Stock/pandas_eval.py

import pandas as pd
import pandas_datareader.data as web
import numpy as np
import math
import matplotlib.pyplot as plt
import datetime as dt
import os

# Stock Investors Financial Math
# https://www.fmlabs.com/reference/default.htm?url=SimpleMA.htm

# Alpha Vantage
# https://www.alphavantage.co/documentation/

# Pandas
# https://pandas.pydata.org/pandas-docs/stable/index.html

# Knowledge
# https://www.investopedia.com/articles/technical/02/050602.asp
# https://ntguardian.wordpress.com/2016/09/19/introduction-stock-market-data-python-1

key = '0UO7Z2MVZ2YSQSVE'
thresh_macd = 1.5
show_range_days = 20
show_title = ['FB2A.DE']
k_euro = 1 / 1.11
N_poly = 7

titles = [
	'AAPL',
	'XLNX',
	'QCOM',
	'DPW.DE',
	'CSCO',
	'AIR',
	'BA',
	'NVDA',
	'MSFT',
	'DIS',
	'NFLX',
	'OHB.DE',
	'ERCA.DE',
	'VAR1.DE',
	'HD',
	'AMZN',
	'GOOGL',
	'ZIL2.DE',
	'SIS.DE',
	'SIE.DE',
	'GFT.DE',
	'AMD.DE',
	'CAP.DE',
	'ADBE',
	'PANW',
	'AMAT',
	'RIB.DE',
	'WAF.DE',
	'EVT.DE',
	'VOW.DE',
	'BMW.DE',
	'NSU.DE',
	'DAI.DE',
	'SHA.DE',
	'CON.DE',
	'DHER.DE',
	'BSL.DE',
	'D6H.DE',
	'TC1.DE',
	'VODI.DE',
	'ATVI',
	'GME',
	'NTO.F',
	'UBSFF',
	'NXPRF',
	'IMPUF',
	'NMPNF',
	'ALSMY',
	'ARRD.F',
	'PHG',
	'KEYS',
	'ORCL',
	'UBER',
	'VMW',
	'AVGO',
	'CY',
	'QABSY',
	'BLDP',
	'D7G.F',
	'ITMPF',
	'PLUG',
	'PCELF',
	'SU.PA',
#	'SON1.DE',
	'STM.DE',
	'BC8.DE',
	'MOR.DE',
	'BAYN.DE',
	'BEI.DE',
	'EUZ.DE',
	'SLAB',
	'SPLK',
	'IAG',
	'INTC',
	'I']

def ema(data, key, alpha=0.5, ic=None):
	result = []
	if ic is None:
		r = data[key][0]
	else:
		r = ic
	for v in data[key]:
		r = alpha*r + (1-alpha)*v
		result.append(r)

	return np.array(result)

def sma(data, key, window_days, ic=None):
	mem = [0] * window_days
	result = []
	k=0
	cumsum = 0
	for v in data[key]:
		cumsum += (v - mem[k])
		mem[k] = v
		k += 1
		if k >= window_days:
			k=0

		result.append(cumsum/window_days)

	return result

def sd(data, key, window_days, ic=None):
	mem = [0] * window_days
	result = []
	k=0
	cumsum = 0
	data_mean = data[key] - sma(data, key, window_days, ic)
	for v in data_mean:
		v2 = v * v
		cumsum += (v2 - mem[k])
		mem[k] = v2
		k += 1
		if k >= window_days:
			k=0

		var = max(0, cumsum) / window_days
		result.append(math.sqrt(var))

	return result


def colsum(data, keys):
	result = np.zeros(data.shape[0])
	for key in keys:
		result += data[key]

	return result

def macd(data, key):
	ema_short = ema(data, key, alpha=0.85)
	ema_long = ema(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))

	mid = np.array(sma(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'])
	result['lower'] = lower
	result['mid'] = mid
	return result

class Symbol(object):
	def __init__(self, name):
		self.name = name
		self.ax = None
		self.fig = None

	def fetch(self):
		filename = self.name.replace('.','_') + '.h5'
		fetch = False
		today = dt.date.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 = True
		except:
			fetch = True

		if fetch:
			print ("Fetching \"{}\"".format(self.name))
			# Get stock price via data reader
			start = dt.datetime(today.year,1,1)
			end = dt.date.today()
			data = web.DataReader(self.name, "av-daily", start, end, api_key=key)
			hdf = pd.HDFStore(filename)
			hdf[self.name] = data
		else:
			hdf = pd.HDFStore(filename, 'r')

		self.data_full = hdf[self.name]*k_euro
		hdf.close()

	def __plot(self, d, keys):
		dates = d.index

		for key in keys:
			data = d[key]
			data.plot()

		def hover(event):
			if event.inaxes == self.ax:
				x_idx = int(event.xdata)
				self.ax.format_xdata = lambda x: dates[x_idx]
				self.ax.format_ydata = lambda y: '€{:.2f}'.format(data[x_idx])

		self.fig.canvas.mpl_connect("motion_notify_event", hover)

	def analyze(self):
		self.data_full['ema'] = ema(self.data_full, key='close', alpha=0.75)
		self.data_full['macd'] = macd(self.data_full, key='close')
		self.data_full['sma'] = sma(self.data_full, key='close', window_days=30)
		self.boll_full = bollinger(self.data_full, window_days=30)

		_macd_curr = self.data_full['macd'][len(self.data_full)-1]

		self._macd_data = self.data_full['macd'][len(self.data_full) - show_range_days:]
		x_r = list(range(0, len(self._macd_data)+1))
		y_r = self._macd_data.to_numpy()
		y_r = np.append(y_r, y_r[len(y_r)-1])
		poly = np.polyfit(x_r, y_r, N_poly)
		poly_d = np.polyder(poly)

		yf = np.polyval(poly, x_r[:len(x_r)-1])
		yf_d = np.polyval(poly_d, x_r[:len(x_r)-1])
		self._macd_fitted = pd.DataFrame(np.transpose([yf,yf_d]), index=self._macd_data.index, columns=['macd_fitted', 'macd_fitted_d'])

		_macd_f_curr = self._macd_fitted['macd_fitted'][len(self._macd_fitted)-1]
		_macd_fd_curr = self._macd_fitted['macd_fitted_d'][len(self._macd_fitted)-1]

		return {'macd' : _macd_curr, 'macd_f' : _macd_f_curr, 'macd_fd' : _macd_fd_curr}


	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'])
		plt.title(self.name)
		plt.legend()
		plt.grid()

		plt.subplot(312)
		boll['upper'].plot()
		boll['mid'].plot()
		boll['lower'].plot()
		plt.legend()
		plt.grid()


		plt.subplot(313)
		self._macd_data.plot()
		self._macd_fitted['macd_fitted'].plot()
		self._macd_fitted['macd_fitted_d'].plot()
		plt.legend()
		plt.grid()

figNum = 1

if len(show_title) > 0:
	for title in show_title:
		sym = Symbol(title)
		sym.fetch()
		print(sym.analyze())
		sym.show(figNum)
		figNum += 1
else:
	for title in titles:
		sym = Symbol(title)
		sym.fetch()
		result = sym.analyze()
		if result['macd_f'] > thresh_macd and result['macd_fd'] > 0:
			sym.show(figNum)
			figNum += 1

plt.show()