Merge pull request #37 from SynergyX-AI-Pattern/feat/#36_add_highlight_range_to_backtest_response

Feat/#36 add highlight range to backtest response

Author: Soomxn

app/schemas/backtest.py

@@ -1,5 +1,6 @@
+from typing import Optional
 from pydantic import BaseModel
-from datetime import date
+from datetime import date, datetime
 
 class BacktestRequest(BaseModel):
     startDate: date
@@ -14,6 +15,19 @@ class BacktestRequest(BaseModel):
         }
     }
 
+class HighlightRange(BaseModel):
+    fromDate: datetime
+    toDate: datetime
+
+    model_config = {
+        "json_schema_extra": {
+            "example": {
+                "fromDate": "2024-02-01T09:00:00",
+                "toDate": "2024-03-15T15:00:00"
+            }
+        }
+    }
+
 class BacktestResponse(BaseModel):
     matchedCount: int
     winRate: float
@@ -25,6 +39,7 @@ class BacktestResponse(BaseModel):
     totalReturn: float
     lastMatchedDate: date
     lastMatchedReturn: float
+    highlightRange: Optional[HighlightRange] = None
 
     model_config = {
         "json_schema_extra": {
@@ -38,7 +53,11 @@ class BacktestResponse(BaseModel):
                 "minReturnDate": "2024-02-10",
                 "totalReturn": 88.3,
                 "lastMatchedDate": "2024-04-20",
-                "lastMatchedReturn": 9.3
+                "lastMatchedReturn": 9.3,
+                "highlightRange": {
+                    "fromDate": "2024-02-01T09:00:00",
+                    "toDate": "2024-03-15T15:00:00"
+                }
             }
         }
     }

app/services/backtest_service.py

@@ -6,7 +6,7 @@
 from app.crud.stock import get_stock_by_id
 from app.crud.pattern import get_pattern_by_id
 from app.crud.stock_timeseries import get_stock_timeseries_by_unit
-from app.schemas.backtest import BacktestResponse, BacktestRequest
+from app.schemas.backtest import BacktestResponse, BacktestRequest, HighlightRange
 from app.exceptions.base import APIException
 from app.api_payload.code.status_code import ErrorStatus
 from app.utils.timeseries_calculator import (
@@ -110,7 +110,7 @@ def _calculate_returns(
         unit: str,
         value: int,
         pat_len: int
-    ) -> List[Tuple[datetime, float]]:
+    ) -> List[Tuple[datetime, float, datetime, datetime]]:
         """
         각 매칭 구간의 수익률 계산합니다.
 
@@ -122,7 +122,7 @@ def _calculate_returns(
             pat_len: 패턴 길이
 
         Returns:
-            (진입 시점, 수익률) 리스트
+            (진입 시점, 수익률, 구간 시작 지점, 구간 종료 지점) 리스트
         """
         returns = []
 
@@ -150,26 +150,35 @@ def _calculate_returns(
             # 범위 초과 시 마지막 종가로 설정
             if pos >= len(closes):
                 pos = len(closes) - 1
+
             # 이전 시점이 더 가까우면 뒤로 이동
             elif pos > 0 and (timestamps[pos] - tgt) > (tgt - timestamps[pos - 1]):
                 pos -= 1
+
             # 진입가, 청산가 활용하여 수익률 계산
             entry_p, exit_p = closes[entry_i], closes[pos]
-            # 수익률
-            returns.append((entry_time, (exit_p - entry_p) / entry_p * 100))
+            ret = (exit_p - entry_p) / entry_p * 100
+
+            # 매칭 구간 저장
+            match_start = timestamps[idx]
+            match_end = timestamps[entry_i]
+
+            # 수익률 포함 결과 매칭 구간 저장
+            returns.append((entry_time, ret, match_start, match_end))
+
         return returns
 
     @staticmethod
     def _aggregate_results(
-        returns: List[Tuple[datetime, float]],
+        returns: List[Tuple[datetime, float, datetime, datetime]],
         start_date: date,
         match_count: int
     ) -> BacktestResponse:
         """
         최종 응답을 반환합니다.
 
         Parameters:
-            returns: (진입 시점, 수익률) 리스트
+            returns: (진입 시점, 수익률, 구간 시작 지점, 구간 종료 지점) 리스트
             start_date : 시작 날짜
             match_count: 매칭된 구간 수
 
@@ -187,20 +196,27 @@ def _aggregate_results(
                 minReturnDate=start_date,
                 totalReturn=0.0,
                 lastMatchedDate=start_date,
-                lastMatchedReturn=0.0
+                lastMatchedReturn=0.0,
+                highlightRange=None
             )
-        dates, vals = zip(*returns)
+        dates, vals, match_starts, match_ends = zip(*returns)
         wins = [v for v in vals if v > 0]
 
+        max_idx = vals.index(max(vals))
+
         return BacktestResponse(
             matchedCount=match_count,
             winRate=len(wins)/len(vals) * 100,
             averageReturn=sum(vals)/len(vals),
             maxReturn=max(vals),
-            maxReturnDate=dates[vals.index(max(vals))].date(),
+            maxReturnDate=dates[max_idx].date(),
             minReturn=min(vals),
             minReturnDate=dates[vals.index(min(vals))].date(),
             totalReturn=sum(vals),
             lastMatchedDate=dates[-1].date(),
-            lastMatchedReturn=vals[-1]
+            lastMatchedReturn=vals[-1],
+            highlightRange=HighlightRange(
+                fromDate=match_starts[max_idx].isoformat(),
+                toDate=match_ends[max_idx].isoformat()
+            )
         )