# Implementation Plan: Score Transparency & Trade Overlay

## Overview

Extend the scoring API and UI to expose full score breakdowns (sub-scores, weights, raw values, formulas) for each dimension and the composite score. Add trade setup chart overlays (entry, stop-loss, take-profit zones) to the candlestick chart on the ticker detail page. Backend changes are in Python/FastAPI, frontend in React/TypeScript.

## Tasks

- [x] 1. Add score breakdown schemas and refactor scoring service
  - [x] 1.1 Add breakdown Pydantic models to `app/schemas/score.py`
    - Add `SubScoreResponse`, `ScoreBreakdownResponse`, `CompositeBreakdownResponse` models
    - Extend `DimensionScoreResponse` with optional `breakdown: ScoreBreakdownResponse` field
    - Extend `ScoreResponse` with optional `composite_breakdown: CompositeBreakdownResponse` field
    - _Requirements: 1.1, 1.7_

  - [x] 1.2 Refactor `_compute_technical_score` in `app/services/scoring_service.py` to return breakdown
    - Change return type to `tuple[float | None, ScoreBreakdown | None]`
    - Return sub-scores for ADX (weight 0.4), EMA (weight 0.3), RSI (weight 0.3) with raw indicator values
    - Include formula description string
    - Add unavailable sub-scores with reason when data is insufficient
    - _Requirements: 1.2, 1.8_

  - [x] 1.3 Refactor `_compute_sentiment_score` to return breakdown
    - Return record count, decay rate, lookback window, and weighted average formula parameters as sub-score metadata
    - _Requirements: 1.3, 1.8_

  - [x] 1.4 Refactor `_compute_fundamental_score` to return breakdown
    - Return PE Ratio, Revenue Growth, Earnings Surprise sub-scores with raw metric values and normalization formula
    - _Requirements: 1.4, 1.8_

  - [x] 1.5 Refactor `_compute_momentum_score` to return breakdown
    - Return 5-day ROC (weight 0.5) and 20-day ROC (weight 0.5) sub-scores with raw ROC percentages
    - _Requirements: 1.5, 1.8_

  - [x] 1.6 Refactor `_compute_sr_quality_score` to return breakdown
    - Return strong count (max 40 pts), proximity (max 30 pts), avg strength (max 30 pts) sub-scores with input values
    - _Requirements: 1.6, 1.8_

  - [x] 1.7 Update `get_score` to assemble composite breakdown and pass dimension breakdowns through
    - Build `CompositeBreakdownResponse` with original weights, available/missing dimensions, re-normalized weights, and formula
    - Wire dimension breakdowns into the response dict
    - _Requirements: 1.7, 3.2_

  - [x] 1.8 Update `read_score` in `app/routers/scores.py` to populate breakdown fields from service response
    - Map breakdown dicts from service into the new Pydantic response models
    - _Requirements: 1.1_

  - [ ]* 1.9 Write property test: Dimension breakdown contains correct sub-scores (Property 1)
    - **Property 1: Dimension breakdown contains correct sub-scores**
    - Use `hypothesis` to generate valid input data for each dimension type
    - Verify returned breakdown has expected sub-score names, correct weights, and non-null raw values
    - **Validates: Requirements 1.1, 1.2, 1.3, 1.4, 1.5, 1.6**

  - [ ]* 1.10 Write property test: Composite re-normalization correctness (Property 2)
    - **Property 2: Composite re-normalization correctness**
    - Use `hypothesis` to generate random subsets of dimensions with random weights
    - Verify re-normalized weights sum to 1.0 and each equals `original_weight / sum(available_weights)`
    - **Validates: Requirements 1.7, 3.2**

- [x] 2. Checkpoint — Backend score breakdown
  - Ensure all tests pass, ask the user if questions arise.

- [x] 3. Add frontend types and DimensionBreakdownPanel component
  - [x] 3.1 Extend frontend types in `frontend/src/lib/types.ts`
    - Add `SubScore`, `ScoreBreakdown`, `CompositeBreakdown` interfaces
    - Extend `DimensionScoreDetail` with optional `breakdown` field
    - Extend `ScoreResponse` with optional `composite_breakdown` field
    - _Requirements: 1.1, 1.7_

  - [x] 3.2 Create `frontend/src/components/ticker/DimensionBreakdownPanel.tsx`
    - Expandable panel showing sub-score rows: name, score value, weight badge, raw input value
    - Formula description text section
    - Muted "unavailable" labels for missing sub-scores with reason
    - _Requirements: 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8_

  - [x] 3.3 Modify `frontend/src/components/ui/ScoreCard.tsx` for composite transparency
    - Make each dimension row expandable, rendering `DimensionBreakdownPanel` when expanded
    - Show dimension weight next to each bar
    - Show missing dimensions with muted styling and "redistributed" indicator
    - Add tooltip/inline text explaining weighted average with re-normalization
    - _Requirements: 3.1, 3.2, 3.3_

  - [ ]* 3.4 Write property test: Dimension breakdown UI rendering completeness (Property 3)
    - **Property 3: Dimension breakdown UI rendering completeness**
    - Use `fast-check` to generate random `ScoreBreakdown` objects with 1-5 sub-scores
    - Render `DimensionBreakdownPanel` and verify DOM contains exactly N sub-score rows with all required fields
    - **Validates: Requirements 2.1**

  - [ ]* 3.5 Write property test: Composite weight display (Property 4)
    - **Property 4: Composite weight display**
    - Use `fast-check` to generate random score responses with random available/missing dimension combinations
    - Render `ScoreCard` and verify weight labels present and missing dimensions visually distinct
    - **Validates: Requirements 3.1, 3.2**

- [x] 4. Checkpoint — Score transparency UI
  - Ensure all tests pass, ask the user if questions arise.

- [x] 5. Add trade setup chart overlay
  - [x] 5.1 Modify `frontend/src/components/charts/CandlestickChart.tsx` to accept and render trade overlay
    - Add optional `tradeSetup?: TradeSetup` prop
    - Draw entry price as dashed horizontal line (blue/white) spanning full chart width
    - Draw stop-loss zone as red semi-transparent rectangle between entry and stop-loss
    - Draw take-profit zone as green semi-transparent rectangle between entry and target
    - Include entry, stop-loss, target in y-axis price range calculation
    - Add hover tooltip showing direction, entry, stop, target, R:R ratio
    - Render no overlay when prop is absent
    - _Requirements: 4.1, 4.2, 4.3, 4.4, 4.5, 4.6_

  - [ ]* 5.2 Write property test: Trade overlay y-axis range includes all trade levels (Property 5)
    - **Property 5: Trade overlay y-axis range includes all trade levels**
    - Use `fast-check` to generate random OHLCV data and random trade setups
    - Extract the y-axis range computation logic and verify all three trade levels fall within `[lo, hi]`
    - **Validates: Requirements 4.4**

- [x] 6. Integrate trade setup data on ticker detail page
  - [x] 6.1 Update `frontend/src/hooks/useTickerDetail.ts` to include trades data
    - Add trades query to the hook return value
    - _Requirements: 5.1_

  - [x] 6.2 Modify `frontend/src/pages/TickerDetailPage.tsx` to wire trade overlay
    - Fetch trade setups via `useTrades()`, filter for current symbol, pick latest by `detected_at`
    - Pass `tradeSetup` prop to `CandlestickChart`
    - Render trade setup summary card below chart (direction, entry, stop, target, R:R)
    - Handle trades API failure gracefully — chart renders without overlay, error logged
    - _Requirements: 5.1, 5.2, 5.3, 5.4, 5.5_

  - [ ]* 6.3 Write property test: Trade setup selection picks latest matching symbol (Property 6)
    - **Property 6: Trade setup selection picks latest matching symbol**
    - Use `fast-check` to generate random lists of trade setups with random symbols and timestamps
    - Verify selection logic returns the latest setup for the target symbol, or null if no match
    - **Validates: Requirements 5.1, 5.5**

- [x] 7. Final checkpoint — Ensure all tests pass
  - Ensure all tests pass, ask the user if questions arise.

## Notes

- Tasks marked with `*` are optional and can be skipped for faster MVP
- Each task references specific requirements for traceability
- Property tests use `hypothesis` (Python) and `fast-check` (TypeScript) with minimum 100 iterations
- No new database tables — breakdowns are computed on-the-fly from existing data
- Trade overlay uses the existing `lightweight-charts` rendering pipeline, no new library needed
