MetaTrader5 MCP Server

# Core MetaTrader5 and MCP dependencies # Use environment markers to keep compatibility across Python versions MetaTrader5>=5.0.5260 mcp>=1.14.1 # requires Python >=3.10 fastmcp>=2.12.3 # requires Python >=3.10 # Python standard library backports typing-extensions>=4.15.0 # Additional useful packages for development python-dotenv>=1.1.1 dateparser>=1.2.2 # pandas pandas>=2.3.2; python_version >= "3.10" pandas>=2.0.3; python_version < "3.10" # pandas-ta (install classic fork from GitHub). Requires Git available on PATH. # Use distribution name to match package metadata (pandas-ta-classic) pandas-ta-classic @ git+https://github.com/xgboosted/pandas-ta-classic@main # ARCH (heteroskedasticity models) arch>=7.2.0; python_version >= "3.9" arch>=6.3.0; python_version < "3.9" python-dateutil>=2.9.0.post0 pytz>=2025.2 # SciPy (bounded to <1.16.0 to satisfy statsforecast<->scipy compatibility) scipy>=1.15.3,<1.16.0; python_version >= "3.10" scipy>=1.10.1,<1.16.0; python_version < "3.10" # statsmodels statsmodels>=0.14.5; python_version >= "3.9" statsmodels>=0.14.0; python_version < "3.9" # API server fastapi>=0.117.1 uvicorn[standard]>=0.36.0; python_version >= "3.9" uvicorn[standard]>=0.23.2; python_version < "3.9" # Optional: classical/ML/DL forecasting frameworks (enabled to support all methods) # statsforecast (classical, numba-accelerated) statsforecast>=2.0.2 # mlforecast (tree/GBM over lags); LightGBM optional mlforecast>=1.0.2 lightgbm>=4.6.0 # neuralforecast (deep learning; include torch extras). Not available on Python 3.12 due to ray wheels. neuralforecast[torch]>=3.0.2; python_version < "3.12" # Ensure torch/torchvision available (CPU wheels by default) # For Python <3.12: constrained to satisfy neuralforecast (<=2.6.0) torch>=2.0,<2.7; python_version < "3.12" torchvision>=0.21.0,<0.22; python_version < "3.12" # For Python >=3.12: neuralforecast is skipped; keep torch for Chronos/Accelerate torch>=2.3.0; python_version >= "3.12" torchvision>=0.17.0; python_version >= "3.12" # Optional: foundation models (Transformers) — enabled for Chronos/TimesFM transformers>=4.56.2 accelerate>=1.10.1 chronos-forecasting==1.5.3 # for Chronos-Bolt timesfm>=2.0.0 # Optional: pattern search ANN and DTW backends # hnswlib>=0.8.0 # tslearn>=0.6.3 # dtaidistance>=2.3.10 # Optional: dimensionality reduction methods / ML utilities scikit-learn>=1.7.2; python_version >= "3.10" scikit-learn>=1.4.2; python_version >= "3.9" and python_version < "3.10" scikit-learn>=1.3.2; python_version < "3.9" # umap-learn>=0.5.5 # pydiffmap>=0.2.0 # pykeops>=2.2.3 # optional acceleration for CNE graph (GPU) # Optional: DREAMS-CNE (install from source) # git+https://github.com/berenslab/DREAMS-CNE@tp