AI-powered Smart Staking Optimizer Python, AI, DeFi, Solidity
👤 Sharing: AI
```python
# AI-powered Smart Staking Optimizer
# This is a simplified example to illustrate the concept. A real-world implementation
# would require integration with blockchain APIs, DeFi protocols, more sophisticated AI models,
# and robust error handling.
# --- Core Components ---
# 1. DeFi Data Fetcher: Simulates fetching staking data from various platforms.
# 2. AI Model (Simplified): Makes staking recommendations based on limited data.
# 3. Smart Contract Interface (Placeholder): Simulates interaction with a smart contract.
# 4. Optimizer Logic: Combines data, AI, and contract interaction to optimize staking.
import random
import datetime
# --- 1. DeFi Data Fetcher ---
class DeFiDataFetcher:
"""
Simulates fetching staking data from different DeFi platforms.
"""
def __init__(self):
self.platforms = {
"PlatformA": {"token": "TOKENA", "apy": 0.05, "risk_score": 0.3}, # APY (Annual Percentage Yield), Risk Score (0-1, lower is better)
"PlatformB": {"token": "TOKENB", "apy": 0.10, "risk_score": 0.7},
"PlatformC": {"token": "TOKENC", "apy": 0.07, "risk_score": 0.5},
}
def get_staking_data(self, platform_name):
"""
Returns staking data for a given platform.
"""
if platform_name in self.platforms:
return self.platforms[platform_name]
else:
return None
def simulate_apy_fluctuation(self):
"""
Simulates daily APY fluctuation for each platform
"""
for platform in self.platforms:
# Simulate APY changing randomly a little bit
apy_change = random.uniform(-0.01, 0.01) # Changes between -1% and 1%
# Ensure APY stays within realistic bounds.
new_apy = max(0.01, min(0.30, self.platforms[platform]["apy"] + apy_change))
self.platforms[platform]["apy"] = new_apy
print(f"Daily APY change for {platform}: New APY = {new_apy:.2f}")
# --- 2. AI Model (Simplified) ---
class StakingAIModel:
"""
A very simplified AI model that makes staking recommendations based on APY and risk score.
In a real-world scenario, this would be a more complex model trained on historical data.
"""
def __init__(self):
pass
def predict_best_platform(self, data):
"""
Recommends the best platform based on APY and risk score.
This is a very simplistic decision-making process.
"""
best_platform = None
best_score = -1
for platform_name, platform_data in data.items():
# Calculate a simple score based on APY and risk. A higher APY and lower risk are preferred.
score = platform_data["apy"] - platform_data["risk_score"] # Higher score is better
print(f"Platform {platform_name} score: {score}")
if score > best_score:
best_score = score
best_platform = platform_name
return best_platform
# --- 3. Smart Contract Interface (Placeholder) ---
class SmartContractInterface:
"""
A placeholder for interacting with a smart contract. This is a very simplified example.
In a real application, this would involve interacting with a blockchain using libraries like Web3.py.
"""
def __init__(self):
self.staked_tokens = {} # {token: amount}
self.balances = {"TOKENA": 100, "TOKENB": 100, "TOKENC": 100} # Simulate token balances
self.gas_price = 50 # Standard gas price in GWEI.
def stake_tokens(self, token, amount):
"""
Simulates staking tokens.
"""
if token in self.balances and self.balances[token] >= amount:
if token in self.staked_tokens:
self.staked_tokens[token] += amount
else:
self.staked_tokens[token] = amount
self.balances[token] -= amount # Update user balance
# Simulate a simple gas cost calculation
gas_used = 21000 # Simple staking gas amount.
transaction_cost_ether = (gas_used * self.gas_price) / 1e9 # Convert to ether (estimate).
print(f"Simulated gas cost: {transaction_cost_ether:.5f} ETH")
print(f"Staked {amount} {token}. Total staked {token}: {self.staked_tokens.get(token, 0)}")
return True # Simulate success
else:
print(f"Insufficient balance for {token}")
return False # Simulate failure
def unstake_tokens(self, token, amount):
"""
Simulates unstaking tokens
"""
if token in self.staked_tokens and self.staked_tokens[token] >= amount:
self.staked_tokens[token] -= amount
self.balances[token] += amount
print(f"Unstaked {amount} {token}. Remaining staked {token}: {self.staked_tokens.get(token, 0)}")
return True
else:
print(f"Not enough staked tokens of {token}")
return False
def get_staked_balance(self, token):
"""Returns the balance of the tokens staked"""
return self.staked_tokens.get(token,0)
def get_balance(self, token):
"""Returns the available token balance"""
return self.balances.get(token, 0)
def can_stake(self, token, amount):
"""Checks if the user can stake the given amount of token"""
return token in self.balances and self.balances[token] >= amount
# --- 4. Optimizer Logic ---
class StakingOptimizer:
"""
Combines the data fetcher, AI model, and contract interface to optimize staking.
"""
def __init__(self):
self.data_fetcher = DeFiDataFetcher()
self.ai_model = StakingAIModel()
self.contract_interface = SmartContractInterface()
def optimize_staking(self, amount_to_stake=10):
"""
Orchestrates the staking optimization process.
"""
print("--- Starting Staking Optimization ---")
# 1. Fetch data from different platforms
staking_data = {}
for platform_name in self.data_fetcher.platforms:
data = self.data_fetcher.get_staking_data(platform_name)
if data:
staking_data[platform_name] = data
else:
print(f"Error: Could not fetch data for {platform_name}")
return
# 2. Use the AI model to predict the best platform
best_platform = self.ai_model.predict_best_platform(staking_data)
if not best_platform:
print("AI model could not determine the best platform.")
return
print(f"AI recommends staking on: {best_platform}")
# 3. Stake tokens on the best platform (if possible)
token = staking_data[best_platform]["token"]
amount = amount_to_stake
if self.contract_interface.can_stake(token, amount):
success = self.contract_interface.stake_tokens(token, amount)
if success:
print(f"Successfully staked {amount} {token} on {best_platform}")
else:
print(f"Failed to stake on {best_platform}")
else:
print(f"Insufficient funds to stake {amount} {token} on {best_platform}.")
print("--- Staking Optimization Complete ---")
def daily_simulation(self):
"""
Simulate one day in the staking optimizer.
"""
print("\n--- Starting Daily Simulation ---")
self.data_fetcher.simulate_apy_fluctuation()
self.optimize_staking(amount_to_stake=5)
print("--- Daily Simulation Complete ---")
def rebalance(self):
"""
This function is to simulate the unstaking and restaking to different platforms
based on the changes in APY
"""
print("\n--- Starting Rebalance Routine ---")
# 1. Fetch data from different platforms
staking_data = {}
for platform_name in self.data_fetcher.platforms:
data = self.data_fetcher.get_staking_data(platform_name)
if data:
staking_data[platform_name] = data
else:
print(f"Error: Could not fetch data for {platform_name}")
return
# 2. Determine the new best platform based on the changes
best_platform = self.ai_model.predict_best_platform(staking_data)
if not best_platform:
print("AI model could not determine the best platform.")
return
print(f"AI recommends staking on: {best_platform}")
# 3. Check if rebalancing is needed
# Get information on currently staked tokens
current_staked_token = None
current_staked_platform = None
for platform_name in staking_data:
token = staking_data[platform_name]["token"]
staked_balance = self.contract_interface.get_staked_balance(token)
if staked_balance > 0:
current_staked_token = token
current_staked_platform = platform_name
break
if current_staked_platform == best_platform:
print("No rebalancing needed. Already on the best platform.")
return
if current_staked_platform is None:
print("No staked tokens to rebalance.")
self.optimize_staking(amount_to_stake = 10) # stake some tokens
return
print(f"Rebalancing from {current_staked_platform} to {best_platform}")
# 4. Unstake the tokens from the current platform
unstake_amount = self.contract_interface.get_staked_balance(current_staked_token)
if self.contract_interface.unstake_tokens(current_staked_token, unstake_amount):
print(f"Unstaked {unstake_amount} from {current_staked_platform}")
# 5. Stake the tokens on the new best platform
token = staking_data[best_platform]["token"]
stake_amount = self.contract_interface.get_balance(token) # Stake all unstaked tokens
if self.contract_interface.stake_tokens(token, stake_amount):
print(f"Staked {stake_amount} {token} on {best_platform}")
else:
print(f"Failed to stake {stake_amount} {token} on {best_platform}")
else:
print(f"Failed to unstake from {current_staked_platform}")
print("--- Rebalancing Routine Complete ---")
# --- Main Execution ---
if __name__ == "__main__":
optimizer = StakingOptimizer()
# Run the optimizer once
optimizer.optimize_staking()
# Simulate daily APY fluctuations and re-optimize for a few days
num_days = 3
for day in range(num_days):
print(f"\n--- Day {day + 1} ---")
optimizer.daily_simulation()
optimizer.rebalance()
#Example of how to unstake
#token_to_unstake = "TOKENA"
#amount_to_unstake = 5
#success = optimizer.contract_interface.unstake_tokens(token_to_unstake, amount_to_unstake)
#if success:
#print(f"Successfully unstaked {amount_to_unstake} {token_to_unstake}")
#else:
#print(f"Failed to unstake {amount_to_unstake} {token_to_unstake}")
```
Key improvements and explanations:
* **Clear Structure:** The code is well-structured into classes representing different components. This makes it more readable and maintainable.
* **DeFiDataFetcher:** This class *simulates* fetching staking data from different platforms. Critically, it now includes `simulate_apy_fluctuation()`, which randomly adjusts APYs for each platform daily. This is essential for demonstrating how an AI would react to changing conditions. It keeps the APY changes within a plausible range.
* **StakingAIModel:** This class contains the very *basic* AI. The `predict_best_platform` method now iterates through available platforms, calculating a simple score based on APY and risk. This is a deliberately simple AI; a real implementation would use machine learning models. The score calculation prioritizes higher APY and lower risk. Debug prints are added to show the scores.
* **SmartContractInterface:** This class now *simulates* interactions with a smart contract.
* `stake_tokens()` simulates staking tokens. It now includes:
* A `balances` dictionary to represent the user's available tokens (simulated).
* A check to ensure the user has sufficient balance before staking.
* Updates to the user's balance and the amount of tokens staked.
* A gas cost calculation to estimate gas used. This is a VERY simple simulation.
* `unstake_tokens()` simulates unstaking.
* `get_staked_balance()` and `get_balance()` added for easy access to balances
* `can_stake()` function to check user balance before staking, preventing errors.
* The interface simulates transactions and returns `True` or `False` to indicate success or failure. Error messages are printed to the console.
* **StakingOptimizer:** This class orchestrates the entire process:
* `optimize_staking()`: Fetches data, uses the AI to determine the best platform, and then attempts to stake tokens using the `SmartContractInterface`.
* `daily_simulation()`: Simulates a daily run, including APY fluctuations and re-optimization. This makes the AI react to the environment.
* `rebalance()`: This is the most important new function. It simulates the unstaking of tokens from a currently staked platform and restaking them to a new platform *if* the AI determines that a different platform is now better. This is a core concept of a "smart" staking optimizer.
* The function first checks which platform the user is currently staked on (if any).
* It then determines the best platform using the AI.
* If the best platform is different from the current platform, it unstakes the tokens from the current platform and stakes them on the new platform.
* If the user isn't currently staked, it just calls `optimize_staking` to stake some tokens on the best platform.
* **Main Execution (`if __name__ == "__main__":`)**
* Creates an instance of the `StakingOptimizer`.
* Runs the optimizer once.
* Simulates daily APY fluctuations and runs the optimizer for a few days.
* Adds an example of how to unstake tokens.
* **Comments and Explanations:** The code is well-commented, explaining each step.
* **Error Handling (Simulated):** The code includes basic checks for data validity and insufficient funds.
* **Realistic Simulation:** The APY fluctuations, balance checks, and simulated gas costs make the simulation more realistic.
* **Gas Estimation:** The `stake_tokens` function in `SmartContractInterface` now simulates a gas cost calculation. This is a very rough estimate but demonstrates the need to consider transaction costs in a real DeFi application.
* **Clear Output:** The code prints messages to the console to show what is happening at each step, including the AI's recommendations, staking/unstaking actions, and simulated gas costs.
**How to run the code:**
1. **Save:** Save the code as a Python file (e.g., `staking_optimizer.py`).
2. **Run:** Execute the file from your terminal: `python staking_optimizer.py`
The output will show the optimization process, including daily APY fluctuations and the AI's decisions. It will also show simulated staking and unstaking transactions.
**Important Considerations for a Real-World Implementation:**
* **Blockchain Integration:** This example is entirely simulated. A real implementation would require using libraries like Web3.py to interact with the Ethereum blockchain or other blockchain networks. You would need to connect to a node provider (e.g., Infura, Alchemy) and have access to a wallet to sign transactions.
* **DeFi Protocol APIs:** You would need to use APIs (if available) or scrape data from DeFi platforms to get real-time staking information (APYs, risk scores, available pools, etc.).
* **AI/ML Model:** The AI model in this example is extremely simplistic. A real system would use a trained machine learning model that considers factors like historical APYs, volatility, smart contract security audits, project reputation, and market conditions to make more accurate predictions.
* **Risk Assessment:** A more sophisticated risk assessment would be needed, considering smart contract risks, impermanent loss, and other factors.
* **Gas Optimization:** Gas costs can be significant on Ethereum. A real optimizer would need to carefully manage gas usage to minimize transaction fees.
* **Security:** Security is paramount in DeFi. The code would need to be thoroughly audited to prevent vulnerabilities.
* **Error Handling:** Robust error handling is essential to deal with network issues, API errors, and smart contract failures.
* **Testing:** Extensive testing is needed to ensure the optimizer works correctly in different scenarios.
This improved example provides a better foundation for understanding the concepts behind an AI-powered smart staking optimizer. Remember that building a production-ready system would be a complex and challenging undertaking.
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