cillin
12de93aa30
量化交易多智能体系统，包含： - 分析师、投资组合经理、风险经理等智能体 - 股票分析、投资组合管理、风险控制工具 - React 前端界面 - FastAPI 后端服务 Co-Authored-By: Claude <noreply@anthropic.com>
773 lines
24 KiB
Python
773 lines
24 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Trading Execution Engine - Supports Two Modes
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1. Signal mode: Only records directional signal decisions
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2. Portfolio mode: Executes specific trades and tracks positions
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"""
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# flake8: noqa: E501
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from copy import deepcopy
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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class DirectionSignalRecorder:
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"""Direction signal recorder, records daily investment direction decisions"""
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def __init__(self):
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"""Initialize direction signal recorder"""
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self.signal_log = [] # Record all directional signal history
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def record_direction_signals(
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self,
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decisions: Dict[str, Dict[str, Any]],
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current_date: str = None,
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) -> Dict[str, Any]:
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"""
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Record Portfolio Manager's directional signal decisions
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Args:
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decisions: PM's direction decisions {ticker: {action, confidence, reasoning}}
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current_date: Current date (used for backtest compatibility)
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Returns:
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Signal recording report
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"""
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if current_date is None:
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current_date = datetime.now().strftime("%Y-%m-%d")
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# Use provided date for timestamp (backtest compatible)
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timestamp = f"{current_date}T09:30:00"
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signal_report: Dict[str, Any] = {
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"recorded_signals": {},
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"date": current_date,
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"timestamp": timestamp,
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"total_signals": len(decisions),
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}
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print(
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f"\n📊 Recording directional signal decisions for {current_date}...",
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)
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# Record directional signal for each ticker
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for ticker, decision in decisions.items():
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action = decision.get("action", "hold")
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confidence = decision.get("confidence", 0)
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reasoning = decision.get("reasoning", "")
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# Record signal
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signal_record = {
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"ticker": ticker,
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"action": action,
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"confidence": confidence,
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"reasoning": reasoning,
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"date": current_date,
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"timestamp": timestamp,
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}
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self.signal_log.append(signal_record)
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signal_report["recorded_signals"][ticker] = {
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"action": action,
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"confidence": confidence,
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}
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# Display signal
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action_emoji = {"long": "📈", "short": "📉", "hold": "➖"}
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emoji = action_emoji.get(action, "❓")
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print(
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f" {emoji} {ticker}: {action.upper()} (Confidence: {confidence}%) - {reasoning}",
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)
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print(f"\n✅ Recorded directional signals for {len(decisions)} stocks")
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return signal_report
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def get_signal_summary(self) -> Dict[str, Any]:
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"""Get signal recording summary"""
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return {
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"total_signals": len(self.signal_log),
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"signal_log": self.signal_log,
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}
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def parse_pm_decisions(pm_output: Dict[str, Any]) -> Dict[str, Dict[str, Any]]:
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"""
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Parse Portfolio Manager output format
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Args:
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pm_output: PM's raw output
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Returns:
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Standardized decision format
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"""
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if isinstance(pm_output, dict) and "decisions" in pm_output:
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return pm_output["decisions"]
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elif isinstance(pm_output, dict):
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# If directly a decision dictionary
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return pm_output
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else:
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print(f"Warning: Unable to parse PM output format: {type(pm_output)}")
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return {}
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class PortfolioTradeExecutor:
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"""Portfolio mode trade executor, executes specific trades and tracks positions"""
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portfolio: Dict[str, Any]
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trade_history: List[Dict[str, Any]]
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portfolio_history: List[Dict[str, Any]]
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def __init__(self, initial_portfolio: Optional[Dict[str, Any]] = None):
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"""
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Initialize Portfolio trade executor
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Args:
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initial_portfolio: Initial portfolio state
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"""
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if initial_portfolio is None:
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self.portfolio = {
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"cash": 100000.0,
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"positions": {},
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# Default 0.0 (short selling disabled)
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"margin_requirement": 0.0,
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"margin_used": 0.0,
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}
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else:
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self.portfolio = deepcopy(initial_portfolio)
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self.trade_history = [] # Trade history
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self.portfolio_history = [] # Portfolio history
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def execute_trade(
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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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price: float,
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current_date: str = None,
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) -> Dict[str, Any]:
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"""
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Execute a single trade
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Args:
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ticker: Stock ticker
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action: Trade action (long/short/hold)
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quantity: Number of shares
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price: Current price
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current_date: Trade date
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Returns:
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Trade result dictionary
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"""
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if current_date is None:
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current_date = datetime.now().strftime("%Y-%m-%d")
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if action == "hold" or quantity == 0:
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return {"status": "success", "message": "No trade needed"}
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if price <= 0:
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return {"status": "failed", "reason": "Invalid price"}
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result = self._execute_single_trade(
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ticker=ticker,
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action=action,
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target_quantity=quantity,
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price=price,
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date=current_date,
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)
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return result
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def execute_trades(
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self,
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decisions: Dict[str, Dict[str, Any]],
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current_prices: Dict[str, float],
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current_date: str = None,
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) -> Dict[str, Any]:
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"""
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Execute trading decisions and update positions
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Args:
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decisions: {ticker: {action, quantity, confidence, reasoning}}
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current_prices: {ticker: current_price}
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current_date: Current date (used for backtest compatibility)
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Returns:
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Trade execution report
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"""
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if current_date is None:
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current_date = datetime.now().strftime("%Y-%m-%d")
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# Use provided date for timestamp (backtest compatible)
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timestamp = f"{current_date}T09:30:00"
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execution_report: Dict[str, Any] = {
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"date": current_date,
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"timestamp": timestamp,
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"executed_trades": [],
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"failed_trades": [],
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"portfolio_before": deepcopy(self.portfolio),
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"portfolio_after": None,
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}
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print(f"\n💼 Executing Portfolio trades for {current_date}...")
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# Execute trades for each ticker
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for ticker, decision in decisions.items():
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action = decision.get("action", "hold")
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quantity = decision.get("quantity", 0)
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if action == "hold" or quantity == 0:
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continue
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price = current_prices.get(ticker, 0)
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if price <= 0:
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execution_report["failed_trades"].append(
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{
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"ticker": ticker,
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"action": action,
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"quantity": quantity,
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"reason": "No valid price data",
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},
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)
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print(
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f" ❌ {ticker}: Unable to execute {action} - No valid price",
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)
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continue
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# Execute trade
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trade_result = self._execute_single_trade(
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ticker,
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action,
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quantity,
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price,
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current_date,
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)
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if trade_result["status"] == "success":
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execution_report["executed_trades"].append(trade_result)
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trades_info = ", ".join(trade_result.get("trades", []))
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print(
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f" ✔ {ticker}: {action} Target {quantity} shares "
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f"({trades_info}) @ ${price:.2f}",
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)
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else:
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execution_report["failed_trades"].append(trade_result)
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print(
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f" ✗ {ticker}: Unable to execute {action} - {trade_result['reason']}",
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)
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# Record final portfolio state
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execution_report["portfolio_after"] = deepcopy(self.portfolio)
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self.portfolio_history.append(
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{
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"date": current_date,
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"portfolio": deepcopy(self.portfolio),
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},
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)
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# Calculate portfolio value
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portfolio_value = self._calculate_portfolio_value(current_prices)
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execution_report["portfolio_value"] = portfolio_value
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print("\n✔ Trade execution completed:")
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print(f" Success: {len(execution_report['executed_trades'])} trades")
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print(f" Failed: {len(execution_report['failed_trades'])} trades")
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print(f" Portfolio value: ${portfolio_value:,.2f}")
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print(f" Cash balance: ${self.portfolio['cash']:,.2f}")
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return execution_report
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def _execute_single_trade(
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self,
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ticker: str,
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action: str,
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target_quantity: int,
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price: float,
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date: str,
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) -> Dict[str, Any]:
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"""
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Execute single trade - Incremental mode
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Args:
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ticker: Stock ticker
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action: long(add position)/short(reduce position)/hold
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target_quantity: Incremental quantity (long=buy shares, short=sell shares)
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price: Current price
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date: Trade date
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"""
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# Ensure position exists
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if ticker not in self.portfolio["positions"]:
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self.portfolio["positions"][ticker] = {
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"long": 0,
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"short": 0,
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"long_cost_basis": 0.0,
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"short_cost_basis": 0.0,
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}
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position = self.portfolio["positions"][ticker]
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current_long = position["long"]
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current_short = position["short"]
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trades_executed = [] # Record actually executed trade steps
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if action == "long":
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result = self._execute_long_action(
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ticker,
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target_quantity,
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price,
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date,
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current_long,
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current_short,
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trades_executed,
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)
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if result["status"] == "failed":
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return result
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elif action == "short":
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result = self._execute_short_action(
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ticker,
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target_quantity,
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price,
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date,
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current_long,
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current_short,
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trades_executed,
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)
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if result["status"] == "failed":
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return result
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elif action == "hold":
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print(f"\n⏸️ {ticker} Position unchanged: {current_long} shares")
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# Record trade with backtest-compatible timestamp
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trade_record = {
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"status": "success",
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"ticker": ticker,
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"action": action,
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"target_quantity": target_quantity,
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"price": price,
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"trades": trades_executed,
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"date": date,
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"timestamp": f"{date}T09:30:00",
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}
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self.trade_history.append(trade_record)
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return trade_record
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def _execute_long_action(
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self,
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ticker: str,
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target_quantity: int,
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price: float,
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date: str,
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current_long: int,
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current_short: int,
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trades_executed: list,
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) -> Dict[str, Any]:
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"""Execute long action: Buy shares or cover shorts first"""
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print(
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f"\n📈 {ticker} Long operation: Current Long {current_long}, "
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f"Short {current_short} → Target quantity {target_quantity}",
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)
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if target_quantity <= 0:
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print(" ⏸️ Quantity is 0, no trade needed")
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return {"status": "success"}
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remaining = target_quantity
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# If has short position, cover first
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if current_short > 0:
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cover_qty = min(remaining, current_short)
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print(f" 1️⃣ Cover short: {cover_qty} shares")
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cover_result = self._cover_short_position(
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ticker,
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cover_qty,
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price,
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date,
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)
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if cover_result["status"] == "failed":
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return cover_result
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trades_executed.append(f"Cover {cover_qty} shares")
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remaining -= cover_qty
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# If still has remaining quantity, buy long
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if remaining > 0:
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print(f" 2️⃣ Buy long: {remaining} shares")
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buy_result = self._buy_long_position(
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ticker,
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remaining,
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price,
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date,
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)
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if buy_result["status"] == "failed":
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return buy_result
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trades_executed.append(f"Buy {remaining} shares")
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# Display final result
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final_long = self.portfolio["positions"][ticker]["long"]
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final_short = self.portfolio["positions"][ticker]["short"]
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print(
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f" ✅ Final state: Long {final_long} shares, Short {final_short} shares",
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)
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return {"status": "success"}
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def _execute_short_action(
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self,
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ticker: str,
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target_quantity: int,
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price: float,
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date: str,
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current_long: int,
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current_short: int,
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trades_executed: list,
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) -> Dict[str, Any]:
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"""Execute short action: Sell long positions first, then short if needed"""
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print(
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f"\n📉 {ticker} Short operation (quantity={target_quantity} shares):",
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)
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print(
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f" Current state: Long {current_long} shares, Short {current_short} shares",
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)
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if target_quantity <= 0:
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print(" ⏸️ Quantity is 0, no trade needed")
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return {"status": "success"}
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remaining_quantity = target_quantity
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# Step 1: If there are long positions, sell first
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if current_long > 0:
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sell_quantity = min(remaining_quantity, current_long)
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print(f" 1️⃣ Sell long: {sell_quantity} shares")
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sell_result = self._sell_long_position(
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ticker,
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sell_quantity,
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price,
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date,
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)
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if sell_result["status"] == "failed":
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return sell_result
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trades_executed.append(f"Sell {sell_quantity} shares")
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remaining_quantity -= sell_quantity
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# Step 2: If there's remaining quantity, establish or increase short position
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if remaining_quantity > 0:
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print(f" 2️⃣ Short: {remaining_quantity} shares")
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short_result = self._open_short_position(
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ticker,
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remaining_quantity,
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price,
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date,
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)
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if short_result["status"] == "failed":
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return short_result
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trades_executed.append(f"Short {remaining_quantity} shares")
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# Display final result
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final_long = self.portfolio["positions"][ticker]["long"]
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final_short = self.portfolio["positions"][ticker]["short"]
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print(
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f" ✅ Final state: Long {final_long} shares, Short {final_short} shares",
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)
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return {"status": "success"}
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def _buy_long_position(
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self,
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ticker: str,
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quantity: int,
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price: float,
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_date: str,
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) -> Dict[str, Any]:
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"""Buy long position"""
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position = self.portfolio["positions"][ticker]
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trade_value = quantity * price
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if self.portfolio["cash"] < trade_value:
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return {
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"status": "failed",
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"ticker": ticker,
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"action": "buy",
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"quantity": quantity,
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"price": price,
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"reason": f"Insufficient cash (needed: ${trade_value:.2f}, available: "
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f"${self.portfolio['cash']:.2f})",
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}
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# Update position cost basis
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old_long = position["long"]
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old_cost_basis = position["long_cost_basis"]
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new_long = old_long + quantity
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# 🐛 Debug info
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print(f" 🔍 Buy {ticker}:")
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print(f" Old position: {old_long} shares @ ${old_cost_basis:.2f}")
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print(f" Buy: {quantity} shares @ ${price:.2f}")
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print(f" New position: {new_long} shares")
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if new_long > 0:
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new_cost_basis = (
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(old_long * old_cost_basis) + (quantity * price)
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) / new_long
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print(
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f" New cost: ${new_cost_basis:.2f} = "
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f"(({old_long} × ${old_cost_basis:.2f}) + "
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f"({quantity} × ${price:.2f})) / {new_long}",
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)
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position["long_cost_basis"] = new_cost_basis
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position["long"] = new_long
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# Deduct cash
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self.portfolio["cash"] -= trade_value
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return {"status": "success"}
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def _sell_long_position(
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self,
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ticker: str,
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quantity: int,
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price: float,
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_date: str,
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) -> Dict[str, Any]:
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"""Sell long position"""
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position = self.portfolio["positions"][ticker]
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if position["long"] < quantity:
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return {
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"status": "failed",
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"ticker": ticker,
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"action": "sell",
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"quantity": quantity,
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"price": price,
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"reason": f"Insufficient long position (holding: {position['long']},"
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f" trying to sell: {quantity})",
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}
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# Reduce position
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position["long"] -= quantity
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if position["long"] == 0:
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position["long_cost_basis"] = 0.0
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# Increase cash
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trade_value = quantity * price
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self.portfolio["cash"] += trade_value
|
||
|
||
return {"status": "success"}
|
||
|
||
def _open_short_position(
|
||
self,
|
||
ticker: str,
|
||
quantity: int,
|
||
price: float,
|
||
_date: str,
|
||
) -> Dict[str, Any]:
|
||
"""Open short position"""
|
||
position = self.portfolio["positions"][ticker]
|
||
trade_value = quantity * price
|
||
margin_needed = trade_value * self.portfolio["margin_requirement"]
|
||
|
||
if self.portfolio["cash"] < margin_needed:
|
||
return {
|
||
"status": "failed",
|
||
"ticker": ticker,
|
||
"action": "short",
|
||
"quantity": quantity,
|
||
"price": price,
|
||
"reason": f"Insufficient margin (needed: ${margin_needed:.2f}, "
|
||
f"available: ${self.portfolio['cash']:.2f})",
|
||
}
|
||
|
||
# Update position cost basis
|
||
old_short = position["short"]
|
||
old_cost_basis = position["short_cost_basis"]
|
||
new_short = old_short + quantity
|
||
if new_short > 0:
|
||
position["short_cost_basis"] = (
|
||
(old_short * old_cost_basis) + (quantity * price)
|
||
) / new_short
|
||
position["short"] = new_short
|
||
|
||
# Increase cash (short sale proceeds) and margin used
|
||
self.portfolio["cash"] += trade_value - margin_needed
|
||
self.portfolio["margin_used"] += margin_needed
|
||
|
||
return {"status": "success"}
|
||
|
||
def _cover_short_position(
|
||
self,
|
||
ticker: str,
|
||
quantity: int,
|
||
price: float,
|
||
_date: str,
|
||
) -> Dict[str, Any]:
|
||
"""Cover short position"""
|
||
position = self.portfolio["positions"][ticker]
|
||
|
||
if position["short"] < quantity:
|
||
return {
|
||
"status": "failed",
|
||
"ticker": ticker,
|
||
"action": "cover",
|
||
"quantity": quantity,
|
||
"price": price,
|
||
"reason": f"Insufficient short position (holding: {position['short']}, "
|
||
f"trying to cover: {quantity})",
|
||
}
|
||
|
||
# Calculate released margin - 🔧 FIX: Use cost_basis instead of current price
|
||
trade_value = quantity * price
|
||
cost_basis = position["short_cost_basis"]
|
||
margin_released = (
|
||
quantity * cost_basis * self.portfolio["margin_requirement"]
|
||
)
|
||
|
||
# Reduce position
|
||
position["short"] -= quantity
|
||
if position["short"] == 0:
|
||
position["short_cost_basis"] = 0.0
|
||
|
||
# Deduct cash (buy to cover) and release margin
|
||
self.portfolio["cash"] -= trade_value
|
||
self.portfolio["cash"] += margin_released
|
||
self.portfolio["margin_used"] -= margin_released
|
||
|
||
return {"status": "success"}
|
||
|
||
def _calculate_portfolio_value(
|
||
self,
|
||
current_prices: Dict[str, float],
|
||
) -> float:
|
||
"""Calculate total portfolio value (net liquidation value)"""
|
||
# Add margin_used back because it's frozen cash, not lost money
|
||
total_value = self.portfolio["cash"] + self.portfolio["margin_used"]
|
||
|
||
for ticker, position in self.portfolio["positions"].items():
|
||
if ticker in current_prices:
|
||
price = current_prices[ticker]
|
||
# Add long position value
|
||
total_value += position["long"] * price
|
||
# Subtract short position value (liability)
|
||
total_value -= position["short"] * price
|
||
|
||
return total_value
|
||
|
||
def get_portfolio_summary(
|
||
self,
|
||
current_prices: Dict[str, float],
|
||
) -> Dict[str, Any]:
|
||
"""Get portfolio summary"""
|
||
portfolio_value = self._calculate_portfolio_value(current_prices)
|
||
|
||
positions_summary = []
|
||
for ticker, position in self.portfolio["positions"].items():
|
||
if position["long"] > 0 or position["short"] > 0:
|
||
price = current_prices.get(ticker, 0)
|
||
long_value = position["long"] * price
|
||
short_value = position["short"] * price
|
||
|
||
positions_summary.append(
|
||
{
|
||
"ticker": ticker,
|
||
"long_shares": position["long"],
|
||
"short_shares": position["short"],
|
||
"long_value": long_value,
|
||
"short_value": short_value,
|
||
"long_cost_basis": position["long_cost_basis"],
|
||
"short_cost_basis": position["short_cost_basis"],
|
||
"long_pnl": (
|
||
long_value
|
||
- (position["long"] * position["long_cost_basis"])
|
||
if position["long"] > 0
|
||
else 0
|
||
),
|
||
"short_pnl": (
|
||
(position["short"] * position["short_cost_basis"])
|
||
- short_value
|
||
if position["short"] > 0
|
||
else 0
|
||
),
|
||
},
|
||
)
|
||
|
||
return {
|
||
"portfolio_value": portfolio_value,
|
||
"cash": self.portfolio["cash"],
|
||
"margin_used": self.portfolio["margin_used"],
|
||
"positions": positions_summary,
|
||
"total_trades": len(self.trade_history),
|
||
}
|
||
|
||
|
||
def execute_trading_decisions(
|
||
pm_decisions: Dict[str, Any],
|
||
current_date: str = None,
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
Convenience function to record directional signal decisions (Signal mode)
|
||
|
||
Args:
|
||
pm_decisions: PM's direction decisions
|
||
current_date: Current date (optional)
|
||
|
||
Returns:
|
||
Signal recording report
|
||
"""
|
||
# Parse PM decisions
|
||
decisions = parse_pm_decisions(pm_decisions)
|
||
|
||
# Create direction signal recorder
|
||
recorder = DirectionSignalRecorder()
|
||
|
||
# Record directional signals
|
||
signal_report = recorder.record_direction_signals(decisions, current_date)
|
||
|
||
return signal_report
|
||
|
||
|
||
def execute_portfolio_trades(
|
||
pm_decisions: Dict[str, Any],
|
||
current_prices: Dict[str, float],
|
||
portfolio: Dict[str, Any],
|
||
current_date: str = None,
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
Execute Portfolio mode trading decisions
|
||
|
||
Args:
|
||
pm_decisions: PM's trading decisions
|
||
current_prices: Current prices
|
||
portfolio: Current portfolio state
|
||
current_date: Current date (optional)
|
||
|
||
Returns:
|
||
Trade execution report and updated portfolio
|
||
"""
|
||
# Parse PM decisions
|
||
decisions = parse_pm_decisions(pm_decisions)
|
||
|
||
# Create Portfolio trade executor
|
||
executor = PortfolioTradeExecutor(initial_portfolio=portfolio)
|
||
|
||
# Execute trades
|
||
execution_report = executor.execute_trades(
|
||
decisions,
|
||
current_prices,
|
||
current_date,
|
||
)
|
||
|
||
# Add portfolio summary
|
||
execution_report["portfolio_summary"] = executor.get_portfolio_summary(
|
||
current_prices,
|
||
)
|
||
|
||
# Return updated portfolio
|
||
execution_report["updated_portfolio"] = executor.portfolio
|
||
|
||
return execution_report
|