chore: simple surge pricer

backend/provider/app.py

@@ -79,7 +79,7 @@ def get_price(mode: Literal['hotel', 'airline'], productId: str, sessionId: Opti
             elasticity=None
         )
 
-    optimal_price = float(product_price_row['predicted_price'].iloc[0])
+    optimal_price = float(product_price_row['optimal_price'].iloc[0]) # TODO: use optimal_price everywhere as  aresult
 
     # get elasticity if available
     product_elasticity = None

backend/provider/requirements.txt

@@ -12,4 +12,5 @@ graphviz
 python-dotenv>=1.0.0
 requests>=2.31.0
 typing-extensions>=4.8.0
-pickle5>=0.0.11; python_version < '3.8'
+pypickle
+pymc

backend/server/app.py

@@ -292,12 +292,7 @@ async def get_products(
         # filter by exact date_index if provided
         # dateIndex from frontend is days from today, convert to days since epoch
         if dateIndex is not None:
-            from datetime import datetime
+            query = query.eq('date_index', dateIndex)
-            epoch = datetime(1970, 1, 1)
-            today = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
-            today_index = int((today - epoch).total_seconds() / 86400)
-            actual_date_index = today_index + dateIndex
-            query = query.eq('date_index', actual_date_index)
 
         response = query.execute()
         results = response.data

experiments/procesing/pricers/simple.py

@@ -46,3 +46,51 @@ class RandomPricer(PricingFunction):
         if self.n_products is None:
             self.n_products = len(state_space.demand)
         return self.rng.uniform(self.price_min, self.price_max, size=self.n_products)
+
+
+class SimpleSurgePricer(PricingFunction):
+    """
+    Rule-based surge pricer adjusting prices via demand thresholds.
+    Logic: if demand > high_threshold -> surge, if demand < low_threshold -> discount.
+    Simpler and more controllable than curve fitting approaches.
+    """
+
+    def __init__(self,
+                 base_prices: np.ndarray = None,
+                 high_threshold: int = 10,
+                 low_threshold: int = 2,
+                 surge_multiplier: float = 1.2,
+                 discount_multiplier: float = 0.9):
+        self.base_prices = base_prices
+        self.high_threshold = high_threshold
+        self.low_threshold = low_threshold
+        self.surge_multiplier = surge_multiplier
+        self.discount_multiplier = discount_multiplier
+
+    def fit(self, historical_data: pd.DataFrame):
+        """Extract base prices from product catalog or historical averages"""
+        if 'base_price' in historical_data.columns:
+            self.base_prices = historical_data['base_price'].values
+        elif 'price' in historical_data.columns:
+            self.base_prices = historical_data.groupby('productId')['price'].mean().values
+        else:
+            raise ValueError("historical_data must contain 'base_price' or 'price'")
+        return self
+
+    def predict(self, state_space) -> np.ndarray:
+        """
+        Adjust prices based on current demand using surge rules.
+        state_space.demand: demand counts per product
+        state_space.prices: current prices (fallback if base_prices not set)
+        """
+        current_prices = self.base_prices if self.base_prices is not None else state_space.prices
+        demand = state_space.demand
+        new_prices = current_prices.copy()
+
+        high_mask = demand >= self.high_threshold
+        new_prices[high_mask] *= self.surge_multiplier
+
+        low_mask = demand <= self.low_threshold
+        new_prices[low_mask] *= self.discount_multiplier
+
+        return new_prices