Initial commit: Django quantitative strategy executor

- Django 5.2.7 project with Python 3.13+
- Quant strategy management system with version control
- Strategy implementations using registry pattern
- API endpoints for strategy listing and execution
- Sample strategy implementations (MovingAverage, RSI, BollingerBand)
- Async strategy execution with status tracking

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

strategies/implementations.py

@@ -0,0 +1,202 @@
+from typing import Dict, Any
+import time
+import random
+import math
+from .base import BaseQuantStrategy, strategy
+
+
+@strategy
+class MovingAverageCrossover(BaseQuantStrategy):
+    """이동평균선 교차 전략"""
+
+    @property
+    def name(self) -> str:
+        return "MovingAverageCrossover"
+
+    @property
+    def description(self) -> str:
+        return "단기 이동평균선이 장기 이동평균선을 상향 돌파할 때 매수, 하향 돌파할 때 매도하는 전략"
+
+    @property
+    def version(self) -> str:
+        return "1.0.0"
+
+    @property
+    def default_parameters(self) -> Dict[str, Any]:
+        return {
+            "short_window": 20,
+            "long_window": 50,
+            "initial_capital": 100000,
+            "position_size": 0.1
+        }
+
+    def validate_parameters(self, parameters: Dict[str, Any]) -> bool:
+        required_params = ["short_window", "long_window", "initial_capital"]
+        for param in required_params:
+            if param not in parameters:
+                return False
+
+        if parameters["short_window"] >= parameters["long_window"]:
+            return False
+
+        return True
+
+    def execute(self, parameters: Dict[str, Any] = None) -> Dict[str, Any]:
+        if parameters is None:
+            parameters = self.default_parameters
+
+        if not self.validate_parameters(parameters):
+            raise ValueError("Invalid parameters")
+
+        # 시뮬레이션 실행
+        time.sleep(1)  # 실행 시간 시뮬레이션
+
+        # 모의 결과 생성
+        profit_rate = random.uniform(-0.15, 0.25)
+        trades_count = random.randint(15, 60)
+        win_rate = random.uniform(0.45, 0.75)
+
+        return {
+            "strategy": self.name,
+            "version": self.version,
+            "profit_loss": round(parameters["initial_capital"] * profit_rate, 2),
+            "profit_rate": round(profit_rate * 100, 2),
+            "trades_executed": trades_count,
+            "win_rate": round(win_rate, 3),
+            "execution_time": "1.2s",
+            "parameters_used": parameters,
+            "final_capital": round(parameters["initial_capital"] * (1 + profit_rate), 2)
+        }
+
+
+@strategy
+class RSIMeanReversion(BaseQuantStrategy):
+    """RSI 평균회귀 전략"""
+
+    @property
+    def name(self) -> str:
+        return "RSIMeanReversion"
+
+    @property
+    def description(self) -> str:
+        return "RSI 지표를 이용한 평균회귀 전략. RSI가 과매수/과매도 구간에서 반대 방향으로 거래"
+
+    @property
+    def version(self) -> str:
+        return "1.0.0"
+
+    @property
+    def default_parameters(self) -> Dict[str, Any]:
+        return {
+            "rsi_period": 14,
+            "oversold_threshold": 30,
+            "overbought_threshold": 70,
+            "initial_capital": 100000,
+            "position_size": 0.05
+        }
+
+    def validate_parameters(self, parameters: Dict[str, Any]) -> bool:
+        required_params = ["rsi_period", "oversold_threshold", "overbought_threshold", "initial_capital"]
+        for param in required_params:
+            if param not in parameters:
+                return False
+
+        if not (0 < parameters["oversold_threshold"] < parameters["overbought_threshold"] < 100):
+            return False
+
+        return True
+
+    def execute(self, parameters: Dict[str, Any] = None) -> Dict[str, Any]:
+        if parameters is None:
+            parameters = self.default_parameters
+
+        if not self.validate_parameters(parameters):
+            raise ValueError("Invalid parameters")
+
+        # 시뮬레이션 실행
+        time.sleep(1.5)  # 실행 시간 시뮬레이션
+
+        # 모의 결과 생성
+        profit_rate = random.uniform(-0.10, 0.18)
+        trades_count = random.randint(25, 80)
+        win_rate = random.uniform(0.40, 0.65)
+
+        return {
+            "strategy": self.name,
+            "version": self.version,
+            "profit_loss": round(parameters["initial_capital"] * profit_rate, 2),
+            "profit_rate": round(profit_rate * 100, 2),
+            "trades_executed": trades_count,
+            "win_rate": round(win_rate, 3),
+            "execution_time": "1.5s",
+            "parameters_used": parameters,
+            "final_capital": round(parameters["initial_capital"] * (1 + profit_rate), 2),
+            "max_drawdown": round(random.uniform(0.05, 0.20), 3)
+        }
+
+
+@strategy
+class BollingerBandBreakout(BaseQuantStrategy):
+    """볼린저 밴드 돌파 전략"""
+
+    @property
+    def name(self) -> str:
+        return "BollingerBandBreakout"
+
+    @property
+    def description(self) -> str:
+        return "볼린저 밴드 상한선 돌파시 매수, 하한선 돌파시 매도하는 돌파 전략"
+
+    @property
+    def version(self) -> str:
+        return "2.0.0"
+
+    @property
+    def default_parameters(self) -> Dict[str, Any]:
+        return {
+            "period": 20,
+            "std_dev": 2.0,
+            "initial_capital": 100000,
+            "position_size": 0.08,
+            "stop_loss": 0.05
+        }
+
+    def validate_parameters(self, parameters: Dict[str, Any]) -> bool:
+        required_params = ["period", "std_dev", "initial_capital"]
+        for param in required_params:
+            if param not in parameters:
+                return False
+
+        if parameters["std_dev"] <= 0 or parameters["period"] <= 0:
+            return False
+
+        return True
+
+    def execute(self, parameters: Dict[str, Any] = None) -> Dict[str, Any]:
+        if parameters is None:
+            parameters = self.default_parameters
+
+        if not self.validate_parameters(parameters):
+            raise ValueError("Invalid parameters")
+
+        # 시뮬레이션 실행
+        time.sleep(2)  # 실행 시간 시뮬레이션
+
+        # 모의 결과 생성
+        profit_rate = random.uniform(-0.20, 0.30)
+        trades_count = random.randint(10, 40)
+        win_rate = random.uniform(0.35, 0.70)
+
+        return {
+            "strategy": self.name,
+            "version": self.version,
+            "profit_loss": round(parameters["initial_capital"] * profit_rate, 2),
+            "profit_rate": round(profit_rate * 100, 2),
+            "trades_executed": trades_count,
+            "win_rate": round(win_rate, 3),
+            "execution_time": "2.0s",
+            "parameters_used": parameters,
+            "final_capital": round(parameters["initial_capital"] * (1 + profit_rate), 2),
+            "sharpe_ratio": round(random.uniform(0.5, 2.5), 2),
+            "volatility": round(random.uniform(0.15, 0.35), 3)
+        }