finance

🧠 Curated collection of 214+ best OpenClaw personas — weekly updated by Deskclaw.net from GitHub & community. AI, productivity, dev, marketing, finance & more.

82 ready-to-deploy agents for Microsoft Copilot Chat, no Copilot Premium required. Paste the instruction block into the Copilot Studio builder. Writing, HR, PM, IT ops, Sales, Finance, Engineering and more. RAI reviewed, source-quality labelled, tested. No coding.

Deep Learning in Quantitative Finance: Transformer Networks for Time Series Prediction

GPTStonks is a financial chatbot powered by LLMs and enhanced with data frameworks. It provides natural language conversation capabilities for financial topics, making it an ideal choice for a wide range of financial applications.

This Repository contains the real life use cases of GenAI (LLM+RAG) in Finance Domain. I covers many projects use cases with theory and projects.

Telegram bot wrapping TradingAgents — chat-driven watchlist with parallel multi-ticker analysis, cancellation, and Telegraph reports.

The first Open-Souce RAG-LLM tool to analyse macroeconomic data and forecasts 🦜🔗

[MM 2025] A Multimodal Finance Benchmark for Expert-level Understanding and Reasoning

SentimentArcs: a large ensemble of dozens of sentiment analysis models to analyze emotion in text over time

Local-first AI agent for personal finance

In this dataset, the price of the dollar to the Iranian rial in the years 2011 to 2023 has been collected by our crawler.

Chat interface for OpenBB based on a instruct finetuning of a LLM, similar to ChatGPT.

LLM-powered financial analyst using LoRA-tuned Llama-3 and RAG pipeline to answer complex queries over SEC 10-K filings with contextual accuracy.

Pizzly, financial market analysis combining technical indicators with LLMs, featuring real-time data processing and AI-powered market insights ⚡️

Engineered an advanced deep learning model to automate the classification of financial documents, including Balance Sheets, Cash Flow and Income Statements using Bidirectional LSTM and TensorFlow. The model achieved an impressive accuracy of 96.2%, enhancing efficiency and reducing errors in document management for the finance and banking sectors.