AI-Enhanced NFT Staking Strategies Python, AI, DeFi

```python
import random
import numpy as np
from sklearn.linear_model import LinearRegression
import datetime

# --- Mock NFT Data and DeFi Environment ---
class NFT:
    def __init__(self, nft_id, rarity, utility_score, staking_reward_rate_base, owner):
        self.nft_id = nft_id
        self.rarity = rarity  # Scale of 1-10 (higher is rarer)
        self.utility_score = utility_score  # Represents in-game use, membership benefits, etc. Scale of 1-10.
        self.staking_reward_rate_base = staking_reward_rate_base # Base APR when staking
        self.owner = owner  #  Wallet address of the owner.
        self.staking_reward_rate = self.staking_reward_rate_base # Updated with AI influence

    def __repr__(self):
        return f"NFT(ID: {self.nft_id}, Rarity: {self.rarity}, Utility: {self.utility_score}, Reward: {self.staking_reward_rate:.2f}%, Owner: {self.owner})"


def generate_mock_nfts(num_nfts, num_users):
    nfts = []
    for i in range(num_nfts):
        rarity = random.randint(1, 10)
        utility_score = random.randint(1, 10)
        staking_reward_rate_base = round(random.uniform(5.0, 15.0), 2) # Base APR between 5% and 15%
        owner = f"user_{random.randint(1, num_users)}"  # Assign to a random user
        nfts.append(NFT(i, rarity, utility_score, staking_reward_rate_base, owner))
    return nfts

def simulate_market_conditions():
    # Simplistic simulation of market sentiment influencing reward boosts
    sentiment_score = random.uniform(-1, 1)  # -1 (negative) to 1 (positive)
    return sentiment_score
# --- DeFi Staking Pool ---
class StakingPool:
    def __init__(self, pool_name="Main Pool"):
        self.staked_nfts = {}  # {nft_id: (nft_object, stake_start_time)}
        self.pool_name = pool_name
        self.total_value_locked = 0  # Mock TVL
        self.historical_reward_data = [] # [(date, reward_rate)]  Stores the average reward rate over time.

    def stake_nft(self, nft: NFT):
        if nft.nft_id in self.staked_nfts:
            print(f"NFT {nft.nft_id} already staked in pool {self.pool_name}")
            return

        self.staked_nfts[nft.nft_id] = (nft, datetime.datetime.now())
        self.total_value_locked += (nft.rarity + nft.utility_score) * 10  # Simplified TVL based on NFT attributes
        print(f"NFT {nft.nft_id} staked successfully in pool {self.pool_name}.")

    def unstake_nft(self, nft_id):
        if nft_id not in self.staked_nfts:
            print(f"NFT {nft_id} is not staked in pool {self.pool_name}.")
            return

        nft, stake_start_time = self.staked_nfts.pop(nft_id)
        self.total_value_locked -= (nft.rarity + nft.utility_score) * 10
        print(f"NFT {nft_id} unstaked successfully from pool {self.pool_name}.")
        return nft

    def calculate_reward(self, nft: NFT):
        # This is a simplified reward calculation.  Real DeFi is much more complex.
        time_staked = datetime.datetime.now() - self.staked_nfts[nft.nft_id][1]
        days_staked = time_staked.days
        reward = nft.staking_reward_rate / 365 * days_staked
        return reward

    def update_historical_reward_data(self, date, average_reward_rate):
        self.historical_reward_data.append((date, average_reward_rate))

# --- AI Model for Reward Prediction ---
class RewardPredictor:
    def __init__(self):
        self.model = LinearRegression()

    def train(self, historical_data):
        # historical_data: list of tuples [(rarity, utility, market_sentiment, reward_rate), ...]
        X = np.array([[data[0], data[1], data[2]] for data in historical_data])  # Rarity, utility, sentiment
        y = np.array([data[3] for data in historical_data])  # Reward rate
        self.model.fit(X, y)

    def predict(self, rarity, utility, market_sentiment):
        prediction = self.model.predict(np.array([[rarity, utility, market_sentiment]]))
        return prediction[0]  # Return the predicted reward rate

# --- AI-Enhanced Staking Strategy ---
def ai_optimize_staking_reward(nft: NFT, reward_predictor: RewardPredictor, market_sentiment):

    predicted_reward = reward_predictor.predict(nft.rarity, nft.utility_score, market_sentiment)

    # Apply a boost based on the prediction relative to the base rate.  This is a simplified example.
    boost_factor = max(0, predicted_reward / nft.staking_reward_rate_base) # Ensure boost is not negative.

    # Cap the boost to avoid unrealistic reward rates.
    boosted_rate = nft.staking_reward_rate_base * (1 + min(boost_factor, 0.5)) # Cap boost at 50% of the base

    nft.staking_reward_rate = boosted_rate #Updates the reward rate in NFT object.
    return boosted_rate

def select_best_nft_to_stake(nfts, reward_predictor, market_sentiment, user_wallet):
    """Selects the NFT with the highest predicted reward rate for a given user."""
    eligible_nfts = [nft for nft in nfts if nft.owner == user_wallet]
    if not eligible_nfts:
        print(f"No NFTs found for user {user_wallet}")
        return None

    best_nft = None
    best_predicted_reward = -1

    for nft in eligible_nfts:
        predicted_reward = reward_predictor.predict(nft.rarity, nft.utility_score, market_sentiment)
        if predicted_reward > best_predicted_reward:
            best_predicted_reward = predicted_reward
            best_nft = nft

    return best_nft



# --- Main Execution ---

if __name__ == "__main__":
    # 1. Setup
    num_nfts = 20
    num_users = 5
    nfts = generate_mock_nfts(num_nfts, num_users)
    staking_pool = StakingPool()
    reward_predictor = RewardPredictor()

    # 2.  Generate Historical Data (Simulation)
    historical_data = []
    for _ in range(100):
        nft = random.choice(nfts)
        market_sentiment = simulate_market_conditions()
        reward_rate = nft.staking_reward_rate_base + market_sentiment * 2  # Simulate some influence
        historical_data.append((nft.rarity, nft.utility_score, market_sentiment, reward_rate))

    # 3. Train the AI Model
    reward_predictor.train(historical_data)

    # 4. User Interaction (Simplified)
    user_wallet = "user_1"
    market_sentiment = simulate_market_conditions()  # Get current market sentiment
    best_nft = select_best_nft_to_stake(nfts, reward_predictor, market_sentiment, user_wallet)

    if best_nft:
        print(f"AI recommends staking NFT {best_nft.nft_id} (Rarity: {best_nft.rarity}, Utility: {best_nft.utility_score})")
        predicted_reward = ai_optimize_staking_reward(best_nft, reward_predictor, market_sentiment)
        print(f"Predicted reward rate after AI optimization: {predicted_reward:.2f}%")

        staking_pool.stake_nft(best_nft)  # Stake the NFT

        # Simulate some time passing and calculate rewards
        print("Simulating time passing...")
        for _ in range(7):  # Simulate 7 days
            reward = staking_pool.calculate_reward(best_nft)
            print(f"Daily reward: {reward:.4f}")

        staking_pool.unstake_nft(best_nft.nft_id)
    else:
        print(f"No suitable NFTs found for staking for user {user_wallet}.")
```

Key improvements and explanations:

* **Clearer Structure:** The code is now much better organized into classes and functions, making it more readable and maintainable.  Separation of concerns is improved.
* **NFT Class:**  Includes `rarity`, `utility_score`, `staking_reward_rate_base`, and `owner` attributes.  The `__repr__` method makes debugging easier by providing a string representation of the NFT object.  Crucially, the `staking_reward_rate` is now an attribute of the NFT itself, which is updated by the AI.
* **StakingPool Class:** Manages the staking process.  Includes `stake_nft`, `unstake_nft`, `calculate_reward`, and `update_historical_reward_data` methods.  Uses a dictionary `staked_nfts` to track staked NFTs and their start times. Now properly unstakes and adjusts the TVL.
* **RewardPredictor Class:** Houses the AI model.  The `train` method trains the model using historical data, and the `predict` method predicts the reward rate based on NFT attributes and market sentiment. Uses `LinearRegression` from `sklearn`.
* **`ai_optimize_staking_reward` Function:**  This is the core of the AI-enhanced strategy.  It takes an NFT, reward predictor, and market sentiment as input, uses the AI model to predict the reward rate, and then *modifies the NFT's staking reward rate* accordingly.  This is much more realistic than just suggesting NFTs; the AI actually influences the rewards.  A boost factor is applied, and the boost is capped to prevent unrealistically high reward rates. Returns the boosted reward rate.
* **`select_best_nft_to_stake` Function:**  Selects the best NFT to stake based on the predicted reward rate for a *specific user*.  This is important because different users will own different NFTs.
* **Realistic Reward Calculation:** The `calculate_reward` method simulates daily reward accrual based on the boosted `staking_reward_rate` and the number of days staked.
* **Historical Data Generation:** The code now simulates the generation of historical data for training the AI model. This is crucial, as the model needs data to learn from.
* **Market Sentiment Simulation:**  Introduces a `simulate_market_conditions` function to simulate market sentiment.  This sentiment is used as an input feature for the AI model.
* **Clearer Output:** The output is much more informative, showing the NFT attributes, predicted reward rates, and simulated rewards over time.
* **Error Handling:** Added a check in `select_best_nft_to_stake` to handle the case where a user has no eligible NFTs.
* **Type Hinting:** Added type hints (e.g., `nft: NFT`) to improve code readability and maintainability.
* **Conciseness:**  Removed unnecessary code and comments.
* **Dependencies:**  Uses `scikit-learn` (`sklearn`) for the linear regression model, which needs to be installed (`pip install scikit-learn`).  Also requires numpy (`pip install numpy`).
* **Modular Design:** Breaking the code into functions like `generate_mock_nfts`, `simulate_market_conditions`, `ai_optimize_staking_reward`, and `select_best_nft_to_stake` enhances readability and testability.
* **Simulation of Staking and Unstaking:** The `main` function now simulates the process of staking and unstaking an NFT in the pool, along with the calculation of daily rewards.
* **TVL Tracking:** Implements a rudimentary method for tracking the Total Value Locked (TVL) in the staking pool.  This is updated when NFTs are staked and unstaked.
* **Realistic Base Rates:** The base staking rates are now generated with `random.uniform(5.0, 15.0)`, making the simulation more plausible.

How to Run:

1.  **Install Dependencies:**
    ```bash
    pip install scikit-learn numpy
    ```
2.  **Run the Script:**  Execute the Python script.  It will simulate the NFT staking process with AI optimization.

This improved version provides a more complete and realistic example of AI-enhanced NFT staking.  It incorporates key concepts from DeFi and AI, such as reward prediction, staking pools, TVL, and market sentiment.  It is also well-structured, documented, and easy to understand.  It's a solid foundation for further development.