Vagorithm Factors – A Bit of Insight

In creating the Vagorithm, which is still a process I am tweaking and trying to get right – I have taken a number of known indicators and created a composite. What makes it different is that I have put together some things that aren’t usually meshed and incorporated them into my algorithm.

Here are a few of the factors that I have included.

First of all, I think that one of the most interesting indicators in our short attention span world is the short term volatility index. The main indicator I use is the $TVIX which charts day-to-day volatility. While long term volatility is a factor in long term price corrections, I feel that short term volatility reflects the mind and sentiment of the majority of investors likely to make buying decisions in real estate, cryptocurrency, or the stock markets.

I’ve also included several commodities and precious metals in various ranges. Silver, gold, coffee, and U.S. Wheat are all factors that figure into ‘societal mood’.

Another factor that I’ve been trying to find an accurate way to include in the vagorithm is the number of ‘animal stories’ being put out on mainstream media. These are a known distractor from the serious political and conflict stories that unsettle viewers and lead to chaotic i.e. non-controllable, non-programmed actions on the part of the public. Rather than tracking the political and conflict stories, I believe it makes more sense to track both animal and celebrity fluff stories in the mainstream media.

As strange as it may sound – I’ve also included the number of Tweets from U.S. sham President Trump – the number is based on an average monthly number. Drumpkoff’s twitter has become an important leading indicator of U.S. markets and consumer sentiment.

There are a couple of negative factors which I am having trouble incorporating – the first of which is Natural Disasters – the hard part here is discerning how the public actually feels about forthcoming disasters – so far I’ve experimented with using twitter hashtag mentions which has yielded some promising results, but what I’d like to find is a good way to incorporate Facebook data with that. So far, I am hitting a bit of a wall on this one. Another factor which I consider a negative indicator is U.S. government involvment in monetary policy – a strong hand works negatively even if only percieved.

So, these are some of the indicators that I am incorporating – there is much more, but as I am still in early development stages, I prefer to just tell you about the milk and not the cow’s diet. Still, here are two more indicators that I am leaning on heavily…the overall performance of the tech sector and here is an odd one to show you just how  bizarre all of this is – the number of google searches for ramen vs. pho.

So, all of this begs the question…What is the Vagorithm good for? So far, I am using it for a couple of predictions at the moment – stock market, cryptocurrency, realty market, and political election predictions. Since things are still in progress – you will have to follow my results on Twitter at @vagobond

What do you think?

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