Canonical AI Agent Demos

LumiBot includes three canonical AI agent demo strategies that serve as both reference implementations and end-to-end acceptance tests for agentic backtesting. Each demo uses a real external MCP server, the full built-in tool set, replay caching, and benchmarked tearsheet output.

These are complete, runnable strategies – not snippets. They demonstrate how to backtest an AI trading agent with real external data sources, and they validate that LumiBot’s AI-driven trading strategy backtest pipeline works end to end.

The Three Demos

  • News Sentiment Strategy – event-driven stock selection using news data

  • Macro Risk Strategy – macro regime allocation using economic indicators

  • M2 Liquidity Strategy – liquidity-driven allocation using money supply data

News Sentiment Strategy

This strategy uses the Alpha Vantage MCP server to search for recent US stock news and trade on strong catalysts.

MCP server: Alpha Vantage (https://mcp.alphavantage.co/mcp)

What it demonstrates:

  • External MCP server connected via a single URL

  • Agent-driven stock discovery from news flow

  • Portfolio rotation between opportunities and a defensive parking asset (SHV)

  • No-trade decisions when conviction is weak

  • Replay caching of deterministic backtest runs

  • Trace inspection for tool calls, results, and warnings

What it is useful for:

  • Event-driven AI trading strategies

  • Research agents that compare current holdings to new ideas

  • Validating that the agent reacts to real point-in-time news, not hallucinated data

Macro Risk Strategy

This strategy uses the Smithery-hosted FRED MCP server to read economic data and allocate between TQQQ (risk-on) and SHV (risk-off).

MCP server: Smithery FRED (https://server.smithery.ai/@kablewy/fred-mcp-server/mcp)

What it demonstrates:

  • MCP server with Bearer token authentication via headers

  • Agent discovery of relevant macro indicators (interest rates, inflation, growth)

  • Binary allocation between a leveraged risk asset and a defensive asset

  • De-risking during adverse macro regimes (e.g., 2022 inflation/rate hiking)

  • Built-in DuckDB time-series analysis alongside external data

  • Benchmarked evaluation against SPY

What it is useful for:

  • Macro regime AI trading strategies

  • Concentrated AI strategies where concentration is intentional

  • Validating entry and exit behavior across changing economic conditions

M2 Liquidity Strategy

This strategy uses the same Smithery-hosted FRED MCP server to read money supply and liquidity data and allocate between TQQQ and SHV.

MCP server: Smithery FRED (https://server.smithery.ai/@kablewy/fred-mcp-server/mcp)

What it demonstrates:

  • AI reasoning over macro and liquidity inputs

  • Concentration in a single risk asset when the liquidity thesis is strong

  • Defensive parking when the agent determines liquidity is contracting

  • Long-horizon backtest (2015-2026) with dividend handling

  • Benchmarked tearsheets and trade artifacts

What it is useful for:

  • Long-horizon AI-guided allocation logic

  • Validating defensive-asset behavior over multiple market cycles

  • Checking cashflow accounting and observability in real artifacts

How to Use These Demos

Use the demos for:

  • Prompt design patterns (short system prompts, let LumiBot handle the rest)

  • Strategy lifecycle placement (agent created in initialize(), run in on_trading_iteration())

  • External MCP server wiring (URL-based, no local scripts)

  • Observability and debugging (traces, summaries, warnings)

  • Replay cache validation (warm reruns with zero model calls)

  • Tearsheet interpretation (benchmarked against SPY)

Do not copy them blindly. Instead:

  • Keep the shape that matches your use case

  • Point the MCP server URL at whatever data source you need

  • Write a 2-3 sentence system prompt about your strategy

  • Inspect the trace when the behavior surprises you

What to Inspect After a Run

For each demo, review:

  • The tearsheet and benchmark comparison

  • The trades chart

  • trades.csv and trade_events.csv

  • The agent trace JSON

  • The per-run summary log lines

These artifacts answer:

  • Why did the agent trade (or not trade)?

  • What tools did it call?

  • What evidence did it use?

  • Did the run replay from cache?

  • Were there any observability warnings?