def __init__(self, symbols,
start_date=dt.datetime(2008,1,1),
end_date= dt.datetime(2009,1,1)):
self.dates_range = pd.date_range(start_date, end_date)
self.check = 0
self.cum_reward = 0
# initialize portfolio's cash
self.init_cash = 100000
# value of each position (long or short)
self.buy_volume = 8000
self.cum_operations = 0
self.cum_cash = self.init_cash
self.date_start = 1
#for visualization
self.data_out = []
# preprocessing time series
# stock symbol data
stock_symbols = symbols[:]
# price data
prices_all = util.get_prices(symbols, start_date, end_date)
self.stock_A = stock_symbols[0]
self.stock_B = stock_symbols[1]
# first trading day
self.dateIdx = 0
self.date = prices_all.index[0]
self.start_date = start_date
self.end_date = end_date
self.prices = prices_all[stock_symbols]
self.prices_ibovespa = prices_all['ibovespa']
# keep track of portfolio value as a series.
#########################
# the variables 'longA', 'longB' and 'longC' keep track of the number of opened positions on each stock. When a stock is
# bought, the variables are increased 1. When a stock is sold, the variables are decreased 1. When the variables' values are
# zero, no positions are opened.
#########################
# variable 'a_vol' keeps track of the number of A shares traded (either bought or sold) in the form of a list.
# Whenever a position is opened, it is feeded. Whenever a position is closed, the respective register is deleted. The
# positions are closed beginning with the last ones.
#########################
# variable 'a_price' is a list that keeps track of the price of the stock A when a trade occurs. Likewise, Whenever a
# position is opened, it is feeded. Whenever a position is closed, the respective register is deleted.
#########################
# The same observations apply to 'b_vol', 'c_vol', 'b_price' and 'c_price'. All those variables are needed for the
# computation of returns when the positions are closed.
self.portfolio = {'cash': self.init_cash, 'a_vol': [], 'a_price': [], 'b_vol': [], 'b_price': [], 'longA': 0, 'longB': 0}
self.port_val = self.port_value()
self.port_val_market = self.port_value_for_output()
def main():
start_date = dt.datetime(2010, 1, 1)
end_date = dt.datetime(2011, 12, 31)
symbols = ['JPM']
start_val = 100000
benchmark_days = get_prices(symbols, pd.date_range(start_date, end_date))
ps.usePossibleStrategy(benchmark_days, symbols, start_date, end_date, start_val)
imps.useImpossibleStrategy(benchmark_days, symbols, start_date, end_date, start_val)
def show_search(a=""):
print("done")
self.steam1['text'] = ""
self.instant1['text'] = ""
self.g2a1['text'] = ""
self.uplay['text'] = ""
self.steam1['fg'] = "black"
self.instant1['fg'] = "black"
self.g2a1['fg'] = "black"
self.uplay['fg'] = "black"
self.instnat_label['text'] = "Instant-Gaming.com"
self.steam_label['text'] = "Steam"
self.uplay_label['text'] = "Uplay"
self.g2a_label['text'] = "g2a.com"
search_saved = self.search.get()
result = get_prices(search_saved, self.search_for_dlc)
#self.g2a2['text'] = search_saved
for i in result:
if i[2].lower() == "instant-gaming":
self.instant1['text'] = self.instant1['text'] + i[0] + "\n(Price: " + str(i[1]) + ")" + "\n\n"
elif i[2].lower() == "steam":
self.steam1['text'] = self.steam1['text'] + i[0] + "\n(Price: " + str(i[1]) + ")" + "\n\n"
elif i[2].lower() == "g2a":
self.g2a1['text'] = self.g2a1['text'] + i[0] + "\n(Price: " + str(i[1]) + ")" + "\n\n"
elif i[2].lower() == "uplay":
self.uplay['text'] = self.uplay['text'] + i[0] + "\n(Price: " + str(i[1]) + ")" + "\n\n"
if self.instant1['text'] == "":
self.instant1['fg'] = "red"
self.instant1['text'] = " ERROR "
tk.messagebox.showerror(title="ERROR", message="Couldn't connect to instant-gaming.com\nor couldn't find game")
if self.steam1['text'] == "":
self.steam1['fg'] = "red"
self.steam1['text'] = " ERROR "
tk.messagebox.showerror(title="ERROR", message="Couldn't connect to steam\nor couldn't find game")
if self.g2a1['text'] == "":
self.g2a1['fg'] = "red"
self.g2a1['text'] = " ERROR "
tk.messagebox.showerror(title="ERROR", message="Couldn't connect to g2a.com\nor couldn't find game")
if self.uplay['text'] == "":
self.uplay['fg'] = "red"
self.uplay['text'] = " ERROR "
tk.messagebox.showerror(title="ERROR", message="Couldn't connect to uplay\nor couldn't find game")
print(result)
def get_indicators(symbols, start_date, end_date):
dates = pd.date_range(start_date, end_date)
df_prices = get_prices(symbols, dates)
# Get the Rolling Mean
rm = get_rolling_mean(df_prices['JPM'], window=20)
# Get the Rolling Standard Deviation
rstd = get_rolling_std(df_prices['JPM'], window=20)
# Get the Momentum
mom = get_momentum(df_prices['JPM'])
# Get the Upper and Lower Bollinger Bands
df_bollinger_bands = get_bollinger_bands(rm, rstd)
# Plot Raw Prices and Bollinger Bands
plt.figure(1)
plt.suptitle('Bollinger Bands', fontsize=20)
plt.plot(df_prices.index, df_prices['JPM'], color='r', label='Raw Price')
plt.plot(df_prices.index,
df_bollinger_bands['Upper'],
color='b',
label='_nolegend_')
plt.plot(df_prices.index,
df_bollinger_bands['Lower'],
color='b',
label='Bollinger Bands')
plt.legend(loc='best')
plt.xticks(rotation=45)
# Plot Raw Prices and Moving Average
plt.figure(2)
plt.suptitle('Moving Average', fontsize=20)
plt.plot(df_prices.index, df_prices['JPM'], color='r', label='Raw Price')
plt.plot(df_prices.index, rm, color='b', label='Moving Average')
plt.legend(loc='best')
plt.xticks(rotation=90)
# Plot Raw Price and Momentum
f, axarr = plt.subplots(2, sharex='all')
axarr[0].plot(df_prices.index, df_prices['JPM'], color='r')
axarr[0].set_title('Raw Price')
axarr[1].set_title('Momentum')
axarr[1].plot(df_prices.index, mom, color='b')
plt.suptitle('Raw Price and Momentum', fontsize=20)
plt.xticks(rotation=90)
# plt.show(block=False)
# plt.gcf().clear()
return
def __init__(
self,
symbols,
start_date=dt.datetime(2008, 1, 1),
end_date=dt.datetime(2009, 1, 1),
test_date=dt.datetime(2008, 8, 1),
):
self.dates_range = pd.date_range(start_date, end_date)
self.test_date = test_date
self.check = 0
self.tickers = symbols
#self.cum_reward = 0
self.date_start = 1
# for visualization
self.data_out = []
self.costs = []
self.cum_return = []
# preprocessing time series
# stock symbol data
stock_symbols = symbols[:]
# price data
prices_all = util.get_prices(symbols, start_date, end_date)
self.stock_A = stock_symbols[0]
self.stock_B = stock_symbols[1]
# first trading day start from day 100
self.dateIdx = 100
self.date = prices_all.index[0]
self.start_date = start_date
self.end_date = end_date
self.prices = prices_all[stock_symbols]
# print (self.prices)
#self.prices_ibovespa = prices_all['ibovespa']
self.portfolio = {'longA': 0, 'longB': 0}
print('========1.init environment===================')
print('\t{}Price: {:>6} \tWebsite: {:<20}{}'.format(
bcolors.OKGREEN, e[1], e[2], bcolors.ENDC))
else:
print('\t{}Price: {:>6} \tWebsite: {:<20}{}'.format(
bcolors.WARNING, e[1], e[2], bcolors.ENDC))
last = e[0].lower()
if __name__ == '__main__':
from util import get_prices
import argparse
parser = argparse.ArgumentParser(
description=
'Commandline tool to compare prices between different websites',
usage='python3 cli.py <game> [options]')
parser.add_argument('query', type=str, help='Game you\'re searching for')
parser.add_argument('--dlc',
dest='dlc',
action='store_true',
help='Are you searching a dlc?')
parser.set_defaults(dlc=False)
parser.add_argument('--max',
type=int,
help='Max number of results per website',
default=3)
args = parser.parse_args()
print_data(get_prices(args.query, args.dlc, args.max))