working on gdp
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179
main.py
179
main.py
@ -79,98 +79,90 @@ class Market:
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The market is updated every turn.
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"""
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def __init__(self):
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self.current_turn = 0
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self.companies = {}
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self.products = {'Coal': 10.0, 'Copper': 15.0, 'Conductor': 20.0}
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self.starting_prices = self.products.copy()
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self.events = load_json('market_events.json')["events"]
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self.event_effects = {
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"double": lambda x: x * 2,
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"halve": lambda x: x / 2,
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"increase": lambda x: x + 3.0,
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"decrease": lambda x: max(1, x - 3.0),
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"tech_boost": self.tech_boost_effect,
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"resource_scarcity": self.resource_scarcity_effect,
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"recession": self.recession_effect
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}
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self.stock_ledger = {}
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self.inflation_rate = 0.05 # Initial inflation rate
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self.inflation_growing = True # Start with inflation growing
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self.inflation_change = random.uniform(0.0001, 0.001) # Initial change rate self.unemployment_rate = 0.05
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self.inflation_rate = 0.05
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self.inflation_growing = True
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self.inflation_change = random.uniform(0.0001, 0.001)
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self.gdp = 500000
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self.unemployment_rate = 0.04
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self.trade_volume = 0
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self.total_bought = {'Coal': 0, 'Copper': 0, 'Conductor': 0}
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self.total_sold = {'Coal': 0, 'Copper': 0, 'Conductor': 0}
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self.total_bought = {product: 0 for product in self.products}
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self.total_sold = {product: 0 for product in self.products}
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self.previous_prices = self.products.copy()
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self.last_event_name = None
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self.trend_factor = 1.01
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def print_market_events(self):
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console = Console()
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panel_text = Text()
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def update_market(self):
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self.current_turn += 1
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self.previous_prices = self.products.copy()
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self.update_prices()
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self.reset_trade_volumes()
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self.trend_factor = self.generate_market_trend()
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self.update_economic_indicators()
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self.handle_market_events()
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# Event information
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panel_text.append(f"Last Market Event: {self.last_event_name}\n")
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def update_prices(self):
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for product in self.products:
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demand_factor = self.total_bought[product] - self.total_sold[product]
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self.products[product] *= (1 + demand_factor * 0.05)
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# Economic indicators
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panel_text.append(f"Inflation Rate: {self.inflation_rate:.2f}%\n", style="grey70")
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panel_text.append(f"Unemployment Rate: {self.unemployment_rate:.2f}%\n", style="grey70")
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panel_text.append(f"GDP: {self.gdp:.2f} €", style="grey70")
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def reset_trade_volumes(self):
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self.total_bought = dict.fromkeys(self.total_bought, 0)
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self.total_sold = dict.fromkeys(self.total_sold, 0)
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# Display in a panel
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console.print(Panel(panel_text, title="[bold]Market Events and Indicators", border_style="grey50"))
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def generate_market_trend(self):
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trend_change = random.uniform(-0.02, 0.02)
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return max(0.5, min(self.trend_factor + trend_change, 1.5))
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def update_economic_indicators(self):
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"""Updates key economic indicators like inflation and unemployment rates."""
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# Update inflation_rate
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self.inflation_rate += self.inflation_change if self.inflation_growing else -self.inflation_change
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self.inflation_rate = max(0.0001, min(self.inflation_rate, 0.05)) # Clamp the inflation_rate
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# Update GDP
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self.gdp *= (1 + (self.inflation_rate / 2))
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# Update unemployment_rate
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self.unemployment_rate = min(max(self.unemployment_rate + random.uniform(-0.005, 0.005), 0), 1)
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# Optional: Print statements for debugging
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print(f"Inflation Rate: {self.inflation_rate}")
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print(f"GDP: {self.gdp}")
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print(f"Unemployment Rate: {self.unemployment_rate}")
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def update_inflation_rate(self):
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lower_bound = 0.0001
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upper_bound = 0.049
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if self.inflation_rate <= lower_bound:
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self.inflation_growing = True
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elif self.inflation_rate >= upper_bound:
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self.inflation_growing = False
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self.inflation_rate += self.inflation_change if self.inflation_growing else -self.inflation_change
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def update_unemployment_rate(self):
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fluctuation = random.uniform(-0.005, 0.005)
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self.unemployment_rate = max(0, min(self.unemployment_rate + fluctuation, 1))
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def handle_market_events(self):
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event = random.choices(self.events, weights=[e["probability"] for e in self.events], k=1)[0]
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self.last_event_name = event["name"]
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getattr(self, event["effect"])() if event["effect"] in dir(self) else None
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def tech_boost_effect(self):
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"""
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Handles the effect of a technological boost in the market.
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This can increase the price of high-tech products.
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"""
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self.products['Conductor'] *= 1.2 # Example: Increase by 20%
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self.products['Conductor'] *= 1.2
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def resource_scarcity_effect(self):
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"""
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Handles the effect of resource scarcity in the market.
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This can increase the price of a random basic resource.
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"""
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random_resource = random.choice(['Coal', 'Copper'])
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self.products[random_resource] *= 1.5 # Example: Increase by 50%
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def regulatory_change_effect(self):
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"""
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Handles the effect of a regulatory change in the market.
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This can either increase or decrease the price of all products.
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"""
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change_factor = random.uniform(0.9, 1.1) # Example: Change between -10% to +10%
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for product in self.products:
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self.products[product] *= change_factor
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def innovation_breakthrough_effect(self):
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"""
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Handles the effect of an innovation breakthrough.
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This can drastically reduce the production cost of a random product.
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"""
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breakthrough_product = random.choice(list(self.products.keys()))
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self.products[breakthrough_product] *= 0.8 # Example: Reduce by 20%
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def global_event_effect(self):
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"""
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Handles the effect of a global event (like a pandemic or economic crisis).
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This can significantly affect all market prices.
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"""
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impact_factor = random.uniform(0.7, 1.3) # Example: Change between -30% to +30%
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for product in self.products:
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self.products[product] *= impact_factor
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def tech_boost_effect(self, price):
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"""Handles the effect of a technology boost event on product prices."""
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# Example: Increases the price of high-tech products like 'Conductor'
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return price * 1.2 # Increase by 20%
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def resource_scarcity_effect(self, price):
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"""Handles the effect of a resource scarcity event on product prices."""
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# Example: Increase the price of a resource due to scarcity
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return price * 1.5 # Increase by 50%
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self.products[random_resource] *= 1.5
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def trade_agreement_effect(self, price):
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"""Handles the effect of a new trade agreement event."""
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@ -184,6 +176,25 @@ class Market:
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"""Handles the effect of a recession event on product prices."""
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return price * 0.7
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def print_market_events(self):
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"""
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Prints the latest market events and key economic indicators in a formatted panel.
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"""
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# Prepare the console and panel text for printing
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console = Console()
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event_info = f"Last Market Event: {self.last_event_name}\n"
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economic_indicators = (
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f"Inflation Rate: {self.inflation_rate:.2f}%\n"
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f"Unemployment Rate: {self.unemployment_rate:.2f}%\n"
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f"GDP: {self.gdp:.2f} €"
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)
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# Create a formatted panel text
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panel_text = Text(event_info + economic_indicators)
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# Print the panel with styling
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console.print(Panel(panel_text, title="[bold]Market Events and Indicators", border_style="grey50"))
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def update_stock_ledger(self, company_id, owner_id, amount):
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"""Updates the stock ledger for a given company and owner based on the transaction amount."""
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self.stock_ledger[company_id, owner_id] = self.stock_ledger.get((company_id, owner_id), 0) + amount
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@ -200,26 +211,30 @@ class Market:
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self.current_turn += 1
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self.previous_prices = self.products.copy()
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# Adjust prices based on demand
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# Update prices based on supply and demand
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for product in self.products:
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supply_chain_impact = self.simulate_supply_chain_impact(product)
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demand_factor = self.total_bought[product] - self.total_sold[product]
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self.products[product] *= (1 + demand_factor * 0.05)
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self.products[product] *= (1 + demand_factor * 0.05) * supply_chain_impact
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self.reset_trade_volumes()
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# Update inflation rate with random fluctuation
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self.inflation_rate = random.uniform(-0.001, 0.05)
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# Procedural generation of market trends
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self.trend_factor = self.generate_market_trend()
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# GDP update logic - refined for more controlled growth
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self.gdp *= (1 + self.inflation_rate / 2)
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# Update economic indicators
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self.update_economic_indicators()
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# Handle market events
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event = random.choices(self.events, weights=[e["probability"] for e in self.events], k=1)[0]
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self.last_event_name = event["name"]
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self.products = {k: self.event_effects[event["effect"]](v) for k, v in self.products.items()}
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self.handle_market_events()
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# Update other economic indicators after processing the event
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self.update_economic_indicators()
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def simulate_supply_chain_impact(self, product):
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return random.uniform(0.9, 1.1)
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def generate_market_trend(self):
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trend_change = random.uniform(-0.02, 0.02)
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new_trend = self.trend_factor + trend_change
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return max(0.5, min(new_trend, 1.5))
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def reset_trade_volumes(self):
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for product in self.total_bought.keys():
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