fynance.algorithms.allocation.rolling_allocation¶

fynance.algorithms.allocation.rolling_allocation(f, X, n=252, s=63, ret=True, drift=True, **kwargs)¶

Roll an algorithm of portfolio allocation.

Parameters:

Parameters:	f : callable Allocation algorithm that take as parameters a subarray of `X` and `kwargs`, and return a vector (as `np.ndarray`) of weights. X : array_like Data matrix, each columns is a series of prices, indexes or performances, each row is a observation at time `t`. n, s : int Respectively the number of observations to compute weights and the number of observations to roll. Default is `n=252` and `s=63`. ret : bool, optional If True (default) pass to `f` the returns of `X`. Otherwise pass `X` to `f`. drift : bool, optional If False performance of the portfolio is computed as if we rebalance the weights of asset at each timeframe. Otherwise we let to drift the weights. Default is True. kwargs Any keyword arguments to pass to `f`.
Returns:	pd.Series Performance of the portfolio allocated following `f` algorithm. pd.DataFrame Weights of the portfolio allocated following `f` algorithm.

f : callable: Allocation algorithm that take as parameters a subarray of X and **kwargs, and return a vector (as np.ndarray) of weights.
X : array_like: Data matrix, each columns is a series of prices, indexes or performances, each row is a observation at time t.
n, s : int: Respectively the number of observations to compute weights and the number of observations to roll. Default is n=252 and s=63.
ret : bool, optional: If True (default) pass to f the returns of X. Otherwise pass X to f.
drift : bool, optional: If False performance of the portfolio is computed as if we rebalance the weights of asset at each timeframe. Otherwise we let to drift the weights. Default is True.
**kwargs: Any keyword arguments to pass to f.

Returns:

Notes

Weights are computed on the past data from t - n to t and are applied to backtest on data from t to t + s.

\[\forall t \in [n, T], w_{t:t+s} = f(X_{t-n:t})\]