基于机器学习进行智能投资组合优化

Intelligent Portfolio Optimization Based on Machine Learning

近年来,随着投资者数量的不断增加,交易员频繁买卖市场波动性较大的产品,如黄金,比特币用以获取最大的利润。本文研究利用交易市场中货币的历史价格数据来规避投资过程中的风险,预测产品价格的方向,进而获得最大的收益。对于投资者来说,如果他们能有一个好的交易策略,这将给他带来稳定的收入,同时也保证了交易市场的平稳运行和稳定。为了更好的预测货币趋势,本文使用历史数据来预测第二天的货币价格从而构建了LSTM,随机森林,梯度回升树和xgboost模型进行单步预测,通过对比,随机森林模型的预测效果最好。本文利用预测的结果提出了一种基于动态规划来进行投资组合优化的方法,该方法考虑了风险因素对投资组合管理过程的影响,能依据市场状态和资产信息自动转换投资组合优化模式以应对市场风格变化,通过投资组合内部资产与外部资产池动态交易的形式来实时调整投资组合资产构成及资产配置,使得实现理论上的利润最大化。

In recent years, with the increasing number of investors, traders frequently buy and sell products with high market volatility, such as gold, and bitcoin is used to maximize profits. This article studies the use of historical price data of currencies in the trading market to avoid risks in the investment process, predict the direction of product prices, and obtain maximum returns. For investors, if they can have a good trading strategy, this will bring them a stable income, while also ensuring the smooth operation and stability of the trading market. In order to better predict the currency trend, this paper uses historical data to predict the next day’s currency price to construct LSTM, random forest, gradient recovery tree and xgboost model for single-step prediction, through comparison, the random forest model has the best prediction effect. This paper uses the prediction results to propose a method for portfolio optimization based on dynamic programming, which considers the impact of risk factors on the portfolio management process, can automatically convert the portfolio optimization mode according to market conditions and asset information to cope with market style changes, and adjust the portfolio asset composition and asset allocation in real time through the dynamic trading of portfolio internal assets and external asset pools, so as to maximize theoretical profits.