Trade like an engineer.
Most trading education sells a feeling: confidence, conviction, a guru’s certainty on camera. Engineering sells a process: a hypothesis, a test, a measured result, and a decision made from the number rather than the mood. Trading like an engineer means writing the strategy down in plain rules before risking anything, running it against historical data long enough to see it fail at least once, and only then deciding whether the failure was tolerable or disqualifying.
Risk management is the actual curriculum, not the footnote. A position-sizing rule that caps every single trade’s risk to a small, fixed percentage of capital, a hard stop-loss set before entry rather than negotiated after the trade moves against you, and a maximum daily or weekly loss limit that shuts the system down for the day once it is hit: these three rules, followed without exception, prevent more account-ending losses than any entry signal ever could.
Backtesting discipline separates a system from a hunch. A strategy has to survive a full market cycle in historical data, bull and bear both, not just the stretch that happens to make the backtest chart look good. The honest version of backtesting includes transaction costs, slippage, and the worst historical drawdown on record for that instrument, because a strategy that only works in frictionless spreadsheet math will not survive contact with a real order book.
Cohort dates and a formal masterclass structure are not scheduled yet. The real proving ground already exists: KADUSHI.io, the high-frequency AI trading platform currently building inside the AW Ventures portfolio. A cohort opens once there is an actual track record worth teaching from, not before.