f7289606fc
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decide() ignored market_data["prompt_override"], always building a generic trade-decision prompt. This caused pre_market_planner playbook generation to fail with JSONDecodeError on every market, falling back to defensive playbooks. Now prompt_override takes priority over both optimization and standard prompt building. Closes #143 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
760 lines
27 KiB
Python
760 lines
27 KiB
Python
"""Decision engine powered by Google Gemini.
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Constructs prompts from market data, calls Gemini, and parses structured
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JSON responses into validated TradeDecision objects.
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Includes token efficiency optimizations:
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- Prompt compression and abbreviation
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- Response caching for common scenarios
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- Smart context selection
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- Token usage tracking and metrics
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Includes external data integration:
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- News sentiment analysis
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- Economic calendar events
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- Market indicators
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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from dataclasses import dataclass
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from typing import Any
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from google import genai
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from src.config import Settings
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from src.data.news_api import NewsAPI, NewsSentiment
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from src.data.economic_calendar import EconomicCalendar
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from src.data.market_data import MarketData
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from src.brain.cache import DecisionCache
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from src.brain.prompt_optimizer import PromptOptimizer
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logger = logging.getLogger(__name__)
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VALID_ACTIONS = {"BUY", "SELL", "HOLD"}
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@dataclass(frozen=True)
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class TradeDecision:
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"""Validated decision from the AI brain."""
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action: str # "BUY" | "SELL" | "HOLD"
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confidence: int # 0-100
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rationale: str
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token_count: int = 0 # Estimated tokens used
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cached: bool = False # Whether decision came from cache
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class GeminiClient:
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"""Wraps the Gemini API for trade decision-making."""
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def __init__(
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self,
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settings: Settings,
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news_api: NewsAPI | None = None,
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economic_calendar: EconomicCalendar | None = None,
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market_data: MarketData | None = None,
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enable_cache: bool = True,
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enable_optimization: bool = True,
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) -> None:
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self._settings = settings
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self._confidence_threshold = settings.CONFIDENCE_THRESHOLD
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self._client = genai.Client(api_key=settings.GEMINI_API_KEY)
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self._model_name = settings.GEMINI_MODEL
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# External data sources (optional)
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self._news_api = news_api
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self._economic_calendar = economic_calendar
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self._market_data = market_data
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# Token efficiency features
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self._enable_cache = enable_cache
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self._enable_optimization = enable_optimization
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self._cache = DecisionCache(ttl_seconds=300) if enable_cache else None
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self._optimizer = PromptOptimizer()
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# Token usage metrics
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self._total_tokens_used = 0
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self._total_decisions = 0
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self._total_cached_decisions = 0
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# ------------------------------------------------------------------
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# External Data Integration
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# ------------------------------------------------------------------
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async def _build_external_context(
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self, stock_code: str, news_sentiment: NewsSentiment | None = None
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) -> str:
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"""Build external data context for the prompt.
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Args:
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stock_code: Stock ticker symbol
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news_sentiment: Optional pre-fetched news sentiment
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Returns:
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Formatted string with external data context
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"""
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context_parts: list[str] = []
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# News sentiment
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if news_sentiment is not None:
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sentiment_str = self._format_news_sentiment(news_sentiment)
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if sentiment_str:
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context_parts.append(sentiment_str)
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elif self._news_api is not None:
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# Fetch news sentiment if not provided
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try:
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sentiment = await self._news_api.get_news_sentiment(stock_code)
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if sentiment is not None:
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sentiment_str = self._format_news_sentiment(sentiment)
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if sentiment_str:
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context_parts.append(sentiment_str)
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except Exception as exc:
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logger.warning("Failed to fetch news sentiment: %s", exc)
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# Economic events
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if self._economic_calendar is not None:
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events_str = self._format_economic_events(stock_code)
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if events_str:
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context_parts.append(events_str)
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# Market indicators
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if self._market_data is not None:
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indicators_str = self._format_market_indicators()
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if indicators_str:
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context_parts.append(indicators_str)
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if not context_parts:
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return ""
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return "EXTERNAL DATA:\n" + "\n\n".join(context_parts)
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def _format_news_sentiment(self, sentiment: NewsSentiment) -> str:
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"""Format news sentiment for prompt."""
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if sentiment.article_count == 0:
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return ""
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# Select top 3 most relevant articles
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top_articles = sentiment.articles[:3]
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lines = [
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f"News Sentiment: {sentiment.avg_sentiment:.2f} "
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f"(from {sentiment.article_count} articles)",
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]
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for i, article in enumerate(top_articles, 1):
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lines.append(
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f" {i}. [{article.source}] {article.title} "
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f"(sentiment: {article.sentiment_score:.2f})"
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)
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return "\n".join(lines)
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def _format_economic_events(self, stock_code: str) -> str:
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"""Format upcoming economic events for prompt."""
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if self._economic_calendar is None:
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return ""
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# Check for upcoming high-impact events
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upcoming = self._economic_calendar.get_upcoming_events(
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days_ahead=7, min_impact="HIGH"
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)
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if upcoming.high_impact_count == 0:
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return ""
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lines = [
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f"Upcoming High-Impact Events: {upcoming.high_impact_count} in next 7 days"
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]
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if upcoming.next_major_event is not None:
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event = upcoming.next_major_event
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lines.append(
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f" Next: {event.name} ({event.event_type}) "
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f"on {event.datetime.strftime('%Y-%m-%d')}"
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)
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# Check for earnings
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earnings_date = self._economic_calendar.get_earnings_date(stock_code)
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if earnings_date is not None:
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lines.append(
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f" Earnings: {stock_code} on {earnings_date.strftime('%Y-%m-%d')}"
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)
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return "\n".join(lines)
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def _format_market_indicators(self) -> str:
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"""Format market indicators for prompt."""
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if self._market_data is None:
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return ""
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try:
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indicators = self._market_data.get_market_indicators()
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lines = [f"Market Sentiment: {indicators.sentiment.name}"]
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# Add breadth if meaningful
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if indicators.breadth.advance_decline_ratio != 1.0:
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lines.append(
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f"Advance/Decline Ratio: {indicators.breadth.advance_decline_ratio:.2f}"
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)
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return "\n".join(lines)
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except Exception as exc:
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logger.warning("Failed to get market indicators: %s", exc)
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return ""
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# ------------------------------------------------------------------
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# Prompt Construction
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# ------------------------------------------------------------------
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async def build_prompt(
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self, market_data: dict[str, Any], news_sentiment: NewsSentiment | None = None
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) -> str:
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"""Build a structured prompt from market data and external sources.
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The prompt instructs Gemini to return valid JSON with action,
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confidence, and rationale fields.
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"""
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market_name = market_data.get("market_name", "Korean stock market")
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# Build market data section dynamically based on available fields
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market_info_lines = [
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f"Market: {market_name}",
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f"Stock Code: {market_data['stock_code']}",
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f"Current Price: {market_data['current_price']}",
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]
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# Add orderbook if available (domestic markets)
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if "orderbook" in market_data:
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market_info_lines.append(
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f"Orderbook: {json.dumps(market_data['orderbook'], ensure_ascii=False)}"
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)
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# Add foreigner net if non-zero
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if market_data.get("foreigner_net", 0) != 0:
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market_info_lines.append(
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f"Foreigner Net Buy/Sell: {market_data['foreigner_net']}"
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)
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market_info = "\n".join(market_info_lines)
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# Add external data context if available
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external_context = await self._build_external_context(
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market_data["stock_code"], news_sentiment
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)
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if external_context:
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market_info += f"\n\n{external_context}"
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json_format = (
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'{"action": "BUY"|"SELL"|"HOLD", '
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'"confidence": <int 0-100>, "rationale": "<string>"}'
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)
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return (
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f"You are a professional {market_name} trading analyst.\n"
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"Analyze the following market data and decide whether to "
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"BUY, SELL, or HOLD.\n\n"
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f"{market_info}\n\n"
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"You MUST respond with ONLY valid JSON in the following format:\n"
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f"{json_format}\n\n"
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"Rules:\n"
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"- action must be exactly one of: BUY, SELL, HOLD\n"
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"- confidence must be an integer from 0 to 100\n"
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"- rationale must explain your reasoning concisely\n"
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"- Do NOT wrap the JSON in markdown code blocks\n"
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)
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def build_prompt_sync(self, market_data: dict[str, Any]) -> str:
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"""Synchronous version of build_prompt (for backward compatibility).
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This version does NOT include external data integration.
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Use async build_prompt() for full functionality.
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"""
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market_name = market_data.get("market_name", "Korean stock market")
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# Build market data section dynamically based on available fields
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market_info_lines = [
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f"Market: {market_name}",
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f"Stock Code: {market_data['stock_code']}",
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f"Current Price: {market_data['current_price']}",
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]
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# Add orderbook if available (domestic markets)
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if "orderbook" in market_data:
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market_info_lines.append(
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f"Orderbook: {json.dumps(market_data['orderbook'], ensure_ascii=False)}"
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)
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# Add foreigner net if non-zero
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if market_data.get("foreigner_net", 0) != 0:
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market_info_lines.append(
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f"Foreigner Net Buy/Sell: {market_data['foreigner_net']}"
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)
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market_info = "\n".join(market_info_lines)
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json_format = (
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'{"action": "BUY"|"SELL"|"HOLD", '
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'"confidence": <int 0-100>, "rationale": "<string>"}'
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)
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return (
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f"You are a professional {market_name} trading analyst.\n"
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"Analyze the following market data and decide whether to "
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"BUY, SELL, or HOLD.\n\n"
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f"{market_info}\n\n"
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"You MUST respond with ONLY valid JSON in the following format:\n"
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f"{json_format}\n\n"
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"Rules:\n"
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"- action must be exactly one of: BUY, SELL, HOLD\n"
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"- confidence must be an integer from 0 to 100\n"
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"- rationale must explain your reasoning concisely\n"
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"- Do NOT wrap the JSON in markdown code blocks\n"
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)
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# ------------------------------------------------------------------
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# Response Parsing
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# ------------------------------------------------------------------
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def parse_response(self, raw: str) -> TradeDecision:
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"""Parse a raw Gemini response into a TradeDecision.
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Handles: valid JSON, JSON wrapped in markdown code blocks,
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malformed JSON, missing fields, and invalid action values.
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On any failure, returns a safe HOLD with confidence 0.
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"""
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if not raw or not raw.strip():
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logger.warning("Empty response from Gemini — defaulting to HOLD")
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return TradeDecision(action="HOLD", confidence=0, rationale="Empty response")
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# Strip markdown code fences if present
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cleaned = raw.strip()
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match = re.search(r"```(?:json)?\s*\n?(.*?)\n?```", cleaned, re.DOTALL)
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if match:
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cleaned = match.group(1).strip()
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try:
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data = json.loads(cleaned)
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except json.JSONDecodeError:
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logger.warning("Malformed JSON from Gemini — defaulting to HOLD")
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return TradeDecision(
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action="HOLD", confidence=0, rationale="Malformed JSON response"
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)
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# Validate required fields
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if not all(k in data for k in ("action", "confidence", "rationale")):
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logger.warning("Missing fields in Gemini response — defaulting to HOLD")
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return TradeDecision(
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action="HOLD", confidence=0, rationale="Missing required fields"
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)
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action = str(data["action"]).upper()
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if action not in VALID_ACTIONS:
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logger.warning("Invalid action '%s' from Gemini — defaulting to HOLD", action)
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return TradeDecision(
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action="HOLD", confidence=0, rationale=f"Invalid action: {action}"
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)
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confidence = int(data["confidence"])
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rationale = str(data["rationale"])
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# Enforce confidence threshold
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if confidence < self._confidence_threshold:
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logger.info(
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"Confidence %d < threshold %d — forcing HOLD",
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confidence,
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self._confidence_threshold,
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)
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action = "HOLD"
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return TradeDecision(action=action, confidence=confidence, rationale=rationale)
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# ------------------------------------------------------------------
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# API Call
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# ------------------------------------------------------------------
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async def decide(
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self, market_data: dict[str, Any], news_sentiment: NewsSentiment | None = None
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) -> TradeDecision:
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"""Build prompt, call Gemini, and return a parsed decision.
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Args:
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market_data: Market data dictionary with price, orderbook, etc.
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news_sentiment: Optional pre-fetched news sentiment
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Returns:
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Parsed TradeDecision
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"""
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# Check cache first
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if self._cache:
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cached_decision = self._cache.get(market_data)
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if cached_decision:
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self._total_cached_decisions += 1
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self._total_decisions += 1
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logger.info(
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"Cache hit for decision",
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extra={
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"action": cached_decision.action,
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"confidence": cached_decision.confidence,
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"cache_hit_rate": self.get_cache_hit_rate(),
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},
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)
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# Return cached decision with cached flag
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return TradeDecision(
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action=cached_decision.action,
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confidence=cached_decision.confidence,
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rationale=cached_decision.rationale,
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token_count=0,
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cached=True,
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)
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# Build prompt (prompt_override takes priority for callers like pre_market_planner)
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if "prompt_override" in market_data:
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prompt = market_data["prompt_override"]
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elif self._enable_optimization:
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prompt = self._optimizer.build_compressed_prompt(market_data)
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else:
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prompt = await self.build_prompt(market_data, news_sentiment)
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# Estimate tokens
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token_count = self._optimizer.estimate_tokens(prompt)
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self._total_tokens_used += token_count
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logger.info(
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"Requesting trade decision from Gemini",
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extra={"estimated_tokens": token_count, "optimized": self._enable_optimization},
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)
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try:
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response = await self._client.aio.models.generate_content(
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model=self._model_name,
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contents=prompt,
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)
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raw = response.text
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except Exception as exc:
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logger.error("Gemini API error: %s", exc)
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return TradeDecision(
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action="HOLD", confidence=0, rationale=f"API error: {exc}", token_count=token_count
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)
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decision = self.parse_response(raw)
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self._total_decisions += 1
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# Add token count to decision
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decision_with_tokens = TradeDecision(
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action=decision.action,
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confidence=decision.confidence,
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rationale=decision.rationale,
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token_count=token_count,
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cached=False,
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)
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# Cache if appropriate
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if self._cache and self._cache.should_cache_decision(decision):
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self._cache.set(market_data, decision)
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logger.info(
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"Gemini decision",
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extra={
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"action": decision.action,
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"confidence": decision.confidence,
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"tokens": token_count,
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"avg_tokens": self.get_avg_tokens_per_decision(),
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},
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)
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return decision_with_tokens
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# ------------------------------------------------------------------
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# Token Efficiency Metrics
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# ------------------------------------------------------------------
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def get_token_metrics(self) -> dict[str, Any]:
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"""Get token usage metrics.
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Returns:
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Dictionary with token usage statistics
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"""
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metrics = {
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"total_tokens_used": self._total_tokens_used,
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"total_decisions": self._total_decisions,
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"total_cached_decisions": self._total_cached_decisions,
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"avg_tokens_per_decision": self.get_avg_tokens_per_decision(),
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"cache_hit_rate": self.get_cache_hit_rate(),
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}
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if self._cache:
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cache_metrics = self._cache.get_metrics()
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metrics["cache_metrics"] = cache_metrics.to_dict()
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return metrics
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def get_avg_tokens_per_decision(self) -> float:
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"""Calculate average tokens per decision.
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Returns:
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Average tokens per decision
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"""
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if self._total_decisions == 0:
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return 0.0
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return self._total_tokens_used / self._total_decisions
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def get_cache_hit_rate(self) -> float:
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"""Calculate cache hit rate.
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Returns:
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Cache hit rate (0.0 to 1.0)
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"""
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if self._total_decisions == 0:
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return 0.0
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return self._total_cached_decisions / self._total_decisions
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def reset_metrics(self) -> None:
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"""Reset token usage metrics."""
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self._total_tokens_used = 0
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self._total_decisions = 0
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self._total_cached_decisions = 0
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if self._cache:
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self._cache.reset_metrics()
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logger.info("Token metrics reset")
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def get_cache(self) -> DecisionCache | None:
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"""Get the decision cache instance.
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Returns:
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DecisionCache instance or None if caching disabled
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"""
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return self._cache
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# ------------------------------------------------------------------
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# Batch Decision Making (for daily trading mode)
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# ------------------------------------------------------------------
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async def decide_batch(
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self, stocks_data: list[dict[str, Any]]
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) -> dict[str, TradeDecision]:
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|
"""Make decisions for multiple stocks in a single API call.
|
|
|
|
This is designed for daily trading mode to minimize API usage
|
|
when working with Gemini Free tier (20 calls/day limit).
|
|
|
|
Args:
|
|
stocks_data: List of market data dictionaries, each with:
|
|
- stock_code: Stock ticker
|
|
- current_price: Current price
|
|
- market_name: Market name (optional)
|
|
- foreigner_net: Foreigner net buy/sell (optional)
|
|
|
|
Returns:
|
|
Dictionary mapping stock_code to TradeDecision
|
|
|
|
Example:
|
|
>>> stocks_data = [
|
|
... {"stock_code": "AAPL", "current_price": 185.5},
|
|
... {"stock_code": "MSFT", "current_price": 420.0},
|
|
... ]
|
|
>>> decisions = await client.decide_batch(stocks_data)
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|
>>> decisions["AAPL"].action
|
|
'BUY'
|
|
"""
|
|
if not stocks_data:
|
|
return {}
|
|
|
|
# Build compressed batch prompt
|
|
market_name = stocks_data[0].get("market_name", "stock market")
|
|
|
|
# Format stock data as compact JSON array
|
|
compact_stocks = []
|
|
for stock in stocks_data:
|
|
compact = {
|
|
"code": stock["stock_code"],
|
|
"price": stock["current_price"],
|
|
}
|
|
if stock.get("foreigner_net", 0) != 0:
|
|
compact["frgn"] = stock["foreigner_net"]
|
|
compact_stocks.append(compact)
|
|
|
|
data_str = json.dumps(compact_stocks, ensure_ascii=False)
|
|
|
|
prompt = (
|
|
f"You are a professional {market_name} trading analyst.\n"
|
|
"Analyze the following stocks and decide whether to BUY, SELL, or HOLD each one.\n\n"
|
|
f"Stock Data: {data_str}\n\n"
|
|
"You MUST respond with ONLY a valid JSON array in this format:\n"
|
|
'[{"code": "AAPL", "action": "BUY", "confidence": 85, "rationale": "..."},\n'
|
|
' {"code": "MSFT", "action": "HOLD", "confidence": 50, "rationale": "..."}, ...]\n\n'
|
|
"Rules:\n"
|
|
"- Return one decision object per stock\n"
|
|
"- action must be exactly: BUY, SELL, or HOLD\n"
|
|
"- confidence must be 0-100\n"
|
|
"- rationale should be concise (1-2 sentences)\n"
|
|
"- Do NOT wrap JSON in markdown code blocks\n"
|
|
)
|
|
|
|
# Estimate tokens
|
|
token_count = self._optimizer.estimate_tokens(prompt)
|
|
self._total_tokens_used += token_count
|
|
|
|
logger.info(
|
|
"Requesting batch decision for %d stocks from Gemini",
|
|
len(stocks_data),
|
|
extra={"estimated_tokens": token_count},
|
|
)
|
|
|
|
try:
|
|
response = await self._client.aio.models.generate_content(
|
|
model=self._model_name,
|
|
contents=prompt,
|
|
)
|
|
raw = response.text
|
|
except Exception as exc:
|
|
logger.error("Gemini API error in batch decision: %s", exc)
|
|
# Return HOLD for all stocks on API error
|
|
return {
|
|
stock["stock_code"]: TradeDecision(
|
|
action="HOLD",
|
|
confidence=0,
|
|
rationale=f"API error: {exc}",
|
|
token_count=token_count,
|
|
cached=False,
|
|
)
|
|
for stock in stocks_data
|
|
}
|
|
|
|
# Parse batch response
|
|
return self._parse_batch_response(raw, stocks_data, token_count)
|
|
|
|
def _parse_batch_response(
|
|
self, raw: str, stocks_data: list[dict[str, Any]], token_count: int
|
|
) -> dict[str, TradeDecision]:
|
|
"""Parse batch response into a dictionary of decisions.
|
|
|
|
Args:
|
|
raw: Raw response from Gemini
|
|
stocks_data: Original stock data list
|
|
token_count: Token count for the request
|
|
|
|
Returns:
|
|
Dictionary mapping stock_code to TradeDecision
|
|
"""
|
|
if not raw or not raw.strip():
|
|
logger.warning("Empty batch response from Gemini — defaulting all to HOLD")
|
|
return {
|
|
stock["stock_code"]: TradeDecision(
|
|
action="HOLD",
|
|
confidence=0,
|
|
rationale="Empty response",
|
|
token_count=0,
|
|
cached=False,
|
|
)
|
|
for stock in stocks_data
|
|
}
|
|
|
|
# Strip markdown code fences if present
|
|
cleaned = raw.strip()
|
|
match = re.search(r"```(?:json)?\s*\n?(.*?)\n?```", cleaned, re.DOTALL)
|
|
if match:
|
|
cleaned = match.group(1).strip()
|
|
|
|
try:
|
|
data = json.loads(cleaned)
|
|
except json.JSONDecodeError:
|
|
logger.warning("Malformed JSON in batch response — defaulting all to HOLD")
|
|
return {
|
|
stock["stock_code"]: TradeDecision(
|
|
action="HOLD",
|
|
confidence=0,
|
|
rationale="Malformed JSON response",
|
|
token_count=0,
|
|
cached=False,
|
|
)
|
|
for stock in stocks_data
|
|
}
|
|
|
|
if not isinstance(data, list):
|
|
logger.warning("Batch response is not a JSON array — defaulting all to HOLD")
|
|
return {
|
|
stock["stock_code"]: TradeDecision(
|
|
action="HOLD",
|
|
confidence=0,
|
|
rationale="Invalid response format",
|
|
token_count=0,
|
|
cached=False,
|
|
)
|
|
for stock in stocks_data
|
|
}
|
|
|
|
# Build decision map
|
|
decisions: dict[str, TradeDecision] = {}
|
|
stock_codes = {stock["stock_code"] for stock in stocks_data}
|
|
|
|
for item in data:
|
|
if not isinstance(item, dict):
|
|
continue
|
|
|
|
code = item.get("code")
|
|
if not code or code not in stock_codes:
|
|
continue
|
|
|
|
# Validate required fields
|
|
if not all(k in item for k in ("action", "confidence", "rationale")):
|
|
logger.warning("Missing fields for %s — using HOLD", code)
|
|
decisions[code] = TradeDecision(
|
|
action="HOLD",
|
|
confidence=0,
|
|
rationale="Missing required fields",
|
|
token_count=0,
|
|
cached=False,
|
|
)
|
|
continue
|
|
|
|
action = str(item["action"]).upper()
|
|
if action not in VALID_ACTIONS:
|
|
logger.warning("Invalid action '%s' for %s — forcing HOLD", action, code)
|
|
action = "HOLD"
|
|
|
|
confidence = int(item["confidence"])
|
|
rationale = str(item["rationale"])
|
|
|
|
# Enforce confidence threshold
|
|
if confidence < self._confidence_threshold:
|
|
logger.info(
|
|
"Confidence %d < threshold %d for %s — forcing HOLD",
|
|
confidence,
|
|
self._confidence_threshold,
|
|
code,
|
|
)
|
|
action = "HOLD"
|
|
|
|
decisions[code] = TradeDecision(
|
|
action=action,
|
|
confidence=confidence,
|
|
rationale=rationale,
|
|
token_count=token_count // len(stocks_data), # Split token cost
|
|
cached=False,
|
|
)
|
|
self._total_decisions += 1
|
|
|
|
# Fill in missing stocks with HOLD
|
|
for stock in stocks_data:
|
|
code = stock["stock_code"]
|
|
if code not in decisions:
|
|
logger.warning("No decision for %s in batch response — using HOLD", code)
|
|
decisions[code] = TradeDecision(
|
|
action="HOLD",
|
|
confidence=0,
|
|
rationale="Not found in batch response",
|
|
token_count=0,
|
|
cached=False,
|
|
)
|
|
|
|
logger.info(
|
|
"Batch decision completed for %d stocks",
|
|
len(decisions),
|
|
extra={"tokens": token_count},
|
|
)
|
|
|
|
return decisions
|