def init(self):
self.windows = self.param['window']
self.cols = self.param['col']
self.types = self.param['type']
self.translation_cols = self.param.get('translation')
self.scale_cols = self.param.get('scale')
self.move_window_mapping = {
"mean":
lambda c, s, t, w: bn.move_mean(c, w) * s + t,
"std":
lambda c, s, t, w: bn.move_std(c, w) * s,
"var":
lambda c, s, t, w: bn.move_var(c, w) * s * s,
"min":
lambda c, s, t, w: bn.move_min(c, w) * s + t,
"max":
lambda c, s, t, w: bn.move_max(c, w) * s + t,
"rank":
lambda c, s, t, w: bn.move_rank(c, w),
"sum":
lambda c, s, t, w: bn.move_sum(c, w) * s + t * w,
"ema":
lambda c, s, t, w: F.
ema(c, 2.0 /
(w + 1), start_indices=self.base.start_indices) * s + t,
"rsi":
lambda c, s, t, w: F.rsi(
c, w, start_indices=self.base.start_indices),
"psy":
lambda c, s, t, w: F.psy(
c, w, start_indices=self.base.start_indices),
"bias":
lambda c, s, t, w: F.bias(
c, w, start_indices=self.base.start_indices)
}
def Ts_min(A, n):
'''
计算n天(包括当天)的最小值
n >= 1
'''
if n < 1:
#print ("计算n天的最小值，n不得小于1，返回输入")
return A
result = bk.move_min(A, n, min_count=1, axis=0)
result[np.isnan(A)] = np.nan
return result
def _wr(arr_h, arr_l, arr_c, arr_o, window, start_indices):
high_n = bn.move_max(arr_h,window)
low_n = bn.move_min(arr_l,window)
res = np.where(high_n - low_n!=0, 100*(high_n - arr_c) / (high_n - low_n), 50)
pre_cnt = 0.0
N = arr_c.shape[0]
N_GROUP = start_indices.shape[0]
j = 0
for i in range(N):
if j < N_GROUP and start_indices[j] == i:
pre_cnt = 0
j += 1
if pre_cnt < window:
res[i] = np.nan
pre_cnt += 1
return res
def plot(self,
window=100,
alpha=0.2,
save=False,
close_plots=False,
pre_fix=""):
plt.figure()
plt.title(pre_fix + "Mean")
p = plt.plot(bn.move_mean(self.reward_hist, window=window))[0]
if alpha > 0:
plt.plot(self.reward_hist, color=p.get_color(), alpha=alpha)
plt.xlim(xmin=0)
plt.grid(True)
if not save is False:
plt.savefig(os.path.join(save, "move_mean.svg"))
if not close_plots:
plt.show()
else:
plt.close()
plt.figure()
plt.title(pre_fix + "Min")
plt.plot(bn.move_min(self.reward_hist, window=window))
plt.xlim(xmin=0)
plt.grid(True)
if not save is False:
plt.savefig(os.path.join(save, "move_min.svg"))
if not close_plots:
plt.show()
else:
plt.close()
plt.figure()
plt.title(pre_fix + "Max")
plt.plot(bn.move_max(self.reward_hist, window=window))
plt.xlim(xmin=0)
plt.grid(True)
if not save is False:
plt.savefig(os.path.join(save, "move_max.svg"))
if not close_plots:
plt.show()
else:
plt.close()
def time_move_min(self, dtype, shape, order, axis, window):
bn.move_min(self.arr, window, axis=axis)
def time_move_min(self, dtype, shape, window):
bn.move_min(self.arr, window)