The platform builds a quantitative execution layer around perception, decision-making, risk control, and execution, while providing six types of trading skills, non-custodial execution, multi-exchange connectivity, and real-time operational monitoring
Tiger-Agent has officially launched its quantitative AI agent platform, integrating real-time market data, strategy decision-making, risk control, and trade execution into a unified quantitative execution system.
Tiger-Agent positions the platform as a Quant Execution Layer for digital asset trading. The platform builds an AI agent execution loop around four core stages: “perception, decision-making, risk control, and execution.” After completing account registration and API connection, users can deploy AI agents based on the corresponding trading skills and risk parameters, and view operational status and execution information through the platform.
According to the product architecture currently presented by Tiger-Agent, during the perception stage, the system continuously synchronizes market depth, trade data, and order book information. During 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 risk control, where limits, slippage, and drawdown constraints are incorporated into the decision process. Once the relevant conditions are met, trading instructions are executed directly through APIs, while order confirmations and position information are written back to the platform in real time.
This process forms the core operating model of Tiger-Agent’s AI agents and enables market data, strategy decisions, risk control, and trade execution to be connected within a single system.
At present, Tiger-Agent offers six trading skills, corresponding to six execution methods: GRID, GRID+, DCA, TREND, MEAN, and BREAK.
Among them, 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.
In addition, VectorRun (TREND) uses momentum vector channels to follow directional market trends while operating with drawdown protection mechanisms; OrbitBand (MEAN) performs reverse position opening and closing after prices deviate from their moving averages, primarily targeting mean-reverting market environments; 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 Tiger-Agent’s AI agent system. Users can select the appropriate execution method based on the relevant strategy configurations and risk parameters.
In terms of asset and account connectivity, Tiger-Agent identifies “non-custodial execution” as one of the platform’s key features. According to the platform, user funds remain in their own exchange accounts at all times. Tiger-Agent executes trades through APIs and does not require users to transfer trading assets directly to the platform.
During the API connection process, the platform also instructs users to configure withdrawal whitelists, withdrawal permissions, IP whitelists, and other relevant settings.
Tiger-Agent also provides wallet login functionality. According to the current login interface, wallet connection is used solely to verify wallet ownership. No transactions are initiated during the login process, and no Gas is consumed.
In addition to non-custodial execution, Tiger-Agent currently offers autonomous monitoring, transparent parameters, and multi-exchange connectivity.
For autonomous monitoring, AI agents can monitor the market 24/7 without requiring users to continuously watch the market, while protective logic can be triggered when abnormal market conditions occur.
For parameter configuration, the platform allows users to view and modify parameters such as leverage, position size, stop-loss levels, and grid spacing, while strategy behavior can also be audited.
For multi-exchange connectivity, Tiger-Agent connects trading environments through a unified account view and supports switching between different trading environments.
The Tiger-Agent platform also integrates real-time market data and execution status monitoring.
The current market interface displays digital asset trading pairs including BTC/USDT, ETH/USDT, SOL/USDT, XRP/USDT, BNB/USDT, ADA/USDT, DOGE/USDT, AVAX/USDT, and LINK/USDT, allowing users to view the latest prices and 24-hour market changes.
For execution monitoring, the platform provides Live Exec Log Streaming, which displays execution statuses including TICK, RISK, PNL, OPEN, and CLOSE, corresponding to market information, risk checks, profit and loss data, position openings, position closings, and other stages of AI agent operation.
The platform also includes a performance leaderboard where users can view overall and weekly rankings, along with return data associated with different accounts.
According to Tiger-Agent’s current onboarding process, users can deploy an AI agent in three steps: first, register an account; second, connect an API and complete the relevant permission, whitelist, and IP configurations; and finally, select a trading skill and risk-control parameters before deploying the AI agent.
Once deployment is complete, the AI agent enters the execution process based on the predefined trading skill and parameters, while users can continuously monitor market conditions, AI agent status, and relevant execution information through the platform.
Tiger-Agent’s current homepage also displays several platform operating metrics, including 14 ms execution latency, 2,244 signals processed per minute, 99.99% AI agent availability, and 47N online nodes. All of the above figures are operating metrics currently displayed on the Tiger-Agent platform interface.
With this official launch, Tiger-Agent has brought its quantitative AI agent system into live platform operation. Built around the core logic of “perception, decision-making, risk control, and execution,” the platform integrates trading skills, API connectivity, risk parameters, market data, and execution logs into a unified quantitative execution framework.
Digital asset markets are highly volatile, and automated trading strategies may also result in losses. Users should configure 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, autonomous monitoring, parameter configuration, multi-exchange connectivity, real-time market data, and execution logs.
Official Website: Tiger-Agent.com