This project uses python and scikit-learn to make stock predictions. The code is based on Sentdex's excellent machine learning tutorial.

This was my first proper python project, as well as the first time I've used GitHub, so apologies for poor documentation and bad coding.

The program looks at historical stock fundamentals (e.g PE ratio, Debt/equity etc), and also historical prices. The program then tries to 'learn' if there is any relationship between those fundamentals and the resulting change in price.

Then, we feed in the current stock fundamentals. The program should then output a list of stocks which have 'good fundamentals', which in the past have corresponded to a price increase.

Note that this repository does not include all the backtesting etc. It is just the final product. During the backtesting, I was getting returns of about 17%, which is quite a decent outperformance of the market.

All stocks are US based, from the S&P500. This also behaves as our benchmark.

We need three datasets:

1. Historical stock fundamentals
2. Historical stock price changes (including data for the S&P500).
3. Current stock prices.

This is actually very difficult to find. However, it turns out that there is a way to parse it from yahoo finance. I do not know how to do this. Fortunately, Sentdex has done it for us. On this page you will be able to find a file called intraQuarter.zip.

intraQuarter contains a subfolder called KeyStats, which contains fundamentals for all stocks in the S&P500 back to around 2003, sorted by stock.

For the historical stock prices, I used Quandl, which has a free python API.

Quandl has nicely cleaned data, but more importantly its stock data has been adjusted to include things like share splits.

The historical S&P500 values can be downloaded from yahoo finance, which we name YAHOO-INDEX_GSPC.csv.

Current data is parsed from Yahoo finance. The original code did the parsing using regex, but since the code was written Yahoo massively changed their UI. As such, we actually parse the data from Yahoo Finance Singapore, which still uses the old UI. Once this UI is changed, the program will no longer work.

Uses the quandl API to get historic adjusted stock prices, returning stock_prices.csv.

This is the bulk of the project. It looks through intraQuarter to parse the historical fundamental data into a pandas dataframe. Then, it adds to this dataframe the stock percentage change in a year. We compare this with the change in the S&P500 in the same year, to determine if the stock underperformed or outperformed.

Requires intraQuarter, stock_prices.csv, and YAHOO-INDEX_GSPC.csv.

Outputs a csv called key_stats_NO_NA_enhanced.csv.

Parses the current stock fundamentals from sg.finance.yahoo, and puts them into a dataframe, finally returning a csv called forward_sample_NO_NA.csv.

The machine learning. Uses a SVM to fit the data, then predicts the outcome. Returns a list of stocks to invest in.

Being lazy, I have copied all the unique import statements.

import numpy as np
from sklearn import svm, preprocessing
import pandas as pd
from collections import Counter
import os
import re
import time
import urllib.request
from datetime import datetime
from Quandl import Quandl