201 lines
6.0 KiB
Python
201 lines
6.0 KiB
Python
from copy import deepcopy
|
|
|
|
from langgraph.graph import END, START, StateGraph
|
|
|
|
from tradingagents.agents.utils.agent_states import AgentState
|
|
from tradingagents.agents.managers.portfolio_manager import create_portfolio_manager
|
|
from tradingagents.agents.managers.research_manager import create_research_manager
|
|
from tradingagents.graph.propagation import Propagator
|
|
|
|
|
|
EXPECTED_VALUATION_DATA = {
|
|
"fair_value_range": {"low": None, "high": None},
|
|
"expected_return_pct": None,
|
|
"primary_method": "",
|
|
"thesis": "",
|
|
}
|
|
|
|
EXPECTED_SEGMENT_DATA = {
|
|
"segments": [],
|
|
"dominant_segment": "",
|
|
"thesis": "",
|
|
}
|
|
|
|
EXPECTED_SCENARIO_CATALYST_DATA = {
|
|
"bull_case": {"probability": None, "price_target": None, "thesis": ""},
|
|
"base_case": {"probability": None, "price_target": None, "thesis": ""},
|
|
"bear_case": {"probability": None, "price_target": None, "thesis": ""},
|
|
"catalysts": [],
|
|
"invalidation_triggers": [],
|
|
}
|
|
|
|
EXPECTED_POSITION_SIZING_DATA = {
|
|
"conviction": "",
|
|
"target_weight_pct": None,
|
|
"initial_weight_pct": None,
|
|
"max_loss_pct": None,
|
|
}
|
|
|
|
EXPECTED_CHIEF_ANALYST_DATA = {
|
|
"action": "",
|
|
"summary": "",
|
|
"thesis": "",
|
|
"confidence": "",
|
|
}
|
|
|
|
STRUCTURED_PASSTHROUGH_KEYS = {
|
|
"valuation_data",
|
|
"segment_data",
|
|
"scenario_catalyst_data",
|
|
"position_sizing_data",
|
|
"chief_analyst_data",
|
|
}
|
|
|
|
|
|
class DummyMemory:
|
|
def get_memories(self, _situation, n_matches=2):
|
|
return []
|
|
|
|
|
|
class DummyResponse:
|
|
def __init__(self, content):
|
|
self.content = content
|
|
|
|
|
|
class DummyLLM:
|
|
def __init__(self, content):
|
|
self.content = content
|
|
|
|
def invoke(self, _prompt):
|
|
return DummyResponse(self.content)
|
|
|
|
|
|
def assert_structured_stock_fields(payload):
|
|
assert payload["valuation_data"] == EXPECTED_VALUATION_DATA
|
|
assert payload["segment_data"] == EXPECTED_SEGMENT_DATA
|
|
assert payload["scenario_catalyst_data"] == EXPECTED_SCENARIO_CATALYST_DATA
|
|
assert payload["position_sizing_data"] == EXPECTED_POSITION_SIZING_DATA
|
|
assert payload["chief_analyst_data"] == EXPECTED_CHIEF_ANALYST_DATA
|
|
|
|
|
|
def assert_manager_update_omits_structured_passthrough(
|
|
payload, expected_present_keys
|
|
):
|
|
for key in expected_present_keys:
|
|
assert key in payload
|
|
assert STRUCTURED_PASSTHROUGH_KEYS.isdisjoint(payload)
|
|
|
|
|
|
def compile_single_node_graph(node_name, node):
|
|
workflow = StateGraph(AgentState)
|
|
workflow.add_node(node_name, node)
|
|
workflow.add_edge(START, node_name)
|
|
workflow.add_edge(node_name, END)
|
|
return workflow.compile()
|
|
|
|
|
|
def test_propagator_initializes_structured_stock_underwriting_fields():
|
|
initial_state = Propagator().create_initial_state("NVDA", "2026-03-24")
|
|
|
|
assert_structured_stock_fields(initial_state)
|
|
|
|
|
|
def test_research_manager_update_omits_structured_stock_passthrough_fields(monkeypatch):
|
|
monkeypatch.setattr(
|
|
"tradingagents.agents.managers.research_manager.build_instrument_context",
|
|
lambda _ticker: "instrument context",
|
|
)
|
|
|
|
state = Propagator().create_initial_state("NVDA", "2026-03-24")
|
|
research_manager = create_research_manager(
|
|
DummyLLM("Research manager output"),
|
|
DummyMemory(),
|
|
)
|
|
research_result = research_manager(deepcopy(state))
|
|
|
|
assert research_result["investment_plan"] == "Research manager output"
|
|
assert research_result["investment_debate_state"]["judge_decision"] == (
|
|
"Research manager output"
|
|
)
|
|
assert_manager_update_omits_structured_passthrough(
|
|
research_result,
|
|
{"investment_debate_state", "investment_plan"},
|
|
)
|
|
|
|
|
|
def test_research_manager_graph_preserves_structured_stock_underwriting_fields(
|
|
monkeypatch,
|
|
):
|
|
monkeypatch.setattr(
|
|
"tradingagents.agents.managers.research_manager.build_instrument_context",
|
|
lambda _ticker: "instrument context",
|
|
)
|
|
|
|
research_manager = create_research_manager(
|
|
DummyLLM("Research manager output"),
|
|
DummyMemory(),
|
|
)
|
|
state = Propagator().create_initial_state("NVDA", "2026-03-24")
|
|
|
|
final_state = compile_single_node_graph("Research Manager", research_manager).invoke(
|
|
state
|
|
)
|
|
|
|
assert final_state["investment_plan"] == "Research manager output"
|
|
assert final_state["investment_debate_state"]["judge_decision"] == (
|
|
"Research manager output"
|
|
)
|
|
assert_structured_stock_fields(final_state)
|
|
|
|
|
|
def test_portfolio_manager_update_omits_structured_stock_passthrough_fields(
|
|
monkeypatch,
|
|
):
|
|
monkeypatch.setattr(
|
|
"tradingagents.agents.managers.portfolio_manager.build_instrument_context",
|
|
lambda _ticker: "instrument context",
|
|
)
|
|
|
|
state = Propagator().create_initial_state("NVDA", "2026-03-24")
|
|
state["investment_plan"] = "Existing investment plan"
|
|
portfolio_manager = create_portfolio_manager(
|
|
DummyLLM("Portfolio manager output"),
|
|
DummyMemory(),
|
|
)
|
|
portfolio_result = portfolio_manager(deepcopy(state))
|
|
|
|
assert portfolio_result["final_trade_decision"] == "Portfolio manager output"
|
|
assert portfolio_result["risk_debate_state"]["judge_decision"] == (
|
|
"Portfolio manager output"
|
|
)
|
|
assert_manager_update_omits_structured_passthrough(
|
|
portfolio_result,
|
|
{"risk_debate_state", "final_trade_decision"},
|
|
)
|
|
|
|
|
|
def test_portfolio_manager_graph_preserves_structured_stock_underwriting_fields(
|
|
monkeypatch,
|
|
):
|
|
monkeypatch.setattr(
|
|
"tradingagents.agents.managers.portfolio_manager.build_instrument_context",
|
|
lambda _ticker: "instrument context",
|
|
)
|
|
|
|
portfolio_manager = create_portfolio_manager(
|
|
DummyLLM("Portfolio manager output"),
|
|
DummyMemory(),
|
|
)
|
|
state = Propagator().create_initial_state("NVDA", "2026-03-24")
|
|
state["investment_plan"] = "Existing investment plan"
|
|
|
|
final_state = compile_single_node_graph(
|
|
"Portfolio Manager", portfolio_manager
|
|
).invoke(state)
|
|
|
|
assert final_state["final_trade_decision"] == "Portfolio manager output"
|
|
assert final_state["risk_debate_state"]["judge_decision"] == (
|
|
"Portfolio manager output"
|
|
)
|
|
assert_structured_stock_fields(final_state)
|