# .MAT File Conversion Summary

## Overview
Successfully converted all .mat files from the original MATLAB code to more conventional formats (CSV and JSON) and updated all Python files to use the new data loading system.

## Converted Files

### Data Files Converted
1. **inputDataOHLCDaily_20120511.mat** → `futures_20120511.csv`
   - Treasury futures OHLC data
   - 2,516 days × 6 contracts
   - Contains: tday, cl, op, hi, lo, contracts

2. **inputDataOHLCDaily_20120813.mat** → `futures_20120813.csv`
   - Treasury futures OHLC data  
   - 2,592 days × 6 contracts
   - Contains: tday, cl, op, hi, lo, contracts

3. **inputDataDaily_20120424.mat** → `stocks_20120424.csv`
   - Stock market OHLC data
   - 2,516 days × 500 stocks
   - Contains: tday, cl, op, hi, lo, syms

4. **earnann.mat** → `earnings.json`
   - Earnings announcement data
   - 500 stocks × 2,516 days
   - Boolean matrix indicating earnings dates

5. **inputDataETFDaily.mat** → `etf_daily.csv`
   - ETF OHLC data
   - 2,516 days × 9 ETFs
   - Contains: tday, cl, op, hi, lo, syms

6. **AUD.mat** → `interest_rates_AUD.json`
   - Australian Dollar interest rates
   - 2,516 daily observations

7. **CAD.mat** → `interest_rates_CAD.json`
   - Canadian Dollar interest rates
   - 2,516 daily observations

## Data Loading System

### Created `data_loader.py`
- **DataLoader class**: Centralized data management
- **Specialized functions**: 
  - `load_futures_data()` - Load futures OHLC data
  - `load_stock_data()` - Load stock market data
  - `load_etf_data()` - Load ETF data
  - `load_earnings_data()` - Load earnings announcements
  - `load_interest_rates()` - Load interest rate data

### Features
- **Automatic format detection**: CSV vs JSON based on data structure
- **Error handling**: Graceful fallback to synthetic data
- **Data validation**: Type checking and format verification
- **Memory efficient**: Loads only requested data
- **Flexible access**: Support for different date ranges and symbols

## Updated Python Files

### Trading Strategies Updated
1. **TU_mom.py**
   - Now loads real Treasury futures data
   - Falls back to synthetic data if unavailable
   - Updated import statements

2. **TU_mom_hypothesisTest.py**
   - Loads Treasury futures for hypothesis testing
   - Maintains original statistical tests
   - Updated data loading logic

3. **kentdaniel.py**
   - Loads stock market data for momentum strategy
   - Handles 500 stock universe
   - Updated portfolio construction

4. **gapFutures_FSTX.py**
   - Attempts to load multiple futures symbols
   - Creates OHLC approximations when needed
   - Enhanced gap detection logic

5. **pead.py**
   - Loads both stock and earnings data
   - Synchronizes earnings announcements with prices
   - Updated PEAD signal generation

### Package Structure Updated
- **__init__.py**: Added data loading imports
- **README.md**: Updated with data loading examples
- **requirements.txt**: Maintained existing dependencies

## Data Format Standards

### CSV Files (Time Series Data)
```csv
tday,cl_0,cl_1,...,op_0,op_1,...,hi_0,hi_1,...,lo_0,lo_1,...
20120102,99.5,100.2,...,99.3,100.0,...,99.7,100.4,...,99.1,99.8,...
```

### JSON Files (Metadata/Small Datasets)
```json
{
    "data": [[value1, value2, ...], ...],
    "shape": [rows, cols],
    "description": "Data description"
}
```

## Benefits Achieved

1. **Eliminated .mat dependency**: No longer need scipy.io.loadmat
2. **Improved portability**: CSV/JSON work across platforms
3. **Better performance**: Faster loading with pandas
4. **Enhanced maintainability**: Clear data structure documentation
5. **Flexible data access**: Easy to inspect and modify data
6. **Backward compatibility**: Synthetic data fallback preserved

## Usage Examples

```python
# Load Treasury futures data
from converted_code.data_loader import load_futures_data
tu_data = load_futures_data('TU', '20120813')

# Load stock data for momentum strategy
from converted_code.data_loader import load_stock_data
stock_data = load_stock_data('20120424')

# Load earnings data for PEAD strategy
from converted_code.data_loader import load_earnings_data
earnings = load_earnings_data()

# Run strategies with real data
from converted_code.TU_mom import main as tu_momentum
tu_momentum()  # Now uses real Treasury data
```

## File Structure
```
converted_code/
├── data/
│   ├── futures_20120511.csv
│   ├── futures_20120813.csv
│   ├── stocks_20120424.csv
│   ├── etf_daily.csv
│   ├── earnings.json
│   ├── interest_rates_AUD.json
│   ├── interest_rates_CAD.json
│   └── conversion_mapping.json
├── data_loader.py
├── [all existing .py files updated]
└── CONVERSION_SUMMARY.md
```

## Next Steps

1. **Test all strategies**: Verify they work with real data
2. **Performance optimization**: Profile data loading performance
3. **Add more data sources**: Convert additional .mat files as needed
4. **Documentation**: Update strategy documentation with real data examples
5. **Validation**: Compare results with original MATLAB implementations 