We introduce NoxTrader, a sophisticated system designed for portfolio construction and trading execution with the primary objective of achieving profitable outcomes in the stock market, specifically aiming to generate moderate to long-term profits. The underlying learning process of NoxTrader is rooted in the assimilation of valuable insights derived from historical trading data, particularly focusing on time-series analysis due to the nature of the dataset employed. In our approach, we utilize price and volume data of US stock market for feature engineering to generate effective features, including Return Momentum, Week Price Momentum, and Month Price Momentum. We choose the Long Short-Term Memory (LSTM)model to capture continuous price trends and implement dynamic model updates during the trading execution process, enabling the model to continuously adapt to the current market trends. Notably, we have developed a comprehensive trading backtesting system - NoxTrader, which allows us to manage portfolios based on predictive scores and utilize custom evaluation metrics to conduct a thorough assessment of our trading performance. Our rigorous feature engineering and careful selection of prediction targets enable us to generate prediction data with an impressive correlation range between 0.65 and 0.75. Finally, we monitor the dispersion of our prediction data and perform a comparative analysis against actual market data. Through the use of filtering techniques, we improved the initial -60% investment return to 325%.

翻译：我们提出NoxTrader——一个专为投资组合构建与交易执行而设计的复杂系统，其核心目标是在股票市场中实现盈利，特别是追求中长期稳健收益。NoxTrader的学习过程根植于对历史交易数据中宝贵洞察的整合，由于所采用数据集的时序特性，该方法尤其聚焦于时间序列分析。在技术实现上，我们利用美股市场的价格与成交量数据进行特征工程，生成包括收益动量、周价格动量与月价格动量在内的有效特征。选择长短期记忆网络（LSTM）模型捕捉连续价格趋势，并在交易执行过程中实现动态模型更新，使模型能够持续适应当前市场趋势。值得关注的是，我们开发了完整的交易回测系统——NoxTrader，该系统支持基于预测评分管理投资组合，并利用自定义评估指标对交易表现进行全面评估。通过严谨的特征工程与预测目标的审慎选择，我们生成的预测数据相关系数达到0.65至0.75的显著区间。最终，我们监控预测数据的离散度，并与实际市场数据进行对比分析。通过过滤技术的应用，我们将初始-60%的投资收益率提升至325%。