---
title: SuperAI Forecast
emoji: "📈"
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
---
# SuperAI Forecast

SuperAI Forecast is a FastAPI + Lightweight Charts application for live market analysis with a multi-model AI forecast stack: Kronos, Google TimesFM, and Amazon Chronos.

## Core Features

- multi-source OHLCV data with automatic fallback
- technical indicator calculation
- independent Kronos / TimesFM / Chronos forecasting
- unified OHLC4 forecast contract across all AI models
- real-time WebSocket price streaming
- single-origin frontend served directly from the backend

## Runtime Requirements

- Windows with Python 3.11
- Internet access for market data
- Internet access on the first TimesFM model load if the checkpoint is not cached locally

## Quick Start

```bat
run.bat
```

The launcher validates the virtual environment, ensures the local AI model dependencies are ready, applies the TimesFM compatibility patch, starts the backend, and opens the dashboard automatically.

## Manual Start

```bat
py -3.11 -m venv venv
venv\Scripts\python.exe -m pip install -r requirements.txt
venv\Scripts\python.exe -m backend.launcher
```

## Project Layout

```text
SuperAI Forecast/
|-- backend/
|   |-- main.py
|   |-- frontend_assets.py
|   |-- launcher.py
|   |-- server_runtime.py
|   |-- forecasting/
|   `-- startup_utils.py
|-- frontend/
|   |-- app.js
|   |-- forecast-models.js
|   |-- index.html
|   |-- workspace.js
|   `-- workspace.css
|-- libs/
|   `-- chronos-forecasting/
|-- scripts/
|   `-- patch_timesfm.py
|-- requirements.txt
`-- run.bat
```

## Key Endpoints

- `GET /api/health`
- `GET /api/symbols`
- `GET /api/historical/{symbol}`
- `GET /api/indicators/{symbol}`
- `GET /api/forecast/{symbol}`
- `GET /api/market-status`
- `GET /api/crypto/market`
- `WS /ws/price/{symbol}`

## SuperAI Forecast Contract

- Input to every AI model is a single `OHLC4` series built as `(open + high + low + close) / 4`.
- The recommended context length is `512`, with model-specific fallback windows when the source history is shorter.
- The default horizon is `10`, so each enabled AI model predicts `T+1 ... T+10`.
- Output from each AI model is an independent future `OHLC4` line with quantiles `p10`, `p50`, `p90`.
- API responses expose both `last_close` (market close) and `last_ohlc4` (forecast baseline) so downstream analysis stays explicit and stable.
- Frontend rendering uses one forecast line per model, a shared horizon/timeframe contract, and a versioned asset registry for consistent model/UI alignment.

## Health Expectations

When the system is healthy:

- `/api/health` returns `status: online`
- `timesfm.available` is `true`
- `chronos.available` is `true`
- `kronos.available` is `true`
- each model reports its own readiness state after warmup or after the first forecast
- the frontend loads from the same backend origin

## Troubleshooting

- Install Python 3.11 if it is missing.
- Rebuild `venv` if it was copied from another machine.
- If forecasts fail, check `/api/health` and confirm the target model is available and loaded.
- If market data fails, verify connectivity to Binance, Bybit, CoinGecko, Twelve Data, Finnhub, and Yahoo Finance.