V. V

is creating Stock Market Trading Algorithm

Select a membership level

recommended stock market security trade
per month




per month



I have been working on this Stock Market model now for the past 13 years. I first started learning about Fundamental Analysis for Stock Market Investing in 2005. I red everything from Benjamin Graham, The Intelligent Investor to Margin of Safety: Risk-averse Value Investing Strategies for the Thoughtful Investor by Seth Klarman.

Although I gained a lot of insides reading these kinds of fundamental analysis books, they did not prepare me for the horrific 2008 stock market crash that occurred. Feeling lost and confused why fundamental analysis did not work. I started to do more research and was introduced to Technical Analysis. After spending couple of years learning about Technical Analysis and reading books such as; Trading For A Living by Dr. Alexander Elder, Pit Bill Martin Schwartz, Reminiscences of a Stock Operator by Edwin Lefevre, Trending Following by Michael W. Covel, Market Wizards by Jack D. Schwager and many more. I gained a deeper understanding of how markets work.

Even more, understanding fundamental analysis and technical analysis made me a better investor. I did not beat the benchmark. So I started to learn about Intermarket Analysis, to perfect my entries and exits. Which helped me gain a better understanding why markets behave the way they do. However, even though I started to feel more confident about the knowledge I gained. I still did not feel I truly understood how the markets worked. Thus, in 2011 while I was getting my degree in Economics from University Of Minnesota. I was fortunate to have full access to all scholarly papers free of charge. Where I continued my research to better understand how markets work. I red scholarly papers on Fama and French Three Factor Model, Eugene Fama the Efficient market hypothesis, Robert J. Shiller on Earnings, Dividends, and Interest Rates and the Black–Scholes model.
Feeling confident that I gained enough knowledge to beat the average investor, I started to build my model. The system I created performed well. But it had a flaw, all trades had to be submitted manually. At the time it did not seem to be an issue. However, psychologically it meant I was being biased on what investment trades I was picking. Even though, I was not losing money, it made me feel uncomfortable. If I wanted to create a reliable model that would work for years, this issue needed to be addressed now.

To overcome the biased flaw, from 2013 – 2015 I taught myself how to write code in Python. Where I continued my research in quantitative analysis, statistical analysis and artificial neural network such as support vector machine, KNN, Logistic regression and even Image recognition being applied to the model.

The System:
In 2017 I finally completed the python code. It has 15,000 lines of code; it incorporates everything from fundamental analysis, technical analysis, Intermarket analysis, three Factor Model, quantitative analysis, statistical analysis and artificial neural network analysis. Primary results look promising. The system only trades Dow Jones Top 30 Securities, which have the highest volume of shares traded per day. It benefits our model as we can increase buying more shares without us impacting the security. Second, volatility for Dow Jones Top 30 Securities is a lot lower than Russell 2000 or even SP500, which benefits us by reducing the risk of a major drawdown.

Third, the model enters a trade at the open of the market and closes the trade at the end of the day where liquidity is the highest. This benefits the model because top institutions and private funds keep the spread between the ask and the bid stable and narrow. Fourth, the model has a 2-1 profit/loss ratio with 63% success rate. Even if you would get a bad fill or your cost per trade is high you would have a good safety of margin of being profitable. Finally, the model is dynamic and not static, which means it adapts to new environments. However, it can only adapt less than 5% per X number of data points. The model is design to change slowly and steady, not fast and erratic.

Why I need your pledge:
With all the subscriptions needed to make this model work, such as tick data, security data, fundamental data, technical data going back 10+ years and cloud computing. It costs me about $500/month. With only a small investment account the net positive percentage I get back does not cover the cost of the subscriptions needed to run the model.
How it works

Get started in 2 minutes

Choose a membership
Sign up
Add a payment method
Get benefits