smart wallet trading agent control stack

The problem

Building a Solana trading bot usually focuses on strategy while neglecting the operational safeguards that prevent catastrophic capital loss. Most custom scripts lack the modular control planes, risk circuit breakers, and post-mortem memory needed to survive volatile market conditions.

What it does

• Generates a modular seven-layer architecture covering data ingestion, signal evaluation, and secure execution.
• Defines a comprehensive risk engine with per-trade limits, global drawdown protection, and automated circuit breakers.
• Structures a persistent memory and learning layer to detect and block repeated trade failure patterns.
• Provides detailed schemas for wallet tracking, source scoring, and HSM-based key management.
• Produces ready-to-use YAML configurations and deployment manifests for local or cloud environments.

Frameworks & tools

Solana RPCs, Jupiter Aggregator, Raydium, TypeScript, Rust, Python, PostgreSQL, TimescaleDB, and Docker.

Why this beats prompting it yourself

Standard LLM prompts often overlook the "boring" but critical infrastructure like human-in-the-loop gates and encrypted backup drills. This skill enforces a production-ready blueprint that treats a trading agent like a high-stakes financial system rather than a hobby script.

Use cases

• Architecting a professional-grade Solana MEV or directional trading bot.
• Implementing automated risk management for an existing decentralized fund.
• Designing a self-improving agent that adjusts its own signal weights based on P&L.
• Building a secure dashboard for real-time monitoring of multiple hot/cold wallet clusters.