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ALGO/DISCRETIONARY TRADING INCL . ALTERNATIVE DATA  \nBY PRABHU RAMAMOORTHY, NVIDIA FINANCIAL SERVICES & TECHNOLOGY TEAM CFA, FRM, CAIA  \nNVIDIA AI LEADER FOR LLM/GENERATIVE AI  \n2  \nAPPLICATION  \nFRAMEWORKS PLATFORM  \nNVIDIA HPC  \nNVIDIA AI ENTERPRISE  \nNVIDIA  \nOMNIVERSE  \nSYSTEM SOFTWARE  \nACCELERATED HARDWARE  \nRTXUCF  \nRTX  \nGPU  \nDOCA  \nDGX  \nCPU  \nMAG  \nHGX  \nDPU  \nCUDA-X  \nBASE CMD  \nEGX OVX  \nNIC  \nFLEET CMD  \nSUPER POD  \nSWITCH  \nPHYSX AERIAL  \nAGX  \nSOC  \nFull Stack-Software + Hardware  \nData Center Scale  \n(Cloud/Onprem)  \n2,700 Accelerated Applications  \n450 SDKs, AI Models  \nWHY LLMs/GENERATIVE AI in Capital Markets  \nUsed in Systematic Trading/Discretionary Trading  \nMcKinsey reports -Advanced analytics in asset management beyond the buzz,  Generative AI is here to change biz  \n\n| \u003Cbr>Problem statementStaying on Top of Market\u003Cbr>Results and Consequences\u003Cbr>Industry Needs & Solutions |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n| ▪ Extremely Volatile Market |  | ▪Lower Sharpe -> Redemptions |  | Needs |  |\n| ▪Market signals are viral – |  | ▪Downside Risk - Failures such |  | ▪Continuous Real time exposure |  |\n| Behavioral finance is hard |  | as Credit Suisse, SVB, Banks, |  | ▪Unstructured -> Alpha/Beta |  |\n| ▪Unstructured potential is not |  | Melvin Capital HF (Game stop) |  | Solutions |  |\n| tapped fully |  | ▪ Upside - Unable to generate excess returns/beat market |  | ▪NLP Older -> Newer accuracy\u003Cbr>▪ Early Warning Indicators |  |\n| \u003Cbr>Step 1 – Use Unstructured Data – Alternative data\u003Cbr>Alpha, Beta\u003Cbr>Step 2-Generate Investment/Trading Signals with LLMs\u003Cbr>Step 3- Knowledge Metrics Aggregated by Ticker & Themes\u003Cbr>Step 4-Downstream uses – Investing/Trading Indices, Systematic/Discretionary\u003Cbr>3\u003Cbr>|  |  |  |  |  |\n\nLeverage Accelerated workloads in Capital Markets for you  \nOne Stop Platform -Not only LLMs but use it with Quant Finance, ETL/ML, DL Algos  \nAI (Neural nets) – LLMs/Generative AI/others  \nQuant Finance Aka HPC  \nData Processing ETL/ML (Non neural nets)  \n• AI Unstructured Data using NLP with LLMs, Other Systematic Trading Algos  \n• Framework –PyTorch/TensorFlow, NVIDIA NeMo LLM, NVIDIA RIVA  \n• Pricing, Risk (MC Sim, Margin, FRTB, CVA, SIMM, XVA) & Back testing  \n• Framework – CUDA C/C++, Parallel Algorithms C++, NVIDIA Accelerated Python – RAPIDS, Open ACC  \n• Feature Engineering, Data Prep, & Data Science (e.g. , XGBOOST)  \n• Framework – NVIDIA Accelerated Python - RAPIDS, Spark on GPU  \nForrester Wave ™ : AI Infrastructure, Q4 2021- “NVIDIA’s DNA is in every other AI infrastructure solution we evaluated. It’s an understatement to say that NVIDIA GPUs are synonymous with AI infrastructure.”  \n4  \nHow to Get Started With LLMs/Generative AI at your firm  \nGenerate your AI ROI  \nMcKinsey Modelling Impact of AI-Front-runner companies could “Double their cash flow”  \nor  \nNVIDIA LLM Nemo Foundry  \nOn DGX Cloud  \nNVIDIA LLM Nemo  \ntuning, guard  \n– Prompt rails  \nTest/Deploy on Platform of choice  \nNLP- News, Docs Sentiment & Q/A Alt Data  \nLLM/Generative-> AI ROI  \n• Step 1: Workshops to prioritize,& define the problem statement, success criteria in  \nLLM/Generative AI (2-4 hours)  \n• Step 2: Leverage NVIDIA LLM NeMo Foundry on cloud to prototype with your proprietary data (2-3 weeks)  \n• Step 3: Test & document (1 weeks)  \n• Step 4: Present findings & ROI to key stakeholders (1 week)  \nWhat Would an LLM/Gen AI Journey Look Like?  \nTHANK YOU","cbCaictIrMz9su2l","https://ap.wps.com/l/cbCaictIrMz9su2l","pdf",982508,"English","# Application frameworks platform\n## NVIDIA HPC\n## NVIDIA AI Enterprise\n## NVIDIA Omniverse\n## System software and accelerated hardware\n# Why LLMs/Generative AI in capital markets\n## Staying on top of market: problems and consequences\n## Industry needs and solutions\n# Workflow for LLM-driven trading\n## Step 1: Use unstructured alternative data\n## Step 2: Generate trading signals with LLMs\n## Step 3: Aggregate knowledge metrics by ticker and themes\n## Step 4: Downstream investing and trading uses\n# Getting started and measuring AI ROI\n## Workshops and problem definition\n## Prototype with NVIDIA LLM NeMo Foundry\n## Test, document, and present ROI","[{\"question\":\"How does the document position LLMs and generative AI for capital markets trading?\",\"answer\":\"It explains that LLMs and generative AI can convert unstructured alternative data into actionable investment and trading signals, supporting both systematic and discretionary trading use cases.\"},{\"question\":\"What problems in the market does the framework address?\",\"answer\":\"It highlights extreme market volatility, viral market signals, difficulty in behavioral-finance signal extraction, and challenges in downside risk and generating excess returns.\"},{\"question\":\"What are the recommended steps to get started at a firm?\",\"answer\":\"The document proposes workshops to define the problem and success criteria, prototyping with NVIDIA LLM NeMo Foundry on cloud using proprietary data, then testing/documenting and presenting findings and ROI to stakeholders.\"}]","STAC New York - LLM/Generative AI for Systematic and Discretionary Trading - NVIDIA | PDF",15]