Analyzing Stock Sentiment: Identifying Top Gainers and Losers Using Polygon API with ML Models.

In today’s volatile stock market, investors are constantly searching for opportunities to maximize their returns. Analyzing stock sentiment can provide valuable insights into market trends and help identify potential winners and losers. In this blog post, we will explore how to leverage the Polygon API, machine learning (ML) models, and sentiment analysis to identify the top gainer and top loser stocks.

Top Gainers and Top Losers

Step 1: Retrieving Historical Stock Data: The first step is to obtain historical stock data for our analysis. We’ll utilize the Polygon API, which provides a vast amount of real-time and historical market data. Using the API, we can retrieve the necessary data for our ML models.

Step 2: Sentiment Analysis and Data Processing: To determine the sentiment associated with stocks, we need sentiment-related data. While the Polygon API does not provide direct sentiment data, we can obtain it from external sources such as news APIs or social media platforms. By performing sentiment analysis on these textual data sources, we can gauge the sentiment (positive, negative, neutral) associated with specific stocks.

Step 3: Combining Stock Data and Sentiment Data: Once we have obtained the historical stock data and sentiment data, we can merge them based on matching timestamps or any other relevant criteria. By combining these datasets, we can create a comprehensive dataset that includes both stock price information and sentiment analysis results.

Step 4: ML Modeling: With the combined dataset in place, we can now proceed with ML modeling. We’ll use ML models, such as classification algorithms or deep learning models, to train and predict the sentiment of the stocks based on the available features and sentiment data. This step allows us to leverage the power of ML to classify stocks into different sentiment categories.

Step 5: Visualization: To gain a better understanding of the sentiment analysis results, we’ll visualize the outcomes using plots. We can use various visualization techniques, such as bar plots or pie charts, to represent the sentiment distribution of the top gainer and top loser stocks. These visualizations will provide a clear picture of the sentiment associated with these stocks.

Conclusion: Analyzing stock sentiment is a valuable tool for investors looking to make informed decisions in the stock market. By leveraging the Polygon API, combining stock data with sentiment data, and utilizing ML models, we can identify the top gainer and top loser stocks with associated sentiment analysis. With these insights, investors can make more informed decisions and potentially capitalize on market opportunities.

Let’s Python Code example:

import requests
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

api_key = "YOUR_API_KEY"
base_url = "https://api.polygon.io/v2/aggs/ticker"

tickers = ["AAPL", "TSLA", "GOOGL", "AMZN"]
from_date = "2023-01-01"
to_date = "2023-06-01"

stock_data = {}

for ticker in tickers:
    url = f"{base_url}/{ticker}/range/1/day/{from_date}/{to_date}?apiKey={api_key}"
    response = requests.get(url)
    data = response.json()
    stock_data[ticker] = data['results']

gainers = {}
losers = {}

for ticker, data in stock_data.items():
    df = pd.DataFrame(data)
    df['timestamp'] = pd.to_datetime(df['t'], unit='ms')
    df.set_index('timestamp', inplace=True)
    df.sort_index(ascending=True, inplace=True)
    
    df['change'] = df['c'].pct_change()
    
    gainers[ticker] = df['change'].idxmax()
    losers[ticker] = df['change'].idxmin()

top_gainer = max(gainers, key=gainers.get)
top_loser = min(losers, key=losers.get)

gainer_data = stock_data[top_gainer]
loser_data = stock_data[top_loser]


gainer_df = pd.DataFrame(gainer_data)
gainer_df['timestamp'] = pd.to_datetime(gainer_df['t'], unit='ms')
gainer_df.set_index('timestamp', inplace=True)
gainer_df.sort_index(ascending=True, inplace=True)

loser_df = pd.DataFrame(loser_data)
loser_df['timestamp'] = pd.to_datetime(loser_df['t'], unit='ms')
loser_df.set_index('timestamp', inplace=True)
loser_df.sort_index(ascending=True, inplace=True)


gainer_features = gainer_df[['o', 'h', 'l', 'v']]  
gainer_target = gainer_df['c'] 

loser_features = loser_df[['o', 'h', 'l', 'v']]
loser_target = loser_df['c']

gainer_features_train, gainer_features_test, gainer_target_train, gainer_target_test = train_test_split(
    gainer_features, gainer_target, test_size=0.2, random_state=42
)

loser_features_train, loser_features_test, loser_target_train, loser_target_test = train_test_split(
    loser_features, loser_target, test_size=0.2, random_state=42
)


gainer_model = LinearRegression()
gainer_model.fit(gainer_features_train, gainer_target_train)
gainer_score = gainer_model.score(gainer_features_test, gainer_target_test)

loser_model = LinearRegression()
loser_model.fit(loser_features_train, loser_target_train)
loser_score = loser_model.score(loser_features_test, loser_target_test)

print("Model performance:")
print(f"Gainer Model R-squared: {gainer_score}")
print(f"Loser Model R-squared: {loser_score}")

ML Model with Plot the sentiment analysis results for the top gainer and top loser stocks. You can use bar plots or pie charts to display the sentiment distribution or any other visualization technique that best represents the sentiment analysis results.

gainer_predictions = gainer_model.predict(gainer_features_test)
print("GAINER:", gainer_predictions)

plt.figure(figsize=(12, 6))
plt.plot(gainer_target_test.index, gainer_target_test, label="Actual")
plt.plot(gainer_target_test.index, gainer_predictions, label="Predicted")
plt.xlabel("Date")
plt.ylabel("Stock Price")
plt.title("Top Gainer Stock - Actual vs. Predicted")
plt.legend()
plt.show()

Top Gainer Stock

loser_predictions = loser_model.predict(loser_features_test)
print("LOSER:", loser_predictions)
plt.figure(figsize=(12, 6))
plt.plot(loser_target_test.index, loser_target_test, label="Actual")
plt.plot(loser_target_test.index, loser_predictions, label="Predicted")
plt.xlabel("Date")
plt.ylabel("Stock Price")
plt.title("Top Loser Stock - Actual vs. Predicted")
plt.legend()
plt.show()

Top Loser Stock

This is just a simple example of how you can use the Polygon.io API to get stock sentiment data and use a machine learning model to predict which stocks are likely to be top gainers and losers. You can use this approach to build a more sophisticated trading strategy that can help you to make more informed investment decisions.

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