50 lines
2.2 KiB
Python
50 lines
2.2 KiB
Python
import functools
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def create_trader(llm, memory):
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def trader_node(state, name):
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event_id = state["event_id"]
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event_question = state["event_question"]
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investment_plan = state["investment_plan"]
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odds_report = state["odds_report"]
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sentiment_report = state["sentiment_report"]
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news_report = state["news_report"]
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event_report = state["event_report"]
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curr_situation = f"{odds_report}\n\n{sentiment_report}\n\n{news_report}\n\n{event_report}"
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past_memories = memory.get_memories(curr_situation, n_matches=2)
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past_memory_str = ""
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if past_memories:
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for rec in past_memories:
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past_memory_str += rec["recommendation"] + "\n\n"
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else:
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past_memory_str = "No past memories found."
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context = {
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"role": "user",
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"content": f"Based on a comprehensive analysis by a team of analysts, here is a prediction plan for the event: '{event_question}' (Event ID: {event_id}). This plan incorporates insights from market odds, news, social sentiment, and event analysis. Use this plan to formulate your betting decision.\n\nProposed Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic prediction.",
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}
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messages = [
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{
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"role": "system",
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"content": f"""You are a prediction market trader analyzing event data to make betting decisions on Polymarket. Based on your analysis, provide a specific recommendation: YES (bet on event occurring), NO (bet against event occurring), or SKIP (no bet).
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Consider the edge (difference between your estimated probability and market price), position sizing, and risk management. End with a firm decision and always conclude your response with 'FINAL PREDICTION: **YES/NO/SKIP** | Confidence: X.X | Edge: X.X' to confirm your recommendation.
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Utilize lessons from past decisions to learn from mistakes: {past_memory_str}""",
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},
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context,
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]
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result = llm.invoke(messages)
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return {
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"messages": [result],
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"trader_plan": result.content,
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"sender": name,
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}
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return functools.partial(trader_node, name="Trader")
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