fegger
a763ab0774
- Detect AMD GPUs via ROCm device name in config - Replace single-timestep LSTM in GNN model with leaner post-GNN MLP - Pass edge_attr through AMDGATConv propagate and use ones for self-loops - Fix live trading to sell existing positions on negative signals instead of skipping them entirely - Use per-file try/except in data pipeline and batch SQLite inserts - Import torch directly in backtester instead of dynamic __import__ - Update AMP autocast import for PyTorch 2.0+ compatibility
211 lines
6.7 KiB
Python
211 lines
6.7 KiB
Python
import os
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from datetime import datetime, timedelta
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import torch
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from dotenv import load_dotenv
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load_dotenv()
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def _check_amd_gpu() -> bool:
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"""Detect AMD GPU via ROCm device name."""
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if not torch.cuda.is_available():
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return False
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try:
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name = torch.cuda.get_device_name(0)
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return any(kw in name for kw in ("AMD", "Radeon", "gfx"))
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except Exception:
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return False
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class Config:
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# Project settings
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PROJECT_NAME = "StockGNN_R9700"
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VERSION = "1.0.0"
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# Data directories
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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DATA_DIR = os.path.join(BASE_DIR, "data")
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RAW_DATA_DIR = os.path.join(DATA_DIR, "raw")
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PROCESSED_DATA_DIR = os.path.join(DATA_DIR, "processed")
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EXTERNAL_DATA_DIR = os.path.join(DATA_DIR, "external")
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MODEL_DIR = os.path.join(BASE_DIR, "models")
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# Stock universe settings
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INITIAL_TICKERS = [
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"AAPL",
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"MSFT",
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"GOOGL",
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"AMZN",
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"META",
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"TSLA",
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"NVDA",
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"JPM",
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"V",
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"WMT",
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"PG",
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"DIS",
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"NFLX",
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"ADBE",
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"PYPL",
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"INTC",
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"CSCO",
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"PEP",
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"KO",
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"XOM",
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"BAC",
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"VZ",
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"T",
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"CRM",
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"CMCSA",
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"PFE",
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"NKE",
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"MRK",
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"CVX",
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"HD",
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]
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INDEX_TICKER = "^GSPC" # S&P 500
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DELISTED_TICKERS_FILE = os.path.join(EXTERNAL_DATA_DIR, "delisted_stocks.csv")
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# Date settings
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START_DATE = "2015-01-01"
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END_DATE = datetime.now().strftime("%Y-%m-%d")
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TRAIN_END_DATE = "2022-12-31"
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VAL_END_DATE = "2023-06-30"
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TEST_END_DATE = END_DATE
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# AMD GPU settings (Radeon R9700 AI Pro - 32GB)
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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AMD_GPU = _check_amd_gpu()
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GPU_MEMORY_LIMIT = 0.9 # Use 90% of 32GB = 28.8GB
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ROCM_OPT_LEVEL = "O2" # Optimization level for ROCm ('O0', 'O1', 'O2')
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PIN_MEMORY = True # Enable pinned memory for faster data transfer
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MIXED_PRECISION = True # Enable mixed precision training
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PRECISION = "bf16" # 'fp16' or 'bf16' for mixed precision
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# Model settings
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MODEL_NAME = "stock_gnn_r9700"
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HIDDEN_CHANNELS = 128 # Increased for R9700's compute power
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NUM_HEADS = 16 # Increased number of attention heads
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DROPOUT = 0.3 # Reduced dropout for better GPU utilization
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LEARNING_RATE = 0.0005 # Lower learning rate for stability
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EPOCHS = 200 # More epochs with larger batch sizes
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BATCH_SIZE = 128 # Larger batch size for R9700's memory
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SEQUENCE_LENGTH = 60 # Longer sequences with more memory
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PREDICTION_HORIZON = 10 # Number of steps to predict ahead
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# Intraday trading settings
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TRADING_FREQUENCY = "5min" # '1min', '5min', '15min', '30min', '1h'
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TRADING_HOURS = {
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"start": "09:30", # Market open (ET)
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"end": "16:00", # Market close (ET)
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}
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PRE_MARKET_HOURS = {
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"start": "04:00", # Pre-market start
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"end": "09:30", # Pre-market end
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}
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AFTER_HOURS = {
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"start": "16:00", # After-hours start
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"end": "20:00", # After-hours end
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}
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MAX_POSITION_HOLD_TIME = "4h" # Maximum time to hold a position
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MIN_POSITION_HOLD_TIME = "10min" # Minimum time to hold a position
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MAX_DAILY_POSITIONS = 50 # Maximum number of positions per day
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MAX_POSITION_SIZE = 0.03 # Maximum % of portfolio per position (3%)
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# Data pipeline settings
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LOOKBACK_WINDOW = 60 # Days for feature calculation
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REALTIME_FEATURE_WINDOW = 30 # Number of data points for real-time features
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DATA_BUFFER_SIZE = 5000 # Number of data points to keep in memory
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DATA_FLUSH_INTERVAL = 300 # seconds - how often to flush data to database
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# Alternative data settings (set via .env or environment variables)
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NEWS_API_KEY = os.environ.get("NEWS_API_KEY", "")
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TWITTER_BEARER_TOKEN = os.environ.get("TWITTER_BEARER_TOKEN", "")
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REDDIT_CLIENT_ID = os.environ.get("REDDIT_CLIENT_ID", "")
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REDDIT_CLIENT_SECRET = os.environ.get("REDDIT_CLIENT_SECRET", "")
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NEWS_LOOKBACK_DAYS = 7 # Number of days to look back for news
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SOCIAL_MEDIA_LOOKBACK_DAYS = 3 # Number of days to look back for social media
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# Feature definitions (referenced by models)
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NEWS_FEATURES = [
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"sentiment",
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"volume",
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"recency",
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"source_reliability",
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"topic_relevance",
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]
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SOCIAL_FEATURES = [
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"twitter_sentiment",
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"twitter_volume",
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"reddit_sentiment",
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"reddit_volume",
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"social_momentum",
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]
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INTRADAY_FEATURES = [
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"return",
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"volatility",
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"momentum",
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"volume_momentum",
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"bid_ask_spread",
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"bid_ask_spread_pct",
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"volume_imbalance",
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"order_flow",
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"vwap_deviation",
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]
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# Stateful prediction
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STATEFUL_PREDICTION = False
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REALTIME_UPDATE_INTERVAL = 60 # seconds
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# Backtesting settings
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INITIAL_CAPITAL = 100000
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TRANSACTION_COST = 0.0005 # 0.05% per trade
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SLIPPAGE_MODEL = "volume_curve" # 'volume_curve', 'constant', or 'none'
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SLIPPAGE_RATE = 0.0002 # 0.02% slippage
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# Live trading settings
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LIVE_DATA_ENABLED = True
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DATA_PROVIDER = "polygon" # 'polygon', 'alphavantage', 'ib', 'tdameritrade'
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POLYGON_API_KEY = os.environ.get("POLYGON_API_KEY", "")
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ALPHA_VANTAGE_API_KEY = os.environ.get("ALPHA_VANTAGE_API_KEY", "")
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IB_HOST = "127.0.0.1"
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IB_PORT = 7497
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IB_CLIENT_ID = 1
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# WebSocket settings
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WEBSOCKET_RECONNECT_DELAY = 5 # seconds
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WEBSOCKET_MAX_RETRIES = 20
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WEBSOCKET_PING_INTERVAL = 30 # seconds
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# Data loading settings
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NUM_WORKERS = 8 # Number of data loading workers
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PREFETCH_FACTOR = 4 # Number of batches to prefetch
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# Online learning settings
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ONLINE_LEARNING = True # Enable online learning
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ONLINE_LEARNING_RATE = 0.0001 # Learning rate for online updates
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ONLINE_LEARNING_INTERVAL = 3600 # seconds - how often to perform online learning
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# Risk management settings
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MAX_DAILY_LOSS = 0.01 # 1% max daily loss
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MAX_DRAWDOWN = 0.05 # 5% max drawdown
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VOLATILITY_TARGET = 0.15 # Annualized volatility target
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POSITION_SIZING = (
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"volatility_target" # 'volatility_target', 'equal_weight', 'kelly'
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)
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# Execution settings
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EXECUTION_ALGORITHM = "vwap" # 'vwap', 'twap', 'pov', 'implementation_shortfall'
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EXECUTION_TIME_HORIZON = "5min" # Time to complete execution
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MARKET_IMPACT_MODEL = "kyle" # 'kyle', 'almgren_chriss', or 'none'
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def __init__(self):
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os.makedirs(self.RAW_DATA_DIR, exist_ok=True)
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os.makedirs(self.PROCESSED_DATA_DIR, exist_ok=True)
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os.makedirs(self.EXTERNAL_DATA_DIR, exist_ok=True)
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os.makedirs(self.MODEL_DIR, exist_ok=True)
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config = Config()
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