wehub-resource-sync
300 lines
9.0 KiB
TOML
300 lines
9.0 KiB
TOML
[project]
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name = "ml4t"
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version = "3.0.0"
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description = "Machine Learning for Algorithmic Trading — 3rd Edition"
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readme = "README.md"
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requires-python = ">=3.14"
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license = "MIT"
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authors = [{ name = "Stefan Jansen" }]
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dependencies = [
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# ===================
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# Core Data Science
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# ===================
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"numpy>=1.26",
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# scipy<1.18: 1.18 removed the private scipy.cluster.hierarchy._LINKAGE_METHODS
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# that PyPortfolioOpt 1.6.0 (latest) still references for HRP linkage validation
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# (Ch17 08_library_comparison). Lift once pypfopt ships a scipy>=1.18 fix.
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"scipy>=1.12,<1.18",
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"pandas>=2.2",
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"polars>=1.0",
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"pyarrow>=15.0",
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# ===================
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# Visualization
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# ===================
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"plotly>=5.20",
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"matplotlib>=3.8",
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"seaborn>=0.13",
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"kaleido==0.2.1",
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# ===================
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# Machine Learning
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# ===================
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"scikit-learn>=1.4",
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"xgboost>=3.1",
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"lightgbm>=4.6",
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"catboost>=1.2.8",
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"optuna>=3.6",
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"optuna-integration[lightgbm]>=4.8",
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"shap>=0.45",
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"tabpfn>=2.0",
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"gymnasium>=1.2",
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"stable-baselines3>=2.7",
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# ===================
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# ML4T Libraries (PyPI, pre-release)
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# ===================
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"ml4t-data>=0.1.0b15",
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"ml4t-diagnostic>=0.1.0b21",
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"ml4t-engineer>=0.1.0b8",
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"ml4t-backtest>=0.1.0b20",
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"ml4t-live>=0.1.0b3",
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# ===================
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# Deep Learning
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# ===================
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"torch>=2.2",
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"pytorch-lightning>=2.2",
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"torchdiffeq>=0.2",
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# torchode (Lienen & Günnemann 2022) would offer per-sample adaptive
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# integration in Ch5 NB04 but its torchtyping dependency is incompatible
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# with torch>=2.10, so the notebook uses torchdiffeq instead.
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"transformers>=4.40,<5",
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"sentencepiece>=0.2",
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"sentence-transformers>=2.6",
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"einops>=0.7",
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"granite-tsfm>=0.3",
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"chronos-forecasting>=1.4",
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# ===================
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# Time Series
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# ===================
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"statsmodels>=0.14",
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"arch>=8.0",
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"pmdarima>=2.0",
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"sktime>=0.30",
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"darts>=0.28",
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"statsforecast>=1.7",
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"hmmlearn>=0.3",
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"filterpy>=1.4",
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"pykalman>=0.11",
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"pywavelets>=1.6",
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"ruptures>=1.1",
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# ===================
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# Causal Inference
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# ===================
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"dowhy>=0.11",
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"econml>=0.15",
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"causalml>=0.15",
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"tigramite>=5.2",
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# tfcausalimpact (BSTS, Ch15 NB06): py312 image only — requires Python <3.13.
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# See envs/py312/pyproject.toml; pycausalimpact (ARIMA, 2020-dead) removed.
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"causal-learn>=0.1",
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"linearmodels>=6.0",
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# ===================
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# NLP / Text
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# ===================
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# gensim: word2vec/Doc2Vec only (Ch10 NB01-02). No Python 3.14 support.
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# Use Docker: docker compose --profile word2vec run --rm word2vec python ...
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"datasets>=2.19",
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"evaluate>=0.4",
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# ===================
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# Portfolio / Finance
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# ===================
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"PyPortfolioOpt>=1.5",
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"riskfolio-lib>=6.0",
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"skfolio>=0.3",
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"vectorbt>=0.26",
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# ===================
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# Data Sources
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# ===================
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"yfinance>=0.2",
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"edgartools>=5.9",
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# sec-edgar-api: removed — no notebook imports it (they use edgartools).
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# sec-edgar-api 1.1.0 is broken with pyrate-limiter 4.x anyway.
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"secedgar>=0.6",
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"iex-parser>=1.7",
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"beautifulsoup4>=4.12",
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"cot-reports>=0.1",
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"databento>=0.47.0",
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"gdown>=5.1",
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"oandapyV20>=0.7",
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# ===================
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# Technical Analysis
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# ===================
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"ta-lib>=0.6.8",
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"ta>=0.11",
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# ===================
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# Synthetic Data
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# ===================
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"be-great>=0.0.5",
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"opacus>=1.4",
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# signatory/esig: separate env (envs/py312/). Only Ch5 NB03, Ch9 NB06/12.
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# ===================
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# Strategy
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# ===================
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"exchange-calendars>=4.5",
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# ===================
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# Utilities
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# ===================
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"python-dotenv>=1.0",
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"pyyaml>=6.0",
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"tqdm>=4.66",
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"rich>=13.7",
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"requests>=2.31",
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"nest-asyncio>=1.6",
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"neo4j>=5.28",
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"thefuzz>=0.22",
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"rapidfuzz>=3.6",
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"psutil>=5.9",
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"sympy>=1.12",
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"openpyxl>=3.1",
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"numba>=0.59",
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"papermill>=2.6",
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# ===================
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# Jupyter
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# ===================
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"jupyter>=1.0",
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"notebook>=7.0",
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"jupytext>=1.16",
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"jupyterlab>=4.1",
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"ipython>=8.22",
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"anywidget>=0.9",
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"nbconvert>=7.16",
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"nbformat>=5.10",
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# ===================
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# File Formats
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# ===================
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"tables>=3.10",
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"duckdb>=1.0",
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"mlflow>=3.10.1",
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"arviz>=0.23.4,<1.0",
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"feast>=0.61",
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"pymc>=5.25.1",
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"pyvis>=0.3.2",
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"llama-index-core>=0.14.20",
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"llama-index-embeddings-huggingface>=0.7.0",
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"chromadb>=1.5.5",
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"pfhedge>=0.22.0",
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"ml4t-models>=0.1.0a4",
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"llama-index-vector-stores-chroma>=0.5.5",
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"llama-index-llms-openai>=0.7.7",
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# Ch24 Autonomous Agents (required at notebook runtime; Docker installs
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# main deps only, so these must not live in an optional-dependencies group)
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"crewai>=0.86",
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# litellm enters transitively via crewai. Pin the security floor directly:
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# crewai's newest 0.x (0.118) caps litellm <1.83, which carries proxy-server
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# CVEs (GHSA OIDC bypass / proxy-config priv-esc). The book only uses litellm
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# as a client lib, but we keep the patched line anyway; this floor holds
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# crewai at 0.95 (last 0.x allowing litellm>=1.83). crewai 1.x needs
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# chromadb<1.2, which conflicts with the Ch22 RAG stack.
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"litellm>=1.83.0",
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"langgraph>=0.2",
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"google-genai>=0.3",
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# Anthropic is the first provider in the Ch24 agent auto-detect cascade
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# (agent_providers.create_llm_client). It is a runtime dependency, not a
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# dev tool — readers with ANTHROPIC_API_KEY set need it in the Docker image.
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"anthropic>=0.89.0",
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]
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[project.optional-dependencies]
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# Database benchmark extras (requires Docker for servers)
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db-benchmark = [
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"arcticdb>=4.5",
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"clickhouse-connect>=0.7",
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"psycopg2-binary>=2.9",
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"influxdb-client>=1.45",
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"questdb>=2.0",
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]
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# Bayesian modeling
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bayesian = [
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"pymc>=5.14",
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"arviz>=0.18,<1.0",
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]
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# MLOps (Ch26)
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mlops = [
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"feast>=0.61",
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"mlflow>=2.16",
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]
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# Live Trading (Ch25)
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live = [
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"ib_insync>=0.9",
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"python-okx>=0.3",
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"alpaca-py>=0.33",
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]
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# Development tools
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dev = [
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"pytest>=8.0",
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"pytest-rerunfailures>=15.0",
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"jupytext>=1.16",
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"pre-commit>=3.6",
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"ruff>=0.8",
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]
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[tool.uv]
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# ml4t-engineer only has pre-release versions on PyPI
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prerelease = "if-necessary-or-explicit"
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# Force protobuf >= 5.0 for Python 3.14 compatibility.
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# protobuf 4.x has a C extension metaclass bug on Python 3.14.
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# opentelemetry-proto 1.27.0 pins protobuf<5; override until it upgrades.
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override-dependencies = [
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"protobuf>=5.0",
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]
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[build-system]
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requires = ["setuptools>=77", "wheel"]
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build-backend = "setuptools.build_meta"
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[dependency-groups]
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dev = [
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"httpx>=0.28.1",
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]
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[tool.setuptools.packages.find]
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include = ["utils", "data", "case_studies"]
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namespaces = false
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# Auto-loaded at interpreter startup to put numbered chapter dirs on sys.path
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# (see sitecustomize.py). Shipped as a top-level module so both the editable
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# install (local) and a wheel install resolve it.
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[tool.setuptools]
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py-modules = ["sitecustomize"]
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[tool.ruff]
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line-length = 100
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# Vendored upstream code (MIT-licensed PatchTST reference) — don't lint/modify.
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extend-exclude = ["**/_reference/**"]
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# Keep at py312 even though requires-python is >=3.14. With
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# target-version = "py314", ruff/pyupgrade rewrites `except (A, B):`
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# to PEP 758's unparenthesized `except A | B:` form. Until our minimum
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# runtime support and reader tooling catches up to py314, we pin py312
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# so ruff preserves the parenthesized tuple-catch form.
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target-version = "py312"
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[tool.ruff.lint]
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select = ["E", "F", "I", "UP", "B", "SIM"]
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ignore = [
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"E501", # Line length handled by formatter
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"E402", # Module level import not at top - OK for notebooks
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"F401", # Imported but unused - OK for conditional imports in notebooks
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"F811", # Redefinition of unused variable - OK in notebook cells
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"B006", # Mutable argument defaults - common pattern in notebooks
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"B007", # Loop variable not used - intentional in some cases
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"B018", # Useless expression - intentional in notebooks to display output
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"B905", # zip without strict= - bare zip reads cleaner in teaching code
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"F822", # Undefined in __all__ - forward declarations
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"E741", # Ambiguous variable names (l, I, O) - common in math/ML code
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"F841", # Local variable assigned but never used - common in exploratory notebooks
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"SIM102", # Use single if statement - readability preference
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"SIM105", # contextlib.suppress over try/except/pass - explicit form clearer
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"SIM108", # Use ternary operator - readability preference
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"SIM113", # Use enumerate - readability preference
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"SIM118", # Use `key in dict` - both forms acceptable
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]
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[tool.jupytext]
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formats = "ipynb,py:percent"
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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python_files = ["test_*.py"]
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python_functions = ["test_*"]
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addopts = "-v --tb=short -p no:torchtyping"
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