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Overview

The Portfolio Tearsheet turns a list of companies - a watchlist, portfolio, or comps set - into a single, consolidated data grid. Instead of calling a tearsheet company by company, it returns one row per company covering price, 1-day price change, market capitalization, the earnings per share a company last reported alongside the analyst estimate for that same period and the surprise between them, the analyst consensus price target, and media sentiment.

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When to Use

The Portfolio Tearsheet is ideal for:
  • Cross-portfolio views: Getting a quick view of price and performance across many companies at once
  • Daily monitoring: Tracking a fund, watchlist, or portfolio on a recurring basis
  • Universe screening: Screening a large universe or comps set before drilling into individual names for deeper research
This is a multi-entity grid, not a single-entity deep dive. To research one company in depth, use bigdata_company_tearsheet, bigdata_sentiment_tearsheet, or bigdata_search.

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How It Works

The Portfolio Tearsheet follows a multi-step process to resolve every company in the list before retrieving the grid:
  1. Optionally: Name the metric columns you need, which drops the fetches behind the columns you leave out. Omitting metrics asks for every column
  2. Call: A call is made to find_securities (or get_securities) to resolve each company name, ticker, or ISIN
  3. Extract: The id field is extracted from each result and used as an rp_entity_id
  4. Call: A single call is made to bigdata_portfolio_tearsheet with the full list of rp_entity_id values
This workflow resolves the whole list up front, so the grid is retrieved in one call instead of one call per company.

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Parameters

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How many companies you can ask for

The limit depends on the columns, because they are not fetched the same way. Price, 1-day change, and market capitalization come back in bulk, so those grids take up to 3,000 companies. The EPS columns, the price target, and sentiment are each fetched one company at a time, so a request naming any of them is limited to 100 companies.
Omitting metrics asks for every column, so it is subject to the 100-company limit. For a watchlist larger than that, either request ["PRICE", "PRICE_CHANGE_1D", "MARKET_CAP"], or ask for the EPS columns, the price target, or sentiment over a shortlist.

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Important Notes

  • Each rp_entity_id must be exactly 6 characters - always obtain IDs from find_securities or get_securities
  • Companies without market data (for example ETFs or unlisted entities) keep their row, with blank cells rather than an error
  • Malformed IDs are skipped and reported back in the response
  • A column whose data a provider could not return comes back blank for that company rather than failing the grid
  • Sentiment renders blank rather than a misleading zero when a company has no qualifying news in the trailing 90 days, or when its coverage never clears the thresholds the score is built from

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Data Returned

The Portfolio Tearsheet returns a single data grid with one row per company: Only the columns you asked for are present. The response reports the columns it actually returned, which can be fewer than requested.

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Practical Tips

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Reducing Latency on Large Lists

A metric you leave out is not fetched, so naming only the columns you need keeps the response smaller and faster - useful when monitoring hundreds or thousands of names. PRICE, PRICE_CHANGE_1D, and MARKET_CAP are the only columns fetched in bulk, so any combination of them is what scales to a 3,000-name watchlist. EPS, EPS_ESTIMATE, and EPS_SURPRISE are the exception among the per-company columns: all three are read off the same earnings report, so asking for all three costs no more than asking for one.

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Reading EPS Against Its Estimate

The three EPS columns describe one earnings report, the last one the company filed, and EPS Fiscal Period says which period that is. Read them together: the reported figure on its own says how the company did, while the estimate beside it and the surprise between them say whether that was what the market was expecting. A large positive surprise on a name whose price has not moved is usually the row worth opening. Because every row shows whatever period that company last reported, two rows in the same grid can cover different quarters - a company that reported last week and one that reported two months ago sit side by side. Check EPS Fiscal Period before comparing figures across rows.

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Reading the Price Target

The consensus target is the headline figure; Target Low, Target Median, and Target High are the spread of analyst estimates, broken out as their own columns and quoted in the same currency as the consensus. A wide spread is worth more attention than the consensus alone - it is disagreement about the name, not a single view of it. Compare the consensus against the Price column to see implied upside or downside.

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Reading Sentiment

Treat a blank sentiment cell as “no strongly-toned coverage,” not as neutral - a company with genuinely mixed or quiet news and a company the fetch couldn’t complete in time can both render blank. Because the score matches what bigdata_sentiment_tearsheet reports as “Current,” a name worth a closer look is a good candidate for that tool’s narrative and cited sources. Sentiment is also the most expensive column to fetch - narrow the watchlist further than the 100-company cap if you’re combining it with other per-company metrics and latency matters.

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Reading Market Capitalization

Market capitalization is a snapshot, latest price times shares outstanding, so it moves with the Price column rather than with fundamentals. Values are shown in local currency, so keep that in mind when comparing companies listed in different markets. Use it to segment a watchlist by size (large-, mid-, small-cap) before comparing names on EPS or the price target, since those figures mean different things at different scales.

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From Grid to Deep Dive

Use the grid to identify which names warrant attention - notable movers, outliers, a large earnings surprise, a price far from its consensus target, or a sentiment score at odds with the price action - then follow up on those names individually:

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Frequency of Monitoring

  • Daily: For active fund, watchlist, or portfolio monitoring
  • Weekly: For broader universe screening and comps review