Merge remote-tracking branch 'upstream/main' into crypto-analysis-mvp

# Conflicts:
#	tradingagents/agents/researchers/bear_researcher.py
#	tradingagents/agents/researchers/bull_researcher.py
#	tradingagents/graph/propagation.py
This commit is contained in:
CadeYu committed 2026-05-08 18:57:09 +08:00
commit 5bae826749
39 files changed
+3259 -458

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@@ -1,6 +1,6 @@
def create_bear_researcher(llm, memory):
def create_bear_researcher(llm):
def bear_node(state) -> dict:
investment_debate_state = state["investment_debate_state"]
history = investment_debate_state.get("history", "")
@@ -19,13 +19,6 @@ def create_bear_researcher(llm, memory):
else "Asset fundamentals report (may be unavailable for crypto)"
)
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""You are a Bear Analyst making the case against investing in the {target_label}. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.
Key points to focus on:
@@ -44,8 +37,7 @@ Latest world affairs news: {news_report}
{fundamentals_label}: {fundamentals_report}
Conversation history of the debate: {history}
Last bull argument: {current_response}
Reflections from similar situations and lessons learned: {past_memory_str}
Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the {target_label}. You must also address reflections and learn from lessons and mistakes you made in the past.
Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the {target_label}.
"""
response = llm.invoke(prompt)