claude_dev
164690373f
- 资金占用成本(spec§195): StrategyRunner.daily_borrow_cost(used×risk_free/365) 归因per_strategy_pnl(不碰account总账, account.equity真实净值不变); config risk_free_rate=0.02; engine.step mark_to_market后计扣; =0向后兼容跳过 - 分红送股(spec§295): dividend_source.py(akshare stock_history_dividend_detail, 实测600000/000001纯现金分红); PositionLedger.apply_split(volume×factor/avg÷factor); Account.apply_cash_dividend; engine._apply_dividends(除权日调整,现金先split后); mark_to_market停牌prev_close兜底(今收→前收→均价); _run_replay注入dividends日历 - 修_restore_ledger预存bug: PositionLedger.__init__加volume/frozen/avg_price参数 (原只symbol, live_orchestrator跨日恢复4参数调用会TypeError, 首次step空仓未暴露) - 139 passed(119基准+20分红+3占用成本), 无回归 - live_step dividends注入待分期项(每日拉全市场分红慢, 需run_daily_update预拉日历)
113 lines
3.9 KiB
Python
113 lines
3.9 KiB
Python
"""资金占用成本（borrow cost）归因测试（spec §195）。
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每策略占用资金按无风险利率日扣，归因到 per_strategy_pnl；不影响账户总账净值。
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"""
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import json
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from types import SimpleNamespace
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import pytest
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from sanguo_trader.account import Account
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from sanguo_trader.cta_adapter import PaperCtaEngine
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from sanguo_trader.engine import PaperEngine
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from sanguo_trader.models import AccountConfig, MatchSession
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from sanguo_trader.persistence import init_db, list_daily_balance, save_account
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from sanguo_trader.position_ledger import PositionLedger
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from sanguo_trader.strategy_runner import StrategyRunner
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def _bar(date, o, h, l, c):
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return SimpleNamespace(open_price=o, high_price=h, low_price=l,
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close_price=c, datetime=date)
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class _FakeDataSource:
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def __init__(self, sections):
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self.sections = sections
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def iter_bars(self, symbols, start, end, interval, adjust="qfq", cfg=None):
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del symbols, start, end, interval, adjust, cfg
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for date, bars in self.sections:
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yield date, bars
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class _NoOrderStrategy:
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"""空策略：on_bar 不下单（仅驱动 step 走完盯市/归因路径）。"""
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def __init__(self, engine, vt_symbol):
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self.cta_engine = engine
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self.vt_symbol = vt_symbol
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def on_bar(self, bar):
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pass
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def _make_position(symbol, volume, avg_price):
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pos = PositionLedger(symbol)
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pos.volume = volume
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pos.avg_price = avg_price
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return pos
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def test_daily_borrow_cost_calculation():
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# Arrange：200 股 @10 → 占用 2000，rate=0.02 → 日成本 2000*0.02/365
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runner = StrategyRunner("s1", symbol="600000")
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runner.positions["600000"] = _make_position("600000", 200, 10.0)
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closes = {"600000": 10.0}
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# Act
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cost = runner.daily_borrow_cost(closes, risk_free_rate=0.02)
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# Assert
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assert cost == pytest.approx(2000 * 0.02 / 365)
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def test_holding_cost_greater_than_empty():
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# Arrange：持仓 runner vs 空仓 runner
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runner_holding = StrategyRunner("s1", symbol="600000")
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runner_holding.positions["600000"] = _make_position("600000", 200, 10.0)
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runner_empty = StrategyRunner("s2", symbol="600000")
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closes = {"600000": 10.0}
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# Act
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cost_holding = runner_holding.daily_borrow_cost(closes, 0.02)
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cost_empty = runner_empty.daily_borrow_cost(closes, 0.02)
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# Assert
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assert cost_empty == 0.0
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assert cost_holding > 0
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assert cost_holding > cost_empty
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def test_borrow_cost_attributed_not_affecting_account(tmp_path):
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# Arrange：预置持仓 200 股 @10（占用 2000），空策略不下单，rate=0.03
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db = str(tmp_path / "borrow.db")
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init_db(db)
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aid = save_account(db, {"name": "t", "initial_capital": 1_000_000})
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cfg = AccountConfig(initial_capital=1_000_000)
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account = Account(1_000_000)
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account.cash = 998000 # 已花 2000 买入
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account.positions["600000"] = _make_position("600000", 200, 10.0)
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cta = PaperCtaEngine("s1", match_session=MatchSession.CURRENT_CLOSE)
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strat = _NoOrderStrategy(cta, "600000.SSE")
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cta.set_strategy(strat)
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runner = StrategyRunner("s1", strategy=strat, paper_cta_engine=cta,
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symbol="600000")
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runner.positions["600000"] = _make_position("600000", 200, 10.0)
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sections = [("2024-01-01", {"600000": _bar("2024-01-01", 10.0, 10.5, 9.5, 10.0)})]
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pe = PaperEngine(account, [runner], _FakeDataSource(sections), cfg, db, aid,
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symbols=["600000"], start="2024-01-01", end="2024-01-31",
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risk_free_rate=0.03)
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cash_before = account.cash
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# Act
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pe.run()
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# Assert：账户总账不受占用成本影响（仅归因记账）
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assert account.cash == cash_before
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balances = list_daily_balance(db, aid)
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assert len(balances) == 1
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psp = json.loads(balances[0]["per_strategy_pnl"])
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assert "s1" in psp
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assert psp["s1"]["borrow_cost"] == pytest.approx(2000 * 0.03 / 365)
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