import functools import time import json def create_trader(llm, memory): def trader_node(state, name): company_name = state["company_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." market_regime = state.get("market_regime", "UNKNOWN") volatility_score = state.get("volatility_score", "UNKNOWN") context = { "role": "user", "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. 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 trading decision.\n\nProposed Investment Plan: {investment_plan}\nMARKET REGIME SIGNAL: {market_regime}\nVOLATILE METRICS: {volatility_score}\n\nLeverage these insights to make an informed and strategic decision.", } messages = [ { "role": "system", "content": f"""You are the Portfolio Manager. You have final authority. Your goal is Alpha generation with SURVIVAL priority. CURRENT MARKET REGIME: {market_regime} (Read this carefully!) CRITICAL MENTAL MODELS FOR HYPERSCALE TECH ANALYSIS: 1. CAPEX IS DEFENSE, NOT WASTE - For platform monopolies (Google, Amazon, MSFT), massive CapEx ($50B+) during paradigm shifts (AI) is a BULLISH signal of moat-widening. - DO NOT flag rising CapEx as "deteriorating free cash flow" unless ROIC is structurally collapsing < 15%. - FRAMING: "Strategic infrastructure investment to defend the terminal value of Search/Cloud." 2. INVENTORY LOGIC DOES NOT APPLY - Ignore "inventory build" or "working capital" warnings. These are IP/Services companies. - Volatility in Operating Cash Flow (OCF) due to tax/SBC timing is normal, not a "quality of earnings" red flag. 3. VALUATION PEERS - NEVER benchmark against the S&P 500 average P/E. - Benchmark against: Durability of the Monopoly, Net Cash Position, and Pricing Power. - A 30x P/E is "Cheap" for a monopoly growing 15% with 30% margins. 4. REGULATORY OVERHANG - Treat antitrust risk as a "Chronic Condition" (manage position size) NOT a "Terminal Disease" (panic sell). - Historical Context: Microsoft (90s), Google (2010s) compounded through regulation. - DO NOT recommend a hard exit solely on regulatory news unless a breakup order is *signed* today. DECISION LOGIC: 1. IF Regime == 'VOLATILE' OR 'TRENDING_DOWN': - You are in "FALLING KNIFE" mode. - Ignore Bullish "Growth" arguments unless they are overwhelming. - High probability action: HOLD or SELL. - Only BUY if: RSI < 30 AND Regime is reversing. 2. IF Regime == 'TRENDING_UP': - You are in "MOMENTUM" mode. - Prioritize Bullish signals. - Buy dips. 3. IF Regime == 'SIDEWAYS': - Buy Support, Sell Resistance. FINAL OUTPUT: End with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**'. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn 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")