mirofish-prediction

name	mirofish-prediction
description	Qualitative scenario prediction via MiroFish swarm simulation. Builds simulated digital worlds from seed data (news, market signals, policy changes), runs thousands of agents with behavioral logic, and produces narrative prediction reports. Use as complement to TimesFM quantitative forecasts for trading and strategy decisions.
license	AGPL-3.0
metadata	{"author":"<your-name>","version":"1.0.0","depends_on":["aster-trading","timesfm-forecasting"],"external_service":true}
tags	["prediction","simulation","trading","strategy","research"]
created	2026-04-04T00:00:00.000Z

MiroFish Scenario Prediction

Overview

MiroFish is a swarm intelligence engine that builds simulated digital worlds from real-world seed data. Thousands of agents with distinct personalities, memory, and behavioral logic interact to produce qualitative scenario predictions.

Complementary to TimesFM: TimesFM gives quantitative price curves with confidence intervals. MiroFish gives qualitative scenario narratives — what happens if a specific event occurs and how it cascades through markets and public opinion.

When to Use

• "What happens if the Fed raises rates?" — scenario simulation
• "Impact of a whale dumping 10K BTC?" — cascading effect analysis
• Public opinion modeling for token launches or policy changes
• Complementing TimesFM forecasts with narrative context
• Any council question in the trading or strategy domain
• Strategic decisions that need "what if" scenario exploration

How to Start MiroFish

# Backend (Python 3.10, port 5001)
source ~/Projects/MiroFish/backend/.venv/bin/activate
cd ~/Projects/MiroFish && python backend/app/main.py

# Frontend (port 3002 — avoid conflict with Agent Orchestrator on 3000)
PORT=3002 npm --prefix ~/Projects/MiroFish/frontend start

Prerequisites: Real API keys in ~/Projects/MiroFish/.env:

• LLM_API_KEY — OpenRouter or Claude API key
• ZEP_API_KEY — Zep Cloud key for agent memory

Integration with Council

When /council is invoked for trading or strategy domains:

1. Phase 0: If MiroFish is running, seed a scenario simulation with the council question
2. Feed the simulation results as additional intelligence to the advisors
3. The Contrarian advisor should specifically challenge MiroFish's assumptions
4. The Executor should reference both TimesFM numbers and MiroFish scenarios

Integration with /plan

When /plan routes a trading or strategy task:

1. Use MiroFish for "what-if" analysis before committing to a trading strategy
2. Compare MiroFish qualitative predictions against TimesFM quantitative forecasts
3. If predictions diverge significantly, escalate to council decision

Cost Warning

Each simulation runs thousands of LLM-powered agents. A single scenario can consume significant API tokens. Use judiciously — prefer TimesFM for routine forecasting, reserve MiroFish for high-stakes scenario analysis.

Combo: Full Prediction Stack

Aster MCP (live market data)
    → TimesFM (quantitative: price curves + confidence intervals)
    → MiroFish (qualitative: scenario narratives + cascading effects)
    → Council (multi-advisor synthesis of both)
    → autoresearch (measure prediction accuracy over time)