alexrios22

Risk Parity & Budgeting

With Python

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

Python for Quant Finance Meetup, London, 16. November 2022

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

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#   Name:           c_usda_quick_stats.py
#   Author:         Randy Runtsch
#   Date:           March 29, 2022
#   Project:        Query USDA QuickStats API
#   Author:         Randall P. Runtsch
#
#   Description:    Query the USDA QuickStats api_GET API with a specified set of 
#                   parameters. Write the retrieved data, in CSV format, to a file.
#
#   See Quick Stats (NASS) API user guide:  https://quickstats.nass.usda.gov/api

alexrios22

Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide

alexrios22

from typing import List


def http_error(status: int) -> str:
    match status:
        case 400:
            return "Bad request"
        case 404:
            return "Not found"
        case 418:

alexrios22

## docker-compose para correr wordpress con una base de datos en mysql
## by PeladoNerd https://youtu.be/eoFxMaeB9H4

version: '3.1'

services:

  wordpress:
    image: wordpress:php7.1-apache
    ports:

alexrios22

# -*- coding: utf-8 -*-
"""
Created on Fri Jun 02 17:02:32 2017
@author: [email protected]
This script will pull the symbols from your specified Amibroker database and download historical EOD dividend adjusted data from Tiingo.com and will store them 
in a folder (Destop/Data/TiingoEOD) as csv files, one for each stock (APPLE.csv SPY.csv, etc)
It will then attempt to open Amibroker and import the csv files.
You need:
1. To add your own Token number you will get when you register at Tiingo.com (line 51)
2. Specify the Amibroker database you want updated (line 99)

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def treatoutliers(self, df=None, columns=None, factor=1.5, method='IQR', treament='cap'):
    """
    Removes the rows from self.df whose value does not lies in the specified standard deviation
    :param columns:
    :param in_stddev:
    :return:
    """
    #     if not columns:
    #         columns = self.mandatory_cols_ + self.optional_cols_ + [self.target_col]
    if not columns:

alexrios22

window = 253
# detrend tome series by MA
ratesM = data['Adj Close'].rolling(window).mean().dropna()
ratesD = data['Adj Close'].reindex(ratesM.index).sub(ratesM)

fig, axs = plt.subplots(2, figsize=(16, 8), sharex=True)
data['Adj Close'].plot(ax=axs[0], title="Trend")
ratesM.plot(ax=axs[0])
ratesD.plot(ax=axs[1], c="g", title="Residuals");

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seasonal["2019-11":"2019-12"].plot(label='Seasonality', color="blue", figsize=(20,8));

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seasonal["2019"].plot(label='Seasonality', color="blue", figsize=(20,8));