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 demonstrates how to construct a Retrieval-Augmented Generation (RAG) chatbot, which operates entirely on an Android device utilizing Termux and Ollama. The chatbot can ingest research materials such as PDFs, markdown notes, and option-chain exports without requiring an API key or incurring monthly costs. No data is transmitted from the device, ensuring privacy and security.
To begin, the research documents are organized in a "docs/" folder, encompassing markdown files, CSV exports, and PDFs. The script subsequently loads these documents into the system. Next, the text is chunked into smaller segments using a sliding window approach, which preserves context during the split. After chunking, the chunks are embedded using the nomic-embed-text model through Ollama's API, generating local vector representations. These vectors are then saved to a file named "index_vecs.npy" and saved as JSON.
With the local index established, the chatbot is capable of answering questions by retrieving the most relevant chunks and having the local LLM generate responses based solely on those sources. This eliminates the need for external API calls and keeps all operations within the user's device, ensuring data privacy and avoiding continuous costs. The entire process is encapsulated within approximately 200 lines of Python code, requiring only a basic level of Python proficiency.
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