Open Finance@Columbia
@Ai4Finance
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MOF, MOT, FinGPT, FinRL | Open Finance @ Columbia University | Academic member of Linux Foundation, PyTorch, LF AI & Data, FinOS, CRAFT
New York City
Joined January 2021
FinRL Contests: Benchmarking Data-driven Financial Reinforcement Learning Agents.
arxiv.org
Financial reinforcement learning (FinRL) is now a practical paradigm for financial engineering. However, applying RL strategies to real-world trading tasks remains a challenge for individuals, as...
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Plz use your best judgement before real-life trading!
This Free AI Made a Trader $1.2 Million!! That’s exactly what happened when a Reddit trader used FinGPT — a free, open-source AI that scans real-time news, earnings reports, and tweets — to turn $280K in a week… & $1.2 million in 3 months. #FinGPT #AITrading #FreeAI #AlgoTrading
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«Финуслуги» от Мосбиржи представил обновленную версию FinGPT — своего инвестиционного ИИ-помощника. Он может предоставлять инвесторам информацию о происходящем на российском рынке, транслировать биржевые котировки, объяснять финансовые термины и тонкости… https://t.co/exdBEMBqPN
forbes.ru
«Финуслуги» от Мосбиржи представили обновленную версию FinGPT — своего инвестиционного ИИ-помощника. Он может предоставлять инвесторам информацию о происходящем на российском рынке, транслировать...
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Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges.
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FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets.
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Parallel Market Environments for FinRL Contests
arxiv.org
Financial reinforcement learning (FinRL) is now a practical paradigm for financial engineering. However, applying RL strategies to real-world trading tasks remains a challenge for individuals, as...
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💡FinRL: A Python package for Financial Reinforcement Learning Let's explore: 🧵
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Parallel Market Environments for FinRL Contests.
arxiv.org
Financial reinforcement learning (FinRL) is now a practical paradigm for financial engineering. However, applying RL strategies to real-world trading tasks remains a challenge for individuals, as...
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FinRL Contest 2025 starts! Welcome to explore
open-finance-lab.github.io
Website for the FinRL Contest 2025.
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🚀 Day 5 of #OpenSourceWeek: 3FS, Thruster for All DeepSeek Data Access Fire-Flyer File System (3FS) - a parallel file system that utilizes the full bandwidth of modern SSDs and RDMA networks. ⚡ 6.6 TiB/s aggregate read throughput in a 180-node cluster ⚡ 3.66 TiB/min
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🚀 Day 4 of #OpenSourceWeek: Optimized Parallelism Strategies ✅ DualPipe - a bidirectional pipeline parallelism algorithm for computation-communication overlap in V3/R1 training. 🔗 https://t.co/GBtxSvWLT4 ✅ EPLB - an expert-parallel load balancer for V3/R1. 🔗
github.com
A bidirectional pipeline parallelism algorithm for computation-communication overlap in DeepSeek V3/R1 training. - deepseek-ai/DualPipe
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🚀 Day 3 of #OpenSourceWeek: DeepGEMM Introducing DeepGEMM - an FP8 GEMM library that supports both dense and MoE GEMMs, powering V3/R1 training and inference. ⚡ Up to 1350+ FP8 TFLOPS on Hopper GPUs ✅ No heavy dependency, as clean as a tutorial ✅ Fully Just-In-Time compiled
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🚀 Day 2 of #OpenSourceWeek: DeepEP Excited to introduce DeepEP - the first open-source EP communication library for MoE model training and inference. ✅ Efficient and optimized all-to-all communication ✅ Both intranode and internode support with NVLink and RDMA ✅
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🚀 Day 1 of #OpenSourceWeek: FlashMLA Honored to share FlashMLA - our efficient MLA decoding kernel for Hopper GPUs, optimized for variable-length sequences and now in production. ✅ BF16 support ✅ Paged KV cache (block size 64) ⚡ 3000 GB/s memory-bound & 580 TFLOPS
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Great job!
Just open-sourced PrimoGPT – an automated trading system using Deep Reinforcement Learning (DRL) & LLMs! Built as part of my PhD dissertation, inspired by FinRL & FinGPT. It features an RAG-based approach for generating trading signals. 🔗 Repo: https://t.co/ZjVbg4j5oS It's
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🚀 Day 0: Warming up for #OpenSourceWeek! We're a tiny team @deepseek_ai exploring AGI. Starting next week, we'll be open-sourcing 5 repos, sharing our small but sincere progress with full transparency. These humble building blocks in our online service have been documented,
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FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents.
arxiv.org
This paper presents a novel risk-sensitive trading agent combining reinforcement learning and large language models (LLMs). We extend the Conditional Value-at-Risk Proximal Policy Optimization...
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FinRLlama: A Solution to LLM-Engineered Signals Challenge at FinRL Contest 2024.
arxiv.org
In response to Task II of the FinRL Challenge at ACM ICAIF 2024, this study proposes a novel prompt framework for fine-tuning large language models (LLM) with Reinforcement Learning from Market...
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Transparency, reproducibility, and openness are key to advancing AI. 🌐 The Model Openness Framework by LF AI & Data’s GenAI Commons offers a structured, tiered approach to evaluate ML models. Align with open science principles! 📄 Download the MOF Spec: https://t.co/gkXQayRn4h
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