Fireside (virtual) chat with Dr Ken Long: Hosted by Jeff Boccaccio
Fireside (virtual) chat with Dr Ken Long: Hosted by Jeff Boccaccio
Well before yesterday evening’s event, a detailed email had been sent to STA members trumpeting this Zoom meeting and containing links to several texts** relevant to the talk. The chat basically consisted of a lengthy explanation of his trading methods and how to learn trading tactics, with a brief digression into creativity.
Though retired from active service as a Lieutenant Colonel in the US army many years ago, he is still associated with the organisation of which he is clearly very fond. Ken started investing in 1986, encouraged by his new father-in-law. Now semi-retired Ken has several links to US academia where he helps US army officers with 10 years’ service ‘’make the transition to organisational leadership…I focus on logistics, change management and creativity.’’ As he has focused on this for the last 20 years, his sentences are peppered with Human Resources jargon and he also trots out a long list of suggested reading – mainly what I would call of the ‘get rich quick’ genre.
Despite all of the above, he finds time to ‘’do the thing’’, producing lots of content daily for traders (where he has all the vocab and patter) and has thousands of videos available on YouTube. He describes himself as a ‘’systematic trader with discretionary tendencies’’ where ‘’95% of the world isn’t looking through my time frame’’ – which is day trading, mainly using 1 and 3 minute charts. His basic building block is mean regression because this ‘’best expresses the trend of price’’. Looking back over 10, 30 and 90 days (thirds of each other), he may then overlay Average True Range and moving averages when price is close to these. Bandying about statistical concepts, don’t be put off because what he’s using is pretty basic stuff; nothing Bayesian or non-parametric.
His website is: www.tortoisecapital.net
** Accompanying text files:
Harvard Business Review article on AAR process
Sniper Trade Executive Summary
Tags: Mean Regression, probability
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