NSE LLaMA Sentiment Forecast Pipeline

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@sakshitiwari2323

➡️ Build an NSE LLaMA Sentiment Forecast Pipeline Having meticulously evaluated your project, I’m confident it aligns with my expertise in NLP, deep learning, and financial forecasting pipelines. I’m ready to deliver a production-grade end-to-end system using fine-tuned LLaMA for sentiment-driven equity return prediction. ✅ My Blueprint ✔ Data Pipeline ➡️ Ingest NSE PDFs/HTML from cloud storage with automated parsing and chunking ➡️ Clean and preprocess unstructured text for model input ✔ LLaMA Sentiment Model ➡️ Fine-tune LLaMA with sentiment head for numeric scoring and explanation tokens ➡️ Generate high-quality embeddings and vectorized representations ✔ Feature & Forecasting Engine ➡️ Merge sentiment with technical/fundamental features in a feature store ➡️ Train multi-horizon regression model (1–60 days) nightly with PyTorch ✔ Delivery & Deployment ➡️ Deploy FastAPI inference service for real-time predictions and dashboard ➡️ Provide Docker/Compose one-click setup with GPU support and documentation ✅ Solution Framework ➲ Python + PyTorch for LLaMA fine-tuning and forecasting ➲ FastAPI for low-latency inference endpoint ➲ S3 + Feature Store (Feast/Redis) for data management ➲ Docker + Terraform for reproducible deployment ➽ Let’s launch a powerful AI-driven equity forecasting engine with explainable sentiment! Sakshi.V

@citijayamala

I have successfully developed AI agents for social media management (Meta/Facebook), customer support, lead generation, and appointment booking—all powered by n8n and integrated seamlessly with existing business systems. My expertise lies in designing end-to-end automation workflows that combine n8n orchestration with advanced AI models such as OpenAI GPT-4, Claude, LLaMA, and other state-of-the-art LLMs, enabling intelligent, context-aware, and scalable business solutions. Sure, I can handle your project on NSE LLaMA Sentiment Forecast Pipeline. Kindly please connect in chat to discuss. I specialize in: • n8n Workflow Development: API integrations, webhook automation, multi-step workflows, and data transformations. • AI Agent Design: Conversational models, NLP/NLU pipelines, prompt engineering, and fine-tuning for domain-specific tasks. • Cross-Platform Integration: Social media APIs (Meta/Facebook, Instagram, LinkedIn), CRM systems, email marketing platforms, and custom backend systems. • Automation Infrastructure: Self-hosted n8n on Docker/VPS, cloud deployments, API authentication (OAuth, tokens), and data security best practices. • Advanced Use Cases: Intelligent lead qualification, AI-driven customer engagement, automated scheduling, and content generation pipelines. Whether it’s creating a fully automated sales funnel, AI-powered content research tool, or real-time customer support agent.

@Web1devloper

Drawing from my extensive 8+ years of experience in data analytics and science and a strong foundation in Python, I am confident about my ability to deliver a top-notch, production-ready pipeline for your NSE LLaMA Sentiment Forecast project. From Python to TensorFlow or PyTorch as per your preference, I have comprehensive skills across the ML and Data Science spectrum Your requirement for sentiment analysis, fine-tuning models, time-series prediction, feature engineering and deployment all find great synergy with my expertise. My familiarity with working on huge datasets coupled with complex data manipulation will help ensure seamless ingestion,preprocessing process that is essential for generating accurate forecasts. Having created concise inference services on multiple projects (using FastAPI among others), I’m skilled at building systems that provide you with sentiment insights and predictions at your fingertips. Lastly, my passion for extracting meaningful insights from complex data and experience in forecasting business outcomes makes me a holistic fit for your project. With an initial focus on accuracy and reproducibility - using hold-out sets -I'll ensure gilt-edge quality for you. Should this first phase be successful, I'm excited to continue expanding coverage and strive to integrate streaming social data as well. Let’s power up your GPU instance!

@ullahimran914

Hello, I can build the full production-ready pipeline you described: ingesting NSE PDFs and HTML into cloud storage, parsing and cleaning the text, chunking it, and fine-tuning a LLaMA sentiment model that outputs both numeric sentiment scores and explanation tokens. I have strong experience with deep-learning NLP pipelines, financial-text preprocessing, feature engineering, and multi-horizon forecasting models. I will deliver the complete workflow in Python: ingestion, preprocessing, LLaMA fine-tuning, vectorisation, feature store integration, and a nightly training script for the 1, 3, 7, 15, 30 and 60-day return models. I’ll also provide a clean FastAPI inference service plus a one-click deployment setup using Docker or Terraform, along with clear documentation. A couple quick questions to align everything: Do you have any labeled sentiment data available for the LLaMA head? Which cloud platform and GPU instance will you deploy on? Do you prefer PyTorch or TensorFlow for the training stack? I’m ready to start immediately and deliver a reliable, reproducible pipeline.

@sumits840

Hello, I’ve reviewed the complete system design, and I can deliver a production-ready pipeline exactly as outlined—covering ingestion of NSE reports, preprocessing, LLaMA-based sentiment scoring, feature engineering, and multi-horizon return prediction. I will build the full workflow in Python, with clean modular scripts for every stage, nightly retraining, and an inference API that returns sentiment, explanations, engineered features, and 9-horizon forecasts. I am an AI prompt engineer and automation expert with more than 2 years of experience. Currently I am researching on different LLMs by running them locally and focused on fine tuning them. You’ll receive reproducible training runs, hold-out evaluation, a FastAPI service, a dashboard for signals, and one-click deployment via Docker/Terraform. I’ve worked extensively with financial ML, LLM fine-tuning, and multi-output modeling, so the architecture will be accurate, stable, and ready for scaling. I can start immediately and deliver the full pipeline with documentation and deployment guides exactly as specified. We can discuss more about this project. Waiting for your valuable reply. Regards, Sumit Singh

@williamh790

I can deliver a full production-ready Python pipeline for your NSE sentiment-to-returns project. I’ll handle ingestion (PDF/HTML), preprocessing, LLaMA fine-tuning, vectorization, feature engineering, and multi-horizon regression modeling. The stack includes a FastAPI inference service returning sentiment, features, and 1–60 day predictions, plus clear documentation and a one-click Docker deployment. I ensure reproducible results on hold-out sets and can extend to streaming social data in follow-on phases. PyTorch or TensorFlow can be used per your preference.

@CodeniaTechno

Dear , I have carefully reviewed your project requirements for the NSE LLaMA Sentiment Forecast Pipeline and am excited about the opportunity to collaborate. With expertise in Python, deep learning frameworks like PyTorch and TensorFlow, and experience in developing end-to-end ML pipelines, I am well-equipped to deliver the solution you envision. I will create a robust pipeline that seamlessly handles the ingestion, preprocessing, sentiment analysis using LLaMA, feature engineering, and multi-horizon regression model training. The final deliverables will include a concise inference service using FastAPI, comprehensive documentation, and a deployment script for easy setup on your GPU instance. I am confident in my ability to meet your expectations for accuracy and reproducibility, and I am eager to discuss how we can bring your project to life. Let's connect to explore further. Looking forward to the opportunity to work together. Best regards,

@AnuragNagare

Hi, I’m excited to submit my proposal for your project to build a production-ready pipeline for NSE report ingestion, sentiment analysis with a fine-tuned LLaMA model, feature engineering, and multi-horizon equity return predictions. With extensive experience in Python development, deep learning (especially fine-tuning transformer-based models like LLaMA), and building end-to-end ML pipelines, I am confident in delivering a robust, scalable system. I have worked with document parsing, sentiment modeling, and feature store integrations, combined with deployment via FastAPI and Docker, which perfectly matches the requirements you outlined. My approach will cover: Reliable ingestion and preprocessing of NSE PDFs/HTML and additional market text Developing and fine-tuning the LLaMA sentiment head with explanation token output Engineering features by combining sentiment, technical, and fundamental indicators Training a multi-horizon regression model with nightly updates Building a clean inference API service Creating clear documentation and a one-click deployment script (Docker/Compose or Terraform) I am available to start immediately and will ensure reproducible, accurate results tested on hold-out data. Looking forward to discussing the project details and next steps. Best regards, Anurag

@SourceX

Excited to support your vision! This project aligns perfectly with our recent work. We've tackled similar projects where creating a clean, professional, and scalable pipeline was crucial. Proposed Solution: Our team will use Python and the preferred deep learning framework (PyTorch or TensorFlow) to build a robust pipeline. With expertise in data ingestion, preprocessing, model training, and API integration, we ensure accurate sentiment analysis and equity return predictions. Core Expertise: Our team excels in deep learning, natural language processing, API development, and cloud deployment. While we are new to Freelancer, we have extensive off-site experience delivering top-quality projects with proven timelines and communication. Happy to take this to my team - feel free to share any additional needs, there's no obligation. Regards, Neliaan.