- refactored

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

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()