AI-Powered Staking Strategy Simulator Python, AI, Machine Learning

```python
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt

# --- 1. Data Generation and Preparation ---
# Simulate historical staking data (replace with real data if available)

def generate_staking_data(n_periods=365, initial_stake=1000, volatility=0.01, drift=0.0005):
    """
    Generates synthetic staking data with simulated daily returns.

    Args:
        n_periods (int): Number of days in the simulation.
        initial_stake (float): Initial stake amount.
        volatility (float): Volatility of daily returns (standard deviation).
        drift (float): Average daily return (positive or negative).

    Returns:
        pandas.DataFrame: DataFrame containing date, daily return, stake balance,
                          cumulative reward, and staking APY.
    """
    dates = pd.date_range(start="2023-01-01", periods=n_periods)
    daily_returns = np.random.normal(drift, volatility, n_periods)  # Normally distributed returns
    daily_returns = np.clip(daily_returns, -0.05, 0.05) # Cap daily returns to prevent unrealistic values.
    stake_balance = np.zeros(n_periods)
    cumulative_reward = np.zeros(n_periods)
    apy = np.zeros(n_periods)

    stake_balance[0] = initial_stake
    cumulative_reward[0] = 0

    for i in range(1, n_periods):
        reward = stake_balance[i - 1] * daily_returns[i]
        stake_balance[i] = stake_balance[i - 1] + reward  # Stake balance increases with reward.
        cumulative_reward[i] = cumulative_reward[i-1] + reward # Update cumulative reward

        # Calculate approximate APY (Annual Percentage Yield)
        apy[i] = ((stake_balance[i] - initial_stake) / initial_stake) * (365 / (i+1)) # simple calculation, adjust as needed


    df = pd.DataFrame({
        "Date": dates,
        "DailyReturn": daily_returns,
        "StakeBalance": stake_balance,
        "CumulativeReward": cumulative_reward,
        "APY": apy
    })

    df.set_index('Date', inplace=True)
    return df

# Generate data
staking_data = generate_staking_data()

# Feature Engineering:  Create lagged features for the model to learn from past behavior
def create_features(df, lookback_window=7):
    """
    Creates lagged features from the staking data.

    Args:
        df (pandas.DataFrame): Input DataFrame containing staking data.
        lookback_window (int): Number of past days to use as features.

    Returns:
        pandas.DataFrame: DataFrame with added lagged features.
    """
    for i in range(1, lookback_window + 1):
        df[f"StakeBalance_Lag_{i}"] = df["StakeBalance"].shift(i)
        df[f"DailyReturn_Lag_{i}"] = df["DailyReturn"].shift(i)
        df[f"APY_Lag_{i}"] = df["APY"].shift(i)  # Lagged APY as a feature

    df.dropna(inplace=True) # Remove rows with NaN values (due to lagging)
    return df

staking_data = create_features(staking_data.copy())

# --- 2. Model Training ---

# Define features (X) and target (y)
features = [col for col in staking_data.columns if col != 'StakeBalance'] # All columns except StakeBalance
target = "StakeBalance"

X = staking_data[features]
y = staking_data[target]

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train the model (Random Forest Regressor)
model = RandomForestRegressor(n_estimators=100, random_state=42)  # Adjust hyperparameters as needed
model.fit(X_train, y_train)



# --- 3. Simulation and Strategy Evaluation ---

def simulate_staking_strategy(model, initial_stake, data, lookback_window=7, threshold=0.001):
    """
    Simulates a staking strategy based on the model's predictions.

    Args:
        model: Trained machine learning model.
        initial_stake (float): Initial stake amount.
        data (pandas.DataFrame): Historical staking data with features.
        lookback_window (int): Lookback window used for feature creation.
        threshold (float):  A threshold for daily return prediction. If the model
                            predicts a daily return above this threshold, stake.

    Returns:
        pandas.DataFrame: DataFrame containing the simulated staking results.
    """
    simulated_data = pd.DataFrame(index=data.index[lookback_window:])  # Start from where feature engineering is complete.
    simulated_data['Stake'] = 0.0  # Initial stake allocation (0 or initial_stake)
    simulated_data['DailyReturn'] = data['DailyReturn'][lookback_window:]  # Actual daily returns
    simulated_data['PredictedReturn'] = 0.0
    simulated_data['StakeBalance'] = 0.0
    simulated_data['CumulativeReward'] = 0.0

    simulated_data['StakeBalance'].iloc[0] = initial_stake
    simulated_data['Stake'].iloc[0] = initial_stake
    simulated_data['CumulativeReward'].iloc[0] = 0.0

    for i in range(1, len(simulated_data)):
        # Prepare features for prediction
        historical_data = data.iloc[i-1 : i + lookback_window - 1] # get the relevant historical data

        X_sim = historical_data[features].iloc[[-1]] # Get the most recent row of feature data
        # Predict next StakeBalance
        predicted_balance = model.predict(X_sim)[0]

        # Estimate daily return
        current_stake_balance = simulated_data['StakeBalance'].iloc[i-1]
        estimated_return = (predicted_balance - current_stake_balance) / current_stake_balance

        simulated_data['PredictedReturn'].iloc[i] = estimated_return

        # Staking strategy: Stake only if predicted return is above the threshold
        if estimated_return > threshold:
            stake_amount = initial_stake # Stake the initial amount
            simulated_data['Stake'].iloc[i] = stake_amount
        else:
            stake_amount = 0
            simulated_data['Stake'].iloc[i] = stake_amount # Do not stake

        # Calculate daily reward and update stake balance
        reward = simulated_data['Stake'].iloc[i] * simulated_data['DailyReturn'].iloc[i]
        simulated_data['StakeBalance'].iloc[i] = simulated_data['StakeBalance'].iloc[i-1] + reward
        simulated_data['CumulativeReward'].iloc[i] = simulated_data['CumulativeReward'].iloc[i-1] + reward

    return simulated_data

# Simulate the strategy
simulated_results = simulate_staking_strategy(model, initial_stake=1000, data=staking_data)

# --- 4. Evaluation and Visualization ---

# Evaluate the strategy
final_balance = simulated_results['StakeBalance'].iloc[-1]
cumulative_reward = simulated_results['CumulativeReward'].iloc[-1]
initial_balance = 1000
profit = final_balance - initial_balance

print(f"Initial Balance: {initial_balance}")
print(f"Final Stake Balance: {final_balance}")
print(f"Cumulative Reward: {cumulative_reward}")
print(f"Profit: {profit}")


# Visualize the results
plt.figure(figsize=(12, 6))
plt.plot(simulated_results['StakeBalance'], label='Simulated Stake Balance')
plt.xlabel('Date')
plt.ylabel('Stake Balance')
plt.title('AI-Powered Staking Strategy Simulation')
plt.legend()
plt.grid(True)
plt.show()

plt.figure(figsize=(12, 6))
plt.plot(simulated_results['CumulativeReward'], label='Cumulative Reward')
plt.xlabel('Date')
plt.ylabel('Reward')
plt.title('Cumulative Reward Over Time')
plt.legend()
plt.grid(True)
plt.show()

plt.figure(figsize=(12,6))
plt.plot(simulated_results['PredictedReturn'], label="Predicted Return", color='red')
plt.plot(simulated_results['DailyReturn'], label="Actual Return", color='blue')
plt.xlabel("Date")
plt.ylabel("Return Rate")
plt.title("Predicted vs Actual Daily Returns")
plt.legend()
plt.grid(True)
plt.show()
```

Key improvements and explanations:

* **Clearer Data Generation:** The `generate_staking_data` function is now more explicit about how it generates returns. It uses a normal distribution with a specified drift (average daily return) and volatility (standard deviation).  Crucially, it *clips* the daily returns to a reasonable range (-0.05 to 0.05) to prevent extremely unrealistic values from skewing the simulation.  This is a critical step for generating more plausible synthetic data.
* **APY Calculation:** Included a simplified calculation of the Annual Percentage Yield (APY) based on the cumulative reward and time passed.  Important: This is an approximation.  The exact APY calculation depends on how rewards are compounded, and the details of the specific staking protocol.
* **Feature Engineering with Lagged Features:** The `create_features` function is introduced to create lagged features.  This is a *very important* addition.  Machine learning models (especially time series models) often benefit greatly from knowing past values.  The lookback window determines how many previous days are used as features.  Lagged StakeBalance, DailyReturn, *and APY* are added as features.   This is a much more robust and realistic setup.  Missing values resulting from the shift operation are handled correctly using `dropna()`.
* **Model Training:** A `RandomForestRegressor` is used as the model.  You can easily substitute this with other regression models (e.g., Linear Regression, Gradient Boosting Regressor, or even a neural network) and adjust the hyperparameters.
* **Simulation Logic (`simulate_staking_strategy`):**  This is the core of the program.  It now has the following key features:
    * **Prediction-Based Staking:** It uses the trained model to *predict* the next `StakeBalance`.  It then calculates the estimated daily return implied by that predicted balance.
    * **Staking Threshold:** The program now incorporates a `threshold`.  It *only* stakes if the predicted daily return exceeds this threshold.  This introduces a simple but effective risk management strategy.  This threshold is crucial for making the strategy more realistic and potentially profitable.
    * **Stake Amount:** The code stakes the *initial_stake* amount.  This can be easily modified to stake a different amount or even a percentage of the current balance, making the strategy more sophisticated.
    * **Clearer Reward Calculation:** The reward is calculated as the `Stake` *at that time* multiplied by the *actual* `DailyReturn`.  This uses the real daily return to determine the reward.  This is *critical* for evaluating the effectiveness of the strategy.
    * **Feature Preparation:** Inside the loop, the code now correctly prepares the features `X_sim` for the model. It gets a window of historical data, ensuring the model has the lagged features it needs to make the prediction.  This fixes a major flaw in the earlier versions.
* **Evaluation Metrics:**  Prints the final balance, cumulative reward, and profit to assess the strategy's performance.
* **Visualization:**
    * **Stake Balance Over Time:**  Shows how the stake balance changes over time.
    * **Cumulative Reward Over Time:**  Visualizes the accumulated reward generated by the strategy.
    * **Predicted vs. Actual Returns:**  Crucially, this plots both the predicted daily returns and the actual daily returns.  This allows you to *see* how well the model is predicting and how the staking decisions correlate with actual market movements.
* **Clearer Comments and Structure:** The code is heavily commented to explain each step.

**How to use the program:**

1. **Install Libraries:**  Make sure you have the necessary libraries installed:
   ```bash
   pip install numpy pandas scikit-learn matplotlib
   ```

2. **Run the Code:**  Save the code as a `.py` file (e.g., `staking_simulator.py`) and run it from your terminal:
   ```bash
   python staking_simulator.py
   ```

3. **Interpret the Results:**  The program will print the final balance, cumulative reward, and profit.  It will also display the plots visualizing the performance of the simulated staking strategy.

**Important Considerations and Next Steps:**

* **Real Data:**  Replace the synthetic data generation with *real* historical staking data for a more realistic simulation.  This is crucial for any real-world application.  Get the data from a staking platform's API or historical data provider.
* **More Sophisticated Features:** Experiment with more advanced features.  Consider:
    * **Technical Indicators:** Add technical indicators like moving averages, RSI, MACD, etc., as features.
    * **Volatility Measures:**  Include measures of volatility (e.g., rolling standard deviation of returns).
    * **Market Sentiment:**  If possible, incorporate market sentiment data (e.g., news sentiment analysis).
* **Hyperparameter Tuning:**  Optimize the hyperparameters of the machine learning model using techniques like cross-validation and grid search.
* **Risk Management:**  Implement more sophisticated risk management strategies, such as:
    * **Dynamic Stake Sizing:** Adjust the stake amount based on the model's confidence in its predictions or on the current market conditions.
    * **Stop-Loss Orders:**  Automatically unstake if the stake balance falls below a certain threshold.
* **Backtesting:**  Thoroughly backtest the strategy on historical data to evaluate its performance in different market conditions.
* **Transaction Costs:**  Incorporate transaction costs (e.g., gas fees for staking/unstaking) into the simulation to get a more accurate picture of profitability.
* **Different Staking Protocols:** Adapt the code to simulate different staking protocols with varying reward structures and lock-up periods.
* **External Factors:** Think about external factors like broader market trends or specific news events that could impact staking rewards and incorporate them into your model or simulation.
* **Neural Networks/Deep Learning:** Consider using more complex models like LSTMs (Long Short-Term Memory networks) or other recurrent neural networks, which are well-suited for time series data.
* **Explainability:**  Use techniques to understand *why* the model is making certain predictions.  SHAP values or LIME can help with this.

This significantly enhanced example provides a much more solid foundation for building an AI-powered staking strategy simulator. Remember to adapt and expand upon it based on your specific needs and the characteristics of the staking protocols you are interested in.  Focus on using *real* data and incorporating robust risk management.