wiredtoserve

wiredtoserve

def daily_demand(mean, sd, probability):
    random_num = np.random.uniform(0, 1)
    if random_num > probability:
        return 0
    else:
        return np.exp(np.random.normal(mean, sd))

wiredtoserve

def monte_carlo_ray(M, product, review_period=30):
    inventory = product.starting_stock
    mean = product.mean
    sd = product.sd
    lead_time = product.lead_time
    probability = product.probability
    demand_lead = product.demand_lead

    q = 0
    stock_out = 0

wiredtoserve

def calculate_profit(data, product):
    unit_cost = product.unit_cost
    selling_price = product.selling_price
    holding_cost = product.holding_cost
    order_cost = product.ordering_cost
    size = product.size
    days = 365

    revenue = sum(data['units_sold']) * selling_price
    Co = len(data['orders']) * order_cost

wiredtoserve

def cc_monte_carlo_ray(product, q, r):
    inventory = product.starting_stock
    mean = product.mean
    sd = product.sd
    lead_time = product.lead_time
    probability = product.probability

    order_placed = False
    order_time = 0
    stock_out = 0

wiredtoserve

import pandas as pd

# get a list of stocks
ticker_df = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
stocks_df = ticker_df[0]

# dataframe output
stocks_df.head()

wiredtoserve

import pandas_datareader as pdr
from datetime import datetime

# get a list of ticker names
tickers = [tick for tick in stocks_df['Symbol']]

# initialize start and end times
start = datetime(2019, 12, 1)
end = datetime.today()

wiredtoserve

import pandas as pd
from collections import defaultdict

# creating a returns data frame
returns = pd.DataFrame()

# computes the percentage change from the immediately previous row by default
for key in stock_dict.keys():
    returns[key + ' Return'] = stock_dict[key]['Adj Close'].pct_change()

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# dictionary to store all the values
discount_dict = {}

def calculate_discount(df):
    max_price = df['Adj Close'].describe()['75%']
    min_price = df['Adj Close'].min()
    return (max_price - min_price)/max_price
  
for tick in stock_dict.keys():
    discount_dict[tick] = calculate_discount(stock_dict[tick])

wiredtoserve

import numpy as np
import pandas as pd

# calculating volatility
risk_df = pd.DataFrame(returns.std(), columns = ['returns'])
# output
risk_df.head()