ShaneLee

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>Background Color</key>
	<dict>
		<key>Blue Component</key>
		<real>0.0901960784</real>
		<key>Green Component</key>
		<real>0.0666666667</real>

ShaneLee

"""
Bulk-delete custom Goodreads shelves.

HOW THIS WORKS
Goodreads' real shelf-management page lives at a fixed URL:
    https://www.goodreads.com/shelf/edit
(paginated: ?page=2, ?page=3, ...)

Each deletable (non-default) shelf row on that page has a delete "x"
whose onclick handler fires a plain AJAX DELETE request:

ShaneLee

<plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-checkstyle-plugin</artifactId>
                <version>${checkstyle-maven-plugin.version}</version>
                <configuration>
                    <dependency>
                        <groupId>com.puppycrawl.tools</groupId>
                        <artifactId>checkstyle</artifactId>
                        <version>${checkstyle.version}</version>
                    </dependency>

ShaneLee

import subprocess, urllib, random
class NoBlocks(Exception): pass
def getblocks():
    r = urllib.urlopen("http://{?REDACTED?}/grab").read()
    if '<html' in r.lower(): raise NoBlocks
    return r.split()


import sys
if len(sys.argv) > 1:

ShaneLee

def get_expected_portfolio_return(porfolio):
    return np.sum(portfolio['weight'] * porfolio['returns'])
  
expected_portfolio_return = get_expected_portfolio_return(portfolio)
final_value = expected_portfolio_return * PORTFOLIO_VALUE

print('Estimated value of Portfolio in {} : £{:,.2f} \nExpected Portfolio Return: {:,.2f}%').format(datetime.now().year + YEARS, final_value, expected_portfolio_return)
print('Estimated Income £{:,.2f}').format(final_value * 0.04)

ShaneLee

def get_simulation(data):
    # Get the logarithmic returns of the % change of prices from one trading day to the next.
    log_returns = np.log(1 + data.pct_change()) 
    # Get the mean of these returns
    u = log_returns.mean() 
    # Get the variance of these returns 
    var = log_returns.var()
    # Get the change in the average value of these values
    drift = u - (0.5 * var)
    # Get the standard deviation

ShaneLee

def get_data(stock):
    data = pd.DataFrame()
    if stock['type'] == 'index':
        data = wb.DataReader(stock['ticker'], 'stooq', start=START_DATE)['Close']
        return get_simulation(data)
    data = wb.DataReader(stock['ticker'], 'yahoo', start=START_DATE)['Adj Close']
    return get_simulation(data)

ShaneLee

def get_portfolio():
    with open('portfolio.json', 'r') as portfolio_file:
        portfolio_json = portfolio_file.read()
        return json.loads(portfolio_json)['portfolio']

portfolio_data = get_portfolio()
portfolio = pd.DataFrame(portfolio_data).assign(returns = [get_data(stock) for stock in portfolio_data])

ShaneLee

START_DATE = '2007-1-1' # Date from which we want to get the financial year.
YEARS = 40 # Years until retirement
DAYS_IN_YEAR = 253 # Average number of trading days in the year
PORTFOLIO_VALUE = 1000

ShaneLee

# -*- coding: utf-8 -*-
from datetime import datetime
import json
import numpy as np
import pandas as pd
from pandas_datareader import data as wb
from scipy.stats import norm, gmean