VergeX guides
Understand AI trading before choosing a strategy
AI trading uses models to interpret market information and support or automate trading decisions. A useful evaluation starts with what the system does, how orders reach the market and how losses are controlled. This guide connects those questions to the AI traders, prompt strategies and code strategies available on VergeX.
AI trading, trading bots and quantitative strategies
A trading bot automates a workflow. A quantitative strategy expresses market assumptions through data and rules. AI can contribute to either approach by interpreting inputs or producing decisions. These categories overlap: calling a system “AI” does not explain its strategy, execution quality or expected return.
Identify the markets it trades, the information it uses, when it can place an order and which risk settings constrain it. A language-model-based agent also depends on its instructions and the context supplied to the model. Model changes, missing data and changing market conditions can all affect behavior.
Explore the different strategy types on VergeX
The AI Trader directory provides public trader descriptions and available historical metrics. Open a trader’s detail page to review the markets and history behind a headline figure. Prompt strategies describe instructions used by an AI agent; code strategies express trading logic in code. Review the particular strategy and account requirements before using either.
Copying a trader and running a strategy can have different execution paths. Read the relevant product flow and configuration rather than assuming every agent mirrors the same orders. The exchanges, models and settings shown in the application are the current source of availability information.
Read performance with the right context
Compare results over the same period. Return is a percentage; P&L is an amount. Review the reporting period, drawdown, trade count and market exposure alongside returns. A high win rate can coexist with large losses. An unavailable metric provides no evidence that risk is zero.
Backtests describe results under historical data and assumptions. Live results reflect actual execution but still depend on the account and observation period. Fees, funding, slippage, entry time and position size can make your outcome differ from a published record. Historical results do not establish future profitability.
Understand costs and operating boundaries
Check the current model or Credits pricing in VergeX, the exchange’s trading fees and any funding or strategy charges that apply to your chosen workflow. Frequent decisions and frequent trades are different activities, and can create different costs.
Review account permissions, strategy parameters and risk controls before enabling an agent. Know where to inspect decisions and positions, and how to pause the workflow. Data interruptions, model errors and exchange failures remain possible even when an interface is available.
A checklist for comparing AI trading systems
- Can you explain the strategy and the markets it trades?
- Are the reporting period and backtest or live status clear?
- Can you inspect losses, drawdown and costs alongside returns?
- Do the account permissions and risk settings match the intended workflow?
- Can you review decisions and stop the agent when needed?