- buy_BTFD: added params

- stock::fetch: support slice by date if loaded from file

git-svn-id: http://moon:8086/svn/projects/Stock@334 fda53097-d464-4ada-af97-ba876c37ca34

agent.py

@@ -1,7 +1,7 @@
 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):
+def buy_BTFD(name, data, range_days, params, marker_key='close_n', thresh_max=-5, thresh_min=1, buy_callback=None):
 	N = len(data['index'])
 	key = 'BTFD'
 	buy_list = {'index' : np.array([None]*N), key : np.array([None]*N)}
@@ -10,20 +10,21 @@ def buy_BTFD(name, data, range_days, cand_window, marker_key='close_n', thresh_m
 	for n in range(N-range_days, N):
 		vmax = data['Qmax'][n]
 		vmin = data['Qmin'][n]
-		trend = data['macd_fdd'][n]
+		trend = data['macd_fd'][n]
+		trend2 = data['macd_fdd'][n]
 		index = data['index'][n]
 		value = data[marker_key][n]
-		if vmin <= thresh_min:
-			if vmax <= thresh_max:
+		if vmin <= params['thresh_min']:
+			if vmax <= params['thresh_max']:
 				cand = n
-				if trend >= 0:
+				if trend2 >= 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:
+			if n - cand <= params['cand_window']:
+				if trend2 >= 0:
 					do_buy = True
 					former = data[marker_key][cand]
 					print("{}: Delayed buy on {} at {:0.2f} ({:0.2f})".format(name, index, value, former-value))

robot.py

@@ -16,6 +16,7 @@ from stock import Stock
 
 params = {
 	'show_range_days' : 5,
+	'btfd' : {'thresh_max' : -10, 'thresh_min' : 1, 'cand_window' : 5},
 	'k_euro' : 1 / 1.11,
 	'ema_alpha' : 0.75,
 	'sma_days' : 10,

stock.py

@@ -72,11 +72,18 @@ class Stock(object):
 			hdf = pd.HDFStore(filename, 'r')
 
 		data = hdf[self.symbol] * self.currency_corr
-		N = len(data.index)
-		self.data['index'] = np.array(data.index[0:N])
-		self.data['close'] = np.array(data['close'][0:N])
-		self.data['high'] = np.array(data['high'][0:N])
-		self.data['low'] = np.array(data['low'][0:N])
+		start_pos = 0
+		end_pos = 0
+
+		for index in data.index:
+			end_pos += 1
+			if str(end) in index:
+				break
+
+		self.data['index'] = np.array(data.index[start_pos:end_pos])
+		self.data['close'] = np.array(data['close'][start_pos:end_pos])
+		self.data['high'] = np.array(data['high'][start_pos:end_pos])
+		self.data['low'] = np.array(data['low'][start_pos:end_pos])
 
 		hdf.close()
 
@@ -113,7 +120,7 @@ class Stock(object):
 		self.data['macd_fdd'] = np.transpose(yf_dd)
 
 	def analyze(self, buy_callback, range_days):
-		return agent.buy_BTFD(self.symbol, self.data, marker_key='close_n', cand_window=5, range_days=range_days, buy_callback=buy_callback)
+		return agent.buy_BTFD(self.symbol, self.data, params=self.params['btfd'], marker_key='close_n', range_days=range_days, buy_callback=buy_callback)
 
 	@staticmethod
 	def has_candidate(data, key):