CryptoHawk AI Long-Short Strategy — AI & Trading Strategy in Action

Machine learning and data science depend heavily on significant time-series data to identify and leverage the patterns to achieve meaningful predictions. The CryptoHawk AI Long-Short prediction model is based on Bitcoin and Ethereum because they have the most history compared to other coins. The resulting model, which includes many market indicators and co-integration, is based on what has been a relatively bullish history. This is why the model performs well in detecting buy signals, whereas the sell/short signals have been less effective and favor investors with shorting options.

After more than a year and a half of predictions and disruptive crypto market volatility, our data science team has experimented with several different models and strategies to leverage our Long-Short signals.

This document will help you combine the strongest and most consistent predictions with a buy and stop-loss strategy.

As its name indicates, the CryptoHawk AI Long-Short model was modeled to consider short selling. The Buy & Stop-Loss Threshold Strategy acknowledges that most casual traders do not engage in short selling. Our data science team analyzed the performance of the predictions and applied two thresholds, one for one upper gain limit and one for a lower sell/stop-loss limit have been established.

In this exercise, we display several upper and lower limit thresholds. This strategy uses the first buy signal from the machine learning (ML) strategy and holds it until it reaches the upper or lower limits. A sell signal is then generated when gain/loss reaches the established thresholds.

When the gain reaches the lower threshold limit, we check what the suggestion from the ML strategy at that point is. If it also calls a sell signal, then we show High confidence; otherwise, we are moderate about our call. As we move forward, we will see the comparison of different strategies during different time intervals of the test dataset. The Moving Average (MA) and ML are used in our research to establish a baseline and analyze our predictions. The threshold strategies are the ones we are demonstrating to illustrate the various performances. We also apply a set fee of 0.1% for each transaction.

By applying the different threshold distributions to the predictions to mark a sell and stop-sell position, we discovered that the strategy offered significant gains compared to a market hold strategy. The most impressive gains were measured on the 12% and -3% settings, with an initial balance of $100 returning an end trading balance of $407.46. The following detail the different distributions applied to the strategy.

Our Machine Learning model is best combined with strategies that are supported by backtesting and being mindful of the volatility of crypto. Financial and technical indicators, social media, regulations, seasonality, absence of traditional supply chains, and business reports contribute to positioning crypto predictions in a class of their own. During such a paradigm shift, unexpected events have disrupted how people perceive and engage with cryptocurrencies. Complementary predictions with strategies are key to a successful outcome despite the market’s high uncertainties and volatility.

Our machine learning and data science team are not only strengthening our AI models but also exploring and sharing strategies that make CryptoHawk AI a valuable predictive tool for crypto traders.

With this information and the upcoming API for our Pro CryptoHawk AI subscribers, the use of our predictions and published backtested strategies become a powerful tool for trades building their trading bots.

1. Thierry Hubert, CTO

1. ‭+1 (833) 344-4629‬

• thierry@digimax-global.com

1. Dr. Shahzad Cheema, Chief Data Scientist

1. Dr. Muhammad Kamran Malik, Lead Data Scientist‬

• Hassan Masood, Data Scientist

For more information about CryptoHawk AI, visit www.CryptoHawk.ai or download our mobile app at Apple App Store or Google Play