Reddit mentions of How To Make Money In Stocks: A Winning System in Good Times or Bad, 3rd Edition

I'm making money too. Most if my algorithms are live since 2015, although they are backtested since 1999 with non-survivalship bias and translanting ideas / research into model reveals the power of algo trading. I've been investing since 1998 and trading since 2012.

I'm building a website so people can subscribe to follow the buy/sell signals on a given strategy. Understanding the idea behind the model is paramount, since not all models will outperform all the time. But it's more likely for one to stick to it and endure down moments if one understand the rationale for the model.

Just some ideas on what I have developed. The goal is to have a trading portfolio made of models that are driven by different factors, with low correlation between them. I got models for both US and Canada exchanges. Model live performance and simulation includes costs for commissions (1 cent per share, which is what Interactive Brokers charges me) and variable slippage according to the stock liquidity, which is what affect real life performance. A backtest done with these conditions are more realistic to what we might encounter in out-of-sample.

For example, one model is focused in income, by seeking quality companies with low volatility. This research paper has the details behind to why it works: https://www.researchaffiliates.com/documents/True%20Grit_The%20Durable%20Low%20Volatility%20Effect%20pdf.pdf

This model makes use of market timing based on economic factors, to switch to other asset classes during times that equities underperform, such as in recessions.

Another model is based on growth, exploring inefficiencies from Nasdaq companies, which are the great for growth as they drive innovation and are strategic for mature companies to continue to be competitive. This model relies on both fundamentals and technical analysis, to take advantage of price momentum (and therefore, overvaluation), which wouldn't be possible to capture with a value approach focused on fundamentals only. The technical analysis uses the principal of this book: https://www.amazon.com/How-Make-Money-Stocks-Winning/dp/0071373616

Given the technical analysis in place, this model also makes use of market timing based on economic factors to switch to other asset types during recessions or crisis.

Another model is focused on momentum of fundamentals, basically exploring the inefficiencies of small cap companies with decent fundamentals but with price disconnected from that quality, which are also increasing the rate of which fundamentals keep getting better (while stock price doesn't keep up with the same pace). This is based on this research paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2538867

This paper also explains the benefits when combining value, size and momentum, as per this paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1720139

The model also makes use of these criteria regarding quality: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287202


This model does not make any use of market timing - it either buys if there's momentum or sell and keep in cash if the criteria are not met.

I've also implemented a model trading inverse volatility, as per descritpion and formulas of this paper: http://www.scirp.org/journal/PaperDownload.aspx?paperID=19158

The model looks good thanks for the leverage on inverse volaility when in contango model, and it makes use of other leveraged ETFs when not in XIV. The strategy is a variation of #3 described here: http://www.naaim.org/wp-content/uploads/2013/10/00R_Easy-Volatility-Investing-+-Abstract-Tony-Cooper.pdf

So far, using the approach of tagging along well researched papers or books and putting the automation in place to do the hard work for us has been fruitful to complement a portfolio meant for short term gains (these ones, amongst others) and long term gains (investing portfolio made of companies with history of increasing dividends, so I can live off their dividends perpetually and remain invested at all times).

Trading is much harder than investing because it requires 2 luxuries not needed when investing: locking profits in the short term and reducing drawdown. For this reason, develop a model based on a sound idea, not just performance from a backtest.

The hardest skill is temperament. It's hard to stick to an idea when a bunch of trades keeping closing at a loss. It's hard to not question the model and to stay away from modifying it on the fly. That's where algo thrives; If you come up with a good model and decide to use real money, stick to it for at least 1 year. Algo trading is mechanical, so there will be losses. Maybe a bunch in a row. It doesn't mean it's broken and no longer works. If one can remember this everytime a trade is closed for a loss, algo trading can be very profitable even with a simple model.