wolverine-strategy

WOLVERINE v1.1 — HYPE Alpha Hunter with Position Lifecycle

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Install skill "wolverine-strategy" with this command: npx skills add senpi-ai/senpi-skills/senpi-ai-senpi-skills-wolverine-strategy

WOLVERINE v1.1 — HYPE Alpha Hunter with Position Lifecycle

One asset. Every signal. Maximum conviction. Reload-on-dip.

v1.1 Changes — DSL Recalibrated for HYPE Speed

Day 1 data: Wolverine caught a textbook HYPE LONG — score 10+, SM aligned, peaked at +9.3% ROE (+$18.54). Then gave it ALL back because the DSL was too loose. Price dropped from +9.3% to -2.3% ROE faster than the 3-minute scan could react. The old Tier 1 locked only 20% of high water = 1.86% floor, which was meaningless.

v1.1 fix: Phase 2 trigger lowered from 6% to 3% ROE. Tier 1 lock raised from 20% to 40%. Breaches reduced from 3 to 2. HYPE reverses fast — the DSL must lock profits aggressively.

With v1.1 DSL, that same trade locks +3.7% ROE at worst instead of exiting at -2.3%.

Also in v1.1: BTC correlation is bonus-only, never a gate or thesis exit. HYPE moves independently of BTC — up 50% while BTC dropped 30%. When BTC confirms, HYPE runs harder (+2 score bonus). When BTC diverges, it's a non-event.

WOLVERINE stares at HYPE and nothing else. Every signal source available — smart money positioning, funding rate, open interest, 4-timeframe trend structure, volume, BTC correlation — feeds into a single thesis: is there a high-conviction HYPE trade right now?

Based on GRIZZLY v2.0's three-mode lifecycle, adapted for HYPE's volatility profile.

The Three-Mode Lifecycle

MODE 1 — HUNTING (default)

Scan HYPE every 3 minutes. All signals must align (4h trend, 1h momentum, SM, funding, OI, volume). Score 10+ to enter. When a position opens, switch to MODE 2.

MODE 2 — RIDING

Active position. DSL High Water trails it. Thesis re-evaluation every scan. If thesis breaks (4h trend flips, SM flips, funding extreme, volume dies) -> thesis exit and reset to MODE 1. If DSL closes the position -> switch to MODE 3. Note: BTC divergence does NOT invalidate HYPE thesis — HYPE moves independently.

MODE 3 — STALKING

DSL locked profits. The trend may not be over. Watch for a reload opportunity. Every scan checks:

Reload conditions (ALL must pass):

  • At least one completed 1h candle since exit (~30 min minimum)

  • Fresh 5m momentum impulse in the exit direction

  • OI stable or growing

  • Volume at least 50% of original entry

  • Funding not spiked into crowded territory

  • SM still aligned in the exit direction

  • 4h trend structure still intact

If ALL pass -> RELOAD. Re-enter same direction, same leverage. Switch to MODE 2.

Kill conditions (ANY triggers reset to MODE 1):

  • 4h trend reversed

  • SM flipped against exit direction

  • OI collapsed 20%+

  • Stalking for more than 6 hours with no reload

  • Funding spiked above 100% annualized

maxPositions: 1. WOLVERINE holds one HYPE position at a time.

MANDATORY: DSL High Water Mode

WOLVERINE MUST use DSL High Water Mode. This is not optional.

Spec: https://github.com/Senpi-ai/senpi-skills/blob/main/dsl-dynamic-stop-loss/dsl-high-water-spec%201.0.md

DSL tiers in wolverine-config.json . Arm DSL immediately after every entry fill. Zero naked positions.

Why HYPE-Only at 5-10x Leverage

  • Native to Hyperliquid — deepest on-exchange liquidity for HYPE

  • Platform momentum — HYPE price correlates with Hyperliquid volume/TVL growth

  • High volatility — moves fast, rewards conviction entries with wide trails

  • BTC as bonus, not filter — when BTC runs with HYPE, the move is amplified (+2 score bonus). But HYPE often decouples during platform-specific events, so BTC divergence is never a penalty or exit signal

  • Lower leverage compensates for volatility — 10x on HYPE captures large structural moves without overexposure

How WOLVERINE Trades

Entry (score >= 10 required)

Every 3 minutes, the scanner evaluates HYPE across all signal sources:

Signal Points Required?

4h trend structure (higher lows / lower highs) 3 Yes

1h trend agrees with 4h 2 Yes

15m momentum confirms direction 0-1 Yes

5m alignment (all 4 timeframes agree) 1 No

SM aligned with direction 2-3 Hard block if opposing

Funding pays to hold the direction 2 No

Volume above average 1-2 No

OI growing 1 No

BTC confirms move 2 No (bonus only — HYPE moves independently of BTC)

RSI has room 1 No (blocks overbought/oversold)

4h momentum strength 1 No

Maximum score: ~18. Minimum to enter: 10.

Conviction-Scaled Leverage

Score Leverage

10-11 8x

12-13 9x

14+ 10x

Conviction-Scaled Margin

Score Margin

10-11 20% of account

12-13 25%

14+ 30%

Risk Management

Rule Value

Max positions 1

Phase 1 floor 2.5% notional (~25% ROE at 10x)

Drawdown halt 25% from peak

Daily loss limit 10%

Cooldown 120 min after 3 consecutive losses

Stagnation TP 10% ROE stale 75 min

Cron Architecture

Cron Interval Session Purpose

Scanner 3 min isolated Thesis builder + re-evaluator + stalk/reload

DSL v5 3 min isolated High Water Mode trailing stops

Both MUST be isolated sessions with agentTurn . Use NO_REPLY for idle cycles.

Notification Policy

ONLY alert: Position OPENED (direction, leverage, score, reasons), position CLOSED (DSL or thesis exit), risk guardian triggered, critical error. NEVER alert: Scanner found no thesis, thesis re-eval passed, DSL routine, any reasoning.

Bootstrap Gate

On EVERY session, check config/bootstrap-complete.json . If missing:

  • Verify Senpi MCP

  • Create scanner cron (3 min, isolated) and DSL cron (3 min, isolated)

  • Write config/bootstrap-complete.json

  • Send: "🦡 WOLVERINE is online. Watching HYPE. DSL High Water Mode active. Silence = no conviction."

Expected Behavior

Metric Expected

Trades/day 1-3

Avg hold time 1-12 hours

Win rate ~45-55%

Avg winner 20-50%+ ROE

Avg loser -20 to -40% ROE

Files

File Purpose

scripts/wolverine-scanner.py

HYPE thesis builder + re-evaluator + stalk/reload

scripts/wolverine_config.py

Shared config, MCP helpers, state I/O

config/wolverine-config.json

All configurable variables with DSL High Water tiers

License

MIT — Built by Senpi (https://senpi.ai). Source: https://github.com/Senpi-ai/senpi-skills

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