normanlmfung

using Newtonsoft.Json;
using System;
using System.Collections.Generic;
using System.Net.WebSockets;
using System.Text;
using System.Threading;
using System.Threading.Tasks;


/*

normanlmfung

using System;
using System.Collections.Generic;
using System.Text.RegularExpressions;

class Program
{
    static void Main(string[] args)
    {
        // Example 1. Simple List
        List<float> floatList = new List<float> { 1.1f, 2.2f, 3.3f, 4.4f, 5.5f };

normanlmfung

 var lazyString = new Lazy<string>(
			() =>
			{
				// Here you can do some complex processing
				// and then return a value.
				Console.WriteLine("Inside lazy loader");
				return "Lazy loading!";
			});

Console.WriteLine($"Is value created: {lazyString.IsValueCreated}"); // false

normanlmfung

import pandas as pd
import numpy as np
from datetime import datetime
from tabulate import tabulate

# Illusttration 1. Load some dummy data
pd_total_equity = None
strategies = [ 'trend_following', 'statsarb', 'triarb' ]
strategies_total_equities = {}
for strategy in strategies:

normanlmfung

[
    {
        "book": "trend_following",
        "datetime": "20210101",
        "total_equity": 1000,
        "turnover": 1000000
    },
    {
        "book": "trend_following",
        "datetime": "20210201",

normanlmfung

import json
import pandas as pd
import numpy as np
import math
from datetime import datetime
from tabulate import tabulate

# Illustration 1. Array initialization: ones/zeros/arrange/linspace
np.arange(0, 10, 2) # array([0, 2, 4, 6, 8])
np.zeros(10)

normanlmfung

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

X_train = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y_train = np.array([[0], [1], [1], [0]])

model = Sequential()
model.add(Dense(2, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

normanlmfung

class square_all:
	def __init__(self, numbers):
		self.numbers = iter(numbers)
	def __next__(self):
		return next(self.numbers) ** 2
	def __iter__(self):
		return self

normanlmfung

def fibonacci():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

# Generate Fibonacci numbers up to a certain limit
def fibonacci_up_to(limit):
    fib = fibonacci()
    while True:

normanlmfung

class MessageWriter(object):
		def __init__(self, file_name):
			self.file_name = file_name
		 
		def __enter__(self):
			self.file = open(self.file_name, 'w')
			return self.file
	 
		def __exit__(self, *args):
			self.file.close()