Initial commit of integrated agent system
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cillin
321
backend/utils/msg_adapter.py
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321
backend/utils/msg_adapter.py
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# -*- coding: utf-8 -*-
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"""
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Message Adapter - Converts AgentScope Msg to frontend JSON format
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Ensures compatibility with existing frontend without modifications
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"""
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import json
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import logging
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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from agentscope.message import Msg
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logger = logging.getLogger(__name__)
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class FrontendAdapter:
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"""
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Adapter to convert AgentScope messages to frontend-compatible format
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Frontend expects specific message types:
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- agent: Agent thinking/analysis messages
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- team_summary: Portfolio summary with equity curves
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- team_holdings: Current portfolio holdings
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- team_stats: Portfolio statistics
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- team_trades: Trade history
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- team_leaderboard: Agent performance rankings
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- price_update: Real-time price updates
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- system: System notifications
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"""
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@staticmethod
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def parse(msg: Msg) -> Optional[Dict[str, Any]]:
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"""
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Parse AgentScope Msg to frontend format
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Args:
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msg: AgentScope Msg object
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Returns:
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Dictionary in frontend format, or None if message should be skipped
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"""
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if msg is None:
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return None
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# Determine message type based on metadata or content
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msg_type = FrontendAdapter._determine_type(msg)
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if msg_type == "agent":
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return FrontendAdapter._format_agent_msg(msg)
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elif msg_type == "portfolio_update":
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return FrontendAdapter._format_portfolio_msg(msg)
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elif msg_type == "system":
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return FrontendAdapter._format_system_msg(msg)
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else:
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# Default: treat as agent message
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return FrontendAdapter._format_agent_msg(msg)
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@staticmethod
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def _determine_type(msg: Msg) -> str:
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"""Determine frontend message type from Msg"""
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# Check metadata for explicit type
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if hasattr(msg, "metadata") and msg.metadata:
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if "type" in msg.metadata:
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return msg.metadata["type"]
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# Check if message contains portfolio update
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if "portfolio" in msg.metadata:
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return "portfolio_update"
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# Check message name/role
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if msg.name == "system":
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return "system"
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# Default to agent message
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return "agent"
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@staticmethod
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def _format_agent_msg(msg: object) -> Dict[str, Any]:
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"""
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Format agent message for frontend
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Args:
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msg: Either AgentScope Msg or dict from pipeline results
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Frontend expects:
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{
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"type": "agent",
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"role_key": "analyst_id",
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"content": "message text",
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"timestamp": "ISO timestamp"
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}
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"""
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# Handle dict from pipeline results
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if isinstance(msg, dict):
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name = msg.get("agent", "unknown")
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content = msg.get("content", "")
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else:
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# Handle Msg object
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name = msg.name
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content = msg.content
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return {
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"type": "agent",
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"role_key": name,
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"content": content
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if isinstance(content, str)
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else json.dumps(content),
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"timestamp": datetime.now().isoformat(),
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}
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@staticmethod
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def _format_portfolio_msg(msg: Msg) -> Dict[str, Any]:
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"""
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Format portfolio update message
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This typically generates multiple frontend messages:
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- team_summary
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- team_holdings
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- team_stats
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- team_trades (if trades were executed)
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"""
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metadata = msg.metadata or {}
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portfolio = metadata.get("portfolio", {})
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messages: List[Dict[str, Any]] = []
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# Generate holdings message
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holdings = FrontendAdapter.build_holdings(portfolio)
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if holdings:
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messages.append(
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{
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"type": "team_holdings",
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"data": holdings,
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"timestamp": datetime.now().isoformat(),
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},
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)
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# Generate stats message
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stats = FrontendAdapter.build_stats(portfolio)
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if stats:
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messages.append(
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{
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"type": "team_stats",
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"data": stats,
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"timestamp": datetime.now().isoformat(),
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},
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)
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# Generate trades message if execution logs exist
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execution_logs = metadata.get("execution_logs", [])
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if execution_logs:
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trades = FrontendAdapter.build_trades(execution_logs)
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messages.append(
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{
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"type": "team_trades",
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"mode": "incremental",
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"data": trades,
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"timestamp": datetime.now().isoformat(),
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},
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)
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# Return composite message
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return {
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"type": "composite",
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"messages": messages,
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}
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@staticmethod
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def _format_system_msg(msg: Msg) -> Dict[str, Any]:
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"""Format system message"""
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return {
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"type": "system",
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"content": msg.content
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if isinstance(msg.content, str)
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else json.dumps(msg.content),
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"timestamp": datetime.now().isoformat(),
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}
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@staticmethod
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def build_holdings(
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portfolio: Dict[str, Any],
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prices: Dict[str, float] = None,
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) -> List[Dict[str, Any]]:
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"""Build holdings array from portfolio state"""
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holdings = []
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prices = prices or {}
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positions = portfolio.get("positions", {})
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cash = portfolio.get("cash", 0.0)
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# Calculate total value using current prices
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total_value = cash
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for ticker, position in positions.items():
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long_shares = position.get("long", 0)
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short_shares = position.get("short", 0)
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price = prices.get(ticker) or position.get("avg_price", 0)
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total_value += (long_shares - short_shares) * price
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# Build holdings for each position
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for ticker, position in positions.items():
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long_shares = position.get("long", 0)
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short_shares = position.get("short", 0)
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avg_price = position.get("avg_price", 0)
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current_price = prices.get(ticker) or avg_price
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net_shares = long_shares - short_shares
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if net_shares == 0:
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continue
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market_value = net_shares * current_price
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weight = market_value / total_value if total_value > 0 else 0
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holdings.append(
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{
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"ticker": ticker,
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"quantity": net_shares,
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"avg": avg_price,
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"currentPrice": current_price,
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"marketValue": market_value,
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"weight": weight,
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},
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)
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# Add cash as a holding
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if cash > 0:
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holdings.append(
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{
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"ticker": "CASH",
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"quantity": 1,
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"avg": cash,
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"currentPrice": cash,
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"marketValue": cash,
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"weight": cash / total_value if total_value > 0 else 0,
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},
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)
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return holdings
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@staticmethod
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def build_stats(
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portfolio: Dict[str, Any],
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prices: Dict[str, float] = None,
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) -> Dict[str, Any]:
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"""Build stats dictionary from portfolio"""
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prices = prices or {}
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positions = portfolio.get("positions", {})
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cash = portfolio.get("cash", 0.0)
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margin_used = portfolio.get("margin_used", 0.0)
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# Calculate total value using current prices
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total_value = cash
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for ticker, position in positions.items():
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long_shares = position.get("long", 0)
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short_shares = position.get("short", 0)
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price = prices.get(ticker) or position.get("avg_price", 0)
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total_value += (long_shares - short_shares) * price
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# Calculate ticker weights
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ticker_weights = {}
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for ticker, position in positions.items():
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long_shares = position.get("long", 0)
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short_shares = position.get("short", 0)
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price = prices.get(ticker) or position.get("avg_price", 0)
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market_value = (long_shares - short_shares) * price
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if market_value != 0:
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ticker_weights[ticker] = (
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market_value / total_value if total_value > 0 else 0
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)
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# Calculate total return
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initial_cash = portfolio.get("initial_cash", 100000.0)
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total_return = (
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((total_value - initial_cash) / initial_cash * 100)
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if initial_cash > 0
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else 0.0
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)
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return {
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"totalAssetValue": round(total_value, 2),
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"totalReturn": round(total_return, 2),
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"cashPosition": round(cash, 2),
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"tickerWeights": ticker_weights,
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"marginUsed": round(margin_used, 2),
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}
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@staticmethod
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def build_trades(execution_logs: List[str]) -> List[Dict[str, Any]]:
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"""
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Build trades array from execution logs
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Frontend expects:
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[{
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"ts": 1234567890,
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"ticker": "AAPL",
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"side": "LONG",
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"qty": 100,
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"price": 150.0,
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"reason": "Buy signal"
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}, ...]
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"""
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trades = []
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timestamp = int(datetime.now().timestamp() * 1000)
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for log in execution_logs:
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# Parse execution log (simplified - should use structured data)
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if "Executed" in log:
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# Extract trade details from log string
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# in real implementation, pass structured data
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trades.append(
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{
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"ts": timestamp,
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"ticker": "UNKNOWN", # Should parse from log
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"side": "LONG", # Should parse from log
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"qty": 0, # Should parse from log
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"price": 0.0, # Should parse from log
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"reason": log,
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},
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)
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return trades
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