feat(portfolio): add optimizer, risk model, rebalance engine, CLI pfactor command, bump v1.13.0

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "easy-tdx"
-version = "1.12.0"
+version = "1.13.0"
 description = "通达信 TCP 协议行情数据客户端，支持在线行情、离线数据读取与写入同步"
 readme = "README.md"
 requires-python = ">=3.10"

src/easy_tdx/cli/__init__.py

@@ -20,6 +20,7 @@ from .cmd_capital import capital_flow
 from .cmd_chanlun import chanlun
 from .cmd_ex import ex
 from .cmd_factor import factor
+from .cmd_pfactor import pfactor
 from .cmd_finance import f10, fund_flow
 from .cmd_indicator import indicator, indicator_list
 from .cmd_info import server_info, symbol_info
@@ -83,6 +84,7 @@ cli.add_command(indicator_list)
 cli.add_command(offline)
 cli.add_command(chanlun)
 cli.add_command(factor)
+cli.add_command(pfactor)
 cli.add_command(backtest)
 cli.add_command(portfolio)
 cli.add_command(run_all)

src/easy_tdx/cli/cmd_pfactor.py

@@ -0,0 +1,58 @@
+"""组合因子选股 CLI 命令。"""
+
+from __future__ import annotations
+
+import json
+
+import click
+
+
+@click.group("pfactor")
+def pfactor() -> None:
+    """组合因子选股工具。"""
+    pass
+
+
+@pfactor.command("backtest")
+@click.argument("factor_name")
+@click.option("--n-stocks", default=50, type=int, help="持仓数量")
+@click.option("--rebalance-freq", default="M", help="调仓频率: W/M/Q")
+@click.option(
+    "--optimizer",
+    "opt_name",
+    default="equal",
+    help="优化器: equal/factor_weighted/risk_parity/mean_variance",
+)
+@click.option("--cash", default=1000000.0, type=float, help="初始资金")
+def pfactor_backtest(
+    factor_name: str, n_stocks: int, rebalance_freq: str, opt_name: str, cash: float
+) -> None:
+    """运行组合因子回测。
+
+    示例：
+
+      easy-tdx pfactor backtest momentum_20d
+
+      easy-tdx pfactor backtest rsi_14 --n-stocks 10 --optimizer factor_weighted
+    """
+    click.echo(
+        json.dumps(
+            {
+                "message": "pfactor backtest 需要行情数据，请使用 Python API",
+                "example": (
+                    f"from easy_tdx.portfolio import RebalanceEngine, EqualWeightOptimizer\n"
+                    f"engine = RebalanceEngine(\n"
+                    f"    optimizer=EqualWeightOptimizer(),\n"
+                    f"    factor_name='{factor_name}',\n"
+                    f"    n_stocks={n_stocks},\n"
+                    f"    rebalance_freq='{rebalance_freq}',\n"
+                    f"    cash={cash},\n"
+                    f")\n"
+                    f"result = engine.run(data, start_date=20230101, end_date=20240101)\n"
+                    f"print(f'年化收益={{result.performance[\"annual_return\"]:.2%}}')"
+                ),
+            },
+            ensure_ascii=False,
+            indent=2,
+        )
+    )

src/easy_tdx/portfolio/__init__.py

@@ -1 +1,26 @@
 """组合管理模块。"""
+
+from easy_tdx.portfolio.optimizer import (
+    EqualWeightOptimizer,
+    FactorWeightedOptimizer,
+    MeanVarianceOptimizer,
+    RiskParityOptimizer,
+    WeightOptimizer,
+    get_optimizer,
+)
+from easy_tdx.portfolio.rebalance import RebalanceEngine
+from easy_tdx.portfolio.risk import RiskModel
+from easy_tdx.portfolio.types import PortfolioState, RebalanceResult
+
+__all__ = [
+    "WeightOptimizer",
+    "EqualWeightOptimizer",
+    "FactorWeightedOptimizer",
+    "RiskParityOptimizer",
+    "MeanVarianceOptimizer",
+    "get_optimizer",
+    "RiskModel",
+    "RebalanceEngine",
+    "PortfolioState",
+    "RebalanceResult",
+]

src/easy_tdx/portfolio/optimizer.py

@@ -1,4 +1,5 @@
 """权重优化器。"""
+
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
@@ -26,9 +27,11 @@ _OPTIMIZER_REGISTRY: dict[str, type[WeightOptimizer]] = {}
 
 def register_optimizer(name: str) -> type[WeightOptimizer]:
     """注册优化器。"""
+
     def wrapper(cls: type[WeightOptimizer]) -> type[WeightOptimizer]:
         _OPTIMIZER_REGISTRY[name] = cls
         return cls
+
     return wrapper
 
 
@@ -122,6 +125,7 @@ class MeanVarianceOptimizer(WeightOptimizer):
     ) -> dict[str, float]:
         try:
             from scipy.optimize import minimize  # noqa: F401
+
             return self._optimize_with_scipy(factor_scores, n_stocks)
         except ImportError:
             fallback = EqualWeightOptimizer()

src/easy_tdx/portfolio/rebalance.py

@@ -1,4 +1,5 @@
 """多期调仓回测引擎。"""
+
 from __future__ import annotations
 
 import numpy as np
@@ -111,21 +112,38 @@ class RebalanceEngine:
                     if holdings[c] > 0 and c in prices:
                         weights[c] = holdings[c] * prices[c] / total_value
 
-            states.append(PortfolioState(
-                date=date_int, weights=weights, holdings=dict(holdings),
-                cash=cash, total_value=total_value,
-                positions_count=len([s for s in holdings.values() if s > 0]),
-            ))
-            equity_records.append({"datetime": date_int, "total": total_value, "cash": cash, "position_value": position_value})
+            states.append(
+                PortfolioState(
+                    date=date_int,
+                    weights=weights,
+                    holdings=dict(holdings),
+                    cash=cash,
+                    total_value=total_value,
+                    positions_count=len([s for s in holdings.values() if s > 0]),
+                )
+            )
+            equity_records.append(
+                {
+                    "datetime": date_int,
+                    "total": total_value,
+                    "cash": cash,
+                    "position_value": position_value,
+                }
+            )
 
         equity_curve = pd.DataFrame(equity_records)
-        trades_df = pd.DataFrame(trades_list) if trades_list else pd.DataFrame(
-            columns=["datetime", "direction", "code", "shares", "price", "cost"]
+        trades_df = (
+            pd.DataFrame(trades_list)
+            if trades_list
+            else pd.DataFrame(columns=["datetime", "direction", "code", "shares", "price", "cost"])
         )
         performance = self._compute_performance(equity_curve)
         return RebalanceResult(
-            rebalance_dates=rebalance_dates, states=states, trades=trades_df,
-            equity_curve=equity_curve, performance=performance,
+            rebalance_dates=rebalance_dates,
+            states=states,
+            trades=trades_df,
+            equity_curve=equity_curve,
+            performance=performance,
         )
 
     def _rebalance(
@@ -145,7 +163,16 @@ class RebalanceEngine:
                     sell_value = holdings[code] * price
                     cost = sell_value * (self._commission + self._slippage)
                     cash += sell_value - cost
-                    trades_list.append({"datetime": date_int, "direction": "SELL", "code": code, "shares": holdings[code], "price": price, "cost": cost})
+                    trades_list.append(
+                        {
+                            "datetime": date_int,
+                            "direction": "SELL",
+                            "code": code,
+                            "shares": holdings[code],
+                            "price": price,
+                            "cost": cost,
+                        }
+                    )
                 del holdings[code]
 
         new_holdings: dict[str, float] = {}
@@ -159,7 +186,16 @@ class RebalanceEngine:
                 new_holdings[code] = shares
                 trade_value = shares * price
                 cost = trade_value * (self._commission + self._slippage)
-                trades_list.append({"datetime": date_int, "direction": "BUY", "code": code, "shares": shares, "price": price, "cost": cost})
+                trades_list.append(
+                    {
+                        "datetime": date_int,
+                        "direction": "BUY",
+                        "code": code,
+                        "shares": shares,
+                        "price": price,
+                        "cost": cost,
+                    }
+                )
 
         cash = total_value - sum(new_holdings.get(c, 0) * prices.get(c, 0) for c in new_holdings)
         holdings.clear()
@@ -178,13 +214,31 @@ class RebalanceEngine:
         max_drawdown = float(np.min(drawdown))
         daily_ret = np.diff(total) / total[:-1]
         daily_ret = daily_ret[~np.isnan(daily_ret)]
-        sharpe = float(np.mean(daily_ret) / np.std(daily_ret) * np.sqrt(252)) if len(daily_ret) > 1 and np.std(daily_ret) > 0 else 0.0
-        return {"total_return": total_return, "annual_return": annual_return, "max_drawdown": max_drawdown, "sharpe": sharpe, "total_trades": len(equity_curve)}
+        sharpe = (
+            float(np.mean(daily_ret) / np.std(daily_ret) * np.sqrt(252))
+            if len(daily_ret) > 1 and np.std(daily_ret) > 0
+            else 0.0
+        )
+        return {
+            "total_return": total_return,
+            "annual_return": annual_return,
+            "max_drawdown": max_drawdown,
+            "sharpe": sharpe,
+            "total_trades": len(equity_curve),
+        }
 
     def _empty_result(self) -> RebalanceResult:
         return RebalanceResult(
-            rebalance_dates=[], states=[],
-            trades=pd.DataFrame(columns=["datetime", "direction", "code", "shares", "price", "cost"]),
+            rebalance_dates=[],
+            states=[],
+            trades=pd.DataFrame(
+                columns=["datetime", "direction", "code", "shares", "price", "cost"]
+            ),
             equity_curve=pd.DataFrame(columns=["datetime", "total", "cash", "position_value"]),
-            performance={"total_return": 0.0, "annual_return": 0.0, "max_drawdown": 0.0, "sharpe": 0.0},
+            performance={
+                "total_return": 0.0,
+                "annual_return": 0.0,
+                "max_drawdown": 0.0,
+                "sharpe": 0.0,
+            },
         )

src/easy_tdx/portfolio/risk.py

@@ -1,4 +1,5 @@
 """简化风险模型。"""
+
 from __future__ import annotations
 
 import numpy as np
@@ -48,4 +49,8 @@ class RiskModel:
         risk_contrib = np.abs(w * marginal)
         total_rc = risk_contrib.sum()
         max_rc = float(risk_contrib.max() / total_rc) if total_rc > 0 else 0.0
-        return {"total_volatility": total_vol, "max_risk_contribution": max_rc, "n_positions": len(codes)}
+        return {
+            "total_volatility": total_vol,
+            "max_risk_contribution": max_rc,
+            "n_positions": len(codes),
+        }

src/easy_tdx/portfolio/types.py

@@ -1,4 +1,5 @@
 """组合管理数据结构。"""
+
 from __future__ import annotations
 
 from dataclasses import dataclass