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fixed economic indicators

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This commit is contained in:
Isaak Buslovich 2023-11-19 16:59:43 +01:00
parent 7b46be7c85
commit e5b7709ce3
Signed by: Isaak
GPG Key ID: EEC31D6437FBCC63
1 changed files with 29 additions and 16 deletions
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main.py
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@ -95,9 +95,11 @@ class Market:
"recession": self.recession_effect "recession": self.recession_effect
} }
self.stock_ledger = {} self.stock_ledger = {}
self.inflation_rate = 0.05 self.inflation_rate = 0.05 # Initial inflation rate
self.unemployment_rate = 0.05 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.gdp = 500000
self.unemployment_rate = 0.04
self.trade_volume = 0 self.trade_volume = 0
self.total_bought = {'Coal': 0, 'Copper': 0, 'Conductor': 0} self.total_bought = {'Coal': 0, 'Copper': 0, 'Conductor': 0}
self.total_sold = {'Coal': 0, 'Copper': 0, 'Conductor': 0} self.total_sold = {'Coal': 0, 'Copper': 0, 'Conductor': 0}
@ -199,24 +201,21 @@ class Market:
self.previous_prices = self.products.copy() self.previous_prices = self.products.copy()
# Adjust prices based on demand # 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] demand_factor = self.total_bought[product] - self.total_sold[product]
self.products[product] *= (1 + demand_factor * 0.05) self.products[product] *= (1 + demand_factor * 0.05)
self.reset_trade_volumes() self.reset_trade_volumes()
# Update GDP # Update inflation rate with random fluctuation
self.gdp += self.trade_volume * 0.1 # Example formula self.inflation_rate = random.uniform(-0.001, 0.05)
self.trade_volume = 0
# Update inflation rate # GDP update logic - refined for more controlled growth
self.inflation_rate += (self.gdp / 500000 - 1) * 0.05 # Example adjustment self.gdp *= (1 + self.inflation_rate / 2)
# Handle market events # Handle market events
event = random.choices(self.events, weights=[e["probability"] for e in self.events])[0] event = random.choices(self.events, weights=[e["probability"] for e in self.events], k=1)[0]
self.last_event_name = event["name"] # Update the last event name self.last_event_name = 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()} self.products = {k: self.event_effects[event["effect"]](v) for k, v in self.products.items()}
# Update other economic indicators after processing the event # Update other economic indicators after processing the event
@ -246,9 +245,23 @@ class Market:
def update_economic_indicators(self): def update_economic_indicators(self):
"""Updates key economic indicators like inflation and unemployment rates.""" """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.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): def calculate_final_scores(self):
"""Calculates and returns final scores for each company based on value and stock ownership.""" """Calculates and returns final scores for each company based on value and stock ownership."""