docs: Add technical design and implementation evaluation for AssetTrendChart

[Max97k]

This PR introduces the technical evaluation document for enhancing the AssetTrendChart functionality in the money2 project. The requested file, docs/asset_chart_implementation_evaluation.md, contains a detailed assessment covering:

1. Data flow and architecture design adjustments for handling multiple time ranges.
2. Canvas interactive cursor implementation strategy using Jetpack Compose's pointerInput.
3. Market index comparison and normalization plan to track benchmarks like S&P 500.
4. Step-by-step implementation plan categorized into clear milestones.

The report is written in Traditional Chinese (zh-TW).

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[gemini-code-assist]

Code Review

This pull request adds a technical evaluation and implementation plan for enhancing the AssetTrendChart, covering data flow, interactive Canvas gestures, benchmark normalization, and a milestone-based roadmap. The review feedback provides valuable technical improvements: recommending a normalized Room database schema instead of storing serialized lists to optimize caching, suggesting detectHorizontalDragGestures instead of detectDragGestures to prevent blocking vertical scrolling in parent containers, and pointing out a potential division-by-zero bug in the normalization formula when the initial asset value is zero.

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[gemini-code-assist]

When designing the Room database schema for caching historical prices, storing a serialized list of pairs mapped by (Symbol, Interval) as a single row can make querying, updating, or partially clearing the cache difficult.

Instead, it is highly recommended to design a normalized database table where each historical data point is a separate row. This allows leveraging SQL queries for filtering by date ranges, updating specific points, and automatic cleanup of old data.

Suggested change

	目前的 `historicalDataCache` 是儲存了 2Y 的 1d 資料。未來需將 Cache 結構升級，支援 `(Symbol, Interval) -> List<Pair<Long, Double>>`。可以利用 Room 資料庫進行持久化快取，減少每次啟動時的網路請求。針對分K資料，由於時效性高，可保留在 Memory Cache (如 `MutableStateFlow`)。
	目前的 `historicalDataCache` 是儲存了 2Y 的 1d 資料。未來需將 Cache 結構升級，在 Room 資料庫中設計一個正規化的資料表（以 `symbol`、`interval` 與 `timestamp` 作為複合主鍵），以支援高效的區間查詢與單點更新，避免直接序列化儲存整個 List。針對分K資料，由於時效性高，可保留在 Memory Cache (如 `MutableStateFlow`)。

[gemini-code-assist]

Using detectDragGestures will intercept and consume drag gestures in all directions (both horizontal and vertical). If the chart is placed inside a scrollable container (such as a LazyColumn or a scrollable Column), this will prevent the user from scrolling the page when dragging vertically over the chart area.

Since the interactive cursor only tracks horizontal movement (the X coordinate) to select the data point, it is highly recommended to use detectHorizontalDragGestures instead. This ensures vertical scroll gestures are correctly propagated to parent scrollable containers, providing a much smoother user experience.

Suggested change

	detectDragGestures(
	onDragStart = { offset -> /* 計算 selectedIndex */ },
	onDrag = { change, _ ->
	val x = change.position.x
	/* 根據 X 座標找出最近的資料點更新 selectedIndex */
	},
	onDragEnd = { selectedIndex = null },
	onDragCancel = { selectedIndex = null }
	)
	detectHorizontalDragGestures(
	onDragStart = { offset -> /* 計算 selectedIndex */ },
	onHorizontalDrag = { change, _ ->
	val x = change.position.x
	/* 根據 X 座標找出最近的資料點更新 selectedIndex */
	},
	onDragEnd = { selectedIndex = null },
	onDragCancel = { selectedIndex = null }
	)

[gemini-code-assist]

The normalization formula NormalizedValue(t) = (Value(t) / Value(t_0)) - 1.0 assumes that Value(t_0) (the value at the start of the selected time range) is always non-zero.

However, in real-world scenarios, Value(t_0) can be zero (e.g., if the user had no assets at that time, or if there is a temporary data anomaly/missing price). If Value(t_0) == 0, this formula will result in a division by zero, leading to NaN or Infinity values, which will crash the Canvas rendering or result in an empty chart.

Please update the design to explicitly handle the case where Value(t_0) == 0 (e.g., fallback to 0.0 or use a small epsilon/alternative baseline).

Suggested change

	計算公式：`NormalizedValue(t) = (Value(t) / Value(t_0)) - 1.0` (得出如 +0.05 代表 5%)。
	計算公式：`NormalizedValue(t) = if (Value(t_0) != 0.0) (Value(t) / Value(t_0)) - 1.0 else 0.0` (需特別處理 `Value(t_0) == 0` 的邊界情況，避免除以零導致 `NaN` 或 `Infinity` 造成圖表繪製崩潰)。