jens
947a843128
git-svn-id: http://moon:8086/svn/projects/Stock@326 fda53097-d464-4ada-af97-ba876c37ca34
43 lines
1.1 KiB
Python
43 lines
1.1 KiB
Python
import numpy as np
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def buy_BTFD(name, data, range_days, cand_window, marker_key='close_n', thresh_max=-10, thresh_min=1, buy_callback=None):
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N = len(data['index'])
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key = 'BTFD'
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buy_list = {'index' : np.array([None]*N), key : np.array([None]*N)}
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cand = None
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do_buy = False
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for n in range(N-range_days, N):
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vmax = data['Qmax'][n]
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vmin = data['Qmin'][n]
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trend = data['macd_fdd'][n]
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index = data['index'][n]
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value = data[marker_key][n]
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if vmin <= thresh_min:
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if vmax <= thresh_max:
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cand = n
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if trend >= 0:
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do_buy = True
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print ("{}: Buy on {} at {:0.2f}".format(name, index, value))
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cand = None
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if cand is not None:
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if n - cand <= cand_window:
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if trend >= 0:
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do_buy = True
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former = data[marker_key][cand]
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print("{}: Delayed buy on {} at {:0.2f} ({:0.2f})".format(name, index, value, former-value))
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cand = None
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else:
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cand = None
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if do_buy:
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do_buy = False
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buy_list['index'][n] = index
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buy_list[key][n] = value
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if buy_callback is not None:
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buy_callback({'name': name, 'item': {'value': value, 'date': index}})
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return buy_list
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