133 lines
3.9 KiB
Python
133 lines
3.9 KiB
Python
"""Tests for BacktestEngine risk-aware execution."""
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from __future__ import annotations
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from datetime import date
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import pytest
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from app.agents.base import AgentAction, AgentContext
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from app.agents.game import Decision
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from app.backtest.engine import BacktestEngine, BacktestResult, BtConfig, PortfolioState
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def _make_context(price: float, features: dict | None = None) -> AgentContext:
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scope_values = {"daily.close": price}
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return AgentContext(
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ts_code="000001.SZ",
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trade_date="2025-01-10",
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features=features or {},
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market_snapshot={},
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raw={"scope_values": scope_values},
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)
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def _make_decision(action: AgentAction, target_weight: float = 0.0) -> Decision:
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return Decision(
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action=action,
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confidence=0.8,
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target_weight=target_weight,
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feasible_actions=[],
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utilities={},
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)
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def _engine_with_params(params: dict[str, float]) -> BacktestEngine:
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cfg = BtConfig(
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id="test",
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name="test",
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start_date=date(2025, 1, 10),
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end_date=date(2025, 1, 10),
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universe=["000001.SZ"],
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params=params,
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)
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return BacktestEngine(cfg)
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def test_buy_respects_risk_caps():
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engine = _engine_with_params(
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{
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"max_position_weight": 0.2,
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"fee_rate": 0.0,
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"slippage_bps": 0.0,
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"max_daily_turnover_ratio": 1.0,
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}
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)
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state = PortfolioState(cash=100_000.0)
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result = BacktestResult()
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features = {
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"liquidity_score": 0.7,
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"risk_penalty": 0.25,
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}
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context = _make_context(100.0, features)
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decision = _make_decision(AgentAction.BUY_L, target_weight=0.5)
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engine._apply_portfolio_updates(
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date(2025, 1, 10),
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state,
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[("000001.SZ", context, decision)],
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result,
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)
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expected_qty = (100_000.0 * 0.2 * (1 - 0.25)) / 100.0
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assert state.holdings["000001.SZ"] == pytest.approx(expected_qty)
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assert state.cash == pytest.approx(100_000.0 - expected_qty * 100.0)
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assert result.trades and result.trades[0]["status"] == "executed"
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assert result.nav_series[0]["turnover"] == pytest.approx(expected_qty * 100.0)
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def test_buy_blocked_by_limit_up_records_risk():
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engine = _engine_with_params({})
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state = PortfolioState(cash=50_000.0)
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result = BacktestResult()
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features = {"limit_up": True}
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context = _make_context(100.0, features)
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decision = _make_decision(AgentAction.BUY_M, target_weight=0.1)
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engine._apply_portfolio_updates(
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date(2025, 1, 10),
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state,
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[("000001.SZ", context, decision)],
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result,
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)
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assert "000001.SZ" not in state.holdings
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assert not result.trades
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assert result.risk_events
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assert result.risk_events[0]["reason"] == "limit_up"
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def test_sell_applies_slippage_and_fee():
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engine = _engine_with_params(
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{
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"max_position_weight": 0.3,
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"fee_rate": 0.001,
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"slippage_bps": 20.0,
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"max_daily_turnover_ratio": 1.0,
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}
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)
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state = PortfolioState(
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cash=0.0,
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holdings={"000001.SZ": 100.0},
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cost_basis={"000001.SZ": 90.0},
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opened_dates={"000001.SZ": "2024-12-01"},
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)
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result = BacktestResult()
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context = _make_context(100.0, {})
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decision = _make_decision(AgentAction.SELL)
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engine._apply_portfolio_updates(
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date(2025, 1, 10),
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state,
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[("000001.SZ", context, decision)],
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result,
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)
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trade = result.trades[0]
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assert pytest.approx(trade["price"], rel=1e-6) == 100.0 * (1 - 0.002)
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assert pytest.approx(trade["fee"], rel=1e-6) == trade["value"] * 0.001
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assert state.cash == pytest.approx(trade["value"] - trade["fee"])
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assert state.realized_pnl == pytest.approx((trade["price"] - 90.0) * 100 - trade["fee"])
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assert not state.holdings
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assert result.nav_series[0]["turnover"] == pytest.approx(trade["value"])
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assert not result.risk_events
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