starchild-ai-agent/@1247/trade-simulator

🐟 Trade Simulator (MiroFish Architecture)

Multi-agent scenario analysis for traders. Not a spreadsheet — a behavioral simulation. Built on MiroFish's swarm intelligence architecture, adapted from social simulation to market simulation.

MiroFish Integration

This skill implements MiroFish's 5-stage prediction pipeline, replacing social media environments with financial markets:

| MiroFish Stage | Original (Social) | Our Adaptation (Markets) | |---|---|---| | 1. Graph Construction | Zep knowledge graph from news/docs | Market State Graph from live Coinglass/HL data | | 2. Environment Setup | Twitter/Reddit agent profiles | Market participant profiles (Whale, MM, Retail, etc.) | | 3. Simulation | OASIS dual-platform social interaction | Round-based market interaction with LLM reasoning | | 4. Report Generation | ReACT report with Zep tools | ReACT report with market data tools | | 5. Deep Interaction | Interview any social agent | Interview any market participant |

Key MiroFish Patterns Used

1. LLM-driven agent reasoning (from oasis_profile_generator.py) — agents don't use if/else rules. Each agent has a persona prompt and "thinks" each round via LLM call
2. Simulation config auto-generation (from simulation_config_generator.py) — describe scenario in natural language, LLM generates agent roster, parameters, event timeline, activity patterns
3. ReACT report generation (from report_agent.py) — multi-step reasoning with tool use: plan outline → generate sections → cite evidence → synthesize predictions
4. Post-simulation interviews (from zep_tools.py Interview system) — chat with any agent after simulation to understand their reasoning
5. Knowledge graph backbone — entities, relationships, and facts structured for agent retrieval (we use in-memory graph instead of Zep Cloud)

What We Don't Use

• ❌ OASIS / camel-ai (social media simulation runtime — irrelevant to markets)
• ❌ Zep Cloud (replaced with local in-memory knowledge graph)
• ❌ Flask frontend (we output to agent conversation)
• ❌ Twitter/Reddit environments (replaced with market environment)

Architecture

skills/trade-simulator/
├── SKILL.md                          # This file
└── scripts/
    ├── mirofish_engine.py            # Core engine — 5-stage pipeline
    ├── market_graph.py               # Stage 1: Market state graph builder
    ├── profile_generator.py          # Stage 2: LLM agent profile generation
    ├── simulation_runner.py          # Stage 3: Round-based market simulation
    ├── report_agent.py               # Stage 4: ReACT report generation
    └── interview.py                  # Stage 5: Post-sim agent interviews

Usage

Quick Scenario Analysis

Agent: "Run a trade simulation: What happens to my BTC short if ETF inflows spike 500%?"

The engine will:

1. Build market state graph from live data (OI, funding, liquidations, whale positions)
2. Auto-generate 5-8 market participant agents calibrated to current conditions
3. Run 6-round simulation where each agent LLM-reasons about their actions
4. Generate ReACT analysis report with turning points, cascade analysis, recommendations
5. Offer interactive interviews with any simulated agent

Supported Scenarios

• Directional shocks: "What if BTC pumps/dumps 10-20%?"
• Catalyst events: "What if ETF inflows spike?" / "What if Tether depegs?"
• Market structure: "What if funding goes extreme?" / "What if OI doubles?"
• Portfolio stress: "How does my portfolio react to a black swan?"

Interview Mode

After any simulation:

Agent: "Interview the whale agent — why did they cover at round 4?"
Agent: "Ask the market maker about their liquidity decision"

Data Sources (Live)

| Data | Tool | What It Feeds | |---|---|---| | Open Interest | cg_open_interest() | Market leverage state | | Funding Rates | funding_rate() | Positioning sentiment | | Liquidation Levels | cg_liquidations() | Cascade trigger points | | Whale Positions | cg_hyperliquid_whale_positions() | Whale agent calibration | | Long/Short Ratios | long_short_ratio() | Crowd positioning | | Orderbook Depth | hl_orderbook() | MM agent calibration | | ETF Flows | cg_btc_etf_flows() | Institutional flow context | | Price/OHLC | cg_ohlc_history() | Price context | | Social Sentiment | lunar_coin() | Retail agent behavior |

Workflow

1. Collect live market data using tools above
2. Run simulation: python3 skills/trade-simulator/scripts/mirofish_engine.py
   • Pass market data + scenario + user positions as JSON
   • Engine runs all 5 MiroFish stages
   • Returns structured results (agent actions, report, interview-ready state)
3. Present results with key insights, PnL impact, risk warnings
4. Offer interviews — user can interrogate any agent