evotraders/backend/agents/portfolio_manager.py

# -*- coding: utf-8 -*-
"""
Portfolio Manager Agent - Based on AgentScope ReActAgent
Responsible for decision-making (NOT trade execution)

.. deprecated:: 0.2.0
    PMAgent is deprecated and will be removed in a future version.
    Use :class:`backend.agents.base.evo_agent.EvoAgent` instead.
    See docs/CRITICAL_FIXES.md for migration guide.
"""
import warnings
from pathlib import Path
from typing import Any, Dict, Optional, Callable

from agentscope.agent import ReActAgent
from agentscope.memory import InMemoryMemory, LongTermMemoryBase
from agentscope.message import Msg, TextBlock
from agentscope.tool import Toolkit, ToolResponse

from ..utils.progress import progress
from .prompt_factory import build_agent_system_prompt, clear_prompt_factory_cache
from .team_pipeline_config import update_active_analysts
from ..config.constants import ANALYST_TYPES

# Emit deprecation warning on module import
warnings.warn(
    "PMAgent is deprecated. Use EvoAgent instead. "
    "See docs/CRITICAL_FIXES.md for migration guide.",
    DeprecationWarning,
    stacklevel=2,
)


class PMAgent(ReActAgent):
    """
    Portfolio Manager Agent - Makes investment decisions

    Key features:
    1. PM outputs decisions only (action + quantity per ticker)
    2. Trade execution happens externally (in pipeline/executor)
    3. Supports both backtest and live modes

    .. deprecated:: 0.2.0
        Use :class:`backend.agents.base.evo_agent.EvoAgent` with
        workspace-driven configuration instead.
    """
    """
    Portfolio Manager Agent - Makes investment decisions

    Key features:
    1. PM outputs decisions only (action + quantity per ticker)
    2. Trade execution happens externally (in pipeline/executor)
    3. Supports both backtest and live modes
    """

    def __init__(
        self,
        name: str = "portfolio_manager",
        model: Any = None,
        formatter: Any = None,
        initial_cash: float = 100000.0,
        margin_requirement: float = 0.25,
        config: Optional[Dict[str, Any]] = None,
        long_term_memory: Optional[LongTermMemoryBase] = None,
        toolkit_factory: Any = None,
        toolkit_factory_kwargs: Optional[Dict[str, Any]] = None,
        toolkit: Optional[Toolkit] = None,
    ):
        # Emit runtime deprecation warning
        warnings.warn(
            "PMAgent is deprecated. Use EvoAgent via UnifiedAgentFactory instead.",
            DeprecationWarning,
            stacklevel=2,
        )

        object.__setattr__(self, "config", config or {})

        # Portfolio state
        object.__setattr__(
            self,
            "portfolio",
            {
                "cash": initial_cash,
                "positions": {},
                "margin_used": 0.0,
                "margin_requirement": margin_requirement,
            },
        )

        # Decisions made in current cycle
        object.__setattr__(self, "_decisions", {})
        toolkit_factory_kwargs = toolkit_factory_kwargs or {}
        object.__setattr__(self, "_toolkit_factory", toolkit_factory)
        object.__setattr__(
            self,
            "_toolkit_factory_kwargs",
            toolkit_factory_kwargs,
        )
        object.__setattr__(self, "_create_team_agent_cb", None)
        object.__setattr__(self, "_remove_team_agent_cb", None)

        # Create toolkit after local state is ready so bound tool methods can be registered.
        if toolkit is None:
            if toolkit_factory is not None:
                toolkit = toolkit_factory(
                    name,
                    self.config.get("config_name", "default"),
                    owner=self,
                    **toolkit_factory_kwargs,
                )
            else:
                toolkit = self._create_toolkit()
        object.__setattr__(self, "toolkit", toolkit)

        sys_prompt = build_agent_system_prompt(
            agent_id=name,
            config_name=self.config.get("config_name", "default"),
            toolkit=self.toolkit,
        )

        kwargs = {
            "name": name,
            "sys_prompt": sys_prompt,
            "model": model,
            "formatter": formatter,
            "toolkit": toolkit,
            "memory": InMemoryMemory(),
            "max_iters": 10,
        }
        if long_term_memory:
            kwargs["long_term_memory"] = long_term_memory
            kwargs["long_term_memory_mode"] = "both"

        super().__init__(**kwargs)

    def _create_toolkit(self) -> Toolkit:
        """Create toolkit with decision recording tool"""
        toolkit = Toolkit()
        toolkit.register_tool_function(self._make_decision)
        return toolkit

    def _make_decision(
        self,
        ticker: str,
        action: str,
        quantity: int,
        confidence: int = 50,
        reasoning: str = "",
    ) -> ToolResponse:
        """
        Record a trading decision for a ticker.

        Args:
            ticker: Stock ticker symbol (e.g., "AAPL")
            action: Decision - "long", "short" or "hold"
            quantity: Number of shares to trade (0 for hold)
            confidence: Confidence level 0-100
            reasoning: Explanation for this decision

        Returns:
            ToolResponse confirming decision recorded
        """
        if action not in ["long", "short", "hold"]:
            return ToolResponse(
                content=[
                    TextBlock(
                        type="text",
                        text=f"Invalid action: {action}. "
                        "Must be 'long', 'short', or 'hold'.",
                    ),
                ],
            )

        self._decisions[ticker] = {
            "action": action,
            "quantity": quantity if action != "hold" else 0,
            "confidence": confidence,
            "reasoning": reasoning,
        }

        return ToolResponse(
            content=[
                TextBlock(
                    type="text",
                    text=f"Decision recorded: {action} "
                    f"{quantity} shares of {ticker}"
                    f" (confidence: {confidence}%)",
                ),
            ],
        )

    def _add_team_analyst(self, agent_id: str) -> ToolResponse:
        """Add one analyst to active discussion team."""
        config_name = self.config.get("config_name", "default")
        project_root = Path(__file__).resolve().parents[2]
        active = update_active_analysts(
            project_root=project_root,
            config_name=config_name,
            available_analysts=list(ANALYST_TYPES.keys()),
            add=[agent_id],
        )
        return ToolResponse(
            content=[
                TextBlock(
                    type="text",
                    text=(
                        f"Active analyst team updated. Added: {agent_id}. "
                        f"Current active analysts: {', '.join(active)}"
                    ),
                ),
            ],
        )

    def _remove_team_analyst(self, agent_id: str) -> ToolResponse:
        """Remove one analyst from active discussion team."""
        callback_msg = ""
        callback = self._remove_team_agent_cb
        if callback is not None:
            callback_msg = callback(agent_id=agent_id)

        config_name = self.config.get("config_name", "default")
        project_root = Path(__file__).resolve().parents[2]
        active = update_active_analysts(
            project_root=project_root,
            config_name=config_name,
            available_analysts=list(ANALYST_TYPES.keys()),
            remove=[agent_id],
        )
        return ToolResponse(
            content=[
                TextBlock(
                    type="text",
                    text=(
                        f"Active analyst team updated. Removed: {agent_id}. "
                        f"Current active analysts: {', '.join(active)}"
                        + (f" | {callback_msg}" if callback_msg else "")
                    ),
                ),
            ],
        )

    def _set_active_analysts(self, agent_ids: str) -> ToolResponse:
        """Set active analysts from comma-separated agent ids."""
        requested = [
            item.strip() for item in str(agent_ids or "").split(",") if item.strip()
        ]
        config_name = self.config.get("config_name", "default")
        project_root = Path(__file__).resolve().parents[2]
        active = update_active_analysts(
            project_root=project_root,
            config_name=config_name,
            available_analysts=list(ANALYST_TYPES.keys()),
            set_to=requested,
        )
        return ToolResponse(
            content=[
                TextBlock(
                    type="text",
                    text=f"Active analyst team set to: {', '.join(active)}",
                ),
            ],
        )

    def _create_team_analyst(self, agent_id: str, analyst_type: str) -> ToolResponse:
        """Create a runtime analyst instance and activate it."""
        callback = self._create_team_agent_cb
        if callback is None:
            return ToolResponse(
                content=[
                    TextBlock(
                        type="text",
                        text="Runtime agent creation is not available in current pipeline.",
                    ),
                ],
            )
        result = callback(agent_id=agent_id, analyst_type=analyst_type)
        return ToolResponse(
            content=[
                TextBlock(type="text", text=result),
            ],
        )

    def set_team_controller(
        self,
        *,
        create_agent_callback: Optional[Callable[..., str]] = None,
        remove_agent_callback: Optional[Callable[..., str]] = None,
    ) -> None:
        """Inject runtime team lifecycle callbacks from pipeline."""
        object.__setattr__(self, "_create_team_agent_cb", create_agent_callback)
        object.__setattr__(self, "_remove_team_agent_cb", remove_agent_callback)

    async def reply(self, x: Msg = None) -> Msg:
        """
        Make investment decisions

        Returns:
            Msg with decisions in metadata
        """
        if x is None:
            return Msg(
                name=self.name,
                content="No input provided",
                role="assistant",
            )

        # Clear previous decisions
        self._decisions = {}

        progress.update_status(
            self.name,
            None,
            "Analyzing and making decisions",
        )

        result = await super().reply(x)

        progress.update_status(self.name, None, "Completed")

        # Attach decisions to metadata
        if result.metadata is None:
            result.metadata = {}
        result.metadata["decisions"] = self._decisions.copy()
        result.metadata["portfolio"] = self.portfolio.copy()

        return result

    def get_decisions(self) -> Dict[str, Dict]:
        """Get decisions from current cycle"""
        return self._decisions.copy()

    def get_portfolio_state(self) -> Dict[str, Any]:
        """Get current portfolio state"""
        return self.portfolio.copy()

    def load_portfolio_state(self, portfolio: Dict[str, Any]):
        """Load portfolio state"""
        if not portfolio:
            return
        self.portfolio = {
            "cash": portfolio.get("cash", self.portfolio["cash"]),
            "positions": portfolio.get("positions", {}).copy(),
            "margin_used": portfolio.get("margin_used", 0.0),
            "margin_requirement": portfolio.get(
                "margin_requirement",
                self.portfolio["margin_requirement"],
            ),
        }

    def update_portfolio(self, portfolio: Dict[str, Any]):
        """Update portfolio after external execution"""
        self.portfolio.update(portfolio)

    def _has_open_positions(self) -> bool:
        """Return whether the current portfolio still has non-zero positions."""
        for position in self.portfolio.get("positions", {}).values():
            if position.get("long", 0) or position.get("short", 0):
                return True
        return False

    def can_apply_initial_cash(self) -> bool:
        """Only allow cash rebasing before any positions or margin exist."""
        return (
            not self._has_open_positions()
            and float(self.portfolio.get("margin_used", 0.0) or 0.0) == 0.0
        )

    def apply_runtime_portfolio_config(
        self,
        *,
        margin_requirement: Optional[float] = None,
        initial_cash: Optional[float] = None,
    ) -> Dict[str, bool]:
        """Apply safe run-time portfolio config updates."""
        result = {
            "margin_requirement": False,
            "initial_cash": False,
        }

        if margin_requirement is not None:
            self.portfolio["margin_requirement"] = float(margin_requirement)
            result["margin_requirement"] = True

        if initial_cash is not None and self.can_apply_initial_cash():
            self.portfolio["cash"] = float(initial_cash)
            result["initial_cash"] = True

        return result

    def reload_runtime_assets(self, active_skill_dirs: Optional[list] = None) -> None:
        """Reload toolkit and system prompt from current run assets."""
        from .toolkit_factory import create_agent_toolkit

        clear_prompt_factory_cache()
        toolkit_factory = self._toolkit_factory or create_agent_toolkit
        toolkit_kwargs = dict(self._toolkit_factory_kwargs)
        if active_skill_dirs is not None:
            toolkit_kwargs["active_skill_dirs"] = active_skill_dirs

        self.toolkit = toolkit_factory(
            self.name,
            self.config.get("config_name", "default"),
            owner=self,
            **toolkit_kwargs,
        )
        self._apply_runtime_sys_prompt(
            build_agent_system_prompt(
                agent_id=self.name,
                config_name=self.config.get("config_name", "default"),
                toolkit=self.toolkit,
            ),
        )

    def _apply_runtime_sys_prompt(self, sys_prompt: str) -> None:
        """Update the prompt used by future turns and the cached system msg."""
        self._sys_prompt = sys_prompt
        for msg, _marks in self.memory.content:
            if getattr(msg, "role", None) == "system":
                msg.content = sys_prompt
                break