OpenClaw Trading Suite
Use this skill when the user asks for end-to-end trading-agent behavior across analysis, hypothesis creation, risk management, execution, and continuous optimization.
Scope
- - Strategy styles: swing-first, with optional intraday and event-driven variants.
- Assets: equities and crypto by default.
- Lifecycle: research -> hypothesis -> validate -> size risk -> execute -> review -> retrain.
- Data retention: all decisions, signals, fills, outcomes, and model versions are logged for later analysis.
Core workflow
- 1. Ingest market, technical, and optional lightweight sentiment/event data.
- Run screeners to generate candidate tickers/coins for strategy hypotheses.
- Build trade hypotheses with explicit entry, exit, invalidation, and confidence.
- Apply strategy-specific risk profile (not global static policy).
- Gate execution based on drawdown, exposure, and confidence thresholds.
- Log every step to persistent storage (research, signals, orders, fills, P&L).
- Run periodic review: win rate, expectancy, drawdown, and regime-fit diagnostics.
- Feed outcomes into optimization/retraining loop with champion-vs-challenger testing.
Strategy catalog
Load references/strategy_profiles.md when a user asks for concrete strategies or wants to include the "4 bots competition" approaches.
Data model and retention
Load references/dataretention_schema.md when implementing storage, analytics, or RL/ML training.
Autonomy modes
Load references/autonomy_modes.md when implementing user-selected autonomy behavior and approvals.
Adapter extension contract
Load references/adapterplugin_contract.md when adding venues, data feeds, or research tools.
Strategy builder and gates
Load references/strategybuilderand_gates.md when user/agent-defined thresholds are needed for paper-to-live graduation.
Secrets handling
Load references/secrets_management.md when adding providers, credentials, or runtime configuration.
Orchestration
Load references/system_orchestration.md when wiring agents/tools, heartbeat cadence, and execution triggers.
Execution policy defaults
- - Start in paper mode unless user explicitly requests live mode.
- Require per-hypothesis approval for first live deployment of any new strategy.
- Enforce strategy-local risk budgets and portfolio-level circuit breakers.
- Halt strategy if live or paper performance breaches configured drawdown limits.
Reuse notes for this repository
- - Existing modules to reuse first:
market-data-aggregator, technical-analysis-engine, risk-position-manager, strategy-optimizer, trade-signal-processor-executor, performance-reporter-learner, profit-forecaster, and temp-rl-proto. - Treat older module
SKILL.md files as component-level docs; this suite is the orchestrator skill. - Nightly research entry point:
scripts/nightly_research.py.
OpenClaw 交易套件
当用户请求端到端交易代理行为(涵盖分析、假设创建、风险管理、执行和持续优化)时,使用此技能。
范围
- - 策略风格:以波段交易为主,可选日内交易和事件驱动变体。
- 资产:默认支持股票和加密货币。
- 生命周期:研究 -> 假设 -> 验证 -> 风险量化 -> 执行 -> 复盘 -> 重新训练。
- 数据留存:所有决策、信号、成交记录、结果和模型版本均被记录以供后续分析。
核心工作流
- 1. 摄取市场数据、技术指标以及可选的轻量级情绪/事件数据。
- 运行筛选器,为策略假设生成候选股票/币种。
- 构建交易假设,明确入场、出场、失效条件和置信度。
- 应用策略特定的风险配置(非全局静态策略)。
- 基于回撤、敞口和置信度阈值进行执行门控。
- 将每一步记录到持久化存储(研究、信号、订单、成交、盈亏)。
- 运行定期复盘:胜率、期望值、回撤和制度匹配诊断。
- 将结果反馈到优化/重新训练循环中,进行冠军与挑战者测试。
策略目录
当用户询问具体策略或希望包含4个机器人竞赛方法时,加载references/strategy_profiles.md。
数据模型与留存
在实现存储、分析或强化学习/机器学习训练时,加载references/dataretention_schema.md。
自主模式
在实现用户选择的自主行为和审批时,加载references/autonomy_modes.md。
适配器扩展合约
在添加交易场所、数据源或研究工具时,加载references/adapterplugin_contract.md。
策略构建器与门控
当用户/代理需要定义从模拟到实盘升级的阈值时,加载references/strategybuilderand_gates.md。
密钥处理
在添加提供商、凭证或运行时配置时,加载references/secrets_management.md。
编排
在连接代理/工具、心跳节奏和执行触发器时,加载references/system_orchestration.md。
执行策略默认值
- - 除非用户明确请求实盘模式,否则默认从模拟模式开始。
- 任何新策略首次实盘部署需要每个假设的审批。
- 强制执行策略本地风险预算和投资组合级别的熔断机制。
- 如果实盘或模拟表现超过配置的回撤限制,则暂停策略。
本仓库的复用说明
- - 优先复用的现有模块:market-data-aggregator、technical-analysis-engine、risk-position-manager、strategy-optimizer、trade-signal-processor-executor、performance-reporter-learner、profit-forecaster和temp-rl-proto。
- 将较旧的SKILL.md文件视为组件级文档;本套件是编排技能。
- 夜间研究入口点:scripts/nightly_research.py。