AI-Trader
Bring your own broker accounts. We add the AI, risk controls, and automation layer—research, monitor, and run an agent on top of your connected accounts. We are not a broker and do not hold your funds.
Closed beta. Paper first; no guaranteed returns.
What you get
One workspace across assets you already trade at supported brokers.
Crypto
Connect supported venues; automate under your risk limits.
Forex
Research and agent workflows on your FX accounts.
Stocks
Equities via brokers you bring (e.g. Alpaca paper/live).
Closed beta
Access opens gradually. Bring your own broker (or use paper/demo). We sell the software layer—not custody and not a share of your PnL. Leave your email if you want updates.
Latest from the blog
Product notes, engineering updates, and news.
Architecture of an algorithmic trading system
An algorithmic trading system can look like a set of separate modules: data loading, strategy, backtesting, execution, risk management, logs, monitoring. But in real operation, the important part is not the list of components. It is the boundaries between them. Those boundaries are where the link between a convincing research result and the real market most often breaks.
5/31/2026
Trading strategy metrics: how to tell whether a strategy is good
A good trading strategy is not the one with the highest return in a single backtest. Return without context says very little: it can come from excessive leverage, one rare lucky event, parameter overfitting, or a risk that simply has not appeared in the selected history yet.
5/30/2026
Main types of trading strategies in algorithmic trading
Algorithmic trading does not start with the question "where should I buy?" or with choosing an indicator. It starts with a hypothesis about why the market sometimes behaves non-randomly: it continues a move, reverts to an average, temporarily diverges between related instruments, pays for liquidity provision, or rewards the allocation of risk across assets.
5/5/2026