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Refactor and translate comments and docstrings in utility modules to English for better clarity and maintainability. Update Gunicorn and application startup messages for consistency in language. Enhance documentation with English translations for better accessibility.

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dienakdz
2026-04-06 16:47:36 +07:00
parent 3ca291a346
commit 11e2e5aaa6
64 changed files with 2323 additions and 2336 deletions
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backend_api_python/app/services/polymarket_analyzer.py
+79 -79
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@@ -1,6 +1,6 @@
"""
Polymarket预测市场分析器
分析预测市场，生成AI预测和交易机会推荐
Polymarket Prediction Market Analyzer
Analyze prediction markets and generate AI predictions and trading opportunity recommendations
"""
import json
import re
@@ -17,7 +17,7 @@ logger = get_logger(__name__)
class PolymarketAnalyzer:
"""预测市场AI分析器"""
"""Prediction Market AI Analyzer"""
def __init__(self):
self.llm_service = LLMService()
@@ -26,19 +26,19 @@ class PolymarketAnalyzer:
def analyze_market(self, market_id: str, user_id: int = None, use_cache: bool = True, language: str = 'zh-CN', model: str = None) -> Dict:
"""
分析单个预测市场
Analyze a single prediction market
Args:
market_id: 市场ID
user_id: 用户ID（可选，用于用户特定分析）
use_cache: 是否使用缓存的分析结果（默认True）
language: 语言设置（'zh-CN' 或 'en-US'），用于生成对应语言的AI分析结果
market_id: Market ID
user_id: User ID (optional, for user-specific analysis)
use_cache: whether to use cached analysis results (default True)
language: language setting ('zh-CN' or 'en-US'), used to generate AI analysis results in the corresponding language
Returns:
分析结果字典
Analysis results dictionary
"""
try:
# 1. 获取市场数据
# 1. Get market data
market = self.polymarket_source.get_market_details(market_id)
if not market:
return {
@@ -46,21 +46,21 @@ class PolymarketAnalyzer:
"market_id": market_id
}
# 2. 如果使用缓存，检查是否有缓存的分析结果（30分钟有效）
# 2. If cache is used, check whether there are cached analysis results (valid for 30 minutes)
if use_cache:
cached_analysis = self._get_cached_analysis(market_id, user_id)
if cached_analysis:
cache_minutes = 30 # 缓存30分钟
cache_minutes = 30 # Cache for 30 minutes
if self._is_analysis_fresh(cached_analysis, max_age_minutes=cache_minutes):
logger.debug(f"Using cached analysis for market {market_id}")
return cached_analysis
# 3. 收集相关数据
# 3. Collect relevant data
related_news = self._get_related_news(market['question'])
related_assets = self._identify_related_assets(market['question'])
asset_data = self._get_asset_data(related_assets)
# 4. AI分析
# 4. AI analysis
ai_result = self._ai_predict_probability(
question=market['question'],
current_market_prob=market['current_probability'],
@@ -69,20 +69,20 @@ class PolymarketAnalyzer:
language=language
)
# 5. 计算机会评分
# 5. Calculate opportunity scores
opportunity_score = self._calculate_opportunity_score(
ai_prob=ai_result['predicted_probability'],
market_prob=market['current_probability'],
confidence=ai_result['confidence']
)
# 6. 生成推荐
# 6. Generate recommendations
recommendation = self._generate_recommendation(
divergence=ai_result['predicted_probability'] - market['current_probability'],
confidence=ai_result['confidence']
)
# 7. 构建分析结果
# 7. Construct analysis results
analysis_result = {
"market_id": market_id,
"ai_predicted_probability": ai_result['predicted_probability'],
@@ -98,7 +98,7 @@ class PolymarketAnalyzer:
"opportunity_score": opportunity_score
}
# 8. 保存到数据库
# 8. Save to database
self._save_analysis_to_db(analysis_result, user_id)
return analysis_result
@@ -112,54 +112,54 @@ class PolymarketAnalyzer:
def generate_asset_trading_opportunities(self, market_id: str) -> List[Dict]:
"""
基于预测市场生成相关资产的交易机会
Generate trading opportunities for related assets based on prediction markets
Args:
market_id: 预测市场ID
market_id: prediction market ID
Returns:
资产交易机会列表
List of asset trading opportunities
"""
try:
# 1. 分析预测市场
# 1. Analyze prediction markets
market_analysis = self.analyze_market(market_id)
if market_analysis.get('error'):
return []
# 2. 识别相关资产
# 2. Identify relevant assets
related_assets = market_analysis.get('related_assets', [])
if not related_assets:
return []
# 3. 对每个资产进行技术分析
# 3. Perform technical analysis on each asset
opportunities = []
for asset in related_assets:
try:
# 推断市场类型
# Infer market type
market_type = self._infer_market(asset)
# 获取资产数据
# Get asset data
asset_data = self.data_collector.collect_all(
market=market_type,
symbol=asset,
timeframe="1D",
include_polymarket=False # 避免循环
include_polymarket=False # avoid loops
)
# 技术分析
# technical analysis
technical_analysis = self._analyze_technical(asset_data)
# 结合预测市场信号
# Incorporate Predictive Market Signals
if market_analysis['recommendation'] == "YES":
# 预测事件发生概率高 → 相关资产可能上涨
# Predicted event probability is high → related assets may rise
signal = "BUY" if technical_analysis.get('trend') == "bullish" else "HOLD"
elif market_analysis['recommendation'] == "NO":
# 预测事件发生概率低 → 相关资产可能下跌
# The probability of the predicted event is low → the related assets may fall
signal = "SELL" if technical_analysis.get('trend') == "bearish" else "HOLD"
else:
signal = "HOLD"
# 计算综合置信度
# Calculate overall confidence
confidence = (
market_analysis['confidence_score'] * 0.6 +
technical_analysis.get('confidence', 50) * 0.4
@@ -184,7 +184,7 @@ class PolymarketAnalyzer:
logger.debug(f"Failed to analyze asset {asset} for market {market_id}: {e}")
continue
# 保存机会到数据库
# Save opportunity to database
if opportunities:
self._save_opportunities_to_db(market_id, opportunities)
@@ -196,12 +196,12 @@ class PolymarketAnalyzer:
def _ai_predict_probability(self, question: str, current_market_prob: float,
related_news: List, asset_data: Dict, language: str = 'zh-CN') -> Dict:
"""使用AI预测事件概率"""
"""Using AI to predict event probabilities"""
try:
# 根据语言设置构建prompt
# Build prompt based on language settings
is_english = language.lower() in ['en', 'en-us', 'en_us']
# 构建prompt
# build prompt
news_text = "\n".join([f"- {n.get('title', '')[:100]}" for n in related_news[:5]])
asset_text = ""
@@ -287,7 +287,7 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
system_prompt = "你是一个专业的市场分析师，擅长分析预测市场事件。请基于提供的数据，客观评估事件发生的概率。请使用中文回答。"
# 调用LLM
# Call LLM
messages = [
{
"role": "system",
@@ -305,13 +305,13 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
temperature=0.3
)
# 解析结果
# Parse results
if isinstance(result, str):
result = json.loads(result)
# 验证和规范化
# Validation and normalization
predicted_prob = float(result.get('predicted_probability', current_market_prob))
predicted_prob = max(0, min(100, predicted_prob)) # 限制在0-100
predicted_prob = max(0, min(100, predicted_prob)) # Limit to 0-100
confidence = float(result.get('confidence', 70))
confidence = max(0, min(100, confidence))
@@ -326,7 +326,7 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
except Exception as e:
logger.error(f"AI prediction failed: {e}", exc_info=True)
# 返回默认值
# Return to default value
return {
'predicted_probability': current_market_prob,
'confidence': 50.0,
@@ -338,28 +338,28 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
def _calculate_opportunity_score(self, ai_prob: float, market_prob: float,
confidence: float) -> float:
"""
计算机会评分（0-100）
Calculate opportunity rating (0-100)
逻辑：
- AI与市场差异越大，机会越好
- 置信度越高，机会越好
logic:
- The greater the difference between AI and the market, the better the opportunity
- The higher the confidence, the better the chance
"""
divergence = abs(ai_prob - market_prob)
# 差异越大，机会越好（最大40分）
# The bigger the difference, the better the chance (maximum 40 points)
divergence_score = min(divergence * 2, 40)
# 置信度越高，机会越好（最大60分）
# The higher the confidence level, the better the chance (maximum 60 points)
confidence_score = confidence * 0.6
return round(divergence_score + confidence_score, 2)
def _generate_recommendation(self, divergence: float, confidence: float) -> str:
"""
生成推荐：YES/NO/HOLD
Generate recommendations: YES/NO/HOLD
逻辑：
- AI概率 > 市场概率 + 5% 且置信度 > 60 → YES
- AI概率 < 市场概率 - 5% 且置信度 > 60 → NO
- 其他 → HOLD
logic:
- AI Probability > Market Probability + 5% and Confidence > 60 → YES
- AI probability < market probability - 5% and confidence level > 60 → NO
- Others → HOLD
"""
if divergence > 5 and confidence > 60:
return "YES"
@@ -369,7 +369,7 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
return "HOLD"
def _assess_risk(self, market: Dict, ai_result: Dict) -> str:
"""评估风险等级"""
"""Assess risk level"""
confidence = ai_result.get('confidence', 50)
divergence = abs(ai_result.get('predicted_probability', 50) - market.get('current_probability', 50))
@@ -381,19 +381,19 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
return "low"
def _get_related_news(self, question: str) -> List[Dict]:
"""获取相关问题相关的新闻"""
# 提取关键词
"""Get news on relevant issues"""
# Extract keywords
keywords = self._extract_keywords(question)
# 这里可以调用新闻API，暂时返回空列表
# 实际实现时可以调用现有的新闻服务
# Here you can call the news API and temporarily return an empty list
# In actual implementation, existing news services can be called
return []
def _identify_related_assets(self, question: str) -> List[str]:
"""识别问题中提到的相关资产"""
"""Identify related assets mentioned in the question"""
assets = []
# 加密货币关键词映射
# Cryptocurrency Keyword Mapping
crypto_keywords = {
'BTC': ['BTC', 'Bitcoin', 'bitcoin', 'btc'],
'ETH': ['ETH', 'Ethereum', 'ethereum', 'eth'],
@@ -412,11 +412,11 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
if any(kw in question_upper for kw in keywords):
assets.append(f"{symbol}/USDT")
# 去重
# Remove duplicates
return list(set(assets))
def _get_asset_data(self, assets: List[str]) -> Optional[Dict]:
"""获取资产数据（取第一个资产）"""
"""Get asset data (get the first asset)"""
if not assets:
return None
@@ -433,7 +433,7 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
return None
def _analyze_technical(self, asset_data: Dict) -> Dict:
"""简单的技术分析"""
"""simple technical analysis"""
if not asset_data:
return {
'trend': 'neutral',
@@ -445,7 +445,7 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
indicators = asset_data.get('indicators', {})
price_data = asset_data.get('price', {})
# 简单的趋势判断
# Simple trend judgment
rsi = indicators.get('rsi', {}).get('value', 50)
macd_signal = indicators.get('macd', {}).get('signal', 'neutral')
@@ -465,22 +465,22 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
}
def _infer_market(self, symbol: str) -> str:
"""推断市场类型"""
"""Infer market type"""
if '/' in symbol:
return "Crypto"
elif len(symbol) <= 5 and symbol.isupper():
return "USStock"
else:
return "Crypto" # 默认
return "Crypto" # default
def _extract_keywords(self, text: str) -> List[str]:
"""提取关键词"""
# 简单的关键词提取
"""Extract keywords"""
# Simple keyword extraction
words = re.findall(r'\b[A-Z][a-z]+\b|\b[A-Z]{2,}\b', text)
return [w.lower() for w in words if len(w) > 2]
def _get_cached_analysis(self, market_id: str, user_id: int = None) -> Optional[Dict]:
"""获取缓存的分析结果"""
"""Get cached analysis results"""
try:
with get_db_connection() as db:
cur = db.cursor()
@@ -506,7 +506,7 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
cur.close()
if row:
# RealDictCursor返回字典，使用键访问
#RealDictCursor returns the dictionary, accessed using keys
key_factors_raw = row.get('key_factors')
key_factors = []
if key_factors_raw:
@@ -551,19 +551,19 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
def _save_analysis_to_db(self, analysis: Dict, user_id: int = None, language: str = 'en-US', model: str = None):
"""
保存分析结果到数据库
Save analysis results to database
Args:
analysis: 分析结果字典
user_id: 用户ID
language: 语言设置
model: 使用的模型
analysis: dictionary of analysis results
user_id: user ID
language: language settings
model: model used
"""
try:
with get_db_connection() as db:
cur = db.cursor()
# 1. 保存到 qd_polymarket_ai_analysis 表（Polymarket专用表）
# 1. Save to qd_polymarket_ai_analysis table (Polymarket special table)
cur.execute("""
INSERT INTO qd_polymarket_ai_analysis
(market_id, user_id, ai_predicted_probability, market_probability,
@@ -584,7 +584,7 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
analysis.get('related_assets', [])
))
# 2. 同时保存到 qd_analysis_tasks 表（用于管理员统计和统一的历史记录查看）
# 2. Save to the qd_analysis_tasks table at the same time (for administrator statistics and unified historical record viewing)
market_info = analysis.get('market', {})
market_title = market_info.get('question', '') or market_info.get('title', '') or f"Polymarket Market {analysis['market_id']}"
result_json = json.dumps({
@@ -592,7 +592,7 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
'market_title': market_title,
'analysis': analysis,
'market': market_info,
'type': 'polymarket' # 标记为Polymarket分析
'type': 'polymarket' # Mark as Polymarket analysis
}, ensure_ascii=False)
cur.execute("""
@@ -602,8 +602,8 @@ IMPORTANT: All text in the JSON response (reasoning, key_factors, risk_factors)
RETURNING id
""", (
int(user_id) if user_id else 1,
'Polymarket', # market字段
str(analysis['market_id']), # symbol字段存储market_id
'Polymarket', # market field
str(analysis['market_id']), # symbol field stores market_id
str(model) if model else '',
str(language),
'completed',