Moon Dev's AI Trading Agents System

Expert knowledge for working with Moon Dev's experimental AI trading system that orchestrates 48+ specialized AI agents for cryptocurrency trading across Hyperliquid, Solana (BirdEye), Asterdex, and Extended Exchange.

When to Use This Skill

Use this skill when:

• Working with Moon Dev's trading agents repository

• Need to understand agent architecture and capabilities

• Running, modifying, or creating trading agents

• Configuring trading system, exchanges, or LLM providers

• Debugging trading operations or agent interactions

• Understanding backtesting with RBI agent

• Setting up new exchanges or strategies

Environment Setup Note

For New Users: This repo uses Python 3.10.9. If using conda, the README shows setting up an environment named tflow , but you can name it whatever you want. If you don't use conda, standard pip/venv works fine too.

Quick Start Commands

Activate your Python environment (conda, venv, or whatever you use)

Example with conda: conda activate tflow

Example with venv: source venv/bin/activate

Use whatever environment manager you prefer

Run main orchestrator (controls multiple agents)

python src/main.py

Run individual agent

python src/agents/trading_agent.py python src/agents/risk_agent.py python src/agents/rbi_agent.py

Update requirements after adding packages

pip freeze > requirements.txt

Core Architecture

Directory Structure

src/ ├── agents/ # 48+ specialized AI agents (<800 lines each) ├── models/ # LLM provider abstraction (ModelFactory) ├── strategies/ # User-defined trading strategies ├── scripts/ # Standalone utility scripts ├── data/ # Agent outputs, memory, analysis results ├── config.py # Global configuration ├── main.py # Main orchestrator loop ├── nice_funcs.py # Core trading utilities (~1,200 lines) ├── nice_funcs_hl.py # Hyperliquid-specific functions ├── nice_funcs_extended.py # Extended Exchange functions └── ezbot.py # Legacy trading controller

Key Components

Agents (src/agents/)

• Each agent is standalone executable

• Uses ModelFactory for LLM access

• Stores outputs in src/data/[agent_name]/

• Under 800 lines (split if longer)

LLM Integration (src/models/)

• ModelFactory provides unified interface

• Supports: Claude, GPT-4, DeepSeek, Groq, Gemini, Ollama

• Pattern: ModelFactory.create_model('anthropic')

Trading Utilities

• nice_funcs.py : Core functions (Solana/BirdEye)

• nice_funcs_hl.py : Hyperliquid exchange

• nice_funcs_extended.py : Extended Exchange (X10)

Configuration

• config.py : Trading settings, risk limits, agent behavior

• .env : API keys and secrets (never expose these)

Agent Categories

Trading: trading_agent, strategy_agent, risk_agent, copybot_agent

Market Analysis: sentiment_agent, whale_agent, funding_agent, liquidation_agent, chartanalysis_agent

Content: chat_agent, clips_agent, tweet_agent, video_agent, phone_agent

Research: rbi_agent (codes backtests from videos/PDFs), research_agent, websearch_agent

Specialized: sniper_agent, solana_agent, tx_agent, million_agent, polymarket_agent, compliance_agent, swarm_agent

See AGENTS.md for complete list with descriptions.

Common Workflows

1. Run Single Agent

Activate your environment first

python src/agents/[agent_name].py

Each agent is standalone and can run independently.

2. Run Main Orchestrator

python src/main.py

Runs multiple agents in loop based on ACTIVE_AGENTS dict in main.py.

3. Change Exchange

Edit agent file or config:

EXCHANGE = "hyperliquid" # or "birdeye", "extended"

Then import corresponding functions:

if EXCHANGE == "hyperliquid": from src import nice_funcs_hl as nf elif EXCHANGE == "extended": from src import nice_funcs_extended as nf

4. Switch AI Model

Edit src/config.py :

AI_MODEL = "claude-3-haiku-20240307" # Fast, cheap

AI_MODEL = "claude-3-sonnet-20240229" # Balanced

AI_MODEL = "claude-3-opus-20240229" # Most powerful

Or use ModelFactory per-agent:

from src.models.model_factory import ModelFactory model = ModelFactory.create_model('deepseek') # or 'openai', 'groq', etc. response = model.generate_response(system_prompt, user_content, temperature, max_tokens)

5. Backtest Strategy (RBI Agent)

python src/agents/rbi_agent.py

Provide: YouTube URL, PDF, or trading idea text → DeepSeek-R1 extracts strategy logic → Generates backtesting.py compatible code → Executes backtest, returns metrics

See WORKFLOWS.md for more examples.

Development Rules

CRITICAL Rules

• Keep files under 800 lines - split into new files if longer

• NEVER move files - can create new, but no moving without asking

• Use existing environment - don't create new virtual environments, use the one from initial setup

• Update requirements.txt after any pip install: pip freeze > requirements.txt

• Use real data only - never synthetic/fake data

• Minimal error handling - user wants to see errors, not over-engineered try/except

• Never expose API keys - don't show .env contents

Agent Development Pattern

Creating new agents:

1. Use ModelFactory for LLM

from src.models.model_factory import ModelFactory model = ModelFactory.create_model('anthropic')

2. Store outputs in src/data/

output_dir = "src/data/my_agent/"

3. Make independently executable

if name == "main": # Standalone logic here

4. Follow naming: [purpose]_agent.py

5. Add to config.py if needed

Backtesting

• Use backtesting.py library (NOT built-in indicators)

• Use pandas_ta or talib for indicators

• Sample data: src/data/rbi/BTC-USD-15m.csv

Configuration Files

config.py: Trading settings

• MONITORED_TOKENS , EXCLUDED_TOKENS

• Position sizing: usd_size , max_usd_order_size

• Risk: CASH_PERCENTAGE , MAX_LOSS_USD , MAX_GAIN_USD

• Agent: SLEEP_BETWEEN_RUNS_MINUTES , ACTIVE_AGENTS

• AI: AI_MODEL , AI_MAX_TOKENS , AI_TEMPERATURE

.env: Secrets (NEVER expose)

• Trading APIs: BIRDEYE_API_KEY , MOONDEV_API_KEY , COINGECKO_API_KEY

• AI: ANTHROPIC_KEY , OPENAI_KEY , DEEPSEEK_KEY , GROQ_API_KEY , GEMINI_KEY

• Blockchain: SOLANA_PRIVATE_KEY , HYPER_LIQUID_ETH_PRIVATE_KEY , RPC_ENDPOINT

• Extended: X10_API_KEY , X10_PRIVATE_KEY , X10_PUBLIC_KEY , X10_VAULT_ID

Exchange Support

Hyperliquid (nice_funcs_hl.py )

• EVM-compatible perpetuals DEX

• Functions: market_buy() , market_sell() , get_position() , close_position()

• Leverage up to 50x

BirdEye/Solana (nice_funcs.py )

• Solana spot token data and trading

• Functions: token_overview() , token_price() , get_ohlcv_data()

• Real-time market data for 15,000+ tokens

Extended Exchange (nice_funcs_extended.py )

• StarkNet-based perpetuals (X10)

• Auto symbol conversion (BTC → BTC-USD)

• Leverage up to 20x

• Functions match Hyperliquid API for compatibility

See docs/hyperliquid.md, docs/extended_exchange.md for exchange-specific guides.

Data Flow Pattern

Config/Input → Agent Init → API Data Fetch → Data Parsing → LLM Analysis (via ModelFactory) → Decision Output → Result Storage (CSV/JSON in src/data/) → Optional Trade Execution

Common Tasks

Add new package:

Make sure your environment is activated first

pip install package-name pip freeze > requirements.txt

Read market data:

from src.nice_funcs import token_overview, get_ohlcv_data, token_price

overview = token_overview(token_address) ohlcv = get_ohlcv_data(token_address, timeframe='1H', days_back=3) price = token_price(token_address)

Execute trade (Hyperliquid):

from src import nice_funcs_hl as nf nf.market_buy("BTC", usd_amount=100, leverage=10) position = nf.get_position("BTC") nf.close_position("BTC")

Execute trade (Extended):

from src import nice_funcs_extended as nf nf.market_buy("BTC", usd_amount=100, leverage=15) position = nf.get_position("BTC") nf.close_position("BTC")

Git Operations

Current branch: main Main branch for PRs: main

Recent commits:

• dc55e90: websearch agent

• 921ead6: websearch_agent launched and rbi agent updated

• 6bb55c2: backtest dash

Modified files (current):

• .env_example

• src/agents/swarm_agent.py

• src/agents/trading_agent.py

• src/data/ohlcv_collector.py

Documentation

Main docs (docs/):

• CLAUDE.md : Project overview and development guidelines

• hyperliquid.md , hyperliquid_setup.md : Hyperliquid exchange

• extended_exchange.md : Extended Exchange (X10) setup

• rbi_agent.md : Research-Based Inference agent

• websearch_agent.md : Web search capabilities

• swarm_agent.md : Multi-agent coordination

• [agent_name].md : Individual agent docs

README files:

• Root README.md : Project overview

• src/models/README.md : LLM provider guide

Risk Management

• Risk Agent runs FIRST before any trading decisions

• Circuit breakers: MAX_LOSS_USD , MINIMUM_BALANCE_USD

• AI confirmation for position-closing (configurable)

• Default loop: every 15 minutes (SLEEP_BETWEEN_RUNS_MINUTES )

Philosophy

This is an experimental, educational project:

• No guarantees of profitability

• Open source and free

• YouTube-driven development

• Community-supported via Discord

• No official token (avoid scams)

Goal: Democratize AI agent development through practical trading examples.

Additional Resources

For complete agent list, see AGENTS.md For workflow examples, see WORKFLOWS.md For architecture details, see ARCHITECTURE.md

Built with 🌙 by Moon Dev

"Never over-engineer, always ship real trading systems."