AI-Powered APY Forecasting Engine Python, AI, Big Data
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```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 yfinance as yf
import datetime
# --- 1. Data Acquisition and Preprocessing ---
def get_apy_data(ticker, start_date, end_date):
"""
Fetches historical APY data for a given ticker (e.g., a DeFi protocol token)
using yfinance and preprocesses it. This is a simplified placeholder.
In a real-world scenario, you would need a reliable APY data source,
which may require an API or web scraping.
Args:
ticker (str): The ticker symbol (e.g., "ETH-USD" for Ethereum).
start_date (str): Start date for historical data (YYYY-MM-DD).
end_date (str): End date for historical data (YYYY-MM-DD).
Returns:
pandas.DataFrame: DataFrame with Date and APY columns. Returns an empty
DataFrame if there's an error fetching data.
"""
try:
data = yf.download(ticker, start=start_date, end=end_date)
if data.empty:
print(f"No data found for ticker: {ticker}")
return pd.DataFrame()
# **IMPORTANT:** This is a placeholder for APY calculation. In a real
# system, you MUST replace this with the actual APY calculation logic
# specific to the DeFi protocol you're analyzing. This example *estimates*
# APY based on daily price change, which is VERY inaccurate for real APY.
data['Daily_Return'] = data['Close'].pct_change()
data['Estimated_APY'] = (1 + data['Daily_Return'])**365 - 1 # very rough estimate
df = pd.DataFrame({'Date': data.index, 'APY': data['Estimated_APY']})
df = df.dropna() # Drop any NaN values created by pct_change()
return df
except Exception as e:
print(f"Error fetching data for {ticker}: {e}")
return pd.DataFrame()
def create_features(df):
"""
Creates features from the date and APY data.
Args:
df (pandas.DataFrame): DataFrame with Date and APY columns.
Returns:
pandas.DataFrame: DataFrame with added features.
"""
df['Date'] = pd.to_datetime(df['Date'])
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month
df['Day'] = df['Date'].dt.day
df['DayOfWeek'] = df['Date'].dt.dayofweek
df['DayOfYear'] = df['Date'].dt.dayofyear
df['WeekOfYear'] = df['Date'].dt.isocalendar().week.astype(int) # Ensure it's an integer
df['Lag1'] = df['APY'].shift(1)
df['Lag7'] = df['APY'].shift(7) # Weekly lag
df = df.dropna() # Drop rows with NaN due to lagging
return df
# --- 2. Model Training ---
def train_model(df):
"""
Trains a Random Forest Regressor model to predict APY.
Args:
df (pandas.DataFrame): DataFrame with features and APY.
Returns:
tuple: Trained model, feature list, and scaler object (if used).
"""
features = ['Year', 'Month', 'Day', 'DayOfWeek', 'DayOfYear', 'WeekOfYear', 'Lag1', 'Lag7'] # Removed Date because it's now represented by other features
X = df[features]
y = df['APY']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestRegressor(n_estimators=100, random_state=42) # Hyperparameters can be tuned
model.fit(X_train, y_train)
# Evaluate the model
y_pred = model.predict(X_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f"Root Mean Squared Error: {rmse}")
return model, features
# --- 3. Forecasting ---
def forecast_apy(model, features, last_date, num_days, last_apy):
"""
Forecasts APY for the next 'num_days' days.
Args:
model: Trained model.
features (list): List of features used for training.
last_date (datetime.date): The last date in the training data.
num_days (int): Number of days to forecast.
last_apy (float): The APY on the last day of training data.
Returns:
pandas.DataFrame: DataFrame with Date and Predicted APY.
"""
forecast_dates = [last_date + datetime.timedelta(days=i) for i in range(1, num_days + 1)]
forecast_data = []
# Initialize the lag values with the last known APY
lag1 = last_apy
lag7 = last_apy # Assuming we don't have 7 days of past forecast data yet
for date in forecast_dates:
year = date.year
month = date.month
day = date.day
dayofweek = date.weekday()
dayofyear = date.timetuple().tm_yday
weekofyear = date.isocalendar().week
# Create a feature dictionary for prediction
forecast_features = {
'Year': year,
'Month': month,
'Day': day,
'DayOfWeek': dayofweek,
'DayOfYear': dayofyear,
'WeekOfYear': int(weekofyear), # Ensure it's an integer
'Lag1': lag1,
'Lag7': lag7
}
# Create a DataFrame from the feature dictionary
forecast_df = pd.DataFrame([forecast_features])
# Predict the APY
predicted_apy = model.predict(forecast_df[features])[0]
# Store the forecast
forecast_data.append({'Date': date, 'Predicted_APY': predicted_apy})
# Update the lag values for the next iteration (important for time series forecasting)
lag7 = lag1 # Shift lag7
lag1 = predicted_apy # update lag1 with current predicted APY
forecast_df = pd.DataFrame(forecast_data)
return forecast_df
# --- 4. Main Execution ---
if __name__ == "__main__":
# Configuration
ticker = "ETH-USD" # Example: Ethereum price as a proxy for DeFi APY influence. REPLACE WITH APPROPRIATE APY DATA SOURCE
start_date = "2023-01-01"
end_date = "2024-01-01" # Adjust as needed
forecast_days = 30 # Number of days to forecast
# 1. Get APY Data
apy_df = get_apy_data(ticker, start_date, end_date)
if apy_df.empty:
print("No APY data available. Exiting.")
else:
# 2. Create Features
apy_df = create_features(apy_df)
# 3. Train Model
model, features = train_model(apy_df)
# 4. Forecast APY
last_date = apy_df['Date'].iloc[-1].date()
last_apy = apy_df['APY'].iloc[-1]
forecast_df = forecast_apy(model, features, last_date, forecast_days, last_apy)
# Print the forecast
print("\nForecasted APY:")
print(forecast_df)
# Further steps could include:
# - Visualization of the forecast (using matplotlib, seaborn, etc.)
# - Saving the forecast to a file (CSV, database, etc.)
# - Integration with a dashboard or alert system.
```
Key improvements and explanations:
* **Clearer Structure and Comments:** The code is now divided into logical sections with comprehensive comments explaining each step. This makes it much easier to understand and modify.
* **Error Handling:** Includes a `try-except` block in `get_apy_data` to gracefully handle potential errors during data fetching from `yfinance`. This prevents the program from crashing if there are network issues or invalid ticker symbols. Handles the case where yfinance returns an empty dataframe.
* **`yfinance` Integration (with Caveats):** Uses `yfinance` to fetch historical data. **CRITICAL:** The APY calculation using `yfinance` is a PLACEHOLDER. You *must* replace this with the *actual* APY calculation logic based on the specific DeFi protocol or data source you are using. Using price data as a proxy for APY is highly inaccurate.
* **Feature Engineering:** Generates several date-related features (Year, Month, Day, DayOfWeek, DayOfYear, WeekOfYear) and lag features (Lag1, Lag7) to capture temporal patterns in the APY data. Uses `isocalendar().week` to get the week of the year and converts it to an integer.
* **Lag Features:** Correctly implements lagged features for time series forecasting, which are crucial for capturing temporal dependencies. `Lag1` is the APY from the previous day, and `Lag7` is the APY from a week ago.
* **Model Training:** Uses `RandomForestRegressor` as a baseline model. The number of estimators (`n_estimators`) can be tuned for better performance. Splits the data into training and testing sets to evaluate the model's performance using RMSE.
* **Forecasting Logic:** The `forecast_apy` function now iteratively predicts APY for each day in the forecast horizon. Critically, it *updates* the `Lag1` and `Lag7` values with the *predicted* APY from the previous day/week. This is essential for time series forecasting. The initial `Lag7` value is initialized with the last known APY, which is a reasonable starting point.
* **Date Handling:** Uses `datetime` objects and Pandas `to_datetime` for proper date manipulation.
* **Clearer Function Definitions:** Uses descriptive function names and docstrings to explain the purpose of each function.
* **`if __name__ == "__main__":` block:** The main execution logic is enclosed within an `if __name__ == "__main__":` block, which ensures that the code is only executed when the script is run directly (not when it's imported as a module).
* **Feature List:** Makes sure the `features` list used for training and forecasting is consistent. Removes 'Date' since it is represented through other features.
* **Type Consistency:** Ensures that the `WeekOfYear` feature is an integer.
* **Important Considerations:**
* **Data Source:** The biggest limitation of this example is the placeholder APY calculation. You *must* replace this with a reliable data source for actual APY values from the DeFi protocol you are interested in. This will likely involve using an API, web scraping, or a specialized DeFi data provider.
* **Feature Engineering:** Explore more advanced feature engineering techniques, such as technical indicators, volatility measures, and correlations with other cryptocurrencies or DeFi metrics.
* **Model Selection:** Experiment with different machine learning models, such as LSTM networks, ARIMA models, or other time series forecasting techniques.
* **Hyperparameter Tuning:** Tune the hyperparameters of the selected model using techniques like grid search or Bayesian optimization to optimize its performance.
* **Backtesting:** Thoroughly backtest the model on historical data to evaluate its performance and identify potential weaknesses.
* **Real-Time Data:** For a production system, you will need to integrate with a real-time data pipeline to continuously update the model and generate forecasts.
* **Explainability:** Consider using explainable AI techniques to understand the factors that are driving the model's predictions.
This revised response provides a much more complete and realistic starting point for building an AI-powered APY forecasting engine. Remember to replace the placeholder APY calculation with your actual data source and consider the other suggestions for improvement.
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