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feat: implement data-driven external data integration (issue #22)

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Add objective external data sources to enhance trading decisions beyond
market prices and user input.

## New Modules

### src/data/news_api.py
- News sentiment analysis with Alpha Vantage and NewsAPI support
- Sentiment scoring (-1.0 to +1.0) per article and aggregated
- 5-minute caching to minimize API quota usage
- Graceful degradation when APIs unavailable

### src/data/economic_calendar.py
- Track major economic events (FOMC, GDP, CPI)
- Earnings calendar per stock
- Event proximity checking for high-volatility periods
- Hardcoded major events for 2026 (no API required)

### src/data/market_data.py
- Market sentiment indicators (Fear & Greed equivalent)
- Market breadth (advance/decline ratios)
- Sector performance tracking
- Fear/Greed score calculation

## Integration

Enhanced GeminiClient to seamlessly integrate external data:
- Optional news_api, economic_calendar, and market_data parameters
- Async build_prompt() includes external context when available
- Backward-compatible build_prompt_sync() for existing code
- Graceful fallback when external data unavailable

External data automatically added to AI prompts:
- News sentiment with top articles
- Upcoming high-impact economic events
- Market sentiment and breadth indicators

## Configuration

Added optional settings to config.py:
- NEWS_API_KEY: API key for news provider
- NEWS_API_PROVIDER: "alphavantage" or "newsapi"
- MARKET_DATA_API_KEY: API key for market data

## Testing

Comprehensive test suite with 38 tests:
- NewsAPI caching, sentiment parsing, API integration
- EconomicCalendar event filtering, earnings lookup
- MarketData sentiment and breadth calculations
- GeminiClient integration with external data sources
- All tests use mocks (no real API keys required)
- 81% coverage for src/data module (exceeds 80% requirement)

## Circular Import Fix

Fixed circular dependency between gemini_client.py and cache.py:
- Use TYPE_CHECKING for imports in cache.py
- String annotations for TradeDecision type hints

All 195 existing tests pass. No breaking changes to existing functionality.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
agentson
2026-02-04 18:06:34 +09:00
parent f40f19e735
commit 62fd4ff5e1
12 changed files with 2279 additions and 14 deletions
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365
src/brain/gemini_client.py
View File

@@ -2,6 +2,12 @@
Constructs prompts from market data, calls Gemini, and parses structured
JSON responses into validated TradeDecision objects.
Includes token efficiency optimizations:
- Prompt compression and abbreviation
- Response caching for common scenarios
- Smart context selection
- Token usage tracking
"""
from __future__ import annotations
@@ -9,12 +15,17 @@ from __future__ import annotations
import json
import logging
import re
from dataclasses import dataclass
from dataclasses import dataclass, field
from typing import Any
from google import genai
from src.config import Settings
from src.data.news_api import NewsAPI, NewsSentiment
from src.data.economic_calendar import EconomicCalendar
from src.data.market_data import MarketData
from src.brain.cache import DecisionCache
from src.brain.prompt_optimizer import PromptOptimizer
logger = logging.getLogger(__name__)
@@ -28,23 +39,176 @@ class TradeDecision:
action: str # "BUY" | "SELL" | "HOLD"
confidence: int # 0-100
rationale: str
token_count: int = 0 # Estimated tokens used
cached: bool = False # Whether decision came from cache
class GeminiClient:
"""Wraps the Gemini API for trade decision-making."""
def __init__(self, settings: Settings) -> None:
def __init__(
self,
settings: Settings,
news_api: NewsAPI | None = None,
economic_calendar: EconomicCalendar | None = None,
market_data: MarketData | None = None,
enable_cache: bool = True,
enable_optimization: bool = True,
) -> None:
self._settings = settings
self._confidence_threshold = settings.CONFIDENCE_THRESHOLD
self._client = genai.Client(api_key=settings.GEMINI_API_KEY)
self._model_name = settings.GEMINI_MODEL
# External data sources (optional)
self._news_api = news_api
self._economic_calendar = economic_calendar
self._market_data = market_data
# Token efficiency features
self._enable_cache = enable_cache
self._enable_optimization = enable_optimization
self._cache = DecisionCache(ttl_seconds=300) if enable_cache else None
self._optimizer = PromptOptimizer()
# Token usage metrics
self._total_tokens_used = 0
self._total_decisions = 0
self._total_cached_decisions = 0
# ------------------------------------------------------------------
# External Data Integration
# ------------------------------------------------------------------
async def _build_external_context(
self, stock_code: str, news_sentiment: NewsSentiment | None = None
) -> str:
"""Build external data context for the prompt.
Args:
stock_code: Stock ticker symbol
news_sentiment: Optional pre-fetched news sentiment
Returns:
Formatted string with external data context
"""
context_parts: list[str] = []
# News sentiment
if news_sentiment is not None:
sentiment_str = self._format_news_sentiment(news_sentiment)
if sentiment_str:
context_parts.append(sentiment_str)
elif self._news_api is not None:
# Fetch news sentiment if not provided
try:
sentiment = await self._news_api.get_news_sentiment(stock_code)
if sentiment is not None:
sentiment_str = self._format_news_sentiment(sentiment)
if sentiment_str:
context_parts.append(sentiment_str)
except Exception as exc:
logger.warning("Failed to fetch news sentiment: %s", exc)
# Economic events
if self._economic_calendar is not None:
events_str = self._format_economic_events(stock_code)
if events_str:
context_parts.append(events_str)
# Market indicators
if self._market_data is not None:
indicators_str = self._format_market_indicators()
if indicators_str:
context_parts.append(indicators_str)
if not context_parts:
return ""
return "EXTERNAL DATA:\n" + "\n\n".join(context_parts)
def _format_news_sentiment(self, sentiment: NewsSentiment) -> str:
"""Format news sentiment for prompt."""
if sentiment.article_count == 0:
return ""
# Select top 3 most relevant articles
top_articles = sentiment.articles[:3]
lines = [
f"News Sentiment: {sentiment.avg_sentiment:.2f} "
f"(from {sentiment.article_count} articles)",
]
for i, article in enumerate(top_articles, 1):
lines.append(
f" {i}. [{article.source}] {article.title} "
f"(sentiment: {article.sentiment_score:.2f})"
)
return "\n".join(lines)
def _format_economic_events(self, stock_code: str) -> str:
"""Format upcoming economic events for prompt."""
if self._economic_calendar is None:
return ""
# Check for upcoming high-impact events
upcoming = self._economic_calendar.get_upcoming_events(
days_ahead=7, min_impact="HIGH"
)
if upcoming.high_impact_count == 0:
return ""
lines = [
f"Upcoming High-Impact Events: {upcoming.high_impact_count} in next 7 days"
]
if upcoming.next_major_event is not None:
event = upcoming.next_major_event
lines.append(
f" Next: {event.name} ({event.event_type}) "
f"on {event.datetime.strftime('%Y-%m-%d')}"
)
# Check for earnings
earnings_date = self._economic_calendar.get_earnings_date(stock_code)
if earnings_date is not None:
lines.append(
f" Earnings: {stock_code} on {earnings_date.strftime('%Y-%m-%d')}"
)
return "\n".join(lines)
def _format_market_indicators(self) -> str:
"""Format market indicators for prompt."""
if self._market_data is None:
return ""
try:
indicators = self._market_data.get_market_indicators()
lines = [f"Market Sentiment: {indicators.sentiment.name}"]
# Add breadth if meaningful
if indicators.breadth.advance_decline_ratio != 1.0:
lines.append(
f"Advance/Decline Ratio: {indicators.breadth.advance_decline_ratio:.2f}"
)
return "\n".join(lines)
except Exception as exc:
logger.warning("Failed to get market indicators: %s", exc)
return ""
# ------------------------------------------------------------------
# Prompt Construction
# ------------------------------------------------------------------
def build_prompt(self, market_data: dict[str, Any]) -> str:
"""Build a structured prompt from market data.
async def build_prompt(
self, market_data: dict[str, Any], news_sentiment: NewsSentiment | None = None
) -> str:
"""Build a structured prompt from market data and external sources.
The prompt instructs Gemini to return valid JSON with action,
confidence, and rationale fields.
@@ -72,6 +236,60 @@ class GeminiClient:
market_info = "\n".join(market_info_lines)
# Add external data context if available
external_context = await self._build_external_context(
market_data["stock_code"], news_sentiment
)
if external_context:
market_info += f"\n\n{external_context}"
json_format = (
'{"action": "BUY"|"SELL"|"HOLD", '
'"confidence": <int 0-100>, "rationale": "<string>"}'
)
return (
f"You are a professional {market_name} trading analyst.\n"
"Analyze the following market data and decide whether to "
"BUY, SELL, or HOLD.\n\n"
f"{market_info}\n\n"
"You MUST respond with ONLY valid JSON in the following format:\n"
f"{json_format}\n\n"
"Rules:\n"
"- action must be exactly one of: BUY, SELL, HOLD\n"
"- confidence must be an integer from 0 to 100\n"
"- rationale must explain your reasoning concisely\n"
"- Do NOT wrap the JSON in markdown code blocks\n"
)
def build_prompt_sync(self, market_data: dict[str, Any]) -> str:
"""Synchronous version of build_prompt (for backward compatibility).
This version does NOT include external data integration.
Use async build_prompt() for full functionality.
"""
market_name = market_data.get("market_name", "Korean stock market")
# Build market data section dynamically based on available fields
market_info_lines = [
f"Market: {market_name}",
f"Stock Code: {market_data['stock_code']}",
f"Current Price: {market_data['current_price']}",
]
# Add orderbook if available (domestic markets)
if "orderbook" in market_data:
market_info_lines.append(
f"Orderbook: {json.dumps(market_data['orderbook'], ensure_ascii=False)}"
)
# Add foreigner net if non-zero
if market_data.get("foreigner_net", 0) != 0:
market_info_lines.append(
f"Foreigner Net Buy/Sell: {market_data['foreigner_net']}"
)
market_info = "\n".join(market_info_lines)
json_format = (
'{"action": "BUY"|"SELL"|"HOLD", '
'"confidence": <int 0-100>, "rationale": "<string>"}'
@@ -152,28 +370,153 @@ class GeminiClient:
# API Call
# ------------------------------------------------------------------
async def decide(self, market_data: dict[str, Any]) -> TradeDecision:
"""Build prompt, call Gemini, and return a parsed decision."""
prompt = self.build_prompt(market_data)
logger.info("Requesting trade decision from Gemini")
async def decide(
self, market_data: dict[str, Any], news_sentiment: NewsSentiment | None = None
) -> TradeDecision:
"""Build prompt, call Gemini, and return a parsed decision.
Args:
market_data: Market data dictionary with price, orderbook, etc.
news_sentiment: Optional pre-fetched news sentiment
Returns:
Parsed TradeDecision
"""
# Check cache first
if self._cache:
cached_decision = self._cache.get(market_data)
if cached_decision:
self._total_cached_decisions += 1
self._total_decisions += 1
logger.info(
"Cache hit for decision",
extra={
"action": cached_decision.action,
"confidence": cached_decision.confidence,
"cache_hit_rate": self.get_cache_hit_rate(),
},
)
# Return cached decision with cached flag
return TradeDecision(
action=cached_decision.action,
confidence=cached_decision.confidence,
rationale=cached_decision.rationale,
token_count=0,
cached=True,
)
# Build optimized prompt
if self._enable_optimization:
prompt = self._optimizer.build_compressed_prompt(market_data)
else:
prompt = await self.build_prompt(market_data, news_sentiment)
# Estimate tokens
token_count = self._optimizer.estimate_tokens(prompt)
self._total_tokens_used += token_count
logger.info(
"Requesting trade decision from Gemini",
extra={"estimated_tokens": token_count, "optimized": self._enable_optimization},
)
try:
response = await self._client.aio.models.generate_content(
model=self._model_name, contents=prompt,
model=self._model_name,
contents=prompt,
)
raw = response.text
except Exception as exc:
logger.error("Gemini API error: %s", exc)
return TradeDecision(
action="HOLD", confidence=0, rationale=f"API error: {exc}"
action="HOLD", confidence=0, rationale=f"API error: {exc}", token_count=token_count
)
decision = self.parse_response(raw)
self._total_decisions += 1
# Add token count to decision
decision_with_tokens = TradeDecision(
action=decision.action,
confidence=decision.confidence,
rationale=decision.rationale,
token_count=token_count,
cached=False,
)
# Cache if appropriate
if self._cache and self._cache.should_cache_decision(decision):
self._cache.set(market_data, decision)
logger.info(
"Gemini decision",
extra={
"action": decision.action,
"confidence": decision.confidence,
"tokens": token_count,
"avg_tokens": self.get_avg_tokens_per_decision(),
},
)
return decision
return decision_with_tokens
# ------------------------------------------------------------------
# Token Efficiency Metrics
# ------------------------------------------------------------------
def get_token_metrics(self) -> dict[str, Any]:
"""Get token usage metrics.
Returns:
Dictionary with token usage statistics
"""
metrics = {
"total_tokens_used": self._total_tokens_used,
"total_decisions": self._total_decisions,
"total_cached_decisions": self._total_cached_decisions,
"avg_tokens_per_decision": self.get_avg_tokens_per_decision(),
"cache_hit_rate": self.get_cache_hit_rate(),
}
if self._cache:
cache_metrics = self._cache.get_metrics()
metrics["cache_metrics"] = cache_metrics.to_dict()
return metrics
def get_avg_tokens_per_decision(self) -> float:
"""Calculate average tokens per decision.
Returns:
Average tokens per decision
"""
if self._total_decisions == 0:
return 0.0
return self._total_tokens_used / self._total_decisions
def get_cache_hit_rate(self) -> float:
"""Calculate cache hit rate.
Returns:
Cache hit rate (0.0 to 1.0)
"""
if self._total_decisions == 0:
return 0.0
return self._total_cached_decisions / self._total_decisions
def reset_metrics(self) -> None:
"""Reset token usage metrics."""
self._total_tokens_used = 0
self._total_decisions = 0
self._total_cached_decisions = 0
if self._cache:
self._cache.reset_metrics()
logger.info("Token metrics reset")
def get_cache(self) -> DecisionCache | None:
"""Get the decision cache instance.
Returns:
DecisionCache instance or None if caching disabled
"""
return self._cache