🗞️💹 News Sentiment Analysis – NLP-Based Headline Trading Using Python

 

The stock market reacts to news in real-time — but what if you could read the mood of the news automatically and trade before everyone else?

Welcome to the world of News Sentiment Analysis using NLP (Natural Language Processing). In this blog, we’ll explore how to use headlines, AI, and Python to create a headline-based trading strategy.

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🤔 Why Use News for Trading?

Market-moving news breaks every second — earnings results, government policies, global crises, etc. Traders often react emotionally, but AI can do it smarter.

By using NLP models, we can:

• Detect positive or negative market sentiment

• Predict possible stock movement

• Automate buy/sell decisions using logic, not fear

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🔍 What is Sentiment Analysis?

Sentiment analysis is an NLP technique used to determine the emotional tone behind a piece of text.

Example:

• “TCS reports record profit” → Positive

• “Infosys faces data breach scandal” → Negative

We can score these headlines and align them with stock price moves.

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🧪 Python Example: Headline Sentiment Trading

Let’s build a basic sentiment-based trading model using:

• TextBlob for sentiment scoring

• News headlines as input

• A simple rule: Buy if sentiment > 0, Sell if < 0

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📦 Step 1: Install Required Packages

pip install textblob yfinance pandas matplotlib

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🐍 Step 2: Python Code – Sentiment Analysis on Headlines

from textblob import TextBlob
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt

# Sample headlines (replace with live feed or news API)
headlines = [
    "Infosys announces strong quarterly earnings",
    "Infosys faces regulatory investigation",
    "Infosys expands AI services in Europe",
    "Infosys stock under pressure due to IT sector slowdown"
]

# Analyze sentiments
sentiment_scores = []
for headline in headlines:
    sentiment = TextBlob(headline).sentiment.polarity  # Ranges from -1 to 1
    sentiment_scores.append(sentiment)

# Create a dataframe
df = pd.DataFrame({
    'Headline': headlines,
    'Sentiment': sentiment_scores
})

# Decision logic
df['Signal'] = df['Sentiment'].apply(lambda x: 'Buy' if x > 0 else ('Sell' if x < 0 else 'Hold'))
print(df)

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📉 Step 3: Backtest the Idea (with Stock Price)

# Load stock price (example: Infosys)
stock = yf.download('INFY.NS', start='2023-01-01', end='2023-12-31')

# Simple strategy: Buy on positive news, sell on negative
# This is illustrative – in real-world you'd align headlines by date/time
plt.figure(figsize=(12, 6))
stock['Close'].plot(label='INFY Stock Price')
plt.title('Infosys Stock Price Around News Sentiment')
plt.xlabel('Date')
plt.ylabel('Price (INR)')
plt.legend()
plt.grid()
plt.show()

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✅ Output: What Do We Get?

Headline	Sentiment	Signal
Infosys announces strong quarterly earnings	+0.50	Buy
Infosys faces regulatory investigation	-0.25	Sell
Infosys expands AI services in Europe	+0.30	Buy
Infosys stock under pressure due to slowdown	-0.40	Sell

This gives us a signal-based system based purely on how positive or negative the headlines are.

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🚀 How to Take It Further

1. Use live news API:

   • Example: NewsAPI.org

   • Filter by keyword (e.g., Infosys, Nifty50)

2. Use advanced models:

   • Replace TextBlob with VADER, BERT, or FinBERT for finance-specific language.

3. Map news sentiment with actual price moves:

   • Align by timestamp

   • Measure accuracy of sentiment vs. return

4. Automate alerts or trades:

   • Use Zerodha API or Alpaca to automate buy/sell

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📌 Pros & Cons

Pros	Cons
Uses real-time, relevant data	Sentiment models can misinterpret sarcasm
Simple to implement with Python	Hard to predict impact timing
Can be combined with technical signals	News can be ambiguous or conflicting

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🧠 Final Thoughts

NLP + Finance = a powerful edge.

Using AI to read news gives you early signals others might miss. While this blog shows a basic version, the concept can grow into:

• Portfolio-level strategies

• Multi-source sentiment aggregation

• Real-time trade execution bots

📌 Disclaimer:

This blog is intended for educational purposes only. It does not constitute financial, investment, or professional advice. The information provided is based on personal research and for learning use only. Please consult with a certified financial advisor or conduct your own research before making any investment decisions.