- refactored

- cleaned up
- added buy agent

git-svn-id: http://moon:8086/svn/projects/Stock@322 fda53097-d464-4ada-af97-ba876c37ca34

pandas_eval.py

@@ -7,6 +7,9 @@ from scipy.interpolate import UnivariateSpline
 import datetime as dt
 import os
 
+import matplotlib as mpl
+mpl.rc('figure', max_open_warning = 0)
+
 # Stock Investors Financial Math
 # https://www.fmlabs.com/reference/default.htm?url=SimpleMA.htm
 
@@ -21,14 +24,21 @@ import os
 # https://ntguardian.wordpress.com/2016/09/19/introduction-stock-market-data-python-1
 
 key = '0UO7Z2MVZ2YSQSVE'
+q_days = 20
 thresh_macd = 1.5
-show_range_days = 20
+show_range_days = 40
-show_symbols = ['ITMPF', 'PLUG', 'MOR.DE', 'CSCO', 'ERCA.DE', 'AVGO', 'DIS']
+show_symbols = ['OHB.DE', 'ITMPF', 'PLUG', 'MOR.DE', 'CSCO', 'ERCA.DE', 'AVGO', 'DIS', 'UBSFF', 'DHER.DE', 'AIR']
-#show_symbols = []
+#show_symbols = ['CSCO']
+#show_symbols = ['OHB.DE']
+#show_symbols = ['UBSFF']
+#show_symbols = ['DHER.DE']
+show_symbols = []
 k_euro = 1 / 1.11
 N_poly = 7
 ema_alpha = 0.75
 sma_days = 10
+fetch_on_outdated = True
+
 
 symbols = {
 	'WDI.DE' : {'name' : 'WireCard', 'currency' : '€'},
@@ -52,64 +62,64 @@ symbols = {
 	'FB2A.DE' : {'currency' : '€'},
 	'ZIL2.DE' : {'currency' : '€'},
 	'SIS.DE' : {'currency' : '€'},
-	'SIE.DE' : {'currency' : '€'},
+	'SIE.DE' : {'name': 'Siemens', 'currency' : '€'},
 	'GFT.DE' : {'currency' : '€'},
-	'AMD.DE' : {'currency' : '€'},
+	'AMD.DE' : {'name': 'Advanced Micro Devices', 'currency' : '€'},
-	'CAP.DE' : {'currency' : '€'},
+	'CAP.DE' : {'name': 'Encavis', 'currency' : '€'},
-	'ADBE' : {'currency' : '$'},
+	'ADBE' : {'name': 'Adobe', 'currency' : '$'},
-	'PANW' : {'currency' : '$'},
+	'PANW' : {'name': 'Palo Alto Networks', 'currency' : '$'},
-	'AMAT' : {'currency' : '$'},
+	'AMAT' : {'name': 'Applied Materials', 'currency' : '$'},
-	'RIB.DE' : {'currency' : '€'},
+	'RIB.DE' : {'name': 'RIB Software', 'currency' : '€'},
-	'WAF.DE' : {'currency' : '€'},
+	'WAF.DE' : {'name': 'Siltronic', 'currency' : '€'},
-	'EVT.DE' : {'currency' : '€'},
+	'EVT.DE' : {'name': 'Evotec', 'currency' : '€'},
-	'VOW.DE' : {'currency' : '€'},
+	'VOW.DE' : {'name': 'Volkswagen', 'currency' : '€'},
-	'BMW.DE' : {'currency' : '€'},
+	'BMW.DE' : {'name': 'BMW', 'currency' : '€'},
-	'NSU.DE' : {'currency' : '€'},
+	'NSU.DE' : {'name': 'Audi', 'currency' : '€'},
-	'DAI.DE' : {'currency' : '€'},
+	'DAI.DE' : {'name': 'Daimler', 'currency' : '€'},
-	'SHA.DE' : {'currency' : '€'},
+	'SHA.DE' : {'name': 'Schaeffler', 'currency' : '€'},
-	'CON.DE' : {'currency' : '€'},
+	'CON.DE' : {'name': 'Continental', 'currency' : '€'},
 	'DHER.DE' : {'name' : 'Delivery Hero', 'currency' : '€'},
-	'BSL.DE' : {'currency' : '€'},
+	'BSL.DE' : {'name': 'Basler', 'currency' : '€'},
 	'D6H.DE' : {'currency' : '€'},
 	'TC1.DE' : {'currency' : '€'},
 	'VODI.DE' : {'currency' : '€'},
 	'ATVI' : {'currency' : '$'},
 	'GME' : {'currency' : '$'},
-	'NTO.F' : {'currency' : '$'},
+	'NTO.F' : {'name': 'Nintendo', 'currency' : '$'},
-	'UBSFF' : {'currency' : '$'},
+	'UBSFF' : {'name': 'UBI Soft', 'currency' : '$'},
-	'NXPRF' : {'currency' : '$'},
+	'NXPRF' : {'name': 'Nexans', 'currency' : '$'},
-	'IMPUF' : {'currency' : '$'},
+	'IMPUF' : {'name': 'Impala Platinum Holdings', 'currency' : '$'},
-	'NMPNF' : {'currency' : '$'},
+	'NMPNF' : {'name': 'Northam Platinum', 'currency' : '$'},
-	'ALSMY' : {'currency' : '$'},
+	'ALSMY' : {'name': 'Alstom', 'currency' : '$'},
-	'ARRD.F' : {'currency' : '$'},
+	'ARRD.F' : {'name': 'Arcelor Mittal', 'currency' : '$'},
-	'PHG' : {'currency' : '$'},
+	'PHG' : {'name': 'Philips', 'currency' : '$'},
-	'KEYS' : {'currency' : '$'},
+	'KEYS' : {'name': 'KeySight', 'currency' : '$'},
-	'ORCL' : {'currency' : '$'},
+	'ORCL' : {'name': 'Oracle', 'currency' : '$'},
-	'UBER' : {'currency' : '$'},
+	'UBER' : {'name': 'Uber', 'currency' : '$'},
-	'VMW' : {'currency' : '$'},
+	'VMW' : {'name': 'VMWare', 'currency' : '$'},
 	'AVGO' : {'name' : 'Avago', 'currency' : '$'},
-	'CY' : {'currency' : '$'},
+	'CY' : {'name': 'Cypress Semiconductor', 'currency' : '$'},
-	'QABSY' : {'currency' : '$'},
+	'QABSY' : {'name': 'Qanta Airways', 'currency' : '$'},
-	'BLDP' : {'currency' : '$'},
+	'BLDP' : {'name': 'Ballard Power', 'currency' : '$'},
-	'D7G.F' : {'currency' : '$'},
+	'D7G.F' : {'name': 'Nel ASA', 'currency' : '$'},
-	'ITMPF' : {'currency' : '$'},
+	'ITMPF' : {'name': 'ITM Power', 'currency' : '$'},
-	'PLUG' : {'currency' : '$'},
+	'PLUG' : {'name': 'PlugPower', 'currency' : '$'},
-	'PCELF' : {'currency' : '$'},
+	'PCELF' : {'name': 'PowerCell', 'currency' : '$'},
 	'SU.PA' : {'currency' : '$'},
 #	'SON1.DE' : {'currency' : '€'},
-	'STM.DE' : {'currency' : '€'},
+	'STM.DE' : {'name': 'STM Micro', 'currency' : '€'},
-	'BC8.DE' : {'currency' : '€'},
+	'BC8.DE' : {'name': 'Bechtle', 'currency' : '€'},
 	'MOR.DE' : {'name' : 'Morphosys', 'currency' : '€'},
 	'BAYN.DE' : {'currency' : '€'},
 	'BEI.DE' : {'currency' : '€'},
 	'EUZ.DE' : {'currency' : '€'},
-	'SLAB' : {'currency' : '$'},
+	'SLAB' : {'name': 'Silicon Laboratories', 'currency' : '$'},
 	'SPLK' : {'name' : 'Splunk', 'currency' : '$'},
 	'IAG' : {'name' : 'IAG', 'currency' : '$'},
 	'INTC' : {'currency' : '$'},
-	'I' : {'currency' : '$'}}
+	'I' : {'name': 'IntelSat', 'currency' : '$'}}
 
-def ema(data, key, alpha=0.5, ic=None):
+def exponential_moving_average(data, key, alpha=0.5, ic=None):
 	result = []
 	if ic is None:
 		r = data[key][0]
@@ -121,11 +131,11 @@ def ema(data, key, alpha=0.5, ic=None):
 
 	return np.array(result)
 
-def sma(data, key, window_days, ic=None):
+def moving_average(data, key, window_days, ic=0):
-	mem = [0] * window_days
+	mem = [ic] * window_days
 	result = []
 	k=0
-	cumsum = 0
+	cumsum = ic
 	for v in data[key]:
 		cumsum += (v - mem[k])
 		mem[k] = v
@@ -137,12 +147,12 @@ def sma(data, key, window_days, ic=None):
 
 	return result
 
-def sd(data, key, window_days, ic=None):
+def moving_variance(data, key, window_days, ic=0):
-	mem = [0] * window_days
+	mem = [ic] * window_days
 	result = []
 	k=0
-	cumsum = 0
+	cumsum = ic
-	data_mean = data[key] - sma(data, key, window_days, ic)
+	data_mean = data[key] - moving_average(data, key, window_days, ic)
 	for v in data_mean:
 		v2 = v * v
 		cumsum += (v2 - mem[k])
@@ -156,6 +166,48 @@ def sd(data, key, window_days, ic=None):
 
 	return result
 
+def moving_max(data, key, window_days, ic=-1e9):
+	mem = [ic] * window_days
+	result = []
+	k=0
+	for v in data[key]:
+		mem[k] = v
+		k += 1
+		if k >= window_days:
+			k=0
+
+		_max = np.max(mem)
+		result.append(_max)
+
+	return result
+
+def moving_min(data, key, window_days, ic=1e9):
+	mem = [ic] * window_days
+	result = []
+	k=0
+	for v in data[key]:
+		mem[k] = v
+		k += 1
+		if k >= window_days:
+			k=0
+
+		_min = np.min(mem)
+		result.append(_min)
+
+	return result
+
+def normalize(data, key, ic=None):
+	result = []
+	if ic is None:
+		prev = data[key][0]
+	else:
+		prev = ic
+	for v in data[key]:
+		v_n = (v - prev)/prev
+		result.append(100*v_n)
+
+	return result
+
 
 def colsum(data, keys):
 	result = np.zeros(data.shape[0])
@@ -165,17 +217,17 @@ def colsum(data, keys):
 	return result
 
 def macd(data, key):
-	ema_short = ema(data, key, alpha=0.85)
+	ema_short = exponential_moving_average(data, key, alpha=0.85)
-	ema_long = ema(data, key, alpha=0.925)
+	ema_long = exponential_moving_average(data, key, alpha=0.925)
 
 	return ema_short - ema_long
 
 def bollinger(data, window_days, f=2):
 	tp_ser = colsum(data, keys=['high', 'low', 'close']) / 3
 	tp_df = tp_ser.to_frame(name='tp')
-	stddev = np.array(sd(tp_df, 'tp', window_days))
+	stddev = np.array(moving_variance(tp_df, 'tp', window_days))
 
-	mid = np.array(sma(tp_df, key='tp', window_days=window_days))
+	mid = np.array(moving_average(tp_df, key='tp', window_days=window_days))
 	upper = mid + f * stddev
 	lower = mid - f * stddev
 	result = pd.DataFrame(upper, index=data.index, columns=['upper'])
@@ -183,6 +235,31 @@ def bollinger(data, window_days, f=2):
 	result['mid'] = mid
 	return result
 
+def agent_buy(name, data, thresh_max=-10, thresh_min=1):
+	buy_list = []
+	cand = None
+	for n in range(0, len(data.index)):
+		vmax = data['Qmax'][n]
+		vmin = data['Qmin'][n]
+		trend = data['macd_fd'][n]
+		index = data.index[n]
+		if vmin <= thresh_min:
+			if vmax <= thresh_max:
+				cand = n
+				if trend >= 0:
+					print ("{}: Buy on {}".format(name, index))
+					buy_list.append({'name': name, 'date': index})
+					cand = None
+
+		if cand is not None:
+			if n - cand >= 5:
+				if trend >= 0:
+					print("{}: Delayed buy on {}".format(name, data.index[cand]))
+					buy_list.append({'name': name, 'date': data.index[cand]})
+					cand = None
+
+	return buy_list
+
 class Title(object):
 	def __init__(self, symbol, params):
 		self.symbol = symbol
@@ -212,7 +289,7 @@ class Title(object):
 		try:
 			lastmodified = dt.datetime.fromtimestamp(os.stat(filename).st_mtime).date()
 			if lastmodified != today:
-				fetch = True
+				fetch = fetch_on_outdated
 		except:
 			fetch = True
 
@@ -246,52 +323,60 @@ class Title(object):
 		self.fig.canvas.mpl_connect("motion_notify_event", hover)
 
 	def analyze(self):
-		self.data_full['ema'] = ema(self.data_full, key='close', alpha=ema_alpha)
+		N = len(self.data_full['close'])
+		self.data_full['ema'] = exponential_moving_average(self.data_full, key='close', alpha=ema_alpha)
 		self.data_full['macd'] = macd(self.data_full, key='close')
-		self.data_full['sma'] = sma(self.data_full, key='close', window_days=sma_days)
+		self.data_full['sma'] = moving_average(self.data_full, key='close', window_days=sma_days)
+		self.data_full['close_n'] = normalize(self.data_full, key='close')
+		self.data_full['min'] = moving_min(self.data_full, key='close_n', window_days=q_days)
+		self.data_full['max'] = moving_max(self.data_full, key='close_n', window_days=q_days)
+
+		self.data_full['Qmin'] = self.data_full['close_n'] - self.data_full['min']
+		self.data_full['Qmax'] = self.data_full['close_n'] - self.data_full['max']
 		self.boll_full = bollinger(self.data_full, window_days=30)
 
-		_macd_curr = self.data_full['macd'][len(self.data_full)-1]
+		x_r = np.linspace(0, N, N)
-
+		y_r = self.data_full['macd'].to_numpy()
-		self._macd_data = self.data_full['macd'][len(self.data_full) - show_range_days:]
-		x_r = np.linspace(0, len(self._macd_data), len(self._macd_data))
-		y_r = self._macd_data.to_numpy()
 		spl = UnivariateSpline(x_r, y_r)
 		spl.set_smoothing_factor(0.5)
 		yf = spl(x_r)
 		yf_d = spl.derivative()(x_r)
 
-		self._macd_fitted = pd.DataFrame(np.transpose([yf,yf_d]), index=self._macd_data.index, columns=['macd_fitted', 'macd_fitted_d'])
+		self.data_full['macd_f'] = pd.DataFrame(np.transpose([yf]), index=self.data_full['macd'].index, columns=['macd_fitted'])
+		self.data_full['macd_fd'] = pd.DataFrame(np.transpose([yf_d]), index=self.data_full['macd'].index, columns=['macd_fitted_d'])
 
-		_macd_f_curr = self._macd_fitted['macd_fitted'][len(self._macd_fitted)-1]
+		_macd_f_curr = self.data_full['macd_f'][N-1]
-		_macd_fd_curr = self._macd_fitted['macd_fitted_d'][len(self._macd_fitted)-1]
+		_macd_fd_curr = self.data_full['macd_fd'][N-1]
 
-		return {'macd' : _macd_curr, 'macd_f' : _macd_f_curr, 'macd_fd' : _macd_fd_curr}
+		self.data = self.data_full[len(self.data_full) - show_range_days:]
+		self.boll = self.boll_full[len(self.boll_full) - show_range_days:]
+
+
+		return agent_buy(self.symbol, self.data)
 
 
 	def show(self, figNum=1):
-		data = self.data_full[len(self.data_full) - show_range_days:]
-		boll = self.boll_full[len(self.boll_full) - show_range_days:]
 
 		self.fig = plt.figure(figNum)
 		self.ax = plt.subplot(311)
-		self.__plot(data, ['ema', 'sma', 'close'])
+		self.__plot(self.data, ['min', 'max', 'close_n'])
-		plt.title(self.name)
+		plt.title('{} ({})'.format(self.name, self.symbol))
 		plt.legend()
 		plt.grid()
 
 		plt.subplot(312)
-		boll['upper'].plot()
+#		self.boll['upper'].plot()
-		boll['mid'].plot()
+#		self.boll['mid'].plot()
-		boll['lower'].plot()
+#		self.boll['lower'].plot()
+		self.data['Qmin'].plot()
+		self.data['Qmax'].plot()
 		plt.legend()
 		plt.grid()
 
 
 		plt.subplot(313)
-		self._macd_data.plot()
+		self.data['macd_f'].plot()
-		self._macd_fitted['macd_fitted'].plot()
+		self.data['macd_fd'].plot()
-		self._macd_fitted['macd_fitted_d'].plot()
 		plt.legend()
 		plt.grid()
 
@@ -304,15 +389,15 @@ if len(show_symbols) > 0:
 		if symbol in symbols:
 			title = Title(symbol, symbols[symbol])
 			title.fetch()
-			print(title.analyze())
+			title.analyze()
 			title.show(figNum)
 			figNum += 1
 else:
 	for symbol in symbols:
+		print('-----------------------------------------------')
 		title = Title(symbol, symbols[symbol])
 		title.fetch()
-		result = title.analyze()
+		if len(title.analyze()) > 0:
-		if result['macd_f'] > thresh_macd and result['macd_fd'] > 0:
 			title.show(figNum)
 			figNum += 1