Large Language Models (LLMs) can be leveraged to assist in solving complex investment problems. However, the investment decisions generated by existing LLMs often deviate from real-user data. One method to align LLMs with investor decision-making processes is Supervised Fine-Tuning (SFT), which requires a substantial amount of real-user data that is costly to collect and raises concerns about privacy and security. In this work, we propose InvestAlign, an efficient method that constructs large-scale SFT training datasets based on the theoretical solution to a similar and simpler optimal investment problem, rather than the original complex one. We theoretically demonstrate that fine-tuning LLMs with these datasets leads to faster parameter convergence compared to using real-user data. By fine-tuning LLMs, we obtain InvestAgents, which align more with real-user data than pre-SFT LLMs in both the simple and original complex problems. This highlights InvestAlign as a promising approach with the potential to address complex optimal investment problems and align LLMs with investor decision-making processes in economics and finance.