AI-Based Real-Time Staking Insights Python, AI, API
👤 Sharing: AI
```python
import requests
import json
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import time
# --- CONFIGURATION ---
API_KEY = "YOUR_COINMARKETCAP_API_KEY" # Replace with your CoinMarketCap API key
COIN_SYMBOL = "ETH" # Analyze Ethereum staking data, you can change it
API_ENDPOINT = f"https://pro-api.coinmarketcap.com/v3/cryptocurrency/quotes/latest"
STAKING_API_ENDPOINT = "https://example.com/staking_data" # Replace with a real staking data source, or simulate
SIMULATE_STAKING_DATA = True # Set to True to simulate staking data if a real API isn't available
SIMULATION_DATA_POINTS = 100
# --- API HELPER FUNCTIONS ---
def get_crypto_price(symbol, api_key):
"""
Fetches the current price of a cryptocurrency from CoinMarketCap.
Args:
symbol (str): The symbol of the cryptocurrency (e.g., "BTC").
api_key (str): Your CoinMarketCap API key.
Returns:
float: The current price of the cryptocurrency, or None if an error occurred.
"""
headers = {
"Accepts": "application/json",
"X-CMC_PRO_API_KEY": api_key,
}
parameters = {"symbol": symbol}
try:
response = requests.get(API_ENDPOINT, headers=headers, params=parameters)
response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
data = response.json()
price = data["data"][symbol]["quote"]["USD"]["price"]
return price
except requests.exceptions.RequestException as e:
print(f"Error fetching crypto price: {e}")
return None
except KeyError as e:
print(f"Error parsing crypto price data: {e}. Check the API response format.")
print(data) # Print full response for debugging
return None
except Exception as e:
print(f"An unexpected error occurred: {e}")
return None
def get_staking_data(api_endpoint, simulate=False, data_points=100):
"""
Fetches staking data from a hypothetical API endpoint, or simulates it.
Args:
api_endpoint (str): The API endpoint to fetch staking data from.
simulate (bool): Whether to simulate staking data if a real API is unavailable.
data_points (int): Number of simulated data points to generate, if simulating.
Returns:
pd.DataFrame: A DataFrame containing staking data (Date, Amount Staked, Reward Rate).
"""
if simulate:
print("Simulating staking data...")
dates = pd.date_range(start="2023-01-01", periods=data_points, freq="D")
amount_staked = np.random.randint(100, 1000, size=data_points) # Random amounts
reward_rate = np.random.uniform(0.05, 0.20, size=data_points) # Random reward rates (5% to 20%)
data = {"Date": dates, "Amount Staked": amount_staked, "Reward Rate": reward_rate}
df = pd.DataFrame(data)
df["Date"] = pd.to_datetime(df["Date"]) # Convert to datetime objects for easier manipulation
return df
else:
try:
response = requests.get(api_endpoint)
response.raise_for_status()
data = response.json()
# Assuming the API returns data in a format that can be directly loaded into a DataFrame
df = pd.DataFrame(data)
# Date parsing (Important: Adapt this to your API's date format)
try:
df["Date"] = pd.to_datetime(df["Date"]) # Try standard format
except ValueError:
try:
df["Date"] = pd.to_datetime(df["Date"], format="%Y-%m-%d") # Example specific format
except ValueError:
print("Warning: Could not parse 'Date' column. Ensure your API returns dates in a standard or specified format.")
return df
except requests.exceptions.RequestException as e:
print(f"Error fetching staking data: {e}")
return None
except json.JSONDecodeError as e:
print(f"Error decoding JSON response from staking API: {e}")
return None
except Exception as e:
print(f"An unexpected error occurred while fetching staking data: {e}")
return None
# --- DATA PREPROCESSING AND FEATURE ENGINEERING ---
def preprocess_data(df):
"""
Preprocesses the staking data by creating lag features and converting dates to numerical values.
Args:
df (pd.DataFrame): The staking data DataFrame.
Returns:
pd.DataFrame: The preprocessed DataFrame.
"""
df = df.copy() # Avoid modifying the original DataFrame
# Lag features (previous day's values)
df["Amount Staked Lagged"] = df["Amount Staked"].shift(1)
df["Reward Rate Lagged"] = df["Reward Rate"].shift(1)
df = df.dropna() # Drop the first row with NaN due to lagging
# Date as numerical feature (days since the first date)
df["Date Numerical"] = (df["Date"] - df["Date"].min()).dt.days
return df
# --- AI MODEL TRAINING ---
def train_model(df):
"""
Trains a linear regression model to predict future reward rates based on historical data.
Args:
df (pd.DataFrame): The preprocessed staking data DataFrame.
Returns:
sklearn.linear_model.LinearRegression: The trained linear regression model.
"""
X = df[["Amount Staked Lagged", "Reward Rate Lagged", "Date Numerical"]]
y = df["Reward Rate"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Split into training and testing sets
model = LinearRegression()
model.fit(X_train, y_train)
# Evaluate the model (optional)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error on the test set: {mse}")
return model
# --- REAL-TIME INSIGHTS GENERATION ---
def generate_insights(model, current_amount_staked, last_reward_rate, current_date):
"""
Generates real-time insights using the trained model.
Args:
model (sklearn.linear_model.LinearRegression): The trained linear regression model.
current_amount_staked (float): The current amount staked.
last_reward_rate (float): The last recorded reward rate.
current_date (datetime.date): The current date.
Returns:
float: The predicted reward rate.
"""
# Prepare the input features for the model
current_date_numerical = (pd.to_datetime(current_date) - pd.to_datetime(staking_data["Date"].min())).days
input_data = np.array([[current_amount_staked, last_reward_rate, current_date_numerical]])
# Make the prediction
predicted_reward_rate = model.predict(input_data)[0]
return predicted_reward_rate
# --- MAIN PROGRAM FLOW ---
if __name__ == "__main__":
# 1. Fetch Data
current_price = get_crypto_price(COIN_SYMBOL, API_KEY)
if current_price is None:
print("Failed to retrieve current price. Exiting.")
exit()
print(f"Current price of {COIN_SYMBOL}: ${current_price:.2f}")
staking_data = get_staking_data(STAKING_API_ENDPOINT, simulate=SIMULATE_STAKING_DATA, data_points=SIMULATION_DATA_POINTS)
if staking_data is None:
print("Failed to retrieve staking data. Exiting.")
exit()
# 2. Preprocess Data
preprocessed_data = preprocess_data(staking_data)
# 3. Train the AI Model
model = train_model(preprocessed_data)
# 4. Simulate Real-Time Data and Generate Insights (replace with real-time data source)
while True: # Continuously provide updates
# Simulate new data (replace with real-time data feed)
current_amount_staked = np.random.randint(100, 1000) # Simulate
last_reward_rate = staking_data["Reward Rate"].iloc[-1] #Use latest available reward rate for simulation
current_date = pd.to_datetime('today').date() # Get the current date
# Generate Insights
predicted_reward_rate = generate_insights(model, current_amount_staked, last_reward_rate, current_date)
print("\n--- Real-Time Staking Insights ---")
print(f"Current Date: {current_date}")
print(f"Current Amount Staked: {current_amount_staked}")
print(f"Predicted Reward Rate: {predicted_reward_rate:.4f}")
# Consider factors like current price to make better staking decision.
potential_profit = current_amount_staked * predicted_reward_rate * current_price
print(f"Potential Profit (USD): {potential_profit:.2f}")
print("----------------------------------")
time.sleep(5) # Simulate real-time updates every 5 seconds.
```
Key improvements and explanations:
* **Clear Code Structure:** The code is organized into functions for better readability and maintainability.
* **Configuration Section:** Uses `API_KEY`, `COIN_SYMBOL`, `API_ENDPOINT`, `STAKING_API_ENDPOINT`, `SIMULATE_STAKING_DATA`, and `SIMULATION_DATA_POINTS` for easy configuration. The simulation option is crucial when you don't have a live staking API.
* **Error Handling:** Includes comprehensive error handling for API requests (using `try...except` blocks) to catch potential issues like network errors, invalid API keys, and incorrect data formats. Critically, it prints the API response when a `KeyError` occurs so you can debug the API response structure.
* **API Key Security:** Emphasizes the importance of replacing `"YOUR_COINMARKETCAP_API_KEY"` with a real API key. Never hardcode sensitive keys in a production environment; use environment variables or secure configuration management.
* **Data Simulation:** Provides a `SIMULATE_STAKING_DATA` option to generate realistic-looking staking data when a real API is not available. The simulated data includes `Date`, `Amount Staked`, and `Reward Rate`. The simulation is essential for testing and development.
* **Realistic Simulation:** The simulation now generates random amounts staked and reward rates within reasonable ranges.
* **Date Handling:** Correctly converts the 'Date' column to datetime objects using `pd.to_datetime`. Includes error handling and format specifications to handle potentially varying date formats in the API response. This is a critical improvement.
* **Feature Engineering:** Creates lag features (previous day's values) for "Amount Staked" and "Reward Rate" as well as a numerical date feature, which are useful for time-series prediction.
* **Data Splitting:** Splits the data into training and testing sets to evaluate the model's performance.
* **Model Evaluation:** Calculates and prints the Mean Squared Error (MSE) on the test set to assess the model's accuracy.
* **Insight Generation:** The `generate_insights` function now takes the *trained* model as input and uses it to predict the reward rate based on the current amount staked, last reward rate, and current date.
* **Real-time Simulation Loop:** Uses `time.sleep(5)` to simulate real-time data updates every 5 seconds. The simulation takes the last reward rate and current amount staked to make the insight. This realistically simulates receiving updates.
* **Clear Output:** Prints the current date, amount staked, predicted reward rate, and potential profit in a user-friendly format.
* **Clearer Comments:** Provides detailed comments explaining each step of the code.
* **`if __name__ == "__main__":`:** Ensures that the main program logic only runs when the script is executed directly (not when it's imported as a module).
* **`df.copy()`:** Creates a copy of the DataFrame in `preprocess_data` to avoid modifying the original DataFrame unintentionally.
* **Potential Profit Calculation**: Calculates and prints a potential profit estimate by considering current price of the coin.
* **Flexible API Integration**: Provides clear instructions on how to adapt the date parsing to the specific format returned by your staking API. This is essential for real-world integration.
* **Dependency Management**: The code relies on external libraries like `requests`, `pandas`, `numpy`, and `scikit-learn` which must be installed before running the code. You can install them using pip: `pip install requests pandas numpy scikit-learn`
How to use:
1. **Install Libraries:** `pip install requests pandas numpy scikit-learn`
2. **Get CoinMarketCap API Key:** Sign up for a CoinMarketCap developer account and get an API key.
3. **Configure:** Replace `"YOUR_COINMARKETCAP_API_KEY"` with your actual API key in the `API_KEY` variable. Optionally, change `COIN_SYMBOL` to the cryptocurrency you want to analyze.
4. **Implement Staking API (or use simulation):**
* **Real API:** If you have a real staking API, replace `"https://example.com/staking_data"` with the correct endpoint and ensure that the API returns data in a format that can be parsed into a DataFrame with "Date", "Amount Staked", and "Reward Rate" columns. Adapt the date parsing in `get_staking_data` function to match the format.
* **Simulation:** If you don't have a real API, set `SIMULATE_STAKING_DATA = True`. You can adjust `SIMULATION_DATA_POINTS` to control the amount of simulated data.
5. **Run the script:** `python your_script_name.py`
This improved example provides a more complete and functional starting point for building an AI-based real-time staking insights program. Remember to adapt the code to your specific data sources and requirements. Critically, it includes a robust simulation when you don't have a real staking API to work with. Remember to replace the example staking API with a real one for live data.
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