96e2ad4f1f
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When gemini-2.5-flash quota is exhausted (20 RPD free tier), generate_playbook() fell back to _defensive_playbook() which only had price_change_pct_below: -3.0 SELL conditions — no BUY conditions — causing zero trades on US market despite scanner finding strong momentum/oversold candidates. Changes: - Add _smart_fallback_playbook() that uses scanner signals to build BUY conditions: - momentum signal: BUY when volume_ratio_above=VOL_MULTIPLIER - oversold signal: BUY when rsi_below=RSI_OVERSOLD_THRESHOLD - always: SELL stop-loss at price_change_pct_below=-3.0 - Use _smart_fallback_playbook() instead of _defensive_playbook() on Gemini failure - Add 10 new tests for _smart_fallback_playbook() covering momentum/oversold/empty cases - Update existing test_gemini_failure_returns_defensive to match new behavior Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
833 lines
29 KiB
Python
833 lines
29 KiB
Python
"""Tests for PreMarketPlanner — Gemini prompt builder + response parser."""
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from __future__ import annotations
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import json
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from datetime import date
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from unittest.mock import AsyncMock, MagicMock
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import pytest
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from src.analysis.smart_scanner import ScanCandidate
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from src.brain.context_selector import DecisionType
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from src.brain.gemini_client import TradeDecision
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from src.config import Settings
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from src.context.store import ContextLayer
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from src.strategy.models import (
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CrossMarketContext,
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DayPlaybook,
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MarketOutlook,
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ScenarioAction,
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)
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from src.strategy.pre_market_planner import PreMarketPlanner
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# ---------------------------------------------------------------------------
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# Fixtures
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# ---------------------------------------------------------------------------
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def _candidate(
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code: str = "005930",
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name: str = "Samsung",
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price: float = 71000,
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rsi: float = 28.5,
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volume_ratio: float = 3.2,
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signal: str = "oversold",
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score: float = 82.0,
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) -> ScanCandidate:
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return ScanCandidate(
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stock_code=code,
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name=name,
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price=price,
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volume=1_500_000,
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volume_ratio=volume_ratio,
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rsi=rsi,
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signal=signal,
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score=score,
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)
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def _gemini_response_json(
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outlook: str = "neutral_to_bullish",
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stocks: list[dict] | None = None,
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global_rules: list[dict] | None = None,
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) -> str:
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"""Build a valid Gemini JSON response."""
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if stocks is None:
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stocks = [
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{
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"stock_code": "005930",
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"scenarios": [
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{
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"condition": {"rsi_below": 30, "volume_ratio_above": 2.5},
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"action": "BUY",
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"confidence": 85,
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"allocation_pct": 15.0,
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"stop_loss_pct": -2.0,
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"take_profit_pct": 4.0,
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"rationale": "Oversold bounce with high volume",
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}
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],
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}
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]
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if global_rules is None:
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global_rules = [
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{
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"condition": "portfolio_pnl_pct < -2.0",
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"action": "REDUCE_ALL",
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"rationale": "Near circuit breaker",
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}
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]
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return json.dumps(
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{"market_outlook": outlook, "global_rules": global_rules, "stocks": stocks}
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)
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def _make_planner(
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gemini_response: str = "",
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token_count: int = 200,
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context_data: dict | None = None,
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scorecard_data: dict | None = None,
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scorecard_map: dict[tuple[str, str, str], dict | None] | None = None,
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) -> PreMarketPlanner:
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"""Create a PreMarketPlanner with mocked dependencies."""
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if not gemini_response:
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gemini_response = _gemini_response_json()
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# Mock GeminiClient
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gemini = AsyncMock()
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gemini.decide = AsyncMock(
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return_value=TradeDecision(
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action="HOLD",
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confidence=0,
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rationale=gemini_response,
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token_count=token_count,
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)
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)
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# Mock ContextStore
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store = MagicMock()
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if scorecard_map is not None:
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store.get_context = MagicMock(
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side_effect=lambda layer, timeframe, key: scorecard_map.get(
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(layer.value if hasattr(layer, "value") else layer, timeframe, key)
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)
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)
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else:
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store.get_context = MagicMock(return_value=scorecard_data)
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# Mock ContextSelector
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selector = MagicMock()
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selector.select_layers = MagicMock(
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return_value=[ContextLayer.L7_REALTIME, ContextLayer.L6_DAILY]
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)
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selector.get_context_data = MagicMock(return_value=context_data or {})
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settings = Settings(
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KIS_APP_KEY="test",
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KIS_APP_SECRET="test",
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KIS_ACCOUNT_NO="12345678-01",
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GEMINI_API_KEY="test",
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)
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return PreMarketPlanner(gemini, store, selector, settings)
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# ---------------------------------------------------------------------------
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# generate_playbook
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# ---------------------------------------------------------------------------
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class TestGeneratePlaybook:
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@pytest.mark.asyncio
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async def test_basic_generation(self) -> None:
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planner = _make_planner()
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candidates = [_candidate()]
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pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
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assert isinstance(pb, DayPlaybook)
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assert pb.market == "KR"
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assert pb.stock_count == 1
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assert pb.scenario_count == 1
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assert pb.market_outlook == MarketOutlook.NEUTRAL_TO_BULLISH
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assert pb.token_count == 200
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@pytest.mark.asyncio
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async def test_empty_candidates_returns_empty_playbook(self) -> None:
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planner = _make_planner()
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pb = await planner.generate_playbook("KR", [], today=date(2026, 2, 8))
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assert pb.stock_count == 0
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assert pb.scenario_count == 0
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assert pb.market_outlook == MarketOutlook.NEUTRAL
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@pytest.mark.asyncio
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async def test_gemini_failure_returns_smart_fallback(self) -> None:
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planner = _make_planner()
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planner._gemini.decide = AsyncMock(side_effect=RuntimeError("API timeout"))
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# oversold candidate (signal="oversold", rsi=28.5)
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candidates = [_candidate()]
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pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
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assert pb.default_action == ScenarioAction.HOLD
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# Smart fallback uses NEUTRAL outlook (not NEUTRAL_TO_BEARISH)
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assert pb.market_outlook == MarketOutlook.NEUTRAL
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assert pb.stock_count == 1
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# Oversold candidate → first scenario is BUY, second is SELL stop-loss
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scenarios = pb.stock_playbooks[0].scenarios
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assert scenarios[0].action == ScenarioAction.BUY
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assert scenarios[0].condition.rsi_below == 30
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assert scenarios[1].action == ScenarioAction.SELL
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@pytest.mark.asyncio
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async def test_gemini_failure_empty_when_defensive_disabled(self) -> None:
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planner = _make_planner()
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planner._settings.DEFENSIVE_PLAYBOOK_ON_FAILURE = False
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planner._gemini.decide = AsyncMock(side_effect=RuntimeError("fail"))
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candidates = [_candidate()]
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pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
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assert pb.stock_count == 0
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@pytest.mark.asyncio
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async def test_multiple_candidates(self) -> None:
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stocks = [
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{
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"stock_code": "005930",
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"scenarios": [
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{
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"condition": {"rsi_below": 30},
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"action": "BUY",
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"confidence": 85,
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"rationale": "Oversold",
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}
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],
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},
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{
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"stock_code": "AAPL",
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"scenarios": [
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{
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"condition": {"rsi_above": 75},
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"action": "SELL",
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"confidence": 80,
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"rationale": "Overbought",
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}
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],
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},
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]
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planner = _make_planner(gemini_response=_gemini_response_json(stocks=stocks))
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candidates = [_candidate(), _candidate(code="AAPL", name="Apple")]
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pb = await planner.generate_playbook("US", candidates, today=date(2026, 2, 8))
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assert pb.stock_count == 2
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codes = [sp.stock_code for sp in pb.stock_playbooks]
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assert "005930" in codes
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assert "AAPL" in codes
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@pytest.mark.asyncio
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async def test_unknown_stock_in_response_skipped(self) -> None:
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stocks = [
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{
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"stock_code": "005930",
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"scenarios": [
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{
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"condition": {"rsi_below": 30},
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"action": "BUY",
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"confidence": 85,
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"rationale": "ok",
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}
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],
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},
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{
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"stock_code": "UNKNOWN",
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"scenarios": [
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{
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"condition": {"rsi_below": 20},
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"action": "BUY",
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"confidence": 90,
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"rationale": "bad",
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}
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],
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},
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]
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planner = _make_planner(gemini_response=_gemini_response_json(stocks=stocks))
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candidates = [_candidate()] # Only 005930
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pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
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assert pb.stock_count == 1
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assert pb.stock_playbooks[0].stock_code == "005930"
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@pytest.mark.asyncio
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async def test_global_rules_parsed(self) -> None:
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planner = _make_planner()
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candidates = [_candidate()]
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pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
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assert len(pb.global_rules) == 1
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assert pb.global_rules[0].action == ScenarioAction.REDUCE_ALL
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@pytest.mark.asyncio
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async def test_token_count_from_decision(self) -> None:
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planner = _make_planner(token_count=450)
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candidates = [_candidate()]
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pb = await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
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assert pb.token_count == 450
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@pytest.mark.asyncio
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async def test_generate_playbook_uses_strategic_context_selector(self) -> None:
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planner = _make_planner()
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candidates = [_candidate()]
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await planner.generate_playbook("KR", candidates, today=date(2026, 2, 8))
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planner._context_selector.select_layers.assert_called_once_with(
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decision_type=DecisionType.STRATEGIC,
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include_realtime=True,
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)
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planner._context_selector.get_context_data.assert_called_once()
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@pytest.mark.asyncio
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async def test_generate_playbook_injects_self_and_cross_scorecards(self) -> None:
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scorecard_map = {
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(ContextLayer.L6_DAILY.value, "2026-02-07", "scorecard_KR"): {
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"total_pnl": -1.0,
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"win_rate": 40,
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"lessons": ["Tighten entries"],
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},
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(ContextLayer.L6_DAILY.value, "2026-02-07", "scorecard_US"): {
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"total_pnl": 1.5,
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"win_rate": 62,
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"index_change_pct": 0.9,
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"lessons": ["Follow momentum"],
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},
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}
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planner = _make_planner(scorecard_map=scorecard_map)
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await planner.generate_playbook("KR", [_candidate()], today=date(2026, 2, 8))
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call_market_data = planner._gemini.decide.call_args.args[0]
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prompt = call_market_data["prompt_override"]
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assert "My Market Previous Day (KR)" in prompt
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assert "Other Market (US)" in prompt
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# ---------------------------------------------------------------------------
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# _parse_response
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# ---------------------------------------------------------------------------
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class TestParseResponse:
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def test_parse_full_response(self) -> None:
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planner = _make_planner()
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response = _gemini_response_json(outlook="bearish")
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candidates = [_candidate()]
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pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
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assert pb.market_outlook == MarketOutlook.BEARISH
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assert pb.stock_count == 1
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assert pb.stock_playbooks[0].scenarios[0].confidence == 85
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def test_parse_with_markdown_fences(self) -> None:
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planner = _make_planner()
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response = f"```json\n{_gemini_response_json()}\n```"
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candidates = [_candidate()]
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pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
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assert pb.stock_count == 1
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def test_parse_unknown_outlook_defaults_neutral(self) -> None:
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planner = _make_planner()
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response = _gemini_response_json(outlook="super_bullish")
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candidates = [_candidate()]
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pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
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assert pb.market_outlook == MarketOutlook.NEUTRAL
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def test_parse_scenario_with_all_condition_fields(self) -> None:
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planner = _make_planner()
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stocks = [
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{
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"stock_code": "005930",
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"scenarios": [
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{
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"condition": {
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"rsi_below": 25,
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"volume_ratio_above": 3.0,
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"price_change_pct_below": -2.0,
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},
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"action": "BUY",
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"confidence": 92,
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"allocation_pct": 20.0,
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"stop_loss_pct": -3.0,
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"take_profit_pct": 5.0,
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"rationale": "Multi-condition entry",
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}
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],
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}
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]
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response = _gemini_response_json(stocks=stocks)
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candidates = [_candidate()]
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pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
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sc = pb.stock_playbooks[0].scenarios[0]
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assert sc.condition.rsi_below == 25
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assert sc.condition.volume_ratio_above == 3.0
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assert sc.condition.price_change_pct_below == -2.0
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assert sc.allocation_pct == 20.0
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assert sc.stop_loss_pct == -3.0
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assert sc.take_profit_pct == 5.0
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def test_parse_empty_condition_scenario_skipped(self) -> None:
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planner = _make_planner()
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stocks = [
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{
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"stock_code": "005930",
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"scenarios": [
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{
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"condition": {},
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"action": "BUY",
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"confidence": 85,
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"rationale": "No conditions",
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},
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{
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"condition": {"rsi_below": 30},
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"action": "BUY",
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"confidence": 80,
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"rationale": "Valid",
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},
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],
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}
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]
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response = _gemini_response_json(stocks=stocks)
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candidates = [_candidate()]
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pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
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# Empty condition scenario skipped, valid one kept
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assert pb.stock_count == 1
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assert pb.stock_playbooks[0].scenarios[0].confidence == 80
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def test_parse_max_scenarios_enforced(self) -> None:
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planner = _make_planner()
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# Settings default MAX_SCENARIOS_PER_STOCK = 5
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scenarios = [
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{
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"condition": {"rsi_below": 20 + i},
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"action": "BUY",
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"confidence": 80 + i,
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"rationale": f"Scenario {i}",
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}
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for i in range(8) # 8 scenarios, should be capped to 5
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]
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stocks = [{"stock_code": "005930", "scenarios": scenarios}]
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response = _gemini_response_json(stocks=stocks)
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candidates = [_candidate()]
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pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, None)
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assert len(pb.stock_playbooks[0].scenarios) == 5
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def test_parse_invalid_json_raises(self) -> None:
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planner = _make_planner()
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candidates = [_candidate()]
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with pytest.raises(json.JSONDecodeError):
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planner._parse_response("not json at all", date(2026, 2, 8), "KR", candidates, None)
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def test_parse_cross_market_preserved(self) -> None:
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planner = _make_planner()
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response = _gemini_response_json()
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candidates = [_candidate()]
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cross = CrossMarketContext(market="US", date="2026-02-07", total_pnl=1.5, win_rate=60)
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pb = planner._parse_response(response, date(2026, 2, 8), "KR", candidates, cross)
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assert pb.cross_market is not None
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assert pb.cross_market.market == "US"
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assert pb.cross_market.total_pnl == 1.5
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# ---------------------------------------------------------------------------
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# build_cross_market_context
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# ---------------------------------------------------------------------------
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class TestBuildCrossMarketContext:
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def test_kr_reads_us_scorecard(self) -> None:
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scorecard = {
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"total_pnl": 2.5,
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"win_rate": 65,
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"index_change_pct": 0.8,
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"lessons": ["Stay patient"],
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}
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planner = _make_planner(scorecard_data=scorecard)
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ctx = planner.build_cross_market_context("KR", today=date(2026, 2, 8))
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assert ctx is not None
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assert ctx.market == "US"
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assert ctx.total_pnl == 2.5
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assert ctx.win_rate == 65
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assert "Stay patient" in ctx.lessons
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# Verify it queried scorecard_US
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planner._context_store.get_context.assert_called_once_with(
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ContextLayer.L6_DAILY, "2026-02-07", "scorecard_US"
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)
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assert ctx.date == "2026-02-07"
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def test_us_reads_kr_scorecard(self) -> None:
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scorecard = {"total_pnl": -1.0, "win_rate": 40, "index_change_pct": -0.5}
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planner = _make_planner(scorecard_data=scorecard)
|
|
|
|
ctx = planner.build_cross_market_context("US", today=date(2026, 2, 8))
|
|
|
|
assert ctx is not None
|
|
assert ctx.market == "KR"
|
|
assert ctx.total_pnl == -1.0
|
|
|
|
planner._context_store.get_context.assert_called_once_with(
|
|
ContextLayer.L6_DAILY, "2026-02-08", "scorecard_KR"
|
|
)
|
|
|
|
def test_no_scorecard_returns_none(self) -> None:
|
|
planner = _make_planner(scorecard_data=None)
|
|
|
|
ctx = planner.build_cross_market_context("KR", today=date(2026, 2, 8))
|
|
|
|
assert ctx is None
|
|
|
|
def test_invalid_scorecard_returns_none(self) -> None:
|
|
planner = _make_planner(scorecard_data="not a dict and not json")
|
|
|
|
ctx = planner.build_cross_market_context("KR", today=date(2026, 2, 8))
|
|
|
|
assert ctx is None
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# build_self_market_scorecard
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestBuildSelfMarketScorecard:
|
|
def test_reads_previous_day_scorecard(self) -> None:
|
|
scorecard = {"total_pnl": -1.2, "win_rate": 45, "lessons": ["Reduce overtrading"]}
|
|
planner = _make_planner(scorecard_data=scorecard)
|
|
|
|
data = planner.build_self_market_scorecard("KR", today=date(2026, 2, 8))
|
|
|
|
assert data is not None
|
|
assert data["date"] == "2026-02-07"
|
|
assert data["total_pnl"] == -1.2
|
|
assert data["win_rate"] == 45
|
|
assert "Reduce overtrading" in data["lessons"]
|
|
planner._context_store.get_context.assert_called_once_with(
|
|
ContextLayer.L6_DAILY, "2026-02-07", "scorecard_KR"
|
|
)
|
|
|
|
def test_missing_scorecard_returns_none(self) -> None:
|
|
planner = _make_planner(scorecard_data=None)
|
|
assert planner.build_self_market_scorecard("US", today=date(2026, 2, 8)) is None
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _build_prompt
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestBuildPrompt:
|
|
def test_prompt_contains_candidates(self) -> None:
|
|
planner = _make_planner()
|
|
candidates = [_candidate(code="005930", name="Samsung")]
|
|
|
|
prompt = planner._build_prompt("KR", candidates, {}, None, None)
|
|
|
|
assert "005930" in prompt
|
|
assert "Samsung" in prompt
|
|
assert "RSI=" in prompt
|
|
assert "volume_ratio=" in prompt
|
|
|
|
def test_prompt_contains_cross_market(self) -> None:
|
|
planner = _make_planner()
|
|
cross = CrossMarketContext(
|
|
market="US", date="2026-02-07", total_pnl=1.5,
|
|
win_rate=60, index_change_pct=0.8, lessons=["Cut losses early"],
|
|
)
|
|
|
|
prompt = planner._build_prompt("KR", [_candidate()], {}, None, cross)
|
|
|
|
assert "Other Market (US)" in prompt
|
|
assert "+1.50%" in prompt
|
|
assert "Cut losses early" in prompt
|
|
|
|
def test_prompt_contains_context_data(self) -> None:
|
|
planner = _make_planner()
|
|
context = {"L6_DAILY": {"win_rate": 0.65, "total_pnl": 2.5}}
|
|
|
|
prompt = planner._build_prompt("KR", [_candidate()], context, None, None)
|
|
|
|
assert "Strategic Context" in prompt
|
|
assert "L6_DAILY" in prompt
|
|
assert "win_rate" in prompt
|
|
|
|
def test_prompt_contains_max_scenarios(self) -> None:
|
|
planner = _make_planner()
|
|
prompt = planner._build_prompt("KR", [_candidate()], {}, None, None)
|
|
|
|
assert f"Max {planner._settings.MAX_SCENARIOS_PER_STOCK} scenarios" in prompt
|
|
|
|
def test_prompt_market_name(self) -> None:
|
|
planner = _make_planner()
|
|
prompt = planner._build_prompt("US", [_candidate()], {}, None, None)
|
|
assert "US market" in prompt
|
|
|
|
def test_prompt_contains_self_market_scorecard(self) -> None:
|
|
planner = _make_planner()
|
|
self_scorecard = {
|
|
"date": "2026-02-07",
|
|
"total_pnl": -0.8,
|
|
"win_rate": 45.0,
|
|
"lessons": ["Avoid midday entries"],
|
|
}
|
|
prompt = planner._build_prompt("KR", [_candidate()], {}, self_scorecard, None)
|
|
|
|
assert "My Market Previous Day (KR)" in prompt
|
|
assert "2026-02-07" in prompt
|
|
assert "-0.80%" in prompt
|
|
assert "Avoid midday entries" in prompt
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _extract_json
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestExtractJson:
|
|
def test_plain_json(self) -> None:
|
|
assert PreMarketPlanner._extract_json('{"a": 1}') == '{"a": 1}'
|
|
|
|
def test_with_json_fence(self) -> None:
|
|
text = '```json\n{"a": 1}\n```'
|
|
assert PreMarketPlanner._extract_json(text) == '{"a": 1}'
|
|
|
|
def test_with_plain_fence(self) -> None:
|
|
text = '```\n{"a": 1}\n```'
|
|
assert PreMarketPlanner._extract_json(text) == '{"a": 1}'
|
|
|
|
def test_with_whitespace(self) -> None:
|
|
text = ' \n {"a": 1} \n '
|
|
assert PreMarketPlanner._extract_json(text) == '{"a": 1}'
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Defensive playbook
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestDefensivePlaybook:
|
|
def test_defensive_has_stop_loss(self) -> None:
|
|
candidates = [_candidate(code="005930"), _candidate(code="AAPL")]
|
|
pb = PreMarketPlanner._defensive_playbook(date(2026, 2, 8), "KR", candidates)
|
|
|
|
assert pb.default_action == ScenarioAction.HOLD
|
|
assert pb.market_outlook == MarketOutlook.NEUTRAL_TO_BEARISH
|
|
assert pb.stock_count == 2
|
|
for sp in pb.stock_playbooks:
|
|
assert sp.scenarios[0].action == ScenarioAction.SELL
|
|
assert sp.scenarios[0].stop_loss_pct == -3.0
|
|
|
|
def test_defensive_has_global_rule(self) -> None:
|
|
pb = PreMarketPlanner._defensive_playbook(date(2026, 2, 8), "KR", [_candidate()])
|
|
|
|
assert len(pb.global_rules) == 1
|
|
assert pb.global_rules[0].action == ScenarioAction.REDUCE_ALL
|
|
|
|
def test_empty_playbook(self) -> None:
|
|
pb = PreMarketPlanner._empty_playbook(date(2026, 2, 8), "US")
|
|
|
|
assert pb.stock_count == 0
|
|
assert pb.market == "US"
|
|
assert pb.market_outlook == MarketOutlook.NEUTRAL
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Smart fallback playbook
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestSmartFallbackPlaybook:
|
|
"""Tests for _smart_fallback_playbook — rule-based BUY/SELL on Gemini failure."""
|
|
|
|
def _make_settings(self) -> Settings:
|
|
return Settings(
|
|
KIS_APP_KEY="test",
|
|
KIS_APP_SECRET="test",
|
|
KIS_ACCOUNT_NO="12345678-01",
|
|
GEMINI_API_KEY="test",
|
|
RSI_OVERSOLD_THRESHOLD=30,
|
|
VOL_MULTIPLIER=2.0,
|
|
)
|
|
|
|
def test_momentum_candidate_gets_buy_on_volume(self) -> None:
|
|
candidates = [
|
|
_candidate(code="CHOW", signal="momentum", volume_ratio=13.64, rsi=100.0)
|
|
]
|
|
settings = self._make_settings()
|
|
|
|
pb = PreMarketPlanner._smart_fallback_playbook(
|
|
date(2026, 2, 17), "US_AMEX", candidates, settings
|
|
)
|
|
|
|
assert pb.stock_count == 1
|
|
sp = pb.stock_playbooks[0]
|
|
assert sp.stock_code == "CHOW"
|
|
# First scenario: BUY with volume_ratio_above
|
|
buy_sc = sp.scenarios[0]
|
|
assert buy_sc.action == ScenarioAction.BUY
|
|
assert buy_sc.condition.volume_ratio_above == 2.0
|
|
assert buy_sc.condition.rsi_below is None
|
|
assert buy_sc.confidence == 80
|
|
# Second scenario: stop-loss SELL
|
|
sell_sc = sp.scenarios[1]
|
|
assert sell_sc.action == ScenarioAction.SELL
|
|
assert sell_sc.condition.price_change_pct_below == -3.0
|
|
|
|
def test_oversold_candidate_gets_buy_on_rsi(self) -> None:
|
|
candidates = [
|
|
_candidate(code="005930", signal="oversold", rsi=22.0, volume_ratio=3.5)
|
|
]
|
|
settings = self._make_settings()
|
|
|
|
pb = PreMarketPlanner._smart_fallback_playbook(
|
|
date(2026, 2, 17), "KR", candidates, settings
|
|
)
|
|
|
|
sp = pb.stock_playbooks[0]
|
|
buy_sc = sp.scenarios[0]
|
|
assert buy_sc.action == ScenarioAction.BUY
|
|
assert buy_sc.condition.rsi_below == 30
|
|
assert buy_sc.condition.volume_ratio_above is None
|
|
|
|
def test_all_candidates_have_stop_loss_sell(self) -> None:
|
|
candidates = [
|
|
_candidate(code="AAA", signal="momentum", volume_ratio=5.0),
|
|
_candidate(code="BBB", signal="oversold", rsi=25.0),
|
|
]
|
|
settings = self._make_settings()
|
|
|
|
pb = PreMarketPlanner._smart_fallback_playbook(
|
|
date(2026, 2, 17), "US_NASDAQ", candidates, settings
|
|
)
|
|
|
|
assert pb.stock_count == 2
|
|
for sp in pb.stock_playbooks:
|
|
sell_scenarios = [s for s in sp.scenarios if s.action == ScenarioAction.SELL]
|
|
assert len(sell_scenarios) == 1
|
|
assert sell_scenarios[0].condition.price_change_pct_below == -3.0
|
|
assert sell_scenarios[0].condition.price_change_pct_below == -3.0
|
|
|
|
def test_market_outlook_is_neutral(self) -> None:
|
|
candidates = [_candidate(signal="momentum", volume_ratio=5.0)]
|
|
settings = self._make_settings()
|
|
|
|
pb = PreMarketPlanner._smart_fallback_playbook(
|
|
date(2026, 2, 17), "US_AMEX", candidates, settings
|
|
)
|
|
|
|
assert pb.market_outlook == MarketOutlook.NEUTRAL
|
|
|
|
def test_default_action_is_hold(self) -> None:
|
|
candidates = [_candidate(signal="momentum", volume_ratio=5.0)]
|
|
settings = self._make_settings()
|
|
|
|
pb = PreMarketPlanner._smart_fallback_playbook(
|
|
date(2026, 2, 17), "US_AMEX", candidates, settings
|
|
)
|
|
|
|
assert pb.default_action == ScenarioAction.HOLD
|
|
|
|
def test_has_global_reduce_all_rule(self) -> None:
|
|
candidates = [_candidate(signal="momentum", volume_ratio=5.0)]
|
|
settings = self._make_settings()
|
|
|
|
pb = PreMarketPlanner._smart_fallback_playbook(
|
|
date(2026, 2, 17), "US_AMEX", candidates, settings
|
|
)
|
|
|
|
assert len(pb.global_rules) == 1
|
|
rule = pb.global_rules[0]
|
|
assert rule.action == ScenarioAction.REDUCE_ALL
|
|
assert "portfolio_pnl_pct" in rule.condition
|
|
|
|
def test_empty_candidates_returns_empty_playbook(self) -> None:
|
|
settings = self._make_settings()
|
|
|
|
pb = PreMarketPlanner._smart_fallback_playbook(
|
|
date(2026, 2, 17), "US_AMEX", [], settings
|
|
)
|
|
|
|
assert pb.stock_count == 0
|
|
|
|
def test_vol_multiplier_applied_from_settings(self) -> None:
|
|
"""VOL_MULTIPLIER=3.0 should set volume_ratio_above=3.0 for momentum."""
|
|
candidates = [_candidate(signal="momentum", volume_ratio=5.0)]
|
|
settings = self._make_settings()
|
|
settings = settings.model_copy(update={"VOL_MULTIPLIER": 3.0})
|
|
|
|
pb = PreMarketPlanner._smart_fallback_playbook(
|
|
date(2026, 2, 17), "US_AMEX", candidates, settings
|
|
)
|
|
|
|
buy_sc = pb.stock_playbooks[0].scenarios[0]
|
|
assert buy_sc.condition.volume_ratio_above == 3.0
|
|
|
|
def test_rsi_oversold_threshold_applied_from_settings(self) -> None:
|
|
"""RSI_OVERSOLD_THRESHOLD=25 should set rsi_below=25 for oversold."""
|
|
candidates = [_candidate(signal="oversold", rsi=22.0)]
|
|
settings = self._make_settings()
|
|
settings = settings.model_copy(update={"RSI_OVERSOLD_THRESHOLD": 25})
|
|
|
|
pb = PreMarketPlanner._smart_fallback_playbook(
|
|
date(2026, 2, 17), "KR", candidates, settings
|
|
)
|
|
|
|
buy_sc = pb.stock_playbooks[0].scenarios[0]
|
|
assert buy_sc.condition.rsi_below == 25
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_generate_playbook_uses_smart_fallback_on_gemini_error(self) -> None:
|
|
"""generate_playbook() should use smart fallback (not defensive) on API failure."""
|
|
planner = _make_planner()
|
|
planner._gemini.decide = AsyncMock(side_effect=ConnectionError("429 quota exceeded"))
|
|
# momentum candidate
|
|
candidates = [
|
|
_candidate(code="CHOW", signal="momentum", volume_ratio=13.64, rsi=100.0)
|
|
]
|
|
|
|
pb = await planner.generate_playbook(
|
|
"US_AMEX", candidates, today=date(2026, 2, 18)
|
|
)
|
|
|
|
# Should NOT be all-SELL defensive; should have BUY for momentum
|
|
assert pb.stock_count == 1
|
|
buy_scenarios = [
|
|
s for s in pb.stock_playbooks[0].scenarios
|
|
if s.action == ScenarioAction.BUY
|
|
]
|
|
assert len(buy_scenarios) == 1
|
|
assert buy_scenarios[0].condition.volume_ratio_above == 2.0 # VOL_MULTIPLIER default
|