fixed economic indicators

main.py

@@ -95,9 +95,11 @@ class Market:
             "recession": self.recession_effect
         }
         self.stock_ledger = {}
-        self.inflation_rate = 0.05
-        self.unemployment_rate = 0.05
+        self.inflation_rate = 0.05  # Initial inflation rate
+        self.inflation_growing = True  # Start with inflation growing
+        self.inflation_change = random.uniform(0.0001, 0.001)  # Initial change rate        self.unemployment_rate = 0.05
         self.gdp = 500000
+        self.unemployment_rate = 0.04
         self.trade_volume = 0
         self.total_bought = {'Coal': 0, 'Copper': 0, 'Conductor': 0}
         self.total_sold = {'Coal': 0, 'Copper': 0, 'Conductor': 0}
@@ -199,25 +201,22 @@ class Market:
         self.previous_prices = self.products.copy()
 
         # Adjust prices based on demand
-        for product in self.products.keys():
+        for product in self.products:
             demand_factor = self.total_bought[product] - self.total_sold[product]
             self.products[product] *= (1 + demand_factor * 0.05)
+
         self.reset_trade_volumes()
 
-        # Update GDP
-        self.gdp += self.trade_volume * 0.1  # Example formula
-        self.trade_volume = 0
+        # Update inflation rate with random fluctuation
+        self.inflation_rate = random.uniform(-0.001, 0.05)
 
-        # Update inflation rate
-        self.inflation_rate += (self.gdp / 500000 - 1) * 0.05  # Example adjustment
+        # GDP update logic - refined for more controlled growth
+        self.gdp *= (1 + self.inflation_rate / 2)
 
         # Handle market events
-        event = random.choices(self.events, weights=[e["probability"] for e in self.events])[0]
-        self.last_event_name = event["name"]  # Update the last event name
-        if event["effect"] in ["new_competitor", "exit_competitor"]:
-            self.handle_competitor_event(event["effect"])
-        else:
-            self.products = {k: self.event_effects[event["effect"]](v) for k, v in self.products.items()}
+        event = random.choices(self.events, weights=[e["probability"] for e in self.events], k=1)[0]
+        self.last_event_name = event["name"]
+        self.products = {k: self.event_effects[event["effect"]](v) for k, v in self.products.items()}
 
         # Update other economic indicators after processing the event
         self.update_economic_indicators()
@@ -246,9 +245,23 @@ class Market:
 
     def update_economic_indicators(self):
         """Updates key economic indicators like inflation and unemployment rates."""
-        self.inflation_rate = max(0.5, self.inflation_rate + random.uniform(-0.01, 0.01))
+
+        # Check bounds and change state if necessary
+        if self.inflation_rate <= 0.0001:  # Lower bound
+            self.inflation_growing = True
+            self.inflation_change = random.uniform(0.0001, 0.001)  # Reset change rate
+        elif self.inflation_rate >= 0.049:  # Upper bound
+            self.inflation_growing = False
+            self.inflation_change = random.uniform(0.0001, 0.001)  # Reset change rate
+
+        # Adjust inflation rate based on the current state
+        if self.inflation_growing:
+            self.inflation_rate += self.inflation_change
+        else:
+            self.inflation_rate -= self.inflation_change
+
+        # Unemployment rate adjustment with random fluctuation
         self.unemployment_rate = min(max(self.unemployment_rate + random.uniform(-0.005, 0.005), 0), 1)
-        self.gdp += self.gdp * (random.uniform(-0.01, 0.03) + self.inflation_rate)
 
     def calculate_final_scores(self):
         """Calculates and returns final scores for each company based on value and stock ownership."""