feat: quant_engine 独立量化引擎（v1.2.0 重构 bootstrap）
从 research_results 抽出纯回测核心能力，形成独立成员仓。 ## 模块（10 个核心） | 模块 | 内容 | |---|---| | alpha_factors | 158 alpha 公式 + 24 基础算子（移植自 qlib alpha158）+ JSONB 工具 | | execution | 执行仿真（成本/滑点/T+1/涨跌停/部分成交/价差）+ 多日 NAV + PnL 拆解 | | indicators | 50+ 技术指标（MACD/KDJ/布林/ATR/ADX/等） | | data_adapter | 桥接 qtdb_pro 长表与新模块（rename/long-wide/复权/vwap 代理） | | backtest | weight-based 多日仿真 | | metrics / perf_stats | 绩效指标 | | factor_library | 通用方法（turnover/IC/winsorize/OLS） | | portfolio_decomp / risk | 组合分解 + 风险指标 | | logging | 统一 logger（标准库 + 可选 loguru） | ## 设计原则 - 零重型依赖（numpy/pandas/scipy） - mypy strict 0 errors（12 source files） - 394 tests passed（从 research_results 复制 + 适配） - ruff clean ## 与 research_results 的关系 - research_results 通过 re-export wrapper 保持向后兼容（src.shared.X → quant_engine.X） - 47 proj 的 import 路径暂时不变，后续逐步迁移 - 本仓角色：researchhub_workspace 引擎层 Co-Authored-By: Mavis <noreply@mavis.local>
This commit is contained in:
George Berkshire
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"""src/shared/execution.py 单元测试（v1.2.0 Phase 0 骨架）。"""
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from __future__ import annotations
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from dataclasses import FrozenInstanceError
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import numpy as np
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import pandas as pd
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import pytest
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from quant_engine.execution import (
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ExecutionConfig,
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ExecutionResult,
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apply_bid_ask_spread,
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apply_volume_constraint,
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check_price_limit,
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check_stop_loss_take_profit,
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check_t1_settlement,
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compute_realized_pnl,
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run_end_to_end_poc,
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simulate_execution,
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simulate_multi_day,
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simulate_with_daily_data,
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total_costs,
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total_turnover,
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)
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# ── ExecutionConfig 校验 ──────────────────────────────
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def test_default_config():
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"""默认配置：A 股典型值。"""
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cfg = ExecutionConfig()
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assert cfg.commission_bps == 3.0
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assert cfg.stamp_tax_bps == 10.0
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assert cfg.slippage_bps == 5.0
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assert cfg.min_trade_amount == 100.0
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def test_config_negative_commission_raises():
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"""负佣金应报错。"""
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with pytest.raises(ValueError, match="non-negative"):
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ExecutionConfig(commission_bps=-1.0)
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def test_config_negative_stamp_tax_raises():
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"""负印花税应报错。"""
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with pytest.raises(ValueError, match="non-negative"):
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ExecutionConfig(stamp_tax_bps=-1.0)
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def test_config_negative_slippage_raises():
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"""负滑点应报错。"""
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with pytest.raises(ValueError, match="non-negative"):
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ExecutionConfig(slippage_bps=-1.0)
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def test_config_negative_min_amount_raises():
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"""负最小成交额应报错。"""
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with pytest.raises(ValueError, match="non-negative"):
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ExecutionConfig(min_trade_amount=-1.0)
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def test_config_is_frozen():
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"""dataclass(frozen=True) 不允许修改。"""
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cfg = ExecutionConfig()
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with pytest.raises(FrozenInstanceError):
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cfg.commission_bps = 100.0 # type: ignore[misc]
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# ── simulate_execution 基础 ──────────────────────────────
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def test_simulate_execution_basic_buy():
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"""买入：目标权重 0.5，组合 100 万 → 目标 50 万 → 实际成交（含滑点）> 50 万。"""
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cfg = ExecutionConfig()
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results = simulate_execution(
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target_weights={"000001.SZ": 0.5},
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portfolio_value=1_000_000.0,
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config=cfg,
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)
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assert len(results) == 1
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r = results[0]
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assert r.stock_code == "000001.SZ"
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assert r.target_value == 500_000.0
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# 滑点 5 bps = 0.0005，500000 * 1.0005 = 500250
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assert r.executed_value == pytest.approx(500_250.0)
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# 佣金 3 bps，按 500250 算 = 150.075
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assert r.commission == pytest.approx(500_250.0 * 3 / 10000)
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# 买入无印花税
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assert r.stamp_tax == 0.0
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# 滑点成本 = executed_value - abs(target_value)
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assert r.slippage_cost == pytest.approx(250.0)
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# 净现金流 = -(executed_value + commission + stamp_tax)
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assert r.net_cash_flow == pytest.approx(-(500_250.0 + 500_250.0 * 3 / 10000))
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def test_simulate_execution_basic_sell():
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"""卖出：目标权重 -0.3（减仓），组合 100 万 → 目标 -30 万。"""
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cfg = ExecutionConfig()
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results = simulate_execution(
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target_weights={"600000.SH": -0.3},
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portfolio_value=1_000_000.0,
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config=cfg,
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)
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assert len(results) == 1
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r = results[0]
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assert r.stock_code == "600000.SH"
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assert r.target_value == -300_000.0
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# 滑点 5 bps = 0.0005，300000 * (1 - 0.0005) = 299850
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assert r.executed_value == pytest.approx(299_850.0)
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# 卖出有印花税 10 bps
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assert r.stamp_tax == pytest.approx(299_850.0 * 10 / 10000)
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# 净现金流 = executed_value - commission - stamp_tax
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assert r.net_cash_flow == pytest.approx(
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299_850.0 - 299_850.0 * 3 / 10000 - 299_850.0 * 10 / 10000,
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)
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def test_simulate_execution_skip_below_min():
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"""成交金额低于 min_trade_amount → 跳过（不返回 ExecutionResult）。"""
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cfg = ExecutionConfig(min_trade_amount=10_000.0)
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results = simulate_execution(
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target_weights={"000001.SZ": 0.005}, # 100万 * 0.005 = 5000 < 10000
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portfolio_value=1_000_000.0,
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config=cfg,
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)
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assert results == []
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def test_simulate_execution_empty_weights():
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"""空权重字典 → 空结果列表。"""
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cfg = ExecutionConfig()
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results = simulate_execution(
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target_weights={},
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portfolio_value=1_000_000.0,
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config=cfg,
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)
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assert results == []
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def test_simulate_execution_multiple_stocks():
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"""多支股票：每支一个 ExecutionResult。"""
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cfg = ExecutionConfig()
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targets = {
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"000001.SZ": 0.3,
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"600000.SH": 0.2,
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"000002.SZ": 0.5,
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}
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results = simulate_execution(
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target_weights=targets,
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portfolio_value=1_000_000.0,
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config=cfg,
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)
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assert len(results) == 3
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codes = {r.stock_code for r in results}
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assert codes == {"000001.SZ", "600000.SH", "000002.SZ"}
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def test_simulate_execution_default_config():
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"""config=None → 使用默认 A 股配置。"""
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results = simulate_execution(
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target_weights={"000001.SZ": 0.5},
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portfolio_value=1_000_000.0,
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)
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assert len(results) == 1
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def test_simulate_execution_zero_costs_config():
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"""零成本配置：executed_value == target_value。"""
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cfg = ExecutionConfig(commission_bps=0, stamp_tax_bps=0, slippage_bps=0)
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results = simulate_execution(
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target_weights={"000001.SZ": 0.5},
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portfolio_value=1_000_000.0,
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config=cfg,
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)
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assert results[0].executed_value == 500_000.0
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assert results[0].commission == 0.0
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assert results[0].stamp_tax == 0.0
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assert results[0].slippage_cost == 0.0
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# ── total_costs / total_turnover ──────────────────────────────
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def test_total_costs_empty():
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"""空结果列表 → 总成本 0。"""
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assert total_costs([]) == 0.0
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def test_total_costs_sum():
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"""总成本 = sum(r.total_cost)。"""
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cfg = ExecutionConfig()
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results = simulate_execution(
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target_weights={"000001.SZ": 0.5, "600000.SH": -0.3},
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portfolio_value=1_000_000.0,
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config=cfg,
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)
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expected = sum(r.total_cost for r in results)
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assert total_costs(results) == expected
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def test_total_turnover_sum():
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"""总换手 = sum(r.executed_value)。"""
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cfg = ExecutionConfig()
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results = simulate_execution(
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target_weights={"000001.SZ": 0.5, "600000.SH": -0.3},
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portfolio_value=1_000_000.0,
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config=cfg,
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)
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expected = sum(r.executed_value for r in results)
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assert total_turnover(results) == expected
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# ── ExecutionResult 数据类 ──────────────────────────────
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def test_execution_result_dataclass():
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"""ExecutionResult 是 dataclass。"""
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r = ExecutionResult(
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stock_code="000001.SZ",
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target_value=500_000.0,
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executed_value=500_250.0,
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commission=150.075,
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stamp_tax=0.0,
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slippage_cost=250.0,
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total_cost=400.075,
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net_cash_flow=-500_400.075,
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)
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assert r.stock_code == "000001.SZ"
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assert r.total_cost == pytest.approx(400.075)
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# ── v1.2.0 Phase 1：止损/止盈（check_stop_loss_take_profit） ──
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def test_stop_loss_triggered():
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"""亏损超过阈值 → 止损。"""
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cfg = ExecutionConfig(stop_loss_pct=0.10)
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assert check_stop_loss_take_profit(100.0, 89.0, cfg) == "stop_loss"
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def test_stop_loss_exact_boundary():
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"""恰好 10% 亏损 → 止损触发（<= -10%）。"""
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cfg = ExecutionConfig(stop_loss_pct=0.10)
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assert check_stop_loss_take_profit(100.0, 90.0, cfg) == "stop_loss"
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def test_stop_loss_not_triggered():
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"""9% 亏损 → 未触发。"""
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cfg = ExecutionConfig(stop_loss_pct=0.10)
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assert check_stop_loss_take_profit(100.0, 91.0, cfg) is None
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def test_take_profit_triggered():
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"""盈利超过阈值 → 止盈。"""
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cfg = ExecutionConfig(take_profit_pct=0.20)
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assert check_stop_loss_take_profit(100.0, 121.0, cfg) == "take_profit"
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def test_take_profit_not_triggered():
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"""15% 盈利 → 未触发（阈值 20%）。"""
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cfg = ExecutionConfig(take_profit_pct=0.20)
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assert check_stop_loss_take_profit(100.0, 115.0, cfg) is None
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def test_no_stop_loss_no_take_profit():
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"""默认配置（inf）→ 永不触发。"""
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cfg = ExecutionConfig() # 默认 inf
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assert check_stop_loss_take_profit(100.0, 50.0, cfg) is None
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assert check_stop_loss_take_profit(100.0, 200.0, cfg) is None
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def test_zero_cost_basis_no_trigger():
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"""成本价 <= 0 → 不触发（避免除零）。"""
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cfg = ExecutionConfig(stop_loss_pct=0.10)
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assert check_stop_loss_take_profit(0.0, 5.0, cfg) is None
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def test_negative_stop_loss_raises():
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"""stop_loss_pct <= 0 应报错。"""
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with pytest.raises(ValueError, match="stop_loss_pct"):
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ExecutionConfig(stop_loss_pct=0.0)
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with pytest.raises(ValueError, match="stop_loss_pct"):
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ExecutionConfig(stop_loss_pct=-0.1)
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def test_negative_take_profit_raises():
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"""take_profit_pct <= 0 应报错。"""
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with pytest.raises(ValueError, match="take_profit_pct"):
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ExecutionConfig(take_profit_pct=0.0)
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# ── v1.2.0 Phase 1：多日仿真（simulate_multi_day） ────────
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def test_simulate_multi_day_basic():
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"""基本多日仿真。"""
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signals = [
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("d1", {"A": 0.5, "B": 0.5}),
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("d2", {"A": 0.6, "B": 0.4}),
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]
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prices = [
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("d1", {"A": 10.0, "B": 20.0}),
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("d2", {"A": 11.0, "B": 21.0}),
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]
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positions = simulate_multi_day(signals, prices, 1_000_000.0)
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assert len(positions) == 2
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assert positions[0].date == "d1"
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assert positions[1].date == "d2"
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def test_simulate_multi_day_empty():
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"""空输入 → 空输出。"""
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assert simulate_multi_day([], [], 1_000_000.0) == []
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def test_simulate_multi_day_length_mismatch_raises():
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"""signals / prices 长度不一致 → 报错。"""
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with pytest.raises(ValueError, match="same length"):
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simulate_multi_day(
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[("d1", {"A": 0.5})],
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[("d1", {"A": 10.0}), ("d2", {"A": 11.0})],
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1_000_000.0,
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)
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def test_simulate_multi_day_first_day_value_equals_initial():
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"""第一天 portfolio_value = initial_cash（无持仓）。"""
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signals = [("d1", {"A": 1.0})]
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prices = [("d1", {"A": 10.0})]
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positions = simulate_multi_day(signals, prices, 1_000_000.0)
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# 第一天 NAV = 1_000_000（无持仓），第二天才是调仓后
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assert positions[0].portfolio_value == 1_000_000.0
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def test_simulate_multi_day_holdings_evolution():
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"""调仓后 holdings 演化。
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注意：positions[i] 是第 i 天 rebalance 之前的快照。
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所以要看 d2 rebalance 后的 holdings，需要看 positions[2]（d3 的快照）。
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"""
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signals = [
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("d1", {"A": 0.5, "B": 0.5}),
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("d2", {"A": 1.0, "B": 0.0}), # 全仓 A
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("d3", {"A": 1.0, "B": 0.0}), # 第三天的快照才能看到 d2 rebalance 后的 holdings
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]
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prices = [
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("d1", {"A": 10.0, "B": 20.0}),
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("d2", {"A": 11.0, "B": 21.0}),
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("d3", {"A": 12.0, "B": 22.0}),
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]
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positions = simulate_multi_day(signals, prices, 1_000_000.0)
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# d3 的 PRE-trade snapshot 应该只有 A（B 在 d2 被平仓）
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assert "B" not in positions[2].holdings
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assert "A" in positions[2].holdings
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# ── v1.2.0 Phase 1：端到端 POC（run_end_to_end_poc） ─────
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def test_run_end_to_end_poc_basic():
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"""端到端 POC 基本 smoke test。"""
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signals = [
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("d1", {"A": 0.5, "B": 0.5}),
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("d2", {"A": 0.6, "B": 0.4}),
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("d3", {"A": 0.5, "B": 0.5}),
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]
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prices = [
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("d1", {"A": 10.0, "B": 20.0}),
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("d2", {"A": 11.0, "B": 21.0}),
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("d3", {"A": 12.0, "B": 22.0}),
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]
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result = run_end_to_end_poc(signals, prices, 1_000_000.0)
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assert "positions" in result
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assert "nav_series" in result
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assert "total_costs" in result
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assert "total_turnover" in result
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assert "total_rebalances" in result
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assert "final_portfolio_value" in result
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assert "return_pct" in result
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def test_run_end_to_end_poc_nav_increases_with_bull_market():
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"""上涨行情：NAV 上升。"""
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signals = [
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("d1", {"A": 1.0}),
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("d2", {"A": 1.0}),
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("d3", {"A": 1.0}),
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]
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prices = [
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("d1", {"A": 10.0}),
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("d2", {"A": 11.0}),
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("d3", {"A": 12.1}),
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]
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result = run_end_to_end_poc(signals, prices, 1_000_000.0)
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nav = result["nav_series"]
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# 持续上涨
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assert nav.iloc[-1] > nav.iloc[0]
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def test_run_end_to_end_poc_with_alpha158_signal():
|
||||
"""与 alpha158 因子联动（端到端 POC 真实场景）。"""
|
||||
import numpy as np
|
||||
from quant_engine.alpha_factors import alpha_005
|
||||
|
||||
np.random.seed(123)
|
||||
n_days = 30
|
||||
# 模拟单只股票
|
||||
close = pd.Series(np.cumsum(np.random.randn(n_days)) + 100)
|
||||
volume = pd.Series(np.random.rand(n_days) * 1000 + 1000)
|
||||
# 计算 alpha_005（相关性）
|
||||
alpha_series = alpha_005(close, volume).fillna(0)
|
||||
# 信号：alpha > 0 时持有，否则空仓
|
||||
signals = []
|
||||
prices = []
|
||||
for i, (date_idx, alpha_val) in enumerate(zip(close.index, alpha_series)):
|
||||
date = f"d{i + 1}"
|
||||
target = {"A": 1.0} if alpha_val > 0 else {"A": 0.0}
|
||||
signals.append((date, target))
|
||||
prices.append((date, {"A": float(close.iloc[date_idx])}))
|
||||
result = run_end_to_end_poc(signals, prices, 1_000_000.0)
|
||||
# 至少有一次调仓
|
||||
assert result["total_rebalances"] > 0
|
||||
|
||||
|
||||
def test_run_end_to_end_poc_empty_signals():
|
||||
"""空 signals → NAV 全是 initial_cash。"""
|
||||
result = run_end_to_end_poc([], [], 1_000_000.0)
|
||||
assert result["final_portfolio_value"] == 1_000_000.0
|
||||
assert result["return_pct"] == 0.0
|
||||
|
||||
|
||||
def test_run_end_to_end_poc_length_mismatch_raises():
|
||||
"""signals / prices 长度不一致 → 报错。"""
|
||||
with pytest.raises(ValueError, match="same length"):
|
||||
run_end_to_end_poc(
|
||||
[("d1", {"A": 1.0})],
|
||||
[("d1", {"A": 10.0}), ("d2", {"A": 11.0})],
|
||||
1_000_000.0,
|
||||
)
|
||||
|
||||
|
||||
def test_run_end_to_end_poc_costs_recorded():
|
||||
"""有调仓 → total_costs > 0。"""
|
||||
signals = [("d1", {"A": 1.0})]
|
||||
prices = [("d1", {"A": 10.0})]
|
||||
result = run_end_to_end_poc(signals, prices, 1_000_000.0)
|
||||
assert result["total_costs"] > 0
|
||||
assert result["total_turnover"] > 0
|
||||
|
||||
|
||||
# ── v1.2.0 Phase 2: T+1 / 涨跌停 / 部分成交 / 买卖价差 ─────
|
||||
|
||||
|
||||
# ── check_t1_settlement ────────────────────────────────────────
|
||||
|
||||
|
||||
def test_t1_disabled_always_allows():
|
||||
"""T+1 关闭 → 总是可以卖。"""
|
||||
assert check_t1_settlement("2024-01-01", "2024-01-01", enable_t1=False) is True
|
||||
assert check_t1_settlement("2024-01-01", "2024-01-02", enable_t1=False) is True
|
||||
|
||||
|
||||
def test_t1_enabled_same_day_blocked():
|
||||
"""T+1 启用 + 同日 → 不能卖。"""
|
||||
assert check_t1_settlement("2024-01-01", "2024-01-01", enable_t1=True) is False
|
||||
|
||||
|
||||
def test_t1_enabled_next_day_allowed():
|
||||
"""T+1 启用 + 次日 → 可以卖。"""
|
||||
assert check_t1_settlement("2024-01-01", "2024-01-02", enable_t1=True) is True
|
||||
|
||||
|
||||
# ── check_price_limit ────────────────────────────────────────
|
||||
|
||||
|
||||
def test_price_limit_normal_trade():
|
||||
"""正常价格（昨收±5%）→ 可以成交。"""
|
||||
cfg = ExecutionConfig()
|
||||
can_trade, reason = check_price_limit(
|
||||
prev_close=10.0, current_price=10.3, config=cfg, is_sell=True
|
||||
)
|
||||
assert can_trade is True
|
||||
assert reason == ""
|
||||
|
||||
|
||||
def test_price_limit_up_blocks_buy():
|
||||
"""涨停（+10%）→ 不能买。"""
|
||||
cfg = ExecutionConfig(price_limit_up_pct=0.10)
|
||||
can_trade, reason = check_price_limit(
|
||||
prev_close=10.0, current_price=11.5, config=cfg, is_sell=False
|
||||
)
|
||||
assert can_trade is False
|
||||
assert "limit_up" in reason
|
||||
|
||||
|
||||
def test_price_limit_down_blocks_sell():
|
||||
"""跌停（-10%）→ 不能卖。"""
|
||||
cfg = ExecutionConfig(price_limit_down_pct=0.10)
|
||||
can_trade, reason = check_price_limit(
|
||||
prev_close=10.0, current_price=8.5, config=cfg, is_sell=True
|
||||
)
|
||||
assert can_trade is False
|
||||
assert "limit_down" in reason
|
||||
|
||||
|
||||
def test_price_limit_chinext_20pct():
|
||||
"""创业板 ±20% 限制。"""
|
||||
cfg = ExecutionConfig(price_limit_up_pct=0.20, price_limit_down_pct=0.20)
|
||||
# +15% 在创业板允许范围
|
||||
can, _ = check_price_limit(10.0, 11.5, cfg, is_sell=False)
|
||||
assert can is True
|
||||
# +25% 触发涨停
|
||||
can, _reason = check_price_limit(10.0, 12.5, cfg, is_sell=False)
|
||||
assert can is False
|
||||
|
||||
|
||||
def test_price_limit_invalid_prev_close():
|
||||
"""prev_close <= 0 → 不做检查（返回 True）。"""
|
||||
cfg = ExecutionConfig()
|
||||
can, _ = check_price_limit(0.0, 10.0, cfg, is_sell=True)
|
||||
assert can is True
|
||||
|
||||
|
||||
# ── apply_volume_constraint ────────────────────────────────────────
|
||||
|
||||
|
||||
def test_volume_constraint_default_unlimited():
|
||||
"""默认 max_volume_pct=1.0 → 不限制。"""
|
||||
cfg = ExecutionConfig()
|
||||
actual = apply_volume_constraint(target_shares=1000, daily_volume_shares=100, config=cfg)
|
||||
assert actual == 100.0 # 100 * 1.0 = 100（限制是 100%）
|
||||
|
||||
|
||||
def test_volume_constraint_caps():
|
||||
"""max_volume_pct=0.05 → 单笔最大 5% 当日量。"""
|
||||
cfg = ExecutionConfig(max_volume_pct=0.05)
|
||||
actual = apply_volume_constraint(target_shares=1000, daily_volume_shares=1000, config=cfg)
|
||||
assert actual == 50.0 # 1000 * 0.05 = 50
|
||||
|
||||
|
||||
def test_volume_constraint_target_below_cap():
|
||||
"""目标低于上限 → 不变。"""
|
||||
cfg = ExecutionConfig(max_volume_pct=0.05)
|
||||
actual = apply_volume_constraint(target_shares=10, daily_volume_shares=1000, config=cfg)
|
||||
assert actual == 10.0
|
||||
|
||||
|
||||
def test_volume_constraint_zero_volume():
|
||||
"""当日成交量为 0 → 不能成交。"""
|
||||
cfg = ExecutionConfig(max_volume_pct=0.05)
|
||||
actual = apply_volume_constraint(target_shares=100, daily_volume_shares=0, config=cfg)
|
||||
assert actual == 0.0
|
||||
|
||||
|
||||
def test_volume_constraint_invalid_config_raises():
|
||||
"""max_volume_pct=0 应报错。"""
|
||||
with pytest.raises(ValueError, match="max_volume_pct"):
|
||||
ExecutionConfig(max_volume_pct=0.0)
|
||||
|
||||
|
||||
# ── apply_bid_ask_spread ────────────────────────────────────────
|
||||
|
||||
|
||||
def test_bid_ask_spread_zero_default():
|
||||
"""默认 spread=0 → 无影响。"""
|
||||
cfg = ExecutionConfig()
|
||||
assert apply_bid_ask_spread(1000.0, is_buy=True, config=cfg) == 1000.0
|
||||
assert apply_bid_ask_spread(1000.0, is_buy=False, config=cfg) == 1000.0
|
||||
|
||||
|
||||
def test_bid_ask_spread_buy():
|
||||
"""买入：实际支付 = target + spread。"""
|
||||
cfg = ExecutionConfig(bid_ask_spread_bps=10.0) # 10 bps = 0.1%
|
||||
actual = apply_bid_ask_spread(1000.0, is_buy=True, config=cfg)
|
||||
assert actual == pytest.approx(1001.0)
|
||||
|
||||
|
||||
def test_bid_ask_spread_sell():
|
||||
"""卖出：实际获得 = target - spread。"""
|
||||
cfg = ExecutionConfig(bid_ask_spread_bps=10.0)
|
||||
actual = apply_bid_ask_spread(1000.0, is_buy=False, config=cfg)
|
||||
assert actual == pytest.approx(999.0)
|
||||
|
||||
|
||||
def test_bid_ask_spread_negative_raises():
|
||||
"""bid_ask_spread_bps < 0 应报错。"""
|
||||
with pytest.raises(ValueError, match="bid_ask_spread"):
|
||||
ExecutionConfig(bid_ask_spread_bps=-1.0)
|
||||
|
||||
|
||||
# ── ExecutionConfig 新字段校验 ────────────────────────────────
|
||||
|
||||
|
||||
def test_execution_config_t1_default_false():
|
||||
"""默认 T+1 关闭。"""
|
||||
cfg = ExecutionConfig()
|
||||
assert cfg.enable_t1_settlement is False
|
||||
|
||||
|
||||
def test_execution_config_price_limit_default_10pct():
|
||||
"""默认涨跌停 ±10%（主板）。"""
|
||||
cfg = ExecutionConfig()
|
||||
assert cfg.price_limit_up_pct == 0.10
|
||||
assert cfg.price_limit_down_pct == 0.10
|
||||
|
||||
|
||||
def test_execution_config_max_volume_default_100pct():
|
||||
"""默认 max_volume_pct=1.0（不限）。"""
|
||||
cfg = ExecutionConfig()
|
||||
assert cfg.max_volume_pct == 1.0
|
||||
|
||||
|
||||
def test_execution_config_invalid_price_limit_raises():
|
||||
"""price_limit_up_pct > 1 应报错。"""
|
||||
with pytest.raises(ValueError, match="price_limit_up_pct"):
|
||||
ExecutionConfig(price_limit_up_pct=1.5)
|
||||
|
||||
|
||||
def test_execution_result_partial_fill_default():
|
||||
"""ExecutionResult 默认 partial_fill_pct=1.0（全部成交）。"""
|
||||
r = ExecutionResult(
|
||||
stock_code="A",
|
||||
target_value=1000.0,
|
||||
executed_value=1000.0,
|
||||
commission=3.0,
|
||||
stamp_tax=0.0,
|
||||
slippage_cost=0.0,
|
||||
total_cost=3.0,
|
||||
net_cash_flow=-1003.0,
|
||||
)
|
||||
assert r.partial_fill_pct == 1.0
|
||||
assert r.blocked_reason == ""
|
||||
|
||||
|
||||
# ── v1.2.0 O: simulate_with_daily_data + compute_realized_pnl ─────
|
||||
|
||||
|
||||
def test_simulate_with_daily_data_basic():
|
||||
"""simulate_with_daily_data 应返回 DailyPosition 列表。"""
|
||||
dates = pd.date_range("2024-01-01", periods=10)
|
||||
prices = pd.DataFrame(
|
||||
{f"S{i}": 10 + np.random.rand(10) * 0.1 for i in range(3)},
|
||||
index=dates,
|
||||
)
|
||||
positions = simulate_with_daily_data(prices, initial_cash=1_000_000.0)
|
||||
assert len(positions) == 10
|
||||
|
||||
|
||||
def test_simulate_with_daily_data_default_equal_weight():
|
||||
"""默认 target_weights=None → 等权。"""
|
||||
dates = pd.date_range("2024-01-01", periods=5)
|
||||
prices = pd.DataFrame(
|
||||
{f"S{i}": 10 + np.random.rand(5) * 0.1 for i in range(3)},
|
||||
index=dates,
|
||||
)
|
||||
positions = simulate_with_daily_data(prices, initial_cash=300_000.0)
|
||||
# 第一天 100k / 3 ≈ 33333 per stock
|
||||
assert positions[1].cash < 300_000.0 # 第一天后 cash 减少
|
||||
|
||||
|
||||
def test_simulate_with_daily_data_custom_weights():
|
||||
"""自定义 target_weights_history 应生效。"""
|
||||
dates = pd.date_range("2024-01-01", periods=5)
|
||||
prices = pd.DataFrame(
|
||||
{f"S{i}": 10 + np.random.rand(5) * 0.1 for i in range(3)},
|
||||
index=dates,
|
||||
)
|
||||
# 第一天全仓 S0，之后空仓
|
||||
targets = [{"S0": 1.0, "S1": 0.0, "S2": 0.0}] + [{"S0": 0.0}] * 4
|
||||
positions = simulate_with_daily_data(
|
||||
prices,
|
||||
initial_cash=1_000_000.0,
|
||||
target_weights_history=targets,
|
||||
)
|
||||
assert len(positions) == 5
|
||||
|
||||
|
||||
def test_simulate_with_daily_data_length_mismatch_raises():
|
||||
"""target_weights_history 长度不一致应报错。"""
|
||||
dates = pd.date_range("2024-01-01", periods=5)
|
||||
prices = pd.DataFrame({"S0": [10.0] * 5}, index=dates)
|
||||
with pytest.raises(ValueError, match="length"):
|
||||
simulate_with_daily_data(
|
||||
prices,
|
||||
target_weights_history=[{"S0": 1.0}],
|
||||
initial_cash=1_000_000,
|
||||
)
|
||||
|
||||
|
||||
def test_simulate_with_daily_data_empty():
|
||||
"""空输入 → 空输出。"""
|
||||
prices = pd.DataFrame()
|
||||
positions = simulate_with_daily_data(prices)
|
||||
assert positions == []
|
||||
|
||||
|
||||
def test_compute_realized_pnl_basic():
|
||||
"""compute_realized_pnl 应返回 DailyPnL 列表（与 positions 同长度）。"""
|
||||
dates = pd.date_range("2024-01-01", periods=10)
|
||||
prices = pd.DataFrame(
|
||||
{f"S{i}": 10 + np.random.rand(10) * 0.1 for i in range(3)},
|
||||
index=dates,
|
||||
)
|
||||
positions = simulate_with_daily_data(prices, initial_cash=1_000_000.0)
|
||||
pnl_list = compute_realized_pnl(positions)
|
||||
assert len(pnl_list) == 10
|
||||
|
||||
|
||||
def test_compute_realized_pnl_empty():
|
||||
"""空 positions → 空 pnl 列表。"""
|
||||
pnl_list = compute_realized_pnl([])
|
||||
assert pnl_list == []
|
||||
|
||||
|
||||
def test_compute_realized_pnl_first_day_zero():
|
||||
"""第一天没有前值，realized 应为 0（pnl=0）。"""
|
||||
dates = pd.date_range("2024-01-01", periods=3)
|
||||
prices = pd.DataFrame({"S0": [10.0, 11.0, 12.0]}, index=dates)
|
||||
positions = simulate_with_daily_data(prices, initial_cash=1_000_000.0)
|
||||
pnl_list = compute_realized_pnl(positions)
|
||||
# 第一天 realized = 0（因为 prev_value = cur_value 时 nav_change = 0）
|
||||
assert pnl_list[0].total_pnl == 0.0
|
||||
|
||||
|
||||
def test_compute_realized_pnl_sell_realizes():
|
||||
"""卖出后 realized_pnl 应反映已实现部分。"""
|
||||
dates = pd.date_range("2024-01-01", periods=3)
|
||||
# 价格大涨 100%，第二天全卖
|
||||
prices = pd.DataFrame({"S0": [10.0, 20.0, 20.0]}, index=dates)
|
||||
targets = [
|
||||
{"S0": 1.0}, # 第一天买入
|
||||
{"S0": 0.0}, # 第二天全卖
|
||||
{"S0": 0.0}, # 第三天空仓
|
||||
]
|
||||
positions = simulate_with_daily_data(
|
||||
prices,
|
||||
initial_cash=1_000_000.0,
|
||||
target_weights_history=targets,
|
||||
)
|
||||
pnl_list = compute_realized_pnl(positions)
|
||||
# 第三天（卖出兑现）应有 realized 正利润（cash 从 -800 → 2M = +2M）
|
||||
assert pnl_list[2].realized_pnl > 0
|
||||
|
||||
|
||||
# ── O3: end-to-end 端到端测试（集成多个函数） ──────────────
|
||||
|
||||
|
||||
def test_execution_end_to_end_bull_market():
|
||||
"""上涨行情：NAV 应明显增长。"""
|
||||
dates = pd.date_range("2024-01-01", periods=30)
|
||||
# 持续上涨 50%
|
||||
base = 10 + np.arange(30) * 0.5
|
||||
prices = pd.DataFrame(
|
||||
{f"S{i}": base + np.random.rand(30) * 0.2 for i in range(5)},
|
||||
index=dates,
|
||||
)
|
||||
# 等权持有
|
||||
positions = simulate_with_daily_data(prices, initial_cash=1_000_000.0)
|
||||
# NAV 应增长 ~50%
|
||||
final_value = positions[-1].portfolio_value
|
||||
assert final_value > 1_100_000 # 至少 +10%
|
||||
|
||||
|
||||
def test_execution_end_to_end_with_pnl_breakdown():
|
||||
"""完整 end-to-end：prices → positions → pnl。"""
|
||||
dates = pd.date_range("2024-01-01", periods=20)
|
||||
prices = pd.DataFrame(
|
||||
{f"S{i}": 10 + np.random.rand(20) * 0.1 for i in range(3)},
|
||||
index=dates,
|
||||
)
|
||||
positions = simulate_with_daily_data(prices, initial_cash=1_000_000.0)
|
||||
pnl_list = compute_realized_pnl(positions)
|
||||
# 累计 PnL 应等于 final_value - initial_cash（理论上）
|
||||
total_pnl_sum = sum(p.total_pnl for p in pnl_list)
|
||||
nav_change = positions[-1].portfolio_value - positions[0].portfolio_value
|
||||
# 允许微小差异（因为是 NAV 变化减 realized 的累计）
|
||||
assert abs(total_pnl_sum - nav_change) < 1.0
|
||||
Reference in New Issue
Block a user