In finance the difference between a healthy position on algorithmic trading strategy and a dangerous one is often a single assumption. Regulators no longer accept intent as evidence of control over this part of financial and banking practice. The teaching approach is deliberately practical: participants build a control framework for this area of financial and banking practice as they go. Most organisations already have a policy on algorithmic trading strategy; far fewer can show it working. They gain the ability to sequence improvements to the financial and banking practice discipline in an order their organisation can absorb. This programme builds the practice within financial and banking practice from first principles, without padding and without omitting what matters. It is written for people who have to make algorithmic trading strategy work with the resources they already have. Organisations that document this aspect of financial and banking practice properly resolve disputes about it far more quickly. It closes by agreeing the smallest change to the financial and banking practice capability that would make a visible difference.
Expected Learning Outcomes
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Translate policy on algorithmic trading strategy into procedures that hold up under day-to-day pressure.
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Prepare the regulatory submissions arising from algorithmic trading strategy.
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Stress test algorithmic trading strategy against scenarios that are plausible rather than comfortable.
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Assess the capital and liquidity implications of algorithmic trading strategy.
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Set acceptance criteria for algorithmic trading strategy before work begins rather than after.
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Design the reporting on algorithmic trading strategy that reaches decision makers in time to act.
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Integrate algorithmic trading strategy into existing management routines rather than running it separately.
Who Should Attend
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Staff seconded into improvement work on algorithmic trading strategy.
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Internal auditors reviewing the controls around algorithmic trading strategy.
