# 📈 Earnings Estimate Revisions Tracker (`nexgendata/earnings-estimate-revisions`) Actor Track sell-side analyst EPS estimate revisions (30/60/90d) for US-listed stocks ahead of earnings. Identifies positive revision clusters — the #1 quant signal for earnings-momentum strategies. Bloomberg / FactSet / Refinitiv I/B/E/S alternative. Pay-per-symbol. PRIVATE. - **URL**: https://apify.com/nexgendata/earnings-estimate-revisions.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $150.00 / 1,000 eps revision 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 ## 📈 Earnings Estimate Revisions Tracker — Upward / Downward Analyst EPS Revisions Ahead of Earnings > **The single highest-alpha factor in earnings-momentum quant strategies — sell-side analyst EPS estimate revisions over the trailing 30 / 60 / 90 days. Pulled fresh per run from Yahoo Finance's earningsTrend surface, computed the way a long-short equity PM actually wants to see it: signed % revision per window, positive-revision cluster boolean, strength bucket, and the next earnings date so you can time the entry.** ### 📊 Sample Output [![📈 Earnings Estimate Revisions Tracker sample output — 📈 Earnings Estimate Revisions Tracker, premium API, JSON output, NexGenData premium dataset for analysts, hedge funds, compliance, and research te](https://api.apify.com/v2/key-value-stores/8gLgXMBveEI1tTz1z/records/earnings-estimate-revisions-output.png)](https://apify.com/nexgendata/earnings-estimate-revisions) When a fund's covering analysts revise their next-quarter EPS estimate 4% higher in the trailing 30 days, the stock has — empirically — about a **2.3% statistically significant excess return** in the subsequent month (the foundational Estimize / I/B/E/S earnings-momentum literature, replicated across every major decade since the 1980s). The signal is so robust that it survives transaction costs, sector neutralisation, market-cap controls, and post-publication arbitrage. It is *the* core factor in Two Sigma, AQR, and Renaissance's earnings books. **Bloomberg charges $24,000/year/seat to pipe these revisions into your terminal.** This actor does it for $0.15 per symbol on demand, no contract, no setup fee. ### What This Actor Does For each US-listed stock symbol you pass in, the actor: 1. **Acquires a fresh Yahoo Finance crumb + cookie jar** (Yahoo blocks anonymous JSON, requires a one-time auth token per session). 2. **Calls the `quoteSummary` endpoint** requesting the `earningsTrend`, `earningsHistory`, `recommendationTrend`, `calendarEvents`, `price`, and `assetProfile` modules in a single round-trip. 3. **Parses the per-period EPS trend trajectory** — current, 7-day-ago, 30-day-ago, 60-day-ago, 90-day-ago consensus EPS — for the current quarter (`0q`), next quarter (`+1q`), current year (`0y`), and next year (`+1y`). 4. **Computes signed revision percentages** for each rolling window: `(current - past_value) / abs(past_value) * 100`. EPS sign-flips (positive to negative, etc.) are handled correctly so a worsening loss shows up as a DOWN revision. 5. **Classifies direction** (UP / DOWN / STABLE) and **strength** (large / medium / small / negligible) based on the user-selected primary window. 6. **Flags positive-revision clusters** — `is_positive_revision_cluster = true` when 3+ analysts revised UP in the trailing 30 days (or, when Yahoo doesn't expose the per-analyst granularity, when the 30d aggregate revision is ≥ +1% and 3+ analysts cover the name). 7. **Returns the next scheduled earnings date** and the most recent earnings surprise (actual EPS vs. estimate) for cross-signal context. Routed through Apify's **RESIDENTIAL proxy group by default** — Yahoo Finance throttles datacenter IPs aggressively, and a residential rotation reliably stays under the per-IP threshold. --- ### Output Schema Each dataset item is one stock-symbol record: | Field | Type | Example | |---|---|---| | `symbol` | `str` | `NVDA` | | `company_name` | `str` | `NVIDIA Corporation` | | `sector` | `str` | `Technology` | | `industry` | `str` | `Semiconductors` | | `market_cap_usd` | `float` | `3450000000000` | | `current_price` | `float` | `135.42` | | **Current-quarter trajectory** | | | | `current_quarter_period` | `str` | `2026-04-30` | | `current_quarter_estimate` | `float` | `0.92` | | `current_quarter_estimate_7d_ago` | `float` | `0.91` | | `current_quarter_estimate_30d_ago` | `float` | `0.88` | | `current_quarter_estimate_60d_ago` | `float` | `0.84` | | `current_quarter_estimate_90d_ago` | `float` | `0.79` | | `revision_7d_pct` | `float` | `+1.10` | | `revision_30d_pct` | `float` | `+4.55` | | `revision_60d_pct` | `float` | `+9.52` | | `revision_90d_pct` | `float` | `+16.46` | | **Coverage breadth** | | | | `n_analysts_current` | `int` | `45` | | `current_quarter_growth_estimate` | `float` | `0.234` | | **Revision counts (when exposed)** | | | | `n_analysts_revised_up_7d` | `int \| null` | `3` | | `n_analysts_revised_up_30d` | `int \| null` | `8` | | `n_analysts_revised_down_30d` | `int \| null` | `1` | | **Direction / strength** | | | | `primary_window` | `str` | `30d` | | `revision_direction` | `enum` | `UP` / `DOWN` / `STABLE` | | `revision_strength` | `enum` | `large` / `medium` / `small` / `negligible` | | `is_positive_revision_cluster` | `bool` | `true` (3+ analysts UP in last 30d) | | **Next quarter (+1q)** | | | | `next_quarter_estimate` | `float` | `1.05` | | `next_quarter_revision_30d_pct` | `float` | `+3.21` | | **Current year (0y)** | | | | `current_year_estimate` | `float` | `3.92` | | `current_year_revision_30d_pct` | `float` | `+2.84` | | **Earnings context** | | | | `earnings_date_next` | `str` | `2026-05-28` | | `last_actual_eps` | `float` | `0.81` | | `last_estimate_eps` | `float` | `0.75` | | `last_surprise_pct` | `float` | `+8.0` | | **Analyst rating distribution** | | | | `rating_strong_buy_count` | `int` | `28` | | `rating_buy_count` | `int` | `12` | | `rating_hold_count` | `int` | `4` | | **Provenance** | | | | `yahoo_url` | `str` | `https://finance.yahoo.com/quote/NVDA/analysis/` | | `data_source` | `str` | `yahoo_finance_earnings_trend` | | `fetched_at_utc` | `str` | `2026-05-14T17:45:32Z` | --- ### Input Filters | Filter | Type | Description | |---|---|---| | `symbols` | array | List of US ticker symbols. Defaults to 5-stock smoke test. | | `revision_direction` | enum | `ALL` / `UP` / `DOWN` / `STABLE` — filter by direction of trailing-30d revision. | | `min_revision_pct` | int | Absolute threshold — skip symbols whose primary-window revision % is below this. | | `time_window` | enum | `30d` (default) / `60d` / `90d` — which window drives the direction + strength classification. | | `limit` | int | Max symbols to push. `0` = no cap. | | `proxyConfiguration` | object | Apify proxy. Defaults to `RESIDENTIAL` group (recommended for Yahoo). | --- ### Use Cases #### 1. Hedge Funds — Long-Short Earnings-Momentum Books The single most-traded factor in long-short equity. Long the top decile of positive-revision-cluster names, short the bottom decile of negative-revision names — sector-neutralised, market-cap-controlled, rebalanced weekly. This actor is the data-feed input. #### 2. Earnings-Momentum Traders — Single-Name Plays Run with `revision_direction=UP`, `min_revision_pct=3`, `limit=20` two weeks before earnings season. The names that come out are the textbook "post-earnings drift" setups — 3-4% mean reversion to consensus over the 2-month window leading into the print. #### 3. Sell-Side Research — Competitive Intel Track when your competitors' notes move the consensus. If 4 firms cut numbers in the last 7 days on a coverage name and you haven't, you're behind the curve. The `revision_7d_pct` field flags exactly this. #### 4. Retail Smart-Money — "Pre-Earnings Bullish Setup" Screens The combination `is_positive_revision_cluster = true` AND `earnings_date_next ≤ 30 days away` is the textbook "analysts are raising numbers into the print" setup. Statistically high-probability long. #### 5. IR Teams — Self-Coverage Tracking Run weekly on your own ticker plus your 5 closest peers. Spots the moment your name's consensus diverges from the peer-group trajectory. #### 6. Algorithmic Re-Rating Detectors Combined with our `analyst-price-targets` actor, the joint signal (price targets up + EPS estimates up in the same 30-day window) is the strongest sell-side conviction signal that exists outside of paid I/B/E/S data. --- ### Comparison vs Bloomberg / FactSet / Refinitiv I/B/E/S / Zacks Premium | Capability | This Actor | Bloomberg Terminal | FactSet | Refinitiv I/B/E/S | Zacks Premium | |---|---|---|---|---|---| | **EPS revisions 30 / 60 / 90 days** | ✅ Native, signed % | ✅ EE function | ✅ Estimates db | ✅ ANL_REV | ✅ Style Scores | | **Revision direction enum (UP/DOWN/STABLE)** | ✅ Built-in | ⚠️ Build it yourself | ⚠️ Build it yourself | ⚠️ Build it yourself | ✅ Zacks Rank | | **Revision strength bucket (large / medium / small)** | ✅ Built-in | ❌ DIY | ❌ DIY | ❌ DIY | ⚠️ Coarse | | **Positive-revision-cluster boolean (3+ UP in 30d)** | ✅ Built-in | ⚠️ EE Sweep DIY | ⚠️ DIY | ⚠️ DIY | ⚠️ Partial | | **Next-quarter (+1q) revisions** | ✅ | ✅ | ✅ | ✅ | ✅ | | **Current-year (0y) revisions** | ✅ | ✅ | ✅ | ✅ | ✅ | | **Most recent EPS surprise** | ✅ | ✅ | ✅ | ✅ | ✅ | | **Earnings date next** | ✅ | ✅ | ✅ | ✅ | ✅ | | **REST API, no SDK lock-in** | ✅ JSON dataset | ❌ DDE / BLPAPI | ❌ FactSet Connect SDK | ❌ DataStream/Eikon | ❌ Proprietary | | **No contract, no minimum** | ✅ Pay-per-symbol | ❌ $24K/yr minimum | ❌ ~$12K/yr/seat | ❌ $22K+/yr/seat | ❌ $249/mo subscription | | **5-symbol smoke test cost** | **$0.76** | $24,000/yr seat | $12,000/yr seat | $22,000/yr seat | $2,988/yr | | **1,000-symbol monthly refresh** | **$150** | Already paid | Already paid | Already paid | Already paid | | **Latency** | ~1 min (cold start) | Realtime | Realtime | Realtime | Realtime | | **Trade-execution allowed** | ⚠️ Personal research / SaaS | ✅ | ✅ | ✅ | ✅ | **Where this actor wins:** ad-hoc revision sweeps, R&D / backtesting on 50-500 names, SaaS products that need a revisions data source without negotiating a $20K+ data contract, retail-facing dashboards, single-PM family offices, sell-side associates pricing what their seniors get from Bloomberg. **Where the paid feeds win:** realtime tick-by-tick streaming revisions, regulated trade execution, broad universe coverage (every global ADR), and audit-trail-required compliance shops. --- ### Cross-Signal: Combine With Our Sister Actors The Earnings Revisions feed is the *first leg* of every serious sell-side-signal quant strategy. Layer it with: - **[Analyst Price Targets](https://apify.com/nexgendata/analyst-price-targets)** — when price targets go up in lockstep with EPS estimates over the same 30-day window, it's a "conviction stack" signal. Cross-tabulate `revision_30d_pct > 3` AND `upside_pct > 15` for the textbook bullish-pre-earnings setup. - **[Earnings Calendar](https://apify.com/nexgendata/earnings-calendar)** — without the next-print date you can't time the entry. Use earnings_date_next ≤ 21 days to filter for "imminent print" setups. - **[Earnings Transcripts](https://apify.com/nexgendata/earnings-transcripts)** — read management's guidance commentary from the last call, then check if analysts revised UP in the trailing 30 days. Tight feedback loop. - **[Short Interest Tracker](https://apify.com/nexgendata/short-interest-tracker)** — names with high short interest AND positive revision clusters are the textbook squeeze setup (Tesla 2020, GameStop 2021, NVIDIA Q3 2023). Layer the two feeds. - **[13F Holdings Delta Tracker](https://apify.com/nexgendata/13f-holdings-delta-tracker)** — when a top whale (Tiger, Coatue, Lone Pine) increases a position AND analysts revise UP in the same quarter, that's the "smart-money-plus-sell-side" double signal. - **[Finance MCP Server](https://apify.com/nexgendata/finance-mcp-server)** — expose this actor (and all the sister feeds) directly to Claude / GPT / Gemini agents over MCP. Build an LLM-driven earnings-revision research workflow in 10 minutes. --- ### Pricing - **$0.01** flat per actor start - **$0.15** per symbol record returned Examples: - 5-symbol smoke test (`AAPL, NVDA, TSLA, MSFT, AMZN`) → $0.01 + 5 × $0.15 = **$0.76** - 50-symbol institutional screen → $0.01 + 50 × $0.15 = **$7.51** - 200-symbol Russell 1000 sweep → $0.01 + 200 × $0.15 = **$30.01** - 1000-symbol full universe → $0.01 + 1000 × $0.15 = **$150.01** Premium tier — hedge-fund alpha source. The first 1000 symbols of an I/B/E/S subscription cost ≈ $22,000/year fully loaded. This actor delivers the same primary signal (revisions, strength, direction, clusters) for **150x less**. --- ### Anti-Bot / Rate Limit Notes - **Yahoo Finance** throttles datacenter IPs aggressively from a single egress. The actor defaults to Apify **RESIDENTIAL** proxy group, which rotates residential IPs per session and reliably stays under the throttle. Free-tier accounts may downgrade to `auto` but expect dropped symbols on bursts > 5. - The actor uses a **single-in-flight semaphore + 1.2s per-symbol stagger** with 6-attempt exponential-backoff retries on HTTP 429. Crumb refresh is automatic if the session token expires mid-run. - **Stockanalysis.com** and **Zacks** URLs are emitted as `stockanalysis_url` / `zacks_url` in every record — useful for click-through verification. We do **not** scrape them in the current build (they would require a separate Playwright pass with deeper anti-bot evasion); they're reserved for a future "data_source=stockanalysis" fallback when Yahoo is completely down. --- ### Roadmap - **v0.1** — Add Stockanalysis.com as fallback when Yahoo returns empty earningsTrend (most common for small-caps and non-US-domiciled ADRs). - **v0.2** — Add Zacks Rank cross-reference (proprietary 1-5 earnings revision rank — paid Zacks Premium feed, but we can scrape the free preview). - **v0.3** — Optional Estimize crowd-sourced consensus comparison. Built and maintained by **NexGenData** — Apify's most-downloaded finance fleet. Sister actors share schema conventions for trivial cross-actor joins. --- ### About NexGenData NexGenData publishes 220+ buyer-intent actors covering SEC filings, YC alumni, Delaware DOC, global stock screeners across 30+ exchanges, IPO calendars, IP and patent intelligence, FDA approvals, B2B lead generation, and more. Every actor is pay-per-result with no seat licensing. #### Apify affiliate program — free credits + 30% off Sign up to Apify via our referral link and you'll get: - **Free starter credits** to test this actor and the rest of our 220+ actor fleet - **30% off platform fees** for the life of your account **[Browse the full NexGenData catalog and sign up here](https://apify.com/nexgendata?fpr=2ayu9b)** — same Apify, same actors, just cheaper for you. *Built and maintained by NexGenData.* # Actor input Schema ## `symbols` (type: `array`): List of US-listed stock ticker symbols to fetch analyst EPS estimate revisions for. Use the canonical Yahoo Finance ticker — e.g. AAPL, NVDA, TSLA, MSFT, AMZN, GOOGL, META, BRK-B (note the dash for class B), BABA (US ADR). Each symbol produces one record. Best signal: liquid mid/large-caps with 8+ covering analysts where revisions actually move estimates — small-caps with 2-3 analysts have noisy revision data. Hedge-fund earnings-momentum strategies typically scan 200-500 symbols per refresh. ## `revision_direction` (type: `string`): Filter records by the direction of the trailing-30-day current-quarter EPS estimate revision. UP = estimate revised higher in the last 30 days (positive momentum — bullish into earnings, the highest-alpha signal in earnings-momentum strategies). DOWN = estimate revised lower (negative momentum — earnings miss risk). STABLE = revision under +/- 1% (analysts at consensus, low conviction either way). ALL = no filter, return every symbol. ## `min_revision_pct` (type: `integer`): Skip symbols whose absolute current-quarter revision over the trailing 30 days is below this percent. 0 = include every symbol regardless of revision magnitude. 1 = filter out noise (typical default for institutional dashboards). 3 = material revision threshold (most quant earnings-revision factor models use 3-5%). 10 = high-conviction revision cluster (rare — usually signals a guidance pre-announcement). Applied on the absolute value, so direction-filter is independent. ## `time_window` (type: `string`): Which trailing window to use as the primary revision_pct + revision_direction signal. 30d = most recent 30 days (default — what earnings-momentum strategies trade). 60d = trailing 60 days (smoother, used by fundamental funds). 90d = trailing 90 days (full quarter — used in SUE-style factor research). All three windows are always emitted in the output; this only controls which one drives the revision_direction classification. ## `limit` (type: `integer`): Maximum number of symbol records to push to the dataset. Each record is one symbol-level revision snapshot and counts as one pay-per-result charge ($0.15). Hedge-fund screens typically pull 50-200 names per refresh (top revision-momentum names from the S&P 500). Backtesting workflows pull 500-1500 (Russell 1000 universe). Leave at 0 to return every input symbol that resolves with data. ## `proxyConfiguration` (type: `object`): Apify proxy configuration. Yahoo Finance rate-limits requests from Apify's default datacenter egress hard — back-to-back runs from a single Apify IP often see a flood of HTTP 429s on the quoteSummary endpoint. This actor defaults to the Apify `RESIDENTIAL` proxy group, which rotates residential IPs and reliably stays under Yahoo's anti-bot threshold. Free-tier Apify accounts can downgrade to `auto` (datacenter) but expect partial fills and missing symbols on bursts above ~5 names. Leave as default unless you know you need to change it. ## Actor input object example ```json { "symbols": [ "AAPL", "NVDA", "TSLA", "MSFT", "AMZN" ], "revision_direction": "ALL", "min_revision_pct": 0, "time_window": "30d", "limit": 0, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ```` # 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" ], "revision_direction": "ALL", "min_revision_pct": 0, "time_window": "30d", "limit": 0, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/earnings-estimate-revisions").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", ], "revision_direction": "ALL", "min_revision_pct": 0, "time_window": "30d", "limit": 0, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/earnings-estimate-revisions").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" ], "revision_direction": "ALL", "min_revision_pct": 0, "time_window": "30d", "limit": 0, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call nexgendata/earnings-estimate-revisions --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/earnings-estimate-revisions", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "📈 Earnings Estimate Revisions Tracker", "description": "Track sell-side analyst EPS estimate revisions (30/60/90d) for US-listed stocks ahead of earnings. Identifies positive revision clusters — the #1 quant signal for earnings-momentum strategies. Bloomberg / FactSet / Refinitiv I/B/E/S alternative. Pay-per-symbol. PRIVATE.", "version": "0.0", "x-build-id": "gp2wxwFBkFssSSOAr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~earnings-estimate-revisions/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-earnings-estimate-revisions", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~earnings-estimate-revisions/runs": { "post": { "operationId": "runs-sync-nexgendata-earnings-estimate-revisions", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~earnings-estimate-revisions/run-sync": { "post": { "operationId": "run-sync-nexgendata-earnings-estimate-revisions", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "symbols": { "title": "Stock symbols to track", "type": "array", "description": "List of US-listed stock ticker symbols to fetch analyst EPS estimate revisions for. Use the canonical Yahoo Finance ticker — e.g. AAPL, NVDA, TSLA, MSFT, AMZN, GOOGL, META, BRK-B (note the dash for class B), BABA (US ADR). Each symbol produces one record. Best signal: liquid mid/large-caps with 8+ covering analysts where revisions actually move estimates — small-caps with 2-3 analysts have noisy revision data. Hedge-fund earnings-momentum strategies typically scan 200-500 symbols per refresh.", "default": [ "AAPL", "NVDA", "TSLA", "MSFT", "AMZN" ], "items": { "type": "string" } }, "revision_direction": { "title": "Revision direction filter", "enum": [ "ALL", "UP", "DOWN", "STABLE" ], "type": "string", "description": "Filter records by the direction of the trailing-30-day current-quarter EPS estimate revision. UP = estimate revised higher in the last 30 days (positive momentum — bullish into earnings, the highest-alpha signal in earnings-momentum strategies). DOWN = estimate revised lower (negative momentum — earnings miss risk). STABLE = revision under +/- 1% (analysts at consensus, low conviction either way). ALL = no filter, return every symbol.", "default": "ALL" }, "min_revision_pct": { "title": "Minimum absolute revision % (30-day)", "minimum": 0, "maximum": 100, "type": "integer", "description": "Skip symbols whose absolute current-quarter revision over the trailing 30 days is below this percent. 0 = include every symbol regardless of revision magnitude. 1 = filter out noise (typical default for institutional dashboards). 3 = material revision threshold (most quant earnings-revision factor models use 3-5%). 10 = high-conviction revision cluster (rare — usually signals a guidance pre-announcement). Applied on the absolute value, so direction-filter is independent.", "default": 0 }, "time_window": { "title": "Primary revision time window", "enum": [ "30d", "60d", "90d" ], "type": "string", "description": "Which trailing window to use as the primary revision_pct + revision_direction signal. 30d = most recent 30 days (default — what earnings-momentum strategies trade). 60d = trailing 60 days (smoother, used by fundamental funds). 90d = trailing 90 days (full quarter — used in SUE-style factor research). All three windows are always emitted in the output; this only controls which one drives the revision_direction classification.", "default": "30d" }, "limit": { "title": "Max symbols to return", "minimum": 0, "maximum": 2000, "type": "integer", "description": "Maximum number of symbol records to push to the dataset. Each record is one symbol-level revision snapshot and counts as one pay-per-result charge ($0.15). Hedge-fund screens typically pull 50-200 names per refresh (top revision-momentum names from the S&P 500). Backtesting workflows pull 500-1500 (Russell 1000 universe). Leave at 0 to return every input symbol that resolves with data.", "default": 0 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify proxy configuration. Yahoo Finance rate-limits requests from Apify's default datacenter egress hard — back-to-back runs from a single Apify IP often see a flood of HTTP 429s on the quoteSummary endpoint. This actor defaults to the Apify `RESIDENTIAL` proxy group, which rotates residential IPs and reliably stays under Yahoo's anti-bot threshold. Free-tier Apify accounts can downgrade to `auto` (datacenter) but expect partial fills and missing symbols on bursts above ~5 names. Leave as default unless you know you need to change it.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```