# Generated Python Strategy for: PlayerA (meta)
# Run Index: 7
# Primary LLM: DeepSeek-V3 (deepseek-ai/DeepSeek-V3-0324) Provider: novita
def PlayerA_DeepSeek_V3_MetaAgent_Run7_Iter0(my_history: list, opp_history: list, opponent_program_code: str) -> str:
    # Heuristic analysis of opponent's code
    opponent_program_code = opponent_program_code.lower()
    
    # Check for simple strategies
    is_always_cooperate = "always_cooperate" in opponent_program_code or "return 'c'" in opponent_program_code
    is_always_defect = "always_defect" in opponent_program_code or "return 'd'" in opponent_program_code
    is_tit_for_tat = "tit_for_tat" in opponent_program_code or ("opp_history" in opponent_program_code and "my_history" in opponent_program_code and "return opp_history[-1]" in opponent_program_code)
    
    # Check for random behavior
    is_random = "random.choice(['c', 'd'])" in opponent_program_code or "random.randint" in opponent_program_code
    
    # Check for self-similarity (if opponent's code resembles our own)
    our_keywords = ["deepseek", "v3", "metaagent", "run7", "iter0"]
    is_similar = any(keyword in opponent_program_code for keyword in our_keywords)
    
    # Count occurrences of 'c' and 'd' in return statements
    c_count = opponent_program_code.count("return 'c'") + opponent_program_code.count("return c") + opponent_program_code.count("return 'cooperate'")
    d_count = opponent_program_code.count("return 'd'") + opponent_program_code.count("return d") + opponent_program_code.count("return 'defect'")
    
    # Check for recursive or LLM-based strategies (though we can't call LLM)
    is_recursive = "make_internal_llm_call" in opponent_program_code
    
    # Determine opponent's likely strategy
    if is_always_cooperate:
        return "D"  # Exploit always-cooperate
    elif is_always_defect:
        return "D"  # Always defect against always-defect
    elif is_tit_for_tat:
        if not opp_history:
            return "C"
        else:
            return opp_history[-1]  # Mirror Tit-for-Tat
    elif is_random:
        return "D"  # Defect against random to minimize loss
    elif is_similar:
        return "C"  # Cooperate with similar strategies
    elif c_count > d_count + 2:  # More cooperative returns
        return "C"
    elif d_count > c_count + 2:  # More defecting returns
        return "D"
    else:
        # Fallback to Tit-for-Tat with initial cooperation
        if not opp_history:
            return "C"
        else:
            return opp_history[-1]