# Wawe Homepage Source: https://wawe.finance Wawe is a revolutionary trading education platform that transforms market analysis into interactive lessons, enabling traders to create, share, and monetize their expertise through AI-powered analytics and community-driven insights. Wawe combines the power of real-time market data with social learning features, allowing experienced traders to build educational communities, create interactive trading lessons from real market scenarios, and track student progress with advanced AI analytics. The platform connects traders, educators, and learners in a collaborative environment where knowledge is shared, skills are honed, and trading expertise is transformed into engaging education. Join communities, participate in interactive courses, and leverage AI insights to accelerate your trading journey with Wawe. # Trader Homepage Source: https://wawe.finance/trader Level Up Your Trading Skills with Interactive, Real-Time Learning. Ditch random videos and chaotic chat rooms. Wawe gives you focused lessons, AI-driven insights, and a thriving trader community—so you can trade with confidence. # Communities Source: https://wawe.finance/explore Discover and search trading communities, view community metrics including followers and engagement, filter by subscription plans and verified status. # About Source: https://wawe.finance/about Learn about Wawe's mission to change the world of trading through innovative education technology, meet our team of passionate traders and developers. # Pricing Source: https://wawe.finance/pricing Detailed subscription plans and pricing information for individual traders, educators, and enterprises, including free and premium tiers. # Download Source: https://wawe.finance/download Download Wawe mobile apps for iOS and Android. # Contact Source: https://wawe.finance/contact Get in touch with our support team, submit feature requests, report issues, or inquire about partnerships and enterprise solutions. # Stock Screener Source: https://wawe.finance/screener Advanced stock screening interface with real-time market data, interactive charts, search functionality for stocks by symbol or company name, and detailed intraday price movements. Insights include technical indicators, volume analysis, Sharpe ratio, # Creator Analytics Flywheel Source: https://wawe.finance/docs/creators/analytics-flywheel/md Comprehensive analytics system for content creators built on the quiz architecture to maximize creator value through progressive insights We'll show you how learners interact with your content through our proven 12-cell mastery system, guide you on what to build next based on real coverage gaps, and gradually connect your content to downstream trading improvements—all while keeping the system fair and explainable. ## The Analytics Loop ### Core Infrastructure 4 domains (ANALYSIS, STRATEGY, RISK, EXECUTION) × 3 Bloom levels (Apply, Analyze, Evaluate) Success rate, discrimination index, abandon rate, time Z-scores with automatic status progression Beta model per cell: E[θ] = α/(α+β) with spaced review triggers Exposure-weighted impact, difference-in-differences uplift, coverage gap identification ## Progressive Dashboard Views ### View 1: Getting Started (Days 1-30) For new creators with < 100 learners or in their first 30 days ``` ┌─────────────────────────────────────┐ │ Your Content Health Score: 72/100 │ │ Active Learners (7d): 47 │ │ Items Published: 23 │ │ Best Performing Cell: RISK-Apply │ └─────────────────────────────────────┘ Coverage Grid (Your 12 Cells): ┌─────────┬────────┬─────────┬──────────┐ │ │ Apply │ Analyze │ Evaluate │ ├─────────┼────────┼─────────┼──────────┤ │ANALYSIS │ ✓3 │ ✓2 │ ⚠️1 │ │STRATEGY │ ✓4 │ 📍0 │ 📍0 │ │RISK │ ✓5 │ ✓3 │ ✓2 │ │EXECUTION│ 📍0 │ 📍0 │ 📍0 │ └─────────┴────────┴─────────┴──────────┘ ✓ = Healthy ⚠️ = Needs revision 📍 = Gap Top Priority: Add EXECUTION content (67% need this) ``` #### Health Score Calculation Average discrimination × (1 - abandon_rate) Cells with ≥2 healthy items / 12 Completion rate × median session length factor ### View 2: Growing (100-500 learners) Unlocked after 100+ learners OR 30+ days of activity #### Mastery Flow Visualization ``` ANALYSIS-Apply (87%) → STRATEGY-Apply (73%) → RISK-Apply (68%) ↓ [Struggling Here - Suggest Content] ↓ RISK-Analyze (42%) Your Impact: +8% mastery gain vs baseline (confidence: medium, n=127) ``` #### Item Performance Table | Item Title | Health Status | Success Rate | Discrimination | Action | |------------|--------------|--------------|----------------|--------| | Stop Loss Basics | stable | 72% | 0.31 | — | | Position Sizing | needs_edit | 45% | 0.08 | [Revise] | | Risk/Reward | stable | 68% | 0.28 | — | ### View 3: Established (500+ learners) Full analytics suite with behavioral bridge metrics ``` ┌──────────────────────────────────────────┐ │ Your RISK-Apply Modules Improved: │ ├──────────────────────────────────────────┤ │ • Rules Compliance: +31% (89 competitors)│ │ • Loss Exit Discipline: +23% (147 comp.) │ │ • Position Size Consistency: +18% (112) │ │ │ │ Your RISK-Analyze Modules Reduced: │ │ • Revenge Trading: -42% (203 competitors)│ │ • Pressure Performance Delta: -15% (156) │ │ │ │ Sample: 12 competitions, 2,847 trades │ │ Confidence: HIGH (structured constraints) │ └──────────────────────────────────────────┘ ``` Pressure Performance Delta measures behavioral consistency between practice and competition trading. A -15% improvement means 15% less deviation under competitive pressure. ## Content Creation Flow ### Spec-First Generation ```json { "domain": "RISK", "bloom": "Analyze", "constraints": ["policy-level only"], "grounding": ["doc://your_material#section3"], "expected_impact": { "addresses_gap": true, "learner_need": "68% struggle here after RISK-Apply", "competition_relevance": "reduces oversizing violations" } } ``` ### Publishing Timeline View attempt rate, completion rate, cell coverage Initial health metrics, discrimination index, time normalization Full health status, revision suggestions, impact preview ## The Quality Flywheel Automatic tracking of attempts, mastery updates, gap identification Weekly confusion reports, health monitoring, coverage analytics Real-time suggestions based on gaps, audience progression, behaviors One-click templates, revision workflow, A/B testing Performance comparison, mastery improvements, behavioral tracking ## Implementation Phases ### Phase A: Foundation (Months 1-2) Focus on basic metrics and coverage - Impact MVP with sample gates - Coverage bounties for underserved cells - Basic creator following - Rules-based struggle detection ### Phase B: Behavioral Bridge (Months 3-6) Connect content to trading behaviors - Revenue share pilot with quality multiplier - Impact dashboards - Asynchronous cohorts - Mastery flow visualization ### Phase C: Full Attribution (Months 6-12) Complete impact measurement and prediction - Collaborative filtering - Uplift modeling - Performance forecasting - Competition impact attribution ## What Remains Unchanged These core systems stay separate to maintain fairness ### XP System - XP rewards engagement only - Never tied to difficulty or creator - No "bonus XP" items ### Privacy & Fairness - All views are aggregated - Minimum sample requirements - Market condition normalization - No individual learner data ### Core Classification - 12-cell matrix unchanged - Hybrid classifier consistency - Precedence rules prevent drift ## Success Metrics ### Platform Health (Monthly) - Classification reliability: κ & AC1 ≥ 0.75 - Item health: < 5% in quarantine - Coverage: All cells have ≥10 healthy items ### Creator Success (Quarterly) - 80% of creators check dashboard weekly - 60% of flagged items revised within 14 days - 40% improvement in underserved cells ### Learner Outcomes - D7 retention ≥ 40%, D30 ≥ 20% - +8-12% mastery gain in targeted cells - < 5% recommendations to unhealthy items ## Communication Framework ### Always Tell Creators - Exact health status and why - Sample sizes behind metrics - Confidence levels for impact - Platform fit assessment - Classification changes - Flagging reasons - How recommendations work - Revenue sharing drivers ### Never Share - Other creators' exact metrics - Unsubstantiated causation claims - Predictions without confidence intervals ## Technical Integration ### Data Sources ```sql -- From questions table health_discrimination, abandon_rate, median_time_ms, health_status -- From user_stats mastery_by_cell (Beta parameters) -- From creator_analytics tables creator_exposures_daily, creator_impact_daily ``` ### Dashboard Architecture Pull from existing analytics rollups Apply established calculations and sample thresholds Cache for performance, progressive disclosure Hook into coverage analytics and mastery flows This approach uses everything already built in the quiz architecture, presents it progressively so creators aren't overwhelmed, and maintains system integrity while scaling naturally with the Phase A→B→C plan. # Creator Documentation - Content Creation Guide Source: https://wawe.finance/docs/creators/content-creation-guide/md Comprehensive guide for creating effective trading education content with skill selection, difficulty calculation, and best practices This guide provides comprehensive documentation for creating effective trading education content, including skill selection, difficulty calculation, and question design best practices. ## Skill Selection Guide ### Skill Domain Definitions **Focus:** Visual and statistical patterns in market data Examples: Chart patterns, candlestick formations, volume profiles **Focus:** Analysis frameworks and decision processes Examples: Trade setup evaluation, market scenario planning **Focus:** Capital preservation and position sizing Examples: Stop placement, position sizing, portfolio allocation **Focus:** Execution and adjustment of positions Examples: Entry/exit timing, scaling methods, adjustment triggers **Focus:** Broader market conditions and relationships Examples: Correlation analysis, sector rotation, macro influences **Focus:** Relationships between different time horizons Examples: Timeframe alignment, nested patterns, fractal analysis **Focus:** Specialized instruments and strategies Examples: Options pricing, Greeks, complex strategies **Focus:** Combining technical and fundamental data Examples: Earnings analysis, news impact assessment ### Skill Selection Flowchart Is the question primarily about identifying patterns on charts? → YES: **Pattern Recognition** Is the question about how to manage risk or position sizing? → YES: **Risk Management** Is the question about multiple timeframes or time horizons? → YES: **Multi-Timeframe Analysis** Is the question about options, futures, or other derivatives? → YES: **Options & Derivatives** Is the question about executing or managing an existing position? → YES: **Trade Management** Is the question about analyzing broader market conditions? → YES: **Market Context** Is the question about combining fundamental data with technical analysis? → YES: **Fundamental Integration** Default to **Problem Solving** if the question involves decision frameworks ### Secondary Skill Selection After identifying the primary skill, determine if secondary skills apply: - Does the question require knowledge from another domain? - Does it involve applying concepts from multiple domains? - Would expertise in another domain change the approach? If yes, assign 1-2 secondary skills that most strongly influence the question. ## Difficulty Calculation Worksheet ### Step 1: Rate Each Component (1-5 scale) | Component | Definition | Rating Guidelines | |-----------|------------|-------------------| | **Conceptual Complexity** | Sophistication of trading concepts | 1: Basic concepts (support/resistance)
3: Intermediate (market structure)
5: Advanced (complex options) | | **Information Processing** | Amount and complexity of data | 1: Single data point
3: Multiple related points
5: Multiple sources with conditions | | **Tool Utilization** | Required tools and calculations | 1: Visual observation
3: Basic calculation
5: Multiple specialized tools | | **Prior Knowledge** | Dependency on foundations | 1: Common knowledge
3: Basic trading principles
5: Specialized expertise | | **Decision Complexity** | Variables in decision process | 1: Binary choice
3: Multiple factors
5: Interdependent conditionals | ### Step 2: Calculate Weighted Score ``` Weighted Score = (Conceptual × 0.3) + (Information × 0.25) + (Tool × 0.15) + (Prior × 0.2) + (Decision × 0.1) ``` ### Step 3: Apply Format Adjustments | Question Format | Adjustment | Conditions | |----------------|------------|------------| | **Boolean** | +0.0 | Simple concept verification | | **Boolean** | +0.5 | Requires nuanced judgment | | **Single Choice** | +0.0 | Standard baseline | | **Single Choice** | +0.2 | Highly plausible distractors | | **Multiple Choice** | -0.3 | Correct answers provide context | | **Multiple Choice** | +0.3 | Evaluating factor combinations | | **Chart Interaction** | +0.5 | Requires precise measurements | | **Chart Interaction** | +1.0 | Multiple measurements & analysis | ### Step 4: Determine Final Difficulty Score: 1.0 - 2.0 Score: 2.1 - 3.5 Score: 3.6 - 5.0 ## Question Type Best Practices ### Boolean Choice Questions **Best Used For:** - Testing understanding of market principles - Verifying knowledge of specific concepts - Quick assessment of decision-making abilities **Guidelines:** - Avoid obvious statements that test only recall - Use context that requires analysis, not just memory - Consider adding measurement requirements for complexity - Provide clear, specific statements **Example:** ✅ GOOD: "Based on the historical volatility shown, would a 30% options premium be justified for this stock's earnings?" ❌ BAD: "Do support levels sometimes fail?" ### Single Choice Questions **Best Used For:** - Testing ability to evaluate multiple alternatives - Assessing decision-making processes - Checking understanding of best practices **Guidelines:** - Create plausible distractors testing misconceptions - Ensure only one answer is truly optimal - Make options similar in length and detail - Use parallel construction ### Multiple Choice Questions **Best Used For:** - Testing ability to identify multiple valid factors - Assessing comprehensive understanding - Checking ability to filter relevant information **Guidelines:** - Each option stands alone as correct/incorrect - Avoid patterns in correct answers - Make options similar in construction - Consider requiring explanations ### Chart Drawing Selection Questions **Best Used For:** - Testing practical application of pattern recognition - Assessing ability to identify key price levels - Checking precision in technical analysis **Guidelines:** - Provide clear selection criteria - Consider multiple valid answers - Add measurement tools when precision matters - Include clear visual distinctions ## Chart Element Standards ### Standard Chart Elements | Element Type | Display Standard | Metadata | |-------------|------------------|----------| | **Price Bars** | Consistent colors, clear open/close | Timeframe, date range | | **Support/Resistance** | Horizontal lines, dotted for minor | Line type, strength | | **Trend Lines** | Angled lines, thickness by significance | Slope, touch points | | **Patterns** | Semi-transparent highlights | Pattern type, completion % | | **Volume** | Bar/histogram, color-coded | Relative measure, MA | | **Indicators** | Separate panes when possible | Settings, interpretation | ### Chart Complexity Guidelines Each element should serve a specific purpose related to the question Most important elements should be visually prominent Chart complexity should align with question difficulty Use consistent annotation styles across quizzes ## User Interface Specifications ### Skill Selection Interface ``` Primary Skill Selection: [ Primary Skill Domain ▼ ] (dropdown with 8 domains) └─ [ Skill Category ▼ ] (dynamically populated) Secondary Skills (max 2): [✓] Pattern Recognition > Market Structure Analysis [✓] Market Context > Cross-Asset Analysis [ ] Risk Management > Position Risk Optimization Skill Dimensions: Facets: [✓] Recognition [✓] Analysis [ ] Synthesis Scope: ( ) Micro (•) Medium ( ) Macro Cognitive: ( ) Recall (•) Application ( ) Evaluation ``` ### Difficulty Calculator Interface ``` ┌────────────────────────────────────────────┐ │ Conceptual Complexity: 1 [●━━━━━━━━━] 5 │ │ Information Processing: 1 [━━━●━━━━━━] 5 │ │ Tool Utilization: 1 [━━●━━━━━━━] 5 │ │ Prior Knowledge: 1 [━━━━●━━━━━] 5 │ │ Decision Complexity: 1 [━━━━━●━━━━] 5 │ └────────────────────────────────────────────┘ Calculated Difficulty: 3.2 (Intermediate) Format Adjustment: +0.3 (Multiple Choice) Final Difficulty: 3.5 (High Intermediate) ``` ### Chart Complexity Calculator ``` Base Chart: [Candlestick] (1.0) Indicators: [✓] MA/EMA (0.2) [✓] Volume (0.2) [ ] MACD (0.2) [ ] RSI (0.2) Drawings: [✓] Trend Lines (0.1 × 2) [✓] Support/Res (0.1 × 3) [ ] Fibonacci (0.1) Highlighted Areas: [✓] Zones (0.3 × 1) [ ] Patterns (0.3) [✓] Multiple Timeframes (0.5) Total Chart Complexity: 2.8 ``` ### Quiz Skill Coverage Visualization ``` Pattern Recognition ████████████████░░░░░░ Problem Solving ████████████████████░░ Risk Management █████████░░░░░░░░░░░░░ Trade Management ███████░░░░░░░░░░░░░░░ Market Context ██████░░░░░░░░░░░░░░░░ Multi-Timeframe ░░░░░░░░░░░░░░░░░░░░░░ Options & Deriv. ████░░░░░░░░░░░░░░░░░░ Fund. Integration ░░░░░░░░░░░░░░░░░░░░░░ Recommended additions: Multi-Timeframe Analysis, Fundamental Integration ``` Use this dashboard to ensure balanced skill coverage across your quiz content and identify areas that need more questions. # Creating a New Quiz: An Overview Source: https://wawe.finance/docs/creators/creating-a-quiz/md Learn how to create a new quiz from scratch, including setting up basic information, managing subscriptions, adding questions, and customizing charts. Creating a New Quiz Quizzes are a powerful way to test your community members' knowledge, provide engaging content, and track learning progress. This guide combines all essential steps from initial setup to final publishing, incorporating key details that ensure a smooth quiz creation experience. --- ## Step 1: Accessing the Quiz Creation Interface From the sidebar menu, click the **Education** icon (it looks like a book) to access your education dashboard. Locate and click the **New** button (top-right of the screen). This opens the **New Quiz** creation screen. ## Step 2: Setting Up Basic Quiz Information This initial screen collects your quiz's basic details. Enter a clear and concise title for your quiz in the **Title** field. The platform enforces a minimum of 5 characters and a maximum of 200 characters. Example: **"Tesla Pre-Earnings"** Provide a brief description (maximum 140 characters) in the **Short Description** field. This text highlights the main objective and key takeaways. Appears in marketing pages, sidebars, emails, etc. Example: **"Analyzing Tesla Stock"** Give a more detailed explanation (up to 3000 characters) in the **Full Description** field. Provide context on the quiz's content, goals, and any important details. Add relevant tags to categorize your quiz, making it easier for users to find. Select which subscription plans can access this quiz. If none are selected, the quiz will remain hidden from your community. ## Step 3: Managing Subscriptions and Visibility If you need a new subscription plan, click **Manage Subscriptions**. You'll be redirected to the **Community** section to add a new plan. Choose **Visible** (quiz is shown in the app) or **Hidden** (quiz is only accessible through a direct link). Decide how comments will appear: **Visible**, **Hidden**, or **Locked**. ## Step 4: Creating Your Quiz Episodes Quizzes are organized into "episodes," each with its own interactive content. Click **Create** to proceed to the episode creation page. Each episode is an individual segment of your quiz. Click **Details** to configure the episode or go straight to the **Question** tab if you already know what you want to set up. Enter a description or lesson text in **Episode Content** (e.g., "Slide 1"). Provide the subtopic (optional) in **Episode Subtopic**. Click **Select Tickers** to choose one or more stock symbols for that episode (e.g., TSLA, AAPL). You can remove tickers using the **X** icon. Check the timeframes you want available (1m, 5m, 1h, 4h, etc.). Set a **default timeframe** (e.g., "4h") to display when the episode loads. Pick the date and time that will be shown on the chart for that episode. Choose the episode's length (e.g., 1–5 minutes) using the slider or the numeric input. ## Step 5: Adding Questions and Answers Give your quiz its interactive component by adding questions to each episode. In the episode creation screen, switch to the **Question** tab.
  • **Boolean Choice**: True/False questions
  • **Single Choice**: Multiple-choice with only one correct answer
  • **Multiple Choice**: Multiple-choice with more than one correct answer
  • **Ordered Choice**: Answers must be placed in the correct sequence
  • **Chart Drawing Selection**: Select specific points or areas on a chart
  • **Chart Drawing Completion**: Users complete a drawing on the chart or select the correct element out of all the available
 Add or remove answer options. For Boolean/Single/Multiple Choice, use the **bin** icon to delete and **drag** icon to reorder. Mark the correct answer(s). Provide a question title that appears in the quiz interface. Click **Add Episode** to finalize that episode's setup and repeat for additional episodes, if needed. ## Step 6: Chart Customization Customize the charts for each question to make them more engaging and instructive. In the episode editing page, click **Chart**. If multiple tickers were chosen, select which ticker's chart you want to customize. Choose between **Candlestick**, **Line**, or **Area**. Adjust the start offset (scroll offset) of the chart. Toggle volume bars on or off. Modify width, padding, wick thickness, and rendering method (e.g., **Rect** or **Path**). Click the **+** under **Drawing** to add tools like Horizontal Line, Trend Line, Fibonacci Retracements, etc. Drag these onto the chart and adjust parameters (color, thickness, style, etc.) as needed. Click the **+** under **Indicators** to add technical indicators (e.g., Volume, RSI, or other specialized tools). ## Step 7: Skills Tab A **Skills** tab is where you can define specific skills each episode is testing. This helps track the user's proficiency in particular areas or competencies. ## Step 8: Simulator & Mobile Preview ### Simulator Use the built-in **Simulator** to see how the quiz episode looks and functions in real-time. You can interact with the chart, answer questions, and confirm the user experience. ### Mobile Preview Switch to **Mobile Preview** mode to check how your quiz will appear on smartphones or tablets. ## Step 9: Review and Publishing ### Review Go through your quiz settings, episode details, questions, and chart customizations. Ensure everything is accurate and ready for participants. ### Publish Once satisfied, save and publish the quiz. Published quizzes appear in the **Education** section if set to **Visible**. ## Additional Notes Access the **Community** section to manage subscription plans and community settings. The **Education** section is where your quizzes and lessons reside. Tailor quizzes to your teaching style: from chart interactions to detailed question types. Incorporate real-time stock data, drawings, and multiple-choice questions to keep learners engaged. ## Conclusion You are now ready to build engaging, interactive quizzes for your community! # Analytics Product Requirements Document Source: https://wawe.finance/docs/technical/analytics-prd/md Comprehensive PRD for the quiz platform analytics section focusing on completion rates, question types, and skill mastery tracking This document outlines the requirements for the analytics section of the quiz platform, providing detailed insights into learner performance tracking and analysis. ## Overview The analytics section of the quiz platform will provide quiz creators, educators, and learners with detailed insights into learner performance. The primary focus is on tracking **completion rates** for quiz questions, with an emphasis on analyzing performance by **question type** and **skill mastery**. Monitor completion rates across different question types Analyze learner mastery of specific skills Enable targeted improvements in quiz design ## Objectives The analytics section aims to achieve the following key objectives: Provide a clear, actionable view of how well learners are performing on different question types (Multiple Choice, True/False, Chart Selection) Offer insights into learner mastery of specific skills (Pattern Recognition, Risk Management) across all question types Allow quiz creators and educators to assess performance at varying levels of difficulty (Easy, Medium, Hard) Ensure that the analytics are easy to interpret, even when questions are tagged with multiple skills Support data-driven decisions for improving quiz content and learner outcomes ## Key Requirements ### Dual-Track Analytics Track the percentage of correct answers for each question type. This metric is independent of skills being tested, focusing purely on question format. Track the percentage of correct answers for questions tagged with each specific skill. Questions with multiple skills contribute to all associated skill metrics. ### Metrics to Track For both question types and skills, the following metrics must be tracked: All metrics are calculated as: `(Number of correct answers / Number of questions attempted) × 100` - **Number of questions attempted** - **Number of correct answers** - **Percentage of correct answers** ### Handling Multiple Skills per Question When a question is tagged with multiple skills, a correct or incorrect answer should be counted toward the performance metrics of **all** associated skills. This ensures that the analytics accurately reflect the multi-dimensional nature of the questions without requiring creators to prioritize or weight skills. ### Difficulty Levels Questions are categorized based on a scoring system that considers: Different question types have inherent difficulty levels (True/False is generally easier than Chart Selection) More hints reduce the overall difficulty of a question Based on the sub-skill's progression level: Basic, Intermediate, or Advanced The analytics section should allow users to **filter performance data by difficulty level**. ### Sub-Skill Progression Sub-skills are organized into three progression levels: Foundational concepts like Technical Pattern Foundations Build on basics like Market Structure Analysis Complex applications like Cross-Asset Analysis ### Small Sample Size Handling To avoid misleading metrics due to small sample sizes: - Display a warning ("Low sample size") when questions attempted < 5 - Optionally disable or gray out metrics for categories with insufficient data ### Overall Performance Metrics The analytics section should provide an overall performance summary: - Total questions attempted - Total correct answers - Overall percentage of correct answers ### User Interface and Visualization The analytics dashboard should be intuitive and visually engaging: Performance by question type and skill Performance trends over time Distribution of difficulty levels attempted ### Filtering and Customization Users should be able to filter analytics data by: - **Difficulty Level** (Easy, Medium, Hard) - **Sub-Skill Progression** (Basic, Intermediate, Advanced) - **Specific Skills** or **Question Types** ## Implementation Details ### Data Collection The platform must log each learner's interaction with quiz questions, capturing: Question type, skills tagged, learner's answer, and difficulty level ### Difficulty Scoring System The difficulty of each question is calculated using a points-based system: #### Question Type Points | Question Type | Points | |--------------|--------| | True/False | 1 | | Multiple Choice (4 options) | 2 | | Multiple Choice (>4 options) | 3 | | Chart Drawing Selection | 4 | | Chart Drawing Completion | 5 | #### Hint Adjustment | Number of Hints | Point Adjustment | |----------------|------------------| | 0 hints | +0 points | | 1 hint | -1 point | | 2 hints | -2 points | | 3+ hints | -3 points | #### Topic Complexity Points | Sub-Skill Level | Points | |----------------|--------| | Basic | +1 | | Intermediate | +2 | | Advanced | +3 | #### Difficulty Categorization - **Easy**: Score ≤ 3 - **Medium**: 4 ≤ Score ≤ 6 - **Hard**: Score ≥ 7 ### Analytics Calculation For each question type, sum the number of questions attempted and correct answers, then calculate the percentage For each skill, sum the questions tagged with that skill that were attempted and correctly answered, then calculate the percentage Sum all questions attempted and correctly answered across the quiz, then calculate the overall percentage ### Dashboard Layout ``` ┌─────────────────────────────────────┐ │ Overall Performance Summary │ │ • Total questions: XX │ │ • Correct answers: XX │ │ • Overall percentage: XX% │ ├─────────────────────────────────────┤ │ Performance by Question Type │ │ [Bar Chart Visualization] │ ├─────────────────────────────────────┤ │ Performance by Skill │ │ [Bar Chart Visualization] │ ├─────────────────────────────────────┤ │ Filters │ │ • Difficulty Level │ │ • Sub-skill Progression │ │ • Question Types / Skills │ └─────────────────────────────────────┘ ``` ## Example Scenario Consider a learner who completes 5 episodes: Multiple Choice, Skills: Pattern Recognition + Risk Management, **Correct** True/False, Skill: Risk Management, **Incorrect** Chart Selection, Skills: Pattern Recognition + Decision Making, **Correct** Multiple Choice, Skill: Decision Making, **Correct** Chart Selection, Skills: Risk Management + Decision Making, **Incorrect** ### Results Analysis #### Performance by Question Type | Question Type | Attempted | Correct | Percentage | |--------------|-----------|---------|------------| | Multiple Choice | 2 | 2 | 100% | | True/False | 1 | 0 | 0% | | Chart Selection | 2 | 1 | 50% | True/False shows a "Low sample size" warning due to only 1 question attempted #### Performance by Skill | Skill | Tagged Questions | Correct | Percentage | |-------|-----------------|---------|------------| | Pattern Recognition | 2 | 2 | 100% | | Risk Management | 4 | 1 | 25% | | Decision Making | 3 | 2 | 66.7% | This example demonstrates how the analytics provide clear insights into both question-type performance and skill mastery, with appropriate warnings for small sample sizes. # Trading Education Skill Classification Taxonomy Source: https://wawe.finance/docs/technical/classification-taxonomy/md Standardized framework for classifying trading education questions according to specific skills they assess This document provides a standardized framework for classifying trading education questions according to the specific skills they assess, ensuring consistent categorization across educational content, assessments, and learning pathways. ## Executive Summary This taxonomy enables precise classification of trading questions with: - **One core skill domain** representing the primary decision/action required - **Up to two supportive skill domains** if they are critical to answering the question ### Primary Users Trading education specialists Assessment developers Automated classification tools Skill gap assessors ## Quick Reference Guide Critical Rules to Remember: - Each question receives ONE core skill domain - Add up to TWO supportive skill domains ONLY if critical - NEVER select two supportive skills from the same top-level category - NEVER select a supportive skill from the same category as the core skill ### Simple Classification Flowchart Read the entire question carefully Identify the main decision/action being tested Match to primary skill domain Check if any supportive skills are CRITICAL Verify no supportive skills from same category as core ### Common Question Types - Quick Classification | Question Type | Core Skill Domain | |--------------|-------------------| | Pattern identification | Technical Pattern Recognition | | Trend analysis | Market Structure Analysis | | Future price projections | Technical Scenario Planning | | Where to place stops | Trade Risk Assessment | | How much to allocate | Position Sizing Methodology | | What to do with existing position | Position Modification | | Which approach for new position | Strategy Selection | | Questions about emotions/biases | Trading Psychology | | How options contracts behave | Derivatives Management | ## Skill Classification Taxonomy The taxonomy consists of four top-level categories: ### CHART & PATTERN ANALYSIS Focus: Visual interpretation of price action and technical patterns Elementary chart elements, candlestick types, simple trend lines Established chart patterns (head & shoulders, triangles, flags) Price action, order flow, trend phases, volume profile Moving averages, oscillators (RSI, MACD), volume indicators Integrating data across different timeframes, fractal structures ### MARKET ANALYSIS & DECISION MAKING Focus: Strategic trade planning and market condition assessment Combining economic/company data with technicals Evaluating multiple potential price outcomes Recognizing statistically favorable opportunities Choosing appropriate trading approaches Managing emotional and cognitive aspects ### RISK MANAGEMENT Focus: Capital preservation and exposure control Expected value, return distributions, R-multiples Determining appropriate allocation amounts Stop placement, downside scenarios, R:R calculation Correlation analysis, diversification, exposure limits Options protection, inverse ETFs, paired positions ### EXECUTION & POSITION MANAGEMENT Focus: Implementing trades and managing active positions Order types, entry techniques, exit planning Stop adjustments, partial profits, scaling Spread management, commission reduction Win rate, average R, drawdown, Sharpe ratio Options greeks, expiration management, spreads ## Classification Workflow - Read entire question including options/explanations - Identify what trader must fundamentally decide - Match core decision to most specific skill domain - Identify supporting skills needed - Apply Domain Decision Trees - Identify highest-level decision/action required - Choose most specific skill domain - Cross-check with Question-Type Priority Map - Determine if supportive skills truly needed - Select up to two supportive domains - NEVER select from same top-level category as core - Apply domain-specific tie-breakers - Use Required Format - Include 2-3 sentence reasoning for core skill - Add brief explanations for supportive skills ### Threshold for Supportive Skills Only add supportive skills when they are truly critical to answering the question correctly. Passing mentions or minor aspects should not be included as supportive skills. | Scenario | Action | |----------|--------| | **No Supportive Skills** | Question tests only one distinct skill domain | | **One Supportive Skill** | Question cannot be answered without second domain | | **Two Supportive Skills** | Only for truly multi-faceted questions | ## Domain Decision Trees ### Options & Derivatives Questions ```mermaid graph TD A[START] --> B{Strategy Selection?} B -->|YES| C{Match to timeframe/conditions?} C -->|YES| D[Strategy Selection] C -->|NO| E{Risk mitigation focus?} E -->|YES| F[Trade Risk Assessment] B -->|NO| G{How options behave?} G -->|YES| H[Derivatives Management] ``` ### Chart Analysis vs. Market Structure ```mermaid graph TD A[START] --> B{Named pattern?} B -->|YES| C[Technical Pattern Recognition] B -->|NO| D{Trend dynamics/structure?} D -->|YES| E[Market Structure Analysis] D -->|NO| F[Standard classification] ``` ## Question-Type Decision Rules ### Tie-Breaking Rules When multiple skills seem equally applicable: 1. Identify the Main Question Verb 2. Check for Options or Derivatives Focus 3. Use Cross-Category Disputes rules 4. When in doubt, determine which domain cannot be solved without ### Common Ambiguities Naming pattern → Pattern Recognition Trend dynamics → Market Structure When to close → Position Modification How to close → Order Execution Choosing strategy → Strategy Selection Understanding greeks → Derivatives Management How much to allocate → Position Sizing Where to place stops → Trade Risk Assessment ### Trading Psychology Classification A question should ONLY be classified with Trading Psychology when it EXPLICITLY addresses: - Cognitive biases (confirmation bias, recency bias, etc.) - Emotional responses (fear, greed, hope) - Psychological aspects of trading discipline - Explicit references to trader mindset or mental state ## Example Classifications ### Simple Pattern Recognition **Question:** "What chart pattern is forming on the daily AAPL chart?" ``` CORE SKILL: CHART & PATTERN ANALYSIS > Technical Pattern Recognition REASONING: The question specifically asks to identify a chart pattern. SUPPORTIVE SKILLS: None required for this straightforward question. ``` ### Position Sizing with Risk **Question:** "How do you calculate position size if you risk 1% of $10,000 on a trade with a $2 stop?" ``` CORE SKILL: RISK MANAGEMENT > Position Sizing Methodology REASONING: The primary focus is determining appropriate allocation. SUPPORTIVE SKILLS: None required for this straightforward calculation. ``` ### Options Strategy Selection **Question:** "Which option strategy best fits a bullish bias over two weeks with elevated IV?" ``` CORE SKILL: MARKET ANALYSIS & DECISION MAKING > Strategy Selection REASONING: Primary focus is selecting appropriate strategy for conditions. SUPPORTIVE SKILL: EXECUTION & POSITION MANAGEMENT > Derivatives Management REASONING: Understanding how strategies behave with IV is critical. ``` ### Complex Multi-Domain Question **Question:** "Looking at this triangle pattern, where would you place your stop to maintain a 2:1 R:R ratio?" ``` CORE SKILL: RISK MANAGEMENT > Trade Risk Assessment REASONING: Central question requires determining stop placement for specific R:R. SUPPORTIVE SKILL: CHART & PATTERN ANALYSIS > Technical Pattern Recognition REASONING: Identifying the triangle pattern is prerequisite for risk assessment. ``` ## Required Classification Format Follow one of these three formats exactly based on the number of supportive skills: ### Format 1: No Supportive Skills ``` CORE SKILL: [CATEGORY > Specific Skill Domain] REASONING: [2-3 sentences explaining why this is the core skill] SUPPORTIVE SKILLS: None required for this straightforward question. ``` ### Format 2: One Supportive Skill ``` CORE SKILL: [CATEGORY > Specific Skill Domain] REASONING: [2-3 sentences explaining why this is the core skill] SUPPORTIVE SKILL: [CATEGORY > Specific Skill Domain] REASONING: [1-2 sentences explaining why this is supportive] ``` ### Format 3: Two Supportive Skills ``` CORE SKILL: [CATEGORY > Specific Skill Domain] REASONING: [2-3 sentences explaining why this is the core skill] SUPPORTIVE SKILL: [CATEGORY > Specific Skill Domain] REASONING: [1-2 sentences explaining why this is supportive] ADDITIONAL SUPPORTIVE SKILL: [CATEGORY > Specific Skill Domain] REASONING: [1-2 sentences explaining why this is additional supportive] ``` Critical Rules: - Use no more than two supportive skills - Never select two supportive skills from same top-level category - Never select supportive skill from same category as core skill - Trend analysis belongs to Market Structure Analysis ## Common Classification Errors ### Error 1: Wrong Core Skill ❌ **Incorrect:** ``` Question: "What chart pattern do you see?" CORE: Technical Scenario Planning ``` ✅ **Correct:** ``` CORE: Technical Pattern Recognition ``` ### Error 2: Same Category Support ❌ **Incorrect:** ``` CORE: Market Structure Analysis SUPPORTIVE: Technical Indicator Application (same category!) ``` ✅ **Correct:** ``` CORE: Market Structure Analysis SUPPORTIVE: None or from different category ``` ### Error 3: Minor Mentions as Support ❌ **Incorrect:** ``` Question: "Which pattern is this? Consider a stop if you'd like." SUPPORTIVE: Trade Risk Assessment (minor mention!) ``` ✅ **Correct:** ``` SUPPORTIVE: None required ``` # Cognitive Classification Guide Source: https://wawe.finance/docs/technical/cognitive-classification-guide/md Complete instruction set for classifying trading education questions based on cognitive processes and skills required This document serves as the complete instruction set for classifying trading education questions based on the cognitive processes required to answer them. ## Executive Summary This guide provides a systematic approach to classify trading education questions by analyzing the cognitive processes required to answer them. Each question receives: One mandatory primary cognitive skill that represents the main mental operation Zero to two supportive skills that meet strict prerequisite criteria ### Golden Rules of Classification Output exactly ONE core cognitive skill domain that represents the primary mental operation Output supportive skills only if they meet ALL strict prerequisite criteria (maximum two) Base classification on the mental task required, not trading phase or market context ## Meta-Instructions for Processing These instructions guide how to interpret and apply the rules within this document to achieve accurate classification. ### Core Processing Principles Adhere strictly to the Master Classification Logic Flow step-by-step Rules and definitions take precedence over examples Base classification on cognitive operations in detailed definitions Apply rules based on objective textual evidence Always analyze the entire input question before starting the classification process. Context from the full question is crucial. ## Master Classification Logic Flow Follow these steps sequentially for every input question: - Read and parse the entire input question text - Identify key verbs, concepts, and specific tasks - Note potentially relevant Cognitive Skill Domains Apply the Metacognitive Override Test. If criteria met: - Assign appropriate METACOGNITIVE Skill as Core - Proceed directly to identifying Supportive Skills For non-metacognitive questions, analyze the primary output: - Understanding/interpreting → PERCEPTION & ANALYSIS - Making a choice/plan → DECISION-MAKING - Implementing/adjusting → EXECUTION & ADAPTATION Within the identified category, pinpoint the single most fitting Core Skill using definitions and decision trees If ambiguity exists, apply specific pairwise tie-breaker rules Evaluate other domains using the Strict Supportive Skill Checklist Assemble classification using the required output format ## Core Classification Principles ### The One Core Cognitive Process Rule Every input question requires exactly one Core Skill Domain representing the primary cognitive operation ### Supportive Skill Rules & Strict Checklist A domain qualifies as Supportive ONLY IF ALL THREE criteria are met: Does the Core Skill logically require this skill's output as input for this specific question? Is this skill actively and centrally engaged, not just tangentially related? Would removing this process make the Core Skill impossible or fundamentally different? Assign maximum TWO Supportive Skills. If more qualify, select the most direct prerequisites. ## Cognitive Categories & Skill Domains ### PERCEPTION & ANALYSIS SKILLS How traders observe, interpret, and analyze market information | Skill Domain | Description | Key Indicators | |-------------|-------------|----------------| | **Pattern Recognition** | Identifying pre-defined, named formations in market data | pattern, formation, candlestick, chart pattern names | | **Data Interpretation** | Extracting meaning from market data points and analytical flows | interpret, meaning, implication, suggest, indicate | | **Contextual Integration** | Synthesizing multiple, potentially conflicting data sources | synthesize, integrate, reconcile, multi-timeframe | | **Signal Filtering** | Distinguishing valid signals from noise | valid, confirm, false signal, filter, validate | | **Quantitative Analysis** | Applying statistical/mathematical methods beyond basic arithmetic | statistical test, probability, model, regression | ### DECISION-MAKING SKILLS How traders evaluate options and choose appropriate actions before execution | Skill Domain | Description | Key Indicators | |-------------|-------------|----------------| | **Strategic Assessment** | Evaluating and selecting trading approaches or strategies | strategy selection, approach, evaluate strategy | | **Trade Structuring** | Defining specific parameters of a potential trade | trade setup, entry criteria, exit criteria, targets | | **Risk-Reward Calibration** | Determining position size and risk levels | position size, risk management, R:R ratio, stop loss | | **Execution Planning** | Deciding how trades will be implemented | order type, entry tactic, limit vs market order | | **Contingency Planning** | Preparing responses for potential future scenarios | contingency, if-then plan, what if, scenario | ### EXECUTION & ADAPTATION SKILLS How traders implement decisions and adjust to changing conditions | Skill Domain | Description | Key Indicators | |-------------|-------------|----------------| | **Mechanical Execution** | Implementing planned trading actions during entry | place order, enter trade, execute entry, fill | | **Position Management** | Adjusting active trades as they evolve | manage position, adjust stop, trail stop, exit | | **Market Adaptation** | Responding to actual, unfolding market changes | adapt to, react to, market change, volatility spike | | **Error Correction** | Managing mistakes or unintended outcomes | error, mistake, correct, fix, wrong size | | **Operational Optimization** | Enhancing trading efficiency and reducing costs | commission, slippage, optimize cost, platform | ### METACOGNITIVE SKILLS How traders understand, reflect upon, and regulate their own thinking processes | Skill Domain | Description | Key Indicators | |-------------|-------------|----------------| | **Emotional Regulation** | Managing feelings that influence trading | fear, greed, anxiety, stress, discipline, patience | | **Cognitive Bias Management** | Identifying and counteracting thinking errors | bias, confirmation, recency, hindsight, anchoring | | **Attention Management** | Directing and maintaining mental focus | focus, concentration, distraction, prioritize | | **Performance Evaluation** | Assessing past trading processes and outcomes | review, analyze performance, journal review | | **Knowledge Integration** | Incorporating lessons into future behavior | learn from, integrate lesson, improve, adapt approach | ## Metacognitive Override Logic ### Mandatory Override Test If ANY of these criteria are met, a Metacognitive domain MUST be assigned as Core Skill: Does the question explicitly state an emotion or bias as the main problem to be managed? Does the question directly ask how to regulate emotions, thoughts, or attention? Does the question require reflecting on or evaluating a past decision's validity? Does the question explicitly ask to differentiate between skill and luck? Does the question focus on evaluating decision quality separate from outcome? ## Decision Trees ### Primary Category Selection ```mermaid graph TD A[Input Question Analyzed] --> B{Primary Output/Action?} B -->|Understanding Market Info| C[PERCEPTION & ANALYSIS] B -->|Making Choice/Plan| D[DECISION-MAKING] B -->|Implementing/Adjusting| E[EXECUTION & ADAPTATION] ``` ### Key Distinctions & Tie-Breakers Named pattern → Pattern Recognition Data meaning → Data Interpretation Pre-trade decision → Execution Planning During-trade action → Mechanical Execution Future hypothetical → Contingency Planning Current actual → Market Adaptation Managing feelings → Emotional Regulation Thinking errors → Cognitive Bias Management ## Common Classification Scenarios ### Cognitive Process Integration Matrix This matrix shows common valid relationships when questions engage multiple domains | If Question Combines | And Primarily Asks For | Core Skill Is | Supportive Skills | |---------------------|------------------------|---------------|-------------------| | Pattern ID + Trading Decision | The optimal trade decision | Trade Structuring | Pattern Recognition | | Data + Signal Filtering | Identifying false signals | Signal Filtering | Data Interpretation | | Multi-Source + Strategy | Synthesizing conflicting data | Contextual Integration | Strategic Assessment | | Position Adjustment + Emotions | How to handle emotions | Emotional Regulation | Position Management | | Past Decision + Bias Check | Evaluating decision validity | Cognitive Bias Mgmt | Original decision skill | ## Required Output Format Your output MUST strictly adhere to one of these formats based on the number of Supportive Skills: ### Format 1: Core Skill Only ``` CORE SKILL: [COGNITIVE CATEGORY > Specific Skill Domain] REASONING: [1-2 sentences explaining the core cognitive task] SUPPORTIVE SKILLS: None required for this straightforward question. ``` ### Format 2: Core + One Supportive Skill ``` CORE SKILL: [COGNITIVE CATEGORY > Specific Skill Domain] REASONING: [1-2 sentences explaining the core cognitive task] SUPPORTIVE SKILL: [COGNITIVE CATEGORY > Specific Skill Domain] REASONING: [Explanation addressing Dependency, Critical Threshold, and Removal Test] ``` ### Format 3: Core + Two Supportive Skills ``` CORE SKILL: [COGNITIVE CATEGORY > Specific Skill Domain] REASONING: [1-2 sentences explaining the core cognitive task] SUPPORTIVE SKILL: [COGNITIVE CATEGORY > Specific Skill Domain] REASONING: [Explanation addressing all three criteria] SUPPORTIVE SKILL: [COGNITIVE CATEGORY > Specific Skill Domain] REASONING: [Explanation addressing all three criteria] ``` Use exact domain naming conventions and include ALL CAPS for labels (CORE SKILL, REASONING, SUPPORTIVE SKILL) ## Example Classifications ### Simple Pattern Recognition **Question:** "What candlestick pattern formed on the GOOGL daily chart?" ``` CORE SKILL: PERCEPTION & ANALYSIS > Pattern Recognition REASONING: The question explicitly asks to identify a specific, named candlestick pattern. SUPPORTIVE SKILLS: None required for this straightforward identification task. ``` ### Risk Calibration with Analysis **Question:** "Using backtest results (60% win rate), calculate expectancy and determine optimal position size for $100k account." ``` CORE SKILL: DECISION-MAKING > Risk-Reward Calibration REASONING: Final task is determining optimal position size, a core risk calibration function. SUPPORTIVE SKILL: PERCEPTION & ANALYSIS > Quantitative Analysis REASONING: Calculating expectancy (Dependency) is required for sizing decision (Critical); removing calculation makes optimal sizing impossible (Removal). ``` ### Metacognitive Override Example **Question:** "After three losing trades, you feel intense fear about the next valid setup. How should you manage this fear?" ``` CORE SKILL: METACOGNITIVE > Emotional Regulation REASONING: Metacognitive Override triggered - "intense fear" explicitly stated as primary obstacle. SUPPORTIVE SKILL: DECISION-MAKING > Trade Structuring REASONING: Recognizing valid setup (Dependency) provides context for emotion (Critical); removing it makes fear management undefined (Removal). ``` # Enhanced Skill Matrix with Templates and Interaction Types Source: https://wawe.finance/docs/technical/enhanced-skill-matrix/md Production-ready 4×3 matrix with interaction type mapping for LLM classification and UI implementation Version 2.0 - Production ready skill matrix with comprehensive UI interaction mapping for the quiz platform ## Core Architecture ### 4×3 Matrix Structure **PERCEPTION (A):** Pattern recognition, signal interpretation, quality assessment **STRATEGY (S):** Approach selection, strategy comparison, optimal choice **RISK (R):** Position sizing, risk assessment, trade viability **EXECUTION (E):** Order mechanics, execution planning, policy optimization **Apply:** Single rule on single input **Analyze:** Compare/synthesize ≥2 inputs **Evaluate:** Choose best option with justified trade-offs ## Interaction Type Matrix Each cell maps to primary and secondary interaction types for optimal user experience ### PERCEPTION Domain | Cell | Primary Interaction | Secondary | Implementation Notes | |------|-------------------|-----------|---------------------| | **PERCEPTION–Apply** | Chart Drawing Selection | Multi-Select | Visual identification of features/patterns | | **PERCEPTION–Analyze** | Multi-Select | Chart Selection | Synthesis across views with multi-view guidance | | **PERCEPTION–Evaluate** | Single Choice | Multi-Select | Evaluate quality vs noise with justification | ### STRATEGY Domain | Cell | Primary Interaction | Secondary | Implementation Notes | |------|-------------------|-----------|---------------------| | **STRATEGY–Apply** | Single Choice | Sequencing | Match setup to playbook | | **STRATEGY–Analyze** | Sequencing | Single Choice | Compare playbooks/contexts with ordering | | **STRATEGY–Evaluate** | Single Choice | Multi-Select | Choose best play with trade-offs | ### RISK Domain | Cell | Primary Interaction | Secondary | Implementation Notes | |------|-------------------|-----------|---------------------| | **RISK–Apply** | Single Choice | Multi-Select | Pre-computed sized options (no numeric input) | | **RISK–Analyze** | Multi-Select | Single Choice | Assess multiple risk factors and interactions | | **RISK–Evaluate** | Single Choice | Multi-Select | Decision thresholding with rationale | ### EXECUTION Domain | Cell | Primary Interaction | Secondary | Implementation Notes | |------|-------------------|-----------|---------------------| | **EXECUTION–Apply** | Chart Drawing Completion | Single Choice | Point-click completion of order parameters | | **EXECUTION–Analyze** | Single Choice | Multi-Select | Compare execution options with chart context | | **EXECUTION–Evaluate** | Single Choice | Sequencing | Optimize for slippage vs fill probability | ## Available Interaction Types **Use:** Decisions, classifications, selections **Format:** 3-5 options with one correct answer **Cells:** S1, S3, R3, E2, E3 **Use:** Multiple factor identification, comprehensive analysis **Format:** 5-8 options with 2+ correct answers **Cells:** A2, R2 **Use:** Process flows, priority ordering, procedures **Format:** 4-7 items to order correctly **Cells:** S2 **Use:** Pattern identification, support/resistance marking **Format:** Click/select specific chart areas **Cells:** A1 **Use:** Order placement, stop/target positioning **Format:** Complete partially drawn chart elements **Cells:** E1 ## Parameter Pools & Context System ### Asset Pools (Rotating) Diversified Asset Selection ```javascript ASSET_POOLS = { "equities": ["AAPL", "NVDA", "MSFT", "AMZN", "GOOGL", "META", "TSLA", "JPM", "BAC", "XOM"], "indices": ["SPY", "QQQ", "IWM", "DIA", "VTI", "EEM", "XLF", "XLE"], "forex": ["EURUSD", "GBPUSD", "USDJPY", "AUDUSD"], "crypto": ["BTCUSD", "ETHUSD", "SOLUSD", "BNBUSD"], "commodities": ["GC", "CL", "NG", "SI", "ZC", "ZS"] } ``` ### Market Context Parameters trending_up, trending_down, ranging, volatile_expanding, volatile_contracting pre_earnings, post_earnings, pre_fomc, post_fomc, pre_cpi, normal normal_correlation, decorrelated, high_correlation, breakdown low_sub15, normal_15to20, elevated_20to30, high_above30 ## Template Examples ### PERCEPTION–Apply: Chart Drawing Selection A1 Template Structure ```javascript function generate_A1_chart_selection(complexity="basic") { const patterns = { "basic": { "pattern": "support_touch", "instruction": "Click all points where price touches support", "chart_elements": ["support_line", "price_candles"], "correct_zones": [(x1, y1), (x2, y2)] }, "advanced": { "pattern": "divergence_signals", "instruction": "Mark areas showing price/indicator divergence", "chart_elements": ["price_chart", "RSI_panel", "trend_lines"], "correct_zones": [(x1, y1, x2, y2), (x3, y3, x4, y4)] } }; return { "interaction_type": "chart_drawing_selection", "prompt": `On the chart below, ${params.instruction}`, "chart_config": { "interactive": true, "selection_mode": "multiple_points" }, "correct_selections": params.correct_zones, "tolerance_pixels": 10 }; } ``` ### STRATEGY–Analyze: Sequencing S2 Template Structure ```javascript function generate_S2_sequencing() { const scenario = { "context": "Trading AAPL before earnings with high IV", "considerations": [ "Assess implied vs historical volatility spread", "Check expected move from options pricing", "Review past earnings reactions", "Determine position sizing based on risk", "Select strategy (straddle, condor, directional)", "Set exit plan for post-announcement" ], "correct_order": [1, 2, 3, 4, 5, 6] }; return { "interaction_type": "sequencing", "prompt": `Context: ${scenario.context}. Order these steps:`, "items": shuffled_items, "correct_sequence": ordered_items, "scoring_method": "kendall_tau" }; } ``` ### RISK–Apply: Computed Position Sizing R1 Template with Pre-computed Options ```javascript function generate_R1_computed_choice() { // Calculate correct position size const risk_amount = account * (risk_pct / 100); const stop_distance = Math.abs(entry - stop); const correct_shares = Math.floor(risk_amount / stop_distance); // Generate plausible distractors const distractors = [ Math.floor(correct_shares * 0.5), // Too conservative Math.floor(correct_shares * 2), // Too aggressive Math.floor(correct_shares * 1.3) // Slightly over ]; return { "interaction_type": "single_choice", "prompt": `Account: $${account} | Risk: ${risk_pct}% Entry: $${entry} | Stop: $${stop} Select correct position size:`, "options": [`${shares} shares` for shares in options], "correct_answer": `${correct_shares} shares`, "show_calculation": true }; } ``` ## Gap Mitigation Strategies ### Numeric Input Limitation Since numeric input isn't available, we implement these solutions for RISK domain: Present calculated position sizes as multiple choice answers Offer ranges like "100-150 shares" or "151-200 shares" Express as account percentages: "2% position", "5% position" Future enhancement for continuous value selection ### Multi-Chart Analysis For PERCEPTION-Analyze requiring multiple chart comparisons: Show 2-4 charts simultaneously Quick switching between chart views Superimpose indicators on single chart Static reference with interactive main ## Validation System ### Interaction Validator Validation Requirements ```javascript VALID_INTERACTIONS = { "single_choice": { "required_fields": ["prompt", "options", "correct_answer"], "options_range": [3, 5], "supports_justification": true }, "multi_select": { "required_fields": ["prompt", "options", "correct_answers"], "options_range": [5, 8], "min_correct": 2, "supports_partial_credit": true }, "chart_drawing_selection": { "required_fields": ["prompt", "chart_config", "correct_selections"], "requires_visual": true, "supports_tolerance": true } } ``` ### Cell-Interaction Matching The validator ensures interaction types match expected patterns: - A_1: chart_drawing_selection, multi_select - A_2: multi_select, chart_drawing_selection - A_3: single_choice, multi_select - S_1: single_choice, sequencing - S_2: sequencing, single_choice - And so on... ## Complexity Measurement ### Objective Complexity Metrics - 2 numerical values - 1 calculation step - 0 conditional branches - Whole numbers only - 4 numerical values - 2 calculation steps - 1 conditional branch - 2 decimal places - 6+ numerical values - 3+ calculation steps - 2+ conditional branches - 4 decimal places ### Complexity Scoring Objective Complexity Formula ```javascript function calculate_objective_complexity(question_data) { const metrics = { numerical_values: count_numbers(question_data.prompt), calculation_steps: count_operations(question_data.calculation), conditional_branches: count_conditionals(question_data.logic), interaction_complexity: score_interaction(question_data.interaction_type) }; // Score each metric (total 100 points) let score = 0; score += Math.min(16.67, metrics.numerical_values * 3.33); score += Math.min(16.67, metrics.calculation_steps * 5.56); score += Math.min(16.67, metrics.conditional_branches * 8.33); score += Math.min(16.67, metrics.interaction_complexity * 5.56); const level = score <= 33 ? "basic" : score <= 66 ? "intermediate" : "advanced"; return {score, level, metrics}; } ``` ## Coverage Tracking ### Usage Distribution The CoverageTracker maintains spacing between similar combinations: - Minimum 10 questions between same asset+timeframe+context+interaction - Balances primary vs secondary interaction types per cell - Recommends underused interaction types for variety ### Implementation Example Question Generation with Coverage ```javascript function generate_question_with_interaction(cell_id, complexity="intermediate") { // Get recommended interaction type for balance const interaction_type = coverage_tracker.recommend_next_interaction(cell_id); // Generate using appropriate template const generator = generator_map[`${cell_id}_${interaction_type}`]; const question_data = generator(complexity); // Validate structure and cell-interaction match const [is_valid, message] = validator.validate_question(question_data); // Calculate objective complexity const {score, level, metrics} = calculate_objective_complexity(question_data); // Mark coverage for spacing coverage_tracker.mark_used(cell_id, asset, timeframe, context, interaction_type); return { ...question_data, complexity_score: score, complexity_level: level, complexity_metrics: metrics }; } ``` This system ensures balanced question generation across all 12 cells with varied interaction types, objective complexity measurement, and proper coverage tracking for production use. # Quiz System - XPs, Classification, and Analytics Source: https://wawe.finance/docs/technical/quiz-system/md High-retention practice system design with engagement-first XP, simple taxonomy, analytics-driven personalization, and parametric content generation Launch a high-retention practice system that is simple to use, fair to learners, and useful to creators—while keeping taxonomy light, analytics strong, and recommendations reliable. ## Product Principles XP rewards engagement; skill is inferred separately 4 domains × 3 Blooms; no difficulty-based XP Classification decided upstream; post-gen validation is guardrail κ & AC1 ≥ 0.75 before wide rollout No penalties for help-seeking or slower devices Add complexity only when data demands it ## System Architecture ``` Learner → Quiz Player → XP Engine → XP Ledger ↘︎ events ↘︎ dashboards Creator → Builder/AI → Classification → Questions Store ↘︎ Item Health ↘︎ Coverage Analytics Telemetry → Analytics Layer → Recommender (Mastery/Health/Spacing) Admin/QA → Reliability Suite → Classifier Tuning ``` ## XP & Gamification ### XP Rules XP is immediate and predictable, focused on engagement rather than difficulty #### Base Rewards - **Correct Answer:** +10 XP - **Incorrect Answer:** +2 XP (participation reward) #### Bonus System +10 XP for 100% accuracy +10 XP if accuracy ≥80% and faster than median +5 XP for solution review + reflection +5 XP per day, max 7 days (+35 XP cap) Daily soft cap: After 300 XP/day, payouts at 50% rate to prevent grinding ### Gamification Features Monthly seasons with XP-based leagues (Bronze/Silver/Gold). Leaderboards reset each season. Earn badges like "RISK–Analyze Adept" for 30 verified corrects in that cell. Tiers at 10/30/60. Auto-set daily goals (e.g., "Earn 60 XP today") with progress tracking. ### Anti-Exploit & Accessibility **No-Speed Mode:** Swaps Speed Bonus for Review Bonus to accommodate learners who need more time or have device limitations ## Classification Model ### 4×3 Matrix (12 Cells) #### Domains | Domain | Focus | Example Applications | |--------|-------|---------------------| | **ANALYSIS** | Reading/interpreting market data | Pattern recognition, signal validation | | **STRATEGY** | Choosing approach and playbook | Plan selection, setup comparison | | **RISK** | Position sizing and capital management | Stop placement, size calculation | | **EXECUTION** | Order mechanics and timing policy | Order type selection, venue choice | #### Bloom's Levels One rule on one input Compare/synthesize ≥2 inputs Pick best option with justification ### Classification Precedence Precedence order: EXECUTION > RISK > STRATEGY > ANALYSIS ### Hybrid Classifier Keyword markers + precedence resolution Request domain & Bloom with "why-not" reasoning for other domains Rules == LLM → high confidence; else apply precedence or review κ & AC1 ≥ 0.75 on balanced 12-cell set ## Content Creation ### Doc-Driven AI Pipeline ```json { "domain": "RISK", "bloom": "Analyze", "constraints": ["policy-level execution only"], "grounding": ["doc://ch12#para3", "doc://ch12#fig2"] } ``` ### Manual Builder Features - Coverage-aware prompts ("Low on EXECUTION–Analyze") - Creator chooses domain + bloom or accepts auto-suggestion - Parametric templates offered (not required) - Same validator ensures consistency ## Analytics Layer ### Item Health Metrics | Metric | Description | Use Case | |--------|-------------|----------| | **Success Rate** | Correct answers per item | Basic performance | | **Discrimination** | Δ success between top/bottom quartiles | Item quality | | **Abandon Rate** | Quits/timeouts per item | Difficulty indicator | | **Time Z-score** | Deviation from median | Ambiguity detection | Health status progression: new → stable → needs_edit → quarantined ### Mastery Tracking Beta model per cell: α = 1 + correct, β = 1 + incorrect Mastery = α/(α+β) ### Spacing & Review - If days_since_last_correct > 7 and mastery < 0.70 → inject review - Light-touch spacing without heavy scheduler ### Dashboard Types Coverage analytics, item health, impact metrics Mastery by cell, streaks, recommended focus Retention metrics, reliability scores, inventory health ## Parametric Templates ### Why Parametric Templates? Varied surface prevents pattern matching Classification consistency with creativity Spans different market regimes Express nuance within quality guardrails ### Template Structure Each template includes: - **Inputs/Slots**: {ASSET}, {TIMEFRAME}, {ENTRY_RULE} - **Constraints**: Value ranges, mutually exclusive combos - **Answer Rule**: How to compute correct choice - **Distractors**: Principled wrong answers - **Validation**: Classification guardrails ### Example Template: Position Size Calculation ``` Prompt: Account {EQUITY}. Risk {RISK_PCT}% per trade. Entry {ENTRY}, stop {STOP}. What size? Answer Rule: size = (EQUITY × RISK_PCT) / |ENTRY–STOP| Distractors: - Swap TP for stop - Percent-of-equity share count - Decimal errors ``` ## Implementation Timeline XP engine with base rewards, bonuses, streaks, session goals 4-domain/3-Bloom classifier, precedence rules, builder UI Performance tracking, item health, dashboards v0 LLM validator, confidence scoring, reduce manual review to ~20% Beta mastery, recommender v1, spaced review, badges & leagues Consider info_complexity if analytics justify (analytics-only) ## Success Metrics ### Primary Metrics - κ & AC1 ≥ 0.75 (balanced 12-cell set) - D7 retention ≥ 40%; D30 ≥ 20% ### Secondary Metrics - +8–12% improvement in cell-level mastery within 7 days - Creator efficiency: < 90s median to confirm labels - Content health: < 5% traffic to needs_edit/quarantined items ### Alert Thresholds - Speed bonus payout > 40% → tighten normative threshold - Any domain < 15% inventory for 2 weeks → trigger creator challenges ## Risk Mitigation | Risk | Mitigation | |------|------------| | **Speed farming** | Accuracy gate + normative pace + daily cap | | **Label drift** | Precedence rule + balanced validation | | **Supply imbalance** | Coverage-aware prompts + creator challenges | | **Accessibility** | No-Speed Mode with Review Bonus | | **Data sparsity** | Minimum attempts for quarantine | ## Future Enhancements ### Phase A (Months 1-2) - Creator impact tracking - Basic personalization - Friendly competitions ### Phase B (Months 3-6) - Creator payments based on learning impact - Study groups - Content difficulty ranking (analytics-only) ### Phase C (Months 6-12) - Advanced predictions - Seasonal narratives - Mentorship programs All future features would be for analytics only (no XP impact) and added carefully to avoid complexity bloat # Wawe Platform Launch: Revolutionizing Trading Education Source: https://wawe.finance/blog/wawe-platform-launch/md Introducing the first AI-powered personalized trading education platform with paper trading, interactive quizzes, and gamification. Join our Founding Educator program for exclusive access. Welcome to the future of trading education! Today marks a revolutionary milestone in financial learning with the official launch of the Wawe platform. **🚀 Ready to Transform Your Trading Journey?** [**Start Learning Free**](https://wawe.finance/auth) | [**Join Founding Educators (50% Off)**](https://wawe.finance/pricing) --- ## Transforming How Traders Learn Traditional trading education has long been fragmented, theoretical, and one-size-fits-all. Today, we're changing that forever. Wawe combines cutting-edge AI with proven educational methodologies to create the world's first truly personalized trading education experience. Our platform offers four core innovations: **AI-Powered Learning** - Our advanced classification system adapts to your learning style and tracks mastery across 12 cognitive skill domains, ensuring every lesson is tailored to your current abilities and learning goals. **Real-World Practice** - Practice with live market data through our comprehensive paper trading environment. Experience actual market conditions without financial risk. **Interactive Content** - Engage with chart-based quizzes and interactive lessons that simulate real trading decisions. No more memorizing theory—practice actual trading skills. **Gamified Progress** - Earn XP, unlock achievements, and compete in seasonal leagues while mastering trading skills. Learning becomes engaging and measurable. ## Core Platform Features ### Paper Trading Environment Experience real market conditions without financial risk. Our paper trading system provides: 1. **Live Market Data** - Practice with real-time market feeds across equities, forex, crypto, and commodities 2. **Advanced Order Types** - Master limit orders, stop losses, bracket orders, and complex execution strategies 3. **Performance Analytics** - Track your progress with detailed analytics measuring both engagement and skill development 4. **Risk Management Tools** - Learn proper position sizing, risk assessment, and portfolio management in a safe environment ### Chart-Based Quizzes Our revolutionary quiz system goes beyond traditional multiple choice to include: - **Chart Drawing Selection**: Click and select patterns, support/resistance levels, and key market features - **Interactive Sequencing**: Order trading steps and decision processes in the correct sequence - **Multi-Select Analysis**: Identify multiple factors affecting market conditions simultaneously - **Scenario-Based Evaluation**: Make optimal trading decisions given real market constraints Each quiz question is classified across our 4×3 matrix covering Perception, Strategy, Risk, and Execution skills at Apply, Analyze, and Evaluate cognitive levels. ### Interactive Lessons Learn through doing with our comprehensive lesson library covering six core areas: **Pattern Recognition** - Master chart patterns with interactive identification exercises that help you spot formations in live markets. **Risk Management** - Practice position sizing and stop placement with real scenarios. Learn to protect your capital while maximizing opportunities. **Strategy Development** - Build and test trading strategies across different market conditions. Understand when to apply momentum, mean reversion, or volatility strategies. **Technical Analysis** - Learn indicators, oscillators, and multi-timeframe analysis. Move beyond basic concepts to advanced chart reading skills. **Market Psychology** - Understand behavioral biases and emotional regulation techniques. Master the mental game that separates successful traders. **Execution Mastery** - Optimize order types, timing, and transaction cost management. Turn good analysis into profitable trades. ### Gamification & Engagement Learning trading shouldn't feel like work. Our gamification system includes: **XP & Progression System** - Earn 10 XP for correct answers, 2 XP for participation - Speed bonuses for quick, accurate responses - Daily streaks with freeze protection - Seasonal leagues: Bronze, Silver, and Gold **Achievement System** - Skill-specific badges for mastering different domains - Milestone rewards for consistent practice - Leaderboards that reset monthly for fair competition **Adaptive Difficulty** - Questions automatically adjust to your skill level - Spaced repetition ensures long-term retention - Personalized recommendations based on performance gaps Our XP system rewards engagement and consistency, not just correctness, ensuring all learners can progress at their own pace. ## The Founding Educator Program **Limited Time Opportunity - Join the Foundation of Trading Education** We're launching with an exclusive **Founding Educator Program** for content creators who want to be part of the trading education revolution. ### Program Benefits - **Lifetime Access** to the full Wawe platform at 50% off regular pricing - **Priority Support** with dedicated creator assistance and feedback - **Revenue Sharing** opportunities as our creator economy launches - **Early Access** to new features and AI-powered content creation tools - **Community Recognition** as a founding member of the Wawe educator network ### Creator Tools & Analytics Founding Educators get access to our comprehensive creator dashboard featuring: **Content Performance Analytics** - Track how learners engage with your content across our 12-cell skill matrix - Monitor item health metrics including success rates and discrimination indices - View mastery improvements driven by your content **AI-Assisted Content Creation** - Spec-first question generation with automatic skill classification - Coverage optimization suggesting high-impact content areas - Parametric templates ensuring variety while maintaining quality **Progressive Insight Dashboard** - Getting Started view for new creators (Days 1-30) - Growing creator analytics for established educators (100+ learners) - Full attribution dashboard with behavioral impact measurement (500+ learners) Full program details and enrollment are available at [wawe.finance/pricing](https://wawe.finance/pricing). ## Built on Scientific Foundations Wawe isn't just another education platform - it's built on rigorous scientific principles: ### Cognitive Classification System Our content is classified using a sophisticated taxonomy that maps to specific cognitive processes required for trading success: - **Perception & Analysis**: Pattern recognition, signal interpretation, quality assessment - **Decision-Making**: Strategy selection, trade structuring, risk-reward evaluation - **Execution & Adaptation**: Order mechanics, position management, market adaptation - **Metacognitive Skills**: Emotional regulation, bias management, performance evaluation ### Evidence-Based Learning Our platform implements proven learning science principles: **Spaced Repetition** - Questions resurface at optimal intervals to maximize retention. The system tracks when you last answered correctly and reintroduces concepts just before you're likely to forget. **Interleaved Practice** - Mixed skill practice prevents over-specialization and improves transfer. Instead of drilling one skill repeatedly, you'll practice different skills in rotation. **Immediate Feedback** - Instant explanations and corrections accelerate learning. Every answer receives detailed feedback explaining why options are correct or incorrect. **Difficulty Progression** - Content automatically adapts based on demonstrated mastery. As you improve, the system presents more challenging scenarios to maintain optimal learning pace. ## Join the Revolution The trading education landscape will never be the same. Whether you're a beginner taking your first steps or an experienced trader looking to refine your skills, Wawe provides the personalized, engaging, and effective learning experience you've been waiting for. **Ready to transform your trading education?** 1. **Sign Up** - Create your free account and complete the initial skill assessment 2. **Start Learning** - Dive into interactive lessons tailored to your current skill level 3. **Practice & Progress** - Take quizzes, earn XP, and track your improvement across all skill domains 4. **Apply Knowledge** - Test your skills in our risk-free paper trading environment **Important:** Founding Educator Program enrollment closes soon. Secure your lifetime access at 50% off before it's too late. Welcome to Wawe. Welcome to the future of trading education. --- *Ready to get started? Visit [wawe.finance](https://wawe.finance) to begin your personalized trading education journey today.* # Beyond Multiple Choice: Our Revolutionary Quiz System Source: https://wawe.finance/blog/revolutionary-quiz-system/md Discover how Wawe's AI-powered quiz system uses interactive charts, sequencing, and multi-dimensional skill assessment to transform trading education. Traditional trading education relies on outdated multiple-choice questions that fail to capture the complexity of real trading decisions. Today, we're unveiling the quiz system that changes everything. **⚡ Experience Interactive Learning That Actually Works** Stop memorizing theory—start practicing real trading skills with our revolutionary quiz system. [**Try Interactive Quizzes Free**](https://wawe.finance/auth) | [**Create Content & Earn (50% Off)**](https://wawe.finance/pricing) --- ## The Problem with Traditional Trading Quizzes Most trading education platforms still rely on simple multiple-choice questions that test memorization rather than application. Ask yourself: when was the last time you made a real trading decision by selecting from four preset options? **Real Trading Reality Check:** - Market patterns appear visually on charts, not in text descriptions - Risk decisions involve calculations and trade-offs, not memorization - Strategy selection requires sequencing multiple factors - Execution timing depends on real market conditions The disconnect between how we learn and how we actually trade has been holding back trader development for decades. Until now. ## Introducing Interactive Question Types Our revolutionary quiz system features five distinct interaction types that mirror real trading activities: ### 1. Chart Drawing Selection **Pattern Recognition Made Real** Instead of describing patterns in text, learners click directly on charts to identify: - Support and resistance levels - Chart pattern boundaries - Volume confirmation signals - Divergence indicators - Breakout points Example: "Click all points where price touches the major support level" - learners click directly on the chart, and the system validates proximity to actual support touches. **Why This Works Better:** - Mirrors how traders actually analyze charts - Develops visual pattern recognition skills - Provides immediate spatial feedback - Builds confidence in real-world application ### 2. Multi-Select Analysis **Comprehensive Factor Assessment** Real markets involve multiple simultaneous factors. Our multi-select questions require learners to identify all relevant elements: **Risk Factor Analysis Example:** Select all factors that increase position risk: - High correlation with existing holdings ✓ - Earnings announcement tomorrow ✓ - Tight spreads ✗ - Average daily volume ✗ **Signal Confluence Example:** Which indicators support a bullish outlook: - RSI rising above 50 ✓ - Volume increasing on upticks ✓ - Price above 20-day MA ✓ - VIX declining ✗ This approach teaches traders to synthesize multiple data points simultaneously - a critical skill for successful trading. ### 3. Sequencing Mastery **Strategic Thinking Development** Trading isn't just about knowing what to do - it's about knowing the correct order of operations. Our sequencing questions require learners to arrange trading steps in the optimal sequence: **Example: Pre-Earnings Strategy Development** Drag these steps into the correct order: 1. Assess implied vs historical volatility spread 2. Check expected move from options pricing 3. Review past earnings reactions and patterns 4. Determine position sizing based on risk 5. Select strategy (straddle, condor, directional) 6. Set exit plan for post-announcement The system uses Kendall's Tau correlation to provide partial credit for sequences that are close to optimal, recognizing that trading often involves judgment calls. ### 4. Chart Drawing Completion **Execution Skills Practice** The most realistic interaction type: learners complete partially drawn trading setups by placing: - Entry orders at optimal levels - Stop losses at calculated distances - Profit targets based on risk-reward ratios - Bracket orders with proper parameters This directly simulates the actual process of placing trades in a trading platform. ### 5. Enhanced Single Choice Even our traditional single-choice questions go beyond basic formats: **Pre-Computed Options for Risk Questions** Instead of asking "What position size should you use?" with generic answers, we present: - 127 shares (optimal for 1% risk) - 254 shares (2% risk - too aggressive) - 64 shares (0.5% risk - too conservative) - 85 shares (random/incorrect calculation) This teaches proper position sizing while avoiding the limitations of text-only input. ## The 4×3 Skill Classification Matrix **4 Cognitive Domains** - **PERCEPTION** - Reading market data - **STRATEGY** - Choosing approaches - **RISK** - Managing capital - **EXECUTION** - Implementing trades **3 Bloom Levels** - **APPLY** - Use single rule on single input - **ANALYZE** - Compare/synthesize multiple inputs - **EVALUATE** - Choose best option with trade-offs Every question maps to one of our 12 cells (4 domains × 3 levels), ensuring comprehensive skill coverage and precise learning analytics. ### Mapping Interactions to Skills | Skill Cell | Primary Interaction | Secondary Option | Purpose | |------------|-------------------|------------------|----------| | **PERCEPTION–Apply** | Chart Drawing Selection | Multi-Select | Visual pattern identification | | **PERCEPTION–Analyze** | Multi-Select | Chart Selection | Multi-signal synthesis | | **PERCEPTION–Evaluate** | Single Choice | Multi-Select | Signal quality assessment | | **STRATEGY–Apply** | Single Choice | Sequencing | Playbook matching | | **STRATEGY–Analyze** | Sequencing | Single Choice | Strategy comparison | | **STRATEGY–Evaluate** | Single Choice | Multi-Select | Optimal strategy selection | | **RISK–Apply** | Single Choice | Multi-Select | Position size calculation | | **RISK–Analyze** | Multi-Select | Single Choice | Risk factor assessment | | **RISK–Evaluate** | Single Choice | Multi-Select | Risk-reward decisions | | **EXECUTION–Apply** | Chart Drawing Completion | Single Choice | Order placement | | **EXECUTION–Analyze** | Single Choice | Multi-Select | Execution comparison | | **EXECUTION–Evaluate** | Single Choice | Sequencing | Policy optimization | ## AI-Powered Content Generation Our quiz system leverages advanced AI to generate varied, high-quality questions while maintaining classification consistency: ### Parametric Templates Instead of rigid question forms, we use flexible templates with smart parameter substitution: Asset Pool Rotation ```javascript ASSET_POOLS = { "equities": ["AAPL", "NVDA", "MSFT", "AMZN"], "indices": ["SPY", "QQQ", "IWM", "DIA"], "forex": ["EURUSD", "GBPUSD", "USDJPY"], "crypto": ["BTCUSD", "ETHUSD", "SOLUSD"] } ``` ### Market Context Variation **Market Regimes:** trending_up, trending_down, ranging, volatile_expanding, volatile_contracting **Event Windows:** pre_earnings, post_earnings, pre_fomc, post_fomc, normal **Volatility States:** low_sub15, normal_15to20, elevated_20to30, high_above30 **Correlation Conditions:** normal_correlation, decorrelated, high_correlation, breakdown This ensures learners practice across diverse market conditions, building robust skills that transfer to real trading. ## Objective Complexity Measurement Unlike subjective difficulty ratings, our system measures complexity objectively: 1. **Numerical Values** - Count of numbers to process (2 = basic, 4 = intermediate, 6+ = advanced) 2. **Calculation Steps** - Number of mathematical operations required 3. **Conditional Branches** - If-then logic complexity in the decision process 4. **Information Sources** - Multiple data points that must be synthesized 5. **Interaction Complexity** - Sophistication of the required user interaction This creates consistent difficulty progression while maintaining engagement. ## Gamification That Actually Works ### Fair XP System **Core Principle:** XP rewards engagement, not difficulty - **+10 XP** for correct answers (regardless of question difficulty) - **+2 XP** for incorrect answers (participation reward) - **+10 XP** speed bonus (if accuracy ≥80% and faster than median) - **+5 XP** review bonus (for studying solutions) - **+5 XP** daily streak bonus (max 7 days) ### Accessibility Features **No-Speed Mode:** Learners who need more time or have device limitations can swap speed bonuses for review bonuses, ensuring fair progression for all. **Soft Daily Cap:** After 300 XP per day, rewards are reduced by 50% to prevent unhealthy grinding while allowing continued practice. ## Advanced Analytics for Learners ### Mastery Tracking Our system uses Beta distributions to model mastery probability for each of the 12 skill cells: - **α parameter:** 1 + number of correct answers - **β parameter:** 1 + number of incorrect answers - **Mastery estimate:** α/(α+β) This provides probabilistic confidence in skill levels rather than simple percentages. ### Spaced Review System **Intelligent Forgetting Curves:** If days since last correct answer > 7 AND mastery < 0.70, the system automatically injects review questions for that skill cell. ### Item Health Monitoring Every question is continuously monitored for: - **Success Rate:** Overall correctness percentage - **Discrimination:** Performance difference between high and low ability learners - **Abandon Rate:** How often learners quit or timeout - **Time Z-Score:** Deviation from expected completion time Questions that perform poorly are automatically flagged for review or removal. ## The Creator Advantage ### Coverage Analytics Content creators get detailed dashboards showing: - **Skill Distribution** - Which of the 12 cells your content covers - **Item Health** - Performance metrics for each question - **Learner Impact** - Mastery improvements driven by your content ### AI-Assisted Generation **Spec-First Question Creation** ```json { "domain": "RISK", "bloom": "Analyze", "constraints": ["policy-level only"], "grounding": ["doc://chapter5#section3"], "expected_impact": { "addresses_gap": true, "learner_need": "68% struggle here after RISK-Apply" } } ``` Our AI generates questions based on these specifications, then validates them against our classification system for consistency. ## Real-World Impact Early testing shows remarkable improvements over traditional quiz formats: - **85% increase** in pattern recognition accuracy on live charts - **73% improvement** in risk calculation speed and accuracy - **91% of learners** report higher engagement vs traditional quizzes - **67% better retention** of strategic decision-making skills after 30 days ## The Future of Trading Assessment This is just the beginning. Our roadmap includes: **Enhanced Interactions** - Real-time market simulation questions - Multi-chart comparative analysis - Voice-activated reasoning exercises - Collaborative problem-solving challenges **Advanced AI Features** - Personalized question generation based on individual weaknesses - Adaptive difficulty that responds to learning velocity - Emotional state recognition for optimal timing - Peer comparison and collaborative learning **Creator Tools** - Visual question builder with drag-and-drop interface - A/B testing capabilities for content optimization - Revenue sharing based on learning impact - Community-driven content validation ## Join the Quiz Revolution Traditional multiple-choice questions taught us to memorize. Our interactive quiz system teaches us to trade. Whether you're a learner seeking more effective practice or a creator wanting to build truly impactful content, our quiz system provides the tools and analytics you need to succeed. **The future of trading education is interactive, personalized, and measurable. Don't get left behind with yesterday's static quiz formats.** **Ready to experience the difference?** Start with our free tier and see how interactive learning transforms your trading education. For creators, our Founding Educator program provides lifetime access to our complete creator suite at 50% off regular pricing. Visit [wawe.finance/pricing](https://wawe.finance/pricing) to learn more about our creator program and start building the next generation of trading education content. --- *The quiz system is live now at [wawe.finance](https://wawe.finance). Experience interactive trading education today.*