The platform establishes a quantitative execution framework built around perception, decision-making, risk control, and execution, integrating GRID, DCA, TREND, MEAN, BREAK, and other trading skills into a unified AI agent system.

Tiger-Agent has further unveiled its quantitative AI agent execution architecture, establishing a continuous operating process centered on four core stages: market perception, strategy decision-making, risk control, and trade execution.

Positioned as a Quant Execution Layer for digital asset trading, Tiger-Agent brings market data synchronization, trading skill evaluation, risk constraints, and order execution into a single execution loop.
According to the platform’s current system architecture, during the perception stage, the system continuously synchronizes market depth, trade data, and order book information. In the decision-making stage, the AI agent generates position-opening or position-closing intentions based on the trading skill selected by the user. The process then moves into the risk-control stage, where position limits, slippage, and drawdown constraints are incorporated into the execution decision. Once the relevant conditions are met, trading instructions are executed through APIs, while order confirmations and position information are written back to the platform in real time.

This process forms the core mechanism that enables Tiger-Agent’s AI agents to operate continuously.

Six Trading Skills Integrated Into a Unified Execution System

Tiger-Agent currently provides six trading skills: PulseCore, HyperLattice, StreamVault, VectorRun, OrbitBand, and EdgeBreak, corresponding to GRID, GRID+, DCA, TREND, MEAN, and BREAK.
PulseCore (GRID) uses microstructure pulse capture to perform adaptive execution in ranging or gradually rising market conditions.

HyperLattice (GRID+) uses a high-density grid weighting mechanism to accelerate execution and expand capacity in ranging markets. 

StreamVault (DCA) builds positions through time-sequenced allocation and accumulates positions during upward market volatility.

VectorRun (TREND) follows directional market trends through momentum vector channels while incorporating drawdown protection. 

OrbitBand (MEAN) operates around reverse position-opening and position-closing logic after prices deviate from their moving averages. 

EdgeBreak (BREAK) opens positions in the direction of the trend after prices break above recent highs and exits when prices fall back to the moving average.

All six trading skills operate within the same AI agent execution framework and continue through the four stages of perception, decision-making, risk control, and execution.

Risk Control and Execution Form a Continuous Process

Within Tiger-Agent’s execution architecture, risk control sits between strategy decision-making and final execution.

According to the platform’s current interface, position limits, slippage, and drawdown constraints directly participate in trade execution decisions. The platform also allows users to view and adjust parameters such as leverage, position size, stop-loss levels, and grid spacing, while treating “parameter transparency” as an important part of the AI agent system.

The platform also provides Live Exec Log Streaming, which displays execution statuses including TICK, RISK, PNL, OPEN, and CLOSE, allowing users to view market synchronization, risk checks, profit and loss information, and position-opening and position-closing processes.

After execution, the corresponding order confirmations and position statuses continue to be synchronized back to the platform.

Non-Custodial Model Connects Trading Environments

Tiger-Agent uses a non-custodial execution model, with users’ trading funds remaining in their own exchange accounts while the platform executes relevant trading operations through APIs.

During the API connection process, users are required to complete the relevant permission settings, withdrawal whitelist configuration, and IP whitelist configuration according to the platform’s current instructions.

Tiger-Agent also supports multi-exchange connectivity, connecting different trading environments through a unified account view and allowing users to switch between them.

The platform also provides wallet login functionality. According to the current interface, wallet connection is primarily used to verify wallet ownership. The login process does not initiate transactions and does not consume Gas.

From Trading Skills to a Complete Quant Execution Layer

After completing account registration and API binding, users can deploy AI agents based on selected trading skills and the corresponding risk-control parameters.

Once activated, the AI agent continuously performs market perception, trading evaluation, risk checks, and order execution, while synchronizing the corresponding operational information through the platform interface.

By integrating six trading skills, market data, risk parameters, API connectivity, and real-time execution logs into a single system, Tiger-Agent forms what it defines as a Quant Execution Layer, enabling trading skills to operate within a unified loop of perception, decision-making, risk control, and execution.

Digital asset markets are highly volatile, and automated trading strategies may also result in losses. Users should configure the relevant trading parameters according to their own risk tolerance and properly manage their exchange accounts and API permissions.

About Tiger-Agent

Tiger-Agent is a quantitative AI agent platform for digital asset trading, positioned as a Quant Execution Layer. The platform builds its AI agent operating system around perception, decision-making, risk control, and execution. It currently provides six trading skills and supports non-custodial execution, multi-exchange connectivity, parameter configuration, real-time market data, and execution logs.

Official Website: Tiger-Agent.com