ritchieng

# One day returns calculation through differencing by 1
differencing_factor = 1
df_daily_returns = (df_raw - df_raw.shift(differencing_factor)) / df_raw.shift(differencing_factor) * 100
df_daily_returns.plot(figsize=figsize)
plt.title(f'{asset_name} Daily Returns via Differencing 1 Day');

ritchieng

# Critical imports for GPU cuDF
import nvstrings, nvcategory, cudf

# Other imports
import numpy as np
import pandas as pd
import time
import pandas_datareader.data as web
from datetime import datetime
from matplotlib import pyplot as plt

ritchieng

int raiseToPower(int base, int exponent) {
  int result = 1;
  for (int i = 0; i < exponent; i = i + 1) {
    result = result * base;
  }
  return result;
}

ritchieng

#include <iostream>
using namespace std;

int main() {
    int x = 0;
    for (; x <= 10;) {
        x = x + 1;
        cout << "x is " << x << "\n";
    }
    return 0;

ritchieng

#include <iostream>
using namespace std;

int main() {
    int x = 0;
    int y = 2;
    while (x < 10) {
        x = x + 1;
        cout << "x is " << x << "\n";
    }

ritchieng

#include <iostream>
using namespace std;

int main() {
    int x = 6;
    int y = 2;
    if(x > y)
        cout << "x is greater than y\n";
    else if(y > x)
        cout << "y is greater than x\n";

ritchieng

#include <iostream>
using namespace std;

int main() {
    int x;
    cin >> x;
    cout << x / 3 << ' ' << x * 2;
    return 0;
}

ritchieng

// Just a simple program
#include <iostream>
int main() {
    std::cout << "Hello, world!\n";
    return 0;
}

ritchieng

// Tell the preprocessor to dump the contents into iostream that defines our io
#include <iostream>

// This tells the compiler that it should look in the std namespace for any identifiers we haven't defined
using namespace std;

// Initiate
int main() {
    // We declare the variable with int x then initialize x by specifying a value
    int x = 4 + 2;

ritchieng

import tensorflow as tf
from keras import backend as K
from keras.layers import Dense
from keras.objectives import categorical_crossentropy
from keras.metrics import categorical_accuracy as accuracy
from keras.datasets import cifar10
from keras.utils import np_utils
from keras.layers import Dense, Activation, Flatten, Lambda, Convolution2D, Convolution1D, AveragePooling2D, BatchNormalization, Dropout, Reshape
from keras.engine import merge, Input, Model
from keras.engine.training import collect_trainable_weights