How do I install hft-quant-expert?

Run `npx skills add https://github.com/kasyap1234/delta-go --skill hft-quant-expert` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does hft-quant-expert support?

hft-quant-expert works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is hft-quant-expert free to use?

Yes. hft-quant-expert is free to install and use. It is available from the open explainx.ai skill registry published by kasyap1234.

Where can I read ratings and reviews for hft-quant-expert?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

Productivity

hft-quant-expert▌

kasyap1234/delta-go · updated Apr 8, 2026

$npx skills add https://github.com/kasyap1234/delta-go --skill hft-quant-expert

summary

Quantitative trading expertise for DeFi and crypto derivatives.

skill.md

HFT Quant Expert

Quantitative trading expertise for DeFi and crypto derivatives.

When to Use

Building trading strategies and signals
Implementing risk management
Calculating position sizes
Backtesting strategies
Analyzing volatility and correlations

Workflow

Step 1: Define Signal

Calculate z-score or other entry signal.

Step 2: Size Position

Use Kelly Criterion (0.25x) for position sizing.

Step 3: Validate Backtest

Check for lookahead bias, survivorship bias, overfitting.

Step 4: Account for Costs

Include gas + slippage in profit calculations.

Quick Formulas

# 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

Common Pitfalls

Lookahead bias - Using future data
Survivorship bias - Only existing assets
Overfitting - Too many parameters
Ignoring costs - Gas + slippage
Wrong annualization - 252 daily, 365*24 hourly

general reviews

Ratings

4.5★★★★★10 reviews

★★★★★Shikha Mishra· Oct 10, 2024
hft-quant-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
★★★★★Piyush G· Sep 9, 2024
Keeps context tight: hft-quant-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
★★★★★Chaitanya Patil· Aug 8, 2024
Registry listing for hft-quant-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
★★★★★Sakshi Patil· Jul 7, 2024
hft-quant-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
★★★★★Ganesh Mohane· Jun 6, 2024
I recommend hft-quant-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
★★★★★Oshnikdeep· May 5, 2024
Useful defaults in hft-quant-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
★★★★★Dhruvi Jain· Apr 4, 2024
hft-quant-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
★★★★★Rahul Santra· Mar 3, 2024
Solid pick for teams standardizing on skills: hft-quant-expert is focused, and the summary matches what you get after install.
★★★★★Pratham Ware· Feb 2, 2024
We added hft-quant-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
★★★★★Yash Thakker· Jan 1, 2024
hft-quant-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.