This report aims to provide readers with performance analysis of our trade analytics platform. In this analysis, four coins were chosen to create a portfolio of assets, these being MATIC, BTC, ETH, and XRP. Risk categories chosen represent the amount of risk the majority of users are willing to take when trading. As shown in Tab.1, the column called Risk Category listed the risk classes where Baseline is a fixed point of reference that is used for comparison purposes.

The trade signals suggested by our platform were used to either buy or sell at three different rise and fall events with variable magnitude percentages. In all the experiments carried out, an initial investment of 1000 USD were used, and long positions were taken mainly due to its simplicity compared to other techniques to assess the performance of our proprietary AI algorithms in creating accurate signals.

Tab.1: Shows risk categories and the corresponding buy & sell strategies.

Risk Category	Buy Strategy	Sell Strategy
Baseline	Buy at start	Never sell anything
Risk Taker	Buy at +6% rise prediction	Sell at +2% fall prediction
Moderate Risk	Buy at +7% rise prediction	Sell at +1% fall prediction
Risk Aversek	Buy at +9% rise prediction	Sell at +0.5% fall prediction

In Fig.1, the net value of assets and their fluctuation within November 10, 2022, and December 24, 2022 using different risk appetites is shown where the net value of assets is mostly higher compared to the baseline. However, risk averse traders slightly outperformed others.

Fig. 1: Shows the net value of assets under different risk categories.

In Fig. 2, portfolio charts for different risk categories are shown

Fig.2: Shows the net value of the portfolio under different risk classes. Horizontal axis represents the time and W denotes Week. Vertical axis represents the percentage composition of the coins and cash within 0-100.

The charts above show the position of each coin and its changes over time in each risk category. In all those charts except for the baseline as shown at the top-left, our prediction recommended traders to sell all coins from the first week, although to a lesser extent for MATIC. As an example, in the Risk Taker category, MATIC has been the most valuable coin in the first week (W1) followed by BTC and ETH respectively. As we move towards weeks 3 and 4, all coins except for MATIC start losing value according to our AI prediction, and traders were suggested by our signals to sell.

In Tab. 2, a summary of profit figures are shown where our data has helped traders to consistently make profit of over 3.5% on average, with the risk averse category being the most profitable at 5%.

Tab.2: Average and final percentage of profit for different risk categories. Average profit represents the average of residual profits estimated over the course of 44 days. Final profit is the profit made at the end of the chosen timeframe.

Risk Category	Average Profit	Final Profit
Baseline	1.21 %	-3.54 %
Risk Taker	3.28 %	4.6 %
Moderate Risk	3.39 %	5.07 %
Risk Aversek	4.09 %	5.01 %

Closing Remarks

In this report, the performance of our proprietary AI algorithms for traders with various risk appetites was evaluated and the results of portfolio analysis were discussed. Generally, in the volatile crypto market, the prediction of price movement is challenging mainly because a large number of factors including social, blockchain, and market play a role. From the results presented in this report, it can be seen that our predictions powered by AI can be of significant assistance to traders helping them to not only avoid losing money but also earning money in most cases.