SnowyPainter

#먼저 인바운드 규칙을 만드세요.
# PowerShell 스크립트

# 1. 기존 portproxy 설정 삭제
netsh interface portproxy delete v4tov4 listenport=4000 listenaddress=0.0.0.0

# 2. WSL2의 eth0 IP 주소 가져오기
$wslIP = wsl hostname -I | ForEach-Object { $_.Trim() }

# 3. 새로운 portproxy 설정 추가

SnowyPainter

import numpy as np
import pandas as pd
from scipy.optimize import minimize
import numpy as np

'''
주식 A를 헤지하기 위해서 주식 B1, B2가 사용됨.
조건: (A)와 (B1, B2)는 다른 섹터에서 상관계수가 -1에 가까워야함
조건2: 사실 뭐든 상관없음

SnowyPainter

import mojito
import websockets
import json
import requests
import os
import asyncio
import time

from Crypto.Cipher import AES
from Crypto.Util.Padding import unpad

SnowyPainter

import mojito
import pprint

key = "발급받은 API KEY"
secret = "발급받은 API SECRET"
acc_no = "12345678-01"

broker = mojito.KoreaInvestment(
    api_key=key,
    api_secret=secret,

SnowyPainter

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.optimizers import RMSprop
import numpy as np
import random

class QLearningAgent:
    def __init__(self, env, learning_rate=0.1, discount_factor=0.99, epsilon=0.1):
        self.env = env
        self.step_size = learning_rate

SnowyPainter

import pandas as pd
import numpy as np

files = ["nvda.csv", "vix.csv", "eur.csv"]

def open_df(fn):
    df = pd.read_csv(fn)
    df.drop(["Vol.","Change %","Open","Low","High"], axis=1, inplace=True)
    df["Price"] = df["Price"].astype(float)
    df['Date'] = pd.to_datetime(df['Date'], format='%m/%d/%Y')

SnowyPainter

import random

computer = random.randint(1,3)
player = int(input("가위, 바위, 보 중 하나를 선택하시오. (가위=1, 바위=2, 보=3): "))

if computer == player:
  print("비겼습니다.")
elif player == 1:
  if computer == 2:
    print("졌습니다 (가위<바위)")

SnowyPainter

import matplotlib.pyplot as plt

class Neuron:
    def __init__(self, package):
        self.schwannCells = 0
        self.Ranviers = -1
        self.neurotransmitter = package
        self.Potential = -70
        self.i = 0

SnowyPainter

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

def andrews_curve(data, weights=None):
    num_variables = data.shape[1]
    t = np.linspace(0, 2*np.pi, 100)
    curve = np.zeros((len(t), 2))

SnowyPainter

import random

sn1 = ['It', 'is', 'a fundraising event', 'which', 'is held', 'on', 'a Friday', 'in March', 'every', 'other', 'year']
sn2 = ['Foodbank', 'also supports', 'food drives', 'for', 'individuals', 'who', 'want', 'to', 'share', 'their', 'food', 'with', 'the', 'poor', 'in the', 'country']
sn3 = ['When', 'I', 'called', "Foodbank's office", 'people', 'there', 'let', 'me', 'know', 'in detail', 'how', 'I', 'could', 'donate', 'food', 'to', 'the', 'hungry']
sn4 = ['These thousands', 'of', 'Santas', 'spread', 'the spirit', 'of', 'Christmas', 'to', 'Australian kids', 'who are', 'sick', 'or', 'disadvantaged']
sn5 = ['He', 'led', 'a', 'mostly', 'unremarkable', 'life', ', working', 'as', 'a', 'Paris customs service officer', 'until', 'his', 'late', 'forties']
sn6 = ['The', 'public', 'and', 'critics', 'laughed at', "Rousseau's", 'flat,', 'seemingly', 'childish', 'style', 'of', 'portraying', 'human', 'figures']
sn7 = ['In', 'this', 'way,', 'he', 'created', 'his', 'own', 'mysterious', 'jungle', 'paintings', 'where',