Yeagers has spent over three years building the system that creates Trading Agents. The pipeline solves multiple industry-level challenges, from data ingestion and model training to pairing predictive brains with position-management behaviour. Each agent in the catalog is the output of that process.

1.What an agent is

Every Trading Agent is a piece of software made of two parts: an AI model and a behavioural program. Together they constitute the agent: not a strategy we ship to you, but autonomous software where the model forecasts its market and the behaviour program manages positions. Entries follow the model's signals; exits, stops, and trade management follow the behaviour layer.

Each agent trades one specific asset only. During training, each agent develops its own unique strategy with different data preferences, risk-reward posture, and position-management style. No two agents are identical.

2.Training pipeline

Agents train for approximately eight days before they are eligible for the catalog. During training they receive a broad set of inputs: price data, technical analysis, sentiment, and global market data. Each agent decides which data sources to rely on most. That selection is part of what makes agents behave differently from one another.

Step 1: Build the brain

We train a predictive brain model on the agent's asset and selected data. Brain architectures vary by agent. Our most commonly used model families include neural networks, XGBoost, ridge regression, and random forest. After training, the brain's predictive capability is rigorously tested before it moves forward.

Step 2: Pair behaviour with a genetic algorithm

A genetic algorithm pairs each validated brain with a behaviour program. The behaviour layer controls position management logic, including:

• Conditions for trade exits
• Initial stop loss placement
• Trailing stop loss rules
• Breakeven stop loss logic
• When to close a trade early versus let a winner run

The AI model and behavioural program together constitute a complete Trading Agent, unique model, unique behaviour, one asset, one self-developed strategy.

3.Validation and performance

Before an agent appears in the catalog, its brain is stress-tested and its combined brain-plus-behaviour performance is evaluated on backtests spanning roughly one year. Backtests apply Binance slippage and trading fees so results reflect realistic execution costs.

Live performance stats in the catalog reflect how agents have traded on live accounts since the team launched them on exchange infrastructure. Backtest and live metrics are shown separately so you can compare simulated history against real track records.

4.Runtime infrastructure

Once connected, agents run on Yeagers-owned hardware using our in-house models and systems. Inference is ML-based, not LLM-based. That is why Trading Agents do not consume credits. Your subscription tier controls how many agents you can run, not per-inference billing.

5.Catalog lifecycle

Agents can be retired from the catalog when they no longer meet our standards or market conditions shift. Retired agents are hidden from new connections but remain accessible to users who already have them connected, until those users remove them.

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