Create README.md

README.md

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+---
+tags:
+- time-series
+- forecasting
+- cryptocurrency
+- lstm
+- finance
+library_name: pytorch
+license: mit
+datasets:
+- coingecko_daily
+metrics:
+- rmse
+- mape
+---
+
+# CryptoPriceForecaster: Deep LSTM for Short-Term Price Trend Prediction
+
+## 📑 Overview
+
+**CryptoPriceForecaster** is a deep **Long Short-Term Memory (LSTM)** neural network optimized for predicting the closing price trend (i.e., the price change magnitude) of a major cryptocurrency (e.g., BTC/USD) over a short-term horizon (e.g., next 24 hours). It is trained on historical data including price, volume, and derived technical indicators.
+
+## 🤖 Model Architecture
+
+This is a custom deep learning model implemented in PyTorch/TensorFlow.
+
+* **Type:** Recurrent Neural Network (RNN) - LSTM variant.
+* **Input Features:** 10 features (e.g., Open, High, Low, Close, Volume, 7-day EMA, RSI, MACD Histogram, etc.).
+* **Sequence Length:** 30 time steps (i.e., the model looks at the last 30 daily data points).
+* **Layers:** 3 stacked LSTM layers.
+    * `Hidden Size`: 128 units per layer.
+    * `Dropout`: 0.2 applied between layers.
+* **Output:** A single dense layer predicting the scaled *normalized* closing price. The predicted value needs to be inverse-transformed to get the actual price.
+
+## 🎯 Intended Use
+
+This model is designed for:
+1.  **Short-Term Trading Strategy:** Generating daily signals for predicting price movement direction.
+2.  **Research:** Studying the non-linear dependencies between technical indicators and price volatility.
+3.  **Educational Purposes:** Demonstrating the use of RNNs for financial time-series forecasting.
+
+## ⚠️ Limitations
+
+* **Prediction vs. Reality:** This model predicts price based on historical and technical data; it does *not* account for Black Swan events, regulatory changes, or breaking news. **It is not financial advice.**
+* **Generalization:** Trained primarily on BTC/USD daily data, its performance on altcoins or intraday data may be significantly lower.
+* **Stationarity:** Requires pre-processed, potentially stationary, input data (e.g., log returns) for optimal performance.
+
+## 💻 Example Code
+
+Assuming the input data is a 3D numpy array `X_test` (batch_size, sequence_length, features):
+
+```python
+import torch
+import numpy as np
+from CryptoPriceForecaster_Model import LSTMForecaster # Custom model class
+
+# Load the model weights and config
+config = json.load(open("config.json"))
+model = LSTMForecaster(**config) 
+model.load_state_dict(torch.load("pytorch_model.bin"))
+model.eval()
+
+# Example input: 1 sample, 30 sequence length, 10 features
+X_test_sample = np.random.rand(1, 30, 10).astype(np.float32) 
+input_tensor = torch.from_numpy(X_test_sample)
+
+with torch.no_grad():
+    scaled_prediction = model(input_tensor)
+
+# The output must be inverse-transformed using the original scaler (MinMaxScaler)
+# For demonstration:
+print(f"Scaled Predicted Price Change: {scaled_prediction.item():.4f}")