99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
"""Tests for DecisionEnv risk-aware reward and info outputs."""
|
|
from __future__ import annotations
|
|
|
|
from datetime import date
|
|
|
|
import pytest
|
|
|
|
from app.backtest.decision_env import DecisionEnv, EpisodeMetrics, ParameterSpec
|
|
from app.backtest.engine import BacktestResult, BtConfig
|
|
|
|
|
|
class _StubEngine:
|
|
def __init__(self, cfg: BtConfig) -> None: # noqa: D401
|
|
self.cfg = cfg
|
|
self.weights = {}
|
|
self.department_manager = None
|
|
|
|
def run(self) -> BacktestResult:
|
|
result = BacktestResult()
|
|
result.nav_series = [
|
|
{
|
|
"trade_date": "2025-01-10",
|
|
"nav": 102.0,
|
|
"cash": 50.0,
|
|
"market_value": 52.0,
|
|
"realized_pnl": 1.0,
|
|
"unrealized_pnl": 1.0,
|
|
"turnover": 20000.0,
|
|
"turnover_ratio": 0.2,
|
|
}
|
|
]
|
|
result.trades = [
|
|
{
|
|
"trade_date": "2025-01-10",
|
|
"ts_code": "000001.SZ",
|
|
"action": "buy",
|
|
"quantity": 100.0,
|
|
"price": 100.0,
|
|
"value": 10000.0,
|
|
"fee": 5.0,
|
|
}
|
|
]
|
|
result.risk_events = [
|
|
{
|
|
"trade_date": "2025-01-10",
|
|
"ts_code": "000002.SZ",
|
|
"reason": "limit_up",
|
|
"action": "buy_l",
|
|
"confidence": 0.7,
|
|
"target_weight": 0.2,
|
|
}
|
|
]
|
|
return result
|
|
|
|
|
|
def test_decision_env_returns_risk_metrics(monkeypatch):
|
|
cfg = BtConfig(
|
|
id="stub",
|
|
name="stub",
|
|
start_date=date(2025, 1, 10),
|
|
end_date=date(2025, 1, 10),
|
|
universe=["000001.SZ"],
|
|
params={},
|
|
)
|
|
specs = [ParameterSpec(name="w_mom", target="agent_weights.A_mom", minimum=0.0, maximum=1.0)]
|
|
env = DecisionEnv(bt_config=cfg, parameter_specs=specs, baseline_weights={"A_mom": 0.5})
|
|
|
|
monkeypatch.setattr("app.backtest.decision_env.BacktestEngine", _StubEngine)
|
|
monkeypatch.setattr(DecisionEnv, "_clear_portfolio_records", lambda self: None)
|
|
monkeypatch.setattr(DecisionEnv, "_fetch_portfolio_records", lambda self: ([], []))
|
|
|
|
obs, reward, done, info = env.step([0.8])
|
|
|
|
assert done is True
|
|
assert "risk_count" in obs and obs["risk_count"] == 1.0
|
|
assert obs["turnover"] == pytest.approx(0.2)
|
|
assert obs["turnover_value"] == pytest.approx(20000.0)
|
|
assert info["risk_events"][0]["reason"] == "limit_up"
|
|
assert info["risk_breakdown"]["limit_up"] == 1
|
|
assert info["nav_series"][0]["turnover_ratio"] == pytest.approx(0.2)
|
|
assert reward < obs["total_return"]
|
|
|
|
|
|
def test_default_reward_penalizes_metrics():
|
|
metrics = EpisodeMetrics(
|
|
total_return=0.1,
|
|
max_drawdown=0.2,
|
|
volatility=0.05,
|
|
nav_series=[],
|
|
trades=[],
|
|
turnover=0.3,
|
|
turnover_value=5000.0,
|
|
trade_count=0,
|
|
risk_count=2,
|
|
risk_breakdown={"foo": 2},
|
|
)
|
|
reward = DecisionEnv._default_reward(metrics)
|
|
assert reward == pytest.approx(0.1 - (0.5 * 0.2 + 0.05 * 2 + 0.1 * 0.3))
|