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quantmomstrat.py
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quantmomstrat.py
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import numpy as np
import pandas as pd
import requests
import math
from scipy import stats
import xlsxwriter
from secrets import token
from statistics import mean
def chunks(lst, n):
for i in range(0, len(lst), n):
yield lst[i:i + n]
def get_symbols():
symbols = []
data = requests.get(f'https://cloud.iexapis.com/beta/ref-data/symbols?token={token}').json()
for da in data:
symbols.append(da['symbol'])
return symbols
def portfolio_input():
global portfolio_size
portfolio_size = input('Enter the size of your portfolio: ')
try:
float(portfolio_size)
except ValueError:
print('Not a number')
def excel_dump(final_dataframe):
writer = pd.ExcelWriter('momentum_strategy.xlsx', engine='xlsxwriter')
final_dataframe.to_excel(writer, sheet_name='Momentum Strategy', index=False)
background_color = '#000000'
font_color = "#ffffff"
string_template = writer.book.add_format(
{
'font_color': '#ffffff',
'bg_color' : '#408ec6',
'border' : 1
}
)
dollar_template = writer.book.add_format(
{
'num_format' : '$0.00',
'font_color' : '#ffffff',
'bg_color' : '#408ec6',
'border' : 1
}
)
integer_template = writer.book.add_format(
{
'font_color' : '#ffffff',
'bg_color' : '#408ec6',
'border' : 1
}
)
price_return_template = writer.book.add_format(
{
'num_format' : '0.0%',
'font_color' : '#ffffff',
'bg_color' : '#7a2048',
'border' : 1
}
)
return_percentile_template = writer.book.add_format(
{
'num_format' : '0.0%',
'font_color' : '#ffffff',
'bg_color' : '1e2761',
'border' : 1
}
)
column_formats = {
'A': ['Ticker', string_template],
'B': ['Price', dollar_template],
'C': ['Number of Shares to Buy', integer_template],
'D': ['One-Year Price Return', price_return_template],
'E': ['One-Year Return Percentile', return_percentile_template],
'F': ['Six-Month Price Return', price_return_template],
'G': ['Six-Month Return Percentile', return_percentile_template],
'H': ['Three-Month Price Return', price_return_template],
'I': ['Three-Month Return Percentile', return_percentile_template],
'J': ['One-Month Price Return', price_return_template],
'K': ['One-Month Return Percentile', return_percentile_template],
'L': ['HQM Score', integer_template]
}
for column in column_formats.keys():
writer.sheets['Momentum Strategy'].set_column(f'{column}:{column}', 20, column_formats[column][1])
writer.sheets['Momentum Strategy'].write(f'{column}1', column_formats[column][0], string_template)
writer.save()
portfolio_input()
#symbols = get_symbols()
symbols = pd.read_csv('sp_500_stocks.csv')
#symbol_groups = list(chunks(symbols, 100))
symbol_groups = list(chunks(symbols['Ticker'], 100))
symbol_strings = []
for i in range(0, len(symbol_groups)):
symbol_strings.append(','.join(symbol_groups[i]))
hqm_columns = [
'Ticker',
'Price',
'Number of Shares to Buy',
'One-Year Price Return',
'One-Year Return Percentile',
'Six-Month Price Return',
'Six-Month Return Percentile',
'Three-Month Price Return',
'Three-Month Return Percentile',
'One-Month Price Return',
'One-Month Return Percentile',
'HQM Score'
]
final_dataframe = pd.DataFrame(columns = hqm_columns)
convert_none = lambda x : 0 if x is None else x
for symbol_string in symbol_strings:
batch_api_url = f'https://cloud.iexapis.com/stable/stock/market/batch?symbols={symbol_string}&types=stats,price,quote,news,chart&token={token}'
data = requests.get(batch_api_url).json()
for symbol in symbol_string.split(','):
final_dataframe = final_dataframe.append(
pd.Series(
[
symbol,
data[symbol]['quote']['latestPrice'],
'N/A',
convert_none(data[symbol]['stats']['year1ChangePercent']),
'N/A',
convert_none(data[symbol]['stats']['month6ChangePercent']),
'N/A',
convert_none(data[symbol]['stats']['month3ChangePercent']),
'N/A',
convert_none(data[symbol]['stats']['month1ChangePercent']),
'N/A',
'N/A'
],
index=hqm_columns
), ignore_index=True
)
time_periods = ['One-Year', 'Six-Month', 'Three-Month', 'One-Month']
for row in final_dataframe.index:
for time_period in time_periods:
change_col = f'{time_period} Price Return'
percentile_col = f'{time_period} Return Percentile'
final_dataframe.loc[row, percentile_col] = stats.percentileofscore(final_dataframe[change_col], final_dataframe.loc[row, change_col])/100
for row in final_dataframe.index:
momentum_percentiles = []
for time_period in time_periods:
momentum_percentiles.append(final_dataframe.loc[row, f'{time_period} Return Percentile'])
final_dataframe.loc[row, 'HQM Score'] = mean(momentum_percentiles)
final_dataframe.sort_values('HQM Score', ascending=False, inplace=True)
final_dataframe = final_dataframe[:50]
final_dataframe.reset_index(drop=True, inplace=True)
position_size = float(portfolio_size)/len(final_dataframe.index)
for i in range(0, len(final_dataframe['Ticker'])-1):
final_dataframe.loc[i, 'Number of Shares to Buy'] = math.floor(position_size/final_dataframe['Price'][i])
excel_dump(final_dataframe)