import os
from datetime import datetime


class Config:
    # Data settings
    DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
    RAW_DATA_DIR = os.path.join(DATA_DIR, "raw")
    PROCESSED_DATA_DIR = os.path.join(DATA_DIR, "processed")
    EXTERNAL_DATA_DIR = os.path.join(DATA_DIR, "external")

    # Ensure directories exist
    os.makedirs(RAW_DATA_DIR, exist_ok=True)
    os.makedirs(PROCESSED_DATA_DIR, exist_ok=True)
    os.makedirs(EXTERNAL_DATA_DIR, exist_ok=True)

    # Stock universe settings
    INITIAL_TICKERS = [
        "AAPL",
        "MSFT",
        "GOOGL",
        "AMZN",
        "META",
        "TSLA",
        "NVDA",
        "JPM",
        "V",
        "WMT",
        "PG",
        "DIS",
        "NFLX",
        "ADBE",
        "PYPL",
        "INTC",
        "CSCO",
        "PEP",
        "KO",
        "XOM",
    ]
    INDEX_TICKER = "^GSPC"  # S&P 500
    DELISTED_TICKERS_FILE = os.path.join(EXTERNAL_DATA_DIR, "delisted_stocks.csv")

    # Date settings
    START_DATE = "2010-01-01"
    END_DATE = datetime.now().strftime("%Y-%m-%d")
    TRAIN_END_DATE = "2020-12-31"
    VAL_END_DATE = "2021-12-31"

    # Model settings
    MODEL_DIR = os.path.join(os.path.dirname(__file__), "models")
    os.makedirs(MODEL_DIR, exist_ok=True)
    MODEL_NAME = "stock_gnn"
    HIDDEN_CHANNELS = 64
    NUM_HEADS = 8
    DROPOUT = 0.6
    LEARNING_RATE = 0.001
    EPOCHS = 100
    BATCH_SIZE = 32
    LOOKBACK_WINDOW = 30  # Days for feature calculation

    # Backtesting settings
    INITIAL_CAPITAL = 100000
    TRANSACTION_COST = 0.001  # 0.1% per trade

    # Evaluation settings
    BENCHMARK_TICKER = "^GSPC"

    # News data settings
    NEWS_API_KEY = "your_news_api_key"  # For NewsAPI or similar
    NEWS_SOURCES = ["reuters", "bloomberg", "financial-times", "wsj"]
    NEWS_CATEGORIES = ["business", "financial", "economy"]
    NEWS_LOOKBACK_DAYS = 7  # Number of days to look back for news

    # Social media settings
    TWITTER_BEARER_TOKEN = "your_twitter_bearer_token"
    REDDIT_CLIENT_ID = "your_reddit_client_id"
    REDDIT_CLIENT_SECRET = "your_reddit_client_secret"
    SOCIAL_MEDIA_LOOKBACK_DAYS = 3  # Number of days to look back for social media

    # Sentiment analysis settings
    SENTIMENT_MODEL = "vader"  # 'vader', 'finbert', or 'custom'
    FINBERT_MODEL_PATH = "yiyanghkust/finbert-tone"  # HuggingFace model path

    # Alternative data features
    NEWS_FEATURES = [
        "sentiment_score",
        "mention_count",
        "positive_score",
        "negative_score",
    ]
    SOCIAL_FEATURES = [
        "twitter_sentiment",
        "reddit_sentiment",
        "twitter_volume",
        "reddit_volume",
    ]
    ALTERNATIVE_DATA_WEIGHT = 0.3  # Weight for alternative data in final prediction

    # Database settings for alternative data
    ALTERNATIVE_DATA_DB = os.path.join(DATA_DIR, "alternative_data.db")


config = Config()