def cal_funding_reward_HR(pop):
fitness = np.zeros([pop.shape[0], 1], float)
idx_mtr = np.zeros([pop.shape[0], 6], float)
idx_norm = np.zeros([pop.shape[0], 6], float)
for i in range(pop.shape[0]):
a = ""
b = ""
c = ""
for j in range(10):
a = a + str(pop[i, j])
b = b + str(pop[i, j + 10])
c = c + str(pop[i, j + 20])
w_a = int(a, 2)
w_b = int(b, 2)
w_c = int(c, 2)
check_one = w_a + w_b + w_c
w_GSPC = w_a / check_one
w_SP = w_b / check_one
w_USDX = w_c / check_one
idx_mtr[i, :] = nv.first_fund(GSPC_path, SP_path, USDX_path, w_GSPC,
w_SP,
w_USDX).__call__(GSPC_path, SP_path,
USDX_path, w_GSPC, w_SP,
w_USDX)
for k in range(idx_mtr.shape[1]):
if (idx_mtr[:, k].max() - idx_mtr[:, k].min()) == 0:
idx_norm[:, k] = 1
else:
idx_norm[:, k] = (idx_mtr[:, k] - idx_mtr[:, k].min()) / (
idx_mtr[:, k].max() - idx_mtr[:, k].min())
for l in range(pop.shape[0]):
fitness[l] = idx_norm[l][5] * 0.8 + np.sum(idx_norm[l][0:4]) * 0.2 / 5
return idx_mtr, fitness
def get_idx(weight):
idx = nv.first_fund(GSPC_path, SP_path, USDX_path, weight[0], weight[1],
weight[2]).__call__(GSPC_path, SP_path, USDX_path,
weight[0], weight[1], weight[2])
return idx
# -*- coding: utf-8 -*-
"""
Created on Sun Dec 29 23:37:37 2019
@author: tc_chien
"""
import new_vision as nv
w_GSPC = 0.5
w_SP = 0.3
w_USDX = 0.2
GSPC_path = "GSPC.csv"
SP_path = "SP500BDT.csv"
USDX_path = "USDX.csv"
idx = nv.first_fund(GSPC_path, SP_path, USDX_path, w_GSPC, w_SP,
w_USDX).__call__(GSPC_path, SP_path, USDX_path, w_GSPC,
w_SP, w_USDX)
print(idx)
# idx = [out_dic['sharp'], out_dic['sortino'], out_dic['excess'], out_dic['VAR'], out_dic['E on VaR']]
# np.append(idx, out_dic['history'][0]['unrealized gains'][len(out_dic['history'][0]['unrealized gains'])-1])
# print(idx)