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def dollar_averaging(df, trade_date, trade_amount, f_or_b = 'f'):
    
    '''
    Input:
    1. df (dataframe)
        ---> Datetime object should be set as index

    2. trade_date (int)
        ---> determine which date to invest

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import scipy.stats as st
import numpy as np


def black_scholes_pricing_model(
    option_type: str, 
    s: float, 
    k: float, 
    r: float, 
    v: float, 

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import random

doors = ['Goat', 'Car', 'Goat']

n = 1000
win = 0
lose = 0
for _ in range(n):
    
    #-------------------------#

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import pandas as pd
from io import StringIO
import requests
import time
import random

def get_put_call_ratio_df(q_start_date: str, q_end_date:str):
    url = 'https://www.taifex.com.tw/cht/3/pcRatioDown'
    payload = {
        'queryStartDate': q_start_date,

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from enum import Enum
import numpy as np
import scipy.stats as st


class OptionType(str, Enum):
    call = 'Call'
    put = 'Put'

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import pandas as pd
import requests

# 設定目標日期
target_date = '20230928'

# 把 csv 檔抓下來
url = f'https://www.twse.com.tw/rwd/zh/afterTrading/MI_INDEX?date={target_date}&type=ALL&response=csv'
res = requests.get(url)
data = res.text 

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from urllib.parse import urlencode, urljoin
from datetime import datetime
from pandas.core.frame import DataFrame
import requests
import pandas as pd


def get_etf_dividend_history(
        symbol: str = "", 
        start_date: str = '20050101', 

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import scipy.stats as st
import numpy as np


def binomial_tree_pricing_model(
    option_type: str,
    s: float,
    k: float,
    r: float,
    v: float,

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from pydantic import BaseModel
from enum import Enum
from datetime import date, datetime, timedelta
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter


class OptionType(str, Enum):
    Call = 'Call'
    Put = 'Put'