mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 16:54:20 +08:00
awayingsandClaude
990b4a7802
- backtest_usage（742→485）：CLI 章移交 cli-backtest.md，示例/注意事项 抽至 backtest-examples.md，重建目录 - quantitative-guide（628→325）：第 5-7 章（滑点/执行仿真/归因/工作流） 抽至 quantitative-advanced.md 并重编号 - api_reference（704→478）：删除过期版本横幅（1.16.2）与快速开始教程段； 数据模型/枚举与 field_mapping.md 逐表核对后去重（field_mapping 为唯一权威）； WebSocket 节随 web-api.md 合并移除 - field_mapping：吸收 MAC 协议枚举（Period/Adjust/Category/BoardType/ SortType/ExMarket），全部文档回到 ≤500 行 Co-Authored-By: Claude <noreply@anthropic.com>
309 lines
10 KiB
Markdown
309 lines
10 KiB
Markdown
# 量化进阶：执行仿真与归因
|
||
|
||
滑点建模、执行仿真（TWAP/VWAP/限价单）、归因分析与完整工作流。因子/组合基础见 [quantitative-guide.md](./quantitative-guide.md)。
|
||
|
||
## 1. 高级回测
|
||
|
||
### 1.1 滑点模型
|
||
|
||
4 种可插拔滑点模型，替代原有固定滑点：
|
||
|
||
```python
|
||
from easy_tdx.backtest import BacktestEngine
|
||
from easy_tdx.backtest.slippage import (
|
||
FixedSlippage,
|
||
PercentSlippage,
|
||
SquareRootSlippage,
|
||
VolumeSlippage,
|
||
)
|
||
|
||
# 1. 固定每股滑点（与旧行为一致）
|
||
model1 = FixedSlippage(per_share=0.01)
|
||
|
||
# 2. 按金额百分比
|
||
model2 = PercentSlippage(rate=0.001)
|
||
|
||
# 3. 方根市场冲击模型（Almgren-Chriss 简化版）
|
||
# impact = sigma * sqrt(participation_rate) * price * size * coeff
|
||
# A 股量化主流：参与率 >5% 时冲击显著
|
||
model3 = SquareRootSlippage(impact_coeff=0.1)
|
||
|
||
# 4. 成交量比例滑点
|
||
model4 = VolumeSlippage(base_bps=10.0)
|
||
|
||
# 在 BacktestEngine 中使用
|
||
engine = BacktestEngine(
|
||
MyStrategy,
|
||
cash=1_000_000,
|
||
slippage_model=SquareRootSlippage(impact_coeff=0.1),
|
||
)
|
||
result = engine.run(df)
|
||
```
|
||
|
||
**模型选择建议**：
|
||
|
||
| 场景 | 推荐模型 | 参数 |
|
||
|------|---------|------|
|
||
| 快速原型 | `FixedSlippage` | `per_share=0.01` |
|
||
| 中频策略 | `PercentSlippage` | `rate=0.001` |
|
||
| 大额订单 | `SquareRootSlippage` | `impact_coeff=0.1` |
|
||
| 低流动性股票 | `VolumeSlippage` | `base_bps=10.0` |
|
||
|
||
### 1.2 执行仿真
|
||
|
||
4 种执行模型，将单笔信号拆分为多笔子交易：
|
||
|
||
```python
|
||
from easy_tdx.backtest.execution import (
|
||
ImmediateExecution,
|
||
TWAPExecution,
|
||
VWAPExecution,
|
||
LimitExecution,
|
||
)
|
||
|
||
# 1. 即时成交（默认，与旧行为一致）
|
||
exec1 = ImmediateExecution()
|
||
|
||
# 2. TWAP：时间加权平均价格，N 根 K 线均匀拆单
|
||
exec2 = TWAPExecution(n_bars=5)
|
||
|
||
# 3. VWAP：成交量加权平均价格，按历史量分布拆单
|
||
exec3 = VWAPExecution(n_bars=5, volume_lookback=20)
|
||
|
||
# 4. 限价单：目标价挂单，TTL 内未触发则放弃
|
||
exec4 = LimitExecution(ttl_bars=5)
|
||
|
||
# 在 BacktestEngine 中使用
|
||
engine = BacktestEngine(
|
||
MyStrategy,
|
||
cash=1_000_000,
|
||
execution_model=TWAPExecution(n_bars=3),
|
||
slippage_model=SquareRootSlippage(),
|
||
)
|
||
result = engine.run(df)
|
||
```
|
||
|
||
**执行模型选择**：
|
||
|
||
| 场景 | 推荐模型 | 参数 |
|
||
|------|---------|------|
|
||
| 小额/快速验证 | `ImmediateExecution` | 默认 |
|
||
| 大额建仓/平仓 | `TWAPExecution` | `n_bars=3~5` |
|
||
| 追踪 VWAP 基准 | `VWAPExecution` | `n_bars=5` |
|
||
| 精确入场价位 | `LimitExecution` | `ttl_bars=5` |
|
||
|
||
**TWAP vs VWAP 示例**：
|
||
|
||
```python
|
||
# TWAP: 300 股拆成 3 笔 100 股，在 bar 1/2/3 以 close 执行
|
||
engine = BacktestEngine(
|
||
MyStrategy, cash=100_000,
|
||
execution_model=TWAPExecution(n_bars=3),
|
||
)
|
||
|
||
# VWAP: 按成交量分布拆 300 股 — 成交量大的 bar 分配更多
|
||
engine = BacktestEngine(
|
||
MyStrategy, cash=100_000,
|
||
execution_model=VWAPExecution(n_bars=3, volume_lookback=20),
|
||
)
|
||
|
||
# 限价单：在 50 元挂买入，5 根 K 线内 low <= 50 才成交
|
||
class LimitBuyStrategy(Strategy):
|
||
def init(self): pass
|
||
def next(self):
|
||
if self._bar_index == 0:
|
||
self.buy(size=100, price=50.0) # 指定限价
|
||
|
||
engine = BacktestEngine(
|
||
LimitBuyStrategy, cash=100_000,
|
||
execution_model=LimitExecution(ttl_bars=5),
|
||
)
|
||
```
|
||
|
||
### 1.3 归因分析
|
||
|
||
从回测结果生成归因报告：
|
||
|
||
```python
|
||
from easy_tdx.backtest import BacktestEngine
|
||
from easy_tdx.backtest.attribution import AttributionAnalyzer
|
||
|
||
# 运行回测
|
||
engine = BacktestEngine(MyStrategy, cash=1_000_000)
|
||
result = engine.run(df)
|
||
|
||
# --- 成本归因 ---
|
||
analyzer = AttributionAnalyzer(result.trades, result.equity_curve)
|
||
cost_report = analyzer.cost_attribution()
|
||
print(f"总收益: {cost_report.total_return:.2%}")
|
||
print(f"总交易成本: {cost_report.total_trade_cost:.0f} 元")
|
||
print(f" 佣金: {cost_report.commission_cost:.0f}")
|
||
print(f" 滑点: {cost_report.slippage_cost:.0f}")
|
||
print(f" 印花税: {cost_report.stamp_tax_cost:.0f}")
|
||
|
||
# --- Brinson 归因（需要基准）---
|
||
import numpy as np
|
||
import pandas as pd
|
||
# 构造基准曲线（如沪深300）
|
||
benchmark = pd.DataFrame({
|
||
"datetime": result.equity_curve["datetime"],
|
||
"total": np.linspace(100000, 108000, len(result.equity_curve)),
|
||
})
|
||
analyzer = AttributionAnalyzer(result.trades, result.equity_curve, benchmark=benchmark)
|
||
brinson_report = analyzer.brinson_attribution()
|
||
print(f"配置贡献: {brinson_report.allocation_return:.2%}")
|
||
print(f"选股贡献: {brinson_report.selection_return:.2%}")
|
||
print(f"交叉效应: {brinson_report.interaction_return:.2%}")
|
||
|
||
# --- 因子归因（需要因子数据）---
|
||
exposures = pd.DataFrame({"momentum": [0.5, 0.3, 0.2], "quality": [0.1, -0.1, 0.0]})
|
||
returns = pd.DataFrame({"momentum": [0.05, 0.03, 0.02], "quality": [0.01, -0.02, 0.0]})
|
||
analyzer = AttributionAnalyzer(
|
||
result.trades, result.equity_curve,
|
||
factor_exposures=exposures, factor_returns=returns,
|
||
)
|
||
factor_report = analyzer.factor_attribution()
|
||
for name, ret in factor_report.factor_returns.items():
|
||
print(f" {name}: {ret:.4f}")
|
||
print(f"特质收益: {factor_report.specific_return:.4f}")
|
||
|
||
# --- 完整报告（自动选择最佳归因模式）---
|
||
full_report = analyzer.full_report()
|
||
```
|
||
|
||
**归因模式优先级**：因子归因 > Brinson 归因 > 成本归因。`full_report()` 自动选择数据最完整的模式。
|
||
|
||
---
|
||
|
||
## 2. CLI 命令
|
||
|
||
```bash
|
||
# 列出所有内置因子
|
||
easy-tdx factor list --table
|
||
|
||
# 因子分析（需要数据，输出示例代码）
|
||
easy-tdx factor analyze momentum_20d
|
||
|
||
# 组合因子回测（需要数据，输出示例代码）
|
||
easy-tdx pfactor backtest momentum_20d --n-stocks 10 --optimizer factor_weighted
|
||
```
|
||
|
||
CLI 命令输出 Python API 示例代码，方便复制使用。完整的因子计算和组合回测建议通过 Python API 完成。
|
||
|
||
---
|
||
|
||
## 3. 完整工作流示例
|
||
|
||
从数据获取到组合回测再到归因分析的完整管道：
|
||
|
||
```python
|
||
"""
|
||
easy-tdx 量化研究完整工作流示例。
|
||
|
||
依赖: pip install easy-tdx
|
||
"""
|
||
|
||
from easy_tdx import TdxClient, Market, KlineCategory
|
||
from easy_tdx.factor import FactorEngine, FactorAnalyzer, preprocess
|
||
from easy_tdx.portfolio import RebalanceEngine, FactorWeightedOptimizer
|
||
from easy_tdx.backtest import BacktestEngine
|
||
from easy_tdx.backtest.slippage import SquareRootSlippage
|
||
from easy_tdx.backtest.execution import TWAPExecution
|
||
from easy_tdx.backtest.attribution import AttributionAnalyzer
|
||
|
||
# ── 1. 数据获取 ──────────────────────────────────────
|
||
client = TdxClient()
|
||
stock_pool = ["000001", "000858", "600519", "600036", "601318",
|
||
"000333", "002415", "601012", "600276", "000568"]
|
||
|
||
data = {}
|
||
for code in stock_pool:
|
||
market = Market.SH if code.startswith("6") else Market.SZ
|
||
data[code] = client.get_security_bars(
|
||
market, code, KlineCategory.DAY, 0, 500
|
||
)
|
||
print(f"获取 {len(data)} 只股票数据")
|
||
|
||
# ── 2. 因子计算 ──────────────────────────────────────
|
||
engine = FactorEngine()
|
||
factor_data = engine.compute_cross_section(
|
||
data, ["momentum_20d", "volatility_20d", "rsi_14"]
|
||
)
|
||
print(f"截面因子数据: {len(factor_data)} 行")
|
||
|
||
# ── 3. 因子预处理 ─────────────────────────────────────
|
||
clean = preprocess(
|
||
factor_data,
|
||
factor_names=["momentum_20d", "volatility_20d", "rsi_14"],
|
||
steps=["winsorize", "zscore", "fill_missing"],
|
||
)
|
||
|
||
# ── 4. 因子分析 ──────────────────────────────────────
|
||
forward_returns = engine.compute_forward_returns(data, period=5)
|
||
|
||
for factor_name in ["momentum_20d", "volatility_20d", "rsi_14"]:
|
||
analyzer = FactorAnalyzer(clean, forward_returns)
|
||
report = analyzer.full_report(factor_name)
|
||
print(f"\n── {factor_name} ──")
|
||
print(f" IC均值: {report.mean_ic:.4f} ICIR: {report.icir:.4f}")
|
||
print(f" 多头年化: {report.long_only_annual:.2%}")
|
||
print(f" 多空夏普: {report.long_short_sharpe:.4f}")
|
||
|
||
# ── 5. 组合回测 ──────────────────────────────────────
|
||
rebalancer = RebalanceEngine(
|
||
optimizer=FactorWeightedOptimizer(),
|
||
factor_name="momentum_20d",
|
||
n_stocks=5,
|
||
rebalance_freq="M",
|
||
cash=1_000_000,
|
||
)
|
||
result = rebalancer.run(data, start_date=20230101, end_date=20240101)
|
||
print(f"\n── 组合回测 ──")
|
||
print(f" 总收益: {result.performance['total_return']:.2%}")
|
||
print(f" 年化: {result.performance['annual_return']:.2%}")
|
||
print(f" 最大回撤: {result.performance['max_drawdown']:.2%}")
|
||
print(f" 夏普: {result.performance['sharpe']:.4f}")
|
||
|
||
# ── 6. 高级单策略回测（滑点 + 执行仿真）──────
|
||
from easy_tdx.backtest import Strategy
|
||
|
||
class MomentumStrategy(Strategy):
|
||
def init(self):
|
||
pass
|
||
def next(self):
|
||
if self._bar_index < 20:
|
||
return
|
||
ret = (self.data.close[0] - self.data.close[-20]) / self.data.close[-20]
|
||
if ret > 0.05 and self.position["size"] == 0:
|
||
self.buy(size=0)
|
||
elif ret < -0.03 and self.position["size"] > 0:
|
||
self.sell(size=0)
|
||
|
||
bt_engine = BacktestEngine(
|
||
MomentumStrategy,
|
||
cash=500_000,
|
||
slippage_model=SquareRootSlippage(impact_coeff=0.1),
|
||
execution_model=TWAPExecution(n_bars=3),
|
||
)
|
||
# 选一只股票做回测
|
||
bt_result = bt_engine.run(data["600519"])
|
||
print(f"\n── 高级回测（600519）──")
|
||
print(f" 总收益: {bt_result.performance['total_return']:.2%}")
|
||
print(f" 夏普: {bt_result.performance['sharpe']:.4f}")
|
||
|
||
# ── 7. 归因分析 ──────────────────────────────────────
|
||
att_analyzer = AttributionAnalyzer(bt_result.trades, bt_result.equity_curve)
|
||
cost_report = att_analyzer.cost_attribution()
|
||
print(f"\n── 成本归因 ──")
|
||
print(f" 总交易成本: {cost_report.total_trade_cost:.0f} 元")
|
||
print(f" 佣金: {cost_report.commission_cost:.0f}")
|
||
print(f" 滑点: {cost_report.slippage_cost:.0f}")
|
||
print(f" 印花税: {cost_report.stamp_tax_cost:.0f}")
|
||
|
||
print("\n完成。")
|
||
client.close()
|
||
```
|
||
|
||
---
|
||
|