Alpha-GPT 2.0- Human-in-the-Loop AI for Quantitative.pdf
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Alpha-GPT 2.0: Human-in-the-Loop AI for antitative
Investment
Hang Yuan
∗
The Hong Kong University of Science
and Technology
Hong Kong SAR
hyuanak@connect.ust.hk
Saizhuo Wang
∗
The Hong Kong University of Science
and Technology
Hong Kong SAR
swangeh@connect.ust.hk
Jian Guo
†
IDEA Research
China
guojian@idea.edu.cn
ABSTRACT
Recently, we introduced a new paradigm for alpha mining in the
realm of quantitative investment, developing a new interactive al-
pha mining system framework, Alpha-GPT. This system is centered
on iterative Human-AI interaction based on large language models,
introducing a Human-in-the-Loop approach to alpha discovery.
In this paper, we present the next-generation Alpha-GPT 2.0
1
, a
quantitative investment framework that further encompasses cru-
cial modeling and analysis phases in quantitative investment. This
framework emphasizes the iterative, interactive research between
humans and AI, embodying a Human-in-the-Loop strategy through-
out the entire quantitative investment pipeline. By assimilating the
insights of human researchers into the systematic alpha research
process, we eectively leverage the Human-in-the-Loop approach,
enhancing the eciency and precision of quantitative investment
research.
1 INTRODUCTION
A standard quantitative investment pipeline commences with the
mining of alphas [
1
,
2
] that encapsulate valuable market infor-
mation. These alphas function as features in predictive models,
forecasting future stock returns over a specic period. Following
the creation of these models, asset portfolio optimization [
3
] oc-
curs, formulating trading positions and strategies based on the
synthesized alpha. Subsequently, the developed strategy undergoes
comprehensive analysis and rigorous testing before evaluation and
consequent deployment in live trading. This systematic approach,
responsive to new data and market conditions, exemplies a rened
quantitative investment strategy.
Traditionally, the quantitative investment workow is a collabo-
rative process that requires specialized researchers at each stage.
However, due to a lack of experienced and skilled researchers, scal-
ing up the output of quantitative research can be a formidable
challenge. With the advent of technologies like automated alpha
discovery algorithms [
4
,
5
] and AutoML [
6
], many institutions have
begun adopting AI-based automated processes for quantitative
investment research. While this progression marks a signicant
milestone, it also presents notable problems. Primarily, these AI
algorithms are compute-intensive. As research advances, computa-
tional power tends to yield diminishing returns in terms of strategy
performance improvement, making the approach increasingly cost-
inecient.
∗
Work done during internship at IDEA Research.
†
Corresponding author
1
Draft. Work in progress
To address these challenges, we propose a new paradigm for the
quantitative investment research workow, namely Human-In-The-
Loop AI for Quantitative Investment. This innovative approach
combines the market insights, understanding, and experience of
human researchers with the eciencies of an AI-based automated
quantitative investment research system. Through iterative rounds
of collaboration across crucial stages of the research process, this
dynamic interactive approach facilitates the eective discovery of
trading alphas and investment strategies. It leverages the insights
and experience of human researchers to guide the systems such
as in alpha mining and model searching process more eectively.
Simultaneously, it uses the experimental results of the automated re-
search system to continuously inspire human researchers to better
understand nancial markets. This cyclical, multi-round interactive
process allows for ecient and signicantly eective exploration
in quantitative investment research. The proposed paradigm un-
derscores the potential of a synergistic human-AI collaboration in
advancing the eld of AI-based quantitative investment.
To implement this Human-in-the-Loop paradigm for quantita-
tive investment research, we have adopted a Multi-agent architec-
ture, termed Alpha-GPT 2.0. This architecture employs specialized
agents, each trained and developed to excel in distinct segments of
the quantitative investment workow, such as alpha mining, alpha
modeling, and alpha analysis. These agents are powered by large
language models and serve a dual purpose: they eciently drive
and interact with the various modules and tools within the existing
automated quantitative investment algorithms, and they accurately
interpret the research intentions and operational instructions of
human researchers. The agents, each focused on a specic stage of
the workow, connect the whole quantitative investment process
into a research cycle. This cycle includes mining alphas from data,
combining alphas into models, conducting comprehensive analysis
of research results, and based on the understanding and analysis of
these results, informing the direction for the next round of alpha re-
searching. In a innovative approach, these AI agents—each tailored
for distinct stages of the quantitative investment process—form
a collaborative multi-agent system. They synergize to complete
the research cycle. Importantly, at every stage, human researchers
can infuse their insights and ideas into the research cycle through
their interactions with the respective agents. Concurrently, the
system’s feedback on the experiments allows human researchers
to conveniently and comprehensively receive an extensive under-
standing of the experiments results, thereby guiding the direction
of subsequent experiments.
Our contributions in this work can be summarized from these
standpoints:
arXiv:2402.09746v1 [q-fin.CP] 15 Feb 2024
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