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Specialized documentation, mathematical formulations, and empirical backtesting frameworks for ai & machine learning.
- Application of artificial intelligence and large language models in finance and trading
1. A brief history of the development of LLM
The papers selected for this article span a variety of asset classes and research directions, including but not limited to the following key areas:
In the world of hedge funds, the key to making money is speed, accuracy, and stability. With the emergence of generative AI (genAI) and large language models (LLMs) dramatically accelerating the proce...
In this article we will focus on:
Hello everyone, today I want to talk to you about DeepSeek, which has recently attracted global attention. This Chinese AI research laboratory, founded in 2023 and founded by Liam Wenfeng, is launchin...
In today's increasingly complex financial markets, efficient sell-side securities research often requires the support of automated tools. However, many existing AI solutions only focus on technical in...
In addition, as application scenarios expand from a single target to multiple financial tasks, the influencing factors in financial decision-making will also become complex and diverse depending on th...
TradeMaster is an open source platform for quantitative trading (QT), which fully integrates reinforcement learning (RL) technology into the entire quantitative trading process. From data preparation,...
In recent years, multi-agent collaboration driven by large language models (LLM) has made significant progress in automated decision-making and problem solving. However, in the financial field, most r...
This article introduces a new quantitative investment framework, aiming to mine and optimize the Alpha factor in stock investment strategies through the collaboration of Large Language Models (LLMs) a...
\text{Signal}_{i,t} = \frac{\text{AccountingVar1}_{i,t}}{\text{AccountingVar2}_{i,t}}
To better understand the community's AI + quant projects, we explore here a novel AI-assisted workflow designed to leverage ChatGPT o1 to automatically generate code from quantitative finance papers a...
To help readers better understand quantitative trading in the age of AI and explore the community's AI + quant projects, we introduce here the various possible integrations of ChatGPT with quantitativ...
In stock investing, news sentiment often has a significant impact on stock prices. Real-time monitoring and analysis of news can help us gauge market sentiment and optimize investment decisions. This...
自2022年末ChatGPT亮相以来，其强大的功能和潜力让世界为之震撼，预示着大型语言模型将引领社会变革。尽管这一反应略显仓促，但它真实地反映了人工智能领域的飞速进步及其应用的广泛性。虽然有人担忧这项技术可能威胁到就业，但已有创新者利用GPT开发新产品，并将其融入现有服务中。这些产品迅速获得市场青睐，为全球带来了巨大的价值。
生成式人工智能（Generative AI）正在深刻改变量化交易的格局。本章节将系统性地介绍 生成式AI在量化交易中的应用，帮助读者了解这一前沿领域，比如AI Agent, RAG, 大语言模型等。
In the training stage, noise is added to the picture, and the noise-added picture is input to the network. What the network needs to predict is the added noise.
注意力机制是Transformer的灵魂所在。它不再依赖序列顺序，而是让模型在任意时刻参考上下文中所有位置的词语。
parser.add_argument("--batch_size", type=int, default=32)
In recent years, with the rapid development of deep learning and natural language processing, large language models (LLMs) have demonstrated unprecedented potential in multilingual text understanding,...
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在快速变化的金融市场中，如何用人工智能来助力投资决策正成为越来越多投资人的关注重点。利用大模型进行股票分析和新闻洞察，不仅能让我们高效获取关键数据，还能生成更具参考价值的投资建议。本文将带你一步步搭建两个实用场景：