echolon 0.1.2

See what others can't. Market-agnostic quantitative trading engine for futures backtesting, optimization, and deployment.

Echolon

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An LLM-agent-native backtest framework for futures research. Ships an MCP server, 22 in-package skills, 32 catalogued error codes, and typed Pydantic configs — agents call structured tools instead of guessing API shapes from prose docs. End-to-end on SHFE daily futures.

Production engine inside Qorka, DolphinQuant's AI-native strategy generation product. Exercised by real money on SHFE every trading day.

Quickstart

Three commands cover the natural newcomer arc:

Command	Purpose	Time
echolon hello	Quick demo. Downloads SHFE aluminum (last 2y) via akshare, scaffolds a strategy, runs a backtest. Network required.	~30s
echolon init <workspace> --market SHFE --instrument <i> --start <d> --end <d> --template <t>	Start a real project. Downloads market data via akshare (free, no signup), scaffolds a strategy from a template, writes a workspace marker.	~1–5 min
echolon backtest single <strategy_dir> [--json]	Iterate after editing. Walks up to recover ctx from the workspace marker, recomputes indicators, runs the backtest. No flags needed.	~5–10s

pip install echolon
mkdir -p ~/echolon-playground && cd ~/echolon-playground
echolon hello                  # 30-second demo

echolon hello downloads ~2y of aluminum data, scaffolds the momentum_breakout template, writes .echolon-workspace.json, and runs the backtest. Open ./echolon-hello/strategy/baseline/entry.py, tweak a parameter, then re-run with echolon backtest single ./echolon-hello/strategy/baseline/ to see how the Sharpe shifts.

Three templates ship in-package — minimal, momentum_breakout, rsi_mean_reversion. echolon examples --list shows them; pass --template <name> to echolon init / echolon hello to start from one.

If pip install fails on Linux ARM64 / Alpine / FreeBSD, run echolon doctor — it diagnoses ta-lib's C library, the only dependency that may need source-building outside the standard prebuilt-wheel platforms (Linux x86_64, macOS x86_64+arm64, Windows x86_64; Python 3.11–3.12).

Drive it from your agent

pip install echolon                                # 1. install
claude mcp add -s user echolon -- echolon-mcp      # 2. register MCP server (user-wide)
# 3. restart Claude Code to load mcp__echolon__* tools

Then ask:

"Build a trend-following strategy on copper, backtest 2018–2024."

Behind the scenes the agent calls list_skills → picks patterns and quick_start → load_template("momentum_breakout") → list_indicators(has_lookback=True) → edits entry.py and exit.py → loops validate_strategy_full(strategy_dir) until everything passes → runs the backtest. If anything breaks, it parses [CODE-NNN] from the traceback and calls get_error_doc(code). There's no point where it has to guess.

Runtime	Setup
Claude Code	claude mcp add -s user echolon -- echolon-mcp
Cursor	In ~/.cursor/mcp.json add an entry under mcpServers: "echolon": {"command": "echolon-mcp", "args": []}
OpenAI Codex CLI	codex mcp add echolon -- echolon-mcp (writes [mcp_servers.echolon] to ~/.codex/config.toml)
OpenAI Agents SDK (Python)	MCPServerStdio(name="echolon", params={"command": "echolon-mcp", "args": []})
LangChain / LangGraph	langchain-mcp-adapters: MultiServerMCPClient({"echolon": {"transport": "stdio", "command": "echolon-mcp", "args": []}})
Any other MCP-compatible client (CrewAI, AutoGen, …)	Configure it as a stdio server with command="echolon-mcp", no args. See your client's MCP docs for the call shape.

For Claude Code: -s user makes the registration apply across all your projects (drop it for current-project-only); -- separates the registration name from the launch command. After running once, claude mcp list should show echolon as a connected stdio server. The agent's orientation guide is llms.txt — also dropped at the workspace root by echolon init / hello so an agent walking into the project finds it without needing the package.

What's in scope today

Done end-to-end (production-grade, exercised daily):

• SHFE daily futures research — data ingestion, 214-indicator catalog, Backtrader execution, Optuna TPE optimization (single + multi-objective), walk-forward analysis with deployment-readiness scoring, KMeans-based robust trial selection.
• Agent surface — 23 MCP tools, 22 skills, 32 error codes, 3 working templates.

Not yet (open an issue if you want to drive a slice forward):

• SHFE intraday backtesting — data pipeline ready, engine plumbing being firmed up.
• Live trading via MiniQMT — clean public release in progress.
• Crypto perpetuals (CCXT adapter scaffolded), CME futures, equities.
• Optuna alternatives (no grid, no random, no Bayesian-budget search), distributed orchestration, Python ≤ 3.10.
• Pre-1.0 — public API may change between minor versions. Breaking changes documented in CHANGELOG.md.

Bring your own data

If you already have raw SHFE XLS files (downloaded from shfe.com.cn), run SHFEFileDayExtractor directly instead of using akshare. For other formats (broker CSV, tushare, custom DB), three files must end up under {workspace}/workspace/data/market_data/SHFE/{instrument}/:

File	Schema
sort_by_contract/{contract}.csv	contract, date, prev_close, prev_settlement, open, high, low, close, settlement, price_change, settlement_change, volume, turnover, open_interest
sort_by_date.csv	Same columns, all rows concatenated and sorted by date.
trading_calendar.csv	date, is_trading_day (boolean).

Plus under {workspace}/data/SHFE/{instrument_code}/ (note the SHORT code, e.g. al not aluminum):

File	Schema
main_contract.csv	date, main_contract where main_contract is the contract code with .SF suffix (e.g. al2401.SF). One row per change-of-main-contract date.

Echolon does not auto-derive main_contract.csv from raw OHLCV — it's a USER input that encodes your roll convention (rules based on volume, open interest, or days to expiry). For SHFE via akshare, echolon init derives it for you; otherwise produce it yourself and drop it in place.

Project info

Apache 2.0 — see LICENSE. Use freely, commercially or otherwise. Active development, v0.1.2 beta. Built and maintained by DolphinQuant — the same team running Qorka on SHFE. Issues and pull requests welcome at github.com/dolphinquant/echolon.

@software{echolon,
  title = {Echolon: AI-native quantitative trading engine},
  author = {DolphinQuant},
  year = {2026},
  url = {https://github.com/dolphinquant/echolon},
}