arbitragelab.cointegration_approach.multi_coint

This module generates a cointegration vector for mean-reversion trading of three or more cointegrated assets.

Module Contents

Classes

MultivariateCointegration

This class optimizes bounds for mean-reversion trading of a spread consisting of three and more assets.
class MultivariateCointegration

This class optimizes bounds for mean-reversion trading of a spread consisting of three and more assets.

The implementation is based on the method described by Galenko, A., Popova, E. and Popova, I. in “Trading in the presence of cointegration”

property asset_df: pandas.DataFrame

Property that gives read-only access to the in-sample asset price dataframe.

Returns:

(pd.DataFrame) Dataframe of asset prices.

set_train_dataset(price_df: pandas.DataFrame)

Provide price series for model to calculate the cointegration coefficient and beta.

Parameters:

price_df – (pd.DataFrame) Price series dataframe which contains both series.

fillna_inplace(nan_method: str = 'ffill', order: int = 3)

Replace the class attribute dataframes with imputed training dataframe.

Parameters:

  • nan_method – (str) Missing value imputation method. If “ffill” then use front-fill; if “spline” then use cubic spline.

  • order – (int) Polynomial order for spline function.

static calc_log_price(price_df: pandas.DataFrame) → pandas.DataFrame

Calculate the log price of each asset for cointegration coefficient calculation.

Parameters:

price_df – (pd.DataFrame) Dataframe that contains the raw asset price.

Returns:

(pd.DataFrame) Log prices of the assets.

static calc_price_diff(price_df: pandas.DataFrame) → pandas.DataFrame

Calculate the price difference of day t and day t-1 of each asset.

Parameters:

price_df – (pd.DataFrame) Dataframe that contains the raw asset price.

Returns:

(pd.DataFrame) Log prices of the assets.

fit(log_price: pandas.DataFrame, sig_level: str = '95%', suppress_warnings: bool = False) → numpy.array

Use Johansen test to retrieve the cointegration vector.

Parameters:

  • log_price – (pd.DataFrame) Log price dataframe used to derive cointegration vector.

  • sig_level – (str) Cointegration test significance level. Possible options are “90%”, “95%”, and “99%”.

  • suppress_warnings – (bool) Boolean flag to suppress the cointegration warning message.

Returns:

(np.array) The cointegration vector, b.

get_coint_vec() → Tuple[pandas.DataFrame, Ellipsis]

Generate contegration vector to generate trading signals.

Returns:

(np.array) The cointegration vector, b.

static summary(returns_df: pandas.DataFrame) → pandas.DataFrame

Statistics of the trading strategy returns.

The statistics include: mean, standard deviation, skewness, kurtosis, Sharpe ratio, Sortino ratio, final cumulative returns, percentage of up days and down days, max returns, and min returns.

Parameters:

returns_df – (pd.DataFrame) Daily percentage returns dataframe.

Returns:

(pd.DataFrame) Trading strategy returns statistics dataframe.

static plot_returns(returns: pandas.DataFrame, figw: float = 15.0, figh: float = 15.0, title: str = 'Returns', start_date: pandas.Timestamp | None = None, end_date: pandas.Timestamp | None = None) → matplotlib.pyplot.Figure

Plot the equity curve only.

Parameters:

  • returns – (pd.DataFrame) Daily returns dataframe.

  • figw – (float) Figure width.

  • figh – (float) Figure height.

  • title – (str) Figure title.

  • start_date – (pd.Timestamp) Start point of the plot.

  • end_date – (pd.Timestamp) End point of the plot.

Returns:

(plt.Figure) A single equity curve plot.