ClawFoxyVision 0.2.0

Advanced financial time series forecasting library using LSTM, GRU, and CNN-LSTM neural networks for price prediction with Rust and Burn
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# LSTM Model Implementation Plan

## Overview
This document outlines the step-by-step plan for implementing an LSTM (Long Short-Term Memory) model for stock price prediction using the Burn library.

## Implementation Steps

### 1. Data Preparation
- [ ] Load and preprocess the input DataFrame
- [ ] Split data into training and validation sets
- [ ] Normalize/scale the features
- [ ] Create sequences for LSTM input
- [ ] Convert data to tensors

### 2. Model Architecture
- [ ] Define LSTM layer configuration
  - Input size
  - Hidden size
  - Number of layers
  - Dropout rate
- [ ] Define output layer
- [ ] Implement forward pass
- [ ] Add model initialization

### 3. Training Setup
- [ ] Define loss function (MSE)
- [ ] Configure optimizer (Adam)
- [ ] Set learning rate
- [ ] Implement learning rate scheduler
- [ ] Add early stopping
- [ ] Set up model checkpointing

### 4. Training Loop
- [ ] Implement batch processing
- [ ] Add progress tracking
- [ ] Implement validation step
- [ ] Add metrics logging
- [ ] Implement early stopping logic
- [ ] Add model checkpointing logic

### 5. Evaluation
- [ ] Implement prediction function
- [ ] Add evaluation metrics
  - MSE
  - MAE
  - RMSE
- [ ] Add visualization of results
- [ ] Implement backtesting

### 6. Model Deployment
- [ ] Add model saving/loading
- [ ] Implement prediction API
- [ ] Add model versioning
- [ ] Add documentation

## Technical Details

### Model Architecture
```rust
LSTM(
    input_size: 5,  // close, volume, sma_20, rsi_14, macd
    hidden_size: 32,
    num_layers: 2,
    dropout: 0.1
)
```

### Training Parameters
- Batch size: 32
- Learning rate: 0.001
- Epochs: 100
- Early stopping patience: 10
- Validation split: 0.2

### Data Processing
1. Feature selection:
   - Close price
   - Volume
   - SMA(20)
   - RSI(14)
   - MACD

2. Data normalization:
   - Min-Max scaling for price data
   - Standard scaling for technical indicators

3. Sequence creation:
   - Window size: 60 (1 hour of minute data)
   - Step size: 1

## Implementation Notes

### Dependencies
- burn: Deep learning framework
- polars: Data processing
- anyhow: Error handling

### File Structure
```
src/minute/lstm/
├── step_1_tensor_preparation.rs    # Data loading and preprocessing
├── step_2_lstm_cell.rs  # Technical indicators
├── step_3_lstm_model_arch.rs     # LSTM model implementation
└── step_4_train_model.rs       # Prediction and evaluation
```

### Testing Strategy
1. Unit tests for each component
2. Integration tests for the full pipeline
3. Performance benchmarks
4. Backtesting on historical data

## Progress Tracking
- [ ] Data Preparation
- [ ] Model Architecture
- [ ] Training Setup
- [ ] Training Loop
- [ ] Evaluation
- [ ] Model Deployment