📜Coding Example
Here is a simplified Python implementation that showcases how AI might optimize a cross-chain swap:
import numpy as np
class CrossChainSwapAI:
def __init__(self, gas_prices, network_congestion, liquidity):
self.gas_prices = gas_prices
self.network_congestion = network_congestion
self.liquidity = liquidity
def estimate_cost(self, path):
estimated_cost = 0
for i, segment in enumerate(path):
estimated_cost += self.gas_prices[i] * self.network_congestion[i] /
self.liquidity[i]
return estimated_cost
def find_optimal_path(self, paths):
min_cost = float('inf')
best_path = None
for path in paths:
cost = self.estimate_cost(path)
if cost < min_cost:
min_cost = cost
best_path = path
return best_path, min_cost
# Example usage:
gas_prices = [10, 15, 8] # Hypothetical gas prices for each segment
network_congestion = [1.2, 1.5, 1.0] # Hypothetical congestion levels
liquidity = [100, 80, 120] # Hypothetical liquidity levels
ai = CrossChainSwapAI(gas_prices, network_congestion, liquidity)
paths = [[0, 1, 2], [0, 2], [1, 2]] # Hypothetical paths
best_path, min_cost = ai.find_optimal_path(paths)
print(f"Best path: {best_path} with estimated cost: {min_cost}")
Here is a simplified pseudocode example for AI-powered tax management:
def calculate_tax_liability(user_transactions):
total_liability = 0
for transaction in user_transactions:
gain = transaction['sell_price'] - transaction['buy_price']
liability = gain * get_tax_rate(transaction['holding_period'])
total_liability += liability
return total_liability
def get_tax_rate(holding_period):
# Simplified tax rate determination based on holding period
if holding_period > 365:
return 0.15 # Long-term capital gains tax
else:
return 0.30 # Short-term capital gains tax
# Example usage:
user_transactions = [
{'buy_price': 10000, 'sell_price': 15000, 'holding_period': 400},
{'buy_price': 20000, 'sell_price': 25000, 'holding_period': 200},
]
tax_liability = calculate_tax_liability(user_transactions)
print(f"Total tax liability: ${tax_liability}")
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