jens
947a843128
git-svn-id: http://moon:8086/svn/projects/Stock@326 fda53097-d464-4ada-af97-ba876c37ca34
138 lines
2.5 KiB
Python
138 lines
2.5 KiB
Python
import numpy as np
|
|
import math
|
|
|
|
|
|
def exponential_moving_average(data, key, alpha=0.5, ic=None):
|
|
result = np.zeros_like(data[key])
|
|
if ic is None:
|
|
r = data[key][0]
|
|
else:
|
|
r = ic
|
|
n = 0
|
|
for v in data[key]:
|
|
r = alpha*r + (1-alpha)*v
|
|
result[n] = r
|
|
n += 1
|
|
|
|
return np.array(result)
|
|
|
|
def moving_average(data, key, window_days, ic=0):
|
|
mem = [ic] * window_days
|
|
result = np.zeros_like(data[key])
|
|
k=0
|
|
cumsum = ic
|
|
n = 0
|
|
for v in data[key]:
|
|
cumsum += (v - mem[k])
|
|
mem[k] = v
|
|
k += 1
|
|
if k >= window_days:
|
|
k=0
|
|
|
|
result[n] = cumsum/window_days
|
|
n += 1
|
|
|
|
return result
|
|
|
|
def moving_variance(data, key, window_days, ic=0):
|
|
mem = [ic] * window_days
|
|
result = np.zeros_like(data[key])
|
|
k=0
|
|
cumsum = ic
|
|
data_mean = data[key] - moving_average(data, key, window_days, ic)
|
|
n = 0
|
|
for v in data_mean:
|
|
try:
|
|
v2 = v * v
|
|
except:
|
|
print ('v', v)
|
|
cumsum += (v2 - mem[k])
|
|
mem[k] = v2
|
|
k += 1
|
|
if k >= window_days:
|
|
k=0
|
|
|
|
var = max(0, cumsum) / window_days
|
|
result[n] = math.sqrt(var)
|
|
n += 1
|
|
|
|
return result
|
|
|
|
def moving_max(data, key, window_days, ic=-1e9):
|
|
mem = [ic] * window_days
|
|
result = np.zeros_like(data[key])
|
|
k=0
|
|
n = 0
|
|
for v in data[key]:
|
|
mem[k] = v
|
|
k += 1
|
|
if k >= window_days:
|
|
k=0
|
|
|
|
result[n] = np.max(mem)
|
|
n += 1
|
|
|
|
return result
|
|
|
|
def moving_min(data, key, window_days, ic=1e9):
|
|
mem = [ic] * window_days
|
|
result = np.zeros_like(data[key])
|
|
k=0
|
|
n = 0
|
|
for v in data[key]:
|
|
mem[k] = v
|
|
k += 1
|
|
if k >= window_days:
|
|
k=0
|
|
|
|
result[n] = np.min(mem)
|
|
n += 1
|
|
|
|
return result
|
|
|
|
def normalize(data, key, ic=None):
|
|
result = np.zeros_like(data[key])
|
|
cumsum = 0
|
|
if ic is None:
|
|
prev = data[key][0]
|
|
else:
|
|
prev = ic
|
|
n = 0
|
|
for v in data[key]:
|
|
if prev == 0:
|
|
print (prev)
|
|
cumsum += 100*(v - prev)/(prev)
|
|
result[n] = cumsum
|
|
|
|
if v > 0:
|
|
prev = v
|
|
n += 1
|
|
|
|
return result
|
|
|
|
|
|
def colsum(data, keys):
|
|
result = np.zeros(data['index'].shape)
|
|
for key in keys:
|
|
result += data[key]
|
|
|
|
return result
|
|
|
|
def macd(data, key):
|
|
ema_short = exponential_moving_average(data, key, alpha=0.85)
|
|
ema_long = exponential_moving_average(data, key, alpha=0.925)
|
|
|
|
return ema_short - ema_long
|
|
|
|
def bollinger(data, window_days, f=2):
|
|
tp = colsum(data, keys=['high', 'low', 'close']) / 3
|
|
tp_dict = {'tp': tp}
|
|
stddev = np.array(moving_variance(tp_dict, 'tp', window_days))
|
|
|
|
mid = np.array(moving_average(tp_dict, key='tp', window_days=window_days))
|
|
upper = mid + f * stddev
|
|
lower = mid - f * stddev
|
|
result = {'index' : data['index'], 'upper' : upper, 'mid' : mid, 'lower' : lower}
|
|
return result
|
|
|