AI Finance Oracle Subnet - Bittensor

AI Finance Oracle is a Bittensor subnet (#7) that creates a decentralized competitive marketplace for quantitative financial AI models.

Problem: Institutional-grade predictive models are locked behind hedge funds. Retail traders, DeFi protocols, and smaller funds lack access to state-of-the-art financial intelligence. Existing oracles only fetch historical data — they don't predict.

Solution: Miners compete to build the best AI models for price prediction (60%), sentiment analysis (25%), and risk assessment (15%). Validators verify every prediction against real market data from exchanges like Binance. The most accurate models earn $TAO rewards via Yuma Consensus.

Scoring: Predictions are scored on directional accuracy, magnitude accuracy (MAE), confidence calibration, and latency — with a 1.5x bonus for correct calls in volatile markets.

Key Features:

- 6 specialized AI miners (QuantFlow, AlphaNet, DeepSentiment, RiskForest, etc.)

- 3-4 validators with real-time market data verification

- Interactive web demo with 3 live scenarios (BTC prediction, ETH sentiment, portfolio risk)

- Full REST API with Swagger documentation (20+ endpoints)

- Yuma Consensus with TAO reward distribution

Target Market: DeFi protocols ($100B+ TVL), retail traders, algorithmic trading ($20B+ market), and quantitative researchers.

Tech Stack: Python, FastAPI, Pydantic, Bittensor SDK

Week 1 — Research & Design

- Analyzed existing Bittensor subnets (Taoshi/Prophet) for financial oracle patterns

- Designed miner/validator incentive mechanism with MAE-based scoring

- Wrote SUBNET_PROPOSAL.md with full mechanism specification

Week 2 — Backend Development

- Built FastAPI backend with 20+ API endpoints (miners, validators, network, demos)

- Implemented 3 miner task types: price prediction, sentiment analysis, risk assessment

- Created in-memory database with 8 pre-seeded miners and 3 validators

- Developed scoring engine with weighted multi-factor evaluation

Week 3 — Frontend & Demo

- Built interactive dark-theme web UI with 3 demo scenario cards

- Implemented real-time loading animation simulating subnet broadcast

- Created detailed result views showing miner responses, validator checks, and consensus

- Added Swagger API docs and ReDoc for full API exploration

Week 4 — Polish & Submission

- Tested all 3 demo scenarios end-to-end (BTC prediction, ETH sentiment, portfolio risk)

- Wrote pitch deck, demo video script, and visual guide

- Deployed to GitHub with comprehensive README and judge instructions

- Prepared demo video and pitch presentation

Currently bootstrapped — no external funding raised.

This project was built during the Bittensor Subnet Ideathon as a proof-of-concept. All development has been self-funded by the team.

Future plans:

- Apply for Bittensor subnet registration (requires TAO stake)

- Seek grants from Opentensor Foundation for subnet development

- Explore partnerships with DeFi protocols for integration funding

- Revenue model: Premium API access for institutional-grade predictions

We are open to conversations with investors and partners interested in decentralized financial AI infrastructure.