Fix dataclass field ordering in event_driven.py and format code with ruff

Author: MeridianAlgo-Developer

meridianalgo/advanced_risk.py

@@ -677,7 +677,7 @@ def extreme_value_analysis(
 
             # Calculate extreme quantiles
             extreme_var = threshold - (scale_param / shape_param) * (
-                ((len(returns) / len(excesses) * (1 - 0.01)) ** (-shape_param) - 1)
+                (len(returns) / len(excesses) * (1 - 0.01)) ** (-shape_param) - 1
             )
 
         else:  # GEV method

meridianalgo/api.py

@@ -310,7 +310,6 @@ def get_market_data(
     return api.get_market_data(symbols, start_date, end_date)
 
 
-
 def optimize_portfolio(
     returns: pd.DataFrame, method: str = "sharpe", **kwargs
 ) -> Dict[str, float]:

meridianalgo/backtesting/event_driven.py

@@ -45,12 +45,12 @@ def __lt__(self, other):
 class MarketDataEvent(Event):
     """Market data event."""
 
-    symbol: str
-    open_price: float
-    high_price: float
-    low_price: float
-    close_price: float
-    volume: int
+    symbol: str = ""
+    open_price: float = 0.0
+    high_price: float = 0.0
+    low_price: float = 0.0
+    close_price: float = 0.0
+    volume: int = 0
     bid_price: Optional[float] = None
     ask_price: Optional[float] = None
 
@@ -74,9 +74,9 @@ def __post_init__(self):
 class SignalEvent(Event):
     """Trading signal event."""
 
-    symbol: str
-    signal_type: str  # 'BUY', 'SELL', 'HOLD'
-    strength: float  # Signal strength (0-1)
+    symbol: str = ""
+    signal_type: str = ""  # 'BUY', 'SELL', 'HOLD'
+    strength: float = 0.0  # Signal strength (0-1)
     metadata: Dict[str, Any] = field(default_factory=dict)
 
     def __post_init__(self):
@@ -136,7 +136,7 @@ class Order:
 class OrderEvent(Event):
     """Order event."""
 
-    order: Order
+    order: Order = None
 
     def __post_init__(self):
         self.event_type = EventType.ORDER
@@ -171,7 +171,7 @@ class Fill:
 class FillEvent(Event):
     """Fill event."""
 
-    fill: Fill
+    fill: Fill = None
 
     def __post_init__(self):
         self.event_type = EventType.FILL
@@ -198,7 +198,6 @@ def __init__(
         latency_model: str = "fixed",
         base_latency: float = 0.001,
     ):  # 1ms
-
         self.spread_model = spread_model
         self.base_spread = base_spread
         self.impact_model = impact_model
@@ -694,7 +693,6 @@ def __init__(
         portfolio: Portfolio,
         market_simulator: MarketSimulator = None,
     ):
-
         self.data_handler = data_handler
         self.strategy = strategy
         self.portfolio = portfolio

meridianalgo/computing/distributed.py

@@ -297,7 +297,6 @@ def __init__(
         redis_db: int = 0,
         default_ttl: int = 3600,
     ):
-
         self.redis_host = redis_host
         self.redis_port = redis_port
         self.redis_db = redis_db

meridianalgo/data/processing.py

@@ -160,7 +160,6 @@ def _check_ohlc_consistency(self, data: pd.DataFrame) -> pd.DataFrame:
                     col in data.columns.get_level_values(0)
                     for col in ["Open", "High", "Low", "Close"]
                 ):
-
                     high = data[("High", symbol)]
                     low = data[("Low", symbol)]
                     data[("Open", symbol)]

meridianalgo/diversification.py

@@ -437,12 +437,8 @@ def calculate_alternative_portfolio_allocation(
             Portfolio allocation results
         """
         # Calculate risk-adjusted returns
-        (traditional_returns.mean() * 252) / (
-            traditional_returns.std() * np.sqrt(252)
-        )
-        (alternative_returns.mean() * 252) / (
-            alternative_returns.std() * np.sqrt(252)
-        )
+        (traditional_returns.mean() * 252) / (traditional_returns.std() * np.sqrt(252))
+        (alternative_returns.mean() * 252) / (alternative_returns.std() * np.sqrt(252))
 
         # Optimize allocation (simplified)
         n_alternatives = len(alternative_returns.columns)

meridianalgo/fixed_income/options.py

@@ -495,9 +495,9 @@ class OptionPortfolio:
     """Option portfolio analytics."""
 
     def __init__(self):
-        self.positions: List[Tuple[Option, float, str]] = (
-            []
-        )  # (option, quantity, model)
+        self.positions: List[
+            Tuple[Option, float, str]
+        ] = []  # (option, quantity, model)
 
     def add_position(
         self, option: Option, quantity: float, model: str = "black_scholes"
@@ -865,9 +865,9 @@ def price_rainbow_option(
             for _ in range(n_steps):
                 # Generate correlated random numbers
                 z1 = np.random.normal(0, 1)
-                z2 = correlation * z1 + np.sqrt(
-                    1 - correlation**2
-                ) * np.random.normal(0, 1)
+                z2 = correlation * z1 + np.sqrt(1 - correlation**2) * np.random.normal(
+                    0, 1
+                )
 
                 dW1 = z1 * np.sqrt(dt)
                 dW2 = z2 * np.sqrt(dt)
@@ -964,9 +964,9 @@ class VolatilitySurface:
     """Implied volatility surface construction and analysis."""
 
     def __init__(self):
-        self.data_points: List[Tuple[float, float, float, float]] = (
-            []
-        )  # (strike, expiry, price, vol)
+        self.data_points: List[
+            Tuple[float, float, float, float]
+        ] = []  # (strike, expiry, price, vol)
         self.surface = None
 
     def add_market_data(

meridianalgo/machine_learning/deployment.py

@@ -708,7 +708,7 @@ def check_retraining_triggers(self, model_name: str) -> bool:
         try:
             active_version, _ = self.registry.get_active_model(model_name)
             performance = self.monitor.get_model_performance(
-                active_version,                 hours_back=24
+                active_version, hours_back=24
             )
         except Exception:
             return False

meridianalgo/machine_learning/feature_engineering.py

@@ -89,9 +89,7 @@ def generate_features(self, data: pd.DataFrame, **kwargs) -> pd.DataFrame:
         data["High"].values
         data["Low"].values
         close = data["Close"].values
-        (
-            data["Volume"].values if "Volume" in data.columns else np.ones_like(close)
-        )
+        (data["Volume"].values if "Volume" in data.columns else np.ones_like(close))
         data["Open"].values if "Open" in data.columns else close
 
         # Moving Averages

meridianalgo/machine_learning/models.py

@@ -23,11 +23,9 @@
     warnings.warn("PyTorch not available. Deep learning models will be limited.")
 
 try:
-    from sklearn.ensemble import (GradientBoostingRegressor,
-                                  RandomForestRegressor)
+    from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
     from sklearn.linear_model import ElasticNet, Lasso, LinearRegression, Ridge
-    from sklearn.metrics import (mean_absolute_error, mean_squared_error,
-                                 r2_score)
+    from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
     from sklearn.preprocessing import StandardScaler
     from sklearn.svm import SVR
 
@@ -656,7 +654,7 @@ def fit(self, X: np.ndarray, y: np.ndarray, **kwargs) -> "EnsembleModel":
         """Fit all models in the ensemble."""
 
         for i, model in enumerate(self.models):
-            logger.info(f"Training model {i+1}/{len(self.models)}: {model.name}")
+            logger.info(f"Training model {i + 1}/{len(self.models)}: {model.name}")
             model.fit(X, y, **kwargs)
 
         self.is_fitted = True