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/strategy-pivot-designer

@09a9359

Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.

Use this Skill: https://skilld.dev/gh/tradermonty/claude-trading-skills/strategy-pivot-designer

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referencespivot_proposal_schema.md

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Pivot Proposal Schema

Pivot drafts are strategy_draft-compatible YAML files with an additive pivot_metadata extension. Downstream tools can ignore the extension and treat pivots as regular strategy drafts.

Required Fields (strategy_draft compatible)

# Identity
id: pivot_{source_archetype}_to_{target_archetype}_{timestamp}
as_of: "YYYY-MM-DD"
concept_id: <original concept_id from source strategy>
variant: research_probe
name: "<Target archetype name> (pivoted from <source>)"

# Classification
hypothesis_type: <from target archetype>
mechanism_tag: <from target archetype>
regime: <inherited from source or adjusted>
export_ready_v1: <true only if entry_family in EXPORTABLE_FAMILIES>
entry_family: <from target archetype, or research_only>

# Entry
entry:
  conditions: [<list of entry condition strings>]
  trend_filter: [<list of trend filter strings>]
  note: "Probe setup with small size for hypothesis validation."

# Exit
exit:
  stop_loss_pct: <float, e.g. 0.04>
  take_profit_rr: <float, e.g. 2.0>
  time_stop_days: <int>

# Risk
risk:
  position_sizing: fixed_risk
  risk_per_trade: <float, e.g. 0.005>
  max_positions: <int>
  max_sector_exposure: <float, e.g. 0.3>

# Validation
validation_plan:
  period: "2016-01-01 to latest"
  entry_timing: next_open
  hold_days: [<list of int>]
  success_criteria:
    - "<criterion 1>"
    - "<criterion 2>"

# Context
thesis: "<strategy thesis>"
invalidation_signals: [<list of invalidation signals>]

Pivot Metadata Extension (additive)

pivot_metadata:
  pivot_technique: <assumption_inversion | archetype_switch | objective_reframe>
  source_strategy_id: <id of the original strategy draft>
  target_archetype: <archetype id from catalog>
  what_changed:
    signal: "<description of signal change>"
    horizon: "<description of horizon change>"
    risk: "<description of risk change>"
  why: "<explanation of why this pivot addresses the trigger>"
  targeted_triggers: [<list of trigger IDs this pivot addresses>]
  expected_failure_modes:
    - "<potential failure mode 1>"
    - "<potential failure mode 2>"
  scores:
    quality_potential: <float 0-1>
    novelty: <float 0-1>
    combined: <float 0-1>

Output Directory Structure

{output-dir}/
├── pivot_drafts/
│   ├── research_only/
│   │   └── pivot_{source}_{archetype}_{timestamp}.yaml
│   └── exportable/
│       ├── pivot_{source}_{archetype}_{timestamp}.yaml
│       └── ticket_{source}_{archetype}_{timestamp}.yaml
├── pivot_report_{strategy_id}_{timestamp}.md
└── pivot_manifest_{strategy_id}_{timestamp}.json

Exportable Ticket Generation

Tickets are generated only when entry_family is in EXPORTABLE_FAMILIES:

  • pivot_breakout
  • gap_up_continuation

Ticket format follows build_export_ticket() from design_strategy_drafts.py:222.

Manifest Schema

{
  "generated_at_utc": "ISO-8601",
  "strategy_id": "source strategy id",
  "diagnosis_file": "path to diagnosis JSON",
  "strategy_file": "path to source draft YAML",
  "triggers_fired": ["trigger_id_1", "trigger_id_2"],
  "total_pivots_generated": 3,
  "exportable_count": 1,
  "research_only_count": 2,
  "drafts": [
    {
      "id": "pivot_...",
      "path": "relative path",
      "category": "research_only | exportable",
      "ticket_path": "relative path or null",
      "scores": {"quality_potential": 0.7, "novelty": 0.8, "combined": 0.74}
    }
  ],
  "errors": []
}

Source: SKILL.md on GitHub

1 warning3d5 checks · Risk SAFE
  • Gen Agent Trust Hub3d

    The strategy-pivot-designer skill is a local utility for quantitative trading researchers. It provides logic to identify when a strategy's backtest performance has plateaued and automatically generates alternative strategy architectures (pivots) to explore. The skill operates exclusively on local JSON and YAML data, utilizes secure parsing methods, and follows identifier sanitization best practices. No malicious patterns or security risks were identified.

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  • Snyk3d

    Risk: LOW · No issues

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  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at 09a9359. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 15 hours ago.

Activeupdated 7 months ago

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