hft-quant-expert
Build Crypto Quant Trading Strategies
Crypto derivatives teams need rigorous signals, sizing, and backtests. This skill guides DeFi trading research with practical quantitative workflows and risk checks.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "hft-quant-expert" from https://skillstore.io/skills/barissozen-hft-quant-expert.md and its manifest at https://skillstore.io/api/skills/barissozen-hft-quant-expert/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "hft-quant-expert". Review a z-score strategy for a perpetual futures spread.
Expected outcome:
The review identifies signal construction, entry thresholds, exit logic, cost assumptions, and bias checks. It recommends validating half-life and stress testing slippage.
Using "hft-quant-expert". Help size a crypto derivatives trade with Kelly logic.
Expected outcome:
The response estimates a conservative fractional Kelly size, then applies volatility, liquidity, and drawdown limits before suggesting a smaller risk budget.
Using "hft-quant-expert". Find flaws in a profitable DeFi backtest.
Expected outcome:
The analysis checks data timing, asset selection, parameter count, gas costs, slippage, and annualization. It flags assumptions that need evidence.
Security Audit
SafeThe static external command finding is a false positive. SKILL.md line 39 opens a Python formula code block and lines 40-51 contain quantitative formulas, not executable shell instructions or command execution guidance.
Risk Factors
⚙️ External commands (1)
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<iframe src="https://skillstore.io/embed/skills/barissozen-hft-quant-expert.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
BarisSozen. (2026). hft-quant-expert security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/barissozen-hft-quant-expert/audits/9BibTeX citation
@techreport{barissozen-barissozen-hft-quant-expert-2026,
author = {BarisSozen},
title = {hft-quant-expert security audit report (audit version 9)},
institution = {Skillstore},
year = {2026},
number = {9},
url = {https://skillstore.io/skills/barissozen-hft-quant-expert/audits/9},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "hft-quant-expert security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
- name: "BarisSozen"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/barissozen-hft-quant-expert/audits/9"
identifiers:
- type: other
value: "skillstore:barissozen-hft-quant-expert:audit:9"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Screen a Mean Reversion Idea
Evaluate whether a DeFi spread signal has a clear entry rule, exit rule, and cost model.
Review Backtest Quality
Check a strategy design for lookahead bias, survivorship bias, overfitting, and incorrect annualization.
Size Portfolio Risk
Estimate a conservative position size with Kelly logic, volatility assumptions, and drawdown constraints.
Try These Prompts
Explain how to use a z-score signal for a DeFi mean reversion strategy. Include entry, exit, and risk checks.
Review my crypto derivatives backtest design for lookahead bias, survivorship bias, overfitting, costs, and annualization errors.
Create a position sizing framework using 0.25x Kelly, volatility limits, slippage assumptions, and maximum drawdown constraints.
Assess this DeFi trading strategy from signal definition through execution costs, backtest validity, risk sizing, and failure modes.
Best Practices
- Provide data frequency, transaction cost assumptions, and exchange venue details.
- Ask for bias checks before interpreting a profitable backtest.
- Separate signal research, position sizing, and execution risk in each review.
Avoid
- Do not treat example formulas as a production trading system.
- Do not ignore gas, slippage, or liquidation risk in DeFi strategies.
- Do not optimize many parameters without out-of-sample validation.
Frequently Asked Questions
Can this skill execute trades?
Does it provide financial advice?
What inputs should I provide?
Can it evaluate backtest bias?
Does it support DeFi costs?
Which tools can use this skill?
Developer Details
Author
BarisSozenLicense
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
c4231f0153ccd2a2ed62274c5a372bf8f2386d3f
Maintenance freshness
7/18/2026
Usage
16 downloads · 199 views
File structure
📄 SKILL.md