58 lines
2.7 KiB
Python
58 lines
2.7 KiB
Python
import functools
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import time
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import json
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def create_trader(llm, memory):
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def trader_node(state, name):
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ticker = state["ticker_of_interest"]
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investment_plan = state["investment_plan"]
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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news_report = state["news_report"]
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fundamentals_report = state["fundamentals_report"]
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curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_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 i, rec in enumerate(past_memories, 1):
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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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base_asset = ""
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quote_asset = ""
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if isinstance(ticker, str) and "/" in ticker:
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base_asset, quote_asset = ticker.split("/", 1)
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pair_context = ticker
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if base_asset and quote_asset:
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pair_context = f"{ticker} (base={base_asset}, quote={quote_asset})"
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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 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.",
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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 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 \
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End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** **QUANTITY**' to confirm your recommendation. \
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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}""",
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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_investment_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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