- improved

git-svn-id: http://moon:8086/svn/projects/Stock@333 fda53097-d464-4ada-af97-ba876c37ca34

functions.py

@@ -2,27 +2,27 @@ import numpy as np
 import math
 
 
-def exponential_moving_average(data, key, alpha=0.5, ic=None):
+def exponential_moving_average(data, alpha=0.5, ic=None):
-	result = np.zeros_like(data[key])
+	result = np.zeros_like(data)
 	if ic is None:
-		r = data[key][0]
+		r = data[0]
 	else:
 		r = ic
 	n = 0
-	for v in data[key]:
+	for v in data:
 		r = alpha*r + (1-alpha)*v
 		result[n] = r
 		n += 1
 
 	return np.array(result)
 
-def moving_average(data, key, window_days, ic=0):
+def moving_average(data, window_days, ic=0):
 	mem = [ic] * window_days
-	result = np.zeros_like(data[key])
+	result = np.zeros_like(data)
 	k=0
 	cumsum = ic
 	n = 0
-	for v in data[key]:
+	for v in data:
 		cumsum += (v - mem[k])
 		mem[k] = v
 		k += 1
@@ -34,12 +34,12 @@ def moving_average(data, key, window_days, ic=0):
 
 	return result
 
-def moving_variance(data, key, window_days, ic=0):
+def moving_variance(data, window_days, ic=0):
 	mem = [ic] * window_days
-	result = np.zeros_like(data[key])
+	result = np.zeros_like(data)
 	k=0
 	cumsum = ic
-	data_mean = data[key] - moving_average(data, key, window_days, ic)
+	data_mean = data - moving_average(data, window_days, ic)
 	n = 0
 	for v in data_mean:
 		try:
@@ -58,12 +58,12 @@ def moving_variance(data, key, window_days, ic=0):
 
 	return result
 
-def moving_max(data, key, window_days, ic=-1e9):
+def moving_max(data, window_days, ic=-1e9):
 	mem = [ic] * window_days
-	result = np.zeros_like(data[key])
+	result = np.zeros_like(data)
 	k=0
 	n = 0
-	for v in data[key]:
+	for v in data:
 		mem[k] = v
 		k += 1
 		if k >= window_days:
@@ -74,12 +74,12 @@ def moving_max(data, key, window_days, ic=-1e9):
 
 	return result
 
-def moving_min(data, key, window_days, ic=1e9):
+def moving_min(data, window_days, ic=1e9):
 	mem = [ic] * window_days
-	result = np.zeros_like(data[key])
+	result = np.zeros_like(data)
 	k=0
 	n = 0
-	for v in data[key]:
+	for v in data:
 		mem[k] = v
 		k += 1
 		if k >= window_days:
@@ -90,15 +90,15 @@ def moving_min(data, key, window_days, ic=1e9):
 
 	return result
 
-def normalize(data, key, ic=None):
+def normalize(data, ic=None):
-	result = np.zeros_like(data[key])
+	result = np.zeros_like(data)
 	cumsum = 0
 	if ic is None:
-		prev = data[key][0]
+		prev = data[0]
 	else:
 		prev = ic
 	n = 0
-	for v in data[key]:
+	for v in data:
 		if prev == 0:
 			print (prev)
 		cumsum += 100*(v - prev)/(prev)
@@ -118,18 +118,17 @@ def colsum(data, keys):
 
 	return result
 
-def macd(data, key):
+def macd(data):
-	ema_short = exponential_moving_average(data, key, alpha=0.85)
+	ema_short = exponential_moving_average(data, alpha=0.85)
-	ema_long = exponential_moving_average(data, key, alpha=0.925)
+	ema_long = exponential_moving_average(data, 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, window_days))
-	stddev = np.array(moving_variance(tp_dict, 'tp', window_days))
 
-	mid = np.array(moving_average(tp_dict, key='tp', window_days=window_days))
+	mid = np.array(moving_average(tp, window_days=window_days))
 	upper = mid + f * stddev
 	lower = mid - f * stddev
 	result = {'index' : data['index'], 'upper' : upper, 'mid' : mid, 'lower' : lower}

robot.py

@@ -14,9 +14,8 @@
 import matplotlib.pyplot as plt
 from stock import Stock
 
-key = '0UO7Z2MVZ2YSQSVE'
 params = {
-	'show_range_days' : 10,
+	'show_range_days' : 5,
 	'k_euro' : 1 / 1.11,
 	'ema_alpha' : 0.75,
 	'sma_days' : 10,
@@ -28,13 +27,13 @@ show_symbols = ['OHB.DE', 'ITMPF', 'PLUG', 'MOR.DE', 'CSCO', 'ERCA.DE', 'AVGO',
 show_symbols = ['CSCO']
 #show_symbols = ['OHB.DE']
 #show_symbols = ['UBSFF']
-show_symbols = ['DHER.DE']
+#show_symbols = ['DHER.DE']
 #show_symbols = ['WDI.DE']
 #show_symbols = ['EVT.DE']
 show_symbols = []
 
 symbols = {
-
+	'SSHPF' : {'name' : 'Scanship Holding ASA', 'currency' : '$'},
 	'RIG' : {'name' : 'Transocean Ltd.', 'currency' : '$'},
 	'BBIO' : {'name' : 'BridgeBio Pharma, Inc.', 'currency' : '$'},
 	'APA' : {'name' : 'Apache Corporation', 'currency' : '$'},
@@ -140,20 +139,26 @@ if len(show_symbols) > 0:
 			title = Stock(params, symbol, symbols[symbol])
 			title.fetch()
 			title.statistics()
+			try:
 				ind = title.analyze(buy_callback, range_days=params['show_range_days'])
 				title.show(ind, figNum=figNum)
 				figNum += 1
+			except:
+				print ("Exception occurred for {}".format(symbol))
+
 else:
 	for symbol in symbols:
 		title = Stock(params, symbol, symbols[symbol])
 		title.fetch()
 		title.statistics()
+		try:
 			ind = title.analyze(buy_callback, range_days=params['show_range_days'])
 			if Stock.has_candidate(ind, 'BTFD'):
 				print('-----------------------------------------------')
 				title.show(ind, figNum=figNum)
 				figNum += 1
-
+		except:
+			print ("Exception occurred for {}".format(symbol))
 gain_accum = 0
 num_stocks = 0
 for symbol in buy_list:

stock.py

@@ -12,6 +12,8 @@ import matplotlib as mpl
 
 mpl.rc('figure', max_open_warning = 0)
 
+key = '0UO7Z2MVZ2YSQSVE'
+
 class Stock(object):
 	def __init__(self, params, symbol, symbol_params):
 		self.symbol_params = symbol_params
@@ -84,26 +86,26 @@ class Stock(object):
 
 	def statistics(self):
 		N = len(self.data['index'])
-		self.data['ema'] = exponential_moving_average(self.data, key='close', alpha=self.params['ema_alpha'])
+		self.data['ema'] = exponential_moving_average(self.data['close'], alpha=self.params['ema_alpha'])
-		self.data['sma'] = moving_average(self.data, key='close', window_days=self.params['sma_days'])
+		self.data['sma'] = moving_average(self.data['close'], window_days=self.params['sma_days'])
-		self.data['close_n'] = normalize(self.data, key='close')
+		self.data['close_n'] = normalize(self.data['close'])
-		self.data['macd'] = macd(self.data, key='close_n')
+		self.data['macd'] = macd(self.data['close_n'])
-		self.data['min'] = moving_min(self.data, key='close_n', window_days=self.params['q_days'])
+		self.data['min'] = moving_min(self.data['close_n'], window_days=self.params['q_days'])
-		self.data['max'] = moving_max(self.data, key='close_n', window_days=self.params['q_days'])
+		self.data['max'] = moving_max(self.data['close_n'], window_days=self.params['q_days'])
 
 		self.data['Qmin'] = self.data['close_n'] - self.data['min']
 		self.data['Qmax'] = self.data['close_n'] - self.data['max']
 		self.boll = bollinger(self.data, window_days=30)
 
-		x_r = np.linspace(0, N, N)
+		y_r = np.append(self.data['macd'], self.data['macd'][N-1])
-		y_r = self.data['macd']
+		x_r = np.linspace(1, N, N+1)
 		spl = UnivariateSpline(x=x_r, y=y_r, k=5)
 		spl.set_smoothing_factor(0.25)
 		spl_d = spl.derivative()
 		spl_dd = spl.derivative().derivative()
 		yf = spl(x_r)
 		yf_d = spl_d(x_r)
-		x_r2 = np.linspace(-1, N-1, N)
+		x_r2 = np.linspace(1, N, N+1)
 		yf_dd = spl_dd(x_r2)
 
 		self.data['macd_f'] = np.transpose(yf)