market-regimes▌
0xhubed/agent-trading-arena · updated Apr 8, 2026
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Detect market regimes and apply data-driven trading strategies based on historical performance patterns.
- ›Identifies 32 active market regimes (trending up, moderate bull, mixed choppy, bearish, sideways flat, low volatility) with confidence scores up to 93%
- ›Recommends regime-specific strategies: zero-trade capital preservation in choppy/bearish markets, active 120–200 trades/day with risk validation in confirmed uptrends, asset selection prioritization based on momentum
- ›Highlights cri
Market Regimes
Last updated: 2026-01-17 20:31 UTC Active patterns: 32 Total samples: 0 Confidence threshold: 60%
How to Use This Skill
- Identify the current market regime using price action and volatility
- Look up the recommended strategy for that regime below
- Adjust your trading approach accordingly
- Monitor for regime changes
Regime Strategies
Mixed Choppy
Recommended approach (93% confidence, seen 1x):
Reduce or eliminate trading. In mixed markets (BNB +0.93%, SOL +1.65%, BTC -0.08%, ETH -0.03%, DOGE -1.19%), zero-trade strategies ($0 PnL) outperformed all active traders (all negative PnL). Trade frequency inversely correlates with performance: 0 trades = $0, 2 trades = -$0.29, 160 trades = -$17.96, 201 trades = -$360.24.
- Total observations: 0
- First identified: 2026-01-17
Moderate Bull
Recommended approach (93% confidence, seen 1x):
Zero trading or extremely low frequency (<25 trades/24h). Market showed BNB +2.03%, ETH +1.02%, DOGE +1.07%, BTC +0.24% but all active traders lost money. Only non-traders preserved capital. If trading, long-only on strongest performers (BNB).
- Total observations: 0
- First identified: 2026-01-17
Moderate Bull
Recommended approach (92% confidence, seen 1x):
Zero trading or very low frequency (<30 trades/24h) with long-only bias. Market up +0.63% to +2.15% across all assets - passive strategies outperformed all active traders. Only skill_only_oss with 89 trades and selective longs achieved positive PnL.
- Total observations: 0
- First identified: 2026-01-17
Moderate Bull
Recommended approach (90% confidence, seen 1x):
Avoid shorting entirely. All short-biased strategies lost money despite 'bearish' technical signals. Technical indicators gave false bearish readings in a bullish regime.
- Total observations: 0
- First identified: 2026-01-17
Moderate Bull
Recommended approach (90% confidence, seen 1x):
Avoid shorting entirely. skill_aware_oss lost $-167.78 with 171 trades including BTCUSDT shorts despite 'bearish alignment' signals. Market direction trumps technical signals.
- Total observations: 0
- First identified: 2026-01-17
Moderate Bearish
Recommended approach (88% confidence, seen 1x):
Trade frequency inversely correlates with performance: 0-10 trades = $0 to -$30 loss; 76-103 trades = -$44 to -$180 loss. Optimal is <20 trades or pure capital preservation.
- Total observations: 0
- First identified: 2026-01-16
Mixed Choppy
Recommended approach (88% confidence, seen 1x):
Technical analysis signals (multi-timeframe alignment, MACD, SMA crossovers) generate false signals in mixed markets. Both bullish and bearish aligned trades lost money.
- Total observations: 0
- First identified: 2026-01-17
Moderate Bull
Recommended approach (88% confidence, seen 1x):
Asset selection critical: BNB (+2.03%) significantly outperformed SOL (-0.09%). Agents focusing on SOL longs (llama4_scout) lost heavily despite 'uptrend' reasoning.
- Total observations: 0
- First identified: 2026-01-17
Moderate Bearish
Recommended approach (85% confidence, seen 1x):
Reduce trade frequency dramatically or avoid trading entirely. Zero-trade strategies ($0 PnL) outperformed active trading strategies (avg -$66 PnL). Only agentic_gptoss profited by actively managing shorts and closing positions to lock gains.
- Total observations: 0
- First identified: 2026-01-16
Mixed Choppy
Recommended approach (85% confidence, seen 1x):
If trading, prefer assets showing clear directional movement (SOL +1.65%, BNB +0.93%) over flat assets (BTC -0.08%, ETH -0.03%). Avoid shorting in mixed regimes even with bearish technical signals.
- Total observations: 0
- First identified: 2026-01-17
Moderate Bull
Recommended approach (85% confidence, seen 1x):
Asset selection: BNB (+2.15%) significantly outperformed BTC (+0.63%). Long positions in higher-beta assets would have captured more upside.
- Total observations: 0
- First identified: 2026-01-17
Moderate Bull
Recommended approach (85% confidence, seen 1x):
Technical analysis signals (multi-timeframe alignment, MACD, RSI) are unreliable for direction. Both bullish and bearish technical signals led to losses.
- Total observations: 0
- First identified: 2026-01-17
Moderate Bearish
Recommended approach (82% confidence, seen 1x):
Short positions with active profit-taking outperform long positions. agentic_gptoss (+$34.30) succeeded by opening shorts and closing when 'position is already in profit' rather than holding.
- Total observations: 0
- First identified: 2026-01-16
Moderate Bearish
Recommended approach (80% confidence, seen 1x):
Asset selection matters: DOGE (-3%) showed highest volatility/decline, making it best short target. BNB (-0.61%) showed relative strength, making it worst short target.
- Total observations: 0
- First identified: 2026-01-16
Trending Up
Recommended approach (74% confidence, seen 2x):
Active trading with 120-200 trades/24h, multi-timeframe bullish alignment WITH explicit risk validation (2% equity, 2:1 R:R), proactive position management closing losers quickly. skill_aware_oss achieved +$1236.81 with this approach in +2.5% to +6.2% market
- Total observations: 0
- First identified: 2026-01-14
Trending Up
Recommended approach (72% confidence, seen 1x):
Optimal trade frequency 120-200 trades/24h. Below 50 misses opportunities (ta_baseline -$25.88), above 240 leads to overtrading losses (llama4_scout -$18.95)
- Total observations: 0
- First identified: 2026-01-14
Moderate Bull
Recommended approach (70% confidence, seen 1x):
Zero-trade strategies preserve capital but miss +3-6% opportunity. Active validated trading (skill_aware_oss +$1236.81) dramatically outperforms passive approaches
- Total observations: 0
- First identified: 2026-01-14
Trending Up
Recommended approach (70% confidence, seen 1x):
Asset selection matters: ETH (+6.16%) significantly outperformed BTC (+3.99%) and BNB (+2.54%). Agents focusing on higher-beta assets (ETH, DOGE) captured more upside
- Total observations: 0
- First identified: 2026-01-14
Trending Up
Recommended approach (68% confidence, seen 1x):
Active trading with 120-200 trades/24h, multi-timeframe bullish alignment, explicit risk validation (2% risk, 2:1 reward), and proactive position management. skill_aware_oss (+$1379.66) and agentic_gptoss (+$689.63) demonstrate this approach works when market is up 2.78-6.44% across all assets.
- Total observations: 0
- First identified: 2026-01-14
Trending Up
Recommended approach (68% confidence, seen 1x):
Asset selection matters: ETH (+6.16%) outperformed BTC (+3.99%) and SOL (+2.88%) - agents focusing on highest momentum assets captured more gains
- Total observations: 0
- First identified: 2026-01-14
Trending Up
Recommended approach (68% confidence, seen 1x):
Zero-trade strategies preserve capital but miss +3-6% opportunity cost. In confirmed bull markets, active participation with risk controls outperforms passive approaches
- Total observations: 0
- First identified: 2026-01-14
Trending Up
Recommended approach (65% confidence, seen 1x):
Position sizing at 25% equity limit per position with active monitoring and willingness to close at breakeven to redeploy capital (gptoss_20b_simple: +$27.88 with 125 trades)
- Total observations: 0
- First identified: 2026-01-14
Trending Up
Recommended approach (63% confidence, seen 1x):
Asset selection matters in bull markets: ETH (+6.44%) outperformed BTC (+4.14%) and SOL (+2.94%). Agents focusing on strongest performers (ETH, DOGE at +5.52%) captured more upside.
- Total observations: 0
- First identified: 2026-01-14
Trending Up
Recommended approach (60% confidence, seen 1x):
Multi-timeframe bullish alignment WITH risk validation and position scaling works in genuine uptrends (+3-5%). Moderate trade frequency (150-200) captures moves without overtrading. Focus on strongest movers (ETH, DOGE) not laggards (SOL).
- Total observations: 0
- First identified: 2026-01-14
Trending Up
Recommended approach (60% confidence, seen 1x):
Position sizing at 25% equity limit per position (gptoss_20b_simple reasoning) allows meaningful exposure while maintaining diversification. gptoss_20b_simple achieved +$27.88 with 124 trades using this approach.
- Total observations: 0
- First identified: 2026-01-14
Trending Up
Recommended approach (58% confidence, seen 1x):
Asset selection matters: ETH (+4.96%) and DOGE (+4.77%) outperformed. Agents focusing on BTC (+3.14%) and SOL (+1.55%) captured less alpha.
- Total observations: 0
- First identified: 2026-01-14
Moderate Bull
Recommended approach (57% confidence, seen 1x):
Zero-trade strategies preserve capital but miss opportunity cost. In this window with +2.78% to +6.44% gains, passive strategies underperformed active risk-managed approaches by significant margin.
- Total observations: 0
- First identified: 2026-01-14
Trending Up
Recommended approach (55% confidence, seen 1x):
Active loss management - closing losing positions quickly to free margin for winners. agentic_gptoss and gptoss_120b_simple both used this successfully.
- Total observations: 0
- First identified: 2026-01-14
Sideways Flat
Recommended approach (52% confidence, seen 1x):
Reduce trade frequency to <10 trades/day; passive index allocation outperforms active trading; avoid leveraged positions entirely; wait for clear directional breakout before engaging
- Total observations: 0
- First identified: 2026-01-13
Moderate Bull
Recommended approach (50% confidence, seen 1x):
Zero-trade strategies (gpt_simple, index_fund) preserve capital but miss +3-5% opportunities. In confirmed uptrends, some participation is optimal.
- Total observations: 0
- First identified: 2026-01-14
Low Volatility Mixed
Recommended approach (48% confidence, seen 1x):
When all assets show <0.1% absolute movement, avoid opening new positions; fee drag exceeds potential profit; technical signals (SMA, MACD, multi-timeframe alignment) produce false signals in this regime
- Total observations: 0
- First identified: 2026-01-13
Sideways Flat
Recommended approach (45% confidence, seen 1x):
Mean-reversion and momentum strategies both fail; only capital preservation strategies (hold cash, minimal index allocation) succeed
- Total observations: 0
- First identified: 2026-01-13
Confidence Guide
| Confidence | Interpretation |
|---|---|
| 90%+ | High confidence - strong historical support |
| 70-90% | Moderate confidence - use with other signals |
| 60-70% | Low confidence - consider as one input |
| <60% | Experimental - needs more data |
This skill is automatically generated and updated by the Observer Agent.
How to use market-regimes 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 market-regimes
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches market-regimes from GitHub repository 0xhubed/agent-trading-arena 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 market-regimes. Access the skill through slash commands (e.g., /market-regimes) 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
4.4★★★★★39 reviews- ★★★★★Neel Bhatia· Dec 28, 2024
market-regimes fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Dhruvi Jain· Dec 20, 2024
We added market-regimes from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Xiao Sharma· Dec 12, 2024
market-regimes has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Xiao Anderson· Dec 4, 2024
Keeps context tight: market-regimes is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chen Mensah· Nov 19, 2024
We added market-regimes from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Oshnikdeep· Nov 11, 2024
market-regimes fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Neel Singh· Nov 11, 2024
Registry listing for market-regimes matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Xiao Li· Nov 3, 2024
market-regimes reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Neel Gonzalez· Oct 22, 2024
I recommend market-regimes for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Amelia Rahman· Oct 10, 2024
Solid pick for teams standardizing on skills: market-regimes is focused, and the summary matches what you get after install.
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