How do I use Jesse MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Jesse MCP Server Run a backtest for my TrendFollower strategy on BTC/USDT for the last 6 months" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Jesse MCP Server

An MCP (Model Context Protocol) server that exposes Jesse's algorithmic trading framework capabilities to LLM agents.

Status: Feature Complete ✅

All planned features implemented and tested. 32 tools available (17 core + 15 agent).

Quick Start

# Install dependencies pip install -r requirements.txt # Run server (stdio transport) python -m jesse_mcp # Run server (HTTP transport for opencode integration) python -m jesse_mcp --transport http --port 8100

Features

Backtesting - Single and batch backtest execution via Jesse REST API
Optimization - Hyperparameter tuning with walk-forward validation
Monte Carlo Analysis - Statistical robustness testing
Pairs Trading - Cointegration testing and strategy generation
Strategy Management - CRUD operations for trading strategies
Risk Analysis - VaR, stress testing, comprehensive risk reports
Agent Tools - 15 specialized tools for autonomous trading workflows

Architecture

LLM Agent ←→ MCP Protocol ←→ jesse-mcp ←→ Jesse REST API (localhost:9000) ↓ Mock Fallbacks (when Jesse unavailable)

Available Tools (32 Total)

Core Tools (17)

Phase 1: Backtesting

Tool	Description
`backtest`	Run single backtest with specified parameters
`strategy_list`	List available strategies
`strategy_read`	Read strategy source code
`strategy_validate`	Validate strategy code

Phase 2: Data & Analysis

Tool	Description
`candles_import`	Download candle data from exchanges
`backtest_batch`	Run concurrent multi-asset backtests
`analyze_results`	Extract insights from backtest results
`walk_forward`	Walk-forward analysis for overfitting detection

Phase 3: Optimization

Tool	Description
`optimize`	Optimize hyperparameters using Optuna

Phase 4: Risk Analysis

Tool	Description
`monte_carlo`	Monte Carlo simulations for risk analysis
`var_calculation`	Value at Risk (historical, parametric, Monte Carlo)
`stress_test`	Test under extreme market scenarios
`risk_report`	Comprehensive risk assessment

Phase 5: Pairs Trading

Tool	Description
`correlation_matrix`	Cross-asset correlation analysis
`pairs_backtest`	Backtest pairs trading strategies
`factor_analysis`	Decompose returns into systematic factors
`regime_detector`	Identify market regimes and transitions

Agent Tools (15)

Specialized tools for autonomous trading workflows:

Tool	Description
`strategy_suggest_improvements`	AI-powered strategy enhancement suggestions
`strategy_compare_strategies`	Compare multiple strategies side-by-side
`strategy_optimize_pair_selection`	Optimize pairs trading selection
`strategy_analyze_optimization_impact`	Analyze impact of optimization changes
`risk_analyze_portfolio`	Portfolio-level risk analysis
`risk_stress_test`	Advanced stress testing
`risk_assess_leverage`	Leverage risk assessment
`risk_recommend_hedges`	Hedging recommendations
`risk_analyze_drawdown_recovery`	Drawdown recovery analysis
`backtest_comprehensive`	Full backtest with all metrics
`backtest_compare_timeframes`	Compare performance across timeframes
`backtest_optimize_parameters`	Quick parameter optimization
`backtest_monte_carlo`	Backtest with Monte Carlo analysis
`backtest_analyze_regimes`	Regime-aware backtest analysis
`backtest_validate_significance`	Statistical significance validation

Testing

# Run all tests JESSE_URL=http://localhost:9000 JESSE_PASSWORD=test pytest -v # Run single test pytest tests/test_server.py::test_tools_list -v

Status: 12/12 tests passing

Local Development

Prerequisites

Python 3.10+
Jesse 1.13.x running on localhost:9000
PostgreSQL on localhost:5432
Redis on localhost:6379

Start Jesse Stack (Podman)

# Start infrastructure podman run -d --name jesse-postgres --network host \ -e POSTGRES_USER=jesse_user -e POSTGRES_PASSWORD=password -e POSTGRES_DB=jesse_db \ docker.io/library/postgres:14-alpine podman run -d --name jesse-redis --network host \ docker.io/library/redis:6-alpine redis-server --save "" --appendonly no # Start Jesse podman run -d --name jesse --network host \ -v /path/to/jesse-bot:/home:z \ docker.io/salehmir/jesse:latest bash -c "cd /home && jesse run"

Start Dev MCP Server

./scripts/start-dev-server.sh # Start on port 8100 ./scripts/stop-dev-server.sh # Stop server

Add to OpenCode

Add to ~/.config/opencode/opencode.json:

{ "mcp": { "jesse-mcp-dev": { "type": "remote", "url": "http://localhost:8100/mcp", "enabled": true } } }

Documentation

Using with LLMs - How to use with MCP-compatible LLMs
Production Deployment - Production deployment guide
Jesse Setup - Jesse integration setup
Agent System - Agent architecture
AGENTS.md - Development guidelines for AI agents

API Reference

Jesse REST Client

The jesse_rest_client.py module provides direct access to Jesse's REST API:

from jesse_mcp.core.jesse_rest_client import get_jesse_rest_client client = get_jesse_rest_client() # Run backtest result = client.backtest( strategy="OctopusStrategy", symbol="BTC-USDT", timeframe="1h", start_date="2024-01-01", end_date="2024-01-31" )

Mock Implementations

When Jesse is unavailable, all tools gracefully fall back to mock implementations that return realistic synthetic data. This enables development and testing without a full Jesse installation.

Key Dependencies

Package	Version	Purpose
fastmcp	>=0.3.0	MCP server framework
numpy	>=1.24.0	Numerical computations
pandas	>=2.0.0	Data manipulation
scipy	>=1.10.0	Statistical functions
scikit-learn	>=1.3.0	ML utilities
optuna	>=3.0.0	Hyperparameter optimization

Project Structure

jesse_mcp/ ├── server.py # FastMCP server with 17 core tools ├── optimizer.py # Phase 3: Optimization tools ├── risk_analyzer.py # Phase 4: Risk analysis tools ├── pairs_analyzer.py # Phase 5: Pairs trading tools ├── agent_tools.py # 15 agent-specific tools ├── core/ │ ├── integrations.py # Jesse framework integration │ ├── jesse_rest_client.py # REST API client │ └── mock.py # Mock implementations ├── agents/ │ ├── base.py # Base agent class │ ├── backtester.py # Backtesting specialist │ └── risk_manager.py # Risk management specialist └── scripts/ ├── start-dev-server.sh └── stop-dev-server.sh

License

MIT

Jesse MCP Server

Jesse MCP Server

Status: Feature Complete ✅

Quick Start

Features

Architecture

Available Tools (32 Total)

Core Tools (17)

Phase 1: Backtesting

Phase 2: Data & Analysis

Phase 3: Optimization

Phase 4: Risk Analysis

Phase 5: Pairs Trading

Agent Tools (15)

Testing

Local Development

Prerequisites

Start Jesse Stack (Podman)

Start Dev MCP Server

Add to OpenCode

Documentation

API Reference

Jesse REST Client

Mock Implementations

Key Dependencies

Project Structure

License

Resources

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