# ML4T Configuration Template
# Copy this to .env and configure for your environment:
#   cp .env.example .env
#   # Then edit .env with your actual paths

# ============================================================================
# Path Configuration
# ============================================================================

# Repository path (auto-detected, rarely needs to be set)
# ML4T_PATH=/path/to/third-edition

# Data directory path
# - Default: ./data (repo's data folder)
# - Override for external data: /path/to/your/data
# ML4T_DATA_PATH=/path/to/data

# ============================================================================
# Docker Configuration (Only needed when using Docker)
# ============================================================================

# User mapping to prevent permission issues in Docker
# Set these to your host user ID and group ID:
#   UID=$(id -u)   # Usually 1000
#   GID=$(id -g)   # Usually 1000
UID=1000
GID=1000

# ============================================================================
# Test / CI
# ============================================================================

# Output redirection (used by the pytest harness - not needed for manual runs).
# When set, notebook outputs go here instead of chapter/output/. The harness
# also reduces epochs/folds/rows by injecting papermill `parameters`-tagged
# cells at execution time; there is no global fast-mode flag for manual runs.
# ML4T_OUTPUT_DIR=/tmp/ml4t-test-output

# ============================================================================
# API Keys (Optional - only needed for downloading data)
# ============================================================================

# SEC EDGAR - required for Ch04 NB02 (SEC filing explorer) and NB14 (text
# extraction). The SEC mandates a real User-Agent (your name + email) on every
# EDGAR request and blocks placeholder addresses; the notebook raises if unset.
# Format: "Jane Doe jane@example.org"
EDGAR_IDENTITY=

# FRED - Federal Reserve Economic Data (free API key)
# Get key: https://fred.stlouisfed.org/docs/api/api_key.html
FRED_API_KEY=

# DataBento - Futures and market data (paid)
# Get key: https://databento.com
DATABENTO_API_KEY=

# Oanda - Forex data (free tier available)
# Get key: https://www.oanda.com/
OANDA_API_KEY=

# Alpaca - Equity and crypto trading (free tier available)
# Get key: https://alpaca.markets/
ALPACA_API_KEY=
ALPACA_SECRET_KEY=

# Polygon - Multi-asset market data (free tier available)
# Get key: https://polygon.io/
POLYGON_API_KEY=

# LLM providers for Ch. 24 Autonomous Agent demo (any one is enough; the
# client auto-selects Anthropic -> OpenAI -> Google -> OpenRouter -> local
# Ollama, then falls back to a deterministic mock if none are set).
ANTHROPIC_API_KEY=
OPENAI_API_KEY=
GOOGLE_API_KEY=
# OpenRouter: one key, any model. Optionally pin OPENROUTER_MODEL.
OPENROUTER_API_KEY=
# OPENROUTER_MODEL=anthropic/claude-sonnet-4

# Tavily - Agentic web search for Ch. 24 Autonomous Agent demo
TAVILY_API_KEY=

# TabPFN - Ch. 12 (12_gradient_boosting/03_dl_vs_gbm). The tabpfn package
# requires a one-time license acceptance to download model weights for local
# inference. Register at https://ux.priorlabs.ai, accept the license, and copy
# your API key from https://ux.priorlabs.ai/account. The other models in the
# notebook (LightGBM, MLP, TabM) run without it.
TABPFN_TOKEN=

# Ch. 24 research operator (nb11) — companion skill library (github.com/ml4t/skills).
# Default: cloned next to the code repo (../skills). Set this to override, or
# when running in Docker (where ../skills is not present).
# RESEARCH_OPERATOR_SKILLS_ROOT=/path/to/skills