import functools

def create_trader(llm, memory):
    def trader_node(state, name):
        ticker = state["ticker_of_interest"]
        investment_plan = state["investment_plan"]
        market_research_report = state["market_report"]
        sentiment_report = state["sentiment_report"]
        news_report = state["news_report"]
        fundamentals_report = state["fundamentals_report"]

        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 = ""
        if past_memories:
            for i, rec in enumerate(past_memories, 1):
                past_memory_str += rec["recommendation"] + "\n\n"
        else:
            past_memory_str = "No past memories found."

        base_asset = ""
        quote_asset = ""
        if isinstance(ticker, str) and "/" in ticker:
            base_asset, quote_asset = ticker.split("/", 1)

        pair_context = ticker
        if base_asset and quote_asset:
            pair_context = f"{ticker} (base={base_asset}, quote={quote_asset})"

        context = {
            "role": "user",
            "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan for the crypto pair {pair_context}. This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next crypto trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision for this crypto market.",
        }

        messages = [
            {
                "role": "system",
                "content": f"""You are a crypto trading agent analyzing cryptocurrency market data for a specific trading pair (e.g., BTC/USDT). Based on your analysis, provide a specific recommendation to BUY, SELL, or HOLD the base asset relative to the quote asset for the pair {pair_context}, along with the quantity for BUY and SELL \
                    End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** **QUANTITY**' to confirm your recommendation. \
                    Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situations you traded in and the lessons learned: {past_memory_str}""",
            },
            context,
        ]

        result = llm.invoke(messages)

        return {
            "messages": [result],
            "trader_investment_plan": result.content,
            "sender": name,
        }

    return functools.partial(trader_node, name="Trader")