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: 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)

alexrios22

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");

alexrios22

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

alexrios22

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

alexrios22

import statsmodels.api as sm
decomposition = sm.tsa.seasonal_decompose(data['Adj Close'], model="additive", period = 253)

trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid

fig, axs = plt.subplots(4, figsize=(14, 10), sharex=True)
data['Adj Close'].plot(title='Original', color="blue", ax=axs[0])
trend.plot(title='Trend', color="red", ax=axs[1])

alexrios22

Monthly_Returns = ratesD.groupby([ratesD.index.year.rename('year'), 
                                  ratesD.index.month.rename('month')]
                                ).median().to_frame("Returns")
Monthly_Returns.boxplot(by='month', figsize=(16, 8));