jens
a739314378
git-svn-id: http://moon:8086/svn/projects/Stock@333 fda53097-d464-4ada-af97-ba876c37ca34
137 lines
2.3 KiB
Python
137 lines
2.3 KiB
Python
import numpy as np
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import math
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def exponential_moving_average(data, alpha=0.5, ic=None):
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result = np.zeros_like(data)
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if ic is None:
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r = data[0]
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else:
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r = ic
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n = 0
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for v in data:
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r = alpha*r + (1-alpha)*v
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result[n] = r
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n += 1
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return np.array(result)
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def moving_average(data, window_days, ic=0):
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mem = [ic] * window_days
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result = np.zeros_like(data)
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k=0
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cumsum = ic
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n = 0
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for v in data:
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cumsum += (v - mem[k])
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mem[k] = v
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k += 1
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if k >= window_days:
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k=0
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result[n] = cumsum/window_days
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n += 1
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return result
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def moving_variance(data, window_days, ic=0):
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mem = [ic] * window_days
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result = np.zeros_like(data)
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k=0
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cumsum = ic
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data_mean = data - moving_average(data, window_days, ic)
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n = 0
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for v in data_mean:
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try:
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v2 = v * v
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except:
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print ('v', v)
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cumsum += (v2 - mem[k])
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mem[k] = v2
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k += 1
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if k >= window_days:
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k=0
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var = max(0, cumsum) / window_days
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result[n] = math.sqrt(var)
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n += 1
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return result
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def moving_max(data, window_days, ic=-1e9):
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mem = [ic] * window_days
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result = np.zeros_like(data)
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k=0
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n = 0
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for v in data:
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mem[k] = v
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k += 1
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if k >= window_days:
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k=0
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result[n] = np.max(mem)
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n += 1
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return result
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def moving_min(data, window_days, ic=1e9):
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mem = [ic] * window_days
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result = np.zeros_like(data)
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k=0
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n = 0
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for v in data:
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mem[k] = v
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k += 1
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if k >= window_days:
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k=0
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result[n] = np.min(mem)
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n += 1
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return result
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def normalize(data, ic=None):
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result = np.zeros_like(data)
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cumsum = 0
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if ic is None:
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prev = data[0]
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else:
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prev = ic
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n = 0
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for v in data:
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if prev == 0:
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print (prev)
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cumsum += 100*(v - prev)/(prev)
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result[n] = cumsum
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if v > 0:
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prev = v
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n += 1
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return result
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def colsum(data, keys):
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result = np.zeros(data['index'].shape)
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for key in keys:
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result += data[key]
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return result
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def macd(data):
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ema_short = exponential_moving_average(data, alpha=0.85)
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ema_long = exponential_moving_average(data, alpha=0.925)
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return ema_short - ema_long
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def bollinger(data, window_days, f=2):
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tp = colsum(data, keys=['high', 'low', 'close']) / 3
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stddev = np.array(moving_variance(tp, window_days))
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mid = np.array(moving_average(tp, window_days=window_days))
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upper = mid + f * stddev
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lower = mid - f * stddev
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result = {'index' : data['index'], 'upper' : upper, 'mid' : mid, 'lower' : lower}
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return result
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