Dashboards & Visualization for Insights

Stock lists in tables are useful. But staring at rows and columns isn’t always the fastest way to spot patterns.

That’s where dashboards and visualizations help.

They turn your screener’s output into simple charts, heatmaps, or trend lines — things your eye can understand instantly.

Why Dashboards Matter

• See trends quickly: Spot oversold stocks or sector outliers in seconds.
• Compare easily: ROE, P/E, or RSI across 10 companies’ side by side.
• Track progress: Week-on-week results tell you if your strategy is working.

Tools You Can Use

• Google Sheets / Excel → Easiest starting point. Charts + conditional formatting.
• Google Data Studio / Power BI → Interactive dashboards with filters.
• Python (Matplotlib/Plotly) → For advanced users who want full customization.

How to Build a Simple Dashboard in Google Sheets

Step 1: Import Data

• Take the output of your AI screener (table format).
• Paste it into Google Sheets with columns like: Stock, Price, P/E, ROE, RSI, Volume.

Step 2: Add Conditional Formatting

• Highlight RSI < 30 in green (oversold).
• Highlight RSI > 70 in red (overbought).
• Now you have a visual heatmap right inside your table.

Step 3: Create Charts

• Insert → Chart → Choose chart type.
• Bar Chart: Compare P/E ratios across companies.
• Line Chart: Price trend vs 200-DMA.
• Pie Chart: Sector distribution of shortlisted stocks.

Step 4: Add Filters (Slicers)

• Insert → Slicer → Choose column (Sector, Market Cap).
• Now you can toggle sectors or cap sizes interactively.

Step 5: Link with Automation

• If you followed the last chapter, connect your screener automation script to this sheet.
• Every morning, the sheet updates — and your dashboard stays live without extra work.

How to Build It Fast (Copy–Paste Ready)

Sheet setup (3 tabs)

1. Raw → paste AI output with columns: Stock, Sector, Price, PE, ROE, RSI, 200DMA, Volume.
   
2. Clean → create a tidy view from Raw.
   
3. Charts → all visuals live here.
   

Clean sheet formulas

• Unique, deduped list (handles header row in A1):

=QUERY(Raw!A1:H, "select A,B,C,D,E,F,G,H where A is not null", 1)

• Sector list (for dropdowns if you don’t use Slicers):

=UNIQUE(FILTER(Clean!B2:B, Clean!B2:B<>""))

• Filter by Sector (cell B1 holds chosen sector; leave blank = all):

=IF(B1="", Clean!A1:H, QUERY(Clean!A1:H, "select * where B = '"&B1&"'", 1))

Conditional formatting (on Clean tab)

• RSI oversold (<30): Range F2:F → Format rules → Custom formula:

=$F2<30

• RSI overbought (>70): Range F2:F → Custom formula:

=$F2>70

• Price above 200-DMA (trend strength): Range C2:C and G2:G exist → Custom formula:

=$C2>$G2

Charts (on Charts tab)

1. P/E bar chart (compare valuation) Insert → Chart → Bar → Data range: Clean!A1:D → Series: PE → X-axis: Stock.

2. Price vs 200-DMA line (trend check) Create helper table on Charts:

=QUERY(Clean!A1:H, "select A,C,G label C 'Price', G '200-DMA'", 1)

Chart → Line → X-axis: Stock → Series: Price, 200-DMA.

3. RSI heatmap Use Clean with conditional formatting (already set) — no chart needed.

4. Sector distribution pie

=QUERY(Clean!A1:H, "select B, count(A) where A is not null group by B label count(A)
'Count'", 1)

Chart → Pie.

Interactive filters

• Insert → Slicer → Column: Sector (and optionally Market Cap if present).
• Position slicers on Clean (they’ll auto-filter connected charts if charts pull from filtered ranges).

Auto-refresh tips

• If you’re appending new results daily, make charts read from dynamic ranges (e.g., entire columns) or wrap with:

=INDEX(QUERY(Raw!A1:H, "select * where A is not null", 1), 1, 1)

• Keep project timezone = Asia/Kolkata so daily 9:05 runs align.

Notes:

• The formulas (QUERY, UNIQUE, FILTER) are Google Sheets–specific. Excel users will need Power Query or equivalent.
• Conditional formatting rules ($F2<30, $F2>70) assume RSI values are in column F — adjust if your layout differs.
• Keep separate tabs for “Raw,” “Clean,” and “Charts” to avoid breaking formulas when pasting new AI output.

QA checklist (2 mins)

• Headers in row 1?
  
• No merged cells?
  
• Slicers filter charts?
  
• RSI colours show (<30 green, >70 red)?
  
• Price line sits above 200-DMA for “strong” names?
  

Example: Indian Auto Stocks

Your screener outputs 15 auto stocks under ₹1,000.

• Heatmap shows 5 are oversold (RSI < 30).
• Bar chart shows 2 companies have much higher P/E than the rest.
• Line chart reveals only 3 are trading above their 200-DMA.

In two minutes, you’ve gone from rows of numbers to a clear picture of which auto stocks deserve a closer look.

Going Beyond Sheets

Once you’re comfortable:

• Connect Google Sheets to Data Studio for dashboards with filters and drilldowns.
• Use Power BI if you’re tracking across multiple strategies.
• For advanced learners, Python’s Plotly can create interactive web dashboards.

Final Takeaway

Dashboards convert raw stock data into insights you can act on.

Start simple — a heatmap for RSI, a bar chart for P/E, a line chart for price vs 200-DMA.

As your needs grow, link your screener to live sheets and build dashboards that update automatically.

The goal isn’t pretty visuals. It’s speed: helping you spot patterns and make smarter decisions in less time.