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v2.2.2

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Signed-off-by: TIANHE <TIANHE@GMAIL.COM>
This commit is contained in:
TIANHE
2026-03-01 03:42:10 +08:00
parent 7c067fe61c
commit a6ea4d967c
77 changed files with 3368 additions and 193 deletions
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backend_api_python/app/services/fast_analysis.py
+198 -69
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@@ -46,6 +46,7 @@ class FastAnalysisService:
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）
"""
return self.data_collector.collect_all(
market=market,
@@ -53,6 +54,7 @@ class FastAnalysisService:
timeframe=timeframe,
include_macro=True,
include_news=True,
include_polymarket=True, # 包含预测市场数据
timeout=30
)
@@ -196,6 +198,19 @@ class FastAnalysisService:
return "\n".join(summaries) if summaries else "No recent news available."
def _format_polymarket_summary(self, polymarket_events: List[Dict], max_items: int = 3) -> str:
"""Format prediction market events into a concise summary for the prompt."""
if not polymarket_events:
return "No related prediction market events found."
summaries = []
for event in polymarket_events[:max_items]:
question = event.get('question', '')
prob = event.get('current_probability', 50.0)
summaries.append(f"- {question[:80]}: Market probability {prob:.1f}%")
return "\n".join(summaries) if summaries else "No related prediction market events found."
# ==================== Memory Layer ====================
def _get_memory_context(self, market: str, symbol: str, current_indicators: Dict) -> str:
@@ -247,6 +262,7 @@ class FastAnalysisService:
fundamental = data.get("fundamental") or {}
company = data.get("company") or {}
news_summary = self._format_news_summary(data.get("news") or [])
polymarket_events = data.get("polymarket") or []
# Language instruction - MUST be enforced strictly
lang_map = {
@@ -349,22 +365,31 @@ You are CONSERVATIVE and OBJECTIVE. Your analysis must be based on DATA, not spe
- Consider geopolitical events and their potential impact
- Evaluate how macro trends affect this specific market/symbol
3. **News & Event Analysis**:
- **CRITICAL**: Pay special attention to GEOPOLITICAL EVENTS (wars, conflicts, military actions, sanctions)
- These events can cause sudden and severe market movements, especially for crypto and global markets
- Identify BREAKING NEWS or major events that could cause sudden moves
- Assess news sentiment and its credibility
- Consider regulatory changes, partnerships, scandals, etc.
- Don't ignore major news just because technical indicators look good
4. **Fundamental Analysis**: Evaluate valuation, growth, competitive position if data available. If data is insufficient, say so.
5. **Risk Assessment**:
- Consider regulatory changes, partnerships, scandals, geopolitical tensions, etc.
- **DO NOT ignore major geopolitical news** (e.g., US-Iran conflict, Russia-Ukraine war) even if technical indicators look good
- Global events like wars can override all technical analysis - treat them as HIGHEST PRIORITY
4. **Prediction Market Analysis**:
- Review related prediction market events and their current probabilities
- Prediction markets reflect collective market wisdom and can indicate future price movements
- If prediction markets show high probability for bullish events (e.g., "BTC reaches $100k"), consider this as a positive signal
- If prediction markets show high probability for bearish events, consider this as a risk factor
- Use prediction market probabilities as a sentiment indicator alongside technical analysis
5. **Fundamental Analysis**: Evaluate valuation, growth, competitive position if data available. If data is insufficient, say so.
6. **Risk Assessment**:
- Explain why the stop loss level is appropriate
- List ALL significant risks (technical, macro, news, fundamental)
- Consider tail risks from unexpected events
6. **Clear Recommendation**: BUY/SELL/HOLD with entry, stop loss (near suggested), take profit (near suggested)
7. **Clear Recommendation**: BUY/SELL/HOLD with entry, stop loss (near suggested), take profit (near suggested)
- **BUY**: For long positions when indicators suggest upside
- **SELL**: For short positions when indicators suggest downside - this is a VALID trading opportunity
- **HOLD**: Only when signals are truly unclear - DO NOT default to HOLD just to be safe
- Your decision should reflect the WEIGHTED importance of ALL factors
- If macro/news factors strongly contradict technical, explain why you prioritize one over the other
7. **Trading Opportunity Recognition**:
8. **Trading Opportunity Recognition**:
- When you see RSI > 60, bearish MACD, downtrend → Give SELL signal (short opportunity)
- When you see RSI < 40, bullish MACD, uptrend → Give BUY signal (long opportunity)
- Only choose HOLD when signals are genuinely mixed or unclear
@@ -397,14 +422,16 @@ Output ONLY valid JSON (do NOT include word counts or format hints in your actua
- Do NOT make up facts or exaggerate. Base everything on the provided data.
📊 OBJECTIVE SCORING SYSTEM (Reference):
The system will calculate an objective score based on technical indicators, fundamentals, sentiment, and macro factors.
- Score >= +40: Bullish signal → BUY recommended
- Score <= -40: Bearish signal → SELL recommended
- Score between -40 and +40: Neutral → HOLD recommended
The system will calculate an objective score based on technical indicators, fundamentals, sentiment (including geopolitical events), and macro factors.
- Score >= +20: Bullish signal → BUY recommended
- Score <= -20: Bearish signal → SELL recommended
- Score between -20 and +20: Neutral → HOLD recommended (narrow range)
- Score >= +70: Strong bullish → Strong BUY signal
- Score <= -70: Strong bearish → Strong SELL signal
Your decision should align with this objective score when it's significant (>=40 or <=-40).
When the score is neutral (-40 to +40), you can use your judgment, but still consider giving BUY/SELL if technical indicators are clear."""
- Geopolitical events (wars, conflicts) are heavily weighted in sentiment score and can cause severe negative scores
- Macro factors (VIX, DXY, interest rates) are also heavily weighted
Your decision should align with this objective score when it's significant (>=20 or <=-20).
When the score is neutral (-20 to +20), you can use your judgment, but still consider giving BUY/SELL if technical indicators are clear."""
# Format indicator data for prompt (ensure safe defaults)
rsi_data = indicators.get("rsi") or {}
@@ -439,6 +466,9 @@ When the score is neutral (-40 to +40), you can use your judgment, but still con
📰 MARKET NEWS ({len(data.get('news') or [])} items):
{news_summary}
🎯 PREDICTION MARKETS ({len(polymarket_events)} related events):
{self._format_polymarket_summary(polymarket_events)}
💼 FUNDAMENTALS:
- Company: {company.get('name', data['symbol'])}
- Industry: {company.get('industry', 'N/A')}
@@ -460,10 +490,12 @@ When the score is neutral (-40 to +40), you can use your judgment, but still con
{self._format_earnings_data(fundamental.get('earnings', {}))}
IMPORTANT:
1. Consider the macro environment (especially DXY, VIX, rates, geopolitical events) when making your recommendation.
2. Pay attention to BREAKING NEWS and international events that could cause sudden market moves.
3. For US stocks, analyze financial statements and earnings trends to assess company health.
4. Provide your analysis now. Remember: all prices must be within 10% of ${current_price}."""
1. **CRITICAL**: Check for GEOPOLITICAL EVENTS (wars, conflicts, military actions) in the news section. These events have HIGHEST PRIORITY and can override all technical indicators.
2. Consider the macro environment (especially DXY, VIX, rates, geopolitical events) when making your recommendation.
3. Pay attention to BREAKING NEWS and international events that could cause sudden market moves. Geopolitical tensions (e.g., US-Iran conflict) can cause severe market volatility.
4. For US stocks, analyze financial statements and earnings trends to assess company health.
5. If you see news about wars, conflicts, or major geopolitical events, you MUST mention them in your analysis and adjust your recommendation accordingly.
6. Provide your analysis now. Remember: all prices must be within 10% of ${current_price}."""
return system_prompt, user_prompt
@@ -745,8 +777,8 @@ IMPORTANT:
llm_decision = analysis.get("decision", "HOLD")
if llm_decision != score_based_decision:
score_abs = abs(objective_score['overall_score'])
# 降低阈值，因为现在HOLD区间更小了，±40以上的评分就应该覆盖
if score_abs >= 25: # 如果评分达到±25以上，就覆盖LLM决策（因为阈值是±40）
# 降低阈值，因为现在HOLD区间更小了（±20），±15以上的评分就应该覆盖
if score_abs >= 15: # 如果评分达到±15以上，就覆盖LLM决策（因为阈值是±20）
logger.warning(f"LLM decision '{llm_decision}' conflicts with score-based decision '{score_based_decision}' (score: {objective_score['overall_score']:.1f}). Overriding to score-based decision.")
analysis["decision"] = score_based_decision
# Adjust confidence based on score strength
@@ -908,24 +940,50 @@ 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、重大政策等。
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。
"""
if not news_data:
return False
# 检查新闻标题中的关键词
# 检查新闻标题中的关键词（扩展了地缘政治相关关键词）
major_keywords = [
"regulation", "regulatory", "ban", "approval", "partnership", "merger", "acquisition",
"scandal", "lawsuit", "investigation", "policy", "government", "central bank",
"监管", "禁令", "批准", "合作", "合并", "收购", "丑闻", "诉讼", "调查", "政策", "政府", "央行"
# 监管和政策
"regulation", "regulatory", "ban", "approval", "policy", "government", "central bank",
"监管", "禁令", "批准", "政策", "政府", "央行",
# 商业事件
"partnership", "merger", "acquisition", "scandal", "lawsuit", "investigation",
"合作", "合并", "收购", "丑闻", "诉讼", "调查",
# 地缘政治事件（新增）
"war", "conflict", "military", "attack", "strike", "sanctions", "tension", "crisis",
"geopolitical", "iran", "israel", "russia", "ukraine", "china", "taiwan", "north korea",
"middle east", "gulf", "nato", "united states", "us", "usa", "america",
"战争", "冲突", "军事", "袭击", "打击", "制裁", "紧张", "危机",
"地缘政治", "伊朗", "以色列", "俄罗斯", "乌克兰", "中国", "台湾", "朝鲜",
"中东", "海湾", "北约", "美国"
]
for news in news_data[:5]: # 只检查前5条最新新闻
for news in news_data[:10]: # 检查前10条最新新闻（增加检查范围）
title = (news.get("title") or news.get("headline") or "").lower()
summary = (news.get("summary") or "").lower()
sentiment = news.get("sentiment", "neutral")
# 如果有重大关键词且情绪强烈（非中性），认为是重大新闻
if any(keyword in title for keyword in major_keywords) and sentiment != "neutral":
# 检查标题和摘要中是否包含重大关键词
text_to_check = f"{title} {summary}"
# 地缘政治事件通常很严重，即使情绪是中性也要识别
geopolitical_keywords = [
"war", "conflict", "military", "attack", "strike", "geopolitical",
"战争", "冲突", "军事", "袭击", "打击", "地缘政治"
]
# 如果是地缘政治相关，直接认为是重大新闻
if any(keyword in text_to_check for keyword in geopolitical_keywords):
logger.info(f"Detected major geopolitical event in news: {title[:60]}")
return True
# 其他重大关键词且情绪强烈（非中性），认为是重大新闻
if any(keyword in text_to_check for keyword in major_keywords) and sentiment != "neutral":
logger.info(f"Detected major news event: {title[:60]}")
return True
return False
@@ -1061,7 +1119,8 @@ IMPORTANT:
conflicts.append("MACD bearish")
# 均线趋势向下时不应该BUY（除非有重大利好）
if "downtrend" in ma_trend.lower():
# 只有当趋势非常强烈时才认为是冲突（避免过于敏感）
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:
@@ -1140,12 +1199,13 @@ IMPORTANT:
macro_score = self._calculate_macro_score(macro, data.get("market", ""))
# 5. 综合评分（加权平均）
# 权重：技术40%，基本面25%，情绪20%，宏观15%
# 优化权重：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）
# 提高情绪和宏观权重，因为地缘政治和宏观经济因素对市场影响更大
overall_score = (
technical_score * 0.40 +
fundamental_score * 0.25 +
sentiment_score * 0.20 +
macro_score * 0.15
technical_score * 0.35 +
fundamental_score * 0.20 +
sentiment_score * 0.25 + # 提高情绪权重，包含地缘政治事件
macro_score * 0.20 # 提高宏观权重
)
return {
@@ -1318,16 +1378,49 @@ IMPORTANT:
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)"""
"""
计算新闻情绪评分 (-100 to +100)
包含地缘政治事件的特殊处理
"""
if not news:
return 0.0 # 无新闻，中性
positive_count = 0
negative_count = 0
neutral_count = 0
geopolitical_penalty = 0 # 地缘政治事件惩罚分数
geopolitical_count = 0 # 地缘政治事件数量
for item in news[:10]: # 只看前10条
# 地缘政治关键词
geopolitical_keywords = [
"war", "conflict", "military", "attack", "strike", "sanctions",
"geopolitical", "crisis", "tension", "iran", "israel", "russia",
"ukraine", "middle east", "nato", "united states",
"战争", "冲突", "军事", "袭击", "制裁", "地缘政治", "危机"
]
for item in news[:15]: # 检查前15条新闻
title = (item.get("headline") or item.get("title") or "").lower()
summary = (item.get("summary") or "").lower()
text = f"{title} {summary}"
sentiment = item.get("sentiment", "neutral")
is_global_event = item.get("is_global_event", False)
# 检查是否是地缘政治事件
is_geopolitical = is_global_event or any(keyword in text for keyword in geopolitical_keywords)
if is_geopolitical:
geopolitical_count += 1
# 地缘政治事件通常是利空的，给予严重惩罚
if any(kw in text for kw in ["war", "conflict", "attack", "strike", "战争", "冲突", "袭击", "打击"]):
geopolitical_penalty -= 50 # 战争/冲突事件严重利空
elif any(kw in text for kw in ["sanctions", "crisis", "tension", "制裁", "危机", "紧张"]):
geopolitical_penalty -= 30 # 制裁/危机事件利空
else:
geopolitical_penalty -= 20 # 其他地缘政治事件利空
logger.info(f"Detected geopolitical event in sentiment scoring: {title[:60]}, penalty: {geopolitical_penalty}")
# 统计普通新闻情绪
if sentiment == "positive":
positive_count += 1
elif sentiment == "negative":
@@ -1336,66 +1429,97 @@ IMPORTANT:
neutral_count += 1
total = positive_count + negative_count + neutral_count
if total == 0:
return 0.0
# 计算净情绪
net_sentiment = (positive_count - negative_count) / total
# 计算净情绪（普通新闻）
if total > 0:
net_sentiment = (positive_count - negative_count) / total
base_score = net_sentiment * 60 # 基础情绪分数（-60到+60）
else:
base_score = 0
# 映射到-100到+100
score = net_sentiment * 100
# 地缘政治事件惩罚（如果有地缘政治事件，直接应用惩罚）
if geopolitical_count > 0:
# 地缘政治事件的影响权重很高，直接叠加惩罚
final_score = base_score + geopolitical_penalty
logger.info(f"Sentiment score: base={base_score:.1f}, geopolitical_penalty={geopolitical_penalty}, final={final_score:.1f}")
else:
final_score = base_score
return max(-100, min(100, score))
return max(-100, min(100, final_score))
def _calculate_macro_score(self, macro: Dict, market: str) -> float:
"""计算宏观环境评分 (-100 to +100)"""
"""
计算宏观环境评分 (-100 to +100)
包含VIX、DXY、利率等宏观经济指标
"""
if not macro:
return 0.0 # 无宏观数据，中性
score = 0.0
factors = 0
# VIX 评分（恐慌指数）
# VIX 评分（恐慌指数）- 权重提高
vix = macro.get("VIX", {})
vix_value = vix.get("price", 0)
if vix_value > 0:
if vix_value > 30:
vix_score = -30 # 高恐慌，利空
if vix_value > 35:
vix_score = -50 # 极高恐慌（如战争期间），严重利空
elif vix_value > 30:
vix_score = -40 # 高恐慌，严重利空
elif vix_value > 25:
vix_score = -30 # 较高恐慌，利空
elif vix_value > 20:
vix_score = -15
vix_score = -15 # 中等恐慌，轻微利空
elif vix_value < 12:
vix_score = +20 # 低恐慌，利多
elif vix_value < 15:
vix_score = +15 # 低恐慌，利多
vix_score = +10 # 较低恐慌，轻微利多
else:
vix_score = 0
score += vix_score
factors += 1
# DXY 评分（美元指数）
# DXY 评分（美元指数）- 权重提高
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
if market in ["Crypto", "Forex", "Futures"]:
if dxy_change > 1:
if dxy_change > 2:
dxy_score = -30 # 美元大幅走强，严重利空
elif dxy_change > 1:
dxy_score = -20 # 美元走强，利空
elif dxy_change < -2:
dxy_score = +30 # 美元大幅走弱，利多
elif dxy_change < -1:
dxy_score = +20 # 美元走弱，利多
else:
dxy_score = 0
else:
dxy_score = 0 # 对股票影响较小
# 对股票也有影响，但较小
if dxy_change > 2:
dxy_score = -10
elif dxy_change < -2:
dxy_score = +10
else:
dxy_score = 0
score += dxy_score
factors += 1
# 利率评分（TNX）
# 利率评分（TNX）- 权重提高
tnx = macro.get("TNX", {})
tnx_change = tnx.get("changePercent", 0)
if tnx_change != 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
if market in ["Crypto", "USStock"]:
if tnx_change > 2:
tnx_score = -20 # 利率大幅上升，利空
if tnx_change > 3:
tnx_score = -30 # 利率大幅上升，严重利空
elif tnx_change > 2:
tnx_score = -20 # 利率上升，利空
elif tnx_change < -3:
tnx_score = +30 # 利率大幅下降，利多
elif tnx_change < -2:
tnx_score = +20 # 利率下降，利多
else:
@@ -1405,9 +1529,12 @@ IMPORTANT:
score += tnx_score
factors += 1
# 归一化
# 归一化（考虑权重）
if factors > 0:
score = score / factors * 100 / 3 # 最大可能分数是3个因素各30分=90，归一化到100
# 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
max_possible = 110 # 最大绝对值
score = score / max_possible * 100
return max(-100, min(100, score))
@@ -1415,24 +1542,26 @@ IMPORTANT:
"""
根据客观评分转换为决策
优化后的阈值（缩小HOLD区间，使决策更明确）：
- score >= +40: BUY（利多）
- score <= -40: SELL（利空）
- -40 < score < +40: HOLD（中性）
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）
分级决策（可选，用于更细粒度的判断）：
分级决策（用于更细粒度的判断）：
- score >= +70: 强烈BUY
- +40 <= score < +70: BUY
- +10 < score < +40: 弱利多（倾向于BUY，但可HOLD）
- -10 <= score <= +10: 中性HOLD
- -40 < score < -10: 弱利空（倾向于SELL，但可HOLD）
- -70 < score <= -40: SELL
- +40 <= score < +70: 明显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）
- -40 < score <= -20: SELL
- -70 < score <= -40: 明显SELL
- score <= -70: 强烈SELL
"""
# 使用±40作为主要阈值，缩小HOLD区间
if score >= 40:
# 使用±20作为主要阈值，大幅缩小HOLD区间
if score >= 20:
return "BUY"
elif score <= -40:
elif score <= -20:
return "SELL"
else:
return "HOLD"