News Sentiment API

Retrieve AI-powered sentiment analysis scores derived from financial news. Get sentiment scores for any ticker.

Endpoint

GET /v2/sentiment/{symbol}

Authentication

Supports multiple authentication methods (in order of preference):

Method	Example
Bearer token (recommended)	Authorization: Bearer YOUR_API_KEY
X-API-Key header	X-API-Key: YOUR_API_KEY
Query parameter	?apiKey=YOUR_API_KEY
Legacy query parameter	?token=YOUR_API_KEY

Parameters

Path Parameters

Parameter	Type	Required	Description
symbol	string	Yes	Stock ticker symbol (e.g., AAPL, MSFT)

Query Parameters

Parameter	Type	Required	Description
apiKey	string	No	Your API key (if not using header auth)
startDate	string	No	Start date (YYYY-MM-DD)
endDate	string	No	End date (YYYY-MM-DD)
limit	integer	No	Maximum number of results to return

Request

Python SDK

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

df = fb.sentiments.ticker("AAPL",
                          date_from="2025-01-01",
                          date_to="2025-06-30",
                          as_dataframe=True)
print(df)

cURL

# Get sentiment data
curl -H "Authorization: Bearer YOUR_API_KEY" \
  "https://api.finbrain.tech/v2/sentiment/AAPL"

# With date range and limit
curl -H "Authorization: Bearer YOUR_API_KEY" \
  "https://api.finbrain.tech/v2/sentiment/AAPL?startDate=2026-01-01&endDate=2026-01-31&limit=30"

Python (requests)

import requests

headers = {"Authorization": "Bearer YOUR_API_KEY"}

# Get sentiment data
response = requests.get(
    "https://api.finbrain.tech/v2/sentiment/AAPL",
    headers=headers
)
result = response.json()

# With date range and limit
response = requests.get(
    "https://api.finbrain.tech/v2/sentiment/AAPL",
    headers=headers,
    params={"startDate": "2026-01-01", "endDate": "2026-01-31", "limit": 30}
)
result = response.json()

C++

#include <iostream>
#include <string>
#include <curl/curl.h>
#include <nlohmann/json.hpp>

using json = nlohmann::json;

size_t WriteCallback(void* contents, size_t size, size_t nmemb, std::string* userp) {
    userp->append((char*)contents, size * nmemb);
    return size * nmemb;
}

json get_sentiment(const std::string& symbol, const std::string& api_key) {
    CURL* curl = curl_easy_init();
    std::string response;

    if (curl) {
        std::string url = "https://api.finbrain.tech/v2/sentiment/" + symbol;

        struct curl_slist* headers = nullptr;
        std::string auth_header = "Authorization: Bearer " + api_key;
        headers = curl_slist_append(headers, auth_header.c_str());

        curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
        curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);
        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
        curl_easy_setopt(curl, CURLOPT_WRITEDATA, &response);
        curl_easy_perform(curl);
        curl_slist_free_all(headers);
        curl_easy_cleanup(curl);
    }

    return json::parse(response);
}

int main() {
    auto result = get_sentiment("AAPL", "YOUR_API_KEY");
    auto data = result["data"];

    std::cout << "Symbol: " << data["symbol"].get<std::string>()
              << " (" << data["name"].get<std::string>() << ")" << std::endl;

    for (auto& entry : data["data"]) {
        std::cout << entry["date"].get<std::string>() << ": "
                  << entry["score"].get<double>() << std::endl;
    }

    return 0;
}

Rust

use reqwest::blocking::Client;
use reqwest::header::{AUTHORIZATION, HeaderValue};
use serde::Deserialize;
use std::error::Error;

#[derive(Debug, Deserialize)]
struct ApiResponse {
    success: bool,
    data: SentimentData,
}

#[derive(Debug, Deserialize)]
struct SentimentData {
    symbol: String,
    name: String,
    data: Vec<SentimentEntry>,
}

#[derive(Debug, Deserialize)]
struct SentimentEntry {
    date: String,
    score: f64,
}

fn get_sentiment(symbol: &str, api_key: &str)
    -> Result<ApiResponse, Box<dyn Error>> {
    let url = format!(
        "https://api.finbrain.tech/v2/sentiment/{}",
        symbol
    );

    let client = Client::new();
    let response: ApiResponse = client
        .get(&url)
        .header(AUTHORIZATION, HeaderValue::from_str(&format!("Bearer {}", api_key))?)
        .send()?
        .json()?;

    Ok(response)
}

fn main() -> Result<(), Box<dyn Error>> {
    let result = get_sentiment("AAPL", "YOUR_API_KEY")?;
    let data = result.data;

    println!("Symbol: {} ({})", data.symbol, data.name);
    for entry in &data.data {
        println!("{}: {:.3}", entry.date, entry.score);
    }

    Ok(())
}

JavaScript

const response = await fetch(
  "https://api.finbrain.tech/v2/sentiment/AAPL",
  {
    headers: { "Authorization": "Bearer YOUR_API_KEY" }
  }
);
const result = await response.json();
console.log(result.data);

Response

Success Response (200 OK)

{
  "success": true,
  "data": {
    "symbol": "AAPL",
    "name": "Apple Inc.",
    "data": [
      { "date": "2026-01-19", "score": 0.265 },
      { "date": "2026-01-16", "score": 0.346 },
      { "date": "2026-01-15", "score": 0.279 },
      { "date": "2026-01-14", "score": 0.17 },
      { "date": "2026-01-13", "score": 0.128 }
    ]
  },
  "meta": {
    "timestamp": "2026-01-19T15:06:13.240Z"
  }
}

Response Fields

Field	Type	Description
success	boolean	Whether the request was successful
data	object	Sentiment data container
meta	object	Response metadata

Data Object Fields

Field	Type	Description
symbol	string	Stock ticker symbol
name	string	Company name
data	array	Array of sentiment score entries

Sentiment Entry Fields

Each item in the data array contains:

Field	Type	Description
date	string	Date of the sentiment score (YYYY-MM-DD)
score	number	Sentiment score from -1 (bearish) to 1 (bullish)

Sentiment Score Interpretation

Score Range	Interpretation
0.5 to 1.0	Strong bullish sentiment
0.2 to 0.5	Moderate bullish sentiment
-0.2 to 0.2	Neutral sentiment
-0.5 to -0.2	Moderate bearish sentiment
-1.0 to -0.5	Strong bearish sentiment

Usage Examples

Basic Sentiment Check

import requests

headers = {"Authorization": "Bearer YOUR_API_KEY"}
response = requests.get(
    "https://api.finbrain.tech/v2/sentiment/AAPL",
    headers=headers
)
result = response.json()
entries = result["data"]["data"]

# Get latest sentiment (first entry in array)
latest = entries[0]
score = latest["score"]

if score > 0.5:
    print(f"AAPL sentiment is strongly bullish: {score:.3f}")
elif score > 0:
    print(f"AAPL sentiment is mildly bullish: {score:.3f}")
elif score > -0.5:
    print(f"AAPL sentiment is mildly bearish: {score:.3f}")
else:
    print(f"AAPL sentiment is strongly bearish: {score:.3f}")

Detect Sentiment Spikes

import requests

headers = {"Authorization": "Bearer YOUR_API_KEY"}

def detect_sentiment_spike(symbol):
    """Detect unusual sentiment activity"""
    response = requests.get(
        f"https://api.finbrain.tech/v2/sentiment/{symbol}",
        headers=headers
    )
    result = response.json()
    entries = result["data"]["data"]

    if len(entries) < 10:
        return None

    # Calculate baseline from historical data
    historical_scores = [e["score"] for e in entries[1:10]]
    avg_score = sum(historical_scores) / len(historical_scores)

    # Compare to latest
    latest_score = entries[0]["score"]
    score_change = latest_score - avg_score

    alerts = []
    if abs(score_change) > 0.2:
        direction = "improved" if score_change > 0 else "declined"
        alerts.append(f"Sentiment {direction} significantly ({score_change:+.3f})")

    return alerts

alerts = detect_sentiment_spike("TSLA")
if alerts:
    print("Sentiment Alerts:")
    for alert in alerts:
        print(f"  - {alert}")

Sentiment Trend Analysis

import requests

headers = {"Authorization": "Bearer YOUR_API_KEY"}

def analyze_sentiment_trend(symbol, days=14):
    """Analyze sentiment trend over time"""
    response = requests.get(
        f"https://api.finbrain.tech/v2/sentiment/{symbol}",
        headers=headers,
        params={"limit": days}
    )
    result = response.json()
    entries = result["data"]["data"]

    if len(entries) < days:
        return None

    recent = entries[:days//2]
    older = entries[days//2:days]

    recent_avg = sum(e["score"] for e in recent) / len(recent)
    older_avg = sum(e["score"] for e in older) / len(older)

    change = recent_avg - older_avg

    if change > 0.1:
        trend = "improving"
    elif change < -0.1:
        trend = "deteriorating"
    else:
        trend = "stable"

    return {
        "symbol": symbol,
        "recent_sentiment": recent_avg,
        "older_sentiment": older_avg,
        "change": change,
        "trend": trend
    }

result = analyze_sentiment_trend("NVDA")
print(f"Sentiment trend: {result['trend']} ({result['change']:+.3f})")

Errors

Code	Error	Description
400	Bad Request	Invalid symbol
401	Unauthorized	Invalid or missing API key
403	Forbidden	Authenticated, but not authorized to access this resource
404	Not Found	Ticker not found
429	Too Many Requests	Rate limit exceeded — wait and retry
500	Internal Server Error	Server-side error

Related

• News Sentiment Dataset - Use cases and analysis examples
• Price Forecasts - Get price forecasts
• Analyst Ratings - Get analyst recommendations
• Put/Call Data - Get options sentiment