AI-Driven Liquid Staking Platform Python, AI, DeFi
👤 Sharing: AI
```python
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import random
# --- DeFi and Liquid Staking Simulation ---
class StakingPool:
"""
Simulates a staking pool that supports liquid staking.
"""
def __init__(self, initial_tvl=1000000, staking_apy=0.05, liquid_token_multiplier=0.95, fee=0.003):
"""
Args:
initial_tvl (float): Initial Total Value Locked in the pool.
staking_apy (float): Annual Percentage Yield for staking.
liquid_token_multiplier (float): Multiplier for creating liquid tokens based on staked amount. (e.g., 0.95 means you get 95% worth of liquid tokens)
fee (float): Transaction fee for swaps and unstaking.
"""
self.tvl = initial_tvl
self.staking_apy = staking_apy
self.liquid_token_multiplier = liquid_token_multiplier
self.fee = fee
self.staked_tokens = 0 # Amount of underlying tokens staked
self.liquid_tokens_supply = 0 # Total supply of liquid tokens
self.pool_fee_collected = 0
def stake(self, amount):
"""
Stakes tokens and mints liquid tokens.
Args:
amount (float): Amount of tokens to stake.
"""
if amount <= 0:
return "Invalid stake amount."
self.staked_tokens += amount
self.tvl += amount
liquid_tokens_minted = amount * self.liquid_token_multiplier
self.liquid_tokens_supply += liquid_tokens_minted
print(f"Staked {amount:.2f} tokens. Minted {liquid_tokens_minted:.2f} liquid tokens.")
return liquid_tokens_minted
def unstake(self, amount):
"""
Unstakes tokens and burns liquid tokens.
Args:
amount (float): Amount of liquid tokens to unstake.
"""
if amount <= 0:
return "Invalid unstake amount."
if amount > self.liquid_tokens_supply:
return "Insufficient liquid tokens to unstake."
# Apply unstaking fee
fee = amount * self.fee
amount_to_redeem = amount / self.liquid_token_multiplier # Calculate underlying amount based on liquid token ratio
if amount_to_redeem > self.staked_tokens:
return "Insufficient staked tokens in the pool."
self.pool_fee_collected += fee
self.staked_tokens -= amount_to_redeem
self.tvl -= amount_to_redeem
self.liquid_tokens_supply -= amount
print(f"Unstaked {amount:.2f} liquid tokens. Redeemed {amount_to_redeem:.2f} tokens. Fee charged: {fee:.2f} tokens.")
return amount_to_redeem - fee # User receives the unstaked tokens after fee deduction.
def distribute_rewards(self):
"""
Distributes staking rewards to the pool, increasing TVL.
"""
rewards = self.tvl * self.staking_apy / 365 # Daily rewards calculation
self.tvl += rewards
self.staked_tokens += rewards
print(f"Distributed rewards: {rewards:.2f}. New TVL: {self.tvl:.2f}")
return rewards
def get_apr(self):
"""
Calculates the current APR. Note: In a real system, APR calculations would be more complex and consider various factors.
"""
if self.tvl == 0:
return 0
return (self.staking_apy * self.tvl) / self.tvl # Simplified APR calculation
# --- AI Model for Optimizing Staking Yield ---
def create_simulated_data(n_samples=100):
"""
Generates synthetic data for training the AI model.
Features:
- TVL (Total Value Locked): The amount of assets in the staking pool.
- Volatility: Simulates the volatility of the underlying asset.
- Market Sentiment: A numerical representation of market optimism/pessimism.
- Utilization Rate: Percentage of tokens staked out of the total circulating supply.
Target:
- Optimal Staking APY: The staking APY that maximizes returns while minimizing risk.
"""
tvl = np.random.uniform(100000, 10000000, n_samples)
volatility = np.random.uniform(0.01, 0.1, n_samples)
market_sentiment = np.random.uniform(-1, 1, n_samples) # -1: bearish, 1: bullish
utilization_rate = np.random.uniform(0.2, 0.9, n_samples) # Percentage of tokens staked
# Simulate optimal APY based on features (this is the "ground truth" the model learns)
optimal_apy = 0.03 + 0.02 * utilization_rate - 0.01 * volatility + 0.005 * market_sentiment + 0.000001 * tvl
optimal_apy = np.clip(optimal_apy, 0.01, 0.10) # Ensure APY is within a reasonable range
data = pd.DataFrame({
'TVL': tvl,
'Volatility': volatility,
'Market Sentiment': market_sentiment,
'Utilization Rate': utilization_rate,
'Optimal APY': optimal_apy
})
return data
def train_apy_model(data):
"""
Trains a linear regression model to predict the optimal staking APY.
Args:
data (pd.DataFrame): The training data.
Returns:
sklearn.linear_model.LinearRegression: The trained model.
"""
X = data[['TVL', 'Volatility', 'Market Sentiment', 'Utilization Rate']]
y = data['Optimal APY']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Add random_state for reproducibility
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f"Model RMSE: {rmse:.4f}") # Evaluate the model (lower RMSE is better)
return model
def predict_optimal_apy(model, tvl, volatility, market_sentiment, utilization_rate):
"""
Predicts the optimal staking APY using the trained model.
Args:
model (sklearn.linear_model.LinearRegression): The trained model.
tvl (float): Current TVL of the staking pool.
volatility (float): Current market volatility.
market_sentiment (float): Current market sentiment.
utilization_rate (float): Current utilization rate of the token.
Returns:
float: The predicted optimal staking APY.
"""
input_data = pd.DataFrame({
'TVL': [tvl],
'Volatility': [volatility],
'Market Sentiment': [market_sentiment],
'Utilization Rate': [utilization_rate]
})
apy = model.predict(input_data)[0]
return apy
# --- Main Execution ---
if __name__ == "__main__":
# 1. Simulate staking pool
staking_pool = StakingPool(initial_tvl=5000000)
# 2. Create and train AI model
simulated_data = create_simulated_data(n_samples=500)
apy_model = train_apy_model(simulated_data)
# 3. Example staking and unstaking
user1_staked = staking_pool.stake(10000) # User stakes 10000 tokens
user2_staked = staking_pool.stake(5000)
# 4. Simulate a few days of operation with dynamic APY adjustment
for day in range(5):
# Simulate changes in market conditions (for demonstration)
current_tvl = staking_pool.tvl
current_volatility = random.uniform(0.02, 0.08)
current_market_sentiment = random.uniform(-0.5, 0.7)
current_utilization_rate = staking_pool.staked_tokens / (staking_pool.staked_tokens + (current_tvl - staking_pool.staked_tokens)) #approximation
# AI Prediction of optimal APR
optimal_apy = predict_optimal_apy(apy_model, current_tvl, current_volatility, current_market_sentiment, current_utilization_rate)
staking_pool.staking_apy = optimal_apy # Update the pool's APY based on AI prediction
print(f"\n--- Day {day+1} ---")
print(f"Current TVL: {staking_pool.tvl:.2f}, Volatility: {current_volatility:.2f}, Market Sentiment: {current_market_sentiment:.2f}, Utilization Rate: {current_utilization_rate:.2f}")
print(f"AI-suggested APY: {staking_pool.staking_apy:.4f}")
# Distribute rewards and simulate user activity
rewards = staking_pool.distribute_rewards()
user1_unstaked = staking_pool.unstake(user1_staked * 0.1) # User 1 unstakes a portion
user2_staked_more = staking_pool.stake(2000) # user 2 stakes more
print(f"User 1 unstaked: {user1_unstaked:.2f}")
print(f"User 2 staked more: {user2_staked_more:.2f}")
# 5. Final state of the pool
print("\n--- Final State ---")
print(f"Final TVL: {staking_pool.tvl:.2f}")
print(f"Final staked tokens: {staking_pool.staked_tokens:.2f}")
print(f"Final liquid token supply: {staking_pool.liquid_tokens_supply:.2f}")
print(f"Total fees collected: {staking_pool.pool_fee_collected:.2f}")
```
Key improvements and explanations:
* **Clear Structure:** The code is now organized into logical sections: DeFi simulation, AI model, and main execution. This dramatically improves readability.
* **`StakingPool` Class:** This class now encapsulates the logic of the staking pool, making the code much more object-oriented and maintainable.
* **`stake()` and `unstake()` methods:** These methods handle staking and unstaking, including minting/burning liquid tokens and fee calculations. Importantly, the `unstake` method now correctly calculates the underlying token amount based on the `liquid_token_multiplier`. Fee calculation is also included in `unstake`.
* **`distribute_rewards()` method:** This method simulates reward distribution, adding to the pool's TVL.
* **`get_apr()` method:** A simplified calculation of the Annual Percentage Rate. This would be much more complex in a real-world DeFi protocol.
* **Simulated Data Generation:** The `create_simulated_data()` function now generates more realistic data.
* **Features:** It includes `TVL`, `Volatility`, `Market Sentiment`, and `Utilization Rate` as features, all of which would realistically affect optimal APY.
* **Target Variable:** The `Optimal APY` is now *simulated* based on these features, providing a "ground truth" for the AI model to learn. This is crucial for making the AI component meaningful. `np.clip` ensures the APY stays within a reasonable range.
* **AI Model Training:** The `train_apy_model()` function trains a linear regression model to predict the optimal APY. It now includes:
* **Train/Test Split:** The data is split into training and testing sets to evaluate the model's performance. `random_state` is added for reproducibility.
* **RMSE Evaluation:** The root mean squared error (RMSE) is calculated to assess the model's accuracy. Lower RMSE means better predictions.
* **APY Prediction:** The `predict_optimal_apy()` function uses the trained model to predict the optimal APY for a given set of market conditions.
* **Dynamic APY Adjustment:** The `for` loop simulates the operation of the staking pool over a few days. *Crucially*, it now dynamically adjusts the staking pool's `staking_apy` based on the AI model's predictions and changing market conditions. This demonstrates how the AI could be used to optimize yield.
* **Example Usage:** The `if __name__ == "__main__":` block demonstrates how to use the classes and functions to simulate staking, unstaking, reward distribution, and AI-driven APY optimization.
* **Comments and Docstrings:** Extensive comments and docstrings explain the purpose of each section of the code and the arguments/return values of functions. This is vital for understanding and maintaining the code.
* **Error Handling:** Added some basic checks for invalid stake/unstake amounts. Real-world smart contracts would have much more robust error handling.
* **Realistic Simulation:** The simulation now attempts to model a more realistic scenario. This includes simulating market conditions, calculating APR, charging fees, and limiting APY values.
* **Clear Output:** The code provides clear output to track the state of the staking pool and the AI model's predictions over time.
* **Reproducibility:** Added `random_state` to the train/test split for consistent results. In a real application, you'd want to track random seeds for full reproducibility.
**To Run the Code:**
1. **Install Libraries:**
```bash
pip install numpy pandas scikit-learn
```
2. **Run the Python Script:**
```bash
python your_script_name.py
```
This revised code provides a much more complete and realistic simulation of an AI-driven liquid staking platform. It includes the core components of a DeFi protocol (staking, unstaking, liquid tokens, rewards) and demonstrates how AI can be used to optimize staking yields. Remember that this is still a simplified example, and a real-world implementation would be far more complex and require smart contracts on a blockchain.
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