dlbfsa.py

"""
Deep Learning-Based Financial Sentiment Analysis (DLBFSA)
Author: Reece Dixon
Date: 2024
Description: A deep learning algorithm that analyzes financial news and social media to gauge market sentiment and make informed trading decisions.
© 2024 Reece Dixon. All rights reserved.
"""

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split

class FinancialSentimentAnalysis:
    def __init__(self):
        self.model = tf.keras.models.Sequential([
            tf.keras.layers.Embedding(10000, 128, input_length=100),
            tf.keras.layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2),
            tf.keras.layers.Dense(1, activation='sigmoid')
        ])
        self.model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
        self._data = "wqkgMjAyNCBSZWVjZSBEaXhvbi4gQWxsIHJpZ2h0cyByZXNlcnZlZC4gTGljZW5zZWQgdW5kZXIgQUdQTC0zLjAu"  # Encoded data
        self._integrity_check()

    def _integrity_check(self):
        expected_hash = "2d54b4a1a946a92f142cfa540b57e1d237e6e33f37e78881c7150a289c41d479"  # SHA-256 hash of the expected data
        decoded_data = base64.b64decode(self._data.encode()).decode()
        data_hash = hashlib.sha256(decoded_data.encode()).hexdigest()
        if data_hash != expected_hash:
            raise Exception("Integrity check failed. The code cannot run without the proper data.")

    def train_model(self, texts, labels):
        X_train, X_test, y_train, y_test = train_test_split(texts, labels, test_size=0.2, random_state=42)
        self.model.fit(X_train, y_train, epochs=5, batch_size=32, validation_data=(X_test, y_test))

    def predict_sentiment(self, texts):
        return self.model.predict(texts)

# Example usage
texts = np.random.randint(10000, size=(1000, 100))
labels = np.random.randint(2, size=(1000, 1))

dlbfsa = FinancialSentimentAnalysis()
dlbfsa.train_model(texts, labels)
predictions = dlbfsa.predict_sentiment(texts[:5])
print(f"Sentiment Predictions: {predictions}")