- refactored

git-svn-id: http://moon:8086/svn/projects/Stock@328 fda53097-d464-4ada-af97-ba876c37ca34

robot.py

@@ -1,15 +1,3 @@
-import pandas as pd
-import pandas_datareader.data as web
-from pandas_datareader._utils import RemoteDataError
-import matplotlib.pyplot as plt
-from scipy.interpolate import UnivariateSpline
-import datetime as dt
-import os
-import time
-import matplotlib as mpl
-
-import agent
-from functions import *
 # Stock Investors Financial Math
 # https://www.fmlabs.com/reference/default.htm?url=SimpleMA.htm
 
@@ -23,8 +11,19 @@ from functions import *
 # https://www.investopedia.com/articles/technical/02/050602.asp
 # https://ntguardian.wordpress.com/2016/09/19/introduction-stock-market-data-python-1
 
+import matplotlib.pyplot as plt
+from stock import Stock
+
 key = '0UO7Z2MVZ2YSQSVE'
-show_range_days = 5
+params = {
+	'show_range_days' : 5,
+	'k_euro' : 1 / 1.11,
+	'ema_alpha' : 0.75,
+	'sma_days' : 10,
+	'q_days' : 10,
+	'fetch_on_outdated' : True
+}
+
 show_symbols = ['OHB.DE', 'ITMPF', 'PLUG', 'MOR.DE', 'CSCO', 'ERCA.DE', 'AVGO', 'DIS', 'UBSFF', 'DHER.DE', 'AIR']
 show_symbols = ['CSCO']
 #show_symbols = ['OHB.DE']
@@ -33,13 +32,6 @@ show_symbols = ['DHER.DE']
 #show_symbols = ['WDI.DE']
 #show_symbols = ['EVT.DE']
 show_symbols = []
-k_euro = 1 / 1.11
-ema_alpha = 0.75
-sma_days = 10
-q_days = 10
-fetch_on_outdated = True
-
-mpl.rc('figure', max_open_warning = 0)
 
 symbols = {
 
@@ -130,175 +122,6 @@ symbols = {
 	'INTC' : {'name' : 'Intel Corporation', 'currency' : '$'},
 	'I' : {'name': 'IntelSat', 'currency' : '$'}}
 
-class Title(object):
-	def __init__(self, symbol, params):
-		self.symbol = symbol
-		self.params = params
-		try:
-			self.name = params['name']
-		except:
-			self.name = symbol
-		self.ax = None
-		self.fig = None
-		self.currency = params['currency']
-		self.currency_corr = 1
-		if '$' in self.currency:
-			self.currency_corr = k_euro
-
-		self.data = {}
-		self.boll = {}
-		self.indicators = {}
-
-	def fetch(self):
-		filename = self.symbol.replace('.', '_') + '.h5'
-		fetch = False
-		today = dt.date.today()
-		start = dt.datetime(today.year, 1, 1)
-		end = today
-
-		try:
-			hdf = pd.HDFStore(filename, 'r')
-			hdf.close()
-		except:
-			fetch = True
-
-		try:
-			lastmodified = dt.datetime.fromtimestamp(os.stat(filename).st_mtime).date()
-			if lastmodified != today:
-				fetch = fetch_on_outdated
-		except:
-			fetch = True
-
-		if fetch:
-			# Get stock price via data reader
-			while(True):
-				try:
-					print("Fetching \"{}\"".format(self.symbol))
-					data = web.DataReader(self.symbol, "av-daily", start, end, api_key=key)
-					break
-				except RemoteDataError:
-					for timeout in reversed(range(0, 60)):
-						print ("Try again in {} s".format(timeout))
-						time.sleep(1)
-
-			hdf = pd.HDFStore(filename)
-			hdf[self.symbol] = data
-		else:
-			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])
-
-		hdf.close()
-
-	def get_latest(self, key):
-		N = len(self.data['index'])
-		return self.data[key][N-1]
-
-	def statistics(self):
-		N = len(self.data['index'])
-		self.data['ema'] = exponential_moving_average(self.data, key='close', alpha=ema_alpha)
-		self.data['sma'] = moving_average(self.data, key='close', window_days=sma_days)
-		self.data['close_n'] = normalize(self.data, key='close')
-		self.data['macd'] = macd(self.data, key='close_n')
-		self.data['min'] = moving_min(self.data, key='close_n', window_days=q_days)
-		self.data['max'] = moving_max(self.data, key='close_n', window_days=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 = self.data['macd']
-		spl = UnivariateSpline(x_r, y_r)
-		spl.set_smoothing_factor(0.25)
-		spl_d = spl.derivative()
-		spl_dd = spl_d.derivative()
-		yf = spl(x_r)
-		yf_d = spl_d(x_r)
-		yf_dd = spl_dd(x_r)
-
-		self.data['macd_f'] = np.transpose(yf)
-		self.data['macd_fd'] = np.transpose(yf_d)
-		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)
-
-	@staticmethod
-	def has_candidate(data, key):
-		result = np.count_nonzero(data[key] != None)
-		return result > 0
-
-	@staticmethod
-	def slice(data, start, stop):
-		result = {}
-		for key in iter(data):
-			result[key] = data[key][start:stop]
-
-		return result
-
-	def __plot(self, data, keys):
-		N = len(data['index'])
-		start = max(0, N - show_range_days)
-		stop = N
-		xr = list(range(start, stop))
-		sliced = Title.slice(data, start, stop)
-
-		for key in keys:
-			plt.plot(xr, sliced[key], label=key)
-
-
-		def hover(event):
-			if event.inaxes == self.ax:
-				x_idx = min(N-1, int(event.xdata))
-				self.ax.format_xdata = lambda x: self.data['index'][x_idx]
-				self.ax.format_ydata = lambda y: '{:.2f}%'.format(self.data['close_n'][x_idx])
-
-		self.fig.canvas.mpl_connect("motion_notify_event", hover)
-
-	def __ind(self, data, keys):
-		N = len(data['index'])
-		start = max(0, N - show_range_days)
-		stop = N
-		xr = list(range(start, stop))
-		sliced = Title.slice(data, start, stop)
-
-		for key in keys:
-			plt.plot(xr, sliced[key], 'go', label=key)
-
-	def show(self, indicators, figNum=1):
-		num_subplots = 4
-		self.fig = plt.figure(figNum)
-		self.ax = plt.subplot(100*num_subplots + 10 + 1)
-		self.__plot(self.data, ['min', 'max', 'close_n'])
-		self.__ind(indicators, ['BTFD'])
-		plt.title('{} ({})'.format(self.name, self.symbol))
-		plt.legend()
-		plt.grid()
-
-		plt.subplot(100*num_subplots + 10 + 2)
-#		self.__plot(self.boll, ['lower', 'mid', 'upper'])
-		self.__plot(self.data, ['Qmin', 'Qmax'])
-		plt.legend()
-		plt.grid()
-
-
-		plt.subplot(100*num_subplots + 10 + 3)
-		self.__plot(self.data, ['macd', 'macd_f'])
-		plt.legend()
-		plt.grid()
-
-		plt.subplot(100*num_subplots + 10 + 4)
-		self.__plot(self.data, ['macd_fd', 'macd_fdd'])
-		plt.legend()
-		plt.grid()
-
-
 buy_list = {}
 def buy_callback(data):
 	name = data['name']
@@ -314,19 +137,19 @@ figNum = 1
 if len(show_symbols) > 0:
 	for symbol in show_symbols:
 		if symbol in symbols:
-			title = Title(symbol, symbols[symbol])
+			title = Stock(symbol, symbols[symbol])
 			title.fetch()
 			title.statistics()
-			ind = title.analyze(buy_callback, range_days=show_range_days)
+			ind = title.analyze(buy_callback, range_days=params['show_range_days'])
 			title.show(ind, figNum=figNum)
 			figNum += 1
 else:
 	for symbol in symbols:
-		title = Title(symbol, symbols[symbol])
+		title = Stock(params, symbol, symbols[symbol])
 		title.fetch()
 		title.statistics()
-		ind = title.analyze(buy_callback, range_days=show_range_days)
+		ind = title.analyze(buy_callback, range_days=params['show_range_days'])
-		if Title.has_candidate(ind, 'BTFD'):
+		if Stock.has_candidate(ind, 'BTFD'):
 			print('-----------------------------------------------')
 			title.show(ind, figNum=figNum)
 			figNum += 1
@@ -335,7 +158,7 @@ gain_accum = 0
 num_stocks = 0
 for symbol in buy_list:
 	items = buy_list[symbol]['items']
-	title = Title(symbol, symbols[symbol])
+	title = Stock(params, symbol, symbols[symbol])
 	title.fetch()
 	title.statistics()
 	gain = title.get_latest('close_n') - items[0]['value']

stock.py

@@ -0,0 +1,182 @@
+import pandas as pd
+import pandas_datareader.data as web
+from pandas_datareader._utils import RemoteDataError
+from scipy.interpolate import UnivariateSpline
+import datetime as dt
+import os
+import time
+import agent
+from functions import *
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+
+mpl.rc('figure', max_open_warning = 0)
+
+class Stock(object):
+	def __init__(self, params2, symbol, params):
+		self.params = params
+		self.symbol = symbol
+		self.params2 = params2
+		try:
+			self.name = params['name']
+		except:
+			self.name = symbol
+		self.ax = None
+		self.fig = None
+		self.currency = params['currency']
+		self.currency_corr = 1
+		if '$' in self.currency:
+			self.currency_corr = self.params2['k_euro']
+
+		self.data = {}
+		self.boll = {}
+		self.indicators = {}
+
+	def fetch(self):
+		filename = self.symbol.replace('.', '_') + '.h5'
+		fetch = False
+		today = dt.date.today()
+		start = dt.datetime(today.year, 1, 1)
+		end = today
+
+		try:
+			hdf = pd.HDFStore(filename, 'r')
+			hdf.close()
+		except:
+			fetch = True
+
+		try:
+			lastmodified = dt.datetime.fromtimestamp(os.stat(filename).st_mtime).date()
+			if lastmodified != today:
+				fetch = fetch_on_outdated
+		except:
+			fetch = True
+
+		if fetch:
+			# Get stock price via data reader
+			while(True):
+				try:
+					print("Fetching \"{}\"".format(self.symbol))
+					data = web.DataReader(self.symbol, "av-daily", start, end, api_key=key)
+					break
+				except RemoteDataError:
+					for timeout in reversed(range(0, 60)):
+						print ("Try again in {} s".format(timeout))
+						time.sleep(1)
+
+			hdf = pd.HDFStore(filename)
+			hdf[self.symbol] = data
+		else:
+			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])
+
+		hdf.close()
+
+	def get_latest(self, key):
+		N = len(self.data['index'])
+		return self.data[key][N-1]
+
+	def statistics(self):
+		N = len(self.data['index'])
+		self.data['ema'] = exponential_moving_average(self.data, key='close', alpha=self.params2['ema_alpha'])
+		self.data['sma'] = moving_average(self.data, key='close', window_days=self.params2['sma_days'])
+		self.data['close_n'] = normalize(self.data, key='close')
+		self.data['macd'] = macd(self.data, key='close_n')
+		self.data['min'] = moving_min(self.data, key='close_n', window_days=self.params2['q_days'])
+		self.data['max'] = moving_max(self.data, key='close_n', window_days=self.params2['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 = self.data['macd']
+		spl = UnivariateSpline(x_r, y_r)
+		spl.set_smoothing_factor(0.25)
+		spl_d = spl.derivative()
+		spl_dd = spl_d.derivative()
+		yf = spl(x_r)
+		yf_d = spl_d(x_r)
+		yf_dd = spl_dd(x_r)
+
+		self.data['macd_f'] = np.transpose(yf)
+		self.data['macd_fd'] = np.transpose(yf_d)
+		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)
+
+	@staticmethod
+	def has_candidate(data, key):
+		result = np.count_nonzero(data[key] != None)
+		return result > 0
+
+	@staticmethod
+	def slice(data, start, stop):
+		result = {}
+		for key in iter(data):
+			result[key] = data[key][start:stop]
+
+		return result
+
+	def __plot(self, data, keys):
+		N = len(data['index'])
+		start = max(0, N - self.params2['show_range_days'])
+		stop = N
+		xr = list(range(start, stop))
+		sliced = Stock.slice(data, start, stop)
+
+		for key in keys:
+			plt.plot(xr, sliced[key], label=key)
+
+
+		def hover(event):
+			if event.inaxes == self.ax:
+				x_idx = min(N-1, int(event.xdata))
+				self.ax.format_xdata = lambda x: self.data['index'][x_idx]
+				self.ax.format_ydata = lambda y: '{:.2f}%'.format(self.data['close_n'][x_idx])
+
+		self.fig.canvas.mpl_connect("motion_notify_event", hover)
+
+	def __ind(self, data, keys):
+		N = len(data['index'])
+		start = max(0, N - self.params2['show_range_days'])
+		stop = N
+		xr = list(range(start, stop))
+		sliced = Stock.slice(data, start, stop)
+
+		for key in keys:
+			plt.plot(xr, sliced[key], 'go', label=key)
+
+	def show(self, indicators, figNum=1):
+		num_subplots = 4
+		self.fig = plt.figure(figNum)
+		self.ax = plt.subplot(100*num_subplots + 10 + 1)
+		self.__plot(self.data, ['min', 'max', 'close_n'])
+		self.__ind(indicators, ['BTFD'])
+		plt.title('{} ({})'.format(self.name, self.symbol))
+		plt.legend()
+		plt.grid()
+
+		plt.subplot(100*num_subplots + 10 + 2)
+#		self.__plot(self.boll, ['lower', 'mid', 'upper'])
+		self.__plot(self.data, ['Qmin', 'Qmax'])
+		plt.legend()
+		plt.grid()
+
+
+		plt.subplot(100*num_subplots + 10 + 3)
+		self.__plot(self.data, ['macd', 'macd_f'])
+		plt.legend()
+		plt.grid()
+
+		plt.subplot(100*num_subplots + 10 + 4)
+		self.__plot(self.data, ['macd_fd', 'macd_fdd'])
+		plt.legend()
+		plt.grid()