63 lines
1.4 KiB
Plaintext
63 lines
1.4 KiB
Plaintext
websockets>=10.0
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websocket-client>=1.0.0
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plotly>=5.18.0
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dash>=2.14.0
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pandas>=2.0.0
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numpy>=1.24.0
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python-dotenv>=1.0.0
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psutil>=5.9.0
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tensorboard>=2.15.0
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scikit-learn>=1.3.0
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matplotlib>=3.7.0
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seaborn>=0.12.0
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ta>=0.11.0
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ccxt>=4.0.0
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dash-bootstrap-components>=2.0.0
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asyncio-compat>=0.1.2
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wandb>=0.16.0
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pybit>=5.11.0
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requests>=2.31.0
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asyncpg>=0.29.0
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# COBY System Dependencies
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fastapi>=0.104.0
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uvicorn>=0.24.0
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pydantic>=2.5.0
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python-multipart>=0.0.6
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aiohttp>=3.9.0
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redis>=5.0.0
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scipy>=1.11.0
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structlog>=23.2.0
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click>=8.1.0
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rich>=13.7.0
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# DuckDB for data storage
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duckdb>=0.9.0
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# Flask for ANNOTATE system
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Flask>=3.0.0
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flask-cors>=4.0.0
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# NOTE: PyTorch is intentionally not pinned here to avoid pulling wrong GPU deps.
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# We only need torch (not torchvision/torchaudio) for trading systems.
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#
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# The SAME CODEBASE works with NVIDIA (CUDA) and AMD (ROCm) GPUs!
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# PyTorch abstracts hardware differences - just install the right build.
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#
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# AUTOMATIC SETUP (recommended):
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# ./scripts/setup-pytorch.sh
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#
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# MANUAL INSTALL by hardware:
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#
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# CPU-only (development/testing):
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# pip install torch --index-url https://download.pytorch.org/whl/cpu
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#
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# NVIDIA GPU (CUDA 12.1):
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# pip install torch --index-url https://download.pytorch.org/whl/cu121
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#
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# AMD GPU (ROCm 6.2):
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# pip install torch --index-url https://download.pytorch.org/whl/rocm6.2
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#
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# Verification:
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# python -c "import torch; print(f'GPU: {torch.cuda.is_available()}')"
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