Generator

Trading System Blueprinter

Name: Trading System Blueprinter
Author: AI Fin Hub Research

Pick your data source, LLM, broker, storage, risk engine, logger. Get a Mermaid architecture diagram + starter repo scaffold (ZIP). Browser-only. Free.

AI Fin Hub Research Published Apr 20, 2026 Methodology Corrections

Inputs: Configuration
Runtime: Instant
Privacy: Client-side · no upload
API key: Not required
Methodology: Open →

Education · Not investment advice. BaFin/EU framework. Past performance does not indicate future results. Editorial standards Sponsor disclosure Corrections

1 · Pick your stack

Data source

Where your bars / quotes / book events come from.

Broker

Who your orders route through.

LLM layer

Research + decision-support model. BYO keys.

Storage

How you persist bars, trades, decisions, dossiers.

Risk engine

What stops you from blowing up.

Logging + observability

How you see what the agent actually did.

Scheduler

How cadence is enforced.

Stack signature

Alpaca → Claude Opus 4.8 → Alpaca

7 / 7 layers chosen · risk: Fractional Kelly sizer (quarter Kelly cap)

Schedule: launchd (macOS-native) · Storage: DuckDB + Parquet · Logging: Heartbeat JSON + Telegram alerts

2 · Architecture (Mermaid)

flowchart LR
  SCHED["launchd"] -->|tick| DATA["alpaca"]
  DATA -->|bars / book events| NORM[Normalize + persist]
  NORM --> STORE["duckdb"]
  STORE --> RESEARCH["claude-opus-4-8 · price-blind research"]
  RESEARCH -->|proposal + confidence| RISK["fractional-kelly"]
  RISK -->|sized order| EXEC["alpaca"]
  EXEC -->|fills + errors| LOG["heartbeat-json"]
  LOG --> STORE
  LOG -.-> ALERT[Telegram / email]

Paste into any Mermaid-compatible renderer (Notion, Obsidian, GitHub, VS Code preview).

3 · Starter repo scaffold

trading-system/
├── README.md                    # start here
├── .env.example                 # API keys (never commit real values)
├── scripts/
│   ├── fetch-bars.py            # data source → local duckdb
│   ├── research.py              # LLM call (price-blind context builder)
│   ├── decide.py                # risk sizing + decision logger
│   ├── execute.py               # broker call (idempotent)
│   └── heartbeat.py             # health probe + alert
├── data/
│   ├── heartbeat.json           # last-heartbeat timestamp (watchdog reads)
│   ├── circuit.json             # {"paused": false, "reason": ...}
│   └── tickers/                 # per-ticker markdown dossiers
├── memory/
│   └── decisions.jsonl          # append-only decision log
├── plists/
│   └── com.you.trader-*.plist   # launchd-based schedules
├── tests/
│   ├── test_research_no_price_leak.py
│   ├── test_sizing_caps.py
│   └── test_idempotent_orders.py
└── pyproject.toml

Minimal, opinionated layout. Everything non-runtime lives outsidescripts/. Add pyproject / requirements to taste.

Why this shape

· Schedule → data → research → risk → execute → log enforces the ordering that keeps your LLM out of the price decision loop.
· Heartbeat + circuit breaker are first-class so you can spot dead pipelines in under one cycle.
· Append-only decisions.jsonl makes post-mortems possible; any LLM-driven system is unauditable without it.
· Tests cover the three most dangerous failure modes: price leakage, sizing blow-up, duplicate orders.

See methodology for the principle set this generator encodes.

How to use

Step-by-step

Full calculator guide →

1
Pick scope (retail / single-strategy production / multi-strategy fund). The blueprint scales with scope.
2
Pick asset class and frequency. Equity / daily looks different from FX / minute looks different from crypto / tick.
3
Read the architecture diagram and component spec. Each component lists technology recommendations and known failure modes.
4
Download starter templates: docker-compose, Terraform IaC, monitoring dashboards. They're skeletons — production hardening is your responsibility.
5
Reference the compliance section. Even retail strategies need basic recordkeeping; institutional needs more.

For agents

Use in an agent

Same math, same result shape as the UI above — as a static ES module. No HTTP request, no auth, no rate limit.

import { compute } from "https://aifinhub.io/engines/trading-system-blueprinter.js";

Contract: /contracts/trading-system-blueprinter.json Full agent guide →

Glossary references

Terms used by this tool

All glossary →

Questions people ask next

FAQ

What does the blueprint output look like?

Architecture diagram + component spec for a research-to-production trading system: data ingestion, signal generation, risk checks, execution, monitoring, and deploy/rollback. Each component has technology recommendations, key configuration options, and known failure modes. The methodology page shows a sample blueprint.

Is this for institutional or retail?

The default templates target small-team / single-strategy production deployment — too elaborate for a paper-trading hobbyist, simpler than a multi-strategy fund. The tool exposes a 'scope' selector so you can scale up or down. Each scope tier has different recommended infrastructure.

What does it recommend for execution infrastructure?

For up-to-medium-frequency strategies, a broker API (IBKR, Alpaca) is fine. For higher frequency, the tool walks through colocation tradeoffs and direct exchange connectivity. The methodology page makes the cost-vs-latency tradeoff explicit — you don't need colocation unless your strategy actually depends on sub-millisecond latency.

Does it cover compliance and audit logging?

Yes — every blueprint includes a compliance section: trade-blotter requirements, FINRA recordkeeping, audit-log retention. For US retail or hedge fund tier, the methodology page documents the regulatory minimum and recommends production-grade structured logging from day one.

Are there template configs I can copy?

Yes — the blueprinter outputs starter docker-compose files, IaC templates (Terraform), and example monitoring dashboards. They're skeletons, not production-ready, but they save 1-2 weeks of bootstrap. Methodology page links to the template repos.

Related deep dive

All articles →

Read further

Long-form context behind the tool output.

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