ceshine

ceshine

How To Develop Python Programs in Visual Studio Code

Prerequisites

You have to already have these in your system:

• Python. Version 3.6+ is highly recommended.
  • Official CPython from python.org
  • Anaconda

• (Recommended) Miniconda

ceshine

"""A Simple Strategy Trading Two Stocks

Original code: https://blog.csdn.net/qq_26948675/article/details/80016633
Modified based on: https://www.backtrader.com/blog/posts/2018-04-22-improving-code/improving-code.html

Replaced the local CSV files with online data from IEX.

Unfortunately, this strategy is not profitable for the two stocks picked.
"""

ceshine

class STLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(self, optimizer, max_mul, ratio, steps_per_cycle, decay=1, last_epoch=-1):
        self.max_mul = max_mul - 1
        self.turning_point = steps_per_cycle // (ratio + 1)
        self.steps_per_cycle = steps_per_cycle
        self.decay = decay
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        residual = self.last_epoch % self.steps_per_cycle

ceshine

from torchvision import transforms
from PIL import ImageOps

class ResizeAndPad(object):
    def __init__(self, size, interpolation=Image.BILINEAR):
        assert isinstance(size, int)
        self.size = size
        self.interpolation = interpolation

    def __call__(self, img):

ceshine

tf.reset_default_graph()
graph = tf.Graph()
with graph.as_default():
    tf.set_random_seed(10)
    # tf Graph input
    X = tf.placeholder("float", [None, timesteps, num_input])
    Y = tf.placeholder("float", [None, num_classes])
    is_training = tf.placeholder("bool")
    
    # Define weights

ceshine

class TemporalConvNet(tf.layers.Layer):
    def __init__(self, num_channels, kernel_size=2, dropout=0.2,
                 trainable=True, name=None, dtype=None, 
                 activity_regularizer=None, **kwargs):
        super(TemporalConvNet, self).__init__(
            trainable=trainable, dtype=dtype,
            activity_regularizer=activity_regularizer,
            name=name, **kwargs
        )
        self.layers = []

ceshine

class TemporalBlock(tf.layers.Layer):
    def __init__(self, n_outputs, kernel_size, strides, dilation_rate, dropout=0.2, 
                 trainable=True, name=None, dtype=None, 
                 activity_regularizer=None, **kwargs):
        super(TemporalBlock, self).__init__(
            trainable=trainable, dtype=dtype,
            activity_regularizer=activity_regularizer,
            name=name, **kwargs
        )        
        self.dropout = dropout

ceshine

import tensorflow as tf

class CausalConv1D(tf.layers.Conv1D):
    def __init__(self, filters,
               kernel_size,
               strides=1,
               dilation_rate=1,
               activation=None,
               use_bias=True,
               kernel_initializer=None,

ceshine

library(checkpoint)
checkpoint("2018-02-25")
library(ggplot2)

# number of people
N <- 1000
# probability of event interception
P_E <- 0.075
# probability of lucky event
P_L <- 0.5