00_diy_quant_investing.md

DIY Quant Investing

DIY Quantitative Stock Market Investing

Dr. Yves J. Hilpisch | The Python Quants & The AI Machine

Online, PyData Botswana, 15. September 2022

(short link to this Gist: http://bit.ly/pdb_diy)

Slides

You find the slides under http://certificate.tpq.io/pdb_diy.pdf

Data Provider

Visit the Web page of EODHistoricalData under company page.

Resources

This Gist contains selected resources used during the webinar.

Dislaimer

All the content, Python code, Jupyter Notebooks, and other materials (the “Material”) come without warranties or representations, to the extent permitted by applicable law.

None of the Material represents any kind of recommendation or investment advice.

The Material is only meant as a technical illustration.

Leveraged and unleveraged trading of financial instruments, and of contracts for difference (CFDs) in particular, involves a number of risks (for example, losses in excess of deposits). Make sure to understand and manage these risks.

01_financial_data.ipynb

02_asset_allocation.ipynb

03_risk_budgeting.ipynb

mvp_portfolio.py

#
# Mean-Variance Portfolio Class
# Markowitz (1952)
#
# Python for Asset Management
# (c) Dr. Yves J. Hilpisch
# The Python Quants GmbH
#
import math
import numpy as np
import pandas as pd

def portfolio_return(weights, rets):
    return np.dot(weights.T, rets.mean()) * 252

def portfolio_variance(weights, rets):
    return np.dot(weights.T, np.dot(rets.cov(), weights)) * 252

def portfolio_volatility(weights, rets):
    return math.sqrt(portfolio_variance(weights, rets))

universe.csv