Computes the difference between the mean returns of two quantiles. Optionally, computes the standard error of this difference. Param

Creates an event study tear sheet for analysis of a specific event. Parameters ---------- factor_data : pd.DataFrame - MultiIndex

Creates a full tear sheet for analysis and evaluating single return predicting (alpha) factor. Parameters ---------- factor_data

Simulate a portfolio using the input factor and returns the portfolio performance data properly formatted for Pyfolio analysis. For more

Decorator to set plotting context and axes style during function call.

Convert forward returns to returns relative to mean period wise all-universe or group returns. group-wise normalization incorporates the

Compute the alpha (excess returns), alpha t-stat (alpha significance), and beta (market exposure) of a factor. A regression is run with t

Computes autocorrelation of mean factor ranks in specified time spans. We must compare period to period factor ranks rather than factor value

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Get the mean information coefficient of specified groups. Answers questions like: What is the mean IC for each month? What is the mea

Computes mean returns for factor quantiles across provided forward returns columns. Parameters ---------- factor_data : pd.DataF

Give user a more informative error in case it is not possible to properly calculate quantiles on the input dataframe (factor)

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Plots the cumulative returns of the returns series passed in. Parameters ---------- factor_returns : pd.Series Period wise r

Plots the cumulative returns of various factor quantiles. Parameters ---------- quantile_returns : pd.DataFrame Returns by f

Plots the distribution of events in time. Parameters ---------- events : pd.Series A pd.Series whose index contains at least

Plots factor rank autocorrelation over time. See factor_rank_autocorrelation for more details. Parameters ---------- factor_auto

Plots Spearman Rank Information Coefficient for a given factor over provided forward returns. Separates by group. Parameters -------

Plots Spearman Rank Information Coefficient histogram for a given factor. Parameters ---------- ic : pd.DataFrame DataFrame

Plots Spearman Rank Information Coefficient "Q-Q" plot relative to a theoretical distribution. Parameters ---------- ic : pd.Dat

Plots Spearman Rank Information Coefficient and IC moving average for a given factor. Parameters ---------- ic : pd.DataFrame

Plots mean period wise returns for factor quantiles. Parameters ---------- mean_returns_spread : pd.Series Series with diffe

Plots a heatmap of the information coefficient or returns by month. Parameters ---------- mean_monthly_ic : pd.DataFrame The

Plots sector-wise mean daily returns for factor quantiles across provided forward price movement columns. Parameters ----------

Plots mean period wise returns for factor quantiles. Parameters ---------- mean_ret_by_q : pd.DataFrame DataFrame with quant

Plots a violin box plot of period wise returns for factor quantiles. Parameters ---------- return_by_q : pd.DataFrame - MultiIndex

Plots period wise top and bottom quantile factor turnover. Parameters ---------- quantile_turnover: pd.Dataframe Quantile tu

Pretty print a pandas DataFrame. Uses HTML output if running inside Jupyter Notebook, otherwise formatted text output. Parameters

Computes the proportion of names in a factor quantile that were not in that quantile in the previous period. Parameters ----------

Convert returns to 'one_period_len' rate of returns: that is the value the returns would have every 'one_period_len' if they had grown at a s

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one_period_len standard deviation (or standard error) approximation Parameters ---------- period_std: pd.DataFrame DataFrame

Converts pandas string representations of timedeltas into integers of days. Parameters ---------- sequence : iterable List o

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