x̄ - > Python Example for Sentiment Analysis & Technical Analysis

 

Sentiment analysis and technical analysis are two different methodologies used in data analysis, particularly in the context of finance and social media:

Sentiment Analysis

Definition: Sentiment analysis, also known as opinion mining, involves using natural language processing, text analysis, and computational linguistics to identify and extract subjective information from text. It's often used to determine the attitude of a speaker or writer concerning some topic or the overall contextual polarity of a document.

Python Example for Sentiment Analysis:

Python

import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
import pandas as pd

# Download the VADER lexicon for sentiment analysis
nltk.download('vader_lexicon')

# Sample text data
texts = [
    "I love this product, it's amazing!",
    "This movie was terrible and a waste of time.",
    "The service was okay, but nothing special."
]

# Initialize the sentiment analyzer
sia = SentimentIntensityAnalyzer()

# Analyze the sentiment of each text
sentiments = []
for text in texts:
    sentiment = sia.polarity_scores(text)
    sentiments.append({
        'text': text,
        'compound': sentiment['compound'],
        'pos': sentiment['pos'],
        'neu': sentiment['neu'],
        'neg': sentiment['neg']
    })

# Create a DataFrame for better visualization
df = pd.DataFrame(sentiments)
print(df)

This code uses NLTK's VADER (valence-aware dictionary and sentiment Reasoner) to perform sentiment analysis on a set of sentences. The compound score gives an overall sentiment score, while pos, neu, and neg scores provide proportions for positive, neutral, and negative sentiment.

Technical Analysis

Definition: Technical analysis in finance or trading involves analyzing statistical trends from trading activity such as price movement and volume. It aims to forecast future price movements based on historical data.

Python Example for Technical Analysis:

Python

import pandas as pd
import numpy as np
import yfinance as yf
import matplotlib.pyplot as plt
from ta.trend import MACD

# Fetch data for Apple stock
stock_data = yf.download('AAPL', start='2020-01-01', end='2020-12-31')

# Calculate Moving Average Convergence Divergence (MACD)
macd = MACD(stock_data['Close'])
stock_data['MACD'] = macd.macd()
stock_data['MACD_signal'] = macd.macd_signal()
stock_data['MACD_histogram'] = macd.macd_diff()

# Plotting
plt.figure(figsize=(14, 7))
plt.plot(stock_data.index, stock_data['Close'], label='Close Price')
plt.plot(stock_data.index, stock_data['MACD'], label='MACD')
plt.plot(stock_data.index, stock_data['MACD_signal'], label='Signal Line')
plt.fill_between(stock_data.index, 0, stock_data['MACD_histogram'], where=stock_data['MACD_histogram'] >= 0, facecolor='green', alpha=0.5, interpolate=True)
plt.fill_between(stock_data.index, 0, stock_data['MACD_histogram'], where=stock_data['MACD_histogram'] < 0, facecolor='red', alpha=0.5, interpolate=True)
plt.legend()
plt.title('Apple Stock Price with MACD')
plt.show()

This example fetches historical stock data for Apple, calculates the MACD (Moving Average Convergence Divergence) which is a trend-following momentum indicator, and plots it alongside the closing price. The MACD histogram provides insights into the strength, direction, momentum, and duration of a trend.

These examples give you a basic understanding of how to apply sentiment and technical analysis using Python:

• Sentiment Analysis helps understand opinions and emotions in textual data, which is valuable for marketing, customer feedback, and social media monitoring.
• Technical Analysis aids traders and investors in making decisions based on historical trading data, focusing on patterns and signals rather than the asset's intrinsic value.

This work is licensed under a Creative Commons Attribution 4.0 International License.