drjones/alpaca-llm-bot-v1
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v1.2 strategy upgrade: small-capital fee-aware signals, two-model pipeline, auto deep-research escalation

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drjones
2026-02-26 01:38:55 -08:00
parent 080e66980f
commit cb5edf694d
4 changed files with 153 additions and 48 deletions
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17
config.py
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@@ -9,21 +9,32 @@ class Settings:
alpaca_base = os.getenv("ALPACA_BASE_URL", "https://paper-api.alpaca.markets")
paper_mode = os.getenv("PAPER_MODE", "true").lower() == "true"
# Capital/risk profile
starting_capital_usd = float(os.getenv("STARTING_CAPITAL_USD", "100"))
max_order_usd = float(os.getenv("MAX_ORDER_USD", "5"))
max_daily_notional = float(os.getenv("MAX_DAILY_NOTIONAL", "50"))
max_open_positions = int(os.getenv("MAX_OPEN_POSITIONS", "6"))
max_daily_notional = float(os.getenv("MAX_DAILY_NOTIONAL", "40"))
max_open_positions = int(os.getenv("MAX_OPEN_POSITIONS", "8"))
min_confidence = float(os.getenv("MIN_CONFIDENCE", "0.60"))
# Approx fee model for small-size optimization
fee_per_trade_usd = float(os.getenv("FEE_PER_TRADE_USD", "0.00"))
slippage_bps = float(os.getenv("SLIPPAGE_BPS", "5"))
# Scheduling
trade_interval_hours = int(os.getenv("TRADE_INTERVAL_HOURS", "2"))
curate_interval_minutes = int(os.getenv("CURATE_INTERVAL_MINUTES", "30"))
timezone = os.getenv("TIMEZONE", "America/Los_Angeles")
# LLM stack (small for curation, larger for final decision)
ollama_url = os.getenv("OLLAMA_URL", "http://10.30.20.110:11434")
ollama_model = os.getenv("OLLAMA_MODEL", "gemma3:latest")
ollama_curator_model = os.getenv("OLLAMA_CURATOR_MODEL", "gemma3:latest")
ollama_decision_model = os.getenv("OLLAMA_DECISION_MODEL", "agent-oss:latest")
# Data sources
searx_url = os.getenv("SEARX_URL", "http://10.30.20.35:6969/search")
scraper_api = os.getenv("SCRAPER_API_URL", "http://10.30.20.115:24125")
# App
db_path = os.getenv("DB_PATH", "sqlite:///./bot.db")
host = os.getenv("APP_HOST", "0.0.0.0")
port = int(os.getenv("APP_PORT", "8089"))