#BinanceTradeSmarter

Smart Trading Using Technical Analysis on the Crypto Market: Benchmarking Lstm (Long Short Term Memory), Dqn (Deep Q Network) and Rf (Random Forest) Agents.

The volatile and speculative nature of the cryptocurrency market presents unique challenges and opportunities for traders. This study aims to optimize technical analysis (TA) indicators through the application of advanced computational models, specifically Deep Q-Network (DQN), Long Short-Term Memory (LSTM), and Random Forest (RF) agents. Using a systematic backtesting approach, these models were evaluated based on criteria such as Total Return, Annualized Return, Annualized Volatility, Sharpe Ratio, Sortino Ratio, Max Drawdown, and Calmar Ratio. The DQN model demonstrated superior performance in profitability and risk management, while the LSTM model excelled in generating consistent returns. The RF model was most effective in minimizing Max Drawdown, indicating robust volatility management. A significant finding is the absence of a single optimal TA indicator, underscoring the need for adaptive and diversified trading strategies. This study highlights the potential of integrating advanced computational methods with real-time data analysis to enhance automated trading strategies in the cryptocurrency market. Future work should focus on developing hybrid models, implementing dynamic TA indicator selection, refining risk management strategies, and conducting real-world testing to validate these findings.

#MarketPullback #CryptoMarketWatch #USStocksPlunge #BinanceSquareTalks