accuracy samples

Forecast/ability 2

In the previous post “Forecast/ability” I did refer to the daily a.i. forecasts and I showed the results of a long and extensive research on the quality of the response of the model.

But when we come to the weekly and to the monthly forecast, things change radically and for the best. Undoubtedly, weekly and monthly bars undergo a reduced “noise” and express better the global consent of the partecipants to the market activity. Market is fractal in nature and I have not an explanation for why it behaves differently from daily to weekly and monthly time frames. Maybe because it reflects the attitudes of different categories of actors (investors have a totally different approach to the market than daytraders or position traders). Anyway this is what comes out years of observations of the forecasts produced by my model.

Just as an example, this monthly forecast chart has been produced exactly one year ago, on 5th of December of 2017 and shows how r.Virgeel forecasted correctly the October 2018 correction, ten months in advance. Astonishing, uh? Consider that the monthly model is a long term database of financial and econometric data, so the detected patterns are not only related to the market activity, but also to the underlying economic activity.

(The right part of the chart has been cutted, for respect to paying subscribers, as it refers to current market expectations and it is still valid).

 

 

Here another example, from the weekly model, published to subscribers on the 3rd of March 2018: the deep and scary correction was shaking the markets and r.Virgeel correctly forecasted that in 4 to 5 weeks the S&P 500 should reverse (not reaching the previous low) and go for new all times highs, as it did.

 

Quite obviously, these forecast on the long term side are not much interesting for very short term traders, but they may be invaluable for position traders and investors, that have a global vision of the market totally depurated from the biased news and the so-called experts opinions. hese are not opinions of any kind, they are the result of pure brute force number crunching

 

The psychological advantage of these knowledge is actually the first and best result: less stress, better decisions, more returns on your investments!

 

 

 

Posted by Luca in a.i., accuracy samples, free, generics, model insights, r.Virgeel

Forecast/ability

Following my previous post “New Tools at the Horizon“, one question was twirling in my mind: why the stock market is forecastable, but the forecasts are not affordable?

The forecastability of the market is an evidence, because if it were not – being it just a random walk – there would not be the possibility to have an output from the neural networks that manage the forecast process. For a neural network to work, there must be some sort of structure inside tha data that can be used to produce the forecast/diagnosis.

This chart shows a blind neural network, unable to recognize any pattern in the input data.

And this hidden structure is present indeed inside the market data, otherwise r.Virgeel would be totally blind and dumb. This is a sample chart of a blind network: not structure is evaluated and the output is just an array of zero values.

The fact that we humans do not recognize any structure in the data is irrelevant.

So we have a (hidden) structure, the neural tools recognize it, but the output ranges from nicely precise to totally incorrect, without having the possibility to know how much the result is matching the real future movement of the price.

 

Now, I begin to see the light.

The price of a financial instrument is the result of an ask/bid process, where a multitude of actors (I’m considering liquid markets with a wide audience) buy and sell that instruments under the suggestion of a personal forecast that the price of that instrument will rise or fall in the future.  Every partecipant to this activity actually does a personal forecast every time he/she executes an order. So, the resulting price is the sum of all the collective forecasts and, at the end of the day, this collective forecasting process generates the push that contribute to move the trend.

[revec2t text="Every partecipant to the market activity actually does a personal forecast every time he/she executes an order."]

In other words, every attempt to forecast the market is a process of forecasting a collective forecast activity, a meta-forecast: no surprise that somewhere in the process one or more dimensions are lost and the result is probably something similar to a shadow, that let you recognize the original shape under certain conditions and  totally mistify the original shape under other conditions. When you project a multidimensional event in a field that reduces the dimensions (think to a 3d object projected onto a plane) you lose a significant portion of information and you may generate a lot of ambuguity.

 

A 3d object projected onto a 2d planes may generate very different shapes

 

Now, the forecasting process is just a minor side activity of r.Virgeel, even if it is the most appealing and mind-storming:  r.Virgeel is mostly a diagnostic tool that reads current data and find historical patterns that match the best market position available, with a significant success.

 

 

 

 

 

Posted by Luca in a.i., accuracy samples, educational, free, model insights, r.Virgeel

Daily forecast accuracy sample

I wish to stick to showing you samples of the r.Virgeel activity, in (almost) real time. There is no other way to test r.Virgeel than real time. Also, to avoid the “well chosen sample” effect.

Here, on the left, the forecasted bars evaluated on May 17th, on the right the chart of the actual bars until yesterday close, May 30st.

 

Left: forecast of May 17th Right: actual bars at May 30th

 

I show you the forecasted bars because it is by far the most difficult indicator to calculate, bars come in from the future squishly jigsawing and with modulated velocity.  So whenever I want to evaluate r.Virgeel model, I first look at the future bars. Also, the future bars are a totally unattended calculation, meaning that there is no human intervention at all, just the correlation of a huge number of factors is taken in account.

Looking at the forecast, when it was issued, you might argue that a choppy period was to begin, that should not exceed the last high and that might break the last low.  Two down days, followed by a reaction that flattens and returns slightly negative. You ‘d have known all this in advance, various days in advance. Either you are an investor or a trader or even a daytrader, you might take advantage in various ways from the forecast, but you are psychologically prepared to what is coming.

From the comparison ot the two sequences, I point the attention on the fact that the incoming wave is demonstrating to come in faster than expected by the forecast. This is a fact I’m long pondering. Almost all r.Virgeel forecast show this reverse lag, but I’m still detecting. One possible reason is the fact that the databases of r.Virgeel goes back to an era when everything, in trading activity, was slower: no internet, no direct instant execution, non continuous markets, just newspapers and a phone to pass your orders to your broker or bank. The ever accelerating time implicit in the trading activity is an interesting subject of research. Another possibility is that time in itself might be an accelerating media, but here I can’t go further.

 

 

Posted by Luca in accuracy samples, educational, free, indicators, 0 comments