From Chaos to Code: ALPHALABS

Source: Dev.to

The Problem That Kept Me Up at Night

I wanted to build a platform where anyone could create AI trading agents, backtest strategies, and prove their performance. The challenge was coordinating AI models, real‑time market data, WebSocket streams, and financial calculations.

Discovering Kiro’s specs feature changed everything.

The Inspiration: nof1.ai Meets Algorithmic Trading

Traditional algorithmic trading depends on rigid rules tied to a single trading point.

AI trading is different. Instead of predefined conditions, AI can:

• Analyze multiple indicators
• Consider broader market context
• Adapt to incoming data
• Make nuanced decisions beyond binary checks

AlphaLabs allows users to provide tickers, indicators, and context. The AI uses holistic reasoning rather than single‑condition logic.

Why Kiro Was My Secret Weapon

Specs: The Blueprint That Saved Me

The .kiro/specs directory became the backbone of the project.

Specs covered

• Backend architecture
• Trading engine
• FastAPI migration
• Code quality improvements
• Custom indicator engine

Each spec included:

• Requirements
• Design
• Implementation tasks

This enabled structured development, progress tracking, and consistent architecture.

The Frankenstein Architecture

AlphaLabs combines several components that rarely coexist in one system.

AI + Trading Logic

• OpenRouter integration with multiple AI models
• JSON trading decisions (LONG, SHORT, HOLD, CLOSE)
• Contextual decision‑making
• Retry logic, timeouts, circuit breakers
• Fallback to HOLD

Real‑Time WebSockets

• Live candle streaming
• AI decision logs
• Multiple concurrent sessions
• Heartbeat and reconnection handling

Indicator Engine

• 22+ indicators via pandas‑ta
• Two modes: Monk (RSI + MACD) and Omni (full indicators)
• JSON‑based custom indicator formulas

Position Management & Risk Controls

• Real‑time PnL
• Auto stop‑loss and take‑profit
• Safety mode with –2 % liquidation protection
• Leverage support

Certificate Generation

• PDF certificates
• Shareable PNGs
• Verification codes

AI vs Traditional Algorithms

Traditional Algorithms

• Static rules
• Single trading point focus
• Rigid execution

AI in AlphaLabs

• Contextual reasoning
• Multi‑signal analysis
• Adaptive, flexible logic

The AI reasons using the tickers and indicators you provide, not rigid rules.

Council Mode: When 4–5 LLMs Make Decisions Together

How It Works

1. Query 4–5 LLMs (Claude, GPT‑4, Gemini, DeepSeek, etc.).
2. Each model analyzes the same input data.
3. Each returns a decision and reasoning.
4. Decisions are aggregated via voting or consensus.

The system executes the council’s final decision.

Why It Matters

• Diversity of thought
• Reduced bias
• Higher confidence in decisions

Status

• Backtesting: Fully implemented
• Forward testing: Coming soon

Council Mode enables comparing model reasoning and outcomes across identical data conditions.

Technical Highlights

Backend

• FastAPI with async/await
• PostgreSQL (Supabase)
• WebSockets
• OpenRouter
• pandas‑ta indicators

Frontend

• Next.js 16
• React 19
• TypeScript
• Tailwind CSS + shadcn/ui
• TradingView Lightweight Charts
• Clerk authentication

Features

• Backtests
• Forward tests
• Real‑time streams
• 22+ indicators
• Custom formulas
• Risk controls
• Certificate generation
• Analytics
• Council Mode

The Kiro Advantage

Kiro’s specs provided:

• Structure
• Consistency
• Clear requirements
• Trackable progress
• Living documentation

This prevented the project from becoming chaotic.

Challenges Overcome

AI Response Consistency

Strict JSON validation, retries, and fallback logic.

Real‑Time Synchronization

Timestamped WebSocket events and session IDs.

Performance

Precomputed indicators with caching for O(1) access.

Risk Management

Safety mode, leverage limits, automatic stop‑loss.

Council Coordination

Parallel model requests and consensus algorithms.

What Makes This Special

• Fully functional backtesting and forward testing
• Production‑ready backend architecture
• Real‑time UI
• 22+ technical indicators
• Kiro‑driven development workflow
• AI contextual trading
• Council Mode for multi‑model intelligence

The Numbers

• 20+ backend services
• 22+ indicators
• 100+ endpoints
• Real‑time WebSockets
• Multiple AI models (4–5 model council)
• Comprehensive test coverage

Lessons Learned

• Specs keep complexity manageable
• Async operations are essential
• Error handling must be planned early
• Real‑time systems require careful design
• AI systems need strict schemas
• AI outperforms static rules
• Councils outperform single models

What’s Next

• Council Mode for live forward testing
• Multi‑agent arena battles
• Social sharing and leaderboards
• Advanced analytics (Sharpe ratio, drawdown)
• Paper trading
• Mobile monitoring app

Final Thoughts

Kiro’s specs transformed a complex idea into a structured, trackable development process. AlphaLabs demonstrates the power of contextual AI reasoning over traditional trading logic. Council Mode extends this by combining multiple models for stronger decisions.

Built for the Kiroween Hackathon 2025