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license: mit
language:
- en
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🔍 Model Overview
Feature	Description
📦 Model Type	LSTM (Long Short-Term Memory), a type of Recurrent Neural Network (RNN)
🧠 Frameworks Used	TensorFlow (Keras API), Scikit-learn, yfinance, NumPy, Pandas
📈 Input	Past 60 days of Bitcoin closing prices
🎯 Output	The predicted closing price for the next day
🧪 Goal	Short-term (1-day ahead) price prediction
📊 Evaluation Metric	RMSE (Root Mean Squared Error)

🔧 What It Does
Downloads historical BTC-USD data from Yahoo Finance

Normalizes the closing price data between 0 and 1 using MinMaxScaler

Splits the data into 80% training and 20% testing

Uses a sliding window of 60 days to create time-sequenced input for LSTM

Builds a 2-layer LSTM network with dropout to avoid overfitting

Trains the model to minimize mean squared error

Evaluates the model on test data

Plots actual vs predicted prices

Makes a future prediction for the next day based on the last 60 days

💡 What This Model Is Useful For
Educational purposes (learning LSTM, time series preprocessing)

Research and experimentation with financial prediction models

Visualization and evaluation of model performance

Academic paper support (as you’re already writing)

⚠️ Limitations
It uses only closing price – no volume, sentiment, or indicators like RSI/MACD

It performs single-step prediction (only the next day)

May not adapt well to sudden market shocks

Not suitable as-is for high-frequency trading or live trading bots

🚀 How to Improve It
Include more features: trading volume, high/low prices, technical indicators

Add external data: news sentiment, macroeconomic indicators

Use attention mechanisms or transformer architectures

Convert it to multi-step forecasting (predict next 3, 5, or 7 days)

Wrap it in a REST API or real-time dashboard

Let me know if you want to:

Package this into a web app

Prepare a GitHub README for the code

Extend the model for live trading (e.g., using Binance API)

Or build a production-ready pipeline
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Disclaimer:
This model is developed strictly for experimental and educational purposes. It is not intended for real-world financial forecasting or investment decisions. Users are strongly advised not to rely on the outputs of this model for any form of trading or financial transactions. No guarantees are made regarding the accuracy or reliability of the predictions, and no liability or responsibility is accepted for any losses or damages resulting from the use of this system. By using this model, you acknowledge and agree that no claims or disputes will be accepted under any circumstances.
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