Reactor for people from the world of technology and automation

You can start from ready-made prompt examples. The instructions define the task and the way of thinking; the system settings decide the connections, the scheduling and the authority to act.

Reactor connects the AI to tools that bring in data: prices, candles, technical indicators, positions and account details. In the relevant markets, data such as the order book, funding and open interest is also available.

You can define which data a task needs, and check its source and timestamp. The difference between a live price, a snapshot and a closed candle matters especially when the analysis leads to action.

An Agent can get a goal, gather information and weigh several factors before deciding. The instructions can set priorities, ruling-out conditions, and a way to handle conflicting information.

For example, an Agent can assess an opportunity and choose between acting, waiting and staying out. Binding limits, such as the task scope and the authority to execute, define the frame it works in.

You can split the work into roles: scanning, gathering information, the entry decision, position management and reporting.

One Agent's output can feed the next. At each hand-off you define what information is required, what the result means and when to continue. That way you can test each role separately, and the connection between them too.

With Webhooks you can send an external signal into Reactor and trigger a process from it. The Agent can gather more information, assess the signal and decide how to proceed.

You can also send information out to an execution system, alerts or a dashboard, according to the connection and mapping you defined.

Reactor lets you separate the automation's success from the quality of the trading. You can collect signals, replay price movement after them, and examine the decisions and the trade management.

In a real example, a Mapper scanned for Bullish Engulfing every four hours. The candle tool was used to research the movement after the signals. Based on the findings, a 3% stop-loss and a 4.5% profit target were chosen, and a Buyer that buys the signals was run.

As of preparing this example, by the measurement provided, 800 trades closed with a 65% win rate. The Buyer currently doesn't filter with judgment, in order to collect results before testing the next improvement.

A trading decision has to become an execution plan: asset, direction, quantity, order type, price and protections.

Reactor lets you build orders, including staged entries and different allocations, according to the supported tools. In the relevant path you can review a preview before execution.

If you're new to trading, we'll also show the difference between the allocated amount, market exposure and the possible loss. Using leverage changes both the exposure and the risk.

You can run Agents manually, at set times, after another Agent, or in response to an external signal. The schedule should match the pace of the data and the relevant trading hours.

The run record lets you check which model worked, which tools were called, what each tool returned, what failed, how long it took, and what the usage cost was.

Reactor also supports a connection through MCP, which lets other AI tools access Reactor's capabilities with authorization and subject to controls.

The connection can enable work with market and account data and other tools, depending on the capabilities exposed and the permissions granted.