HumanRupert

from datetime import datetime

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt

factors = pd.read_csv("./factors.csv", index_col="Date")

# convert Date index to datetime object
factors.index= pd.to_datetime(factors.index, format="%Y%m%d")

HumanRupert

import yfinance as yf
import pandas as pd

amzn = yf.Ticker("AMZN")
hist = amzn.history(period="max", auto_adjust=True, rounding=False)
price = hist[["Close"]]
monthly_price = price.resample('BM').last()


# calculate monthly returns

HumanRupert

factors = pd.read_csv("./data/factors.csv", index_col="Date")

# convert Date index to datetime object
factors.index= pd.to_datetime(factors.index, format="%Y%m%d")

# get monthly avg
factors = factors.resample('BM').mean()

factors_cum = factors.cumsum()
factors_cum.plot.line()

HumanRupert

import yfinance as yf
import pandas as pd

etsy = yf.Ticker("ETSY")
hist = etsy.history(period="max", auto_adjust=True, rounding=False)
price = hist[["Close"]]

# resample by latest monthly price
monthly_price = price.resample('BM').last()

HumanRupert

import statsmodels.api as sm

# join factors to returns dfs
data = factors.join(excess_returns).dropna()

y = data[["Returns"]] 
x = data[["Mkt-RF","SMB","HML"]]


ols = sm.OLS(y,x)

HumanRupert

latest_factors = factors.tail(1).to_dict('r')[0]

RRR = latest_factors["RF"]
RRR += betas["Mkt-RF"] * latest_factors["Mkt-RF"]
RRR += betas["SMB"] * latest_factors["SMB"]
RRR += betas["HML"] * latest_factors["HML"]

print(f"Required rate of return is: {RRR}")

HumanRupert

# src/constants.py

DJIA_CONSTITUENTS_URL = "https://financialmodelingprep.com/api/v3/dowjones_constituent"

HumanRupert

# src/models/constituent.py

from pydantic import BaseModel


class Constituent(BaseModel):
    """Represents a ticker received from FMP API when retrieving constituents of an index; see `price.load_djia_constituents` method."""
    symbol: str
    name: str
    sector: str

HumanRupert

# src/price/djia.py

import os
import csv
from typing import List

import requests
from dotenv import load_dotenv
from pydantic import parse_obj_as

HumanRupert

# src/price/endpoints.py

DJIA_CONSTITUENTS = "https://financialmodelingprep.com/api/v3/dowjones_constituent"