You have to already have these in your system:
- Python. Version 3.6+ is highly recommended.
- Official CPython from python.org
- Anaconda
- (Recommended) Miniconda
You have to already have these in your system:
"""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. | |
""" |
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 |
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): |
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 |
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 = [] |
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 |
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, |
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 |
"""Improved dataset loader for Toxic Comment dataset from Kaggle | |
Tested against: | |
* Python 3.6 | |
* Numpy 1.14.0 | |
* Pandas 0.22.0 | |
* PyTorch 0.4.0a0+f83ca63 (should be very close to 0.3.0) | |
* torchtext 0.2.1 | |
* spacy 2.0.5 | |
* joblib 0.11 | |
""" |