def getSynthStock(tgt, date, dte, vol, interest, strike, stock):
'''Sets up iterative process to build the inventory up to initial levels'''
#get exDate
exDate = getExpDate(date)
#create the inventory object
inventory = options.Inventory()
i = 0
if system_status == 1:
tgt_factor = ((tgt) / 100) #adding leverage
else:
tgt_factor = tgt / 50
while i < tgt_factor:
#create the options
call = options.Option(exDate, vol, strike, stock, "Call", interest, 1)
put = options.Option(exDate, vol, strike, stock, "Put", interest, -1)
#build the position
if system_status == 1:
pos_list = [call, put]
else:
pos_list = [call]
pos = options.Position(pos_list)
#add the position to Inventory
inventory.add_position(pos)
i = i + 1
return inventory
def __init__(self, w_width, w_height, caption):
pygame.init()
self.root = tkinter.Tk()
self.root.withdraw()
self.w_width = w_width
self.w_height = w_height
self.caption = caption
self.set_window(self.w_width, self.w_height, self.caption)
font = pygame.font.Font("Retroscape.ttf", 15)
self.menu = []
for i, text in enumerate(("PLAY", "EDIT", "HELP")):
self.menu.append(options.Option(text, self.w_width/2,
(self.w_height/4)+103+(i*50), font))
self.title = pygame.image.load("title.png").convert()
self.title_pos = self.title.get_rect(center=(self.w_width/2,
self.w_height/4))
self.game = game.Game(self)
import options
set = options.OptionSet()
set.add_option([
options.Option("output", ["-o", "-beh"], sep=["", " "]),
options.Option("blah", "-behemoth"),
options.Option("flag", "-f", type="bool")
])
s = set.make_option_string([("flag", True), ("output", "hello")])
print s
print set.parse_option_string("-o hello -behemoth -f -beh_h there")
def run_sim(df):
stock = df.iloc[0]['Close']
vol = df.iloc[0]['Settle']
today = df.iloc[0]['Date']
df.at[0, 'tgtdelta'] = float(port) / float(stock)
today = getDate(today)
y = initiate_sim(df)
price = y.get_inventory_stats(today, stock, vol)
value = price['price']
pDelta = price['delta']
#initiate initial values
df.at[0, 'options'] = value
df.at[0, 'delta'] = pDelta
df.at[0, 'cash'] = float(df.iloc[0]['portfolio'] - df.iloc[0]['options'])
#cycle through by day
for i in range(1, len(df) - 1):
stock = df.iloc[i]['Close']
vol = df.iloc[i]['Settle']
today = df.iloc[i]['Date']
today = getDate(today)
interest = df.iloc[i]['Value']
daily_interest = interest / 36500
#get new cash value and delta
price = y.get_inventory_stats(today, stock, vol)
total_cash = df.iloc[i - 1]['cash']
total_cash = float(total_cash) * (1 + daily_interest)
df.at[i, 'cash'] = total_cash
#update df value
df.at[i, 'options'] = price['price']
#get rid of expiring contracts
y.sellExpirContracts(today)
delta = price['delta']
value = price['price']
###Issue with rolling up cash portfolio...need to checl
df.at[i, 'portfolio'] = total_cash + price['price']
#get new target delta
tgtdelta = (df.iloc[i]['portfolio'] / stock) #adding leverage
#tgt=getTargetDelta(tgtdelta)
df.at[i, 'tgtdelta'] = tgtdelta
df.at[i, 'delta'] = delta
gap = tgtdelta - delta
#set up portfolio rebalance
exDate = getExpDate(today)
strike = getStrike(stock)
if gap > 100:
call = options.Option(exDate, vol, strike, stock, "Call", interest,
1)
put = options.Option(exDate, vol, strike, stock, "Put", interest,
-1)
if system_status == 1:
pos_list = [call, put]
else:
pos_list = [call]
pos = options.Position(pos_list)
#add the position to Inventory
y.add_position(pos)
elif gap < -100:
y.sell_position()
else:
continue
#get new options price
price = y.get_inventory_stats(today, stock, vol)
value = price['price']
df.at[i, 'options'] = value
df.at[i, 'cash'] = df.iloc[i]['portfolio'] - value
if system_status == 1:
df.to_csv('synth.csv')
else:
df.to_csv('call.csv')
import os
import numpy as np
import torch
import copy
import options
TrainingFile = 'ModelNet10'
path = os.walk('./Data/' + TrainingFile)
obj_num = 0
obj_class = dict()
TrainingSet = list()
TestSet = list()
current_class = 'nothing'
now = 0
opt = options.Option()
global TrainingSize
global TestSize
def get_TrainingSize():
global TrainingSize
return TrainingSize
def shuffle_TrainingSet():
np.random.shuffle(TrainingSet)
def normalize_pointnum(point):
if point.shape[0] == opt.pointnum:
return point
elif point.shape[0] < opt.pointnum:
idx = np.random.choice(range(0, point.shape[0]), opt.pointnum - point.shape[0])
