Alexander Rios alexrios22

## 00_risk_budgeting.md

      
              6 files
            
          
              0 forks
            
          
                0 comments
              
            
              0 stars
            
          
                alexrios22
                / 00_risk_budgeting.md
            
            
              Created
              April 6, 2025 15:54
                — forked from yhilpisch/00_risk_budgeting.md
            
          
    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)

  
## c_usda_quick_stats.py
#   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

## list.md

      
              1 file
            
          
              0 forks
            
          
                0 comments
              
            
              0 stars
            
          
                alexrios22
                / list.md
            
            
              Created
              October 10, 2021 00:40
                — forked from ih2502mk/list.md
            
              
                Quantopian Lectures Saved
              
          
    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

  
## basic_examples.py
from typing import List


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

## docker-compose-wordpress.yaml
## 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:

## Update_Amibroker_EOD_Tiingo.py
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 02 17:02:32 2017
@author: Vangelis@logical-invest.com
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)

## autofit_outliertreatment.py
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:

## Trend.ibpynb
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");

## SeasonalDecompose2019NovDec.ibpynb
seasonal["2019-11":"2019-12"].plot(label='Seasonality', color="blue", figsize=(20,8));

## SeasonalDecompose2019.ibpynb
seasonal["2019"].plot(label='Seasonality', color="blue", figsize=(20,8));
	# 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
	from typing import List


	def http_error(status: int) -> str:
	match status:
	case 400:
	return "Bad request"
	case 404:
	return "Not found"
	case 418:
	## 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:
	# -- coding: utf-8 --
	"""
	Created on Fri Jun 02 17:02:32 2017
	@author: Vangelis@logical-invest.com
	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)
	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:
	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");