Bitcoin Price Patterns to Predict Future Trends

I am putting together a report by overlaying recent Bitcoin price movements against the best matching past occurrences to see if there is any predicable continuation.

Currently there are no graphs or back testing to see how the different parameters affect accuracy.

I have some preliminary numbers based on comparing a dataset of 6 months, 8 months and 12 months then looking at the average outcome when looking forward from those matches out to four months.

6 Months  1        2        3        4      
 Upside  115  117  118  118 118  118  118  118 118  118  118  118  118  118  118  118
 Average  94  100  101  102  102  101  100  99  98  97  96  94  93  92  90  89
 Downside  105  96  95  93  88  86  85  81  78  75  73  72  69  67  62  58
8 Months                                
 Upside  115  118  121  126  133  149  160  165  166 166  166  167  171  175  178  183
 Average  94  103  105  108  111  114  118  121  123  124  126  126  127  127  127  127
 Downside  105  102  101  101  100  100  99  99  98  97  97  97  97  97  97  97
 12 Months                                
 Upside  117  128  134  159  195  251  297  323  333  341  350  352  353  362  368  374
 Average  95  106  113  122  132  146  163  178  188  194  199  205  211  216  221  226
 Downside  105  105  105  105  105  105  105  105  105  105  105  105  105  105  105  105

The columns are weeks projected into the future and the numbers are percentages. So when comparing 6 months of data to find the most similar past pattern the best outcome after one week is a return of 115%, the second week is 117% etc. All of these numbers are based on the price data pattern as of July 10, 2018.

A big disclaimer; this is not a prediction, just because the past data set closely matches does not guarantee that the trend will continue.

These are just a small sample of results with random parameters. When using a larger pattern to compare with past data can result in more specific matches but at some point reduce the amount of range to search through.

A big issue with this approach when comparing patterns on historical data is tuning the parameters to the dataset rather than fundamental patterns. This comes through in the fact that the 6 month pattern predicts a decline in price and the 8 and 12 month data set predict an increase in price.

I will have to experiment with back testing different parameters to get a good sense of accuracy if any on historical data and the future prediction. That will come later.

Update July 11, 2018

Here are a small sample of back testing results. The data samples are collected in days back from 180 all the way back to 1275 three and half years ago. The data sets were run using 6, 8 and 12 months of data respectively and the results are shown as projected average divided by the actual percentage change ninety days later.

 Days Back  1275  1095  910  730  545  365 180 
 Date 2014-10-02 2015-04-05

2015-10-07

2016-04-03

2016-10-25 2017-05-26 2018-01-10
 Price  $373  $254  $243  $418  $657  $2177  $14000
 6 Month Set  89/85  105/100  102/177  130/156  120/176  188/199  260/48
 8 Month Set  46/85  93/100  121/177  110/156  116/176  216/199  199/48
 12 Month Set  214/85  113/100  123/177  129/156  148/176  265/199  141/48

Overall the results were kind of interesting. There are some correlation but also some significant misses. More resolution and adjusted parameters may make this a more useful report.

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