82 lines
3.6 KiB
Python
82 lines
3.6 KiB
Python
import time
|
|
import json
|
|
from tradingagents.agents.utils.korean_prompt import (
|
|
KOREAN_INVESTOR_GUIDE,
|
|
KOREAN_DEBATE_GUIDE,
|
|
KOREAN_FINAL_DECISION_GUIDE,
|
|
)
|
|
|
|
|
|
def create_risk_manager(llm, memory):
|
|
def risk_manager_node(state) -> dict:
|
|
|
|
company_name = state["company_of_interest"]
|
|
|
|
history = state["risk_debate_state"]["history"]
|
|
risk_debate_state = state["risk_debate_state"]
|
|
market_research_report = state["market_report"]
|
|
news_report = state["news_report"]
|
|
fundamentals_report = state["fundamentals_report"]
|
|
sentiment_report = state["sentiment_report"]
|
|
trader_plan = state["investment_plan"]
|
|
|
|
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"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Aggressive, Neutral, and Conservative—and determine whether to ENTER a NEW long position in this stock.
|
|
|
|
Context: There is currently NO existing position. The trader is evaluating a fresh entry. The only valid decisions are:
|
|
- **BUY**: Enter a new long position now — the risk/reward is favorable for a new entry.
|
|
- **PASS**: Do not enter — the risks are too high or the timing is wrong; skip this trade.
|
|
|
|
SELL is NOT a valid option since there is no existing position.
|
|
|
|
Guidelines for Decision-Making:
|
|
1. **Summarize Key Arguments**: Extract the strongest points from each analyst about whether this is a good entry point, focusing on entry risk and reward.
|
|
2. **Provide Rationale**: Support your recommendation with direct quotes and counterarguments from the debate.
|
|
3. **Refine the Trader's Plan**: Start with the trader's original plan, **{trader_plan}**, and adjust it based on the analysts' insights about entry risk.
|
|
4. **Learn from Past Mistakes**: Use lessons from **{past_memory_str}** to address prior misjudgments and improve the decision you are making now to make sure you don't make a wrong BUY/PASS call.
|
|
|
|
Deliverables:
|
|
- A clear and actionable recommendation: Buy or Pass.
|
|
- Detailed reasoning anchored in the debate and past reflections.
|
|
|
|
---
|
|
|
|
**Analysts Debate History:**
|
|
{history}
|
|
|
|
---
|
|
|
|
Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes.
|
|
{KOREAN_INVESTOR_GUIDE}
|
|
{KOREAN_DEBATE_GUIDE}
|
|
{KOREAN_FINAL_DECISION_GUIDE}
|
|
"""
|
|
|
|
response = llm.invoke(prompt)
|
|
|
|
new_risk_debate_state = {
|
|
"judge_decision": response.content,
|
|
"history": risk_debate_state["history"],
|
|
"aggressive_history": risk_debate_state["aggressive_history"],
|
|
"conservative_history": risk_debate_state["conservative_history"],
|
|
"neutral_history": risk_debate_state["neutral_history"],
|
|
"latest_speaker": "Judge",
|
|
"current_aggressive_response": risk_debate_state["current_aggressive_response"],
|
|
"current_conservative_response": risk_debate_state["current_conservative_response"],
|
|
"current_neutral_response": risk_debate_state["current_neutral_response"],
|
|
"count": risk_debate_state["count"],
|
|
}
|
|
|
|
return {
|
|
"risk_debate_state": new_risk_debate_state,
|
|
"final_trade_decision": response.content,
|
|
}
|
|
|
|
return risk_manager_node
|