P2:

Trade Decisions and Immediate Rewards

  • Initial Trade Experience

    • A personal experience of a trade leading to a loss of a thousand pounds.
    • The action seemed correct at the moment of execution but later proved to be detrimental.

  • Immediate Reward vs Future Consequences

    • Often, what appears to be an immediate reward might not translate into lasting satisfaction.
    • Decisions that seem optimal at one point may have negative repercussions in the near future.

  • Reinforcement Learning Insight

    • The essence of reinforcement learning lies in evaluating actions not just by immediate outputs but also considering near-future outcomes.
    • Example: Selling only a portion (e.g., 25% vs 50%) of holdings as a strategy to mitigate loss if the market declines.

Understanding Market Data and Feedback Loops

  • Importance of Historical Data

    • When building fintech models, historical data provides crucial insights but may lose relevance with continuous market fluctuations.
    • Data should encompass diverse scenarios allowing for comprehensive analysis even under changing market conditions.

  • Scenario Embedding in Data

    • Trade-related data must contain contextual elements, such as company performance metrics, enabling better-informed decisions (e.g., understanding losses related to supplier issues).

  • Examples of Past Scenarios

    • Analyzing trades based on historical data where certain companies faced specific crises.
    • Understanding gains and losses requires contextual awareness, not just numeric evaluation.

Learning Algorithms: Supervised vs Unsupervised Learning

  • Supervised Learning

    • Typically involves labeled datasets where the output variable (Y) is known and can be categorized.
    • Key task: Classifying features into defined categories (Y) based on historical input data.
    • Categorical outcomes can be ordinal (with order, like ratings) or nominal (without order, like colors).

  • Feature Importance

    • Features extracted during the learning process must consider dynamic environments.
    • Example: Face recognition systems can struggle with variations in user appearance, which affects algorithm accuracy.

  • Dynamic Environment Adaptation

    • Algorithms must adapt to changes in environments, such as new user behavior or unexpected external conditions (e.g., wearing accessories that obscure features).

Challenges Within Machine Learning Algorithms

  • Environment and Feature Extraction

    • Algorithms face difficulties identifying features accurately in noisy settings or with variable conditions.
    • Specific examples include the challenges of recognizing a person with changing appearances (cap, glasses) and the impact of background noise.

  • Spoofing and Authentication

    • Addressing spoofing (e.g., using images to impersonate), the system should incorporate liveliness checks to ensure the identity is accurate.
    • It may require additional verification methods, such as asking users to perform certain actions during identification processes (e.g., moving hands).

  • Algorithm Limitations

    • Most systems operate effectively under ideal conditions but can struggle when environments change.
    • Features required for recognition (e.g., eyes, facial structure) may become obscured, impacting task execution successfully.

  • Need for Robust Algorithms

    • Ongoing improvements in algorithms are vital to minimize errors due to environmental noise or variations in user behavior.
    • TF algorithms must efficiently handle depth, resolution, and the dynamics of the environment to achieve reliable results.