API¶
ffn
Package¶
core
Module¶

class
ffn.core.
GroupStats
(*prices)[source]¶ Bases:
dict
GroupStats enables one to compare multiple series side by side. It is a wrapper around a dict of {price.name: PerformanceStats} and provides many convenience methods.
The order of the series passed in will be preserved. Individual PerformanceStats objects can be accessed via index position or name via the [] accessor.
 Args:
 prices (Series): Multiple price series to be compared.
 Attributes:
 stats (DataFrame): Dataframe containing stats for each
 series provided. Stats in rows, series in columns.
 lookback_returns (DataFrame): Returns for diffrent
 lookback periods (1m, 3m, 6m, ytd...) Period in rows, series in columns.
 prices (DataFrame): The merged and rebased prices.

plot
(freq=None, figsize=(15, 5), title=None, logy=False, **kwargs)[source]¶ Helper function for plotting the series.
 Args:
 freq (str): Data frequency used for display purposes.
 Refer to pandas docs for valid freq strings.
 figsize ((x,y)): figure size
 title (str): Title if default not appropriate
 logy (bool): logscale for y axis
 kwargs: passed to pandas’ plot method

plot_correlation
(freq=None, title=None, figsize=(12, 6), **kwargs)[source]¶ Utility function to plot correlations.
 Args:
 freq (str): Pandas data frequency alias string
 title (str): Plot title
 figsize (tuple (x,y)): figure size
 kwargs: passed to Pandas’ plot_corr_heatmap function

plot_histograms
(freq=None, title=None, figsize=(10, 10), **kwargs)[source]¶ Wrapper around pandas’ hist.
 Args:
 freq (str): Data frequency used for display purposes.
 Refer to pandas docs for valid freq strings.
 figsize ((x,y)): figure size
 title (str): Title if default not appropriate
 kwargs: passed to pandas’ hist method

plot_scatter_matrix
(freq=None, title=None, figsize=(10, 10), **kwargs)[source]¶ Wrapper around pandas’ scatter_matrix.
 Args:
 freq (str): Data frequency used for display purposes.
 Refer to pandas docs for valid freq strings.
 figsize ((x,y)): figure size
 title (str): Title if default not appropriate
 kwargs: passed to pandas’ scatter_matrix method

set_date_range
(start=None, end=None)[source]¶ Update date range of stats, charts, etc. If None then the original date range is used. So to reset to the original range, just call with no args.
 Args:
 start (date): start date
 end (end): end date

class
ffn.core.
PerformanceStats
(prices, rf=0.0)[source]¶ Bases:
object
PerformanceStats is a convenience class used for the performance evaluation of a price series. It contains various helper functions to help with plotting and contains a large amount of descriptive statistics.
 Args:
 prices (Series): A price series.
 rf (float): Riskfree rate used in various calculation. Should be
 expressed as a yearly (annualized) return
 Attributes:
 name (str): Name, derived from price series name
 return_table (DataFrame): A table of monthly returns with
 YTD figures as well.
 lookback_returns (Series): Returns for different
 lookback periods (1m, 3m, 6m, ytd...)
 stats (Series): A series that contains all the stats

display_monthly_returns
()[source]¶ Display a table containing monthly returns and ytd returns for every year in range.

plot
(freq=None, figsize=(15, 5), title=None, logy=False, **kwargs)[source]¶ Helper function for plotting the series.
 Args:
 freq (str): Data frequency used for display purposes.
 Refer to pandas docs for valid freq strings.
 figsize ((x,y)): figure size
 title (str): Title if default not appropriate
 logy (bool): logscale for y axis
 kwargs: passed to pandas’ plot method

plot_histogram
(freq=None, figsize=(15, 5), title=None, bins=20, **kwargs)[source]¶ Plots a histogram of returns given a return frequency.
 Args:
 freq (str): Data frequency used for display purposes.
 This will dictate the type of returns (daily returns, monthly, ...) Refer to pandas docs for valid period strings.
 figsize ((x,y)): figure size
 title (str): Title if default not appropriate
 bins (int): number of bins for the histogram
 kwargs: passed to pandas’ hist method

set_date_range
(start=None, end=None)[source]¶ Update date range of stats, charts, etc. If None then the original date is used. So to reset to the original range, just call with no args.
 Args:
 start (date): start date
 end (end): end date

ffn.core.
annualize
(returns, durations, one_year=365.0)[source]¶ Annualize returns using their respective durations.
 Formula used is:
 (1 + returns) ** (1 / (durations / one_year))  1

ffn.core.
asfreq_actual
(series, freq, method='ffill', how='end', normalize=False)[source]¶ Similar to pandas’ asfreq but keeps the actual dates. For example, if last data point in Jan is on the 29th, that date will be used instead of the 31st.

ffn.core.
calc_cagr
(prices)[source]¶ Calculates the CAGR (compound annual growth rate) for a given price series.
 Args:
 prices (pandas.Series): A Series of prices.
 Returns:
 float – cagr.

ffn.core.
calc_calmar_ratio
(prices)[source]¶ Calculates the Calmar Ratio given a series of prices
 Args:
 prices (Series, DataFrame): Price series

ffn.core.
calc_clusters
(returns, n=None, plot=False)[source]¶ Calculates the clusters based on kmeans clustering.
 Args:
 returns (pd.DataFrame): DataFrame of returns
 n (int): Specify # of clusters. If None, this
 will be automatically determined
 plot (bool): Show plot?
 Returns:
 dict with structure: {cluster# : [col names]}

ffn.core.
calc_erc_weights
(returns, initial_weights=None, risk_weights=None, covar_method='ledoitwolf', risk_parity_method='ccd', maximum_iterations=100, tolerance=1e08)[source]¶ Calculates the equal risk contribution / risk parity weights given a DataFrame of returns.
 Args:
 returns (DataFrame): Returns for multiple securities.
 initial_weights (list): Starting asset weights [default inverse vol].
 risk_weights (list): Risk target weights [default equal weight].
 covar_method (str): Covariance matrix estimation method.
 Currently supported:
 ledoitwolf [default]
 standard
 risk_parity_method (str): Risk parity estimation method.
 Currently supported:
 ccd (cyclical coordinate descent)[default]
 maximum_iterations (int): Maximum iterations in iterative solutions.
 tolerance (float): Tolerance level in iterative solutions.
 Returns:
 Series {col_name: weight}

ffn.core.
calc_ftca
(returns, threshold=0.5)[source]¶ Implementation of David Varadi’s Fast Threshold Clustering Algorithm (FTCA).
http://cssanalytics.wordpress.com/2013/11/26/fastthresholdclusteringalgorithmftca/ # NOQA
More stable than kmeans for clustering purposes. If you want more clusters, use a higher threshold.
 Args:
 returns  expects a pandas dataframe of returns where
 each column is the name of a given security.
 threshold (float): Threshold parameter  use higher value
 for more clusters. Basically controls how similar (correlated) series have to be.
 Returns:
 dict of cluster name (a number) and list of securities in cluster

ffn.core.
calc_inv_vol_weights
(returns)[source]¶ Calculates weights proportional to inverse volatility of each column.
Returns weights that are inversely proportional to the column’s volatility resulting in a set of portfolio weights where each position has the same level of volatility.
Note, that assets with returns all equal to NaN or 0 are excluded from the portfolio (their weight is set to NaN).
 Returns:
 Series {col_name: weight}

ffn.core.
calc_max_drawdown
(prices)[source]¶ Calculates the max drawdown of a price series. If you want the actual drawdown series, please use to_drawdown_series.

ffn.core.
calc_mean_var_weights
(returns, weight_bounds=(0.0, 1.0), rf=0.0, covar_method='ledoitwolf')[source]¶ Calculates the meanvariance weights given a DataFrame of returns.
 Args:
 returns (DataFrame): Returns for multiple securities.
 weight_bounds ((low, high)): Weigh limits for optimization.
 rf (float): Riskfree rate used in utility calculation
 covar_method (str): Covariance matrix estimation method.
 Currently supported:
 ledoitwolf
 standard
 Returns:
 Series {col_name: weight}

ffn.core.
calc_perf_stats
(prices)[source]¶ Calculates the performance statistics given an object. The object should be a Series of prices.
A PerformanceStats object will be returned containing all the stats.
 Args:
 prices (Series): Series of prices

ffn.core.
calc_prob_mom
(returns, other_returns)[source]¶ Probabilistic momentum
Basically the “probability or confidence that one asset is going to outperform the other”.

ffn.core.
calc_risk_return_ratio
(returns)[source]¶ Calculates the return / risk ratio. Basically the Sharpe ratio without factoring in the riskfree rate.

ffn.core.
calc_sharpe
(returns, rf=0.0, nperiods=None, annualize=True)[source]¶ Calculates the Sharpe ratio.
If rf is nonzero, you must specify nperiods. In this case, rf is assumed to be expressed in yearly (annualized) terms.
 Args:
 returns (Series, DataFrame): Input return series
 rf (float): Riskfree rate expressed as a yearly (annualized) return
 nperiods (int): Frequency of returns (252 for daily, 12 for monthly,
 etc.)

ffn.core.
calc_sortino_ratio
(returns, rf=0, nperiods=None, annualize=True)[source]¶ Calculates the sortino ratio given a series of returns
 Args:
 returns (Series or DataFrame): Returns
 rf (float): Riskfree rate expressed in yearly (annualized) terms.
 nperiods (int): Number of periods used for annualization. Must be
 provided if rf is nonzero

ffn.core.
calc_stats
(prices)[source]¶ Calculates performance stats of a given object.
If object is Series, a PerformanceStats object is returned. If object is DataFrame, a GroupStats object is returned.
 Args:
 prices (Series, DataFrame): Set of prices

ffn.core.
calc_total_return
(prices)[source]¶ Calculates the total return of a series.
last / first  1

ffn.core.
deannualize
(returns, nperiods)[source]¶ Convert return expressed in annual terms on a different basis.
 Args:
 returns (float, Series, DataFrame): Return(s)
 nperiods (int): Target basis, typically 252 for daily, 12 for
 monthly, etc.

ffn.core.
drawdown_details
(drawdown)[source]¶ Returns a data frame with start, end, days (duration) and drawdown for each drawdown in a drawdown series.
Note
days are actual calendar days, not trading days
 Args:
 drawdown (pandas.Series): A drawdown Series
 (can be obtained w/ drawdown(prices).
 Returns:
 pandas.DataFrame – A data frame with the following
 columns: start, end, days, drawdown.

ffn.core.
drop_duplicate_cols
(df)[source]¶ Removes duplicate columns from a dataframe and keeps column w/ longest history

ffn.core.
extend_pandas
()[source]¶ Extends pandas’ PandasObject (Series, Series, DataFrame) with some functions defined in this file.
This facilitates common functional composition used in quant finance.
 Ex:
 prices.to_returns().dropna().calc_clusters() (where prices would be a DataFrame)

ffn.core.
get_num_days_required
(offset, period='d', perc_required=0.9)[source]¶ Estimates the number of days required to assume that data is OK.
Helper function used to determine if there are enough “good” data days over a given period.
 Args:
 offset (DateOffset): Offset (lookback) period.
 period (str): Period string.
 perc_required (float): percentage of number of days
 expected required.

ffn.core.
limit_weights
(weights, limit=0.1)[source]¶ Limits weights and redistributes excedent amount proportionally.
 ex:
 weights are {a: 0.7, b: 0.2, c: 0.1}
 call with limit=0.5
 excess 0.2 in a is ditributed to b and c
 proportionally.  result is {a: 0.5, b: 0.33, c: 0.167}
 Args:
 weights (Series): A series describing the weights
 limit (float): Maximum weight allowed

ffn.core.
plot_corr_heatmap
(data, **kwargs)[source]¶ Plots the correlation heatmap for a given DataFrame.

ffn.core.
plot_heatmap
(data, title='Heatmap', show_legend=True, show_labels=True, label_fmt='.2f', vmin=None, vmax=None, figsize=None, label_color='w', cmap='RdBu', **kwargs)[source]¶ Plot a heatmap using matplotlib’s pcolor.
 Args:
 data (DataFrame): DataFrame to plot. Usually small matrix (ex.
 correlation matrix).
 title (string): Plot title
 show_legend (bool): Show color legend
 show_labels (bool): Show value labels
 label_fmt (str): Label format string
 vmin (float): Min value for scale
 vmax (float): Max value for scale
 cmap (string): Color map
 kwargs: Passed to matplotlib’s pcolor

ffn.core.
random_weights
(n, bounds=(0.0, 1.0), total=1.0)[source]¶ Generate pseudorandom weights.
Returns a list of random weights that is of length n, where each weight is in the range bounds, and where the weights sum up to total.
Useful for creating random portfolios when benchmarking.
 Args:
 n (int): number of random weights
 bounds ((low, high)): bounds for each weight
 total (float): total sum of the weights

ffn.core.
rebase
(prices, value=100)[source]¶ Rebase all series to a given intial value.
This makes comparing/plotting different series together easier.
 Args:
 prices: Expects a price series
 value (number): starting value for all series.

ffn.core.
rescale
(x, min=0.0, max=1.0, axis=0)[source]¶ Rescale values to fit a certain range [min, max]

ffn.core.
rollapply
(data, window, fn)[source]¶ Apply a function fn over a rolling window of size window.
 Args:
 data (Series or DataFrame): Series or DataFrame
 window (int): Window size
 fn (function): Function to apply over the rolling window.
 For a series, the return value is expected to be a single number. For a DataFrame, it shuold return a new row.
 Returns:
 Object of same dimensions as data

ffn.core.
to_drawdown_series
(prices)[source]¶ Calculates the drawdown series.
This returns a series representing a drawdown. When the price is at all time highs, the drawdown is 0. However, when prices are below high water marks, the drawdown series = current / hwm  1
The max drawdown can be obtained by simply calling .min() on the result (since the drawdown series is negative)
Method ignores all gaps of NaN’s in the price series.
 Args:
 prices (Series or DataFrame): Series of prices.

ffn.core.
to_excess_returns
(returns, rf, nperiods=None)[source]¶ Given a series of returns, it will return the excess returns over rf.
 Args:
 returns (Series, DataFrame): Returns
 rf (float, Series, DataFrame): RiskFree rate(s)
 nperiods (int): Optional. If provided, will convert rf to different
 frequency using deannualize
 Returns:
 excess_returns (Series, DataFrame): Returns  rf

ffn.core.
to_log_returns
(prices)[source]¶ Calculates the log returns of a price series.
Formula is: ln(p1/p0)
 Args:
 prices: Expects a price series

ffn.core.
to_monthly
(series, method='ffill', how='end')[source]¶ Convenience method that wraps asfreq_actual with ‘M’ param (method=’ffill’, how=’end’).

ffn.core.
to_price_index
(returns, start=100)[source]¶ Returns a price index given a series of returns.
 Args:
 returns: Expects a return series
 start (number): Starting level
Assumes arithmetic returns.
Formula is: cumprod (1+r)

ffn.core.
to_returns
(prices)[source]¶ Calculates the simple arithmetic returns of a price series.
Formula is: (t1 / t0)  1
 Args:
 prices: Expects a price series

ffn.core.
to_ulcer_index
(prices)[source]¶ Converts from prices > Ulcer index
See https://en.wikipedia.org/wiki/Ulcer_index
 Args:
 prices (Series, DataFrame): Prices

ffn.core.
to_ulcer_performance_index
(prices, rf=0.0, nperiods=None)[source]¶ Converts from prices > ulcer performance index.
See https://en.wikipedia.org/wiki/Ulcer_index
 Args:
 prices (Series, DataFrame): Prices
 rf (float): Riskfree rate of return. Assumed to be expressed in
 yearly (annualized) terms
 nperiods (int): Used to deannualize rf if rf is provided (nonzero)
data
Module¶

ffn.data.
DEFAULT_PROVIDER
(ticker, field, start=None, end=None, mrefresh=False)¶

ffn.data.
csv
(ticker, path='data.csv', field='', mrefresh=False, **kwargs)[source]¶ Data provider wrapper around pandas’ read_csv. Provides memoization.

ffn.data.
get
(tickers, provider=None, common_dates=True, forward_fill=False, clean_tickers=True, column_names=None, ticker_field_sep=':', mrefresh=False, existing=None, **kwargs)[source]¶ Helper function for retrieving data as a DataFrame.
 Args:
 tickers (list, string, csv string): Tickers to download.
 provider (function): Provider to use for downloading data.
 By default it will be ffn.DEFAULT_PROVIDER if not provided.
 common_dates (bool): Keep common dates only? Drop na’s.
 forward_fill (bool): forward fill values if missing. Only works
 if common_dates is False, since common_dates will remove all nan’s, so no filling forward necessary.
 clean_tickers (bool): Should the tickers be ‘cleaned’ using
 ffn.utils.clean_tickers? Basically remove nonstandard characters (^VIX > vix) and standardize to lower case.
 column_names (list): List of column names if clean_tickers
 is not satisfactory.
 ticker_field_sep (char): separator used to determine the
 ticker and field. This is in case we want to specify particular, nondefault fields. For example, we might want: AAPL:Low,AAPL:High,AAPL:Close. ‘:’ is the separator.
 mrefresh (bool): Ignore memoization.
 existing (DataFrame): Existing DataFrame to append returns
 to  used when we download from multiple sources
 kwargs: passed to provider
utils
Module¶

ffn.utils.
clean_ticker
(ticker)[source]¶ Cleans a ticker for easier use throughout MoneyTree
Splits by space and only keeps first bit. Also removes any characters that are not letters. Returns as lowercase.
>>> clean_ticker('^VIX') 'vix' >>> clean_ticker('SPX Index') 'spx'

ffn.utils.
memoize
(f, refresh_keyword='mrefresh')[source]¶ Memoize decorator. The refresh keyword is the keyword used to bypass the cache (in the function call).