Stock/stock.py

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)

key = '0UO7Z2MVZ2YSQSVE'

class Stock(object):
	def __init__(self, params, symbol, symbol_params):
		self.symbol_params = symbol_params
		self.symbol = symbol
		self.params = params
		try:
			self.name = symbol_params['name']
		except:
			self.name = symbol
		self.ax = None
		self.fig = None
		self.currency = symbol_params['currency']
		self.currency_corr = 1
		if '$' in self.currency:
			self.currency_corr = self.params['k_euro']

		self.data = {}
		self.boll = {}
		self.indicators = {}

	def fetch(self):
		filename = 'stocks/' + self.symbol.replace('.', '_') + '.h5'
		fetch = False
		today = dt.date.today()
		start = dt.date(today.year-1, today.month, today.day)
		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 = self.params['fetch_on_outdated']
		except:
			fetch = True

		if fetch:
			# Get stocks price via data reader
			while(True):
				try:
					print("Fetching \"{}\"".format(self.symbol))
					data = web.DataReader(name=self.symbol, data_source="av-daily", start=start, end=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
		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()

	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['close'], alpha=self.params['ema_alpha'])
		self.data['sma'] = moving_average(self.data['close'], window_days=self.params['sma_days'])
		self.data['close_n'] = normalize(self.data['close'])
		self.data['macd'] = macd(self.data['close_n'])
		self.data['min'] = moving_min(self.data['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)

		y_r = np.append(self.data['macd'], self.data['macd'][N-1])
		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, N+1)
		yf_dd = spl_dd(x_r2)

		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, params=self.params['btfd'], marker_key='close_n', 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, id, data, keys):
		ax = plt.subplot(id)
		N = len(data['index'])
		start = max(0, N - self.params['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 == ax:
				x_idx = min(N-1, int(event.xdata))
				ax.format_xdata = lambda x: self.data['index'][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.params['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
		subplot_id = 100*num_subplots + 10 + 1
		self.fig = plt.figure(figNum)
		self.__plot(subplot_id, self.data, ['min', 'max', 'close_n'])
		self.__ind(indicators, ['BTFD'])
		plt.title('{} ({})'.format(self.name, self.symbol))
		plt.legend()
		plt.grid()

		subplot_id += 1
#		self.__plot(self.boll, ['lower', 'mid', 'upper'])
		self.__plot(subplot_id, self.data, ['Qmin', 'Qmax'])
		plt.legend()
		plt.grid()

		subplot_id += 1
		self.__plot(subplot_id, self.data, ['macd', 'macd_f'])
		plt.legend()
		plt.grid()

		subplot_id += 1
		self.__plot(subplot_id, self.data, ['macd_fd', 'macd_fdd'])
		plt.legend()
		plt.grid()