# 🎯 Analyst Price Targets — Consensus, Upgrades, Downgrades (`nexgendata/analyst-price-targets`) Actor Track sell-side analyst price targets, consensus ratings, recent upgrades/downgrades across US-listed stocks. Bloomberg-style ratings distribution with upside/downside vs current price. Pay-per-result for portfolio managers, retail smart-money followers, IR teams tracking street sentiment. - **URL**: https://apify.com/nexgendata/analyst-price-targets.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $100.00 / 1,000 analyst target records This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Analyst Price Targets — Consensus, Upgrades & Downgrades Track sell-side analyst price targets, consensus ratings, recent upgrades and downgrades across every US-listed stock. One pay-per-result API call returns the Bloomberg-style consensus snapshot you need to run a portfolio: mean / high / low / median price target, the analyst-count behind it, the buy / hold / sell distribution, and the firm-by-firm trail of recent rating actions from the past 90 days. ### 📊 Sample Output [![🎯 Analyst Price Targets — Consensus, Upgrades, Downgrades sample output — 🎯 Analyst Price Targets — Consensus, Upgrades, Downgrades, premium API, JSON output, NexGenData premium dataset for analysts, ](https://api.apify.com/v2/key-value-stores/8gLgXMBveEI1tTz1z/records/analyst-price-targets-output.png)](https://apify.com/nexgendata/analyst-price-targets) Built for portfolio managers running long/short books, retail "smart money" followers who track street consensus, and IR teams that need to know what the sell-side is actually saying about their tape before earnings, conferences, or roadshows. ### What you get per record Every record is one US-listed ticker, normalized into a clean, ready-to-merge schema. Hit it with one symbol or fan it across the S&P 500. | Field | Description | |---|---| | `symbol` | Ticker (uppercase). | | `company_name` | Yahoo Finance long name. | | `sector` / `industry` | GICS-style sector + finer industry classification. | | `current_price` | Latest regular-session price (USD). | | `consensus_target_price` | Mean of all contributing sell-side 12-month price targets. | | `target_median` | Median of contributing targets — outlier-resistant alternative to mean. | | `target_high` / `target_low` | High and low of the contributing target range. Useful for sizing dispersion. | | `n_analysts` | Number of analysts contributing to the consensus mean. | | `upside_pct` | `(consensus_target - current_price) / current_price * 100`. Positive = upside. | | `recommendation_mean` | Yahoo's 1.0–5.0 sell-side mean (1.0 = Strong Buy, 5.0 = Sell). | | `consensus_rating` | Bucketed rating: `Strong Buy` / `Buy` / `Hold` / `Sell` / `Strong Sell`. | | `rating_strong_buy_count` | Count of Strong Buy ratings (current month). | | `rating_buy_count` | Strong Buy + Buy ratings combined. | | `rating_hold_count` | Count of Hold ratings. | | `rating_sell_count` | Sell + Strong Sell ratings combined. | | `rating_strong_sell_count` | Count of Strong Sell ratings. | | `total_rated_analysts` | Sum of all five rating buckets. | | `recent_actions` | Up to 20 firm-level actions from the last 90 days, formatted as plain-English lines (`"Morgan Stanley upgraded to Overweight from Equal-Weight, target raised to $250.00 (was $230.00) on 2026-05-08"`). | | `n_recent_actions_90d` | Count of analyst actions in the trailing 90 days. | | `last_action_date` | Most recent rating or target change date (ISO yyyy-mm-dd). | | `market_cap_usd` | Latest market capitalization (USD). | | `yahoo_url` | Convenience deep-link to Yahoo's analyst page. | | `stockanalysis_url` | Convenience deep-link to Stockanalysis.com's forecast page. | | `data_source` | Provenance: `yahoo_finance_quote_summary`. | | `fetched_at_utc` | ISO timestamp of the fetch. | ### Why this actor exists Sell-side price targets and consensus ratings are the most-quoted, least-accessible number in equities. Bloomberg, FactSet, and Refinitiv ship clean consensus feeds — at $24K, $18K, and $24K per terminal per year respectively. TipRanks and Zacks ship retail-grade views with anti-bot defenses and watered-down API access. For everyone in between — a fund manager spinning up a quant overlay, a retail aggregator running street-consensus screens, an IR team running a quarterly tape-check — there is no clean, pay-per-call option. This actor fixes that: - **Pay only for what you use.** $0.10 per symbol record. A 500-name S&P 500 sweep costs $50.01 and returns in under a minute. Compare that to a Bloomberg seat. - **Bloomberg-style schema.** Mean / high / low / median target, ratings distribution, recent firm actions — the exact fields a PM wants on their morning sheet. - **No anti-bot wrestling.** We handle Yahoo Finance's crumb/cookie dance for you, refresh tokens transparently on 401s, and ship clean JSON. - **Pinned builds.** `?build=0.0.1` keeps your prod integrations stable; we never silently swap parsing logic underneath you. ### Comparison vs incumbents | Capability | Bloomberg | FactSet | Refinitiv | TipRanks | Zacks | **Analyst Price Targets** | |---|---|---|---|---|---|---| | Annual minimum cost | $24,000+ | $18,000+ | $24,000+ | $1,000+ | $250+ | **$0 (pay-per-call)** | | Per-symbol marginal cost | n/a | n/a | n/a | n/a | n/a | **$0.10** | | Consensus mean target | Yes | Yes | Yes | Yes | Yes | **Yes** | | Target high / low / median | Yes | Yes | Yes | Partial | Partial | **Yes** | | Ratings distribution (SB/B/H/S/SS) | Yes | Yes | Yes | Yes | Partial | **Yes** | | Firm-by-firm recent actions | Yes | Yes | Yes | Yes | Partial | **Yes (last 90d)** | | 1.0–5.0 numeric consensus | Yes | Yes | Yes | No | Partial | **Yes** | | Programmatic API access | Yes (enterprise) | Yes (enterprise) | Yes (enterprise) | Limited | Limited | **Yes (HTTP)** | | No-contract, no-seat licensing | No | No | No | No | No | **Yes** | | Cross-language clients | Yes | Yes | Yes | Limited | No | **Yes (Apify SDKs)** | | Build-pinned stability | Yes | Yes | Yes | No | No | **Yes (`?build=`)** | | Time to first record | Weeks (procurement) | Weeks | Weeks | Days | Days | **Minutes** | ### How to call it #### Apify Console (browser UI) Open the actor, paste symbols into the *Stock symbols* field, hit **Start**. Dataset appears in a table view in seconds. #### Apify API (curl) ```bash curl -X POST \ "https://api.apify.com/v2/acts/nexgendata~analyst-price-targets/runs?token=YOUR_TOKEN&build=0.0.1" \ -H "Content-Type: application/json" \ -d '{ "symbols": ["AAPL", "NVDA", "TSLA", "MSFT", "AMZN"], "include_recent_actions": true, "min_n_analysts": 5 }' ```` The `?build=0.0.1` pin is recommended for production integrations. Without it you ride latest, which can change without notice. #### Apify Python SDK ```python from apify_client import ApifyClient client = ApifyClient("YOUR_TOKEN") run = client.actor("nexgendata/analyst-price-targets").call( run_input={ "symbols": ["AAPL", "NVDA", "TSLA", "MSFT", "AMZN"], "min_upside_pct": 20, ## only names with 20%+ upside "consensus_rating": "Buy", ## only Buy-rated names "include_recent_actions": True, }, build="0.0.1", ) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["symbol"], item["consensus_target_price"], item["upside_pct"]) ``` #### Apify JS SDK ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_TOKEN' }); const run = await client.actor('nexgendata/analyst-price-targets').call({ symbols: ['AAPL', 'NVDA', 'TSLA', 'MSFT', 'AMZN'], min_upside_pct: 20, include_recent_actions: true, }, { build: '0.0.1' }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` ### Input parameters | Parameter | Type | Default | Description | |---|---|---|---| | `symbols` | array | `["AAPL","NVDA","TSLA","MSFT","AMZN"]` | US-listed tickers to fetch. Case-insensitive. Required. | | `limit` | int | `0` (all) | Max records to return after filters. `0` returns everything. | | `min_upside_pct` | number | `-1000` (off) | Minimum upside vs current price. `20` for high-conviction longs. | | `consensus_rating` | enum | `"all"` | Filter to one rating bucket: `Strong Buy`, `Buy`, `Hold`, `Sell`, `Strong Sell`. | | `include_recent_actions` | bool | `true` | Populate `recent_actions` with last-90-day firm actions. | | `min_n_analysts` | int | `0` (off) | Drop names with fewer than N contributing analysts. Use `15` for widely-covered names only. | ### Common workflows #### Morning sheet — high-upside Buy-rated names ```json { "symbols": ["AAPL","MSFT","NVDA","GOOGL","META","AMZN","TSLA","AVGO","ORCL","CRM"], "min_upside_pct": 15, "consensus_rating": "Buy", "min_n_analysts": 10 } ``` Filters to widely-covered names with at least 15% implied upside and a sell-side Buy consensus. Drop the result into your morning meeting deck. #### Short candidate screen — Sell-rated names with downside ```json { "symbols": ["...your watchlist..."], "min_upside_pct": -1000, "consensus_rating": "Sell" } ``` Pair with `nexgendata/short-interest-tracker` to find names where the street and the borrow desk both flag downside. #### IR street-check — what is being said about us ```json { "symbols": ["TICKR"], "include_recent_actions": true } ``` One call before earnings to summarize every analyst action in the last 90 days — target moves, rating changes, initiations, dropped coverage. Drop straight into the prep memo. #### Smart-money following — newly upgraded names Run with `include_recent_actions: true`, then filter results client-side for `n_recent_actions_90d > 3` and parse `recent_actions` for the string "upgraded". Daily delta detection for newsletter writers. ### Data sources & methodology Primary source is Yahoo Finance's official `quoteSummary` endpoint, the same endpoint that powers `finance.yahoo.com/quote/{SYMBOL}/analysis`. We request six modules in a single call: `financialData`, `recommendationTrend`, `upgradeDowngradeHistory`, `price`, `summaryDetail`, and `assetProfile`. Yahoo's own consensus aggregation is itself sourced from sell-side feeds (Thomson Reuters / Refinitiv), so the targets and ratings here trace back to the same primary research desks that populate Bloomberg's consensus screen — just routed through Yahoo's free consumer endpoint. Yahoo's crumb/cookie authentication is handled transparently. We boot consent cookies from `fc.yahoo.com`, exchange them for a crumb at `query2.finance.yahoo.com/v1/test/getcrumb`, and reuse the crumb across the run. Crumb expiry mid-run is detected and refreshed on the fly. Concurrency is capped at 5 in-flight requests to stay below Yahoo's per-IP soft limit. A 500-symbol sweep typically completes in 50–90 seconds. ### Pricing | Event | Charge | |---|---| | Actor start | $0.01 | | Per analyst-target record | $0.10 | A 5-symbol smoke run costs **$0.51**. A full S\&P 500 sweep costs **$50.01**. A Russell 1000 sweep costs **$100.01**. There is no monthly minimum, no seat license, and no contract — you pay the Apify platform fee plus the per-event charges only on runs you actually execute. The 20% Apify margin is included in the prices above; we do not mark up beyond that. ### Sister actors in the NexGenData fleet Layer this actor with the rest of the fleet for a full equity-research stack: - [**`nexgendata/finviz-stock-screener`**](https://apify.com/nexgendata/finviz-stock-screener) — Finviz's full screener universe with 70+ filters. Feed its symbol list into this actor to bulk-pull consensus targets for any screen. - [**`nexgendata/earnings-calendar`**](https://apify.com/nexgendata/earnings-calendar) — Upcoming earnings releases with EPS estimates, fiscal period, prior-period surprises. Run this actor against the upcoming-reporters list to find names where street sentiment has shifted into the print. - [**`nexgendata/short-interest-tracker`**](https://apify.com/nexgendata/short-interest-tracker) — Bi-monthly short-interest reports, days-to-cover, % of float short. Cross-reference Sell-rated names here with high short-interest tickers there to find consensus-confirmed shorts. - [**`nexgendata/sec-form4-insider-tracker`**](https://apify.com/nexgendata/sec-form4-insider-tracker) — Real-time insider buys and sells from SEC Form 4 filings. Stack with this actor to find names where the street and management agree on direction. - [**`nexgendata/etf-holdings-tracker`**](https://apify.com/nexgendata/etf-holdings-tracker) — Daily ETF basket exposure. Useful for tracking which thematic ETFs hold the analyst-favorite names you surface here. - [**`nexgendata/finance-mcp-server`**](https://apify.com/nexgendata/finance-mcp-server) — Anthropic MCP server bundling the entire NexGenData equity stack into one tool surface for Claude / GPT / agentic workflows. Drop it in and let the model pull consensus targets, screens, earnings, short interest, insider activity, and ETF holdings on demand. ### FAQ **Why Yahoo Finance and not Stockanalysis.com / TipRanks / MarketBeat?** We tested all five. Stockanalysis.com forecast pages SSR through Svelte and hydrate from a private API that returns 404 to non-browser callers. TipRanks blocks anonymous JSON. MarketBeat returns HTML with anti-bot challenges. Zacks is rate-limited and protected by Cloudflare. Yahoo's `quoteSummary` endpoint is the cleanest, most-reliable surface and is itself sourced from Refinitiv's institutional feed — so we get Bloomberg-quality consensus for free. **How fresh is the consensus?** Yahoo's financialData snapshot refreshes intra-day. Recommendation trend buckets refresh monthly. Upgrade/downgrade history rows appear within hours of the underlying note. For event-driven workflows we recommend rerunning the actor at the close each trading day. **Can I scan the whole Russell 1000?** Yes. Pass 1000 symbols, set `min_n_analysts: 5` to drop thinly-covered names, and the run completes in ~3 minutes for $100.01. We've stress-tested up to 2000 symbols per run without hitting Yahoo's edge limits. **What if a symbol is an ADR / OTC / dual-listing?** ADRs and major OTC names work. Pure foreign listings (e.g. ASML on AEX without an ADR) return null targets — Yahoo's analyst feed only covers the US-listed share class. **Does it handle option-implied targets or buy-side targets?** No. The output is pure sell-side. For option-implied moves see our forthcoming options-vol actor. **Build pinning?** Pin with `?build=0.0.1`. The build *number* (string), not the build *id*. Build numbers are stable; build ids rotate on every push. We bump 0.0.x for additive changes and 0.x.0 for any breaking schema change. ### Support & licensing Private actor on the NexGenData Apify fleet. Access by request — contact via the Apify console or the affiliate signup below. Bulk-rate licensing, white-label deployments, and dedicated-instance options available on request. **[Sign up via the NexGenData affiliate link](https://apify.com/nexgendata?fpr=2ayu9b)** — supports the fleet at no cost to you, gives you instant access to every NexGenData actor (10+ and growing) including this one, plus first-look at upcoming releases (consensus-EPS-revisions actor, options-vol actor, transcripts-sentiment actor). Affiliate signups stay on a flat 20% rev-share for the lifetime of the account. Built and maintained by NexGenData. We focus on the equity-research stack the institutional vendors charge $20K+ for — and ship it pay-per-call. # Actor input Schema ## `symbols` (type: `array`): Array of US-listed stock tickers to fetch analyst price-target data for. Examples: \['AAPL', 'NVDA', 'TSLA', 'MSFT']. Tickers are case-insensitive — they are upper-cased before lookup. Supports common stock symbols on NYSE, NASDAQ, and major US exchanges. Each symbol returns one record with consensus target, high/low, ratings distribution, and recent analyst actions. ## `limit` (type: `integer`): Maximum number of analyst price-target records to return after all filters apply. Set to 0 to return every input symbol that passes the filters. Useful for testing on small batches before running a full universe scan (S\&P 500, Russell 1000, custom watchlist). ## `min_upside_pct` (type: `number`): Filter out names whose consensus target implies less than this percentage of upside vs current price. Use 20 for high-conviction longs, 50 for deep-value/distressed setups, or a negative number (e.g. -100) to include downside names too. Set to -1000 (default) to disable the filter and return everything. Computed as ((target - current) / current) \* 100. ## `consensus_rating` (type: `string`): Filter records by sell-side consensus rating bucket. 'all' returns everything. Use 'Strong Buy' or 'Buy' to surface street-favorite names, 'Sell' or 'Strong Sell' to surface short candidates and value-trap warnings. The rating is derived from Yahoo Finance's recommendationKey field (1.0–5.0 mean scale). ## `include_recent_actions` (type: `boolean`): If true, populates the 'recent\_actions' field with up to 20 individual sell-side actions from the last 90 days: firm name, rating change, target change, action type (Upgrade/Downgrade/Maintain/Initiate), and date. Disable for lighter payloads if you only need the consensus snapshot. ## `min_n_analysts` (type: `integer`): Drop records where fewer than this many analysts contribute to the consensus target. Use 5 to filter out thinly-covered micro-caps where one outlier dominates the mean. Use 15 to filter to widely-covered names with statistically meaningful consensus. Set to 0 (default) to disable the filter. ## Actor input object example ```json { "symbols": [ "AAPL", "NVDA", "TSLA", "MSFT", "AMZN" ], "limit": 0, "min_upside_pct": -1000, "consensus_rating": "all", "include_recent_actions": true, "min_n_analysts": 0 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "symbols": [ "AAPL", "NVDA", "TSLA", "MSFT", "AMZN" ], "limit": 0, "min_upside_pct": -1000, "consensus_rating": "all", "include_recent_actions": true, "min_n_analysts": 0 }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/analyst-price-targets").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "symbols": [ "AAPL", "NVDA", "TSLA", "MSFT", "AMZN", ], "limit": 0, "min_upside_pct": -1000, "consensus_rating": "all", "include_recent_actions": True, "min_n_analysts": 0, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/analyst-price-targets").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "symbols": [ "AAPL", "NVDA", "TSLA", "MSFT", "AMZN" ], "limit": 0, "min_upside_pct": -1000, "consensus_rating": "all", "include_recent_actions": true, "min_n_analysts": 0 }' | apify call nexgendata/analyst-price-targets --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/analyst-price-targets", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🎯 Analyst Price Targets — Consensus, Upgrades, Downgrades", "description": "Track sell-side analyst price targets, consensus ratings, recent upgrades/downgrades across US-listed stocks. Bloomberg-style ratings distribution with upside/downside vs current price. Pay-per-result for portfolio managers, retail smart-money followers, IR teams tracking street sentiment.", "version": "0.0", "x-build-id": "TvKnoT3JzLF0q1neA" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~analyst-price-targets/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-analyst-price-targets", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~analyst-price-targets/runs": { "post": { "operationId": "runs-sync-nexgendata-analyst-price-targets", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~analyst-price-targets/run-sync": { "post": { "operationId": "run-sync-nexgendata-analyst-price-targets", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "symbols" ], "properties": { "symbols": { "title": "Stock symbols (tickers)", "type": "array", "description": "Array of US-listed stock tickers to fetch analyst price-target data for. Examples: ['AAPL', 'NVDA', 'TSLA', 'MSFT']. Tickers are case-insensitive — they are upper-cased before lookup. Supports common stock symbols on NYSE, NASDAQ, and major US exchanges. Each symbol returns one record with consensus target, high/low, ratings distribution, and recent analyst actions.", "default": [ "AAPL", "NVDA", "TSLA", "MSFT", "AMZN" ], "items": { "type": "string" } }, "limit": { "title": "Maximum number of records to return", "minimum": 0, "maximum": 2000, "type": "integer", "description": "Maximum number of analyst price-target records to return after all filters apply. Set to 0 to return every input symbol that passes the filters. Useful for testing on small batches before running a full universe scan (S&P 500, Russell 1000, custom watchlist).", "default": 0 }, "min_upside_pct": { "title": "Minimum upside percentage filter", "minimum": -1000, "maximum": 1000, "type": "number", "description": "Filter out names whose consensus target implies less than this percentage of upside vs current price. Use 20 for high-conviction longs, 50 for deep-value/distressed setups, or a negative number (e.g. -100) to include downside names too. Set to -1000 (default) to disable the filter and return everything. Computed as ((target - current) / current) * 100.", "default": -1000 }, "consensus_rating": { "title": "Consensus rating filter", "enum": [ "all", "Strong Buy", "Buy", "Hold", "Sell", "Strong Sell" ], "type": "string", "description": "Filter records by sell-side consensus rating bucket. 'all' returns everything. Use 'Strong Buy' or 'Buy' to surface street-favorite names, 'Sell' or 'Strong Sell' to surface short candidates and value-trap warnings. The rating is derived from Yahoo Finance's recommendationKey field (1.0–5.0 mean scale).", "default": "all" }, "include_recent_actions": { "title": "Include recent analyst actions (last 90 days)", "type": "boolean", "description": "If true, populates the 'recent_actions' field with up to 20 individual sell-side actions from the last 90 days: firm name, rating change, target change, action type (Upgrade/Downgrade/Maintain/Initiate), and date. Disable for lighter payloads if you only need the consensus snapshot.", "default": true }, "min_n_analysts": { "title": "Minimum number of contributing analysts", "minimum": 0, "maximum": 100, "type": "integer", "description": "Drop records where fewer than this many analysts contribute to the consensus target. Use 5 to filter out thinly-covered micro-caps where one outlier dominates the mean. Use 15 to filter to widely-covered names with statistically meaningful consensus. Set to 0 (default) to disable the filter.", "default": 0 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```