<story><title>Predicting Price Changes in Ethereum (2017) [pdf]</title><url>http://cs229.stanford.edu/proj2017/final-reports/5244039.pdf</url></story><parent_chain></parent_chain><comment><author>adjkant</author><text>I&amp;#x27;ve actually run a trading algorithm off a very similar approach for the past 8-10 months, which yielded about 87% return in that time. Using regression I can still to this day hit about 55-60% accuracy. What&amp;#x27;s not mentioned in the paper is that accuracy is only a small part of the story. If you&amp;#x27;re accurate 60% of the time but in the 40% accuracy range you&amp;#x27;re very wrong, acting on the information is useless.&lt;p&gt;As a result, it&amp;#x27;s important to develop a trading approach that can actually capitalize on the information. For that, I have found three things to work best:&lt;p&gt;1. Only trading on the highest signals of increase within a model that is a spectrum rather than binary classification. This usually doesn&amp;#x27;t increase accuracy much ironically but does increase the &amp;quot;average value&amp;quot; of buying on the increase signals. I usually set this through historical testing for prediction values and taking a top percentage of the prediction values to set the &amp;quot;threshold&amp;quot;.&lt;p&gt;2. More features and feature selection tuning. Right now I&amp;#x27;m using genetic algorithms to constantly try and test new sets of features, thresholds, &amp;quot;hold times&amp;quot; after buying, etc.&lt;p&gt;3. Work on minutes, not hours. The volatility is so high that you can actually capitalize well on the micro level in my experience.&lt;p&gt;While accuracy is important, the average trade value and trades per day are far more important to returns.&lt;p&gt;Interestingly enough, the algorithm was steadily making money until April or so, when it stagnated. Mind you, it was making money from January-March due to sheer volatility even while the price was dropping most days. I&amp;#x27;ve actually shut mine down for two reasons - the plateau plus the fact that the market was too thin on GDAX to quickly trade on buy signals for the amount I was running with (ending at about $3.5K). If the market thickens, I&amp;#x27;ll likely start running it again.&lt;p&gt;Takeaway: this paper&amp;#x27;s approach may seem simple but honestly the reality is that with something so volatile it&amp;#x27;s surprisingly easy to capitalize on with algorithmic trading that learns even a few small features and trades frequently.</text></comment>
<story><title>Predicting Price Changes in Ethereum (2017) [pdf]</title><url>http://cs229.stanford.edu/proj2017/final-reports/5244039.pdf</url></story><parent_chain></parent_chain><comment><author>maxpert</author><text>I can vouch for the DNN results. I took data from Kaggle and tried training networks with different approaches. Was not able to get prediction any better than 54% (which I am pretty sure would be biased as well). Later on while inspecting data and looking at dips and rises turns out external events (govts banning crypto or a price crash due to other reasons) was causing price drops or surge, my conclusion was if you need to predict anything you need to be aware of these external factors.</text></comment>