import numpy as np


def buy_BTFD(name, data, range_days, cand_window, marker_key='close_n', thresh_max=-10, thresh_min=1, buy_callback=None):
	N = len(data['index'])
	key = 'BTFD'
	buy_list = {'index' : np.array([None]*N), key : np.array([None]*N)}
	cand = None
	do_buy = False
	for n in range(N-range_days, N):
		vmax = data['Qmax'][n]
		vmin = data['Qmin'][n]
		trend = data['macd_fdd'][n]
		index = data['index'][n]
		value = data[marker_key][n]
		if vmin <= thresh_min:
			if vmax <= thresh_max:
				cand = n
				if trend >= 0:
					do_buy = True
					print ("{}: Buy on {} at {:0.2f}".format(name, index, value))
					cand = None

		if cand is not None:
			if n - cand <= cand_window:
				if trend >= 0:
					do_buy = True
					former = data[marker_key][cand]
					print("{}: Delayed buy on {} at {:0.2f} ({:0.2f})".format(name, index, value, former-value))
					cand = None
			else:
				cand = None

		if do_buy:
			do_buy = False
			buy_list['index'][n] = index
			buy_list[key][n] = value
			if buy_callback is not None:
				buy_callback({'name': name, 'item': {'value': value, 'date': index}})

	return buy_list