websockets>=10.0
websocket-client>=1.0.0
plotly>=5.18.0
dash>=2.14.0
pandas>=2.0.0
numpy>=1.24.0
python-dotenv>=1.0.0
psutil>=5.9.0
tensorboard>=2.15.0
scikit-learn>=1.3.0
matplotlib>=3.7.0
seaborn>=0.12.0
ta>=0.11.0
ccxt>=4.0.0
dash-bootstrap-components>=2.0.0
asyncio-compat>=0.1.2
wandb>=0.16.0
pybit>=5.11.0
requests>=2.31.0
asyncpg>=0.29.0

# COBY System Dependencies
fastapi>=0.104.0
uvicorn>=0.24.0
pydantic>=2.5.0
python-multipart>=0.0.6
aiohttp>=3.9.0
redis>=5.0.0
scipy>=1.11.0
structlog>=23.2.0
click>=8.1.0
rich>=13.7.0

# DuckDB for data storage
duckdb>=0.9.0

# Flask for ANNOTATE system
Flask>=3.0.0
flask-cors>=4.0.0

# NOTE: PyTorch is intentionally not pinned here to avoid pulling wrong GPU deps.
# We only need torch (not torchvision/torchaudio) for trading systems.
#
# The SAME CODEBASE works with NVIDIA (CUDA) and AMD (ROCm) GPUs!
# PyTorch abstracts hardware differences - just install the right build.
#
# AUTOMATIC SETUP (recommended):
#   ./scripts/setup-pytorch.sh
#
# MANUAL INSTALL by hardware:
#
# CPU-only (development/testing):
#   pip install torch --index-url https://download.pytorch.org/whl/cpu
#
# NVIDIA GPU (CUDA 12.1):
#   pip install torch --index-url https://download.pytorch.org/whl/cu121
#
# AMD GPU (ROCm 6.2):
#   pip install torch --index-url https://download.pytorch.org/whl/rocm6.2
#
# Verification:
#   python -c "import torch; print(f'GPU: {torch.cuda.is_available()}')"