Accurately Predicting Bitcoin Price
Strong correlations that allow for effective and profitable Bitcoin trading are illusive. The strongest one I have found is Google Trends tracing the frequency of the search quarry “Bitcoin.” It works best at price extremes and at that it is a lagging indicator. You can read more about utilizing Google Trends here: https://crypto-trend-trader.com. Tracking pairs are non-existent for crypto currencies. I am fully aware that this statement will generate some strong comments attempting to refute that conclusion, but I challenge you to try and make a profit by utilizing any of them. The S&P 500 is the one most often cited as being strongly correlated with Bitcoin price.
A more effective crypto forecasting tool has emerged; the Voss Predictive Filter. Dr. Voss describes this filter as “A filter for universal real-time prediction of band-limited signals” This algorithm was developed to provide greater resolution and insight into a wide class of signals generated by deterministic or stochastic systems. It attempts to remove group and phase delays from the Weighted Moving Average output. One of Dr. Voss’s fields of endeavor is working to make MRI images clearer. This is done by extracting the first harmonic of the output using a bandpass filter and then applying a “negative-delay” formula to it. The financial application is very simular.
Forecasting or projecting a financial time series is regarded as one of the most challenging applications of time series prediction due to there volatile nature. However, forecasting is the fundamental element of most investment activities, thus attracting the attention of practitioners and researchers.
“It is concluded that a band-limited time series can be predicted with zero errors by a predictive filter that has a constant magnitude response and constant group delay over the bandwidth of the time series.”  It should also be noted at this point that financial data is not bandwidth limited, as most equity prices do not have an upper pricing boundary.
Time series forecasting methods attempt to discover patterns in historical data series and extrapolate these patterns into the future. The further into the future you attempt to press this filter the less likely you are going to see accurate predictions. About three days is the maximum for a financial forecast. The example below is way out over its skis at 6 days.
The current permeation of the predictive indicator this article describes is called Dragon-X and incorporates the Voss predictive filter with several adjustments that enable it to be utilized as a financial forecasting tool. First a synthetic window or aperture is incorporated that constrains the price bandwidth in order to get this type of filter to work effectively. Most similar filters or indicators utilize some form of weighted average. Often it is the weighted exponential moving average. The input utilized here, the center of gravity, is adopted from the same calculation that determines the center of gravity of a rocket as it burns off fuel during launch.
I have provided several other indicators in the example above, as a reference to the Dragon so you can get a perspective of its signals in relationship to other indicators. An indicator called the Early Warning System generated a Bitcoin SELL signal on 12/17. An indicator called Cycles generated a SELL signal on 12/20. The Dragon is between the two in this time line with its SELL signal generated on 12/19. This same indicator is currently predicting that a BUY signal will be generated on 12/26 or six day from the day the SELL signal was produced.
We can watch together to see if Bitcoin swings according to the prediction of the Dragon.
I greatly appreciate the insights of all traders and look forward to learning from your comments and trading ideas.
Good Luck with Your Trading, Michael
1) A Peek into the Future, By John Ehlers
2) Forecasting Financial Time Series using Linear Predictive Filters; by Bin Li;
A thesis submitted for the degree of Doctor of Philosophy in Electrical and Electronic Engineering, Imperial College London, Imperial College London Filters https://spiral.imperial.ac.uk/bitstream/10044/1/11176/1/Li-B-2013-PhD-Thesis.pdf
3) Henning U. Voss, “A universal negative group delay filter for the prediction of band-limited signals”
4) John Ehlers, “Cycle Analytics for Traders”, Chapter 5, John Wiley & Sons