# Finance Trends (`fundamental_bee/finance-trends`) Actor
- **URL**: https://securitybyobscurity.apify.com/fundamental\_bee/finance-trends.md
- **Developed by:** [hanz33](https://securitybyobscurity.apify.com/fundamental_bee) (community)
- **Categories:** AI
- **Stats:** 3 total users, 2 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
Pay per usage
This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage
## 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
## 📈 Stock Trends Analyzer — AI-Powered Stock Market Movement Explanations
> **Get instant, AI-powered explanations for stock price movements** | Analyze S&P 500, NASDAQ, NYSE & 1000+ stocks | Real-time news sentiment analysis | Perfect for traders, investors, and financial analysts
---
### 📊 What is Stock Trends Analyzer?
**Stock Trends Analyzer** is an AI-powered web scraping tool that extracts and analyzes **real-time stock market data** from Google Finance, combining it with **news sentiment analysis** to explain why stocks are moving.
Stop wondering why NVIDIA is up or why Tesla is down. Get **data-backed explanations** for price movements in categories like **Top Gainers**, **Top Losers**, **Most Active**, and **Trending Stocks**.
#### ⚡ Quick Start — 3 Simple Steps
1️⃣ **Select Trend Category**: Choose from _Top Gainers_, _Top Losers_, _Most Active_, or _Trending Stocks_
2️⃣ **Set Number of Stocks**: Pick how many top stocks to analyze (1-25)
3️⃣ **Get AI Explanations**: Receive structured insights combining price data, volume analysis, and news sentiment
---
### 🎯 Use Cases — Who Uses Stock Trends Analyzer?
| Use Case | Description | Best For |
| -------------------------------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------- |
| 🔍 **Daily Market Research** | Understand why top stocks are moving each day with AI-generated explanations backed by news analysis | Day traders, equity analysts, portfolio managers |
| 📈 **Trading Signal Generation** | Identify unusual volume spikes and sentiment shifts that may indicate trading opportunities | Active traders, algorithmic trading developers |
| 📰 **Financial News Monitoring** | Automatically aggregate and analyze news sentiment for trending stocks without manual research | Financial journalists, newsletter creators, market commentators |
| 🤖 **AI Training Data** | Generate structured datasets combining price movements with explanatory text for ML model training | AI/ML researchers, fintech developers, quantitative analysts |
| 📱 **Stock App Integration** | Power your stock market app, dashboard, or bot with real-time market insights via API | Mobile app developers, Discord/Telegram bot creators |
| 💼 **Investment Due Diligence** | Research why specific stocks gained or lost value before making investment decisions | Long-term investors, fund managers, wealth advisors |
| 📊 **Market Sentiment Analysis** | Track sentiment trends across multiple stocks to gauge overall market mood and sector rotation | Market analysts, sentiment traders, research firms |
---
### ✨ Key Features
✅ **4 Trend Categories**: Top Gainers, Top Losers, Most Active, Trending Stocks
✅ **AI-Powered Analysis**: GPT-4 analyzes news articles to explain price movements
✅ **Real-Time Data**: Scrapes live stock prices from Google Finance
✅ **News Aggregation**: Collects relevant news from WSJ, Bloomberg, Yahoo Finance, Seeking Alpha
✅ **Structured Output**: Get clean JSON data ready for integration into your apps
✅ **Customizable Results**: Analyze 1-25 stocks per run
✅ **Comprehensive Metrics**: Price, volume, market cap, P/E ratio, 52-week change
✅ **API Ready**: Easy integration via Apify API for automated workflows
---
### 📤 Output Examples
#### Top Gainers — Why Stocks Are Rising
Discover the reasons behind today's biggest stock winners
**Example Output:** _Top Gainers_
🏷 Symbol: NKE (NIKE, Inc.)
💲 Price: $72.04
📈 Change: +9.5 (+15.19%)
🔊 Volume: 114,059,000 (vs. avg 20,814,000)
🏢 Market Cap: $106B📅 52-Week Change: -6.23%
**Reasons:**
Strong earnings report: Nike’s Q4 2025 earnings exceeded expectations, boosting investor confidence.
Positive business strategy: Inventory cleanup and Amazon re-entry drove optimism.
Dividend announcement: The $0.40/share dividend attracted income investors.
Leadership changes: New executives signaled proactive growth strategies.
Risk management: Clear plans to address tariffs reassured the market.
```json
[
{
"entity": {
"symbol": "NKE",
"name": "NIKE, Inc.",
"price": 72.04,
"change": 9.5,
"changePercentual": 15.19,
"volume": 114059000,
"averageVolume": 20814000,
"marketCap": 106330000000,
"peRatio": 33.35,
"52WeekChangePercentual": -6.23
},
"explanation": {
"reasons": [
"Nike's Q4 2025 earnings beat expectations, driving +15.19% price growth.",
"Inventory cleanup and Amazon re-entry sparked positive sentiment.",
"A $0.40 dividend announcement attracted income investors.",
"Leadership changes boosted confidence in future growth.",
"Clear plans to mitigate tariff impacts reassured investors."
]
}
},
...
]
````
#### Top Losers — Why Stocks Are Falling
Choose the **Top Losers** category to understand why certain stocks are falling and what's driving market pessimism — whether it's earnings misses, regulatory concerns, or sector-wide sell-offs.
#### Most Active — Unusual Volume Analysis
Analyze stocks with the **Most Active** category to uncover unusual trading volumes and what's fueling the activity. High volume often signals major news events, institutional trading, or significant market moves.
#### Trending Stocks — What's Buzzing
See what's buzzing across news and social platforms and why by selecting the **Trending Stocks** category. Get insights into stocks that are capturing attention and driving market discussions.
***
### 📥 Input Configuration
```json
{
"trendType": "top-gainers", // Options: top-gainers | top-losers | most-active | trending
"stocksAmount": 10 // Number of stocks to analyze (1-25)
}
```
**Input Parameters:**
- `trendType` (required): The trend category to analyze
- `top-gainers` — Stocks with highest % gains
- `top-losers` — Stocks with highest % losses
- `most-active` — Stocks with highest trading volume
- `trending` — Stocks trending in news and social media
- `stocksAmount` (required): Number of top stocks to analyze (1-25)
***
### 🔍 How It Works — The Technology
#### 1. Real-Time Data Scraping
The actor scrapes live stock market data from Google Finance, extracting prices, volume, market cap, P/E ratios, and performance metrics for top stocks.
#### 2. News Aggregation
For each stock, we collect relevant news articles from Google News, pulling from trusted sources like:
- [The Wall Street Journal](https://www.wsj.com/) — Financial news & analysis
- [Bloomberg](https://www.bloomberg.com/) — Business & markets coverage
- [Yahoo Finance](https://finance.yahoo.com/) — Market data & news
- [Seeking Alpha](https://seekingalpha.com/) — Investment research & analysis
#### 3. AI Sentiment Analysis
Our GPT-4 powered AI analyzes news articles, identifying key events, sentiment shifts, and market catalysts that explain the stock's price movement.
#### 4. Structured Output
Get clean, structured JSON data combining quantitative metrics with qualitative AI-generated explanations.
***
### 📊 Stock Metrics Included
| Metric | Description | Use Case |
| -------------------- | -------------------------------------- | -------------------------- |
| **Symbol** | Ticker symbol (e.g., AAPL, TSLA, NVDA) | Identification |
| **Name** | Full company name | Display & reporting |
| **Price** | Current stock price in USD | Real-time valuation |
| **Change** | Absolute price change | Dollar movement tracking |
| **Change %** | Percentage price change | Relative performance |
| **Market Cap** | Total market capitalization | Company size indicator |
| **Volume** | Current trading volume | Activity measure |
| **Avg Volume** | Average daily trading volume | Volume comparison baseline |
| **P/E Ratio** | Price-to-Earnings ratio | Valuation metric |
| **52-Week Change %** | Annual performance | Long-term trend analysis |
***
### 🔗 Integration & API Usage
#### Run via Apify API
```javascript
const { ApifyClient } = require('apify-client');
const client = new ApifyClient({
token: 'YOUR_APIFY_TOKEN',
});
const input = {
trendType: 'most-active',
stocksAmount: 10,
};
const run = await client.actor('QhLdTalbsGrBICNte').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
```
#### Python Integration
```python
from apify_client import ApifyClient
client = ApifyClient('YOUR_APIFY_TOKEN')
run_input = {
"trendType": "most-active",
"stocksAmount": 15
}
run = client.actor("QhLdTalbsGrBICNte").call(run_input=run_input)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
```
#### Schedule Automated Runs
Set up scheduled runs on Apify to get daily stock market analysis delivered automatically. Perfect for:
- Pre-market research reports
- Automated trading signal generation
- Investment database updates
- Financial newsletter content
***
### 💡 Why Choose Stock Trends Analyzer?
🎯 **Save Time**: No more manually researching why stocks moved\
🤖 **AI-Powered**: Leverages GPT-4 for intelligent news analysis\
📊 **Comprehensive**: Combines quantitative data with qualitative insights\
🔄 **Automated**: Run via API or schedule for automated workflows\
💰 **Cost-Effective**: Pay only for what you use on Apify platform\
🚀 **Fast**: Get results in seconds, not hours\
📈 **Actionable**: Use insights for trading, research, or content creation
***
### ❓ FAQ
**Q: How often is the data updated?**\
A: The actor scrapes real-time data from Google Finance and Google News each time it runs, ensuring you always get the latest information.
**Q: Can I analyze specific stocks?**\
A: Currently, the actor focuses on trending categories (top gainers, losers, etc.). For specific stocks, we recommend running multiple category searches or filtering results.
**Q: What stock exchanges are covered?**\
A: The tool covers major US exchanges including NYSE, NASDAQ, and includes major international stocks listed on US exchanges.
**Q: What's the data format?**\
A: Output is provided in clean JSON format, compatible with any programming language and easily importable to databases, spreadsheets, or analytics tools.
**Q: Is this financial advice?**\
A: No. This tool provides data and analysis to help you understand market movements, but it does not constitute financial advice. Always conduct your own research before making investment decisions.
**Q: Can I use this for commercial purposes?**\
A: Yes! You can integrate this into your trading apps, investment platforms, research tools, or any commercial application.
***
### ⚠️ Disclaimer
> This tool is designed to help you understand stock market movements better, but it does **not** provide financial advice. Always conduct your own research and consult with a qualified financial advisor before making any investment decisions.
***
### 🆘 Support & Resources
- 📚 [Apify Documentation](https://docs.apify.com/)
- 💬 [Apify Discord Community](https://discord.com/invite/jyEM2PRvMU)
- 🐛 Report issues via Apify Console
# Actor input Schema
## `trendType` (type: `string`):
Choose the type of stocks trend to analyze
## `stocksAmount` (type: `integer`):
Number of stocks to analyze within the selected trend category
## Actor input object example
```json
{
"trendType": "most-active",
"stocksAmount": 5
}
```
# Actor output Schema
## `trendExplanations` (type: `string`):
Summary view of stock data with key metrics
## `analyzedStocks` (type: `string`):
AI-generated analysis with reasoning for each stock
## `rawData` (type: `string`):
Raw data structure with all fields
# 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 = {
"trendType": "most-active",
"stocksAmount": 5
};
// Run the Actor and wait for it to finish
const run = await client.actor("fundamental_bee/finance-trends").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 = {
"trendType": "most-active",
"stocksAmount": 5,
}
# Run the Actor and wait for it to finish
run = client.actor("fundamental_bee/finance-trends").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 '{
"trendType": "most-active",
"stocksAmount": 5
}' |
apify call fundamental_bee/finance-trends --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=fundamental_bee/finance-trends",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Finance Trends",
"version": "0.0",
"x-build-id": "JMcEPBG9rmITooVaC"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/fundamental_bee~finance-trends/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-fundamental_bee-finance-trends",
"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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/fundamental_bee~finance-trends/runs": {
"post": {
"operationId": "runs-sync-fundamental_bee-finance-trends",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/fundamental_bee~finance-trends/run-sync": {
"post": {
"operationId": "run-sync-fundamental_bee-finance-trends",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": [
"trendType",
"stocksAmount"
],
"properties": {
"trendType": {
"title": "Trend",
"enum": [
"most-active",
"gainers",
"losers",
"trending"
],
"type": "string",
"description": "Choose the type of stocks trend to analyze",
"default": "most-active"
},
"stocksAmount": {
"title": "Stocks Amount",
"minimum": 1,
"maximum": 25,
"type": "integer",
"description": "Number of stocks to analyze within the selected trend category",
"default": 5
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```