Hyperliquid Advanced AT

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Last updated 2 months ago

Hyperliquid Advanced AT

Introduction

Automatic trading (AT) is not a new concept; it emerged in the 1980s when firms began leveraging algorithms to execute trades at optimal market prices. With the growth of the internet, people have developed increasingly sophisticated AT systems, evolving from EAs to trading bots, and now to AI trading.

As an experienced trader with years in the market, I have worked with numerous AT set-up, from simple "Buy low, sell high" strategies on TradingView to complex systems running 24/5 in the Forex market. One crucial lesson from these years: no AT system can run 24/5 for years and remain consistently profitable. Developer intervention is always necessary.

Consider this: technological growth follows an exponential curve. Each year, we advance at a faster pace. However, the primary use cases for AT remain limited to market making or delta-neutral strategies.

Ask any firm if they implement AT in their directional trading strategies, and most will answer "No" or "Partially."

Why do firms shy away from AT?

The answer lies in the dynamic nature of market behavior. Markets in 2000, 2010, and 2020 were fundamentally different, changing with unpredictable variables. Most AT systems operate based on historical market data which, ironically, cannot reliably predict future movements.

To approach AT with an AI Agent, our targets must be practical. The AI Agent can trade on behalf of others, but only for short timeframes. As market dynamics change, developers must frequently update their AI Agents to adapt to market conditions.

This is precisely what the AI Agent component of $TRUST aims to achieve. We are creating a realistic AI Agent bound to the IP of Degen Ape, an experienced and profitable trader. All you need to do is $TRUST him.

1. Understanding The Concept

When preconditions are met, the model executes 3 strategies:

Long/Short at the nearest support/resistance level: Scalping Trade
Long/Short at the strongest support/resistance level: Swing Trade
Long/Short at the current price level: Market Trade. (The model will place a limit order at queue 1 on the orderbook, saving 70% in fees)

Examples:

Here's how a trade setup works.

LTF: Using Daily and Hourly charts, the model employs an in-house rating system to determine directional bias.
Preconditions: A set of criteria and filters that price action must meet before trade execution.

Workflow:

The model verifies:

1. If preconditions are met

2. If LTF directional bias aligns

3. If HTF directional bias aligns

Only when all conditions are met will the model execute trades.

Otherwise, no signals or trades are generated.

2. Pros and Cons

A. Pros:

Follows TRUST Signals 24/7
Potential Hyperliquid airdrop in the future.
Saves up to 70% in trading fees through limit orders (except for STOP LOSS)

B. Cons:

Exposure to risks detailed in section 4

3. Money Management

The balance requirements are: minimum $500.

Example: With a $500 balance, a scalping trade risks $2.5, while a swing trade risks $5.

4. Risk

There is always risk involved in trading.

There is always risk involved when using an AT system (bug/misconfigurations)

There is always risk involved when sharing the trading API (hacked, bad dev, bad security OpSec).

Beyond these systematic risks, "stacking limit orders" is the one we also need to discuss.

While trade risk settings range from 0.5% to 1% per trade, the model may stack positions up to 20-30% of balance in a short period. If the market moves strongly against these positions, you could lose 20-30% of your balance.

To mitigate this, there is an EMERGENCY trigger which is managed by me. We did it with the manual signal just before the Trump Tariff dump last week.