Predict Google's stock trends using this RNN model. Trained on historical data, the model excels in capturing smooth market changes and following trends. While exhibiting a lag during spikes, it aligns with financial principles. Leverage this tool for accurate trend analysis and market forecasting.
Project Title: Stock Price Prediction with Recurrent Neural Networks
Overview: This project utilizes Recurrent Neural Networks (RNNs) to predict Google's stock price trends. The RNN model is trained on historical stock data, and its performance is evaluated against real stock prices. The analysis includes a detailed exploration of the model's behavior in response to fast, nonlinear changes and smooth market transitions.
RNN Model Evaluation Summary:
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Lag in Fast, Nonlinear Changes:
- The model exhibits a lag during spikes, common in stock time irregularities.
- Lag behavior aligns with the Brownian Motion concept, where future variations are independent of past values.
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Normalcy in Lag Behavior:
- The lag in response to sudden changes is considered normal due to the inherent nature of financial markets.
- The model's lag during spikes is justified by the independence of future variations according to financial engineering principles.
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Effective Response to Smooth Changes:
- The model demonstrates effectiveness in capturing and predicting smooth changes.
- Successful tracking of upward and downward trends showcases resilience in response to stable and gradual market shifts.
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Optimal Performance in Trend Following:
- The model performs optimally during stable periods, accurately following trends.
- Notable success in capturing and mirroring the financial market's general direction during smoother transitions.
Conclusion: The RNN model showcases a normal lag in response during spikes, a characteristic of its inability to react rapidly to nonlinear changes. However, its proficiency in tracking and predicting smooth changes positions it as a valuable tool for understanding and forecasting market trends.
Usage:
- Load historical stock data for training.
- Train the RNN model using the provided script.
- Evaluate model predictions against real stock prices.
- Utilize the RNN for trend analysis and forecasting in financial markets.
Important Note: While the absolute values may not be precisely mapped, the model accurately captures and predicts overall trends in the stock market.
Contributions: Contributions and enhancements are welcomed. Feel free to open issues, provide feedback, or submit pull requests.
Tools and Libraries:
- Python, NumPy, Pandas, Matplotlib, Keras , RNNs