mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
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release: v1.27.0 — 通达信公式解析器三通道 + 轮动组合引擎 + 回测页WF/评估开关 + Docker 部署
升级计划 P3 + P4（部分）。全量 1252 单测、ruff/mypy strict、前端 vue-tsc+vite build 全绿。 - 通达信公式解析器（formula.py）：自建 tokenizer + 递归下降 AST + 30+ 函数白名单求值 （不走 Python eval）；命名布尔输出=信号列、数值输出=排名列；除零→NaN、预热期不出信号 - 公式三通道：CLI easy-tdx formula compute|screen|backtest；REST /formula/validate|compute| backtest|screen（run/async）；Python API run_formula_backtest（买/卖列自动挑选） - 轮动组合引擎（rotation.py）：排名定期换仓（打分只用截至当日数据）、槽位等额、 跌出排名自动补位、日/周/月刷新、槽内止盈止损；momentum_score/formula_score 打分； REST /backtest/rotation/run/async - 回测页附加分析开关（Web UI）：勾选后随回测并行跑 WF（逐窗红涨绿跌柱状图+汇总卡， 窗口数 2~12）与一条龙评估（评分分项条/高适配徽标/买入持有对比/8 项适配检查）； 新增 WalkForwardPanel/EvaluatePanel 组件与 store runWalkforward/runEvaluate； WF 端点 ?n_windows= 透传；修复报告 numpy 标量 REST 400（源头清洗） - Docker 部署（Dockerfile + docker-compose.yml，/data 卷 + 健康检查）与 scripts/verify_ci.sh 一键门禁 - 升级计划文档 docs/upgrade-plan-2026H2.md（四阶段全部完成 + 诚实实测数据） - 未做（独立排期）：Playwright E2E、WebSocket 实时联动、引擎逐 bar 向量化
This commit is contained in:
GitHub
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"""轮动组合引擎测试（排名换仓 / 槽位等额 / 止盈止损 / 刷新频率 / 绩效）。"""
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from __future__ import annotations
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import json
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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 easy_tdx.backtest.rotation import RotationEngine, RotationResult, formula_score, momentum_score
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def _stock(
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n: int = 250, seed: int = 1, drift: float = 0.001, start: str = "2024-01-01"
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) -> pd.DataFrame:
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rng = np.random.default_rng(seed)
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close = 10.0 * np.cumprod(1.0 + drift + rng.normal(0, 0.01, n))
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return pd.DataFrame(
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{
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"datetime": pd.date_range(start, periods=n, freq="B"),
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"open": close * 0.999,
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"high": close * 1.02,
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"low": close * 0.98,
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"close": close,
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"vol": 1e6,
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"amount": close * 1e6,
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}
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)
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def _pool(drifts: dict[str, float], n: int = 250) -> dict[str, pd.DataFrame]:
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return {sym: _stock(n, seed=i, drift=drift) for i, (sym, drift) in enumerate(drifts.items())}
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# ── 基础结构 ─────────────────────────────────────────────────────────────────
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def test_rotation_basic_run_and_structure():
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pool = _pool({"SH:600519": 0.002, "SZ:000001": 0.001, "SZ:000858": 0.0005, "SH:601318": 0.0})
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engine = RotationEngine(pool, momentum_score(20), slots=2, refresh="weekly")
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result = engine.run()
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assert isinstance(result, RotationResult)
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assert len(result.equity_curve) >= 200
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assert result.performance.get("total_return") is not None
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assert result.config["slots"] == 2
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# 净值曲线字段完整（可喂组合评级）
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first = result.equity_curve[0]
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assert {"datetime", "cash", "position_value", "total", "drawdown_pct"} <= set(first)
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def test_rotation_strong_pool_makes_money():
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"""普涨池 + 动量排名 → 正收益。"""
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pool = _pool({f"SH:60000{i}": 0.004 for i in range(5)})
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result = RotationEngine(pool, momentum_score(20), slots=3, refresh="monthly").run()
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assert result.performance["total_return"] > 0
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def test_rotation_weak_pool_loses_less_than_buyhold():
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"""普跌池 → 负收益（动量轮动不做空）。"""
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pool = _pool({f"SH:60000{i}": -0.004 for i in range(5)})
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result = RotationEngine(pool, momentum_score(20), slots=2).run()
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assert result.performance["total_return"] < 0
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def test_rotation_trades_have_reasons():
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pool = _pool({f"SH:60000{i}": 0.002 if i % 2 else -0.001 for i in range(6)})
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result = RotationEngine(pool, momentum_score(10), slots=2, refresh="weekly").run()
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reasons = {t["reason"] for t in result.trades}
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assert "rotation" in reasons # 买入
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assert "rank_exit" in reasons # 跌出排名的卖出
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def test_rotation_respects_slots():
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"""持仓数永远 ≤ slots。"""
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pool = _pool({f"SH:60000{i}": 0.001 + 0.0005 * i for i in range(8)})
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engine = RotationEngine(pool, momentum_score(10), slots=3, refresh="weekly")
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# 用逐日持仓推断：trades 序列重放
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holdings = 0
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peak_holdings = 0
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for t in result_trades_sorted(engine):
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if t["direction"] == "BUY":
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holdings += 1
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peak_holdings = max(peak_holdings, holdings)
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else:
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holdings -= 1
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assert peak_holdings <= 3
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def result_trades_sorted(engine: RotationEngine) -> list[dict]:
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result = engine.run()
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return result.trades
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def test_rotation_stop_loss_triggers():
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"""深跌池 + 10% 止损 → 出现 stop_loss 卖出。"""
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pool = _pool({f"SH:60000{i}": -0.006 for i in range(4)})
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result = RotationEngine(
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pool, momentum_score(5), slots=2, refresh="monthly", stop_loss=0.05
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).run()
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reasons = {t["reason"] for t in result.trades}
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assert "stop_loss" in reasons
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def test_rotation_refresh_frequencies():
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pool = _pool({f"SH:60000{i}": 0.001 * (i + 1) for i in range(4)})
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r_daily = RotationEngine(pool, momentum_score(10), slots=2, refresh="daily").run()
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r_monthly = RotationEngine(pool, momentum_score(10), slots=2, refresh="monthly").run()
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# 月调仓的调仓日数 ≤ 日调仓
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assert len(r_monthly.rebalance_dates) <= len(r_daily.rebalance_dates)
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# 月调仓约 12 次/年（250 交易日）
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assert 3 <= len(r_monthly.rebalance_dates) <= 15
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def test_rotation_formula_score_synergy():
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"""公式打分与轮动联动：数值输出作为排名分。"""
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pool = _pool({f"SH:60000{i}": 0.001 * (i + 1) for i in range(4)})
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score = formula_score("动量分: C / REF(C, 20) * 100;")
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result = RotationEngine(pool, score, slots=2, refresh="monthly").run()
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assert result.performance["total_return"] is not None
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def test_rotation_result_serializable():
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pool = _pool({f"SH:60000{i}": 0.001 * (i + 1) for i in range(4)})
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result = RotationEngine(pool, momentum_score(10), slots=2).run()
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d = result.to_dict()
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text = json.dumps(d, ensure_ascii=False)
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assert "equity_curve" in text
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assert d["n_rebalances"] >= 1
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def test_rotation_rejects_bad_config():
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pool = _pool({"SH:600519": 0.001, "SZ:000001": 0.001})
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with pytest.raises(ValueError, match="refresh"):
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RotationEngine(pool, momentum_score(5), refresh="yearly")
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with pytest.raises(ValueError, match="stock_dfs"):
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RotationEngine({}, momentum_score(5))
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with pytest.raises(ValueError, match="slots"):
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RotationEngine(pool, momentum_score(5), slots=0)
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def test_rotation_equal_weight_no_allin_single_stock():
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"""首日建仓是等额分批，不是一把全买一只（槽位预算 = 净值/槽数）。"""
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pool = _pool({f"SH:60000{i}": 0.001 * (i + 1) for i in range(6)})
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result = RotationEngine(pool, momentum_score(10), slots=3, refresh="monthly").run()
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first_day_buys = [
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t for t in result.trades if t["direction"] == "BUY" and t["reason"] == "rotation"
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][:3]
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if len(first_day_buys) >= 2:
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values = [t["size"] * t["price"] for t in first_day_buys]
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# 同日买入的各笔金额接近（等额），差异 < 25%（价格整百取整的摩擦）
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assert max(values) / max(min(values), 1) < 1.25
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def test_momentum_score_helper():
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df = _stock(30, seed=1, drift=0.01)
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score = momentum_score(10)(df)
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assert score > 0
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assert momentum_score(10)(_stock(5)) == 0.0 # 数据不足 → 0
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"""通达信公式解析器测试（tokenizer / AST / 白名单求值 / 信号归类 / 安全性）。"""
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from __future__ import annotations
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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 easy_tdx.formula import FormulaError, compile_formula
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def _df(n: int = 60, seed: int = 3) -> pd.DataFrame:
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rng = np.random.default_rng(seed)
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dates = pd.date_range("2024-01-01", periods=n, freq="B")
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close = 10.0 * np.cumprod(1.0 + 0.002 + rng.normal(0, 0.015, n))
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return pd.DataFrame(
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{
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"datetime": dates,
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"open": close * 0.999,
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"high": close * 1.02,
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"low": close * 0.98,
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"close": close,
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"vol": rng.uniform(1e6, 5e6, n),
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"amount": close * rng.uniform(1e6, 5e6, n),
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}
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)
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# ── 编译与语法 ───────────────────────────────────────────────────────────────
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def test_compile_and_outputs():
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formula = compile_formula(
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"""
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N := 9;
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RSV := (C - LLV(L, N)) / (HHV(H, N) - LLV(L, N)) * 100;
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K := SMA(RSV, 3, 1);
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金叉: CROSS(K, 20);
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强度: K;
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"""
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)
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res = formula.compute(_df())
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assert "金叉" in res.signals
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assert "强度" in res.values
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frame = res.to_frame()
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assert list(frame.columns) == ["金叉", "强度"]
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assert len(frame) == 60
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def test_syntax_error_has_position():
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with pytest.raises(FormulaError):
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compile_formula("A := ;")
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with pytest.raises(FormulaError):
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compile_formula("A := UNKNOWN_FUNC(C)")
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with pytest.raises(FormulaError):
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compile_formula("A := B + ") # 引用未定义变量且语法断裂
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def test_unknown_variable_rejected():
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with pytest.raises(FormulaError, match="未知变量"):
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compile_formula("A: X1;").compute(_df())
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def test_unknown_function_rejected():
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with pytest.raises(FormulaError, match="白名单"):
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compile_formula("A: EVAL(C);").compute(_df())
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def test_empty_formula_rejected():
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with pytest.raises(FormulaError, match="为空"):
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compile_formula("{只有注释}")
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def test_no_python_eval_injection():
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"""公式层不走 Python eval：危险标识符按未知变量/函数拒绝。"""
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with pytest.raises(FormulaError):
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compile_formula("__import__('os'): 1;").compute(_df())
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with pytest.raises(FormulaError):
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compile_formula("A: OPEN(C);").compute(_df())
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# ── 语义正确性 ───────────────────────────────────────────────────────────────
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def test_series_aliases():
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"""C/O/H/L/V/AMOUNT 别名与底层列一致。"""
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df = _df(50)
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res = compile_formula("高价: H; 低价: L; 收盘: CLOSE; 量: VOL; 额: AMOUNT;").compute(df)
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assert np.allclose(res.columns["高价"], df["high"])
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assert np.allclose(res.columns["收盘"], df["close"])
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assert np.allclose(res.columns["量"], df["vol"])
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def test_ma_matches_mytt():
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from easy_tdx.MyTT import MA
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df = _df(50)
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res = compile_formula("均线: MA(C, 5);").compute(df)
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assert np.allclose(res.columns["均线"], MA(df["close"].to_numpy(), 5), equal_nan=True)
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def test_cross_semantics():
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"""CROSS(A,B)：A 上穿 B 的那一根为 1，其余 0。"""
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df = _df(50)
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res = compile_formula(
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"""
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快: MA(C, 3);
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慢: MA(C, 10);
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金叉: CROSS(快, 慢);
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"""
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).compute(df)
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golden = res.columns["金叉"]
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assert set(np.unique(golden[np.isfinite(golden)])).issubset({0.0, 1.0})
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assert golden.sum() >= 0 # 结构完整（趋势数据至少存在或为 0）
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# CROSS 手工复算对拍
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from easy_tdx.MyTT import CROSS, MA
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fast = MA(df["close"].to_numpy(), 3)
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slow = MA(df["close"].to_numpy(), 10)
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assert np.allclose(golden, CROSS(fast, slow), equal_nan=True)
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def test_safe_division_zero_denominator_nan():
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"""除零 → NaN（不炸、不 inf）。"""
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df = _df(30)
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res = compile_formula("比值: C / (C - C);").compute(df) # 分母全 0
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assert np.isnan(res.columns["比值"]).all()
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def test_logic_operators():
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df = _df(40)
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res = compile_formula(
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"""
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条件1: C > MA(C, 5);
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条件2: C > MA(C, 20);
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同时: 条件1 AND 条件2;
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任一: 条件1 OR 条件2;
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取反: NOT(条件1);
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"""
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).compute(df)
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c1 = res.columns["条件1"] > 0.5
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c2 = res.columns["条件2"] > 0.5
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assert np.allclose(res.columns["同时"] > 0.5, c1 & c2)
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assert np.allclose(res.columns["任一"] > 0.5, c1 | c2)
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assert np.allclose(res.columns["取反"] > 0.5, ~c1)
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def test_comparison_and_unary():
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df = _df(30)
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res = compile_formula("跌幅: -(C - REF(C, 1)) / REF(C, 1) * 100; 平: C == C;").compute(df)
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assert (res.columns["平"] == 1.0).all()
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assert "跌幅" in res.values
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def test_warmup_nan_not_signal():
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"""预热期 NaN 不产生信号（比较含 NaN → 0）。"""
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df = _df(30)
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res = compile_formula("信号: CROSS(MA(C, 20), MA(C, 25));").compute(df)
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sig = res.columns["信号"]
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assert np.nanmax(np.nan_to_num(sig[:25])) <= 1.0
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assert np.isnan(sig).sum() == 0 # 布尔输出不含 NaN
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def test_output_classification_boolean_vs_numeric():
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"""比较/逻辑输出 → 信号；数值输出 → 数值列；0/1 值域数值也归信号。"""
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df = _df(40)
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res = compile_formula(
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"""
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布尔输出: C > REF(C, 1);
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数值输出: MA(C, 5) - MA(C, 20);
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"""
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).compute(df)
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assert res.signals == ["布尔输出"]
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assert res.values == ["数值输出"]
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def test_last_row_for_screening():
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df = _df(30)
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res = compile_formula("买入: CROSS(MA(C, 3), MA(C, 10)); 值: MA(C, 5);").compute(df)
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last = res.last_row()
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assert set(last) == {"买入", "值"}
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assert last["买入"] in (0.0, 1.0)
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def test_chinese_identifier_and_comment():
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df = _df(30)
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formula = compile_formula(
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"""
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{这是注释：N 周期}
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周期 := 5;
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均线: MA(C, 周期);
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"""
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)
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res = formula.compute(df)
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assert res.columns["均线"][0] != res.columns["均线"][-1]
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def test_compiled_formula_reusable_across_frames():
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f = compile_formula("值: MA(C, 5);")
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r1 = f.compute(_df(30, seed=1))
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r2 = f.compute(_df(40, seed=2))
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assert len(r1.columns["值"]) == 30
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assert len(r2.columns["值"]) == 40
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def test_compiled_formula_is_dataclass_safe():
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"""CompiledFormula 可 pickle（进程池/后台任务传输）。"""
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import pickle
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|
||||
f = compile_formula("值: MA(C, 5);")
|
||||
f2 = pickle.loads(pickle.dumps(f))
|
||||
assert np.allclose(
|
||||
f.compute(_df(20)).columns["值"], f2.compute(_df(20)).columns["值"], equal_nan=True
|
||||
)
|
||||
@@ -0,0 +1,193 @@
|
||||
"""公式回测适配器 + REST 端点测试（三通道一致性）。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
||||
pytest.importorskip("fastapi")
|
||||
|
||||
from fastapi.testclient import TestClient # noqa: E402
|
||||
|
||||
from easy_tdx.backtest.formula_strategy import ( # noqa: E402
|
||||
FormulaStrategyError,
|
||||
attach_formula_columns,
|
||||
pick_signal_columns,
|
||||
run_formula_backtest,
|
||||
)
|
||||
from easy_tdx.formula import compile_formula # noqa: E402
|
||||
|
||||
|
||||
def _df(n: int = 300, seed: int = 3, drift: float = 0.002) -> pd.DataFrame:
|
||||
rng = np.random.default_rng(seed)
|
||||
close = 10.0 * np.cumprod(1.0 + drift + rng.normal(0, 0.012, n))
|
||||
return pd.DataFrame(
|
||||
{
|
||||
"datetime": pd.date_range("2024-01-01", periods=n, freq="B"),
|
||||
"open": close * 0.999,
|
||||
"high": close * 1.02,
|
||||
"low": close * 0.98,
|
||||
"close": close,
|
||||
"vol": 1e6,
|
||||
"amount": close * 1e6,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
_MA_CROSS = "快: MA(C, 5);\n慢: MA(C, 20);\n买入: CROSS(快, 慢);\n卖出: CROSS(慢, 快);"
|
||||
|
||||
|
||||
# ── attach / pick ─────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def test_attach_formula_columns():
|
||||
df = _df(100)
|
||||
enriched, result = attach_formula_columns(df, compile_formula(_MA_CROSS))
|
||||
assert {"快", "慢", "买入", "卖出"} <= set(enriched.columns)
|
||||
assert len(enriched) == len(df)
|
||||
assert df is not enriched # 副本，不污染原 df
|
||||
|
||||
|
||||
def test_pick_signal_columns_by_hint_and_order():
|
||||
_, result = attach_formula_columns(_df(60), compile_formula(_MA_CROSS))
|
||||
buy, sell = pick_signal_columns(result)
|
||||
assert (buy, sell) == ("买入", "卖出") # 名称提示（买/卖）优先
|
||||
|
||||
_, r2 = attach_formula_columns(_df(60), compile_formula("A: C > MA(C, 5); B: C < MA(C, 5);"))
|
||||
buy2, sell2 = pick_signal_columns(r2)
|
||||
assert (buy2, sell2) == ("A", "B") # 无提示时按声明顺序
|
||||
|
||||
buy3, _ = pick_signal_columns(r2, buy_col="B")
|
||||
assert buy3 == "B" # 显式指定优先
|
||||
|
||||
|
||||
def test_pick_requires_signal():
|
||||
from easy_tdx.formula import FormulaResult
|
||||
|
||||
result = FormulaResult(columns={"x": np.ones(5)}, values=["x"])
|
||||
with pytest.raises(FormulaStrategyError, match="布尔信号"):
|
||||
pick_signal_columns(result)
|
||||
|
||||
|
||||
# ── run_formula_backtest ──────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def test_run_formula_backtest_full_report():
|
||||
out = run_formula_backtest(_df(300), _MA_CROSS)
|
||||
assert out["performance"]["total_trades"] >= 1
|
||||
assert out["formula"]["buy_col"] == "买入"
|
||||
assert out["formula"]["sell_col"] == "卖出"
|
||||
assert out["grade"]["grade"] in ("S", "A", "B", "C", "D")
|
||||
assert 0 <= out["score"]["total"] <= 100
|
||||
assert "trades" in out and "equity_curve" in out
|
||||
|
||||
|
||||
def test_run_formula_backtest_no_sell_col_holds():
|
||||
"""只有买入列 → 买入后持有到末尾（1 笔完成交易=0 卖出，持仓中）。"""
|
||||
out = run_formula_backtest(_df(200, drift=0.004), "买入: CROSS(MA(C,3), MA(C,30));")
|
||||
assert out["formula"]["sell_col"] is None
|
||||
assert out["performance"]["total_return"] > 0
|
||||
|
||||
|
||||
def test_run_formula_backtest_accepts_compiled():
|
||||
compiled = compile_formula(_MA_CROSS)
|
||||
out = run_formula_backtest(_df(200), compiled)
|
||||
assert out["formula"]["buy_col"] == "买入"
|
||||
|
||||
|
||||
def test_run_formula_backtest_rejects_no_signal():
|
||||
with pytest.raises(ValueError, match="布尔信号"):
|
||||
run_formula_backtest(_df(60), "数值: MA(C, 5);")
|
||||
|
||||
|
||||
def test_run_formula_backtest_json_serializable():
|
||||
import json
|
||||
|
||||
out = run_formula_backtest(_df(150), _MA_CROSS)
|
||||
json.dumps(out, default=str)
|
||||
|
||||
|
||||
# ── REST 端点 ─────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _client() -> TestClient:
|
||||
from easy_tdx.web import create_app
|
||||
|
||||
return TestClient(create_app())
|
||||
|
||||
|
||||
def _ohlcv(n: int = 200) -> list[dict[str, object]]:
|
||||
df = _df(n)
|
||||
df["datetime"] = df["datetime"].dt.strftime("%Y-%m-%d")
|
||||
return json_records(df)
|
||||
|
||||
|
||||
def json_records(df: pd.DataFrame) -> list[dict[str, object]]:
|
||||
import json
|
||||
|
||||
return json.loads(df.to_json(orient="records", force_ascii=False))
|
||||
|
||||
|
||||
def test_rest_formula_validate_ok_and_error():
|
||||
client = _client()
|
||||
r = client.post(
|
||||
"/api/v1/formula/validate", json={"text": "金叉: CROSS(MA(C,5), MA(C,20)); 强度: MA(C,5);"}
|
||||
)
|
||||
assert r.status_code == 200
|
||||
body = r.json()
|
||||
assert body["ok"] is True
|
||||
assert body["signals"] == ["金叉"]
|
||||
assert body["values"] == ["强度"]
|
||||
|
||||
r2 = client.post("/api/v1/formula/validate", json={"text": "A := ;"})
|
||||
assert r2.status_code == 200
|
||||
assert r2.json()["ok"] is False
|
||||
assert r2.json()["error"]
|
||||
|
||||
|
||||
def test_rest_formula_compute_inline_ohlcv():
|
||||
client = _client()
|
||||
r = client.post(
|
||||
"/api/v1/formula/compute",
|
||||
json={"text": "买入: C > REF(C, 1); 值: MA(C, 5);", "ohlcv": _ohlcv(100), "tail": 5},
|
||||
)
|
||||
assert r.status_code == 200, r.text
|
||||
body = r.json()
|
||||
assert body["signals"] == ["买入"]
|
||||
assert "last_row" in body and "值" in body["last_row"]
|
||||
assert len(body["recent"]) == 5
|
||||
|
||||
|
||||
def test_rest_formula_backtest_async_task():
|
||||
client = _client()
|
||||
r = client.post(
|
||||
"/api/v1/formula/backtest/run/async",
|
||||
json={"text": _MA_CROSS, "ohlcv": _ohlcv(300), "cash": 100000.0},
|
||||
)
|
||||
assert r.status_code == 202, r.text
|
||||
task_id = r.json()["task_id"]
|
||||
for _ in range(200):
|
||||
st = client.get(f"/api/v1/backtest/tasks/{task_id}").json()
|
||||
if st["status"] in ("done", "failed"):
|
||||
break
|
||||
time.sleep(0.05)
|
||||
assert st["status"] == "done", st.get("error")
|
||||
result = st["result"]
|
||||
assert result["formula"]["buy_col"] == "买入"
|
||||
assert result["performance"]["total_trades"] >= 1
|
||||
|
||||
|
||||
def test_rest_formula_screen_async_task():
|
||||
client = _client()
|
||||
# 两份不同行情：A 上涨（末根 C>REF(C,1) 大概率真）、B 构造末根下跌
|
||||
up = _ohlcv(120)
|
||||
r = client.post(
|
||||
"/api/v1/formula/screen/run/async",
|
||||
json={"text": "买入: C > REF(C, 1);", "symbols": ["SH:600519"], "ohlcv": up[:0]},
|
||||
)
|
||||
# symbols 路径需要行情连接——离线环境预期 400/500（无 mock client）
|
||||
# 这里只验证请求校验（symbols 非空）不炸
|
||||
assert r.status_code in (400, 500, 202)
|
||||
Reference in New Issue
Block a user