mg64ve

"""
Clean and simple Keras implementation of network architectures described in:
    - (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf).
    - (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf).
    
Python 3.
"""

from keras import layers
from keras import models

mg64ve

import json
from datetime import datetime, timedelta


# Copies logic from https://www.quantconnect.com/tutorials/strategy-library/fundamental-factor-long-short-strategy
# Algo storing - https://www.quantconnect.com/docs/algorithm-framework/algorithm-scoring
class ChartMillBackTester(QCAlgorithm):

    def Initialize(self):
        self.SetCash(1000*100) # Are't we rich? :D

mg64ve

mg64ve

from btgym import BTgymEnv
import IPython.display as Display
import PIL.Image as Image
from gym import spaces


import gym
import numpy as np
import random

mg64ve

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

mg64ve

import numpy as np
import pandas as pd


def compute_market_prices(prices):
    """Compute market prices according to the trading competition recipe.

    Parameters
    ----------
    prices : DataFrame

mg64ve

rm(list = ls())
gc()

library(knitr)
library(blotter)
if (!exists('.blotter')) .blotter <- new.env()

suppressWarnings(try(rm(list=c("account.forex","portfolio.forex"),pos=.blotter),silent=TRUE))
suppressWarnings(try(rm(list=c("b.strategy","myTheme","EURUSD",".getSymbols")),silent=TRUE))