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000_stuff_brokers
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000_stuff_brokers
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#!/usr/bin/env python3
import random
import numpy as np
import pandas as pd
import matplotlib
# print(matplotlib.get_backend())
matplotlib.rcParams['backend'] = "Qt4Agg"
import matplotlib.pyplot as plt
# Sources:
# http://moneyweek.com/personal-finance/isas/stocks-shares-isa-providers-cost-comparison-table/
# http://www.moneysavingexpert.com/savings/stocks-shares-isas
# http://www.moneywise.co.uk/compare-investment-isas
# X-O.co.uk - Execution Only Share Dealing
# http://www.x-o.co.uk/
# £5.95 per trade
xo = lambda annual_stock_amount: 5.95
# Hargreaves Lansdown | ISAs, pensions, funds and shares
# http://www.hl.co.uk/
# 0.45% per year (max £45 in Isa, max £200
# in Sipp). £11.95 per trade
hl_max_isa_annual_charge=45
hl = lambda annual_stock_amount: min(0.0045 * annual_stock_amount, hl_max_isa_annual_charge) + 11.95
# AJ Bell Youinvest | Award-winning investment platform
# https://www.youinvest.co.uk/
# https://www.youinvest.co.uk/isa/charges-and-rates
aj = lambda annual_stock_amount: 9.95
# Interactive Investor | Shares, funds,ISAs and SIPPs
# http://www.iii.co.uk/
# £20 per quarter (includes £20 of trades)
# £10 per trade
ii = lambda annual_stock_amount: 20 * 4
# Charles Stanley Direct
# https://www.charles-stanley-direct.co.uk/
# https://www.charles-stanley-direct.co.uk/Our_Services/ISA/
# https://www.charles-stanley-direct.co.uk/Our_Charges/
# 0.25% per year (min £20 max £150), waived
# if 6+ trades every six months. £10 per trade
csd = lambda annual_stock_amount: min(max(0.0025 * annual_stock_amount, 20), 150) + 10
# Alliance Trust Savings - Pensions, ISAs, Share Dealing, Funds
# https://www.alliancetrustsavings.co.uk/
# £18.75 per quarter £12.50 per trade
ats = lambda annual_stock_amount: (18.75 * 4) + 12.50
# AXA Self Investor
# https://www.axaselfinvestor.co.uk/
# https://www.axaselfinvestor.co.uk/isas-and-more/our-stocks-and-shares-isa/
# 0.35% per annum of the value of your smart+ ISA, reducing to 0.20%
# per annum where the total value of your smart+ ISA and smart+
# Trading Account is £250,000 or more.
# Buy/sell/switch transaction FREE
asi = lambda annual_stock_amount: 0.0035 * annual_stock_amount
# Cavendish Online
# http://www.cavendishonline.co.uk/
# http://www.cavendishonline.co.uk/investments/our-service/
# 2. A platform charge of 0.20%
# 3. A Cavendish Online Ongoing fee of 0.05%
co = lambda annual_stock_amount: 0.0025 * annual_stock_amount
# Halifax Share Dealing
# http://www.halifax.co.uk/sharedealing/
# http://www.halifax.co.uk/sharedealing/charges/stocks-and-shares-isa-charges/
# £12.50 per trade
# Stocks and Shares ISA admin charge
# £12.50 per year
# 0.5% per stock for stamp duty for UK shares???
hsd = lambda annual_stock_amount: 12.5 + 12.5
# dictionary
broker_dic = {
'X-O':xo,
'Hargreaves Lansdown':hl,
'AJ Bell Youinvest':aj,
'Interactive Investor':ii,
'Charles Stanley Direct': csd,
'Alliance Trust Savings': ats,
'AXA Self Investor': asi,
'Cavendish Online': co,
'Halifax Share Dealing': hsd,
}
x_step=500
x_min = 0
x_max = 15500 + x_step
x = np.arange(x_min,x_max,x_step)
# data structure pandas
x_series = pd.Series(x)
# figure, axis
# it contains 1 plot with coordinates: row 1, column 1
fig, ax = plt.subplots(1,1)
plt.xticks(rotation=90)
broker_dic_output = {}
legend_lables = []
linestyles = ['-', '--', '-.', ':']
for key in broker_dic.keys():
current_formula = broker_dic[key]
y_series = x_series.apply(current_formula)
broker_dic_output[key] = y_series
ax.plot(x_series, y_series, linestyle=random.choice(linestyles))
legend_lables.append(key)
last_index = len(x_series) - 1
# print the value of a random sample in the list
rand_index_1 = random.randrange(0, last_index)
text_y_value = str(y_series[rand_index_1])
ax.text(x_series[rand_index_1], y_series[rand_index_1], text_y_value)
# print the last value
y_lable = str(y_series[last_index]) + ' ' + key
ax.text(x_series[last_index], y_series[last_index], y_lable)
# setup the legend
ax.set_title('Brokers costs comparisons')
ax.set_xlabel('Stock amount (£, for 1 year)')
ax.set_ylabel('Cost (£)')
ax.set_xticks(x_series, x_step)
ax.grid(True)
# ax.legend(legend_lables, loc='center left', bbox_to_anchor=(1, 0.5))
ax_legend = ax.legend(legend_lables, loc='upper center', bbox_to_anchor=(0.5, -0.2), ncol=3)
fig.savefig('./grapsh/brokers_costs.png', dpi=300, format='png', bbox_extra_artists=(ax_legend,), bbox_inches='tight')
plt.show()