# Recommending Cryptocurrency Trading Points with Deep Reinforcement Learning Approach
## 4 Experiments and Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Training** \\ **Time** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Invested** \\ **Money for** \\ **Trading (5)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Trade** \\ **with** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **“Buy\"** \\ **Actions** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **“Sell\"** \\ **Actions** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Transaction** \\ **Fee (5)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Money after** \\ **Trading (5)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Quality of** \\ **Trading (\%)** \\ \end{tabular} } \\ \hline \multirow{2}{*}{70-time} & \multirow{2}{*}{1 000 000} & DC & \multirow{2}{*}{63} & \multirow{2}{*}{63} & \multirow{2}{*}{63} & \multirow{2}{*}{699.2} & \multirow{2}{*}{1 000 266} & \multirow{2}{*}{\begin{tabular}{c} 100.02 (Grew \\ 0.02) \\ \end{tabular} } \\  & & & & & & & \\ \cline{3-8}  & & & & & & & \\ \hline \multirow{2}{*}{80-time} & \multirow{2}{*}{1 000 000} & DC & \multirow{2}{*}{150} & \multirow{2}{*}{150} & \multirow{2}{*}{1 626.9} & \multirow{2}{*}{996 214.8} & \multirow{2}{*}{Lost 0.4} \\  & & & & & & & \\ \cline{3-8}  & & & & & & & \\ \hline \multirow{2}{*}{90-time} & \multirow{2}{*}{1 000 000} & DC & \multirow{2}{*}{262} & \multirow{2}{*}{230} & \multirow{2}{*}{2 663.1} & \multirow{2}{*}{996 029.8} & \multirow{2}{*}{Lost 0.4} \\  & & & & & & & \\ \cline{3-8}  & & & & & & & \\ \hline \multirow{2}{*}{100-time} & \multirow{2}{*}{1 000 000} & DC & \multirow{2}{*}{268} & \multirow{2}{*}{218} & \multirow{2}{*}{2 677.9} & \multirow{2}{*}{994 529.5} & \multirow{2}{*}{Lost 0.6} \\  & & & & & & & \\ \cline{3-8}  & & & & & & & \\ \hline \multirow{2}{*}{450-time} & \multirow{2}{*}{1 000 000} & DC & \multirow{2}{*}{510} & \multirow{2}{*}{396} & \multirow{2}{*}{5 577.0} & \multirow{2}{*}{1 155 658} & \multirow{2}{*}{\begin{tabular}{c} 115.5 (Grew \\ 15.5) \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} 115.5 (Grew \\ 15.5) \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} (Grew \\ 15.5) \\ \end{tabular} } \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main information about the trading process tested with different training time. Figure 8: The result trading process that tested after 450-time training, where “buy” action matched the first square, “sell” in the second square. For making sure and defining how useful using deep reinforcement learning approach, we took the same period and tested simultaneously with three classical strategies: the double cross strategy, swing trading, and scalping trading. We tested on Bitcoin with the period from the 19 of March to the 21 of April and with the 450 th time training model. Table 4 shows 14 actions brought 0.9% profit to the trader when the double cross strategy was used. At that time, the swing trading showed five actions and ended up with a 0.7% profit. The most satisfactory result between classical approaches was in the scalping trading. During the experiment occurred 130 actions, which brought 6.1% of profit for the trader. In the same period, the deep reinforcement learning (DRL) application performed 400 operations. As a result, the trader's capital grew by 14.4%. Then we extended our experiment by examining Litecoin and Ethereum. As in Bitcoin's case, we trained agents separately with their separate historical data and tested for Litecoin with the period between the 19 of March and 21 of April as well. The results are described in Table 5. By using the double cross strategy, the trader completed 12 actions and lost 2.2% of the money invested within a month. In the same period, the swing trader made four trade and finished with a 3.6% profit. The experiment with scalping trading strategy ended with 23.7% profit and 134 trades. During that period, the DRL application performed 642 actions. As a result, the trader earned 74.6% more money than the invested money.