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Welcome to my Home Page on Stock Picking Algo's

Welcome to my Junk Juice Model

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Model to look at selecting stocks using ML and AI and eventually Deep Learning

Time series forecaster: https://facebook.github.io/prophet/ forecast time series data using additive models

Tuts: https://enlight.nyc/projects/stock-market-prediction/ - basic stock forecast https://www.youtube.com/user/sentdex https://pythonprogramming.net https://pythonprogramming.net/machine-learning-python-sklearn-intro/ IMPORTANT!! :https://pythonprogramming.net/getting-stock-prices-python-programming-for-finance/ https://github.com/PythonProgramming/PythonProgramming.net-Website https://ntguardian.wordpress.com/2018/07/17/stock-data-analysis-python-v2/ https://www.datacamp.com/community/tutorials/finance-python-trading https://www.learndatasci.com/tutorials/python-finance-part-yahoo-finance-api-pandas-matplotlib/ https://managingfundswithpythonandsql.wordpress.com/2017/10/03/setting-up-the-bloomberg-api-to-work-with-python-anaconda/ https://www.datacamp.com/courses/intro-to-python-for-finance https://www.datacamp.com/courses/intro-to-financial-concepts-using-python https://www.datacamp.com/courses/introduction-to-time-series-analysis-in-python https://www.datacamp.com/courses/intro-to-portfolio-risk-management-in-python https://www.datacamp.com/courses/importing-managing-financial-data-in-python https://www.datacamp.com/courses/manipulating-dataframes-with-pandas https://www.datacamp.com/courses/manipulating-time-series-data-in-python https://www.datacamp.com/courses/machine-learning-for-finance-in-python https://www.datacamp.com/courses/machine-learning-for-time-series-data-in-python https://www.datacamp.com/courses/financial-forecasting-in-python https://www.datacamp.com/courses/interactive-data-visualization-with-bokeh

Very important ::: https://github.com/robertmartin8/MachineLearningStocks - the main SH1T !!!!!

Arctiles: https://www.researchgate.net/publication/301847788_Equity_forecast_Predicting_long_term_stock_price_movement_using_machine_learning

API: bloomberg: https://github.com/msitt/blpapi-python context info: http://contextors.com/api/

Sources: https://www.bloomberg.com/quote/SBK:SJ https://finance.yahoo.com/quote/AAPL/financials?p=AAPL

Code: https://pypi.org/project/Yahoo-ticker-downloader/ - Get all the ticker info from Yahoo https://github.com/WillKoehrsen/Data-Analysis/tree/master/stocker - pre built library of stock computations and graphs

Process:

Getting financials data: This consist of Ratios as well as price

1. extract all the tickers from yahoo finance
2. extract data for range of tickers.
	2.1 get the price at each closing day over the range
	2.2 get the financial ratios as and when updated over the range
3. split data into training and testing data
	3.1 use various time period to test with 90 days forward
		ie multiple observation and outcome windows
	3.2 use various stocks to test with
	3.3 think about industry in this process
	3.4 use cross folding in the process
4. Define an outcome period at 90 days from "today"
	4.1 Classify stocks into various growth rates 5%, 7.5%, 10%, 12.5%, 15%, etc in 2.5% upto 50?
	4.2	Use a histogram to understand long term changes in growth rate 
5. Train the model thinking about Geographics, industry, growth rate 
6. Test the model
7. THINGS to think about :
	7.1 create a portfolio of stocks
	7.2 test the portfolio against stocks to be added and removed every iteration (90 days?)
	7.2 factor in cost of switching at some stage

Decide on what model to use : Read about SVM Decide on what model to use : Read about timeseries computation : histogram of returns and volatility + other statistical measures for stocks

https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/list-of-technical-indicators/ https://za.markets.com/education/technical-analysis/mathematical-indicators/ http://financeformulas.net/Stock-and-Bond-Formulas.html https://www.dundas.com/support/learning/documentation/analyze-data/formulas/list-of-formulas https://towardsdatascience.com/technical-analysis-library-to-financial-datasets-with-pandas-python-4b2b390d3543 https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html#momentum-indicators https://www.quantopian.com/posts/technical-analysis-indicators-without-talib-code

Other models to consider: specific Recurrent Neural Networks (RNN) / specific Convolutional Neural Networks (CNN) http://stocksneural.net/ XGBOOst is very powerful, SVM, CART, ANNs, HS-ANN (Artifical Neural network) and Boosting HS-ANN (http://iopscience.iop.org/article/10.1088/1757-899X/322/5/052053/pdf)

Getting News:

News articles that cover:

	industry
	Geographics
	company - Ticker
	directors
	board
	suppliers
	competitors

	Need to think about association between these factors ie some sort of graph element or latice structue impact on the price

	Use Scrapy : https://blog.michaelyin.info/scrapy-tutorial-1-scrapy-vs-beautiful-soup/
	Extract news

	then process data and extract important information:
		Ticker
		industry
		Geographics
		directors
		board
		suppliers
		competitors
		brand
		sentiment (-1,0,1 as scores for now = negative, neutral or positive) 
		tense of article (past - what happened, future - speculation/just discovered)

Exchange rate info:

Given values are in USD think about how we represent growth when moving back to local currency