Are there non-conventional, non-mainstream, less known/documented, equities trading algorithms/strategies!?
Is there anyone who is familiar or heard about non-conventional/manistream, less documented, equities trading strategies/algorithms? Namely, other than trend following, stat arb, pair trading, mean reversion, Elliot waves/Fibonacci etc. (but not wild stuff such as astrological-based and the like).
I know one and I trade one like this, it is a stat monthly aberration that is widely discussed in literature. I improved it by better managing drawdowns so that one could put leverage on it. Ready to trade it as advisory or on 50/50 profit split based yoy performance after a certain watermark.
yes, I am. We work on intelligent strategies which sense biggies movement.
is it similar in concept to the sniffing algorithm (known as “Stealth”) of Deutsche Bank (icebergs detection?)
Try this one:
22 days ago
No indicators at all, what ever it may be… trade is done strictly on the price action only. Cloud 9 & Deep sea algorithm pre-fixes the entry / exit level that is applicable for a particular period of time say monthly / weekly / daily – and trade is done on those levels…
Sample trades – March 2011 — Near month futures on Major Global Indices. ( Nikkei, Dow30, Dax30, FTSE 100. CAC40 etc) –
Trade set-up name: MMS
Sample trade set-up Name : mayaConservative — Commodity: SILVER
Another Sample: EUR_USD – July 2011 _ trade setup name: MMS
Visit our blogs — MMS level on certain assets for the month of August 2011 are updated and let us know your terms and conditions so that we can accelerate / proceed ahead.
Try searching a scientific paper site such as Elsevier. You’ll need a subscription to get access however. I have found literally hundreds of papers on the following topics: neural networks / machine learning, fuzzy logic, genetic optimisation, nearest neighbour classification with relation to time-series forecasting. The approach I’m taking is to treat an algorithm as a control system overseeing a chaotic system as opposed to trying to find a statistical model that accurately reflects the market
I am familiar with all mentioned. I myslef consider neural networks / machine learning, fuzzy logic, genetic optimisation, nearest neighbour classification and the like as “mainstream” and more like “machinery” rather than concept/strategy.
But then offcourse, it depends on point of view.
Ok fair enough, but my point still stands – you’ll need to dig into research journals to find cutting edge techniques and ideas that haven’t made it into the commercial world.
Well, there’s the adventure of ignoring (or actually being ignorant of) everything in print and approaching the problem from first principles yourself. When the focus is solely on price, it’s a matter of looking for repeatedly occurring market behavior patterns. That being said, we currently find ourselves in quite an atypical period. With algorithms trading against algorithms, computer against computer, and high speed data chasing, the markets are currently exhibiting periods of both historically unprecedented smoothing and volatility.
In the midst of these current machinations, the greatest strategic advantage lies now, and probably for some time to come, in a long term, “open air theory” approach — just good ole low-frequency Graham and Dodd analysis, combined with a good sense of concrete realities, especially the metrics of stress between world’s population with its unlimited aspirations and the limited real estate we’re on.
As for me, who works statistical swing trading models with a trend following overlay — and with flat results for over a year now — one observation that I can offer is that it has been my experience that when my models go flat that long, the market is headed for a significant interim low.
You mean outside technical, fundamental, quantitative statistical, and rotation?
no not all, no objection to any on your list. I refer to concpets different than what is well known/documented.
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