## 4 Experimental Results  
Several experiments were conducted on historical data to assess the effectiveness of the trading algorithm. Additionally, a phase of parameter tuning was carried out with the goal of identifying optimal values to maximize profits. The parameters assessed during this process included the take-profit (\(PV\)) and stop-loss (\(LV\)) percentages, as well as the duration of the safety interval before opening a trading operation (\(W\)), and the number of days for data used by the prediction model (\(D\)). In particular, after evaluating different values for such parameters, we identified the following optimal configurations: \(PV=12\%\), \(LV=8\%\), \(W=12\) h, and \(D=3\) days.  
The training of the LSTM model has been carried out using a dataset spanning the period January 2021-December 2021, related to the cryptocurrencies listed in Table 2. Subsequently, we evaluated the obtained profits on data collected on a different period, ranging from January 2022 to March 2023. For our tests, among those listed in Table 2, we selected 28 coins in which to invest. In particular, we decided to invest exclusively in three categories of cryptocurrencies: high capitalization (HC), solid project (SP), and influential meme coin (IM). We decided not to invest in volatile meme coins (VM) due to their unpredictable nature, lack of fundamental value, and susceptibility to market manipulation, which can lead to significant financial losses. The evaluation was carried out starting from a virtual initial capital of USD 1000 for each cryptocurrency (USD 28,000 in total). Subsequently, the profits generated by the trading algorithm were evaluated, also taking into account the transaction fees of 1%. It is worth noting that fees are paid on each transaction, so the greater the number of open trading operations, the greater the amount paid.  
The overall results of the algorithm are presented in Table 6, highlighting an overall gain of 194% when transaction fees are not taken into account, and of 117% when transaction fees are considered. Specifically, starting from an initial capital of USD 28,000, we obtained a final capital of USD 82,359 with no transaction fees and of USD 60,871 by considering fees. The trading algorithm proved to be extremely effective in predicting the price trend of influential meme (IM) coins, which appear to be significantly influenced by trends and popularity on social media. Specifically, trading on IM leads to a very high average profit of 902.48% with fees and 1257.96% without fees. However, it is worth noting that not all cryptocurrencies have produced profits, but some of them have experienced losses. In particular, fees have a significant impact on profits. As an example, in the case of BTC, the algorithm produced a loss of 14.91% with fees, while it produced a gain of 227.38% without fees. This phenomenon is due to the fact that the algorithm, in some situations, tends to open and close trading operations whose profit is not able to cover the cost of commissions. This is an aspect that will need to be better evaluated in the future, introducing additional operational constraints into the algorithm to address such situations. In some other cases (i.e., TRX and CHZ), the algorithm produced zero profits as no trading operations were carried out during the period considered. Finally, in some rare cases (e.g., SAND, GRT, and FTM), losses have occurred, both with and without fees. This behavior is most likely due to the fact that data collected for training were limited and did not provide the LSTM model with sufficient predictive capabilities.