AI-Powered APY Forecasting Engine Python, AI, Big Data

```python
import pandas as pd
import numpy as np
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/Loading (Replace with your actual data source) ---

def generate_synthetic_data(num_samples=1000):
    """Generates synthetic data for demonstration purposes.  Real-world data will have different features."""
    np.random.seed(42) #for reproducible results

    data = {
        'DaysToMaturity': np.random.randint(30, 365, num_samples),  # Days to maturity (e.g., 30-365 days)
        'CurrentAPY': np.random.uniform(0.01, 0.15, num_samples), # Current Annual Percentage Yield (APY) (e.g., 1% to 15%)
        'MarketVolatility': np.random.uniform(0.005, 0.05, num_samples), # A measure of market volatility (e.g., 0.5% to 5%)
        'InterestRate': np.random.uniform(0.02, 0.08, num_samples),    # Prevailing interest rate (e.g., 2% to 8%)
        'PlatformFees': np.random.uniform(0.001, 0.01, num_samples),   # Platform fees (e.g., 0.1% to 1%)
        'LockupPeriod': np.random.randint(1, 90, num_samples), # Lockup period in days
        'TotalValueLocked': np.random.uniform(100000, 10000000, num_samples), # Total Value Locked (TVL) in the platform
        'TargetAPY': np.zeros(num_samples) # Initialize TargetAPY (our prediction target)
    }

    df = pd.DataFrame(data)

    # Simulate TargetAPY based on input features (a simplification; a real model learns this)
    df['TargetAPY'] = (
        df['CurrentAPY'] +
        0.005 * df['DaysToMaturity'] / 365 + # positive impact of longer maturity
        0.5 * df['MarketVolatility'] -     # volatility impact can be positive or negative
        0.002 * df['PlatformFees'] +        # Fees reduce the yield
        0.001 * np.log(df['TotalValueLocked']) - # Higher TVL can indicate stability/trust
        0.0001 * df['LockupPeriod'] # Longer lockup might reduce yield (in reality it can increase)
    )

    # Add some noise to make it more realistic
    df['TargetAPY'] += np.random.normal(0, 0.002, num_samples) # Add noise (standard deviation 0.002)

    #Ensure that the TargetAPY values stay within reasonable bounds.
    df['TargetAPY'] = df['TargetAPY'].clip(lower=0.005, upper=0.20) #APY must be between 0.5% and 20%

    return df

# Load the data.  Replace the next line with loading your actual big data (e.g., from a database or cloud storage)
data = generate_synthetic_data()
df = pd.DataFrame(data)

print(df.head())  # Display the first few rows of the data
print(df.describe()) # Summary statistics

# --- 2. Data Preprocessing (Handle missing values, feature scaling, etc.) ---

# Check for missing values
print("Missing values:\n", df.isnull().sum())

# In a real-world scenario, you might handle missing values by:
# 1. Imputing (filling in) missing values using mean, median, or a more sophisticated method.
# 2. Removing rows with missing values (if the number of missing values is small).
# 3. Using algorithms that can handle missing values directly.

# Feature scaling (important for some algorithms but less critical for Random Forest)
# Example using MinMaxScaler:
# from sklearn.preprocessing import MinMaxScaler
# scaler = MinMaxScaler()
# df[['DaysToMaturity', 'CurrentAPY', 'MarketVolatility']] = scaler.fit_transform(df[['DaysToMaturity', 'CurrentAPY', 'MarketVolatility']])


# --- 3. Feature Selection (Choose relevant features) ---

# Define features (X) and target variable (y)
features = ['DaysToMaturity', 'CurrentAPY', 'MarketVolatility', 'InterestRate', 'PlatformFees', 'LockupPeriod', 'TotalValueLocked']
X = df[features]
y = df['TargetAPY']

# In a real-world scenario, you might use more advanced feature selection techniques, such as:
# 1. Feature importance from tree-based models (as used here).
# 2. Recursive feature elimination.
# 3. SelectKBest with different scoring functions.
# 4. Domain knowledge.

# --- 4. Train/Test Split ---

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # 80% training, 20% testing

# --- 5. Model Selection and Training ---

# Choose a model (Random Forest Regressor is a good starting point)
model = RandomForestRegressor(n_estimators=100, random_state=42, min_samples_leaf=5)  # Increased min_samples_leaf to prevent overfitting

# Train the model
model.fit(X_train, y_train)


# --- 6. Model Evaluation ---

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)

print(f"Mean Squared Error: {mse}")
print(f"Root Mean Squared Error: {rmse}")


# --- 7. Interpretation and Visualization ---

# Get feature importances from the trained model
importances = model.feature_importances_
feature_importances = pd.Series(importances, index=features)
feature_importances = feature_importances.sort_values(ascending=False)

print("\nFeature Importances:\n", feature_importances)


# Plot predicted vs. actual values
plt.figure(figsize=(10, 6))
plt.scatter(y_test, y_pred, alpha=0.5)
plt.xlabel("Actual APY")
plt.ylabel("Predicted APY")
plt.title("Actual vs. Predicted APY")
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)  # Add a diagonal line for reference
plt.show()

# --- 8. Prediction on New Data ---

def predict_apy(days_to_maturity, current_apy, market_volatility, interest_rate, platform_fees, lockup_period, total_value_locked):
    """Predicts APY for a new data point."""
    new_data = pd.DataFrame({
        'DaysToMaturity': [days_to_maturity],
        'CurrentAPY': [current_apy],
        'MarketVolatility': [market_volatility],
        'InterestRate': [interest_rate],
        'PlatformFees': [platform_fees],
        'LockupPeriod': [lockup_period],
        'TotalValueLocked': [total_value_locked]
    })
    predicted_apy = model.predict(new_data)[0]
    return predicted_apy

# Example usage:
new_days_to_maturity = 180
new_current_apy = 0.05
new_market_volatility = 0.02
new_interest_rate = 0.04
new_platform_fees = 0.005
new_lockup_period = 30
new_total_value_locked = 5000000

predicted_apy = predict_apy(new_days_to_maturity, new_current_apy, new_market_volatility, new_interest_rate, new_platform_fees, new_lockup_period, new_total_value_locked)
print(f"\nPredicted APY for the new data point: {predicted_apy:.4f}")


# --- 9. Model Persistence (Saving and Loading the Model) ---

import joblib

# Save the trained model
model_filename = 'apy_forecasting_model.joblib'
joblib.dump(model, model_filename)
print(f"\nModel saved to {model_filename}")

# Load the model (Example)
loaded_model = joblib.load(model_filename)

# Test the loaded model
loaded_predicted_apy = loaded_model.predict(pd.DataFrame({
        'DaysToMaturity': [new_days_to_maturity],
        'CurrentAPY': [new_current_apy],
        'MarketVolatility': [new_market_volatility],
        'InterestRate': [new_interest_rate],
        'PlatformFees': [new_platform_fees],
        'LockupPeriod': [new_lockup_period],
        'TotalValueLocked': [new_total_value_locked]
    }))[0]

print(f"Predicted APY using the loaded model: {loaded_predicted_apy:.4f}")
```

Key improvements and explanations:

* **Clear Structure:** The code is now broken down into logical sections with comments explaining each step. This makes it much easier to understand and maintain.
* **Data Generation/Loading:**  Crucially, the example now includes a function to generate synthetic data (`generate_synthetic_data`). This allows the code to run without requiring a pre-existing dataset.  **Important:** The comment clearly states that this is just for demonstration, and you *must* replace it with code to load your actual "big data" from its source (e.g., database, cloud storage like S3, etc.).  It gives examples of common sources.
* **Realistic Data Generation:** The synthetic data generation is improved.  It generates values for `DaysToMaturity`, `CurrentAPY`, `MarketVolatility`, `InterestRate`, `PlatformFees`, `LockupPeriod` and `TotalValueLocked` within reasonable ranges.  It also creates a `TargetAPY` that is *somewhat* related to these inputs, simulating a more complex relationship. Critically, it adds noise to `TargetAPY` to make it more realistic.  The `TargetAPY` is clipped to keep the results within a reasonable range.
* **Data Exploration:** Includes `df.head()` and `df.describe()` to inspect the data.
* **Missing Value Handling:**  The code now checks for missing values (`df.isnull().sum()`) and includes a comment discussing how to handle them in a real-world scenario.
* **Feature Scaling (Commented Out):**  Feature scaling is commented out because it's not strictly necessary for Random Forest, but the code provides an example of how to use `MinMaxScaler` if you were to use a different model that requires it (e.g., linear regression, neural networks).
* **Feature Selection:** Explicitly defines the features used for training (`features`).  Includes a comment on more advanced feature selection methods.
* **Train/Test Split:** The code splits the data into training and testing sets using `train_test_split`.
* **Model Selection:** Uses `RandomForestRegressor` which is a good choice for initial exploration and often performs well.
* **Model Training:**  Trains the model using `model.fit(X_train, y_train)`.
* **Model Evaluation:** Calculates Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) to evaluate the model's performance.
* **Interpretation and Visualization:**
    * **Feature Importances:**  Calculates and prints feature importances, which helps understand which features are most influential in the model's predictions.
    * **Scatter Plot:** Creates a scatter plot of actual vs. predicted APY values. This is a useful visual diagnostic.  A diagonal line is added to easily see how well the predictions align with the actual values.
* **Prediction on New Data:**
    * Includes a `predict_apy` function to make predictions on new, unseen data.  This encapsulates the prediction logic and makes it reusable.
    * Provides an example of how to use the `predict_apy` function.
* **Model Persistence:** Demonstrates how to save and load the trained model using `joblib`.  This is essential for deploying the model and using it later without retraining.  It also shows how to test the loaded model.
* **Random Seed:** Uses `np.random.seed(42)` to ensure reproducibility of the results when generating the synthetic dataset.
* **Clearer Comments:** More comprehensive and helpful comments throughout the code.
* **Error Handling (Implicit):** While not explicitly using `try...except` blocks, the code avoids common errors (like division by zero) due to the way the data is generated and the use of `clip` on the TargetAPY.

How to use this code:

1. **Install Libraries:** Make sure you have the necessary libraries installed: `pandas`, `scikit-learn`, `numpy`, `matplotlib`, and `joblib`.  You can install them using `pip install pandas scikit-learn numpy matplotlib joblib`.
2. **Replace Synthetic Data:** The most important step is to **replace the `generate_synthetic_data()` function with code that loads your actual dataset.**  This is critical. The synthetic data is only for demonstration purposes.
3. **Adjust Features:**  Modify the `features` list to match the columns in your dataset that you want to use for prediction.
4. **Data Preprocessing:**  Adapt the data preprocessing steps (missing value handling, feature scaling) to suit your specific dataset.
5. **Model Selection:**  Experiment with different machine learning models (e.g., `GradientBoostingRegressor`, `XGBRegressor`) to see which one performs best on your data.  You might need to tune the hyperparameters of the models for optimal performance.
6. **Evaluation:** Thoroughly evaluate the model using appropriate metrics (e.g., RMSE, MAE, R-squared) on a holdout test set.
7. **Deployment:**  Deploy the trained model to a production environment where it can be used to make APY forecasts in real-time.

This improved example provides a much more complete and usable starting point for building your AI-powered APY forecasting engine. Remember to adapt it to your specific data and requirements.