How Do Trading Bots Work?

Every trading bot, regardless of strategy complexity, follows the same fundamental loop: input → decision → action → monitoring. The bot receives market data (price ticks, order book updates, time-of-day, news flags), processes that data through its strategy logic, decides on an action (enter, hold, exit, do nothing), submits any required orders, then monitors the result and updates its internal state.

Step 1 — Data ingestion: the bot subscribes to real-time market data from the broker's server. In MetaTrader's case, the OnTick function is called whenever a new price tick arrives for the chart's symbol. The bot can also query historical bars, indicator values, account state (balance, equity, open positions), and time/date information.

Step 2 — Strategy evaluation: the bot's strategy logic processes the input data. This might be a simple rule ('if EMA50 crosses above EMA200 and current candle closes higher, signal buy') or a complex machine-learning inference ('feed last 50 candles into trained neural network, output probability of upward move'). The output is typically a discrete signal: enter long, enter short, exit, or do nothing.

Step 3 — Order construction: if the strategy signals entry or exit, the bot constructs an order request with all required parameters: symbol, direction, volume (lot size), entry price, stop-loss, take-profit, magic number, and execution type (market, limit, stop). Position sizing typically uses risk-percentage logic — calculate stop distance in pips, divide account risk budget by stop distance, derive lot size.

Step 4 — Submission and management: the bot sends the order to the broker via the platform API. The broker may fill it, partial-fill it, requote it, or reject it. The bot handles each outcome: log fills, retry rejections within tolerance, alert on unexpected failures. After fill, the bot monitors the position — potentially trailing the stop, partial-closing on profit targets, or closing on time-based criteria.

Bots can submit any order type the broker supports. The most common in retail forex:

Trading bots need an interface to the broker's matching engine. Retail forex bots typically use one of three connection types:

MetaTrader API (most common at retail): the bot is an Expert Advisor running inside the MT4 or MT5 terminal. The terminal handles the connection to the broker's server; the bot just uses the high-level API (`OrderSend`, `OrderModify`, `PositionGetTicket`, etc.). This is the simplest path but adds the MetaTrader terminal as a dependency in the execution chain.

FIX API (institutional standard): the bot connects directly to the broker's FIX server using the Financial Information eXchange protocol. Faster than MetaTrader, more reliable, but requires more programming expertise. Available at some brokers as a premium option for high-volume accounts.

REST/WebSocket APIs: many modern brokers offer HTTP REST APIs for trading. Useful for bots written in Python or other non-MQL languages. Latency typically higher than FIX but lower than MetaTrader bridges. Common in cryptocurrency markets; expanding in forex.

Profitable bots share specific characteristics. The biggest determinant is strategy edge — does the bot exploit some persistent inefficiency or pattern? Edge can come from speed (scalping execution-quality differences), pattern recognition (technical or statistical patterns), or risk pricing (capturing premiums others won't pay). Edge decays over time; profitable bots either have edges robust to regime change or are updated as regimes shift.

Equally important is risk management. A bot with positive expectancy can still blow up an account if position sizing is too aggressive. The Kelly criterion provides the mathematical optimum, but practitioners typically use fractional Kelly (25-50% of optimal) to manage drawdown variance. Position sizing of 0.5-2% per trade is standard for retail forex bots.

Operational quality matters as much as strategy quality. The same bot on a tight-spread ECN broker with a co-located VPS performs very differently from the same bot on a wide-spread market-maker with a home PC. Broker selection, execution latency, news handling, and parameter management all contribute to whether a bot's theoretical edge translates to live profitability.