autonomous-trading▌
senpi-ai/senpi-skills · updated Apr 8, 2026
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Give your agent a budget, a target, and a deadline — then let it trade. The agent creates a strategy wallet, scans for opportunities, opens positions, protects profits with DSL, and enforces risk controls.
Autonomous Trading v6
Give your agent a budget, a target, and a deadline — then let it trade. The agent creates a strategy wallet, scans for opportunities, opens positions, protects profits with DSL, and enforces risk controls.
Skill Attribution
When creating a strategy, include skill_name and skill_version in the call. See references/skill-attribution.md for details.
Prerequisites
Install these companion skills first:
- dsl-dynamic-stop-loss — trailing stops, auto-closing, per-tier retrace
- opportunity-scanner — market scanning, scoring, hourly trend gate
- emerging-movers — SM rotation detection, IMMEDIATE_MOVER signals
Minimum budget: $500 (recommend $1k+)
Known Bugs & Gotchas
See references/bugs-and-gotchas.md — critical issues from live trading including the dryRun bug, phantom closes, XYZ DEX margin type, Tier 1 lock misconception, and scanner leverage vs actual max.
The Flow
Step 1: Ask the User
Collect: budget, target, deadline, risk tolerance (conservative/moderate/aggressive), asset preferences.
Step 2: Calculate the Playbook
See references/risk-rules.md for complete risk rules by profile.
v6 Core Rules:
The #1 Rule — Hourly Trend Alignment. ALL trades must confirm with hourly candle structure. Counter-trend = hard skip, no exceptions. This single rule prevents the majority of losing trades.
Max Leverage Check. Always check max-leverage.json before entering. Scanner leverage is conservative, not actual max.
Concentration Over Diversification. At small account sizes ($500-$10k), 2-4 high-conviction positions beat 6 mediocre ones. Cross-margin math: 4 positions → 80.6% margin buffer, 2 positions → 89.7%.
Every Slot Must Maximize ROI. Empty slot > mediocre position. If a position isn't working, cut it and free the slot.
Speed Filter. Best moves happen FAST (XRP hit Tier 3 in 19 min, XMR Tier 2 in 37 min). Slow movers are suspects.
Directional Exposure Guard
Before opening, check total LONG vs SHORT notional. Cap at 70% in one direction.
Position Sizing by Score
| Scanner Score | Position Size |
|---|---|
| 250+ | Up to max per-position |
| 200-250 | 75% of max |
| 175-200 | 50% of max |
| < 175 | Skip |
Step 3: Create the Strategy
strategy_create_strategy(budgetUsd, leverageType, riskLabel)
Returns strategyId + walletAddress. Fund the wallet.
Step 4: Create the Playbook File
JSON config tracking: risk profile, position limits, score thresholds, active positions, trade journal. See references/playbook-schema.md.
Step 5: Set Up Cron Jobs
Race Condition Prevention (v6 — CRITICAL)
Multiple cron jobs (scanner, SM flip, DSL) can all try to close the same position. When ANY job closes a position:
# 1. Close the position
result = close_position(wallet, asset)
# 2. Immediately deactivate DSL state file
state["active"] = False
save_state(state)
# 3. Disable DSL cron for this asset
disable_cron(f"dsl-{asset}")
All three steps MUST happen in the same action. This prevents phantom closes.
Cron Schedule:
| Job | Interval | Purpose |
|---|---|---|
| Opportunity Scanner | 10-30 min (time-aware) | Find setups |
| DSL Monitor | 2-3 min per position | Trailing stops |
| SM Flip Detector | 5 min | Conviction changes |
| Portfolio Update | 15 min | Reporting |
See references/cron-setup.md for detailed cron configuration, time-aware scheduling, and SM flip detection logic.
Step 6: The Trading Loop
SCAN → EVALUATE → TRADE → PROTECT → REPEAT
For each scan result:
1. Check hourly trend alignment (HARD REQUIREMENT)
2. Check directional exposure guard
3. Check max leverage via max-leverage.json
4. Score ≥ 175? → Size by score tier
5. Open position → Create DSL state → Start DSL cron
6. Journal the trade (scanner snapshot at entry)
v6: Dead Weight Cutting
| Condition | Action |
|---|---|
| SM conviction drops 4→1 (e.g., 220→24 traders in 10 min) | Cut immediately |
| Dead weight at conviction 0 | Cut immediately — free the slot |
| Position stagnant, better opportunity available | Rotate |
Step 7: Safety Rails
Hard Stops (automatic):
- Daily loss limit hit → stop trading for the day
- Total drawdown hard stop → close all positions, alert user
- DSL breach → auto-close (handled by script)
What the Agent Should NEVER Do:
- Trade counter-trend on hourly
- Exceed position size limits
- Override DSL
- Average down on a losing position
- Ignore the directional exposure guard
Step 8: Lessons from the Field
See references/lessons.md for what works, what doesn't, retrace tuning, and fee awareness from live trading.
API Reference
See references/api-tools.md for the key Senpi tools used by this skill.
How to use autonomous-trading on Cursor
AI-first code editor with Composer
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 autonomous-trading
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches autonomous-trading from GitHub repository senpi-ai/senpi-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate autonomous-trading. Access the skill through slash commands (e.g., /autonomous-trading) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★69 reviews- ★★★★★Omar Tandon· Dec 28, 2024
autonomous-trading fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Yash Thakker· Dec 24, 2024
autonomous-trading fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ishan Ghosh· Dec 24, 2024
autonomous-trading is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Omar Wang· Dec 20, 2024
autonomous-trading has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ishan White· Dec 16, 2024
We added autonomous-trading from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ava Mensah· Dec 16, 2024
Registry listing for autonomous-trading matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★William Chawla· Dec 8, 2024
autonomous-trading fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★William Johnson· Nov 27, 2024
autonomous-trading is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Soo Thompson· Nov 19, 2024
autonomous-trading is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Pratham Ware· Nov 15, 2024
autonomous-trading is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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