Smarter Strategies.
Harness AI Compute. Capture Market Share.

It means using software to organise market data, surface patterns and support a decision you still make yourself. Typical inputs include price structure, volatility, volume and positioning. The value is consistency and speed of analysis; the limitation is that no model knows what happens next, and any suggestion can be wrong. Treat AI output as structured context, never as an instruction.

You define rules: a market, a price range or trend filter, order sizes, stop conditions and a maximum position. A bot watches the market and applies those rules repeatedly without hesitation. It does exactly what it is told, including in conditions you did not anticipate, which is why limits such as maximum position size and hard stops matter more than the entry logic.

No. Nothing in leveraged crypto markets can guarantee a profit, and any product claiming otherwise should be treated with suspicion. Bots can improve consistency and remove emotional decisions, but they also execute losses just as reliably. Past simulated or historical performance tells you how a rule set behaved under one set of conditions, not how it will behave next month.

Demo trading uses simulated balances and simulated fills, so mistakes cost nothing and you can rehearse a workflow. Live trading adds real money, real fees, real funding payments, real slippage and real liquidation. The biggest difference is behavioural: a strategy that feels comfortable on a demo account can be difficult to hold once the loss is your own capital.

Leverage scales your position without scaling your margin, so it shortens the distance between your entry and the liquidation price. At 100x, roughly a 1% adverse move can consume the entire margin. Higher leverage also means funding costs apply to a larger notional and that normal market noise can trigger liquidation even if your longer-term view turns out correct.

Yes, and you should. A backtest applies your rules to historical data to show how the logic would have behaved, and a demo run applies them forward with simulated money. Both are useful for finding broken assumptions. Neither is proof, because backtests are sensitive to the period chosen, and optimistic assumptions about fills, fees and slippage are the most common way a promising result turns into a real loss.

Three cost layers matter. Maker and taker fees are charged each time you trade, and high-frequency strategies pay them constantly. Funding is paid every eight hours on perpetual positions and can work for or against you depending on the rate. Slippage is the gap between the price you expect and the price you get, and it widens in fast markets when stops trigger. On small margins, fees and funding can easily outweigh the edge a strategy appears to have.