quant-strategy-rules

Alpha-Qwen 量化交易策略规则引擎。应用 SMC (Smart Money Concepts) + 顺势马丁格尔复合策略，覆盖多周期趋势共振、结构验证、入场执行、Grid 切换等核心逻辑。当需要分析行情、判断入场/出场、讨论策略优化时使用此 Skill。

1.75x

Quality

78%

Does it follow best practices?

Impact

95%

1.75x

Average score across 3 eval scenarios

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./.trae/skills/quant-strategy-rules/SKILL.md

Quality

Discovery

85%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a well-crafted skill description that clearly defines a specialized quantitative trading strategy engine. It excels in specificity and completeness with explicit 'Use when' triggers in Chinese. The main weakness is trigger term quality - while domain experts would recognize the terminology, casual users might use simpler terms when seeking trading advice.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: SMC + 顺势马丁格尔复合策略, 多周期趋势共振, 结构验证, 入场执行, Grid 切换. These are specific trading strategy components and actions.	3 / 3
Completeness	Clearly answers both what (量化交易策略规则引擎 with SMC + Martingale strategies covering trend resonance, structure validation, entry execution, Grid switching) AND when (当需要分析行情、判断入场/出场、讨论策略优化时使用此 Skill).	3 / 3
Trigger Term Quality	Includes domain-specific terms like '量化交易', '行情', '入场/出场', '策略优化', but uses technical jargon (SMC, Grid切换, 马丁格尔) that users may not naturally say. Missing common variations like 'trading signals', '买卖点', '交易信号'.	2 / 3
Distinctiveness Conflict Risk	Highly distinctive with specific strategy names (Alpha-Qwen, SMC, 顺势马丁格尔) and clear niche in quantitative trading. Unlikely to conflict with other skills due to specialized terminology and explicit use cases.	3 / 3
	Total	11 / 12 Passed

Implementation

72%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill provides a well-organized, concise overview of a complex quantitative trading strategy with good use of tables and structured sections. However, it lacks executable code examples and explicit validation workflows, making it more of a reference document than an actionable implementation guide. The strategy rules are clear but would benefit from concrete implementation snippets.

Suggestions

Add executable Python code examples showing how to call SMC validation (e.g., `if smc_validator.score(ob, fvg) >= 0.70: execute_trade()`)

Include a numbered workflow for the entry decision process with explicit validation checkpoints (e.g., '1. Check multi-timeframe alignment → 2. Validate SMC score ≥70% → 3. If fail, WAIT and re-check in X minutes')

Add concrete examples of signal evaluation with sample data (e.g., 'Given H1=bullish, M15=bullish, M5=bearish → Result: WAIT')

Dimension	Reasoning	Score
Conciseness	Content is lean and efficient, using tables and bullet points to convey complex trading rules without unnecessary explanation. Assumes reader understands SMC concepts, trading terminology, and doesn't over-explain basics.	3 / 3
Actionability	Provides clear rules and conditions but lacks executable code examples. The entry criteria use AND logic clearly, but there are no concrete code snippets showing how to implement these checks or call the referenced modules.	2 / 3
Workflow Clarity	Entry execution standards are listed as AND conditions but lack explicit sequencing. Grid strategy switching has conditions but no step-by-step validation workflow. Missing explicit feedback loops for when SMC validation fails or signals conflict.	2 / 3
Progressive Disclosure	Well-structured with clear sections for different aspects of the strategy. References to detailed docs, position engine, and trading bot are clearly signaled at the end with one-level-deep file paths.	3 / 3
	Total	10 / 12 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: Lingjie-chen/MT5
Commit: 3069d33

Reviewed: 26 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.