AI-powered market regime detection and backtestable strategy generation for BNB Hack Track 2
Tagline: A CoinMarketCap-native AI strategy skill that detects crypto market regimes and emits backtestable trading rules.
RegimeForge is a CoinMarketCap Agent Hub-compatible AI strategy skill built for BNB Hack: AI Trading Agent Edition, Track 2 (Strategy Skills). It reads market data, classifies the market regime using AI inference (NVIDIA NIM), and generates a structured, backtestable trading strategy — complete with explainability and backtest results.
This is not:
- A trading chatbot
- A portfolio tracker
- A news summarizer
- A copy-trading app
This is:
- A regime-aware strategy skill
- A structured strategy spec generator
- A backtestable trading rule engine
- An explainable AI system for crypto trading
- Data Ingestion — Consumes live market data from CoinMarketCap (price, volume, market cap, 24h high/low). Technical indicators (RSI, MACD, funding rate, fear/greed, on-chain flows) are estimated heuristically from available price/volume data when using the live CMC path; full indicator data available in demo mode.
- Signal Computation — Computes deterministic signals across 5 dimensions: momentum, sentiment, volatility, derivatives, on-chain
- AI Regime Classification — NVIDIA NIM classifies the market into one of 6 regimes
- Strategy Generation — Generates a structured strategy spec with quantifiable entry/exit/invalidation rules
- Validation & Critique — Deterministic validator rejects vague rules. AI critique loop identifies weaknesses
- Backtest — Strategy is applied to historical data. Returns, drawdowns, win rates, and equity curves are produced
- Explainability — Signal weights, reasoning, weak points, and thesis invalidators are shown for every output
| Regime | Description | Strategy |
|---|---|---|
| TREND_UP | Bullish momentum, price above MAs | Continuation long with pullback entries |
| TREND_DOWN | Bearish momentum, price below MAs | Continuation short with bounce entries |
| MEAN_REVERT_UP | Oversold bounce setup | Buy the bounce with tight stops |
| MEAN_REVERT_DOWN | Overbought reversal setup | Fade the rally with tight stops |
| HIGH_VOL_BREAKOUT | Volatility expansion with direction | Breakout entries with vol-based stops |
| CHOP | No clear direction | No trade — stay flat |
- TypeScript — Type-safe throughout
- Next.js 15 — Demo web app
- React — UI components
- Tailwind CSS v4 — Styling
- Zod — Schema validation
- NVIDIA NIM — AI inference (LLaMA 3.3 70B)
- CoinMarketCap — Market data source (live API returns price/volume/market cap; technical indicators estimated heuristically)
# Clone
git clone https://github.com/Rohan5commit/regimforge.git
cd regimforge
# Install
npm install
# Environment
cp .env.example .env.local
# Add your NIM_API_KEY to .env.local
# Run
npm run dev| Variable | Required | Description |
|---|---|---|
NIM_API_KEY |
Yes | NVIDIA NIM API key |
CMC_API_KEY |
No | CoinMarketCap API key — live path returns price/volume/market cap only; technical indicators (RSI, MACD, funding rate, etc.) are heuristic estimates. Omit for demo mode with full indicator data. |
NIM_MODEL |
No | NVIDIA NIM model ID. Defaults to meta/llama-3.3-70b-instruct. Override for faster models (e.g. meta/llama-3.1-8b-instruct). |
- Select an asset (BTC, ETH, SOL, BNB, DOGE)
- Toggle AI inference on/off
- Click "Run Skill"
- Inspect regime classification, strategy rules, explainability, and backtest results
/
src/
ai/
nim-client.ts — NVIDIA NIM client with retry logic
backtest/
engine.ts — Backtesting engine with unit-based position tracking
metrics.ts — Technical indicators (SMA, RSI, ATR)
scenarios.ts — Synthetic data generation for backtests
data/
adapters.ts — Market data adapter layer
cmc-client.ts — CoinMarketCap API client
lib/
utils.ts — Shared utilities
orchestration/
runner.ts — Full orchestration pipeline
critique-loop.ts — AI strategy critique and regeneration
regime/
classifier.ts — Regime classification engine
classifiers.ts — Zod schemas for strategy spec, market context, results
features.ts — Feature extraction from market data
validators.ts — Deterministic rule validation
skill/
cmc-skill.ts — CoinMarketCap Agent Hub skill interface
parser.ts — Skill input/output parsing
prompts.ts — Structured AI prompts (regime, strategy, critique)
ui/
charts.tsx — Equity curve and chart components
inspectors.ts — Output inspectors for regime, strategy, backtest
app/
page.tsx — Main demo page
layout.tsx — Root layout
globals.css — Global styles (Tailwind v4)
api/skill/route.ts — Skill execution API endpoint
architecture/ — Judge-facing architecture page
__tests__/
backtest.test.ts — Backtest engine tests
regime.test.ts — Regime classification tests
schemas.test.ts — Schema validation tests
validator.test.ts — Rule validation tests
docs/ — Documentation
README.md
- Architecture — System architecture overview
- Strategy Methodology — Regime classification and trading logic
- CMC Skill Interface — Input/output schemas and API
- Demo Script — 2-3 minute judge walkthrough
- Setup Guide — Installation and deployment
- Prompts Used — AI prompt documentation
- Build Log — Development journal
- Judging Hook — Why this should win
- Credits — Acknowledgments
- Demo uses synthetic historical data for backtesting (not live CMC historical data)
- AI inference requires NVIDIA NIM API key
- No live trading execution (Track 2 deliverable is a strategy spec, not an execution stack)
- Regime classification is approximate — not financial advice
- Real CMC historical OHLCV data for backtesting
- Multi-asset correlation analysis
- Portfolio-level strategy aggregation
- Live signal monitoring and alerting
- Integration with on-chain execution layers
- Community strategy sharing and rating
MIT