Mate Hegedus hmate9

## short_term_alpha.ipynb

      
              2 files
            
          
              0 forks
            
          
                0 comments
              
            
              0 stars
            
          
                hmate9
                / short_term_alpha.ipynb
            
            
              Created
              March 15, 2022 20:22
                — forked from sebjai/short_term_alpha.ipynb
            
              
                Market Making in Short-Term Alpha (Chapter 10.4.2 of Algorithmic and High-Frequency Trading by Cartea, Jaimungal, Penalva, published by Cambridge University Press)
              
          
      Loading

      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## How we use cloudfront and next.js.md

      
              1 file
            
          
              0 forks
            
          
                0 comments
              
            
              0 stars
            
          
                hmate9
                / How we use cloudfront and next.js.md
            
            
              Last active
              August 24, 2020 12:32
                — forked from rbalicki2/How we use cloudfront and next.js.md
            
          
    How we incorporate next and cloudfront (2018-04-21)


Feel free to contact me at robert.balicki@gmail.com or tweet at me @statisticsftw

This is a rough outline of how we utilize next.js and S3/Cloudfront. Hope it helps!
It assumes some knowledge of AWS.
Goals


## ETFs
AAAU
AADR
AAXJ
ABEQ
ACES
ACIO
ACSG
ACSI
ACT
ACWF

## extract.py
def extract_train_data(winner, replayName):
	with open('replays/' + replayName) as replay_file:
		game = json.load(replay_file)
	height = game['height']
	width = game['width']
	player_id = get_id(game, winner)
	print "Winner:", winner, "(", player_id, ")"
	num_frames = game['num_frames']
	dataX = []
	dataY = []

## dqn_catch.py
import numpy as np
import gym

from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.optimizers import Adam

from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy, LinearAnnealedPolicy, EpsGreedyQPolicy
from rl.memory import SequentialMemory
	def extract_train_data(winner, replayName):
	with open('replays/' + replayName) as replay_file:
	game = json.load(replay_file)
	height = game['height']
	width = game['width']
	player_id = get_id(game, winner)
	print "Winner:", winner, "(", player_id, ")"
	num_frames = game['num_frames']
	dataX = []
	dataY = []
	import numpy as np
	import gym

	from keras.models import Sequential
	from keras.layers import Dense, Activation, Flatten
	from keras.optimizers import Adam

	from rl.agents.dqn import DQNAgent
	from rl.policy import BoltzmannQPolicy, LinearAnnealedPolicy, EpsGreedyQPolicy
	from rl.memory import SequentialMemory