# 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

import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rc('figure', max_open_warning = 0)

from stock import Stock
import argparse
import json

params = {
	'analyze_range_days' : 5,
	'plot_range_days' : 0,
	'btfd' : {'thresh_max' : -7, 'thresh_min' : 1, 'cand_window' : 5},
	'k_euro' : 1 / 1.11,
	'q_range_days' : 10,
	'fetch_on_outdated' : True
}

show_symbols = []

parser = argparse.ArgumentParser()
parser.add_argument('--symbol', help='Symbol to fetch')
parser.add_argument('--analyze-range', type=int, help='Analyze window [days]')
parser.add_argument('--plot-range', type=int, help='Plot range [days]')
parser.add_argument('--q-range', type=int, help='Q range [days]')

args = parser.parse_args()

f = open('stocks.json', 'r')
s = f.read()
symbols = json.loads(s)
f.close()

if args.symbol is not None:
	sfc = args.symbol.split('|')
	symbol = sfc[0]
	show_symbols = [symbol]
	if symbol not in symbols:
		try:
			fullname = sfc[1]
		except:
			fullname = 'Enter full name here'
		try:
			currency = sfc[2]
		except:
			currency = 'USD'

		symbols[symbol] = {'name' : fullname, 'currency' : currency}
		s = json.dumps(symbols, sort_keys=True, indent=4)
		f = open('stocks.json', 'w')
		f.write(s)
		f.close()
	else:
		print ("{} is in database".format(symbol))

if args.analyze_range is not None:
	params['analyze_range_days'] = args.analyze_range

if args.plot_range is not None:
	params['plot_range_days'] = args.plot_range

if args.q_range is not None:
	params['q_range_days'] = args.q_range

do_plot = params['plot_range_days'] > 0
buy_list = {}
def buy_callback(data):
	name = data['name']
	item = data['item']
	if name not in buy_list:
		buy_list[name] = {'items': [item]}
	else:
		buy_list[name]['items'].append(item)

	print(data)

figNum = 1
if len(show_symbols) > 0:
	for symbol in show_symbols:
		if symbol in symbols:
			title = Stock(params, symbol, symbols[symbol])
			title.fetch()
			title.statistics()
			try:
				ind = title.analyze(buy_callback, range_days=params['analyze_range_days'])
				if do_plot:
					title.show(ind, figNum=figNum)
				figNum += 1
			except:
				print ("Exception occurred for {}".format(symbol))

else:
	for symbol in symbols:
		title = Stock(params, symbol, symbols[symbol])
		title.fetch()
		title.statistics()
		try:
			ind = title.analyze(buy_callback, range_days=params['analyze_range_days'])
			if Stock.has_candidate(ind, 'BTFD'):
				print('-----------------------------------------------')
				if do_plot:
					title.show(ind, figNum=figNum)
				figNum += 1
		except:
			print ("Exception occurred for {}".format(symbol))
gain_accum = 0
num_stocks = 0
for symbol in buy_list:
	items = buy_list[symbol]['items']
	title = Stock(params, symbol, symbols[symbol])
	title.fetch()
	title.statistics()
	gain = title.get_latest('close_n') - items[0]['value']
	gain_accum += gain
	num_stocks += 1
	print ('{}: Gain = {} %'.format(symbol, gain))

if num_stocks > 0:
	print ('Gain total = {} %'.format(gain_accum/num_stocks))

if do_plot:
	plt.show()