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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/fast_analysis.py
+199 -199
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@@ -1,12 +1,12 @@
"""
Fast Analysis Service 3.0
系统性重构版本 - 使用统一的数据采集器
Systematic refactoring version - using unified data collector
核心改进：
1. 数据源统一 - 使用 MarketDataCollector，与K线模块、自选列表完全一致
2. 宏观数据 - 新增美元指数、VIX、利率等宏观经济指标
3. 多维新闻 - 使用结构化API，无需深度阅读
4. 单次LLM调用 - 强约束prompt，输出结构化分析
Core improvements:
1. Unified data sources - use MarketDataCollector, which is completely consistent with the K-line module and watch list
2. Macroeconomic data - added macroeconomic indicators such as the US dollar index, VIX, and interest rates
3. Multi-dimensional news - using structured API, no need for in-depth reading
4. Single LLM call - strong constraint prompt, output structured analysis
"""
import json
import os
@@ -185,12 +185,12 @@ def _is_major_geopolitical_news_text(combined_text: str) -> bool:
class FastAnalysisService:
"""
快速分析服务 3.0
Rapid Analysis Service 3.0
架构：
1. 数据采集层 - MarketDataCollector (统一数据源)
2. 分析层 - 单次LLM调用 (强约束prompt)
3. 记忆层 - 分析历史存储和检索
Architecture:
1. Data collection layer - MarketDataCollector (unified data source)
2. Analysis layer - single LLM call (strong constraint prompt)
3. Memory layer - analysis history storage and retrieval
"""
def __init__(self):
@@ -212,14 +212,14 @@ class FastAnalysisService:
timeout: int = 45,
) -> Dict[str, Any]:
"""
使用统一的数据采集器收集市场数据
Collect market data using a unified data collector
数据层次：
1. 核心数据: 价格、K线、技术指标
2. 基本面: 公司信息、财务数据
3. 宏观数据: DXY、VIX、TNX、黄金等
4. 情绪数据: 新闻、市场情绪
5. 预测市场: 相关预测市场事件（新增）
Data level:
1. Core data: price, K-line, technical indicators
2. Fundamentals: Company information, financial data
3. Macro data: DXY, VIX, TNX, gold, etc.
4. Sentiment data: news, market sentiment
5. Prediction market: related prediction market events (new)
"""
return self.data_collector.collect_all(
market=market,
@@ -227,8 +227,8 @@ class FastAnalysisService:
timeframe=timeframe,
include_macro=include_macro,
include_news=include_news,
include_polymarket=include_polymarket, # 包含预测市场数据
timeout=timeout, # 增加超时时间，确保数据收集完成
include_polymarket=include_polymarket, # Contains prediction market data
timeout=timeout, # Increase timeout to ensure data collection is complete
)
def _calculate_indicators(self, kline_data: List[Dict]) -> Dict[str, Any]:
@@ -678,13 +678,13 @@ IMPORTANT:
return system_prompt, user_prompt
def _format_financial_statements(self, statements: Dict[str, Any]) -> str:
"""格式化财务报表数据用于提示词"""
"""Formatting financial statement data for prompt words"""
if not statements:
return "财务报表数据暂不可用"
lines = []
# 资产负债表
# balance sheet
if 'balance_sheet' in statements:
bs = statements['balance_sheet']
lines.append("资产负债表 (Balance Sheet):")
@@ -702,7 +702,7 @@ IMPORTANT:
current_ratio = bs['current_assets'] / bs['current_liabilities'] if bs['current_liabilities'] > 0 else 0
lines.append(f" - 流动比率: {current_ratio:.2f}")
# 利润表
# income statement
if 'income_statement' in statements:
is_stmt = statements['income_statement']
lines.append("利润表 (Income Statement):")
@@ -717,7 +717,7 @@ IMPORTANT:
if is_stmt.get('eps'):
lines.append(f" - 每股收益: ${is_stmt['eps']:.2f}")
# 现金流量表
# cash flow statement
if 'cash_flow' in statements:
cf = statements['cash_flow']
lines.append("现金流量表 (Cash Flow):")
@@ -729,13 +729,13 @@ IMPORTANT:
return "\n".join(lines) if lines else "财务报表数据暂不可用"
def _format_earnings_data(self, earnings: Dict[str, Any]) -> str:
"""格式化盈利数据用于提示词"""
"""Format profit data for prompt words"""
if not earnings:
return "盈利数据暂不可用"
lines = []
# 历史盈利
# historical profit
if 'history' in earnings and earnings['history']:
lines.append("历史盈利 (Earnings History):")
for i, hist in enumerate(earnings['history'][:4], 1):
@@ -753,7 +753,7 @@ IMPORTANT:
line += f", 超预期={surprise_str}"
lines.append(line)
# 未来盈利
# future profit
if 'upcoming' in earnings:
upcoming = earnings['upcoming']
if upcoming.get('next_earnings_date'):
@@ -763,7 +763,7 @@ IMPORTANT:
if upcoming.get('revenue_estimate'):
lines.append(f" - 收入预期: ${upcoming['revenue_estimate']:,.0f}")
# 季度盈利
# quarterly profit
if 'quarterly' in earnings:
q = earnings['quarterly']
if q.get('latest_quarter'):
@@ -776,25 +776,25 @@ IMPORTANT:
return "\n".join(lines) if lines else "盈利数据暂不可用"
def _format_macro_summary(self, macro: Dict[str, Any], market: str) -> str:
"""格式化宏观数据摘要"""
"""Format macro data summaries"""
if not macro:
return "宏观数据暂不可用"
lines = []
# 美元指数
# dollar index
if 'DXY' in macro:
dxy = macro['DXY']
direction = "↑" if dxy.get('change', 0) > 0 else "↓"
lines.append(f"- {dxy.get('name', 'USD Index')}: {dxy.get('price', 'N/A')} ({direction}{abs(dxy.get('changePercent', 0)):.2f}%)")
# 美元强弱对不同资产的影响
# The impact of the strength of the U.S. dollar on different assets
if market == 'Crypto':
impact = "利空加密货币" if dxy.get('change', 0) > 0 else "利好加密货币"
lines.append(f" ⚠️ 美元{direction} {impact}")
elif market == 'Forex':
lines.append(f" ⚠️ 美元{direction} 直接影响外汇走势")
# VIX恐慌指数
# VIX panic index
if 'VIX' in macro:
vix = macro['VIX']
vix_value = vix.get('price', 0)
@@ -808,7 +808,7 @@ IMPORTANT:
level = "低波动 (<15)"
lines.append(f"- {vix.get('name', 'VIX')}: {vix_value:.2f} - {level}")
# 美债收益率
# U.S. Treasury yields
if 'TNX' in macro:
tnx = macro['TNX']
direction = "↑" if tnx.get('change', 0) > 0 else "↓"
@@ -816,19 +816,19 @@ IMPORTANT:
if tnx.get('price', 0) > 4.5:
lines.append(" ⚠️ 高利率环境，对估值不利")
# 黄金
# gold
if 'GOLD' in macro:
gold = macro['GOLD']
direction = "↑" if gold.get('change', 0) > 0 else "↓"
lines.append(f"- {gold.get('name', 'Gold')}: ${gold.get('price', 'N/A'):.2f} ({direction}{abs(gold.get('changePercent', 0)):.2f}%)")
# 标普500
# S&P 500
if 'SPY' in macro:
spy = macro['SPY']
direction = "↑" if spy.get('change', 0) > 0 else "↓"
lines.append(f"- {spy.get('name', 'S&P 500')}: ${spy.get('price', 'N/A'):.2f} ({direction}{abs(spy.get('changePercent', 0)):.2f}%)")
# 比特币 (作为风险指标)
# Bitcoin (as a risk indicator)
if 'BTC' in macro and market != 'Crypto':
btc = macro['BTC']
direction = "↑" if btc.get('change', 0) > 0 else "↓"
@@ -876,8 +876,8 @@ IMPORTANT:
logger.info(f"Fast analysis starting: {market}:{symbol}")
# Consensus timeframes:
# - 默认：用用户传入的 timeframe 作为主周期，再加一个上层周期（1D/4H）提升稳定性
# - 也允许通过 env 覆盖（逗号分隔），例如 AI_ANALYSIS_CONSENSUS_TIMEFRAMES=1D,4H
# - Default: use the timeframe passed in by the user as the main cycle, and add an upper cycle (1D/4H) to improve stability
# - Overriding via env (comma separated) is also allowed, e.g. AI_ANALYSIS_CONSENSUS_TIMEFRAMES=1D,4H
env_tfs = os.getenv("AI_ANALYSIS_CONSENSUS_TIMEFRAMES", "").strip()
if env_tfs:
consensus_timeframes = [t.strip() for t in env_tfs.split(",") if t.strip()]
@@ -1067,16 +1067,16 @@ IMPORTANT:
# Validate we have essential data - with fallback to indicators
current_price = None
# 优先从 price 数据获取
# Get it from price data first
if data.get("price") and data["price"].get("price"):
current_price = data["price"]["price"]
# Fallback: 从 indicators 获取 (如果 K 线成功计算了)
# Fallback: Get from indicators (if the K-line is calculated successfully)
if not current_price and data.get("indicators"):
current_price = data["indicators"].get("current_price")
if current_price:
logger.info(f"Using price from indicators: ${current_price}")
# 构建简化的 price 数据
# Build simplified price data
data["price"] = {
"price": current_price,
"change": 0,
@@ -1084,7 +1084,7 @@ IMPORTANT:
"source": "indicators_fallback"
}
# Fallback: 从 kline 最后一根获取
# Fallback: Get from the last kline
if not current_price and data.get("kline"):
klines = data["kline"]
if klines and len(klines) > 0:
@@ -1346,13 +1346,13 @@ IMPORTANT:
"take_profit": analysis.get("take_profit"),
"position_size_pct": analysis.get("position_size_pct", 10),
"timeframe": analysis.get("timeframe", "medium"),
# camelCase + 语义别名：供私有前端/旧版组件绑定（勿用 indicators.trading_levels 充当计划）
# camelCase + semantic alias: for private front-end/legacy component binding (do not use indicators.trading_levels as a plan)
"entryPrice": analysis.get("entry_price"),
"stopLoss": analysis.get("stop_loss"),
"takeProfit": analysis.get("take_profit"),
"positionSizePct": analysis.get("position_size_pct", 10),
"decision": str(analysis.get("decision", "HOLD") or "HOLD").upper(),
# 与 stop_loss / take_profit 数值相同；命名强调「亏损离场 / 盈利目标」避免与多单参考线混淆
# The same value as stop_loss / take_profit; the naming emphasizes "loss exit / profit target" to avoid confusion with the long order reference line
"loss_exit_price": analysis.get("stop_loss"),
"profit_target_price": analysis.get("take_profit"),
},
@@ -1398,12 +1398,12 @@ IMPORTANT:
def _build_decision_guidance(self, rsi_value: float, macd_signal: str, ma_trend: str, change_24h: float) -> str:
"""
根据技术指标构建决策指导，帮助AI做出更合理的决策。
强调SELL信号是有效的做空机会。
Build decision guidance based on technical indicators to help AI make more reasonable decisions.
Emphasize that the SELL signal is an effective short selling opportunity.
"""
guidance_parts = []
# RSI 指导 - 更积极地识别做空机会
# RSI Guidance - Identify shorting opportunities more aggressively
if rsi_value > 70:
guidance_parts.append("🔴 RSI > 70 (超买): 强烈建议SELL做空，避免BUY")
elif rsi_value > 60:
@@ -1415,7 +1415,7 @@ IMPORTANT:
else:
guidance_parts.append("⚪ RSI 40-60 (中性): 技术面中性，需要结合其他指标判断")
# MACD 指导 - 明确做空信号
# MACD Guidance - Clear Short Signal
if macd_signal == "bullish":
guidance_parts.append("🟢 MACD 看涨: 支持BUY做多")
elif macd_signal == "bearish":
@@ -1423,7 +1423,7 @@ IMPORTANT:
else:
guidance_parts.append("⚪ MACD 中性: 无明显方向")
# MA 趋势指导 - 识别趋势反转机会
# MA Trend Guidance - Identify Trend Reversal Opportunities
if "uptrend" in ma_trend.lower() or "strong_uptrend" in ma_trend.lower():
if rsi_value > 60:
guidance_parts.append("⚠️ 均线向上但RSI超买: 可能接近顶部，考虑SELL做空")
@@ -1434,13 +1434,13 @@ IMPORTANT:
else:
guidance_parts.append("⚪ 均线横盘: 趋势不明确")
# 24小时涨跌幅指导 - 识别过度波动
# 24-hour price range guidance - identifying excessive volatility
if change_24h > 5:
guidance_parts.append("🔴 24h涨幅 > 5%: 可能已过度上涨，建议SELL做空或获利了结")
elif change_24h < -5:
guidance_parts.append("🟢 24h跌幅 > 5%: 可能已过度下跌，可以考虑BUY做多")
# 综合建议
# Comprehensive suggestions
sell_signals = sum([
rsi_value > 60,
macd_signal == "bearish",
@@ -1465,14 +1465,14 @@ IMPORTANT:
def _has_major_news(self, news_data: List[Dict]) -> bool:
"""
检查是否有重大新闻事件。
重大新闻包括：监管变化、重大合作、丑闻、重大政策、地缘政治事件等。
地缘类使用词边界与分级，避免 toward/extension/us 等子串误判。
Check for breaking news events.
Breaking news includes: regulatory changes, major collaborations, scandals, major policies, geopolitical events, etc.
The geographical category uses word boundaries and classification to avoid misjudgment of substrings such as toward/extension/us.
"""
if not news_data:
return False
# 子串关键词（较长词或中文，避免过短英文误匹配）
# Substring keywords (longer words or Chinese to avoid mismatching of too short English)
major_keywords = [
"regulation", "regulatory", "approval", "policy", "government", "central bank",
"监管", "禁令", "批准", "政策", "政府", "央行",
@@ -1481,7 +1481,7 @@ IMPORTANT:
"sanctions", "embargo", "制裁", "中东", "海湾", "北约",
"united states", "middle east",
]
# 短英文词用词边界匹配（不用裸子串）
# Use word boundary matching for short English words (without using naked substrings)
major_short_patterns = [
re.compile(r"\b(?:ban|banned|banning)\b", re.I),
re.compile(r"\b(?:crisis|crises)\b", re.I),
@@ -1510,31 +1510,31 @@ IMPORTANT:
def _has_macro_event(self, macro_data: Dict, market: str) -> bool:
"""
检查是否有重大宏观事件。
重大宏观事件包括：VIX异常高、DXY大幅波动、利率政策变化等。
Check for major macro events.
Major macro events include: abnormally high VIX, large fluctuations in DXY, changes in interest rate policies, etc.
"""
if not macro_data:
return False
# 检查VIX（恐慌指数）
# Check the VIX (fear index)
if "VIX" in macro_data:
vix = macro_data["VIX"]
vix_value = vix.get("price", 0)
if vix_value > 30: # VIX > 30 表示极度恐慌
if vix_value > 30: # VIX > 30 indicates extreme panic
return True
# 检查DXY大幅波动（>1%）
# Check for large DXY swings (>1%)
if "DXY" in macro_data:
dxy = macro_data["DXY"]
change_pct = abs(dxy.get("changePercent", 0))
if change_pct > 1.0: # 美元指数波动超过1%
if change_pct > 1.0: # The U.S. dollar index fluctuates more than 1%
return True
# 检查利率变化（对股票和加密货币影响大）
# Check for interest rate changes (big impact on stocks and cryptocurrencies)
if "TNX" in macro_data and market in ["USStock", "Crypto"]:
tnx = macro_data["TNX"]
change_pct = abs(tnx.get("changePercent", 0))
if change_pct > 2.0: # 利率变化超过2%
if change_pct > 2.0: # Interest rates change by more than 2%
return True
return False
@@ -1688,7 +1688,7 @@ IMPORTANT:
else:
analysis["decision"] = decision
# 基于技术指标验证决策合理性（允许宏观/新闻因素覆盖）
# Validate decision-making rationality based on technical indicators (allow macro/news factor coverage)
if indicators:
analysis = self._validate_decision_against_indicators(
analysis, indicators, confidence,
@@ -1704,14 +1704,14 @@ IMPORTANT:
def _validate_decision_against_indicators(self, analysis: Dict, indicators: Dict, confidence: int,
has_major_news: bool = False, has_macro_event: bool = False) -> Dict:
"""
根据技术指标验证决策的合理性，但允许宏观/新闻因素覆盖技术指标。
Justify decisions against technical indicators, but allow macro/news factors to override technical indicators.
Args:
analysis: AI分析结果
indicators: 技术指标数据
confidence: 置信度
has_major_news: 是否有重大新闻事件
has_macro_event: 是否有重大宏观事件
analysis: AI analysis results
indicators: technical indicator data
confidence: confidence
has_major_news: Is there a major news event?
has_macro_event: Is there a major macro event?
"""
decision = analysis.get("decision", "HOLD")
rsi_data = indicators.get("rsi", {})
@@ -1722,70 +1722,70 @@ IMPORTANT:
macd_signal = macd_data.get("signal", "neutral")
ma_trend = ma_data.get("trend", "sideways")
# 如果置信度太低，强制改为HOLD
# If the confidence level is too low, force it to HOLD
if confidence < 60:
if decision != "HOLD":
logger.warning(f"Decision {decision} with low confidence {confidence}, forcing to HOLD")
analysis["decision"] = "HOLD"
analysis["confidence"] = max(confidence, 45) # 降低置信度
analysis["confidence"] = max(confidence, 45) # Reduce confidence
return analysis
# 如果有重大新闻或宏观事件，允许覆盖技术指标（但记录警告）
# Allows technical indicators to be overridden (but logs warnings) if there is major news or macro events
allow_override = has_major_news or has_macro_event
# 检查BUY决策是否与技术指标矛盾
# Check whether the BUY decision conflicts with technical indicators
if decision == "BUY":
conflicts = []
# RSI > 70 时不应该BUY（除非有重大利好）
# You should not buy when RSI > 70 (unless there is a major upside)
if rsi_value > 70:
conflicts.append(f"RSI {rsi_value:.1f} > 70 (超买)")
# MACD看跌时不应该BUY（除非有重大利好）
# You should not BUY when MACD is bearish (unless there is a major upside)
if macd_signal == "bearish":
conflicts.append("MACD bearish")
# 均线趋势向下时不应该BUY（除非有重大利好）
# 只有当趋势非常强烈时才认为是冲突（避免过于敏感）
# You should not buy when the moving average trend is downward (unless there is a major benefit)
# Only consider a conflict if the trend is very strong (avoid being too sensitive)
if "strong_downtrend" in ma_trend.lower() or ("downtrend" in ma_trend.lower() and rsi_value > 50):
conflicts.append(f"MA trend: {ma_trend}")
if conflicts:
if allow_override:
# 允许覆盖，但降低置信度并添加说明
# Allow override, but lower confidence and add description
logger.info(f"BUY decision conflicts with indicators but major news/macro event allows override: {', '.join(conflicts)}")
analysis["confidence"] = max(confidence - 15, 50)
original_summary = analysis.get("summary", "")
analysis["summary"] = f"{original_summary} [注意：技术指标显示{', '.join(conflicts)}，但重大事件可能改变趋势]"
else:
# 没有重大事件，强制改为HOLD
# If there is no major event, it is forced to be changed to HOLD.
logger.warning(f"BUY decision conflicts with indicators and no major event: {', '.join(conflicts)}. Forcing to HOLD")
analysis["decision"] = "HOLD"
analysis["confidence"] = max(confidence - 20, 40)
original_summary = analysis.get("summary", "")
analysis["summary"] = f"{original_summary} [注意：技术指标显示{', '.join(conflicts)}，建议观望]"
# 检查SELL决策是否与技术指标矛盾（放宽限制，因为SELL是有效的做空机会）
# Check whether SELL decisions contradict technical indicators (relax restrictions because SELL is a valid short opportunity)
elif decision == "SELL":
conflicts = []
# 只有在强烈看涨信号时才阻止SELL（放宽条件）
# RSI < 30 且 MACD看涨 且 均线向上时，才认为矛盾
# Only block SELL (relax conditions) if there is a strong bullish signal
# It is considered a contradiction when RSI < 30 and MACD is bullish and the moving average is upward.
if rsi_value < 30 and macd_signal == "bullish" and "uptrend" in ma_trend.lower():
conflicts.append(f"Strong bullish signals (RSI {rsi_value:.1f} < 30, MACD bullish, uptrend)")
# 或者 RSI < 30 且 均线强烈向上
# Or RSI < 30 and the moving average is strongly upward
elif rsi_value < 30 and "strong_uptrend" in ma_trend.lower():
conflicts.append(f"Very strong uptrend with oversold RSI {rsi_value:.1f}")
if conflicts:
if allow_override:
# 允许覆盖，但降低置信度并添加说明
# Allow override, but lower confidence and add description
logger.info(f"SELL decision conflicts with strong bullish indicators but major news/macro event allows override: {', '.join(conflicts)}")
analysis["confidence"] = max(confidence - 15, 50)
original_summary = analysis.get("summary", "")
analysis["summary"] = f"{original_summary} [注意：技术指标显示{', '.join(conflicts)}，但重大事件可能改变趋势]"
else:
# 只有在非常强烈的看涨信号时才改为HOLD
# Only change to HOLD if there is a very strong bullish signal
logger.warning(f"SELL decision conflicts with very strong bullish indicators: {', '.join(conflicts)}. Forcing to HOLD")
analysis["decision"] = "HOLD"
analysis["confidence"] = max(confidence - 20, 40)
@@ -1796,16 +1796,16 @@ IMPORTANT:
def _calculate_objective_score(self, data: Dict[str, Any], current_price: float) -> Dict[str, float]:
"""
基于客观数据计算量化评分系统
Calculates a quantitative scoring system based on objective data
返回一个-100到+100的分数：
- +100: 强烈利多（强烈BUY）
- +70到+100: 强烈利多（强烈BUY）
- +40到+70: 利多（BUY）
- -40到+40: 中性（HOLD）
- -70到-40: 利空（SELL）
- -100到-70: 强烈利空（强烈SELL）
- -100: 强烈利空（强烈SELL）
Return a score between -100 and +100:
- +100: Strong bullish (strong BUY)
- +70 to +100: Strong bullish (strong BUY)
- +40 to +70: BUY
- -40 to +40: Neutral (HOLD)
- -70 to -40: SELL
- -100 to -70: Strongly bearish (strongly SELL)
- -100: Strongly bearish (strongly SELL)
"""
indicators = data.get("indicators") or {}
fundamental = data.get("fundamental") or {}
@@ -1813,27 +1813,27 @@ IMPORTANT:
macro = data.get("macro") or {}
price_data = data.get("price") or {}
# 1. 技术指标评分 (-100 to +100)
# 1. Technical indicator score (-100 to +100)
technical_score = self._calculate_technical_score(indicators, price_data)
# 2. 基本面评分 (-100 to +100)
# 2. Fundamental score (-100 to +100)
fundamental_score = self._calculate_fundamental_score(fundamental, data.get("market", ""))
# 3. 新闻情绪评分 (-100 to +100)
# 3. News sentiment score (-100 to +100)
sentiment_score = self._calculate_sentiment_score(news)
# 4. 宏观环境评分 (-100 to +100)
# 4. Macro environment score (-100 to +100)
macro_score = self._calculate_macro_score(macro, data.get("market", ""))
# 5. 综合评分（加权平均）
# 优化权重：默认技术35%，基本面20%，情绪25%（包含地缘政治），宏观20%（提高宏观权重）
# 但要做“可用信息重加权”：当某些模块缺失（如新闻/宏观没取到），不要用0分去稀释整体强度，
# 而是重新归一化权重，让技术信号在缺失时仍可发挥主导作用。
# 5. Comprehensive rating (weighted average)
# Optimization weight: Default technical 35%, fundamentals 20%, sentiment 25% (including geopolitics), macro 20% (increase macro weight)
# But we need to "reweight the available information": when some modules are missing (such as news/macro is not obtained), do not use 0 points to dilute the overall strength.
# Instead, the weights are renormalized so that technical signals can still play a leading role in their absence.
market_type = str(data.get("market") or "")
fundamental_present = (market_type == "USStock") and bool(fundamental)
sentiment_present = bool(news)
macro_present = bool(macro)
# indicators 一旦成功计算通常就存在，但这里也做一次保护
# indicators usually exist once they are successfully calculated, but they are also protected here.
technical_present = bool(indicators)
weights = {
@@ -1897,28 +1897,28 @@ IMPORTANT:
return cfg
def _calculate_technical_score(self, indicators: Dict, price_data: Dict) -> float:
"""计算技术指标评分 (-100 to +100)"""
"""Calculate technical indicator score (-100 to +100)"""
score = 0.0
weight_sum = 0.0
# RSI 评分 (-50 to +50)
# RSI score (-50 to +50)
rsi_data = indicators.get("rsi", {})
rsi_value = rsi_data.get("value", 50)
if rsi_value > 0:
if rsi_value > 70:
rsi_score = -50 # 超买，强烈利空
rsi_score = -50 # Overbought, strongly bearish
elif rsi_value > 60:
rsi_score = -30 # 偏超买，利空
rsi_score = -30 # Overbought, negative
elif rsi_value < 30:
rsi_score = +50 # 超卖，强烈利多
rsi_score = +50 # Oversold, strongly bullish
elif rsi_value < 40:
rsi_score = +30 # 偏超卖，利多
rsi_score = +30 # Oversold, bullish
else:
rsi_score = (50 - rsi_value) * 0.6 # 40-60之间，线性映射
rsi_score = (50 - rsi_value) * 0.6 # Between 40-60, linear mapping
score += rsi_score * 0.30
weight_sum += 0.30
# MACD 评分 (-40 to +40)
# MACD score (-40 to +40)
macd_data = indicators.get("macd", {})
macd_signal = macd_data.get("signal", "neutral")
if macd_signal == "bullish":
@@ -1930,7 +1930,7 @@ IMPORTANT:
score += macd_score * 0.25
weight_sum += 0.25
# 均线趋势评分 (-40 to +40)
# Moving average trend score (-40 to +40)
ma_data = indicators.get("moving_averages", {})
ma_trend = ma_data.get("trend", "sideways")
if "strong_uptrend" in ma_trend.lower():
@@ -1946,36 +1946,36 @@ IMPORTANT:
score += ma_score * 0.25
weight_sum += 0.25
# 24小时涨跌幅评分 (-20 to +20)
# 24-hour rise and fall score (-20 to +20)
change_24h = price_data.get("changePercent", 0)
if change_24h > 10:
change_score = -20 # 过度上涨，利空
change_score = -20 # Excessive rise is bad
elif change_24h > 5:
change_score = -10
elif change_24h < -10:
change_score = +20 # 过度下跌，利多
change_score = +20 # Excessive decline, bullish
elif change_24h < -5:
change_score = +10
else:
change_score = change_24h * 2 # 线性映射
change_score = change_24h * 2 # linear mapping
score += change_score * 0.20
weight_sum += 0.20
# ========== 额外技术特征（轻量增强，不改变主体结构） ==========
# 这些特征来自 MarketDataCollector._calculate_indicators 的输出：
# - price_position: 过去20根K线区间位置 0~100
# - volume_ratio: 最新成交量 / 20期均量
# ========== Additional technical features (lightweight enhancement, no change to the main structure) ==========
# These characteristics come from the output of MarketDataCollector._calculate_indicators:
# - price_position: range position of the past 20 K-lines 0~100
# - volume_ratio: latest trading volume / average volume of 20 periods
# - bollinger: BB_upper/BB_lower/BB_width
# - volatility: atr, pct
extra_score = 0.0
extra_weight = 0.0
# 1) 区间位置：接近区间顶部更偏利空，接近区间底部更偏利多
# 1) Range position: Close to the top of the range is more bearish, and close to the bottom of the range is more bullish.
try:
pp = float(indicators.get("price_position", 50.0))
# 0~100 -> -15~+15 (线性映射，中心50为0)
# 0~100 -> -15~+15 (linear mapping, center 50 is 0)
pp_score = (50.0 - pp) * 0.3
# 在极端区域增强信号
# Boost signal in extreme areas
if pp >= 85:
pp_score -= 5
elif pp <= 15:
@@ -1985,7 +1985,7 @@ IMPORTANT:
except Exception:
pass
# 2) 布林带触及：突破上轨偏利空，跌破下轨偏利多
# 2) The Bollinger Bands are touched: a breakthrough of the upper band is negative, and a fall below the lower band is positive.
try:
cur_px = float(indicators.get("current_price") or price_data.get("price") or 0.0)
bb = indicators.get("bollinger") or {}
@@ -2006,7 +2006,7 @@ IMPORTANT:
except Exception:
pass
# 3) 成交量放大：在趋势方向上加分，逆趋势减分（弱信号）
# 3) Trading volume amplification: plus points in the direction of the trend and minus points against the trend (weak signal)
try:
vr = float(indicators.get("volume_ratio") or 1.0)
trend = str(indicators.get("trend") or indicators.get("moving_averages", {}).get("trend") or "").lower()
@@ -2018,22 +2018,22 @@ IMPORTANT:
extra_score += -8
extra_weight += 0.15
else:
# 放量但无趋势：更偏不确定，略微降低（当作偏利空风险）
# Large volume but no trend: more uncertain, slightly lower (considered to be a bearish risk)
extra_score += -3
extra_weight += 0.10
elif vr <= 0.6:
# 缩量：趋势信号可信度下降（轻微回归到0）
# Shrinkage: The credibility of the trend signal decreases (slight return to 0)
extra_score += 0
extra_weight += 0.05
except Exception:
pass
# 4) 高波动：减少强方向自信（用“缩放”形式实现，避免硬反转）
# 4) High volatility: reduce strong directional confidence (implemented in the form of "scaling" to avoid hard reversals)
try:
vol = indicators.get("volatility") or {}
vol_pct = float(vol.get("pct") or 0.0)
if vol_pct >= 6.0:
# 极高波动：把额外分数打折，并轻微把总体拉回0
# Extremely High Volatility: Discounts extra points and slightly brings the total back to 0
extra_score *= 0.6
score *= 0.92
elif vol_pct >= 3.5:
@@ -2049,29 +2049,29 @@ IMPORTANT:
score += extra_norm * 0.15
weight_sum += 0.15
# 归一化到-100到+100
# Normalized to -100 to +100
if weight_sum > 0:
score = score / weight_sum * 100
return max(-100, min(100, score))
def _calculate_fundamental_score(self, fundamental: Dict, market: str) -> float:
"""计算基本面评分 (-100 to +100)"""
"""Calculate fundamental score (-100 to +100)"""
if market != "USStock" or not fundamental:
return 0.0 # 非美股或无基本面数据，返回中性
return 0.0 # Non-U.S. stocks or no fundamental data, return neutral
score = 0.0
factors = 0
# PE Ratio 评分
# PE Ratio score
pe_ratio = fundamental.get("pe_ratio")
if pe_ratio and pe_ratio > 0:
if pe_ratio < 15:
pe_score = +20 # 低PE，利多
pe_score = +20 # Low PE, bullish
elif pe_ratio < 25:
pe_score = +10
elif pe_ratio > 50:
pe_score = -20 # 高PE，利空
pe_score = -20 # High PE, negative
elif pe_ratio > 35:
pe_score = -10
else:
@@ -2079,15 +2079,15 @@ IMPORTANT:
score += pe_score
factors += 1
# ROE 评分
# ROE score
roe = fundamental.get("roe")
if roe:
if roe > 20:
roe_score = +20 # 高ROE，利多
roe_score = +20 # High ROE, Rita
elif roe > 15:
roe_score = +10
elif roe < 5:
roe_score = -20 # 低ROE，利空
roe_score = -20 # Low ROE, bad news
elif roe < 10:
roe_score = -10
else:
@@ -2095,15 +2095,15 @@ IMPORTANT:
score += roe_score
factors += 1
# 营收增长评分
# revenue growth score
revenue_growth = fundamental.get("revenue_growth")
if revenue_growth:
if revenue_growth > 20:
growth_score = +20 # 高增长，利多
growth_score = +20 # High growth, good news
elif revenue_growth > 10:
growth_score = +10
elif revenue_growth < -10:
growth_score = -20 # 负增长，利空
growth_score = -20 # negative growth, bad
elif revenue_growth < 0:
growth_score = -10
else:
@@ -2111,15 +2111,15 @@ IMPORTANT:
score += growth_score
factors += 1
# 利润率评分
# Profitability score
profit_margin = fundamental.get("profit_margin")
if profit_margin:
if profit_margin > 20:
margin_score = +15 # 高利润率，利多
margin_score = +15 # High profit margin, profit
elif profit_margin > 10:
margin_score = +7
elif profit_margin < 0:
margin_score = -15 # 亏损，利空
margin_score = -15 # loss, bad
elif profit_margin < 5:
margin_score = -7
else:
@@ -2127,31 +2127,31 @@ IMPORTANT:
score += margin_score
factors += 1
# 债务权益比评分
# Debt to Equity Ratio Score
debt_to_equity = fundamental.get("debt_to_equity")
if debt_to_equity:
if debt_to_equity < 0.5:
debt_score = +10 # 低负债，利多
debt_score = +10 # Low debt, good profits
elif debt_to_equity > 2.0:
debt_score = -10 # 高负债，利空
debt_score = -10 # High debt, bad
else:
debt_score = 0
score += debt_score
factors += 1
# 归一化（如果有多个因素）
# Normalization (if there are multiple factors)
if factors > 0:
score = score / factors * 100 / 4 # 最大可能分数是4个因素各20分=80，归一化到100
score = score / factors * 100 / 4 # The maximum possible score is 20 points for each of the 4 factors = 80, normalized to 100
return max(-100, min(100, score))
def _calculate_sentiment_score(self, news: List[Dict]) -> float:
"""
计算新闻情绪评分 (-100 to +100)
地缘/冲突类：词边界 + 分级惩罚，单条封顶，避免 extension/toward 等误判叠加。
Calculate news sentiment score (-100 to +100)
Geographical/conflict category: word boundary + hierarchical punishment, single capping, to avoid superposition of misjudgments such as extension/toward.
"""
if not news:
return 0.0 # 无新闻，中性
return 0.0 # No news, neutral
positive_count = 0
negative_count = 0
@@ -2210,55 +2210,55 @@ IMPORTANT:
def _calculate_macro_score(self, macro: Dict, market: str) -> float:
"""
计算宏观环境评分 (-100 to +100)
包含VIX、DXY、利率等宏观经济指标
Calculate macro environment score (-100 to +100)
Contains macroeconomic indicators such as VIX, DXY, interest rates, etc.
"""
if not macro:
return 0.0 # 无宏观数据，中性
return 0.0 # No macro data, neutral
score = 0.0
factors = 0
# VIX 评分（恐慌指数）- 权重提高
# VIX score (fear index) - increased weight
vix = macro.get("VIX", {})
vix_value = vix.get("price", 0)
if vix_value > 0:
if vix_value > 35:
vix_score = -50 # 极高恐慌（如战争期间），严重利空
vix_score = -50 # Extremely high panic (such as during a war), severely negative
elif vix_value > 30:
vix_score = -40 # 高恐慌，严重利空
vix_score = -40 # High panic, serious negative news
elif vix_value > 25:
vix_score = -30 # 较高恐慌，利空
vix_score = -30 # Higher panic, bad news
elif vix_value > 20:
vix_score = -15 # 中等恐慌，轻微利空
vix_score = -15 # Moderate panic, slightly negative
elif vix_value < 12:
vix_score = +20 # 低恐慌，利多
vix_score = +20 # Low panic, bullish
elif vix_value < 15:
vix_score = +10 # 较低恐慌，轻微利多
vix_score = +10 # Lower panic, slightly bullish
else:
vix_score = 0
score += vix_score
factors += 1
# DXY 评分（美元指数）- 权重提高
# DXY Score (USD Index) - Increased weighting
dxy = macro.get("DXY", {})
dxy_value = dxy.get("price", 0)
dxy_change = dxy.get("changePercent", 0)
if dxy_value > 0:
# 对于加密货币和商品，强美元通常是利空
# For Cryptocurrencies and Commodities, A Strong USD Is Typically Bearish
if market in ["Crypto", "Forex", "Futures"]:
if dxy_change > 2:
dxy_score = -30 # 美元大幅走强，严重利空
dxy_score = -30 # The sharp strengthening of the US dollar is seriously negative
elif dxy_change > 1:
dxy_score = -20 # 美元走强，利空
dxy_score = -20 # A stronger U.S. dollar is a negative
elif dxy_change < -2:
dxy_score = +30 # 美元大幅走弱，利多
dxy_score = +30 # The U.S. dollar weakens sharply, which is bullish
elif dxy_change < -1:
dxy_score = +20 # 美元走弱，利多
dxy_score = +20 # A weaker dollar is bullish
else:
dxy_score = 0
else:
# 对股票也有影响，但较小
# It also has an impact on stocks, but it’s smaller
if dxy_change > 2:
dxy_score = -10
elif dxy_change < -2:
@@ -2268,21 +2268,21 @@ IMPORTANT:
score += dxy_score
factors += 1
# 利率评分（TNX）- 权重提高
# Interest Rate Score (TNX) - Increased weighting
tnx = macro.get("TNX", {})
tnx_change = tnx.get("changePercent", 0)
tnx_value = tnx.get("price", 0)
if tnx_change != 0 or tnx_value > 0:
# 利率上升对成长股和加密货币通常是利空
# Rising interest rates are generally negative for growth stocks and cryptocurrencies
if market in ["Crypto", "USStock"]:
if tnx_change > 3:
tnx_score = -30 # 利率大幅上升，严重利空
tnx_score = -30 # Interest rates rise sharply, which is seriously negative
elif tnx_change > 2:
tnx_score = -20 # 利率上升，利空
tnx_score = -20 # Rising interest rates are bad
elif tnx_change < -3:
tnx_score = +30 # 利率大幅下降，利多
tnx_score = +30 # A sharp drop in interest rates is bullish
elif tnx_change < -2:
tnx_score = +20 # 利率下降，利多
tnx_score = +20 # Falling interest rates are bullish
else:
tnx_score = 0
else:
@@ -2290,7 +2290,7 @@ IMPORTANT:
score += tnx_score
factors += 1
# 恐惧贪婪指数（更适合 Crypto）：极端贪婪偏利空，极端恐惧偏利多（弱信号）
# Fear and greed index (more suitable for Crypto): extreme greed is negative, extreme fear is bullish (weak signal)
try:
fg = macro.get("FEAR_GREED", {}) or {}
fg_value = float(fg.get("price") or 0.0)
@@ -2310,12 +2310,12 @@ IMPORTANT:
except Exception:
pass
# 归一化（考虑权重）
# Normalization (considering weights)
if factors > 0:
# 最大可能分数：VIX(-50~+20), DXY(-30~+30), TNX(-30~+30) = 约-110到+80
# 归一化到-100到+100
# 加上 Fear&Greed 的幅度（约 15），给点 buffer
max_possible = 125 # 最大绝对值
# Maximum possible score: VIX(-50~+20), DXY(-30~+30), TNX(-30~+30) = about -110 to +80
# Normalized to -100 to +100
# Add the amplitude of Fear&Greed (about 15) and give some buffer
max_possible = 125 # maximum absolute value
score = score / max_possible * 100
return max(-100, min(100, score))
@@ -2330,23 +2330,23 @@ IMPORTANT:
def _score_to_decision(self, score: float, *, market: str = "Crypto") -> str:
"""
根据客观评分转换为决策
Transformed into decisions based on objective scoring
优化后的阈值（大幅缩小HOLD区间，使决策更明确）：
- score >= +20: BUY（利多）
- score <= -20: SELL（利空）
- -20 < score < +20: HOLD（中性）
Optimized threshold (significantly narrows the HOLD interval to make decisions clearer):
- score >= +20: BUY (profit)
- score <= -20: SELL (bad)
- -20 < score < +20: HOLD (neutral)
分级决策（用于更细粒度的判断）：
- score >= +70: 强烈BUY
- +40 <= score < +70: 明显BUY
Hierarchical decision-making (for finer-grained judgment):
- score >= +70: strong BUY
- +40 <= score < +70: obvious BUY
- +20 <= score < +40: BUY
- +10 < score < +20: 弱利多（倾向于BUY，但可HOLD）
- -10 <= score <= +10: 中性HOLD（真正的中性区间）
- -20 < score < -10: 弱利空（倾向于SELL，但可HOLD）
- +10 < score < +20: Weak profit and long (tend to BUY, but can HOLD)
- -10 <= score <= +10: Neutral HOLD (true neutral interval)
- -20 < score < -10: Weakly bearish (inclined to SELL, but can be HOLD)
- -40 < score <= -20: SELL
- -70 < score <= -40: 明显SELL
- score <= -70: 强烈SELL
- -70 < score <= -40: obviously SELL
- score <= -70: Strong SELL
"""
cfg = self._get_ai_calibration(market=market)
buy_thr = float(cfg.get("buy_threshold") or 20.0)
@@ -2361,14 +2361,14 @@ IMPORTANT:
def _calculate_overall_score(self, analysis: Dict) -> int:
"""Calculate weighted overall score (legacy method, now uses objective score if available)."""
# 优先使用客观评分
# Prioritize objective scoring
if "objective_score" in analysis:
objective = analysis["objective_score"]
overall = objective.get("overall_score", 50)
# 转换为0-100格式（原系统使用）
# Convert to 0-100 format (used by the original system)
return max(0, min(100, int(50 + overall * 0.5)))
# 降级到LLM评分
# Downgraded to LLM rating
tech = analysis.get("technical_score", 50)
fund = analysis.get("fundamental_score", 50)
sent = analysis.get("sentiment_score", 50)