Quantitative trading expertise for DeFi and crypto derivatives.
Works with
Quantitative trading expertise for DeFi and crypto derivatives.
Calculate z-score or other entry signal.
Use Kelly Criterion (0.25x) for position sizing.
Check for lookahead bias, survivorship bias, overfitting.
Include gas + slippage in profit calculations.
# Z-score
zscore = (value - rolling_mean) / rolling_std
# Sharpe (annualized)
sharpe = np.sqrt(252) * returns.mean() / returns.std()
# Kelly fraction (use 0.25x)
kelly = (win_prob * win_loss_ratio - (1 - win_prob)) / win_loss_ratio
# Half-life of mean reversion
half_life = -np.log(2) / lambda_coef
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionhft-quant-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches hft-quant-expert from kasyap1234/delta-go and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate hft-quant-expert. Access via /hft-quant-expert in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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total installs
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this week
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upvotes
Run in your terminal
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Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
erichowens/some_claude_skills
sickn33/antigravity-awesome-skills
erichowens/some_claude_skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
hft-quant-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend hft-quant-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
hft-quant-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend hft-quant-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for hft-quant-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: hft-quant-expert is focused, and the summary matches what you get after install.
hft-quant-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
hft-quant-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: hft-quant-expert is focused, and the summary matches what you get after install.
Useful defaults in hft-quant-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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