P&L System: Historical Tracking & Homepage Integration

by Alex Johnson 55 views

Are you looking to build a robust Profit & Loss (P&L) system that not only tracks your betting performance but also provides a user-friendly experience? This article delves into the creation of a comprehensive P&L tracking system, complete with historical storage, seamless homepage integration, and visually appealing card designs. We'll explore the key components necessary to build a system that handles various bet types, including Golden Bets, Value Bets, and Bet Builders, ensuring you have a clear overview of your betting activities. This comprehensive approach will help you gain insights into your betting strategies and optimize your performance over time.

1. Database Schema: Laying the Foundation for Historical Data

At the heart of any robust tracking system is a well-designed database schema. This section focuses on creating the necessary models to store historical data for different bet types. Let's dive into the specifics of each model to understand what it will take to build a P&L system.

1.1 Golden Bet Model

To create our historical P&L system, the IGoldenBet interface will store information about bets identified by our system's machine learning (ML) model as high-confidence picks. We are looking for the best opportunities identified by our AI, so tracking the details is crucial for analysis. Each golden bet entry will include:

  • bet_id: A unique identifier for the bet.
  • fixture_id: The ID of the sports fixture.
  • date: The date the bet was placed.
  • homeTeam: The home team in the fixture.
  • awayTeam: The away team in the fixture.
  • league: The league the fixture belongs to.
  • kickoff: The scheduled kickoff time.
  • market: The specific betting market (e.g., Over/Under, Both Teams to Score).
  • selection: The selected outcome within the market.
  • odds: The decimal odds offered for the selection.
  • stake: The amount staked on the bet (defaulting to €10 for consistent tracking).
  • ml_probability: The machine learning model's confidence percentage in the selection.
  • ai_reasoning: A concise explanation (40-50 words) of the AI's reasoning behind the pick. This is where we store the AI rationale to understand the drivers behind the betting opportunity.
  • markup_value: Numerical representation of the value identified in the betting opportunity.
  • result: An enum indicating the bet's outcome (win, loss, pending, void). This is where the actual result of the bet is stored, which will be the key factor in calculating the profit and loss.
  • settled_at: The date and time the bet was settled. This is useful for tracking when a bet's outcome was determined, which is crucial for financial P&L system calculation.
  • profit_loss: The calculated profit or loss from the bet, factoring in the stake and odds. This is the core metric for the P&L system, indicating the financial outcome of each bet. For a winning bet, it's calculated as (odds * stake) - stake; for a losing bet, it's -stake.
  • created_at: The timestamp when the bet was created in the system. This is essential for tracking the timeline of bets and for historical analysis.
  • updated_at: The timestamp of the last update to the bet's information. This is important for knowing when the bet's details, such as the result or profit_loss, were last modified.

1.2 Value Bet Model

The IValueBet interface is a new model designed to capture bets where our system identifies a significant discrepancy between the bookmaker's odds and the true probability, so it can be used as an important factor in the P&L system. These so-called value bets are critical for profitable betting strategies, as they represent opportunities where the odds offered are higher than the implied probability of the outcome. This model includes all the fields from the IGoldenBet interface, with a particular emphasis on markup_value as the key differentiator. Markup value quantifies the edge we have over the bookmaker, making it a crucial metric for identifying and tracking value bets. Value bets in the P&L system show where the model identifies discrepancies that could lead to higher profitability.

1.3 Bet Builder Model

The IBetBuilder interface is enhanced to accommodate combination bets consisting of multiple markets within the same fixture. Bet builders allow for higher combined odds, but they also require careful tracking of individual market results to accurately assess the overall outcome. The fields included are:

  • bet_id: A unique identifier for the bet builder.
  • fixture_id: The ID of the sports fixture.
  • date: The date the bet builder was created.
  • homeTeam: The home team in the fixture.
  • awayTeam: The away team in the fixture.
  • league: The league the fixture belongs to.
  • kickoff: The scheduled kickoff time.
  • markets: An array of IMarketPrediction objects, each representing a market included in the bet builder. The array should include 3 or more markets to qualify as a bet builder.
  • combinedConfidence: The average machine learning confidence percentage across all markets in the bet builder. This metric provides an overall confidence level for the entire bet builder, helping to assess the strength of the combined predictions.
  • estimatedCombinedOdds: The estimated combined odds for the bet builder, calculated from the individual odds of each market. This value is crucial for determining the potential return and for P&L system calculations.
  • stake: The amount staked on the bet builder, which is set to a default of €10 for consistency in tracking and P&L system analysis.
  • ai_reasoning: A textual explanation generated by the AI, providing the rationale behind the selection of markets and their convergence within the bet builder. This is important for understanding the AI's thought process in constructing the bet builder.
  • result: An enum indicating the overall outcome of the bet builder (win, loss, pending, void). This is determined based on the results of individual markets and is a critical component for the P&L system.
  • settled_at: The date and time when the bet builder was settled. This is important for historical P&L system analysis and for tracking the resolution time of bets.
  • profit_loss: The calculated profit or loss from the bet builder. This is a key metric for assessing the performance of bet builders and for overall P&L system tracking. The calculation depends on the outcomes of individual markets and the overall result of the bet builder.
  • market_results: An array of objects, each tracking the outcome of an individual market within the bet builder. Each object includes the market name and its result (win or loss). This detailed tracking is essential for analyzing the performance of individual components within the bet builder and for refining selection strategies.
  • created_at: The timestamp when the bet builder was created in the system. This is useful for tracking the creation time and for historical P&L system analysis.
  • updated_at: The timestamp of the last update to the bet builder's information. This is important for knowing when the bet's details, such as the results of individual markets or the overall outcome, were last modified.

By implementing these database schemas, we lay a solid foundation for storing and managing historical betting data, which is crucial for calculating P&L system and generating performance insights.

2. P&L Calculation Service: Crunching the Numbers

With the database schema in place, the next step is to build a service that crunches the numbers and provides meaningful P&L system statistics. This section details the PLService component, which calculates performance metrics for each bet type over various time periods. This service is essential for turning raw betting data into actionable insights, allowing users to understand their betting performance at a glance.

2.1 Defining the Data Structures

Before diving into the calculation logic, let's define the interfaces for our P&L system statistics. We'll start with PLPeriod, which represents the statistics for a specific time period:

  • total_bets: The total number of bets placed during the period. This is a fundamental metric for gauging activity and volume in the P&L system.
  • wins: The number of bets that resulted in a win. Tracking wins is essential for calculating win rates and overall profitability in the P&L system.
  • losses: The number of bets that resulted in a loss. Alongside wins, losses provide a clear picture of betting outcomes and inform the win rate calculation in the P&L system.
  • pending: The number of bets with a pending outcome, which have not yet been settled. These bets are important to track, as they represent potential future profits or losses in the P&L system.
  • win_rate: The percentage of bets that resulted in a win, calculated as (wins / total_bets) * 100. Win rate is a key performance indicator in the P&L system, reflecting the accuracy of betting predictions.
  • total_staked: The total amount staked on bets during the period. This metric is vital for calculating returns and ROI in the P&L system.
  • total_returns: The total amount returned from winning bets. Total returns, combined with the total staked, determine the overall profitability of the P&L system.
  • profit_loss: The net profit or loss, calculated as total_returns - total_staked. This is the most crucial metric in the P&L system, indicating the financial outcome of betting activities.
  • roi: The return on investment percentage, calculated as (profit_loss / total_staked) * 100. ROI is a comprehensive measure of profitability, taking into account the amount staked and the net profit or loss in the P&L system.
  • average_odds: The average odds of the bets placed during the period. This metric provides insights into the risk-reward profile of betting strategies within the P&L system.
  • best_win: The highest profit from a single winning bet. Tracking the best win can highlight successful individual bets and inform future strategies in the P&L system.
  • worst_loss: The highest loss from a single losing bet. Monitoring the worst loss helps to manage risk and avoid significant financial setbacks in the P&L system.

Next, we define the PLStats interface, which aggregates PLPeriod statistics for different timeframes:

  • daily: Statistics for the current day.
  • weekly: Statistics for the current week.
  • monthly: Statistics for the current month.
  • yearly: Statistics for the current year.
  • all_time: Statistics for all historical bets.

2.2 Calculation Functions

With the data structures defined, we can implement the functions to calculate the P&L system statistics for each bet type. We'll create three asynchronous functions:

  • calculateGoldenBetsStats(period): Calculates P&L system statistics for Golden Bets over the specified period.
  • calculateValueBetsStats(period): Calculates P&L system statistics for Value Bets over the specified period.
  • calculateBetBuilderStats(period): Calculates P&L system statistics for Bet Builders over the specified period.

Each function will query the database for bets of the corresponding type within the specified time period, then calculate the metrics defined in the PLPeriod interface. These functions are critical for providing users with a detailed understanding of their betting performance across different bet types and timeframes, ensuring the P&L system is both comprehensive and insightful.

3. Homepage Integration: A Centralized View of Performance

Now that we have the data and the calculation service, let's integrate the P&L system into the homepage. This section outlines the layout structure and key components of the homepage, providing a centralized view of betting performance for the user.

3.1 Layout Structure

The homepage will be structured into distinct sections, each providing specific insights into the user's betting activity. Here's the breakdown:

  • Hero Section: A prominent section at the top of the page, potentially highlighting overall P&L system performance or key metrics.
  • Golden Bets Section: Displays the top 3 Golden Bets, along with an explanation card and bet cards showing P&L system information. This section provides quick access to high-confidence picks and their performance.
  • Value Bets Section: Similar to the Golden Bets section, this displays the top 3 Value Bets, with an explanation card and bet cards showing P&L system data. This highlights opportunities where the odds may be mispriced by bookmakers.
  • Bet Builder of the Day Section: Features the Bet Builder of the Day, accompanied by an explanation card and a detailed bet card with P&L system metrics. This section showcases complex bets with potentially high returns.
  • Performance Dashboard: A dedicated dashboard displaying combined P&L system statistics across all bet types. This provides a comprehensive overview of the user's overall betting performance.

3.2 Key Components

Each section will incorporate key components to present information effectively:

  • Explanation Cards: These cards provide a brief overview of each bet type (Golden Bets, Value Bets, Bet Builders), explaining the criteria for selection and how they work. These cards are essential for user education, ensuring that users understand the rationale behind each type of bet and how they are identified by the system. The inclusion of key details such as the ML model's confidence thresholds, markup value calculations, and multi-market alignment criteria helps users grasp the nuances of each betting strategy. By clearly articulating the value proposition of each bet type, the explanation cards enhance user engagement and trust in the system's recommendations.
  • Bet Cards: These cards display individual bets with relevant information, including ML percentage, AI reasoning, and P&L system metrics. Each bet card serves as a snapshot of a specific betting opportunity, offering users a quick overview of the key details and performance indicators. The inclusion of the ML percentage provides insight into the model's confidence in the prediction, while the AI reasoning offers a brief explanation of the factors driving the selection. The display of P&L system metrics, such as profit or loss, allows users to immediately assess the outcome of the bet and track their overall performance.
  • P&L Dashboard: This dashboard presents combined P&L system statistics, allowing users to track their overall betting performance across different time periods. The dashboard consolidates key performance indicators (KPIs) such as win rate, profit/loss, ROI, and total bets, providing users with a holistic view of their betting activities. By displaying performance data across various timeframes (daily, weekly, monthly, yearly), the dashboard enables users to identify trends and patterns in their betting behavior. This comprehensive overview empowers users to make informed decisions about their betting strategies and optimize their approach for maximum profitability. The P&L system dashboard acts as a central hub for performance monitoring, ensuring that users have the insights they need to succeed in the long run.

By integrating these components into the homepage, we create a user-friendly interface that provides a comprehensive view of betting performance, making the P&L system accessible and informative.

4. Explanation Cards: Demystifying Bet Types

Explanation cards are crucial for educating users about the different bet types offered by the system. This section details the content and structure of these cards, ensuring users understand the rationale behind each bet type. The explanation cards serve as educational tools, clarifying the criteria and methodologies used to identify Golden Bets, Value Bets, and Bet Builders. By providing clear and concise explanations, the cards empower users to make informed decisions about their betting strategies and foster trust in the system's recommendations. The cards also set the stage for a more immersive and engaging user experience, encouraging users to explore different betting options and optimize their performance over time.

4.1 Golden Bets Explanation

The Golden Bets explanation card should highlight the high-confidence nature of these picks, emphasizing the rigorous analysis performed by the ML model. Golden Bets represent the system's highest-conviction selections, typically backed by a confidence level of 80% or higher. The card should emphasize that these bets are the result of advanced analytics, historical data analysis, and AI reasoning. By positioning Golden Bets as the cream of the crop, the explanation card instills confidence in users and encourages them to consider these selections as part of their betting strategy. The card should also underscore the limited number of Golden Bets selected per day, typically no more than three, to reinforce the selectivity and quality of these recommendations.

Key elements to include:

  • A clear definition of Golden Bets as the ML model's highest confidence picks (80%+ probability).
  • Emphasis on the use of advanced analytics, historical data, and AI reasoning.
  • A concise explanation of how Golden Bets are selected (maximum 3 per day).
  • A