wehub-resource-sync
94 lines
3.6 KiB
Bash
94 lines
3.6 KiB
Bash
# ML4T Configuration Template
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# Copy this to .env and configure for your environment:
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# cp .env.example .env
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# # Then edit .env with your actual paths
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# ============================================================================
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# Path Configuration
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# ============================================================================
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# Repository path (auto-detected, rarely needs to be set)
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# ML4T_PATH=/path/to/third-edition
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# Data directory path
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# - Default: ./data (repo's data folder)
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# - Override for external data: /path/to/your/data
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# ML4T_DATA_PATH=/path/to/data
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# ============================================================================
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# Docker Configuration (Only needed when using Docker)
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# ============================================================================
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# User mapping to prevent permission issues in Docker
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# Set these to your host user ID and group ID:
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# UID=$(id -u) # Usually 1000
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# GID=$(id -g) # Usually 1000
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UID=1000
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GID=1000
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# ============================================================================
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# Test / CI
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# ============================================================================
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# Output redirection (used by the pytest harness - not needed for manual runs).
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# When set, notebook outputs go here instead of chapter/output/. The harness
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# also reduces epochs/folds/rows by injecting papermill `parameters`-tagged
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# cells at execution time; there is no global fast-mode flag for manual runs.
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# ML4T_OUTPUT_DIR=/tmp/ml4t-test-output
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# ============================================================================
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# API Keys (Optional - only needed for downloading data)
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# ============================================================================
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# SEC EDGAR - required for Ch04 NB02 (SEC filing explorer) and NB14 (text
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# extraction). The SEC mandates a real User-Agent (your name + email) on every
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# EDGAR request and blocks placeholder addresses; the notebook raises if unset.
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# Format: "Jane Doe jane@example.org"
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EDGAR_IDENTITY=
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# FRED - Federal Reserve Economic Data (free API key)
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# Get key: https://fred.stlouisfed.org/docs/api/api_key.html
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FRED_API_KEY=
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# DataBento - Futures and market data (paid)
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# Get key: https://databento.com
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DATABENTO_API_KEY=
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# Oanda - Forex data (free tier available)
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# Get key: https://www.oanda.com/
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OANDA_API_KEY=
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# Alpaca - Equity and crypto trading (free tier available)
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# Get key: https://alpaca.markets/
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ALPACA_API_KEY=
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ALPACA_SECRET_KEY=
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# Polygon - Multi-asset market data (free tier available)
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# Get key: https://polygon.io/
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POLYGON_API_KEY=
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# LLM providers for Ch. 24 Autonomous Agent demo (any one is enough; the
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# client auto-selects Anthropic -> OpenAI -> Google -> OpenRouter -> local
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# Ollama, then falls back to a deterministic mock if none are set).
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ANTHROPIC_API_KEY=
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OPENAI_API_KEY=
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GOOGLE_API_KEY=
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# OpenRouter: one key, any model. Optionally pin OPENROUTER_MODEL.
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OPENROUTER_API_KEY=
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# OPENROUTER_MODEL=anthropic/claude-sonnet-4
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# Tavily - Agentic web search for Ch. 24 Autonomous Agent demo
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TAVILY_API_KEY=
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# TabPFN - Ch. 12 (12_gradient_boosting/03_dl_vs_gbm). The tabpfn package
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# requires a one-time license acceptance to download model weights for local
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# inference. Register at https://ux.priorlabs.ai, accept the license, and copy
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# your API key from https://ux.priorlabs.ai/account. The other models in the
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# notebook (LightGBM, MLP, TabM) run without it.
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TABPFN_TOKEN=
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# Ch. 24 research operator (nb11) — companion skill library (github.com/ml4t/skills).
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# Default: cloned next to the code repo (../skills). Set this to override, or
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# when running in Docker (where ../skills is not present).
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# RESEARCH_OPERATOR_SKILLS_ROOT=/path/to/skills
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