def dF_dW_numeric(portfolio, F__, w__, dw, wParams):
dF_dW_ = np.empty([portfolio.iMax, portfolio.jLen])
F_base = w.eval_w(F__[0], portfolio.x___[1], w__, wParams)
J = 0
for i in range(portfolio.iMax):
for j in range(portfolio.jLen_[i]):
w__[i][j] += dw
F_ = w.eval_w(F__[0], portfolio.x___[1], w__, wParams)
w__[i][j] -= dw
dF_dW_[:, J] = (F_ - F_base) / dw
J += 1
return dF_dW_
def run_epoch(portfolio, W_, alpha, wParams):
w__ = portfolio.split(W_)
F__ = np.empty([portfolio.tMax, portfolio.iMax])
F__[0] = w.init_f(portfolio.iMax)
dFdWMethod = wParams['dFdW']
if (dFdWMethod == 'numeric'):
dF_dW_ = dF_dW_numeric(portfolio, F__, w__, 0.01, wParams)
elif (dFdWMethod == 'es'):
dF_dW_ = dF_dW_init_ES(portfolio, F__, w__, 0.01)
else:
raise InputError("dFdWMethod %s unknown" % dFdWMethod)
#logging.debug("dFdW %s" % dF_dW_)
#logging.debug("W_ %s" % W_)
sumR = sumR2 = 0
for t in range(1, portfolio.tMax):
x__ = portfolio.x___[t] # (asset x feature)
r_ = portfolio.r__[t] # (asset)
F__[t] = w.eval_w(F__[t - 1], x__, w__, wParams)
dF_ = F__[t] - F__[t - 1]
R = np.dot(F__[t - 1], r_) - np.dot(portfolio.c_, np.absolute(dF_))
sumR += R
sumR2 += R * R
A = sumR / t
B = sumR2 / t
BmA2 = B - A * A
dRdF_ = -portfolio.c_ * np.sign(dF_)
dRdF_1 = r_ + portfolio.c_ * np.sign(dF_)
dSdW_a = np.dot(dRdF_1, dF_dW_)
for n in range(portfolio.iMax):
J = 0
for i in range(portfolio.iMax):
for j in range(portfolio.jLen_[i]):
x = x__[n][j] if (n == i) else 0
dF_dW_[n, J] = F__[t, n] * (1 - F__[t, n]) * (x + w__[n][-1] * dF_dW_[n, J])
J += 1
#logging.debug("x__ %s" % x__)
#logging.debug("dFdW %s" % dF_dW_)
#logging.debug("dRdF %s" % dRdF_)
#logging.debug("dRdF_1 %s" % dRdF_1)
dSdW_b = np.dot(dRdF_, dF_dW_)
c = 0 if (BmA2 == 0) else (B - A * R) / math.pow(BmA2, 1.5)
dSdW_ = c * (dSdW_a + dSdW_b)
#logging.debug("dSdW_ %s" % dSdW_)
W_ += alpha * c * (dSdW_a + dSdW_b)
#logging.debug("W_ %s" % W_)
return W_