itresearch mg64ve

## Nnet-example.r
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))

## optimal_strategy.py
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

## timeseries_cnn.py
#!/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

## myTestLSTM.py
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

## Policy Gradient with Cartpole and PyTorch (Medium Version).ipynb

      
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                mg64ve
                / Policy Gradient with Cartpole and PyTorch (Medium Version).ipynb
            
            
              Created
              August 19, 2019 09:45
            
              
                Solution to the Cartpole problem with policy gradients published on Medium
              
          
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## chartmill-backtest.py
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

## residual_network.py
"""
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
	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))
	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
	#!/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
	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
	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
	"""
	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