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pingukim225

01 Crypto Trading Decision Framework

by pingukim225 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install 01-crypto-trading-decision-framework
Description
Structured decision system for crypto traders — position sizing, entry checklist, exit framework, and halt decision tree. Eliminates ad-hoc calls and enforce...
README (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]
Usage Guidance
This skill appears safe to install as an instruction-only trading framework, but treat its outputs as decision support rather than authorization to trade. Keep human approval mandatory for any live capital movement, and be careful about sharing portfolio or strategy details with multiple LLM providers.
Capability Tags
cryptocan-make-purchases
Capability Assessment
Purpose & Capability
The skill is coherently focused on crypto trade sizing, entries, exits, and risk management, but those recommendations can affect high-impact financial decisions.
Instruction Scope
The instructions are prescriptive for trading decisions, but they include an explicit rule that real-money actions require human approval and must not be executed autonomously.
Install Mechanism
There is no install spec, no required binaries, no code files, and the static scan reported no findings.
Credentials
The skill requests no environment variables, credentials, config paths, local files, or external APIs.
Persistence & Privilege
No persistence, background workers, account privileges, or ongoing state mechanisms are present in the provided artifacts.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install 01-crypto-trading-decision-framework
  3. After installation, invoke the skill by name or use /01-crypto-trading-decision-framework
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of the Crypto Trading Decision Framework. - Provides a structured 3-gate system for every trade: position sizing, entry checklist, and exit plan. - Enforces disciplined risk management to prevent common trader errors like oversizing, moving stops, or holding losers. - Includes decision trees for trade halts and live strategy underperformance. - Standardizes scenario triggers, R:R/win rate requirements, and escalation for major capital actions. - Sets a clear output format and confidence tagging for all trade recommendations.
Metadata
Slug 01-crypto-trading-decision-framework
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is 01 Crypto Trading Decision Framework?

Structured decision system for crypto traders — position sizing, entry checklist, exit framework, and halt decision tree. Eliminates ad-hoc calls and enforce... It is an AI Agent Skill for Claude Code / OpenClaw, with 28 downloads so far.

How do I install 01 Crypto Trading Decision Framework?

Run "/install 01-crypto-trading-decision-framework" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is 01 Crypto Trading Decision Framework free?

Yes, 01 Crypto Trading Decision Framework is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does 01 Crypto Trading Decision Framework support?

01 Crypto Trading Decision Framework is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created 01 Crypto Trading Decision Framework?

It is built and maintained by pingukim225 (@pingukim225); the current version is v1.0.0.

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