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backtest-expert

价格未确认★ 127 GitHub Stars目录更新于 · 2026年10月4日agent-skill

概览

Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.

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Backtest Expert

Systematic approach to backtesting trading strategies based on professional methodology that prioritizes robustness over optimistic results.

Core Philosophy

Goal: Find strategies that "break the least", not strategies that "profit the most" on paper.

Principle: Add friction, stress test assumptions, and see what survives. If a strategy holds up under pessimistic conditions, it's more likely to work in live trading.

When to Use This Skill

Use this skill when:

Developing or validating systematic trading strategies
Evaluating whether a trading idea is robust enough for live implementation
Troubleshooting why a backtest might be misleading
Learning proper backtesting methodology
Avoiding common pitfalls (curve-fitting, look-ahead bias, survivorship bias)
Assessing parameter sensitivity and regime dependence
Setting realistic expectations for slippage and execution costs

Prerequisites

Python 3.9+ (for evaluation script)
No API keys required
No external data dependencies — metrics are user-provided

Workflow

1. State the Hypothesis

Define the edge in one sentence.

Example: "Stocks that gap up >3% on earnings and pull back to previous day's close within first hour provide mean-reversion opportunity."

If you can't articulate the edge clearly, don't proceed to testing.

2. Codify Rules with Zero Discretion

Define with complete specificity:

Entry: Exact conditions, timing, price type
Exit: Stop loss, profit target, time-based exit
Position sizing: Fixed $$, % of portfolio, volatility-adjusted
Filters: Market cap, volume, sector, volatility conditions
Universe: What instruments are eligible

Critical: No subjective judgment allowed. Every decision must be rule-based and unambiguous.

3. Run Initial Backtest

Test over:

Minimum 5 years (preferably 10+)
Multiple market regimes (bull, bear, high/low volatility)
Realistic costs: Commissions + conservative slippage

Examine initial results for basic viability. If fundamentally broken, iterate on hypothesis.

4. Stress Test the Strategy

This is where 80% of testing time should be spent.

Parameter sensitivity:

Test stop loss at 50%, 75%, 100%, 125%, 150% of baseline
Test profit target at 80%, 90%, 100%, 110%, 120% of baseline
Vary entry/exit timing by ±15-30 minutes
Look for "plateaus" of stable performance, not narrow spikes

Execution friction:

Increase slippage to 1.5-2x typical estimates
Model worst-case fills (buy at ask+1 tick, sell at bid-1 tick)
Add realistic order rejection scenarios
Test with pessimistic commission structures

Time robustness:

Analyze year-by-year performance
Require positive expectancy in majority of years
Ensure strategy doesn't rely on 1-2 exceptional periods
Test in different market regimes separately

Sample size:

Absolute minimum: 30 trades
Preferred: 100+ trades
High confidence: 200+ trades

5. Out-of-Sample Validation

Walk-forward analysis:

Optimize on training period (e.g., Year 1-3)
Test on validation period (Year 4)
Roll forward and repeat
Compare in-sample vs out-of-sample performance

Warning signs:

Out-of-sample <50% of in-sample performance
Need frequent parameter re-optimization
Parameters change dramatically between periods

6. Evaluate Results

Questions to answer:

Does edge survive pessimistic assumptions?
Is performance stable across parameter variations?
Does strategy work in multiple market regimes?
Is sample size sufficient for statistical confidence?
Are results realistic, not "too good to be true"?

Decision criteria:

✅ Deploy: Survives all stress tests with acceptable performance
🔄 Refine: Core logic sound but needs parameter adjustment
❌ Abandon: Fails stress tests or relies on fragile assumptions

Use the evaluation script for a structured, quantitative assessment:

python3 skills/backtest-expert/scripts/evaluate_backtest.py \
  --total-trades 150 \
  --win-rate 62 \
  --avg-win-pct 1.8 \
  --avg-loss-pct 1.2 \
  --max-drawdown-pct 15 \
  --years-tested 8 \
  --num-parameters 3 \
  --slippage-tested \
  --output-dir reports/

The script scores across 5 dimensions (Sample Size, Expectancy, Risk Management, Robustness, Execution Realism), detects red flags, and outputs a Deploy/Refine/Abandon verdict.

Key Testing Principles

Punish the Strategy

Add friction everywhere:

Commissions higher than reality
Slippage 1.5-2x typical
Worst-case fills
Order rejections
Partial fills

Rationale: Strategies that survive pessimistic assumptions often outperform in live trading.

Seek Plateaus, Not Peaks

Look for parameter ranges where performance is stable, not optimal values that create performance spikes.

Good: Strategy profitable with stop loss anywhere from 1.5% to 3.0% Bad: Strategy only works with stop loss at exactly 2.13%

Stable performance indicates genuine edge; narrow optima suggest curve-fitting.

Test All Cases, Not Cherry-Picked Examples

Wrong approach: Study hand-picked "market leaders" that worked Right approach: Test every stock that met criteria, including those that failed

Selective examples create survivorship bias and overestimate strategy quality.

Separate Idea Generation from Validation

Intuition: Useful for generating hypotheses Validation: Must be purely data-driven

Never let attachment to an idea influence interpretation of test results.

Common Failure Patterns

Recognize these patterns early to save time:

Parameter sensitivity: Only works with exact parameter values
Regime-specific: Great in some years, terrible in others
Slippage sensitivity: Unprofitable when realistic costs added
Small sample: Too few trades for statistical confidence
Look-ahead bias: "Too good to be true" results
Over-optimization: Many parameters, poor out-of-sample results

See references/failed_tests.md for detailed examples and diagnostic framework.

Output

reports/backtest_eval_<timestamp>.json — structured evaluation with per-dimension scores, red flags, and verdict
reports/backtest_eval_<timestamp>.md — human-readable report with dimension table, key metrics, and red flag details

Resources

Methodology Reference

File: references/methodology.md

When to read: For detailed guidance on specific testing techniques.

Contents:

Stress testing methods
Parameter sensitivity analysis
Slippage and friction modeling
Sample size requirements
Market regime classification
Common biases and pitfalls (survivorship, look-ahead, curve-fitting, etc.)

Failed Tests Reference

File: references/failed_tests.md

When to read: When strategy fails tests, or learning from past mistakes.

Contents:

Why failures are valuable
Common failure patterns with examples
Case study documentation framework
Red flags checklist for evaluating backtests

Critical Reminders

Time allocation: Spend 20% generating ideas, 80% trying to break them.

Context-free requirement: If strategy requires "perfect context" to work, it's not robust enough for systematic trading.

Red flag: If backtest results look too good (>90% win rate, minimal drawdowns, perfect timing), audit carefully for look-ahead bias or data issues.

Tool limitations: Understand your backtesting platform's quirks (interpolation methods, handling of low liquidity, data alignment issues).

Statistical significance: Small edges require large sample sizes to prove. 5% edge per trade needs 100+ trades to distinguish from luck.

Discretionary vs Systematic Differences

This skill focuses on systematic/quantitative backtesting where:

All rules are codified in advance
No discretion or "feel" in execution
Testing happens on all historical examples, not cherry-picked cases
Context (news, macro) is deliberately stripped out

Discretionary traders study differently—this skill may not apply to setups requiring subjective judgment.

查看来源 ↗

文件元数据

name: backtest-expert
description: Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.

查看原始文本

---
name: backtest-expert
description: Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
---

# Backtest Expert

Systematic approach to backtesting trading strategies based on professional methodology that prioritizes robustness over optimistic results.

## Core Philosophy

**Goal**: Find strategies that "break the least", not strategies that "profit the most" on paper.

**Principle**: Add friction, stress test assumptions, and see what survives. If a strategy holds up under pessimistic conditions, it's more likely to work in live trading.

## When to Use This Skill

Use this skill when:
- Developing or validating systematic trading strategies
- Evaluating whether a trading idea is robust enough for live implementation
- Troubleshooting why a backtest might be misleading
- Learning proper backtesting methodology
- Avoiding common pitfalls (curve-fitting, look-ahead bias, survivorship bias)
- Assessing parameter sensitivity and regime dependence
- Setting realistic expectations for slippage and execution costs

## Prerequisites

- Python 3.9+ (for evaluation script)
- No API keys required
- No external data dependencies — metrics are user-provided

## Workflow

### 1. State the Hypothesis

Define the edge in one sentence.

**Example**: "Stocks that gap up >3% on earnings and pull back to previous day's close within first hour provide mean-reversion opportunity."

If you can't articulate the edge clearly, don't proceed to testing.

### 2. Codify Rules with Zero Discretion

Define with complete specificity:
- **Entry**: Exact conditions, timing, price type
- **Exit**: Stop loss, profit target, time-based exit
- **Position sizing**: Fixed $$, % of portfolio, volatility-adjusted
- **Filters**: Market cap, volume, sector, volatility conditions
- **Universe**: What instruments are eligible

**Critical**: No subjective judgment allowed. Every decision must be rule-based and unambiguous.

### 3. Run Initial Backtest

Test over:
- **Minimum 5 years** (preferably 10+)
- **Multiple market regimes** (bull, bear, high/low volatility)
- **Realistic costs**: Commissions + conservative slippage

Examine initial results for basic viability. If fundamentally broken, iterate on hypothesis.

### 4. Stress Test the Strategy

This is where 80% of testing time should be spent.

**Parameter sensitivity**:
- Test stop loss at 50%, 75%, 100%, 125%, 150% of baseline
- Test profit target at 80%, 90%, 100%, 110%, 120% of baseline
- Vary entry/exit timing by ±15-30 minutes
- Look for "plateaus" of stable performance, not narrow spikes

**Execution friction**:
- Increase slippage to 1.5-2x typical estimates
- Model worst-case fills (buy at ask+1 tick, sell at bid-1 tick)
- Add realistic order rejection scenarios
- Test with pessimistic commission structures

**Time robustness**:
- Analyze year-by-year performance
- Require positive expectancy in majority of years
- Ensure strategy doesn't rely on 1-2 exceptional periods
- Test in different market regimes separately

**Sample size**:
- Absolute minimum: 30 trades
- Preferred: 100+ trades
- High confidence: 200+ trades

### 5. Out-of-Sample Validation

**Walk-forward analysis**:
1. Optimize on training period (e.g., Year 1-3)
2. Test on validation period (Year 4)
3. Roll forward and repeat
4. Compare in-sample vs out-of-sample performance

**Warning signs**:
- Out-of-sample <50% of in-sample performance
- Need frequent parameter re-optimization
- Parameters change dramatically between periods

### 6. Evaluate Results

**Questions to answer**:
- Does edge survive pessimistic assumptions?
- Is performance stable across parameter variations?
- Does strategy work in multiple market regimes?
- Is sample size sufficient for statistical confidence?
- Are results realistic, not "too good to be true"?

**Decision criteria**:
- ✅ **Deploy**: Survives all stress tests with acceptable performance
- 🔄 **Refine**: Core logic sound but needs parameter adjustment
- ❌ **Abandon**: Fails stress tests or relies on fragile assumptions

Use the evaluation script for a structured, quantitative assessment:

```bash
python3 skills/backtest-expert/scripts/evaluate_backtest.py \
--total-trades 150 \
--win-rate 62 \
--avg-win-pct 1.8 \
--avg-loss-pct 1.2 \
--max-drawdown-pct 15 \
--years-tested 8 \
--num-parameters 3 \
--slippage-tested \
--output-dir reports/
```

The script scores across 5 dimensions (Sample Size, Expectancy, Risk Management, Robustness, Execution Realism), detects red flags, and outputs a Deploy/Refine/Abandon verdict.

## Key Testing Principles

### Punish the Strategy

Add friction everywhere:
- Commissions higher than reality
- Slippage 1.5-2x typical
- Worst-case fills
- Order rejections
- Partial fills

**Rationale**: Strategies that survive pessimistic assumptions often outperform in live trading.

### Seek Plateaus, Not Peaks

Look for parameter ranges where performance is stable, not optimal values that create performance spikes.

**Good**: Strategy profitable with stop loss anywhere from 1.5% to 3.0%
**Bad**: Strategy only works with stop loss at exactly 2.13%

Stable performance indicates genuine edge; narrow optima suggest curve-fitting.

### Test All Cases, Not Cherry-Picked Examples

**Wrong approach**: Study hand-picked "market leaders" that worked
**Right approach**: Test every stock that met criteria, including those that failed

Selective examples create survivorship bias and overestimate strategy quality.

### Separate Idea Generation from Validation

**Intuition**: Useful for generating hypotheses
**Validation**: Must be purely data-driven

Never let attachment to an idea influence interpretation of test results.

## Common Failure Patterns

Recognize these patterns early to save time:

1. **Parameter sensitivity**: Only works with exact parameter values
2. **Regime-specific**: Great in some years, terrible in others
3. **Slippage sensitivity**: Unprofitable when realistic costs added
4. **Small sample**: Too few trades for statistical confidence
5. **Look-ahead bias**: "Too good to be true" results
6. **Over-optimization**: Many parameters, poor out-of-sample results

See `references/failed_tests.md` for detailed examples and diagnostic framework.

## Output

- `reports/backtest_eval_<timestamp>.json` — structured evaluation with per-dimension scores, red flags, and verdict
- `reports/backtest_eval_<timestamp>.md` — human-readable report with dimension table, key metrics, and red flag details

## Resources

### Methodology Reference
**File**: `references/methodology.md`

**When to read**: For detailed guidance on specific testing techniques.

**Contents**:
- Stress testing methods
- Parameter sensitivity analysis
- Slippage and friction modeling
- Sample size requirements
- Market regime classification
- Common biases and pitfalls (survivorship, look-ahead, curve-fitting, etc.)

### Failed Tests Reference
**File**: `references/failed_tests.md`

**When to read**: When strategy fails tests, or learning from past mistakes.

**Contents**:
- Why failures are valuable
- Common failure patterns with examples
- Case study documentation framework
- Red flags checklist for evaluating backtests

## Critical Reminders

**Time allocation**: Spend 20% generating ideas, 80% trying to break them.

**Context-free requirement**: If strategy requires "perfect context" to work, it's not robust enough for systematic trading.

**Red flag**: If backtest results look too good (>90% win rate, minimal drawdowns, perfect timing), audit carefully for look-ahead bias or data issues.

**Tool limitations**: Understand your backtesting platform's quirks (interpolation methods, handling of low liquidity, data alignment issues).

**Statistical significance**: Small edges require large sample sizes to prove. 5% edge per trade needs 100+ trades to distinguish from luck.

## Discretionary vs Systematic Differences

This skill focuses on **systematic/quantitative** backtesting where:
- All rules are codified in advance
- No discretion or "feel" in execution
- Testing happens on all historical examples, not cherry-picked cases
- Context (news, macro) is deliberately stripped out

Discretionary traders study differently—this skill may not apply to setups requiring subjective judgment.

查看并核实来源

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许可证: MIT
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安装前审查: 避免自动安装

许可证: MIT

Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: shell or command execution, filesystem or document access
Permission surface: shell or command execution, filesystem or document access

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工具列表来自元数据，并非已测试的兼容性；Agent 提示词是建议的交接方式。

从一个小任务开始

1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: BaggaT236/AI-Trading-Skills
许可证: MIT
版本: 1.0.0
最近 GitHub 推送: 2026年9月14日
目录更新于: 2026年10月4日
技能指令路径: skills/backtest-expert/SKILL.md @ 8d77f8949c76

版本来自目录元数据，使用前请核实来源发布记录。

质量

68/100

有潜力

信任

69/100

仅限沙盒

审计

80/100

高风险

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Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: shell or command execution, filesystem or document access
Permission surface: shell or command execution, filesystem or document access

Verified installs: —
结果: —

复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

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本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号，让 Agent 无需抓取界面即可排序。

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      "license": "MIT",
      "repository": "https://github.com/BaggaT236/AI-Trading-Skills/tree/main/skills/backtest-expert",
      "install": "npx skills add BaggaT236/AI-Trading-Skills --skill backtest-expert",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "shell or command execution, filesystem or document access",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "design-creative",
      "agent-skill"
    ],
    "known_risks": [
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Permission surface needs review: shell or command execution, filesystem or document access",
      "Permission surface: shell or command execution, filesystem or document access"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 80,
    "risk_level": "risky",
    "risk_label": "Risky",
    "warnings": [
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Permission surface needs review: shell or command execution, filesystem or document access",
      "Permission surface: shell or command execution, filesystem or document access"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 68,
    "label": "Promising"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Coding agents",
    "maintenance": "28d since push",
    "risk": "Risky"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "Audit risk risky exceeds max_risk=medium",
    "High-risk permission hints: Shell or command execution",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required"
  ],
  "agent_contract": {
    "task_input": "Use backtest-expert in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 77/100 Strong shortlist",
      "Audit: 80/100 Risky",
      "Safety: 52/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "baggat236-backtest-expert (backtest-expert)",
      "install_command": "npx skills add BaggaT236/AI-Trading-Skills --skill backtest-expert",
      "risk_summary": "Risky; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "baggat236-backtest-expert",
      "task": "Use backtest-expert in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/baggat236-backtest-expert",
    "api": "https://www.openagentskill.com/api/agent/skills/baggat236-backtest-expert",
    "audit": "https://www.openagentskill.com/skills/baggat236-backtest-expert/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=baggat236-backtest-expert&task=Use%20backtest-expert%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20backtest-expert%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20backtest-expert%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/baggat236-backtest-expert/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/baggat236-backtest-expert"
  }
}

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创作者: BaggaT236
来源: BaggaT236/AI-Trading-Skills
收录方: OpenAgentSkill 社区索引

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