FusionFinance
Vercel
Unicorn Track
In this project, I used both LLMs and ML trading algorithms. The reason for this is that while some pure-LLM frameworks can achieve a high level of return, such as the TradingAgents framework, this is highly volatile. Given that the LLMs were predicting periods of time that were outside of their knowledge cutoff, the Sharpe ratio was only around 0.27, much lower than the S&P 500. On the other hand, ML algorithms, which are widely used by quant firms, seem more reliable, but they simply cannot make qualitative inferences, such as looking for signals like sentiment. So, I brought the two together in layers, ultimately beating the S&P 500 (in a stable and reproducible manner).
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