用深度学习预测汇率，准确率超99%，但实盘仍亏钱。
Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets
- 用LSTM模型处理2018-2023年汇率数据，捕捉波动趋势。
- 模型准确率达99.449%，RMSE低至0.9858，优于传统方法。
- 适合关注量化交易与风险控制的金融从业者参考。
外汇汇率预测对全球金融市场至关重要，影响贸易、投资与经济稳定。本研究基于2018至2023年雅虎财经提供的美元兑孟加拉塔卡（USD/BDT）历史数据，构建先进的机器学习模型。采用长短期记忆网络（LSTM），实现99.449%的预测准确率，均方根误差（RMSE）为0.9858，测试损失为0.8523，显著优于传统方法如ARIMA（RMSE 1.342）。同时使用梯度提升分类器（GBC）进行方向预测，回测显示初始资本1万美元，49次交易中40.82%盈利，但净亏损20,653.25美元。研究分析历史趋势，发现BDT/USD汇率从0.012降至0.009，并引入归一化日收益率以捕捉波动性。结果表明深度学习在外汇预测中具有潜力，可为交易员和政策制定者提供风险缓解工具。未来可结合情绪分析与实时经济指标提升模型适应性。
原文摘要 · Abstract (English)
The prediction of foreign exchange rates, such as the US Dollar (USD) to Bangladeshi Taka (BDT), plays a pivotal role in global financial markets, influencing trade, investments, and economic stability. This study leverages historical USD/BDT exchange rate data from 2018 to 2023, sourced from Yahoo Finance, to develop advanced machine learning models for accurate forecasting. A Long Short-Term Memory (LSTM) neural network is employed, achieving an exceptional accuracy of 99.449%, a Root Mean Square Error (RMSE) of 0.9858, and a test loss of 0.8523, significantly outperforming traditional methods like ARIMA (RMSE 1.342). Additionally, a Gradient Boosting Classifier (GBC) is applied for directional prediction, with backtesting on a $10,000 initial capital revealing a 40.82% profitable trade rate, though resulting in a net loss of $20,653.25 over 49 trades. The study analyzes historical trends, showing a decline in BDT/USD rates from 0.012 to 0.009, and incorporates normalized daily returns to capture volatility. These findings highlight the potential of deep learning in forex forecasting, offering traders and policymakers robust tools to mitigate risks. Future work could integrate sentiment analysis and real-time economic indicators to further enhance model adaptability in volatile markets.
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