287 lines
13 KiB
Python
287 lines
13 KiB
Python
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"""Native graph execution shares immutable inputs, artifacts and budgeted backtests."""
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import copy
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import pytest
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from app.alphas import upsert_alpha
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from app.models import Alpha, CatalogResource
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from app.research.workspace_contracts import FeatureSpec
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from tests.conftest import alpha
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from tests.test_catalog import SCOPE, prepare, sync
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from tests.test_research_flows import begin, drive, flow_setup, get
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from tests.test_research_workspace import catalog, research_input, template
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__all__ = ["flow_setup", "catalog", "research_input"]
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def graph(*kinds):
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return {
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"name": "原生节点研究",
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"nodes": [
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{
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"id": f"n{i}",
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"type": kind,
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"label": kind,
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"config": {"max_rounds": 2} if kind == "iterate" else {},
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}
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for i, kind in enumerate(kinds)
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],
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"edges": [{"source": f"n{i}", "target": f"n{i + 1}"} for i in range(len(kinds) - 1)],
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}
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async def save(client, kind, content):
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r = await client.post("/api/v1/research/assets", json={"kind": kind, "content": content})
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assert r.status_code == 201, r.text
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return r.json()
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async def launch(client, body, definition):
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asset = await save(client, "workflow", definition)
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return await begin(client, {**body, "workflow_id": asset["id"], "workflow_version": asset["version"]})
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@pytest.mark.parametrize(
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"case", ["cycle", "type", "duplicate", "disconnected", "loop", "config", "filter", "ref"]
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)
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async def test_invalid_graphs_rejected_before_save(logged_in, case):
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value = graph("input", "generate", "expand", "backtest", "evaluate")
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if case == "cycle":
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value["nodes"] += [{"id": "loop_a", "type": "generate"}, {"id": "loop_b", "type": "expand"}]
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value["edges"] += [{"source": "loop_a", "target": "loop_b"}, {"source": "loop_b", "target": "loop_a"}]
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if case == "type":
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value["edges"][0]["target"] = "n3"
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if case == "duplicate":
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value["edges"].append(value["edges"][0])
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if case == "disconnected":
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value["edges"].pop()
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if case == "loop":
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value["nodes"].append({"id": "iterate", "type": "iterate", "config": {"max_rounds": True}})
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value["edges"].append({"source": "n4", "target": "iterate"})
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if case == "config":
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value["nodes"][1]["config"] = {"command": "bash"}
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if case == "filter":
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value["nodes"].append({"id": "filter", "type": "filter", "config": {"verdicts": [{}]}})
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value["edges"].append({"source": "n4", "target": "filter"})
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if case == "ref":
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value["nodes"][2]["config"] = {"asset_id": "x", "version": True}
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for url in ("/api/v1/research/flows/validate", "/api/v1/research/assets"):
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r = await logged_in.post(
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url, json=value if url.endswith("validate") else {"kind": "workflow", "content": value}
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)
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assert r.status_code == 422, r.text
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async def test_versioned_template_branch_join_and_replayed_events(app, logged_in, flow_setup):
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body, platform, lane, calls = flow_setup
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fixed = await save(logged_in, "template", template())
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definition = graph("input", "expand", "backtest", "evaluate", "condition")
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definition["nodes"][1]["config"] = {"asset_id": fixed["id"], "version": 1}
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for verdict in ("pass", "review", "block"):
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definition["nodes"].append({"id": verdict, "type": "summarize"})
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definition["edges"].append({"source": "n4", "target": verdict, "branch": verdict})
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# A shared join receives the condition's evidence as well as direct evaluation.
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definition["nodes"].append({"id": "summary", "type": "summarize"})
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definition["edges"] += [{"source": "n4", "target": "summary"}, {"source": "n3", "target": "summary"}]
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run = await launch(logged_in, body, definition)
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workflow = run["authorization"]["workflow"]
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modified = copy.deepcopy(definition)
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modified["nodes"][1]["config"]["version"] = 2
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assert (
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await logged_in.put(
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f"/api/v1/research/assets/{workflow['id']}",
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json={"kind": "workflow", "version": 1, "content": modified},
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)
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).status_code == 200
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value = template()
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value["expression"] = "unknown({field})"
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assert (
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await logged_in.put(
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f"/api/v1/research/assets/{fixed['id']}",
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json={"kind": "template", "version": 1, "content": value},
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)
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).status_code == 200
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final = await drive(app, logged_in, run["id"], lane)
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assert final["status"] == "completed", final
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assert final["model_calls_used"] == 0 and not calls and final["simulations_used"] == 2
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branches = [s for s in final["steps"] if s["node_id"] in ("pass", "review", "block")]
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verdict = next(s for s in final["steps"] if s["node_id"] == "n3")["output"]["verdict"]
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assert [s["node_id"] for s in branches if s["status"] == "completed"] == [verdict]
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assert sum(s["status"] == "skipped" for s in branches) == 2
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assert len(next(s for s in final["steps"] if s["node_id"] == "summary")["output"]["artifacts"]) == 2
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assert final["authorization"]["node_assets"]["n1"]["version"] == 1
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for _ in range(3):
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await app.state.research.advance(run["id"])
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assert len(platform.posts) == 1
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assert (await logged_in.get("/api/v1/research/flows/runs?kind=quantflow")).json()["total"] == 1
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assert (await logged_in.get("/api/v1/research/flows/runs?kind=pipeline")).json()["total"] == 0
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async def test_feature_reference_conversion_and_bounded_loop(app, logged_in, flow_setup):
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body, _, lane, calls = flow_setup
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feature = await save(
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logged_in,
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"feature",
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{
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"name": "字段方案",
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"hypothesis": "排名稳定性",
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"input_ids": body["input_ids"],
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"steps": [{"name": "排名", "rationale": "截面比较", "expression": "rank(TEST_FIN_001)"}],
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"template": template(),
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},
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)
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definition = graph("input", "feature", "expand", "backtest", "evaluate", "filter", "iterate")
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definition["nodes"][1]["config"] = {"asset_id": feature["id"], "version": 1}
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definition["nodes"][5]["config"] = {"verdicts": ["pass", "review", "block"]}
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run = await launch(logged_in, body, definition)
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final = await drive(app, logged_in, run["id"], lane, ticks=60)
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assert final["status"] == "completed", final
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assert final["round"] == 2 and final["simulations_used"] == 4 and not calls
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outputs = [s["output"] for s in final["steps"] if s["node_id"] == "n1"]
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assert all(o["feature"]["id"] == feature["id"] for o in outputs)
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template_asset = (await logged_in.get(f"/api/v1/research/assets/{outputs[0]['template']['id']}")).json()
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assert template_asset["provenance"]["feature"]["version"] == 1
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async def test_native_feature_model_and_structure_variant(app, logged_in, flow_setup, monkeypatch):
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body, _, lane, calls = flow_setup
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async def feature_model(ai, context, output_type, revision):
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assert output_type is FeatureSpec
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calls.append(context)
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return FeatureSpec.model_validate(
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{
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"name": "模型特征",
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"hypothesis": "排名",
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"input_ids": body["input_ids"],
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"steps": [{"name": "排名", "rationale": "比较", "expression": "rank(TEST_FIN_001)"}],
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"template": template(),
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}
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), {"model": "fixture", "revision": revision}
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with monkeypatch.context() as patch:
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patch.setattr("app.research.runtime.request_model", feature_model)
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run = await launch(logged_in, body, graph("input", "feature", "expand", "backtest", "evaluate"))
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final = await drive(app, logged_in, run["id"], lane)
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assert final["status"] == "completed" and final["model_calls_used"] == 1, final
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assert final["steps"][1]["output"]["feature"]
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async with app.state.sessions.begin() as db:
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await upsert_alpha(db, alpha("seed", regular={"code": "rank(TEST_FIN_001)"}))
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run = await launch(
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logged_in,
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{**body, "request_id": "variant", "parent_alpha_ids": ["seed"]},
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graph("input", "variant", "backtest", "evaluate"),
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)
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final = await drive(app, logged_in, run["id"], lane)
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assert final["status"] == "completed" and final["model_calls_used"] == 1, final
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experiment = (
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await logged_in.get(f"/api/v1/research/experiments/{final['steps'][1]['output']['experiment_id']}")
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).json()
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assert experiment["parents"][0]["id"] == "seed" and experiment["kind"] == "quantflow"
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async def test_settings_variant_uses_frozen_seed_and_target_scope(app, logged_in, research_input, catalog):
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from tests.test_backtests import setup
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body = {
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"request_id": "scope-run",
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"name": "范围变体",
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"input_ids": [research_input["id"]],
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"hypothesis": "跨股票池比较",
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"settings": {"region": "USA", "universe": "TOP3000", "delay": 1},
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"budget": {"max_rounds": 1, "max_simulations": 2, "max_model_calls": 1},
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}
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target = {**SCOPE, "universe": "TOP1000"}
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await sync(catalog, scope=target)
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version = (await sync(catalog, "TEST_FIN", scope=target))["id"]
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fixed = (await prepare(logged_in, version, scope=target)).json()
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async with app.state.sessions.begin() as db:
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await upsert_alpha(db, alpha("seed", regular={"code": "x = TEST_FIN_001; rank(x)"}))
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metadata = await db.get(CatalogResource, "settings")
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metadata.content = {
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"items": metadata.content["items"] + [{**target, "neutralizations": ["INDUSTRY"]}]
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}
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_, lane = await setup(app)
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definition = graph("input", "variant", "backtest", "evaluate")
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definition["nodes"][1]["config"] = {"method": "settings"}
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run = await launch(
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logged_in,
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{**body, "input_ids": body["input_ids"] + [fixed["id"]], "parent_alpha_ids": ["seed"]},
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definition,
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)
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async with app.state.sessions.begin() as db:
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seed = await db.get(Alpha, "seed")
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seed.expression = "rank(unknown)"
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final = await drive(app, logged_in, run["id"], lane)
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assert (
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final["status"] == "completed" and final["simulations_used"] == 1 and final["model_calls_used"] == 0
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), final
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experiment = (
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await logged_in.get(f"/api/v1/research/experiments/{final['steps'][1]['output']['experiment_id']}")
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).json()
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c = experiment["candidates"][0]
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assert c["expression"] == "x = TEST_FIN_001; rank(x)" and c["input_ids"] == [fixed["id"]]
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assert c["settings"]["universe"] == "TOP1000" and len(experiment["inputs"]) == 2
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async def test_empty_filter_skips_downstream_simulation(app, logged_in, flow_setup):
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body, platform, lane, _ = flow_setup
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definition = graph(
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"input", "generate", "expand", "backtest", "evaluate", "filter", "backtest", "evaluate"
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)
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definition["nodes"][5]["config"] = {"verdicts": []}
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run = await launch(logged_in, body, definition)
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final = await drive(app, logged_in, run["id"], lane)
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assert final["status"] == "completed" and len(platform.posts) == 1, final
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assert [s["status"] for s in final["steps"][-3:]] == ["skipped"] * 3
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@pytest.mark.parametrize("action", ["pause", "stop", "account"])
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async def test_collected_variant_output_waits_for_authorized_next_tick(app, logged_in, flow_setup, action):
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from sqlalchemy import func, select
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from app.models import Account, ResearchExperiment
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from app.research.workspace_contracts import TemplateSpec
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body, platform, lane, _ = flow_setup
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async with app.state.sessions.begin() as db:
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await upsert_alpha(db, alpha("seed", regular={"code": "rank(TEST_FIN_001)"}))
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run = await launch(
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logged_in, {**body, "parent_alpha_ids": ["seed"]}, graph("input", "variant", "backtest", "evaluate")
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)
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await app.state.research.advance(run["id"])
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work = await app.state.research.prepare(run["id"])
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current = await get(logged_in, run["id"])
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if action == "account":
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async with app.state.sessions.begin() as db:
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account = await db.get(Account, 1)
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account.wq_user_id = "OTHER_ACCOUNT"
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else:
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assert (
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await logged_in.post(
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f"/api/v1/research/flows/runs/{run['id']}/control",
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json={"action": action, "version": current["version"]},
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)
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).status_code == 200
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await app.state.research.finish_model(
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run["id"], work[0], TemplateSpec.model_validate(template()), {"model": "fixture"}
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)
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await app.state.research.advance(run["id"])
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current = await get(logged_in, run["id"])
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assert current["steps"][1]["status"] == "generated" and current["model_calls_used"] == 1
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assert current["status"] == {"pause": "paused", "stop": "stopped", "account": "interrupted"}[action]
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async with app.state.sessions() as db:
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assert await db.scalar(select(func.count()).select_from(ResearchExperiment)) == 0
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assert not platform.posts
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if action == "pause":
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assert (
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await logged_in.post(
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f"/api/v1/research/flows/runs/{run['id']}/control",
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json={"action": "resume", "version": current["version"]},
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)
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).status_code == 200
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await app.state.research.recover()
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final = await drive(app, logged_in, run["id"], lane)
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assert final["status"] == "completed" and final["model_calls_used"] == 1
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