From c93d118308328e3dd402ab7bd28a23422708aa7c Mon Sep 17 00:00:00 2001 From: MarkLo Date: Wed, 26 Nov 2025 15:28:36 +0800 Subject: [PATCH] --- backend/app/services/pdf_generator.py | 7 ++-- .../agents/risk_mgmt/aggresive_debator.py | 19 +--------- .../agents/risk_mgmt/conservative_debator.py | 19 +--------- .../agents/risk_mgmt/neutral_debator.py | 19 +--------- tradingagents/agents/trader/trader.py | 36 +++---------------- 5 files changed, 11 insertions(+), 89 deletions(-) diff --git a/backend/app/services/pdf_generator.py b/backend/app/services/pdf_generator.py index 09597377..1a5f1e35 100644 --- a/backend/app/services/pdf_generator.py +++ b/backend/app/services/pdf_generator.py @@ -277,9 +277,10 @@ class PDFGenerator: # Remove lines that only contain markdown symbols text = re.sub(r'^[\*_`~#\-\+]+\s*$', '', text, flags=re.MULTILINE) - # 12. Final Unicode check - remove any characters that might cause PDF encoding issues - # Keep only printable characters and common Chinese characters - text = ''.join(char for char in text if char.isprintable() or char in '\n\r\t' or '\u4e00' <= char <= '\u9fff') + # 12. REMOVED problematic Unicode filter that was corrupting Chinese characters + # The string comparison '\u4e00' <= char <= '\u9fff' was comparing UTF-8 bytes, + # not Unicode code points, causing characters like '經' to be corrupted. + # Unicode normalization at the start (line 237) is sufficient. return text.strip() diff --git a/tradingagents/agents/risk_mgmt/aggresive_debator.py b/tradingagents/agents/risk_mgmt/aggresive_debator.py index 7b938c9f..723bca7e 100644 --- a/tradingagents/agents/risk_mgmt/aggresive_debator.py +++ b/tradingagents/agents/risk_mgmt/aggresive_debator.py @@ -46,24 +46,7 @@ def create_risky_debator(llm): # 獲取交易員的決策 trader_decision = state["trader_investment_plan"] - # 定義文本截斷函數以避免超過 token 限制 - def truncate_text(text, max_chars): - """截斷文本到指定字符數""" - if len(text) <= max_chars: - return text - return text[:max_chars] + "\n...(內容已截斷)" - - - # 截斷各類輸入以控制 token 使用量 - # 增加限制以確保 800+ 字的報告不被截斷 - market_research_report = truncate_text(market_research_report, 2000) - sentiment_report = truncate_text(sentiment_report, 2000) - news_report = truncate_text(news_report, 2500) - fundamentals_report = truncate_text(fundamentals_report, 2000) - trader_decision = truncate_text(trader_decision, 2000) - history = truncate_text(history, 1500) - current_safe_response = truncate_text(current_safe_response, 1000) - current_neutral_response = truncate_text(current_neutral_response, 1000) + # 移除截斷邏輯以保留完整報告內容 # 建立提示 (prompt) prompt = f"""**重要：您必須使用繁體中文（Traditional Chinese）回覆所有內容。** diff --git a/tradingagents/agents/risk_mgmt/conservative_debator.py b/tradingagents/agents/risk_mgmt/conservative_debator.py index 05bcc071..e0b3d1af 100644 --- a/tradingagents/agents/risk_mgmt/conservative_debator.py +++ b/tradingagents/agents/risk_mgmt/conservative_debator.py @@ -47,24 +47,7 @@ def create_safe_debator(llm): # 獲取交易員的決策 trader_decision = state["trader_investment_plan"] - # 定義文本截斷函數以避免超過 token 限制 - def truncate_text(text, max_chars): - """截斷文本到指定字符數""" - if len(text) <= max_chars: - return text - return text[:max_chars] + "\n...(內容已截斷)" - - - # 截斷各類輸入以控制 token 使用量 - # 增加限制以確保 800+ 字的報告不被截斷 - market_research_report = truncate_text(market_research_report, 2000) - sentiment_report = truncate_text(sentiment_report, 2000) - news_report = truncate_text(news_report, 2500) - fundamentals_report = truncate_text(fundamentals_report, 2000) - trader_decision = truncate_text(trader_decision, 2000) - history = truncate_text(history, 1500) - current_risky_response = truncate_text(current_risky_response, 1000) - current_neutral_response = truncate_text(current_neutral_response, 1000) + # 移除截斷邏輯以保留完整報告內容 # 建立提示 (prompt) prompt = f"""**重要：您必須使用繁體中文（Traditional Chinese）回覆所有內容。** diff --git a/tradingagents/agents/risk_mgmt/neutral_debator.py b/tradingagents/agents/risk_mgmt/neutral_debator.py index 05bf9357..c62b541f 100644 --- a/tradingagents/agents/risk_mgmt/neutral_debator.py +++ b/tradingagents/agents/risk_mgmt/neutral_debator.py @@ -46,24 +46,7 @@ def create_neutral_debator(llm): # 獲取交易員的決策 trader_decision = state["trader_investment_plan"] - # 定義文本截斷函數以避免超過 token 限制 - def truncate_text(text, max_chars): - """截斷文本到指定字符數""" - if len(text) <= max_chars: - return text - return text[:max_chars] + "\n...(內容已截斷)" - - - # 截斷各類輸入以控制 token 使用量 - # 增加限制以確保 800+ 字的報告不被截斷 - market_research_report = truncate_text(market_research_report, 2000) - sentiment_report = truncate_text(sentiment_report, 2000) - news_report = truncate_text(news_report, 2500) - fundamentals_report = truncate_text(fundamentals_report, 2000) - trader_decision = truncate_text(trader_decision, 2000) - history = truncate_text(history, 1500) - current_risky_response = truncate_text(current_risky_response, 1000) - current_safe_response = truncate_text(current_safe_response, 1000) + # 移除截斷邏輯以保留完整報告內容 # 建立提示 (prompt) prompt = f"""**重要：您必須使用繁體中文（Traditional Chinese）回覆所有內容。** diff --git a/tradingagents/agents/trader/trader.py b/tradingagents/agents/trader/trader.py index 222a7325..d5fd724e 100644 --- a/tradingagents/agents/trader/trader.py +++ b/tradingagents/agents/trader/trader.py @@ -39,47 +39,19 @@ def create_trader(llm, memory): news_report = state["news_report"] fundamentals_report = state["fundamentals_report"] - # 定義文本截斷函數以避免超過 token 限制 - def truncate_text(text, max_chars): - """智能截斷文本到指定字符數，在句子邊界處截斷""" - if len(text) <= max_chars: - return text - - # 在max_chars附近尋找句子結束標記 - truncated = text[:max_chars] - - # 尋找最後一個句號、換行或逗號 - for delimiter in ['。', '\n', '，', '、', ' ']: - last_pos = truncated.rfind(delimiter) - if last_pos > max_chars * 0.8: # 至少保留80%的內容 - return text[:last_pos + 1] + "\n\n...(為控制長度已精簡)" - - # 如果找不到合適的分隔符，直接在字符處截斷 - return truncated + "...(為控制長度已精簡)" - - - # 截斷各類報告以控制 token 使用量 - # 增加限制以確保 800+ 字的報告不被截斷 - market_research_report_truncated = truncate_text(market_research_report, 2000) - sentiment_report_truncated = truncate_text(sentiment_report, 2000) - news_report_truncated = truncate_text(news_report, 2500) - fundamentals_report_truncated = truncate_text(fundamentals_report, 2000) - investment_plan_truncated = truncate_text(investment_plan, 2000) + # 移除截斷邏輯以保留完整報告內容 # 整合當前情況（用於記憶檢索） - curr_situation = f"{market_research_report_truncated}\n\n{sentiment_report_truncated}\n\n{news_report_truncated}\n\n{fundamentals_report_truncated}" + curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}" # 從記憶體中獲取過去相似情況的經驗 past_memories = memory.get_memories(curr_situation, n_matches=2) - # 將過去的經驗格式化為字串（限制長度） + # 將過去的經驗格式化為字串 past_memory_str = "" if past_memories: for i, rec in enumerate(past_memories, 1): recommendation = rec["recommendation"] - # 限制每條記憶的長度 - if len(recommendation) > 200: - recommendation = recommendation[:200] + "...(已截斷)" past_memory_str += recommendation + "\n\n" else: past_memory_str = "找不到過去的記憶。" @@ -96,7 +68,7 @@ def create_trader(llm, memory): 3. **風險管理**：設定清晰的進出場與停損參數，確保風控到位 【可用資訊】 -- 投資計畫：{investment_plan_truncated} +- 投資計畫：{investment_plan} - 過去反思：{past_memory_str} 【輸出要求】