker').last().to_csv(data_directory /
'general_cashflow.csv')
derived_df = sf.load_derived(variant='ttm',
market='us',
refresh_days=refresh_days)
derived_df = derived_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
derived_df.groupby('Ticker').last().to_csv(
data_directory / 'general_fundamental_derived.csv')
cache_args = {
'cache_name': 'financial_signals',
'cache_refresh': refresh_days
}
fin_signal_df = sf.fin_signals(df_income_ttm=income_df,
df_balance_ttm=balance_df,
df_cashflow_ttm=cashflow_df,
**cache_args)
growth_signal_df = sf.growth_signals(
df_income_ttm=income_df,
df_income_qrt=income_quarterly_df,
df_balance_ttm=balance_df,
df_balance_qrt=balance_quarterly_df,
df_cashflow_ttm=cashflow_df,
df_cashflow_qrt=cashflow_quarterlay_df,
**cache_args)
# Remove Columns that exist in other Fundamental DataFrames
balance_columns = balance_df.columns[~balance_df.columns.isin(set(
).union(income_df.columns))]
cashflow_columns = cashflow_df.columns[~cashflow_df.columns.isin(set(
).union(income_df.columns))]
derived_df_columns = derived_df.columns[~derived_df.columns.isin(set(
).union(income_df.columns, growth_signal_df.columns, fin_signal_df.
columns))]
# Merge the fundamental data into a single dataframe
fundamental_df = income_df.join(balance_df[balance_columns]).join(
cashflow_df[cashflow_columns]).join(fin_signal_df).join(
growth_signal_df).join(derived_df[derived_df_columns])
fundamental_df['Dataset'] = 'general'
elif dataset == 'banks':
# Load Data from Simfin
income_df = sf.load_income_banks(variant='ttm',
market='us',
refresh_days=refresh_days)
income_df = income_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
income_df.groupby('Ticker').last().to_csv(data_directory /
'banks_income.csv')
balance_df = sf.load_balance_banks(variant='ttm',
market='us',
refresh_days=refresh_days)
balance_df = balance_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
balance_df.groupby('Ticker').last().to_csv(data_directory /
'banks_balance.csv')
cashflow_df = sf.load_cashflow_banks(variant='ttm',
market='us',
refresh_days=refresh_days)
cashflow_df = cashflow_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
cashflow_df.groupby('Ticker').last().to_csv(data_directory /
'banks_cashflow.csv')
derived_df = sf.load_derived_banks(variant='ttm',
market='us',
refresh_days=refresh_days)
derived_df = derived_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
derived_df.groupby('Ticker').last().to_csv(
data_directory / 'banks_fundamental_derived.csv')
derived_df.groupby('Ticker').last().to_csv(
data_directory / 'banks_fundamental_derived.csv')
# Remove Columns that exist in other Fundamental DataFrames
balance_columns = balance_df.columns[~balance_df.columns.isin(set(
).union(income_df.columns))]
cashflow_columns = cashflow_df.columns[~cashflow_df.columns.isin(set(
).union(income_df.columns))]
derived_df_columns = derived_df.columns[~derived_df.columns.isin(set(
).union(income_df.columns))]
# Merge the fundamental data into a single dataframe
fundamental_df = income_df.join(balance_df[balance_columns]).join(
cashflow_df[cashflow_columns]).join(derived_df[derived_df_columns])
fundamental_df['Dataset'] = 'banks'
elif dataset == 'insurance':
# Load Data from Simfin
income_df = sf.load_income_insurance(variant='ttm',
market='us',
refresh_days=refresh_days)
income_df = income_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
income_df.groupby('Ticker').last().to_csv(data_directory /
'insurance_income.csv')
balance_df = sf.load_balance_insurance(variant='ttm',
market='us',
refresh_days=refresh_days)
balance_df = balance_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
balance_df.groupby('Ticker').last().to_csv(data_directory /
'insurance_balance.csv')
cashflow_df = sf.load_cashflow_insurance(variant='ttm',
market='us',
refresh_days=refresh_days)
cashflow_df = cashflow_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
cashflow_df.groupby('Ticker').last().to_csv(data_directory /
'insurance_cashflow.csv')
derived_df = sf.load_derived_insurance(variant='ttm',
market='us',
refresh_days=refresh_days)
derived_df = derived_df.sort_index(level=['Ticker', 'Report Date'],
ascending=[1, 1])
derived_df.groupby('Ticker').last().to_csv(
data_directory / 'insurance_fundamental_derived.csv')
# Remove Columns that exist in other Fundamental DataFrames
balance_columns = balance_df.columns[~balance_df.columns.isin(set(
).union(income_df.columns))]
cashflow_columns = cashflow_df.columns[~cashflow_df.columns.isin(set(
).union(income_df.columns))]
derived_df_columns = derived_df.columns[~derived_df.columns.isin(set(
).union(income_df.columns))]
# Merge the fundamental data into a single dataframe
fundamental_df = income_df.join(balance_df[balance_columns]).join(
cashflow_df[cashflow_columns]).join(derived_df[derived_df_columns])
fundamental_df['Dataset'] = 'insurance'
# Drop Columns with more then 1-thresh nan values
fundamental_df = fundamental_df.dropna(thresh=int(thresh *
len(fundamental_df)),
axis=1)
# Drop Duplicate Index
fundamental_df = fundamental_df[~fundamental_df.index.duplicated(
keep='first')]
# Replace Report Date with the Publish Date because the Publish Date is when the Fundamentals are known to the Public
fundamental_df['Published Date'] = fundamental_df['Publish Date']
fundamental_df = fundamental_df.reset_index().set_index(
['Ticker', 'Publish Date'])
df = sf.reindex(df_src=fundamental_df,
df_target=shareprices_df,
group_index=TICKER,
method='ffill').dropna(how='all').join(shareprices_df)
# General
# Clean Up
df = df.drop([
'SimFinId', 'Currency', 'Fiscal Year', 'Report Date', 'Restated Date',
'Fiscal Period', 'Published Date'
],
axis=1)
if dataset == 'general':
# Remove Share Prices Over Amazon Share Price
df = df[df['Close'] <= df.loc['AMZN']['Close'].max()]
df = df.dropna(subset=[
'Shares (Basic)', 'Shares (Diluted)', 'Revenue', 'Earnings Growth'
])
non_per_share_cols = [
'Currency', 'Fiscal Year', 'Fiscal Period', 'Published Date',
'Restated Date', 'Shares (Basic)', 'Shares (Diluted)', 'Close',
'Dataset'
] + fin_signal_df.columns.tolist() + growth_signal_df.columns.tolist(
) + derived_df_columns.difference(
['EBITDA', 'Total Debt', 'Free Cash Flow']).tolist()
else:
df = df.dropna(
subset=['Shares (Basic)', 'Shares (Diluted)', 'Revenue'])
non_per_share_cols = [
'Currency', 'Fiscal Year', 'Fiscal Period', 'Published Date',
'Restated Date', 'Shares (Basic)', 'Shares (Diluted)', 'Close',
'Dataset'
] + derived_df_columns.difference(
['EBITDA', 'Total Debt', 'Free Cash Flow']).tolist()
df = df.replace([np.inf, -np.inf], 0)
df = df.fillna(0)
per_share_cols = df.columns[~df.columns.isin(non_per_share_cols)]
df[per_share_cols] = df[per_share_cols].div(df['Shares (Diluted)'], axis=0)
# Add Company and Industry Information and Categorize
df = df.join(company_df).merge(
industry_df, left_on='IndustryId', right_index=True).drop(
columns=['IndustryId', 'Company Name', 'SimFinId'])
categorical_features = [
col for col in df.columns if df[col].dtype == 'object'
]
encoder = OrdinalEncoder(cols=categorical_features,
handle_unknown='ignore',
return_df=True).fit(df)
df = encoder.transform(df)
# Sort
df = df.sort_index(level=['Ticker', 'Date'], ascending=[1, 1])
return df
# Set your API-key for downloading data. This key gets the free data.
sf.set_api_key('free')
# Set the local directory where data-files are stored.
# The directory will be created if it does not already exist.
sf.set_data_dir('~/simfin_data/')
# Download the data from the SimFin server and load into a Pandas DataFrame.
df_companies = sf.load_companies(market='us')
df_industries = sf.load_industries()
# Non financial
df_income = sf.load_income(variant='quarterly', market='us')
df_balance = sf.load_balance(variant='quarterly', market='us')
df_cashflow = sf.load_cashflow(variant='quarterly', market='us')
# Banks
df_income = sf.load_income_banks(variant='quarterly', market='us')
df_balance = sf.load_balance_banks(variant='quarterly', market='us')
df_cashflow = sf.load_cashflow_banks(variant='quarterly', market='us')
# Insurance
df_income = sf.load_income_insurance(variant='quarterly', market='us')
df_balance = sf.load_balance_insurance(variant='quarterly', market='us')
df_cashflow = sf.load_cashflow_insurance(variant='quarterly', market='us')
# Prices
df_prices = sf.load_shareprices(variant='daily', market='us')
# Print the first rows of the data.
df_income_qrt = sf.load_income(variant='quarterly', market=market) #Contains: Qtrly Earnings Growth = amount by with this quarter earnings exceeds # the same quarter earnings for past year # TTM Balance Sheets. df_balance_ttm = sf.load_balance(variant='ttm', market=market) #Contains: Total Debt = Short Term Debt + Long Term Debt, # Total Debt/Equity = Total Debt / Total Equity, # Total Cash = Cash, Cash Equivalents & Short Term Investments # Total Cash Per Share = Total Cash / Shares Diluted # Total Assets # Current Ratio = Total Current Assets / Total Current Liabilities # TTM Cash-Flow Statements. df_cashflow_ttm = sf.load_cashflow(variant='ttm', market=market) #Contains: Operating Cash Flow = Net Cash from Operating Activities #Contains: Free Clash Flow = Net Cash from Operating Activities + Change in Fixed Assets & Intangibles # Daily Share-Prices. df_prices = sf.load_shareprices(variant='daily', market=market) #Contains: Market Cap = Shares Outstanding * Adj. Close, # Enterprise value = Market Cap + Total Debt - Total Cash # Price To Book value = Close / Book value(Total Equity / Shares Outstanding) # Price / Earnings To Growth (PEG) Ratio = PE / TTM Earnings Growth #Render it for ticker list tickers = ['EMN','RRC','EBAY','ADM','HPQ','NVDA','F','AKAM','ADBE','CME','AMZN','QCOM', 'WMT','CRM','DIS','CERN','AMAT','AMGN','ATVI','CAT','CHK','FDX','HSY','TWX','XOM', 'JWN','FLIR','WHR','BIIB','HP','KO','TGT','MRK','COST','FFIV','NTAP','UNH','FCX','SEE', 'NKE','D','MET','INTU','VZ','PG','VLO','JNPR','PEG','ROST','PDCO','NSC','MKC','OKE',