283 lines
9.1 KiB
Python
283 lines
9.1 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Portfolio Manager Agent - Based on AgentScope ReActAgent
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Responsible for decision-making (NOT trade execution)
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"""
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from typing import Any, Dict, Optional
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from agentscope.agent import ReActAgent
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from agentscope.memory import InMemoryMemory, LongTermMemoryBase
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from agentscope.message import Msg, TextBlock
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from agentscope.tool import Toolkit, ToolResponse
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from ..utils.progress import progress
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from .prompt_factory import build_agent_system_prompt, clear_prompt_factory_cache
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class PMAgent(ReActAgent):
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"""
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Portfolio Manager Agent - Makes investment decisions
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Key features:
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1. PM outputs decisions only (action + quantity per ticker)
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2. Trade execution happens externally (in pipeline/executor)
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3. Supports both backtest and live modes
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"""
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def __init__(
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self,
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name: str = "portfolio_manager",
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model: Any = None,
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formatter: Any = None,
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initial_cash: float = 100000.0,
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margin_requirement: float = 0.25,
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config: Optional[Dict[str, Any]] = None,
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long_term_memory: Optional[LongTermMemoryBase] = None,
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toolkit_factory: Any = None,
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toolkit_factory_kwargs: Optional[Dict[str, Any]] = None,
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toolkit: Optional[Toolkit] = None,
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):
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object.__setattr__(self, "config", config or {})
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# Portfolio state
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object.__setattr__(
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self,
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"portfolio",
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{
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"cash": initial_cash,
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"positions": {},
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"margin_used": 0.0,
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"margin_requirement": margin_requirement,
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},
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)
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# Decisions made in current cycle
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object.__setattr__(self, "_decisions", {})
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toolkit_factory_kwargs = toolkit_factory_kwargs or {}
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object.__setattr__(self, "_toolkit_factory", toolkit_factory)
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object.__setattr__(
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self,
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"_toolkit_factory_kwargs",
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toolkit_factory_kwargs,
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)
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# Create toolkit after local state is ready so bound tool methods can be registered.
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if toolkit is None:
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if toolkit_factory is not None:
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toolkit = toolkit_factory(
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name,
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self.config.get("config_name", "default"),
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owner=self,
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**toolkit_factory_kwargs,
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)
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else:
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toolkit = self._create_toolkit()
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object.__setattr__(self, "toolkit", toolkit)
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sys_prompt = build_agent_system_prompt(
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agent_id=name,
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config_name=self.config.get("config_name", "default"),
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toolkit=self.toolkit,
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)
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kwargs = {
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"name": name,
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"sys_prompt": sys_prompt,
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"model": model,
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"formatter": formatter,
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"toolkit": toolkit,
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"memory": InMemoryMemory(),
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"max_iters": 10,
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}
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if long_term_memory:
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kwargs["long_term_memory"] = long_term_memory
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kwargs["long_term_memory_mode"] = "both"
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super().__init__(**kwargs)
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def _create_toolkit(self) -> Toolkit:
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"""Create toolkit with decision recording tool"""
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toolkit = Toolkit()
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toolkit.register_tool_function(self._make_decision)
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return toolkit
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def _make_decision(
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self,
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ticker: str,
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action: str,
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quantity: int,
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confidence: int = 50,
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reasoning: str = "",
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) -> ToolResponse:
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"""
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Record a trading decision for a ticker.
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Args:
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ticker: Stock ticker symbol (e.g., "AAPL")
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action: Decision - "long", "short" or "hold"
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quantity: Number of shares to trade (0 for hold)
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confidence: Confidence level 0-100
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reasoning: Explanation for this decision
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Returns:
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ToolResponse confirming decision recorded
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"""
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if action not in ["long", "short", "hold"]:
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return ToolResponse(
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content=[
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TextBlock(
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type="text",
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text=f"Invalid action: {action}. "
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"Must be 'long', 'short', or 'hold'.",
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),
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],
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)
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self._decisions[ticker] = {
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"action": action,
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"quantity": quantity if action != "hold" else 0,
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"confidence": confidence,
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"reasoning": reasoning,
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}
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return ToolResponse(
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content=[
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TextBlock(
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type="text",
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text=f"Decision recorded: {action} "
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f"{quantity} shares of {ticker}"
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f" (confidence: {confidence}%)",
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),
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],
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)
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async def reply(self, x: Msg = None) -> Msg:
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"""
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Make investment decisions
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Returns:
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Msg with decisions in metadata
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"""
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if x is None:
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return Msg(
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name=self.name,
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content="No input provided",
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role="assistant",
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)
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# Clear previous decisions
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self._decisions = {}
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progress.update_status(
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self.name,
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None,
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"Analyzing and making decisions",
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)
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result = await super().reply(x)
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progress.update_status(self.name, None, "Completed")
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# Attach decisions to metadata
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if result.metadata is None:
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result.metadata = {}
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result.metadata["decisions"] = self._decisions.copy()
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result.metadata["portfolio"] = self.portfolio.copy()
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return result
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def get_decisions(self) -> Dict[str, Dict]:
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"""Get decisions from current cycle"""
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return self._decisions.copy()
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def get_portfolio_state(self) -> Dict[str, Any]:
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"""Get current portfolio state"""
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return self.portfolio.copy()
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def load_portfolio_state(self, portfolio: Dict[str, Any]):
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"""Load portfolio state"""
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if not portfolio:
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return
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self.portfolio = {
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"cash": portfolio.get("cash", self.portfolio["cash"]),
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"positions": portfolio.get("positions", {}).copy(),
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"margin_used": portfolio.get("margin_used", 0.0),
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"margin_requirement": portfolio.get(
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"margin_requirement",
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self.portfolio["margin_requirement"],
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),
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}
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def update_portfolio(self, portfolio: Dict[str, Any]):
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"""Update portfolio after external execution"""
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self.portfolio.update(portfolio)
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def _has_open_positions(self) -> bool:
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"""Return whether the current portfolio still has non-zero positions."""
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for position in self.portfolio.get("positions", {}).values():
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if position.get("long", 0) or position.get("short", 0):
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return True
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return False
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def can_apply_initial_cash(self) -> bool:
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"""Only allow cash rebasing before any positions or margin exist."""
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return (
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not self._has_open_positions()
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and float(self.portfolio.get("margin_used", 0.0) or 0.0) == 0.0
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)
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def apply_runtime_portfolio_config(
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self,
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*,
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margin_requirement: Optional[float] = None,
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initial_cash: Optional[float] = None,
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) -> Dict[str, bool]:
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"""Apply safe run-time portfolio config updates."""
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result = {
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"margin_requirement": False,
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"initial_cash": False,
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}
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if margin_requirement is not None:
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self.portfolio["margin_requirement"] = float(margin_requirement)
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result["margin_requirement"] = True
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if initial_cash is not None and self.can_apply_initial_cash():
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self.portfolio["cash"] = float(initial_cash)
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result["initial_cash"] = True
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return result
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def reload_runtime_assets(self, active_skill_dirs: Optional[list] = None) -> None:
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"""Reload toolkit and system prompt from current run assets."""
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from .toolkit_factory import create_agent_toolkit
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clear_prompt_factory_cache()
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toolkit_factory = self._toolkit_factory or create_agent_toolkit
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toolkit_kwargs = dict(self._toolkit_factory_kwargs)
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if active_skill_dirs is not None:
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toolkit_kwargs["active_skill_dirs"] = active_skill_dirs
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self.toolkit = toolkit_factory(
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self.name,
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self.config.get("config_name", "default"),
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owner=self,
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**toolkit_kwargs,
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)
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self._apply_runtime_sys_prompt(
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build_agent_system_prompt(
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agent_id=self.name,
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config_name=self.config.get("config_name", "default"),
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toolkit=self.toolkit,
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),
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)
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def _apply_runtime_sys_prompt(self, sys_prompt: str) -> None:
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"""Update the prompt used by future turns and the cached system msg."""
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self._sys_prompt = sys_prompt
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for msg, _marks in self.memory.content:
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if getattr(msg, "role", None) == "system":
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msg.content = sys_prompt
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break
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