5 posts tagged with "trading"

TL;DR Hyperliquid runs its own Layer-1 with two execution domains:

• HyperCore — native on-chain central limit order book (CLOB), margin, funding, liquidations.
• HyperEVM — standard EVM runtime (gas metered, paid in HYPE).

Trading on HyperCore is gasless: orders, cancels, TP/SL, TWAP, Scale ladders, etc. are signed actions included in consensus, not EVM transactions.

• You don’t need HYPE to place/cancel orders.
• You pay maker/taker fees and funding, not gas.
• Spam is mitigated with address budgets, rate limits, open-order caps.
• If you need more throughput: buy request weight at $0.0005 per action.

The design enables CEX-style strategies (dense ladders, queue dancing, rebates, hourly hedging) without the friction of gas.

Official GitHub repos:

• Python SDK → https://github.com/hyperliquid-dex/hyperliquid-python-sdk
• Rust SDK → https://github.com/hyperliquid-dex/hyperliquid-rust-sdk
• Node → https://github.com/hyperliquid-dex/node
• Order Book Server (example) → https://github.com/hyperliquid-dex/order_book_server

“The markets are never wrong; opinions often are.”
—Jesse Livermore (quoted by Bruce Kovner in Market Wizards)

Introduction​

Gradient Descent is a fundamental optimization algorithm used in machine learning and quantitative finance. In the context of algorithmic trading, it helps in optimizing predictive models, from price forecasting to portfolio optimization. Understanding how Gradient Descent works and how it can be applied in the financial markets is crucial for developing effective trading strategies.

In this article, we will explore the concept of Gradient Descent, its variations, and its applications in trading.

Introduction​

Euclidean distance is not just a mathematical concept but a crucial tool for data analysis in various fields, including trading and quantitative finance. In algorithmic trading, Euclidean distance can be applied to evaluate the similarity between financial assets, identify trading signals, and optimize portfolio allocation. As a distance metric, it helps in quantifying the relationship between different financial data points, allowing for more effective trading strategies.

In this article, we will discuss what Euclidean distance is, how it's calculated, and where it fits in the world of financial markets and algorithmic trading.

Introduction​

The advent of AI in trading has dramatically transformed the landscape of financial market analysis and execution. AI-driven strategies enable traders to process vast amounts of data, identify patterns, and execute trades with a level of precision and speed that was previously unattainable. By leveraging machine learning algorithms, traders can now develop adaptive models that adjust to market conditions in real-time, providing a competitive edge in the fast-paced world of trading.