# ML4T Configuration Template
# Copy this to .env to get started:
#   cp .env.example .env
# The defaults work as-is for the early chapters — you do NOT need to edit
# anything to run your first notebooks. Come back and fill in a line below only
# when a chapter asks for it. Two of them are free and need no account:
# EDGAR_IDENTITY (your own name and email, for the SEC) and UID/GID (only if
# `id -u` or `id -g` prints something other than 1000). The rest are API keys.

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

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

# Data directory path — LEAVE THIS COMMENTED OUT unless you keep your data on a
# separate drive. The default is the repo's own data folder, and it is the same
# folder whichever directory you start from, so you do not need to set or edit
# this to get started. Only uncomment and set a path to store data outside the
# repository; a relative path is taken relative to the repository root:
# ML4T_DATA_PATH=/path/to/data

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

# User mapping, so files the container writes belong to you rather than to root.
#
# Run `id -u` and `id -g`. They are independent, so check both. If either prints
# something other than 1000, replace that default with the number it printed. On most
# single-user Linux and WSL2 installs both are 1000 and there is nothing to change.
#
# Put the NUMBER, never the command. Docker Compose reads this file without a shell, so
# a literal `UID=$(id -u)` is passed through as text and the container refuses to start:
#   Error response from daemon: unable to find user $(id -u): no matching entries in passwd file
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 - free, no account, no sign-up: put your own name and email on the
# line below. The SEC mandates a real User-Agent on every EDGAR request and
# blocks placeholder addresses, so anything that calls EDGAR live refuses to run
# while this is empty: Ch04 NB02 (SEC filing explorer) and NB14 (text
# extraction), Ch22 NB01 (SEC filing pipeline), and the two download scripts
# data/equities/positioning/form4_download.py and
# data/equities/fundamentals/filings_download.py. Every other SEC-derived
# notebook reads a committed snapshot and needs nothing here.
# Format: "Jane Doe jane@example.org"
EDGAR_IDENTITY=

# NASDAQ Data Link (formerly Quandl) - Wiki US equity prices, used by
# data/equities/market/us_equities/download.py. The variable keeps its legacy
# name; NASDAQ_DATA_LINK_API_KEY is read as an alias.
# Get key: https://data.nasdaq.com/sign-up
QUANDL_API_KEY=

# 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