refactor(factorminer): 将 LLM Prompt 和解析器改造为直接输出本地 DSL

- DSL 规范改为 snake_case、中缀运算符，示例同步替换
- 移除 ExpressionTree 依赖，改为括号匹配等基础校验
- retry prompt 适配本地 DSL 规则

tests/test_factorminer_prompt.py

@@ -0,0 +1,72 @@
+"""Tests for local DSL prompt and output parser in Step 3."""
+
+from src.factorminer.agent.output_parser import (
+    CandidateFactor,
+    _infer_category,
+    parse_llm_output,
+)
+from src.factorminer.agent.prompt_builder import SYSTEM_PROMPT
+
+
+def test_system_prompt_uses_local_dsl():
+    assert "$close" not in SYSTEM_PROMPT
+    assert "CsRank(" not in SYSTEM_PROMPT
+    assert "cs_rank(" in SYSTEM_PROMPT
+    assert "close / ts_delay(close, 1) - 1" in SYSTEM_PROMPT
+    assert "ts_mean(close, 20)" in SYSTEM_PROMPT
+
+
+def test_parse_local_dsl_numbered_list():
+    raw = (
+        "1. momentum: -cs_rank(ts_delta(close, 5))\n"
+        "2. volume: cs_zscore((vol - ts_mean(vol, 20)) / ts_std(vol, 20))\n"
+        "3. vwap_dev: cs_rank((close - amount / vol) / (amount / vol))\n"
+    )
+    candidates, failed = parse_llm_output(raw)
+    assert len(candidates) == 3
+    assert candidates[0].name == "momentum"
+    assert candidates[0].formula == "-cs_rank(ts_delta(close, 5))"
+    assert candidates[0].is_valid
+    assert candidates[1].name == "volume"
+    assert (
+        candidates[1].formula == "cs_zscore((vol - ts_mean(vol, 20)) / ts_std(vol, 20))"
+    )
+    assert candidates[2].name == "vwap_dev"
+    assert not failed
+
+
+def test_parse_local_dsl_formula_only():
+    raw = "cs_rank(close / ts_delay(close, 5) - 1)"
+    candidates, failed = parse_llm_output(raw)
+    assert len(candidates) == 1
+    assert candidates[0].formula == "cs_rank(close / ts_delay(close, 5) - 1)"
+    assert not failed
+
+
+def test_parse_invalid_parentheses():
+    candidates, failed = parse_llm_output("1. bad: cs_rank(ts_delta(close, 5)")
+    assert len(candidates) == 1
+    assert not candidates[0].is_valid
+    assert "括号" in candidates[0].parse_error
+
+
+def test_infer_category_local_dsl():
+    assert _infer_category("cs_rank(ts_delta(close, 5))") == "cross_sectional_momentum"
+    assert _infer_category("ts_corr(vol, close, 10)") == "regression"
+    assert _infer_category("ts_std(close, 20)") == "volatility"
+    assert _infer_category("if_(close > open, 1, -1)") == "conditional"
+
+
+def test_candidate_factor_is_valid_without_tree():
+    cf = CandidateFactor(name="test", formula="cs_rank(close)")
+    assert cf.is_valid
+    assert cf.category == "unknown"
+
+
+def test_parse_json_local_dsl():
+    raw = '{"name": "mom", "formula": "cs_rank(close / ts_delay(close, 5) - 1)"}'
+    candidates, failed = parse_llm_output(raw)
+    assert len(candidates) == 1
+    assert candidates[0].name == "mom"
+    assert candidates[0].formula == "cs_rank(close / ts_delay(close, 5) - 1)"
+    assert not failed