def growth_signals(df_income_ttm,
df_income_qrt,
df_balance_ttm,
df_balance_qrt,
df_cashflow_ttm,
df_cashflow_qrt,
df_prices=None,
fill_method='ffill',
offset=None,
func=None,
date_index=REPORT_DATE,
group_index=TICKER):
"""
Calculate growth-signals such as Sales Growth, Earnings Growth, etc.
for all stocks in the given DataFrames.
Three growth-signals are given for each type of financial data, e.g.:
- SALES_GROWTH is calculated from the TTM Revenue divided by the
TTM Revenue from one year ago.
- SALES_GROWTH_YOY is calculated from the Quarterly Revenue divided by
the Quarterly Revenue from one year ago.
- SALES_GROWTH_QOQ is calculated from the Quarterly Revenue divided by
the Quarterly Revenue from the previous quarter.
This function can take a while to compute, so it will create a cache-file
if you pass the arg `cache_refresh`. The next time you call this function,
the cache-file will get loaded if it is more recent than specified by
`cache_refresh`, otherwise the function will get computed again and the
result saved in the cache-file for future use. See the documentation for
the :obj:`~simfin.cache.cache` wrapper for details on its arguments.
.. warning:: You **MUST** use keyword arguments to this function,
otherwise the first unnamed arguments would get passed to the
:obj:`~simfin.cache.cache` wrapper instead.
:param df_prices:
Optional Pandas DataFrame with share-prices for one or more stocks.
If not `None`, then the signals will be reindexed to the same daily
data-points as `df_prices`, otherwise the signals will be quarterly.
:param df_income_ttm:
Pandas DataFrame with Income Statement TTM data for one or more stocks.
:param df_income_qrt:
Pandas DataFrame with Income Statement Quarterly data for one or more
stocks.
:param df_balance_ttm:
Pandas DataFrame with Balance Sheet TTM data for one or more stocks.
:param df_balance_qrt:
Pandas DataFrame with Balance Sheet Quarterly data for one or more
stocks.
:param df_cashflow_ttm:
Pandas DataFrame with Cash-Flow Statement TTM data for one or more
stocks.
:param df_cashflow_qrt:
Pandas DataFrame with Cash-Flow Statement Quarterly data for one or
more stocks.
:param func:
Function to apply on a per-stock basis after the signals have been
calculated, but before they have been reindexed to daily data-points.
This is useful e.g. to calculate multi-year averages.
For example, to calculate the 2-year averages of TTM data:
`func = lambda df: 0.5 * (df + df.shift(4))`
:param fill_method:
String or callable for the method of filling in empty values when
reindexing financial data to daily data-points.
See :obj:`~simfin.resample.reindex` for valid options.
:param offset:
Pandas DateOffset added to the date-index of the Pandas DataFrames with
the financial data. Example: `pd.DateOffset(days=60)` This is useful if
you want to add a lag of e.g. 60 days to the dates of financial reports
with Income Statements, Balance Sheets, and Cash-Flow Statements, because
the REPORT_DATE is not when it was actually made available to the public,
which can be 1, 2 or even 3 months after the REPORT_DATE.
See :obj:`~simfin.utils.add_date_offset` for more details.
:param date_index:
Name of the date-column for the financial data e.g. REPORT_DATE.
:param group_index:
If the DataFrames have a MultiIndex then group data using this
index-column. By default this is TICKER but it could also be e.g.
SIMFIN_ID if you are using that as an index in your DataFrame.
:return:
Pandas DataFrame with growth signals.
"""
# This implementation uses sf.rel_change() to calculate the growth-rates,
# which means that several groupby operations are performed. But this is
# easier to implement and for large DataFrames it is only about 10% slower
# than using sf.apply() with a function like _signals() in fin_signals().
###############################
# Annual growth using TTM data.
# Select and combine the data we need.
df_ttm1 = df_income_ttm[[REVENUE, NET_INCOME]]
df_ttm2 = free_cash_flow(df_cashflow_ttm)
df_ttm3 = df_balance_ttm[[TOTAL_ASSETS]]
df_ttm = pd.concat([df_ttm1, df_ttm2, df_ttm3], axis=1)
# Dict mapping to the new column-names.
new_names = {
REVENUE: SALES_GROWTH,
NET_INCOME: EARNINGS_GROWTH,
FCF: FCF_GROWTH,
TOTAL_ASSETS: ASSETS_GROWTH
}
# Calculate the growth-rates.
df_growth = rel_change(df=df_ttm,
freq='q',
quarters=4,
future=False,
annualized=False,
new_names=new_names)
#############################################
# Year-Over-Year growth using Quarterly data.
# Select and combine the data we need.
df_qrt1 = df_income_qrt[[REVENUE, NET_INCOME]]
df_qrt2 = free_cash_flow(df_cashflow_qrt)
df_qrt3 = df_balance_qrt[[TOTAL_ASSETS]]
df_qrt = pd.concat([df_qrt1, df_qrt2, df_qrt3], axis=1)
# Dict mapping to the new column-names.
new_names = {
REVENUE: SALES_GROWTH_YOY,
NET_INCOME: EARNINGS_GROWTH_YOY,
FCF: FCF_GROWTH_YOY,
TOTAL_ASSETS: ASSETS_GROWTH_YOY
}
# Calculate the growth-rates.
df_growth_yoy = rel_change(df=df_qrt,
freq='q',
quarters=4,
future=False,
annualized=False,
new_names=new_names)
########################################################
# Quarter-Over-Quarter growth using Quarterly data.
# Note: This uses the same Quarterly DataFrame as above.
# Dict mapping to the new column-names.
new_names = {
REVENUE: SALES_GROWTH_QOQ,
NET_INCOME: EARNINGS_GROWTH_QOQ,
FCF: FCF_GROWTH_QOQ,
TOTAL_ASSETS: ASSETS_GROWTH_QOQ
}
# Calculate the growth-rates.
df_growth_qoq = rel_change(df=df_qrt,
freq='q',
quarters=1,
future=False,
annualized=False,
new_names=new_names)
##################
# Post-processing.
# Combine into a single DataFrame.
df_signals = pd.concat([df_growth, df_growth_yoy, df_growth_qoq], axis=1)
# Add offset / lag to the index-dates of the signals.
if offset is not None:
df_signals = add_date_offset(df=df_signals,
offset=offset,
date_index=date_index)
# Process the signals using the supplied function e.g. to calculate averages.
if func is not None:
df_signals = apply(df=df_signals, func=func, group_index=group_index)
# Reindex to the same daily data-points as the share-prices.
if df_prices is not None:
df_signals = reindex(df_src=df_signals,
df_target=df_prices,
method=fill_method,
group_index=group_index)
# Sort the columns by their names.
df_signals.sort_index(axis='columns', inplace=True)
return df_signals
def val_signals(df_prices,
df_income_ttm,
df_balance_ttm,
df_cashflow_ttm,
fill_method='ffill',
offset=None,
func=None,
date_index=REPORT_DATE,
shares_index=SHARES_DILUTED,
group_index=TICKER):
"""
Calculate valuation signals such as P/E and P/Sales ratios for all stocks
in the given DataFrames.
This function can take a while to compute, so it will create a cache-file
if you pass the arg `cache_refresh`. The next time you call this function,
the cache-file will get loaded if it is more recent than specified by
`cache_refresh`, otherwise the function will get computed again and the
result saved in the cache-file for future use. See the documentation for
the :obj:`~simfin.cache.cache` wrapper for details on its arguments.
.. warning:: You **MUST** use keyword arguments to this function,
otherwise the first unnamed arguments would get passed to the
:obj:`~simfin.cache.cache` wrapper instead.
:param df_prices:
Pandas DataFrame with share-prices for one or more stocks.
:param df_income_ttm:
Pandas DataFrame with Income Statement TTM data for one or more stocks.
:param df_balance_ttm:
Pandas DataFrame with Balance Sheet TTM data for one or more stocks.
:param df_cashflow_ttm:
Pandas DataFrame with Cash-Flow Statement TTM data for one or more stocks.
:param fill_method:
String or callable for the method of filling in empty values when
reindexing financial data to daily data-points.
See :obj:`~simfin.resample.reindex` for valid options.
:param offset:
Pandas DateOffset added to the date-index of the Pandas DataFrames with
the financial data. Example: `pd.DateOffset(days=60)` This is useful if
you want to add a lag of e.g. 60 days to the dates of financial reports
with Income Statements, Balance Sheets, and Cash-Flow Statements, because
the REPORT_DATE is not when it was actually made available to the public,
which can be 1, 2 or even 3 months after the REPORT_DATE.
See :obj:`~simfin.utils.add_date_offset` for more details.
:param func:
Function to apply on a per-stock basis on the financial data, before
calculating the valuation signals. This is useful e.g. to calculate
multi-year averages of the Net Income and Revenue and use those when
calculating P/E and P/Sales ratios.
For example, to calculate the 2-year averages of TTM data:
`func = lambda df: 0.5 * (df + df.shift(4))`
:param date_index:
Name of the date-column for the financial data e.g. REPORT_DATE.
:param shares_index:
String with the column-name for the share-counts. SHARES_DILUTED
takes the potential diluting impact of stock-options into account, so
it results in more conservative valuation ratios than SHARES_BASIC.
:param group_index:
If the DataFrames have a MultiIndex then group data using this
index-column. By default this is TICKER but it could also be e.g.
SIMFIN_ID if you are using that as an index in your DataFrame.
:return:
Pandas DataFrame with valuation signals.
"""
# Get the required data from the Income Statements.
columns = [REVENUE, NET_INCOME_COMMON, SHARES_BASIC, SHARES_DILUTED]
df_inc = df_income_ttm[columns]
# Get the required data from the Balance Sheets.
columns = [
TOTAL_CUR_ASSETS, CASH_EQUIV_ST_INVEST, ACC_NOTES_RECV, INVENTORIES,
TOTAL_LIABILITIES, TOTAL_EQUITY
]
df_bal = df_balance_ttm[columns]
# Get the required data from the Cash-Flow Statements.
columns = [DIVIDENDS_PAID]
df_cf = df_cashflow_ttm[columns]
# Combine all the data. This creates a new copy that we can add columns to.
df = pd.concat([df_inc, df_bal, df_cf], axis=1)
# Calculate derived financial data such as Free Cash Flow (FCF),
# and add it as new columns to the DataFrame.
# This is only TTM data with 4 data-points per year, so it is
# faster than calculating it for the daily data-points below.
df[FCF] = free_cash_flow(df_cashflow_ttm)
df[NCAV] = ncav(df_balance_ttm)
df[NETNET] = netnet(df_balance_ttm)
# Add offset / lag to the index-dates of the financial data.
if offset is not None:
df = add_date_offset(df=df, offset=offset, date_index=date_index)
# Copy the number of shares before applying the user-supplied function,
# which might change the number of shares in the original DataFrame df.
# This tries to use the given share-counts (e.g. SHARES_DILUTED) and
# fill in missing values with the other share-counts (e.g. SHARES_BASIC).
df_shares = shares(df=df, index=shares_index)
# Reindex the share-counts to daily data-points.
df_shares_daily = reindex(df_src=df_shares,
df_target=df_prices,
method=fill_method,
group_index=group_index)
# Process the financial data using the user-supplied function
# e.g. to calculate multi-year averages of Earnings, Sales, etc.
if func is not None:
df = apply(df=df, func=func, group_index=group_index)
# Calculate Per-Share numbers. It is important to use the share-count
# from before the user-supplied function was applied.
df_per_share = df.div(df_shares, axis=0)
# Reindex the per-share financial data to daily data-points.
df_daily = reindex(df_src=df_per_share,
df_target=df_prices,
method=fill_method,
group_index=group_index)
# Create new DataFrame for the signals.
# Setting the index improves performance.
df_signals = pd.DataFrame(index=df_prices.index)
# Use the closing share-price for all signals.
df_price = df_prices[CLOSE]
# Calculate basic signals.
df_signals[PSALES] = df_price / df_daily[REVENUE]
df_signals[PE] = df_price / df_daily[NET_INCOME_COMMON]
df_signals[PFCF] = df_price / df_daily[FCF]
df_signals[PBOOK] = df_price / df_daily[TOTAL_EQUITY]
# Calculate Price / Net Current Asset Value (NCAV).
# This measures the share-price relative to estimated liquidation value.
df_signals[P_NCAV] = df_price / df_daily[NCAV]
# Calculate Price / Net-Net Working Capital (NNWC aka. NetNet).
# This measures the share-price relative to a more conservative estimate
# of liquidation value, which values the Receivables and Inventories at
# a discount to their book-value.
df_signals[P_NETNET] = df_price / df_daily[NETNET]
# Calculate Earnings Yield (inverse of the P/E ratio).
df_signals[EARNINGS_YIELD] = df_daily[NET_INCOME_COMMON] / df_price
# Calculate FCF Yield (inverse of the P/FCF ratio).
df_signals[FCF_YIELD] = df_daily[FCF] / df_price
# Calculate Dividend Yield using TTM Cash-Flow data, which is easier than
# using df_prices[DIVIDEND] because the actual payment dates may differ
# slightly from one year to the next, making it difficult to calculate TTM.
# Note the negation because DIVIDENDS_PAID is negative.
df_signals[DIV_YIELD] = -df_daily[DIVIDENDS_PAID] / df_price
# Calculate Market Capitalization.
df_signals[MARKET_CAP] = df_shares_daily * df_price
# Sort the columns by their names.
df_signals.sort_index(axis='columns', inplace=True)
return df_signals
def fin_signals(df_income_ttm,
df_balance_ttm,
df_cashflow_ttm,
df_prices=None,
offset=None,
func=None,
fill_method='ffill',
date_index=REPORT_DATE,
group_index=TICKER):
"""
Calculate financial signals such as Net Profit Margin, Debt Ratio, ROA,
etc. for all stocks in the given DataFrames.
This function can take a while to compute, so it will create a cache-file
if you pass the arg `cache_refresh`. The next time you call this function,
the cache-file will get loaded if it is more recent than specified by
`cache_refresh`, otherwise the function will get computed again and the
result saved in the cache-file for future use. See the documentation for
the :obj:`~simfin.cache.cache` wrapper for details on its arguments.
.. warning:: You **MUST** use keyword arguments to this function,
otherwise the first unnamed arguments would get passed to the
:obj:`~simfin.cache.cache` wrapper instead.
:param df_prices:
Optional Pandas DataFrame with share-prices for one or more stocks.
If not `None`, then the signals will be reindexed to the same daily
data-points as `df_prices`, otherwise the signals will be quarterly.
:param df_income_ttm:
Pandas DataFrame with Income Statement TTM data for one or more stocks.
:param df_balance_ttm:
Pandas DataFrame with Balance Sheet TTM data for one or more stocks.
:param df_cashflow_ttm:
Pandas DataFrame with Cash-Flow Statement TTM data for one or more stocks.
:param func:
Function to apply on a per-stock basis after the signals have been
calculated, but before they have been reindexed to daily data-points.
This is useful e.g. to calculate multi-year averages.
For example, to calculate the 2-year averages of TTM data:
`func = lambda df: 0.5 * (df + df.shift(4))`
:param fill_method:
String or callable for the method of filling in empty values when
reindexing financial data to daily data-points.
See :obj:`~simfin.resample.reindex` for valid options.
:param offset:
Pandas DateOffset added to the date-index of the Pandas DataFrames with
the financial data. Example: `pd.DateOffset(days=60)` This is useful if
you want to add a lag of e.g. 60 days to the dates of financial reports
with Income Statements, Balance Sheets, and Cash-Flow Statements, because
the REPORT_DATE is not when it was actually made available to the public,
which can be 1, 2 or even 3 months after the REPORT_DATE.
See :obj:`~simfin.utils.add_date_offset` for more details.
:param date_index:
Name of the date-column for the financial data e.g. REPORT_DATE.
:param group_index:
If the DataFrames have a MultiIndex then group data using this
index-column. By default this is TICKER but it could also be e.g.
SIMFIN_ID if you are using that as an index in your DataFrame.
:return:
Pandas DataFrame with financial signals.
"""
# Helper-function for calculating signals for a single stock.
def _signals(df):
# Create new DataFrame for the signals.
# Setting the index improves performance.
df_signals = pd.DataFrame(index=df.index)
# Net Profit Margin.
df_signals[NET_PROFIT_MARGIN] = df[NET_INCOME] / df[REVENUE]
# Gross Profit Margin.
df_signals[GROSS_PROFIT_MARGIN] = df[GROSS_PROFIT] / df[REVENUE]
# R&D / Revenue.
# Note: RESEARCH_DEV must be negated.
df_signals[RD_REVENUE] = -df[RESEARCH_DEV] / df[REVENUE]
# R&D / Gross Profit.
# Note: RESEARCH_DEV must be negated.
df_signals[RD_GROSS_PROFIT] = -df[RESEARCH_DEV] / df[GROSS_PROFIT]
# Return on Research Capital (RORC).
# Note: RESEARCH_DEV must be negated.
df_signals[RORC] = df[GROSS_PROFIT] / -df[RESEARCH_DEV]
# Interest Coverage.
# Note: INTEREST_EXP_NET must be negated.
df_signals[INTEREST_COV] = df[OPERATING_INCOME] / -df[INTEREST_EXP_NET]
# Current Ratio = Current Assets / Current Liabilities.
df_signals[CURRENT_RATIO] = df[TOTAL_CUR_ASSETS] / df[TOTAL_CUR_LIAB]
#: Quick Ratio = (Cash + Equiv. + ST Inv. + Recv.) / Current Liab.
df_signals[QUICK_RATIO] = \
(df[CASH_EQUIV_ST_INVEST] + df[ACC_NOTES_RECV].fillna(0.0)) \
/ df[TOTAL_CUR_LIAB]
# Debt Ratio = (Short-term Debt + Long-term Debt) / Total Assets.
df_signals[DEBT_RATIO] = (df[ST_DEBT] + df[LT_DEBT]) / df[TOTAL_ASSETS]
# NOTE: There are different ways of calculating ROA, ROE,
# ASSET_TURNOVER, etc. See Tutorial 04. For example, we could use the
# Assets or Equity from last year instead of from the current year,
# but the resulting ROA, ROE, etc. are usually very similar, and using
# last year's Assets or Equity would cause us to loose one year of
# data-points for the signals we are calculating here.
# Return on Assets = Net Income / Total Assets. See note above.
df_signals[ROA] = df[NET_INCOME] / df[TOTAL_ASSETS]
# Return on Equity = Net Income / Total Equity. See note above.
df_signals[ROE] = df[NET_INCOME] / df[TOTAL_EQUITY]
# Asset Turnover = Revenue / Total Assets. See note above.
df_signals[ASSET_TURNOVER] = df[REVENUE] / df[TOTAL_ASSETS]
# Inventory Turnover = Revenue / Inventory. See note above.
df_signals[INVENTORY_TURNOVER] = df[REVENUE] / df[INVENTORIES]
# Payout Ratio = Dividends / Free Cash Flow
# Note the negation because DIVIDENDS_PAID is negative.
df_signals[PAYOUT_RATIO] = -df[DIVIDENDS_PAID].fillna(0) / df[FCF]
# Buyback Ratio = Share Buyback / Free Cash Flow
# Note the negation because CASH_REPURCHASE_EQUITY is negative.
df_signals[BUYBACK_RATIO] = \
-df[CASH_REPURCHASE_EQUITY].fillna(0) / df[FCF]
# Payout + Buyback Ratio = (Dividends + Share Buyback) / Free Cash Flow
# Note the negation because DIVIDENDS_PAID and CASH_REP.. are negative.
df_signals[PAYOUT_BUYBACK_RATIO] = \
-(df[DIVIDENDS_PAID].fillna(0) +
df[CASH_REPURCHASE_EQUITY].fillna(0)) / df[FCF]
# Net Acquisitions & Divestitures / Total Assets.
# Note the negation because NET_CASH_ACQ_DIVEST is usually negative.
df_signals[ACQ_ASSETS_RATIO] = \
-df[NET_CASH_ACQ_DIVEST] / df[TOTAL_ASSETS]
return df_signals
# Get relevant data from Income Statements.
columns = [
REVENUE, GROSS_PROFIT, OPERATING_INCOME, INTEREST_EXP_NET, NET_INCOME,
RESEARCH_DEV
]
df1 = df_income_ttm[columns]
# Get relevant data from Balance Sheets.
columns = [
TOTAL_ASSETS, TOTAL_CUR_ASSETS, TOTAL_CUR_LIAB, TOTAL_EQUITY, ST_DEBT,
LT_DEBT, INVENTORIES, CASH_EQUIV_ST_INVEST, ACC_NOTES_RECV
]
df2 = df_balance_ttm[columns]
# Get relevant data from Cash-Flow Statements.
columns = [DIVIDENDS_PAID, CASH_REPURCHASE_EQUITY, NET_CASH_ACQ_DIVEST]
df3 = df_cashflow_ttm[columns]
# Calculate Free Cash Flow.
df_fcf = free_cash_flow(df_cashflow=df_cashflow_ttm)
# Combine the data into a single DataFrame.
df = pd.concat([df1, df2, df3, df_fcf], axis=1)
# Add offset / lag to the index-dates of the financial data.
if offset is not None:
df = add_date_offset(df=df, offset=offset, date_index=date_index)
# Calculate signals and use Pandas groupby if `df` has multiple stocks.
df_signals = apply(df=df, func=_signals, group_index=group_index)
# Process the signals using the supplied function e.g. to calculate averages.
if func is not None:
df_signals = apply(df=df_signals, func=func, group_index=group_index)
# Reindex to the same daily data-points as the share-prices.
if df_prices is not None:
df_signals = reindex(df_src=df_signals,
df_target=df_prices,
method=fill_method,
group_index=group_index)
# Sort the columns by their names.
df_signals.sort_index(axis='columns', inplace=True)
return df_signals