Build a Local RAG Chatbot for Trading Research Using Ollama + Termux (Zero API Cost)

Why a Local RAG Chatbot for Trading Research Most "AI trading assistant" products are black boxes: your notes, strategy docs, and market notes get shipped to a third-party API, billed per token, and stored who-knows-where. For a retail NIFTY trader or a quant researcher, that is the worst of all worlds — you pay continuously, you leak your edge, and you cannot audit what the model actually read. This guide shows how to build a Retrieval-Augmented Generation (RAG) chatbot that runs 100% locally on an Android phone using Termux + Ollama. It ingests your own research (PDFs, markdown notes, option-chain exports) and answers questions grounded only in that data. No OpenAI key. No Anthropic key. No monthly bill. No data leaving the device. OBSERVED: Running ollama run llama3.2 on a mid-range phone inside Termux is slow but usable for document Q&A (3–8 tokens/sec). On a laptop it is smooth. SOURCE: Local testing on Termux 0.118, Ollama 0.3.x, Android 14. DERIVED: For production research volumes, run Ollama on a spare x64 machine and point Termux at it over LAN. What You Will Build A four-part pipeline: Ingest — load your research docs (markdown, PDF, CSV) into chunks. Embed — turn chunks into vectors with a local embedding model. Store — keep vectors in a local file-based index (no server needed). Answer — retrieve top-k chunks and ask a local LLM to answer strictly from them. The whole thing is ~200 lines of Python. No paid APIs. Prerequisites Android phone with Termux installed (F-Droid version, not Play Store). ~2 GB free storage. Basic Python comfort. pkg update && pkg upgrade -y pkg install python clang ffmpeg -y pip install ollama numpy Install Ollama inside Termux: curl -fsSL https://ollama.com/install.sh | sh NOTE: The official install script targets Linux. On Termux you often need the community build. If the script fails, install the ollama package via a Termux-compatible binary or run Ollama on a LAN machine and use ollama serve remotely. Pull a small model and a