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Crypto Trading Decision Framework

作者 pingukim225 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install crypto-trading-decision-framework
功能描述
Structured decision system for crypto traders — position sizing, entry checklist, exit framework, and halt decision tree. Eliminates ad-hoc calls and enforce...
使用说明 (SKILL.md)

Crypto Trading Decision Framework

Bottom line: Every trade decision runs through 3 gates — sizing, entry checklist, exit plan. If any gate fails, the trade doesn't happen. No exceptions.


When to invoke

Any trading discussion that involves:

  • New position entry recommendation
  • Position sizing for any asset
  • Stop loss / take profit calibration
  • Risk-reward analysis
  • Live trade management
  • Strategy halt / kill decisions
  • Portfolio concentration calls

Gate 1 — Position Sizing

Step 1: Risk per trade

  • Default: 1% of total liquid portfolio per single trade
  • Aggressive: 2% if conviction ≥85% AND backtest sample n≥30 trades
  • Conservative: 0.5% on first trade in a new strategy or unfamiliar asset

Step 2: Stop distance

  • Hard stop: Always at the level that technically invalidates the thesis
  • Time stop: Default 48 bars on 4H, 24 bars on 1H (strategy isn't playing out on schedule = exit)
  • Trailing stop: Activate after first 1R achieved; trail at 0.5R below current price

Step 3: Position size formula

Position size (notional) = (Risk % ÷ Stop distance %) × Liquid portfolio
Example: 1% risk, 1.5% stop distance → (1/1.5) × $50,000 = ~$33,333 notional

For leveraged accounts: cap leverage at 3× for new strategies, 5× for proven strategies with n≥50 live trades.


Gate 2 — Entry Checklist (must answer all YES)

  1. ☐ Backtest sample size n ≥ 20 trades
  2. ☐ Profit factor (PF) ≥ 1.3 in out-of-sample test window (not just training)
  3. ☐ Max drawdown (MDD) ≤ 20%
  4. ☐ Out-of-sample (OOS) returns positive
  5. ☐ Strategy has a clear thesis — not just curve-fitting
  6. ☐ Current market regime matches strategy's design regime (mean-reversion in choppy, trend-following in trending)
  7. ☐ Position size compliant with Gate 1 above
  8. ☐ Hard stop level identified pre-entry
  9. ☐ Time exit level identified pre-entry
  10. ☐ Take profit ladder identified (TP1 / TP2 / TP3 if multi-target)

Scoring:

  • 10/10 YES → proceed
  • 8-9/10 YES → proceed with caution, note the gaps
  • \x3C 8/10 YES → DO NOT ENTER
  • \x3C 6/10 YES → KILL THE STRATEGY entirely

Gate 3 — Exit Framework

Priority order for exits

  1. Hard stop hit — thesis invalidated. Cut without question. No re-evaluation during the close.
  2. Time stop hit — strategy hasn't played out in expected timeframe. Exit at market.
  3. Take profit hit — pre-planned TP reached. Exit per ladder (e.g., 50% at TP1, 25% at TP2, 25% trail).
  4. Signal flip — strategy generates the opposite signal. Exit + flip.
  5. Regime change — macro backdrop has shifted materially (e.g., dominance flip, Fear & Greed regime change).
  6. Discretionary — user decision. Done.

What NOT to do (the most common losses)

  • ❌ Moving stops further away mid-trade ("just give it more room")
  • ❌ Adding to losing positions ("averaging down" on a broken thesis)
  • ❌ Early exit on a winner before TP1 unless thesis explicitly broke
  • ❌ Second-guessing a planned exit because of hope or FOMO
  • ❌ Holding past time stop because "it might come back"

R:R Minimums by Strategy Type

Strategy type Minimum R:R Win rate floor
Mean reversion 1.5:1 60%
Trend following 2.5:1 40%
Breakout 3:1 35%
News-driven / event 4:1 30%
Funding/yield carry N/A N/A

If a setup doesn't clear BOTH the R:R minimum and the historical win rate floor → DO NOT RECOMMEND.


Strategy Halt Decision Tree

When a live strategy is underperforming, work through this tree top to bottom:

1. Has the strategy hit its account-level kill-switch loss?
   YES → HALT immediately. Post-mortem before any restart.
   NO → next step.

2. Has the strategy hit -3R drawdown beyond its expected backtest MDD?
   YES → PAUSE for 5 trading days. Re-evaluate regime fit.
   NO → next step.

3. Is the live profit factor ≤ 50% of backtest PF over n≥10 live trades?
   YES → SHRINK position size 50%, run another 10 trades, re-evaluate.
   NO → next step.

4. Has the strategy produced zero signals for N days, where N > 2× expected signal frequency?
   YES → Strategy is dead in current regime. KILL or re-tune thresholds.
   NO → Normal volatility. No action needed.

Real-Money Escalation Rules

These always require human approval — never autonomous execution:

  • New live capital deployment of any size
  • Increasing an existing live capital allocation
  • Moving a strategy from paper to live
  • Stop loss override or removal
  • Adding to a losing position
  • Manual close of an open live position
  • Any single action that reduces account equity by >5%

4-Model Consensus Rule (for large capital decisions)

For any deployment of significant capital:

  1. Primary LLM — full recommendation with confidence tags
  2. Second LLM — independent macro + asset-specific opinion
  3. Third LLM — code/execution path audit + edge case check
  4. Fourth LLM — risk/sizing sanity check

If 2+ models disagree → defer 24h, re-run consensus tomorrow. Disagreement = edge case, not clear enough to act.


Confidence Tags (include on every trading recommendation)

Always attach 3 tags to any trade call:

  • Confidence: % belief the recommendation is correct (60-95% typical)
  • Research depth: % of relevant data actually pulled this session (50-90% typical)
  • Reality gap: % unknowns / black-swan exposure (5-25% typical)

Example: "Confidence 82% / Research Depth 75% / Reality Gap 20%"

This keeps recommendations honest and prevents overconfidence drift.


Output Format

When this framework produces a trade recommendation, structure it as:

## Trade Recommendation: [ASSET] [LONG/SHORT]

**Thesis:** [1-2 sentences — why this setup exists]
**Regime fit:** [why current market supports this strategy type]

**Sizing:**
- Portfolio size: $X
- Risk per trade: $X (1%)
- Stop distance: X%
- Position notional: $X

**Entry checklist:** X/10 YES [list any NO items]
**R:R:** X:1 [minimum met: YES/NO]

**Levels:**
- Entry: $X
- Hard stop: $X (thesis invalidated if price reaches here because: [reason])
- TP1: $X (partial exit X%)
- TP2: $X (partial exit X%)
- Time stop: [date/bar count]

**Confidence:** X% / Research Depth X% / Reality Gap X%

**Escalation required:** YES/NO [why]
安全使用建议
The provided artifacts appear safe to install as an instruction-only trading framework. Treat it as educational risk-management guidance, not investment advice or permission to trade automatically. Keep human approval for all live-capital actions and avoid sending sensitive portfolio or account details to additional LLMs unless you understand and accept the data-sharing implications.
功能分析
Type: OpenClaw Skill Name: crypto-trading-decision-framework Version: 1.0.0 The skill bundle provides a structured decision-making framework for cryptocurrency trading, focusing on risk management, position sizing, and disciplined entry/exit strategies. It includes safety-oriented instructions such as mandatory human approval for financial escalations and a multi-model consensus rule for large capital decisions. No malicious code, data exfiltration attempts, or harmful prompt-injection patterns were identified in SKILL.md or _meta.json.
能力标签
crypto
能力评估
Purpose & Capability
The skill is coherent with its stated finance/trading purpose, but it directly guides financially consequential trading decisions.
Instruction Scope
The instructions are broad within the trading domain and use mandatory language, but they also explicitly require human approval for real-money actions.
Install Mechanism
There is no install spec, no code, no required binaries, no environment variables, and no credentials requested.
Credentials
The skill does not request local or account access, but its 4-model consensus workflow could share trading context with additional LLMs if followed.
Persistence & Privilege
No persistence, background execution, privilege escalation, or account-control mechanism is present in the provided artifacts.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install crypto-trading-decision-framework
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /crypto-trading-decision-framework 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
- Initial release of the Crypto Trading Decision Framework. - Introduces a 3-gate process: position sizing, entry checklist, and exit plan for all trade decisions. - Defines strict risk management and position sizing rules, including leverage caps and formulae. - Establishes detailed entry and exit criteria with scoring, minimum R:R, and win rate floors by strategy type. - Provides a halt/kill decision tree for underperforming strategies and clear escalation rules for major capital decisions. - Includes structured output format and confidence tagging for all trade recommendations.
元数据
Slug crypto-trading-decision-framework
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Crypto Trading Decision Framework 是什么?

Structured decision system for crypto traders — position sizing, entry checklist, exit framework, and halt decision tree. Eliminates ad-hoc calls and enforce... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 51 次。

如何安装 Crypto Trading Decision Framework?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install crypto-trading-decision-framework」即可一键安装,无需额外配置。

Crypto Trading Decision Framework 是免费的吗?

是的,Crypto Trading Decision Framework 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Crypto Trading Decision Framework 支持哪些平台?

Crypto Trading Decision Framework 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Crypto Trading Decision Framework?

由 pingukim225(@pingukim225)开发并维护,当前版本 v1.0.0。

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