def test_load_income():
"""Test simfin.bulk.load_income()"""
for dataset, variant, market in iter_all_datasets(datasets='income'):
kwargs = _create_kwargs(variant=variant, market=market)
sf.load_income(**kwargs)
sf.load_income_banks(**kwargs)
sf.load_income_insurance(**kwargs)
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.
print(df_prices.head())
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
#load in entire annual,quaterly, monthly US income statement data df_a = sf.load_income(variant='annual', market='us') df_q = sf.load_income(variant='quarterly', market='us') df_m = sf.load_income(variant='ttm', market='us') #check how the dataframe looks like df_a.head() df_q.head() df_m.head() #Plot Microsoft's revenue across years #don't have to add quatation marks when using pyhon shortcut df_q.loc['MSFT'][REVENUE].plot(grid=True) #Load in income statement for banks and insurance companies df = sf.load_income_banks(variant='annual', market='us') df = sf.load_balance_insurance(variant='annual', market='us') #load share prices df_prices_latest = sf.load_shareprices(variant='latest', market='us') df_prices = sf.load_shareprices(variant='daily', market='us') df_prices_latest.head() #Load Companies Detail df_companies = sf.load_companies(index=TICKER, market='us') #Load sector and industry details df_industries = sf.load_industries() #Look up industry detail of Microsoft industry_id = df_companies.loc['MSFT'][INDUSTRY_ID]