strategies.py

import numpy as np
import pandas as pd
from scipy.optimize import minimize


def equal_weight(assets):
    optimal = [1/len(assets) for i in range(len(assets))]
    return optimal


def minimum_variance(ret, bound):
    def find_port_variance(weights):
        # this is actually std
        cov = ret.cov()
        port_var = np.sqrt(np.dot(weights.T, np.dot(cov, weights)) * 250)
        return port_var

    def weight_cons(weights):
        return np.sum(weights) - 1


    bounds_lim = [bound for x in range(len(ret.columns))] # change to (-1, 1) if you want to short
    init = [1/len(ret.columns) for i in range(len(ret.columns))]
    constraint = {'type': 'eq', 'fun': weight_cons}

    optimal = minimize(fun=find_port_variance,
                       x0=init,
                       bounds=bounds_lim,
                       constraints=constraint,
                       method='SLSQP'
                       )

    return list(optimal['x'])


def max_sharpe(ret, bound):
    def sharpe_func(weights):
        hist_mean = ret.mean(axis=0).to_frame()
        hist_cov = ret.cov()

        port_ret = np.dot(weights.T, hist_mean.values) * 250
        port_std = np.sqrt(np.dot(weights.T, np.dot(hist_cov, weights)) * 250)
        return -1 * port_ret / port_std

    def weight_cons(weights):
        return np.sum(weights) - 1


    bounds_lim = [bound for x in range(len(ret.columns))] # change to (-1, 1) if you want to short
    init = [1/len(ret.columns) for i in range(len(ret.columns))]
    constraint = {'type': 'eq', 'fun': weight_cons}

    optimal = minimize(fun=sharpe_func,
                       x0=init,
                       bounds=bounds_lim,
                       constraints=constraint,
                       method='SLSQP'
                       )

    return list(optimal['x'])