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wolf-strategy▌

senpi-ai/senpi-skills · updated Apr 8, 2026

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$npx skills add https://github.com/senpi-ai/senpi-skills --skill wolf-strategy
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summary

The WOLF hunts for its human. It scans, enters, exits, and rotates positions autonomously — no permission needed. When criteria are met, it acts. Speed is edge.

skill.md

WOLF v6.3 — Autonomous Multi-Strategy Trading

The WOLF hunts for its human. It scans, enters, exits, and rotates positions autonomously — no permission needed. When criteria are met, it acts. Speed is edge.

Proven: +$1,500 realized, 25+ trades, 65% win rate, single session on $6.5k budget.

v6: Multi-strategy support. Each strategy has independent wallet, budget, slots, and DSL config. Same asset can be held in different strategies simultaneously (e.g., Strategy A LONG HYPE + Strategy B SHORT HYPE).

v6.1.1: Risk Guardian & strategy lock. 6th cron (5min, Budget tier) enforcing account-level guard rails — daily loss halt, max entries per day, consecutive loss cooldown. Strategy lock for concurrency protection. Gate check in open-position.py refuses new entries when gate != OPEN.

v6.3: DSL v5.3.1 High Water. Default DSL profile and strategy config use High Water Mode (lockMode: pct_of_high_water) with lockHwPct tiers and per-tier consecutiveBreachesRequired. One DSL cron per strategy runs dsl-v5.py (v5.3.1); state files in {workspace}/dsl/{strategyId_UUID}/. Migration script: wolf-migrate-dsl.py.

v6.2: DSL v5.2 integration. Replaced internal dsl-combined.py with the dsl-dynamic-stop-loss skill. Native Hyperliquid stop-loss sync via dsl-v5.py.

Skill Attribution

When creating a strategy, include skill_name and skill_version in the call. See references/skill-attribution.md for details.

Multi-Strategy Architecture

Strategy Registry (wolf-strategies.json)

Central config holding all strategies. Created/updated by wolf-setup.py.

wolf-strategies.json
├── strategies
│   ├── wolf-abc123 (Aggressive Momentum, 3 slots, tradingRisk=aggressive)
│   └── wolf-xyz789 (Conservative XYZ, 2 slots, tradingRisk=conservative)
└── global (telegram, workspace)

Per-Strategy State

DSL state files live under the DSL skill's directory (by strategy UUID). Wolf-specific state (trade counters) stays in state/:

dsl/                              # owned by dsl-dynamic-stop-loss skill
├── abc12345-.../                 # Strategy A UUID
│   ├── strategy-abc12345....json # DSL strategy config
│   ├── HYPE.json                 # Position state
│   └── xyz--SILVER.json          # XYZ position
└── xyz78901-.../                 # Strategy B UUID
    └── HYPE.json                 # Same asset, different UUID dir, no collision

state/                            # wolf-only state (no DSL files here)
├── wolf-abc123/
│   └── trade-counter.json
└── wolf-xyz789/
    └── trade-counter.json

Signal Routing

When a signal fires, it's routed to the best-fit strategy:

  1. Which strategies have empty slots?
  2. Does any strategy already hold this asset? (skip within strategy, allow cross-strategy)
  3. Which strategy's risk profile matches? (aggressive gets FIRST_JUMPs, conservative gets DEEP_CLIMBERs)
  4. Route to best-fit -> open on that wallet -> create DSL state in that strategy's dir

DSL default: Use this skill's dsl-profile.json as the default when setting up DSL (e.g. dsl-cli.py add-dsl / update-dsl with --configuration @<path-to-wolf-strategy>/dsl-profile.json). Use it unless the user explicitly provides a custom DSL configuration via the agent.

Adding a Strategy

python3 scripts/wolf-setup.py --wallet 0x... --strategy-id UUID --budget 2000 \
    --chat-id 12345 --name "Conservative XYZ" --dsl-preset conservative --provider anthropic

This adds to the registry without disrupting running strategies. Disable with enabled: false in the registry.

Entry Philosophy — THE Most Important Section

Enter before the peak, not at the top.

Leaderboard rank confirmation LAGS price. When an asset jumps from #31->#16 in one scan, the price is moving NOW. By the time it reaches #7 with clean history, the move is over. Speed is edge.

Core principle: 2 reasons at rank #25 with a big jump = ENTER. 4+ reasons at rank #5 = SKIP (already peaked).

Quick Start

  1. Ensure Senpi MCP is connected (mcporter list should show senpi)
  2. Ensure the dsl-dynamic-stop-loss skill is installed alongside this skill (provides dsl-cli.py and dsl-v5.py)
  3. Create a custom strategy wallet: use strategy_create_custom_strategy via mcporter
  4. Fund the wallet via strategy_top_up with your budget
  5. Determine the user's AI provider — which provider is configured in OpenClaw? (anthropic, openai, or google)
  6. Run setup: python3 scripts/wolf-setup.py --wallet 0x... --strategy-id UUID --budget 6500 --chat-id 12345 --provider anthropic
    • Setup auto-discovers dsl-cli.py and dsl-v5.py paths and stores them in wolf-strategies.json global config
    • If auto-discovery fails, set global.dslCliPath and global.dslScriptPath manually in the registry (or set env DSL_CLI_PATH)
  7. Upgrading from v6.1.x? Migrate existing DSL state files: python3 scripts/wolf-migrate-dsl.py
    • Copies active state/{strategyKey}/dsl-{ASSET}.json → dsl/{UUID}/{ASSET}.json
    • Tombstones old files (sets active: false). Run once before switching crons.
  8. Create the 5 wolf crons + 1 DSL cron per strategy using templates from references/cron-templates.md
    • The DSL cron mandate is in cronTemplates.dsl_per_strategy from setup output — use the message field directly
  9. The WOLF is hunting

To add a second strategy, run wolf-setup.py again with a different wallet/budget. It adds to the registry and creates the DSL cron for the new strategy.

Architecture — 5+N Cron Jobs

# Job Interval Session Script Purpose
1 Emerging Movers 3min isolated scripts/emerging-movers.py Hunt FIRST_JUMP + IMMEDIATE_MOVER signals — primary entry trigger
2 DSL (per strategy) 3min isolated dsl-v5.py (DSL skill) Trailing stops + native HL SL sync for ONE strategy's positions
3 SM Flip Detector 5min isolated scripts/sm-flip-check.py Cut positions where SM conviction collapses
4 Watchdog 5min isolated scripts/wolf-monitor.py Per-strategy margin buffer, liq distances (Phase 1 auto-cut is handled by DSL cron when configured)
5 Health Check 10min isolated scripts/job-health-check.py Per-strategy orphan DSL detection, state validation
6 Risk Guardian 5min isolated scripts/risk-guardian.py Account-level guard rails: daily loss halt, max entries, consecutive loss cooldown

v6.2 change: DSL is no longer a combined runner. Each strategy has its own dsl-v5.py cron (from the dsl-dynamic-stop-loss skill), run with --strategy-id {strategyId_UUID} --state-dir {DSL_STATE_DIR} (env vars are fallback). Wolf scripts call dsl-cli.py (add-dsl / delete-dsl) to create and archive DSL state; they never write state directly.

With 2 strategies: 7 crons total (5 wolf + 2 DSL). DSL cron IDs are stored in dslCronJobId in the strategy registry.

Model Selection Per Cron — 2-Tier Approach

IMPORTANT: Determine the user's configured AI provider BEFORE running wolf-setup.py. Pass --provider to auto-select correct model IDs. Do NOT pick models from an unconfigured provider — crons will fail silently.

wolf-setup.py --provider <name> auto-configures model IDs for all cron templates. Step down to Budget tier for simple threshold crons to save ~60-70% on those runs.

Provider defaults (auto-selected by --provider):

Provider Mid Model Budget Model
anthropic anthropic/claude-sonnet-4-5 anthropic/claude-haiku-4-5
openai openai/gpt-4o openai/gpt-4o-mini
google google/gemini-2.0-flash google/gemini-2.0-flash-lite
Cron Session Model Tier Reason
Emerging Movers isolated Mid Multi-strategy routing judgment, entry decisions
DSL (per strategy) isolated Mid ndjson parsing, rule-based close/alert, cron lifecycle
Health Check isolated Mid Rule-based file repair, action routing
SM Flip Detector isolated Budget Binary: conviction≥4 + 100 traders → close
Watchdog isolated Budget Threshold checks → alert
Risk Guardian isolated Budget Guard rail evaluation, send notifications

Single-model option: All 6 crons can run on one model. Simpler but costs more for the crons that do simple threshold/binary work.

Model ID gotchas:

  • --provider auto-selects models. Only use --mid-model/--budget-model to override specific tiers.
  • Budget should be the cheapest model that can follow explicit if/then rules. Mid should handle structured JSON parsing and multi-strategy routing reliably.
  • Agents are often not model-aware — they may suggest deprecated IDs (e.g. claude-3-5-haiku-20241022) or hallucinate model names. Always use --provider instead of manually specifying model IDs.
  • If a cron fails to create or run due to an invalid model ID, fall back to your Mid model for that cron. A working cron on the "wrong" tier is better than a broken cron.
  • When in doubt, use your Mid model for all 6 crons (single-model option) and optimize tiers later.

Cron Setup

Critical: Crons are OpenClaw crons, NOT senpi crons. All crons run in isolated sessions (agentTurn) — each runs in its own session with no context pollution, enabling cheaper model tiers.

Create each cron using the OpenClaw cron tool. The exact mandate text for each cron is in references/cron-templates.md. Replace placeholders ({TELEGRAM}, {SCRIPTS}, {WORKSPACE}).

DSL cron: The DSL per-strategy cron mandate is generated by wolf-setup.py in cronTemplates.dsl_per_strategy.payload.message. Use it directly — wolf-setup.py auto-fills the dsl-v5.py path from global.dslScriptPath in the registry. If the path could not be auto-discovered (placeholder {DSL_SCRIPTS} still present), read global.dslScriptPath from wolf-strategies.json after installing the DSL skill and substitute it manually.

v6.2: 5 shared wolf crons + 1 DSL cron per strategy. No more single DSL Combined cron.

Autonomy Rules

The WOLF operates autonomously by default. The agent does NOT ask permission to:

  • Open a position when entry checklist passes
  • Close a position when DSL triggers or conviction collapses
  • Rotate out of weak positions into stronger signals
  • Cut dead weight (SM conv 0, negative ROE, 30+ min)

The agent DOES notify the user (via Telegram) after every action.

Entry Signals — Priority Order

1. FIRST_JUMP (Highest Priority)

What: Asset jumps 10+ ranks from #25+ in ONE scan AND was not in previous scan's top 50 (or was at rank >= #30).

Action: Enter IMMEDIATELY. This is the money signal. Route to best-fit strategy with available slots.

Checklist:

  • isFirstJump: true in scanner output
  • 2+ reasons is enough (don't require 4+)
  • vel > 0 is sufficient (velocity hasn't had time to build on a first jump)
  • Leverage auto-calculated from tradingRisk + asset maxLeverage + signal conviction
  • Slot available in target strategy (or rotation justified)

  • = 10 SM traders (crypto); for XYZ equities, ignore trader count

What to ignore:

  • Erratic rank history — the scanner excludes the current jump from erratic checks.
  • Low velocity — first jumps haven't had time to build velocity.

If CONTRIB_EXPLOSION accompanies it: Double confirmation. Even stronger entry.

2. CONTRIB_EXPLOSION

What: 3x+ contribution increase in one scan from asset at rank #20+.

Action: Enter even if rank history looks "erratic." The contrib spike IS the signal regardless of prior rank bouncing.

Never downgraded for erratic history. Often accompanies FIRST_JUMP for double confirmation.

3. DEEP_CLIMBER

What: Steady climb from #30+, positive velocity (>= 0.03), 3+ reasons, clean rank history.

Action: Enter when it crosses into top 20. Route to conservative strategy if available.

4. NEW_ENTRY_DEEP

What: Appears in top 20 from nowhere (wasn't in top 50 last scan).

Action: Instant entry.

Anti-Patterns — When NOT to Enter

  • NEVER enter assets already at #1-10. That's the top, not the entry. Rank = what already happened.
  • NEVER wait for a signal to "clean up." By the time rank history is smooth and velocity is high, the move is priced in.
  • 4+ reasons at rank #5 = SKIP. The asset already peaked. You'd be buying the top.
  • 2 reasons at rank #25 with a big jump = ENTER. The move is just starting.
  • Leaderboard rank != future price direction. Rank reflects past trader concentration. Price moves first, rank follows.
  • Negative velocity + no jump = skip. Slow bleeders going nowhere.
  • Oversold shorts (RSI < 30 + extended 24h move) = skip.

Late Entry Anti-Pattern

This deserves its own section because it's the #1 way to lose money with WOLF.

The pattern: Scanner fires FIRST_JUMP for ASSET at #25->#14. You hesitate. Next scan it's #10. Next scan #7 with 5 reasons and clean history. NOW it looks "safe." You enter. It reverses from #5.

The fix: Enter on the FIRST signal or don't enter at all. If you missed it, wait for the next asset. There's always another FIRST_JUMP coming.

Rule: If an asset has been in the top 10 for 2+ scans already, it's too late. Move on.

Phase 1 Auto-Cut

Phase 1 time-based cuts (hard timeout, weak peak, dead weight) are managed by the DSL cron when the skill supplies phase1.hardTimeout, phase1.weakPeakCut, and/or phase1.deadWeightCut in its DSL config. Wolf-strategy does not implement these in the Watchdog; include them in the skill's dsl-profile (or defaultConfig) if desired.

When configured in DSL:

  • Hard timeout: Close when elapsed in Phase 1 ≥ intervalInMinutes (e.g. 90).
  • Weak peak early cut: After intervalInMinutes (e.g. 45), close if peak ROE < minValue and current ROE < peak ROE.
  • Dead weight cut: Close when ROE was never positive and elapsed ≥ intervalInMinutes (e.g. 30).

Why: Phase 1 positions have no trailing stop protection. If the skill enables these in DSL config, the DSL cron enforces them; wolf-strategy no longer needs to manage them.

Exit Rules

1. DSL v4 Mechanical Exit (Trailing Stops)

All trailing stops handled automatically by dsl-combined.py across all strategies.

2. SM Conviction Collapse

Conv drops to 0 or 4->1 with mass trader exodus -> instant cut.

3. Dead Weight

When DSL config includes phase1.deadWeightCut, the DSL cron closes positions that have never gone positive (ROE negative entire time) after the configured minutes. Other dead-weight logic (e.g. conv 0, negative ROE) can remain in agent mandate if desired.

4. SM Flip

Conviction 4+ in the OPPOSITE direction with 100+ traders -> cut immediately.

5. Race Condition Prevention

When ANY wolf script closes a position:

  1. Call dsl-cli.py delete-dsl {strategyId} {asset} {main|xyz} --state-dir {DSL_STATE_DIR} to archive the DSL state
  2. If CLI output has cron_to_remove → remove that DSL cron (it was the last position for that strategy)
  3. Alert user via Telegram
  4. Evaluate: empty slot in that strategy for next signal?

Never set active: false in place. DSL v5.2 archives files by rename; an in-place deactivation will confuse dsl-v5.py.

DSL v5.2 — Trailing Stop System (via dsl-dynamic-stop-loss skill)

Wolf delegates all DSL logic to the dsl-dynamic-stop-loss skill. Wolf's role:

  • Create: call dsl-cli.py add-dsl {strategyId} {asset} {dex} --skill wolf-strategy --configuration {...} --state-dir {DSL_STATE_DIR} after opening a position
  • Delete: call dsl-cli.py delete-dsl {strategyId} {asset} {dex} --state-dir {DSL_STATE_DIR} after closing a position
  • Run: dsl-v5.py runs as a per-strategy cron with --strategy-id {UUID} --state-dir {DSL_STATE_DIR} (env vars are fallback)

Wolf never writes DSL state files directly.

Phase 1 (Pre-Tier 1): Absolute floor

  • LONG floor = entry × (1 − retraceThreshold/leverage) where retraceThreshold=0.10 (10% ROE)
  • SHORT floor = entry × (1 + retraceThreshold/leverage)
  • 3 consecutive breaches → close
  • Max duration: 90 minutes (enforced by Watchdog, not DSL v5 — see Phase 1 Auto-Cut)
  • Native HL SL: dsl-v5.py sets a real stop-loss order on HL via edit_position. Between cron runs, HL's engine protects the position.

Phase 2 (Tier 1+): Trailing tiers

Tier ROE Trigger Lock % of High-Water Breaches (shared)
1 5% 50% 2 (majority from wolf tier config)
2 10% 65% 2
3 15% 75% 2
4 20% 85% 2

Note: DSL v5.2 uses a single consecutiveBreachesRequired for all tiers. build_wolf_dsl_config() derives it from the majority breach count in wolf's tier config.

DSL State Files

Location: dsl/{strategyId_UUID}/{ASSET}.json (e.g. dsl/6a23783a-.../HYPE.json). See references/state-schema.md for schema. Key difference from v4: phase1.retraceThreshold is a fraction (0.10), not a percentage (10).

Rotation Rules

When slots are full in a strategy and a new FIRST_JUMP or IMMEDIATE fires:

  • Cross-strategy first: If one strategy is full but another has slots, route to the available strategy instead of rotating
  • Rotation cooldown (mandatory): Only rotate a position listed in rotationEligibleCoins from the scanner output. Positions younger than rotationCooldownMinutes (default 45 min) are ineligible — they have flat/negative ROE by design. Do NOT override this with judgment.
  • Rotate if: new signal is FIRST_JUMP or has 3+ reasons + positive velocity AND weakest eligible position (from rotationEligibleCoins) is flat/negative ROE with SM conv 0-1
  • Hold if: current position in Tier 2+ or trending up with SM conv 3+
  • If hasRotationCandidate: false: all positions are in cooldown. Do not rotate. Output HEARTBEAT_OK.

Budget Scaling

All sizing is calculated from budget (30% per slot):

how to use wolf-strategy

How to use wolf-strategy on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add wolf-strategy
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/senpi-ai/senpi-skills --skill wolf-strategy

The skills CLI fetches wolf-strategy from GitHub repository senpi-ai/senpi-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/wolf-strategy

Reload or restart Cursor to activate wolf-strategy. Access the skill through slash commands (e.g., /wolf-strategy) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

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Use Cases▌

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

✓

Save 3-5 hours/week on communication overhead

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client
  • ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • ›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • ⚠Not validating competitive research—verify facts before sharing
  • ⚠Accepting user stories without involving engineering team
  • ⚠Over-relying on frameworks without qualitative judgment
  • ⚠Not customizing outputs to company culture and communication style
  • ⚠Skipping stakeholder validation of generated requirements

Best Practices▌

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • −Don't publish competitive analysis without fact-checking
  • −Don't finalize user stories without engineering review
  • −Don't make prioritization decisions solely on AI scoring
  • −Don't skip customer validation of generated requirements
  • −Don't ignore company-specific context and culture

💡 Pro Tips

  • ★Provide context: company goals, constraints, customer feedback
  • ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • ★Use skill for 70% generation + 30% customization to company needs

When to Use This▌

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.6★★★★★40 reviews
  • ★★★★★Isabella Kapoor· Dec 28, 2024

    Keeps context tight: wolf-strategy is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Luis Mensah· Dec 4, 2024

    I recommend wolf-strategy for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Dev Desai· Nov 23, 2024

    Useful defaults in wolf-strategy — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Isabella Jain· Nov 23, 2024

    wolf-strategy is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Sophia Gupta· Nov 19, 2024

    wolf-strategy has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Arya Okafor· Oct 14, 2024

    Registry listing for wolf-strategy matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Omar Liu· Oct 14, 2024

    wolf-strategy reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★William Shah· Oct 10, 2024

    wolf-strategy fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Sakura Ramirez· Sep 21, 2024

    Keeps context tight: wolf-strategy is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Hiroshi Smith· Sep 21, 2024

    Registry listing for wolf-strategy matched our evaluation — installs cleanly and behaves as described in the markdown.

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1 / 4
Budget Slots Margin/Slot Daily Loss Limit
$500 2 $150 -$75
$2,000 2 $600 -$300
$6,500 3 $1,950 -$975
$10,000+ 3-4