Calculating Crypto Currency Risk Score using Python

Calculating the risk score for a cryptocurrency can be a complex process and there are different approaches that can be used depending on the factors you want to consider. However, here is a general framework that you can follow:

Identify the risk factors: The first step in calculating the risk score for a cryptocurrency is to identify the risk factors you want to consider.

Some of the factors you may want to consider include:

• Volatility: Cryptocurrencies are known for their high volatility, which can make them risky investments.
• Liquidity: Low liquidity can make it difficult to buy or sell a cryptocurrency, which can increase the risk of price manipulation and price instability.
• Security: Cryptocurrencies are prone to hacking and theft, so you may want to consider the security measures implemented by the cryptocurrency project.
• Adoption: The level of adoption of a cryptocurrency can impact its value and stability, so you may want to consider the number of users, merchants, and exchanges that accept the cryptocurrency.
• Regulatory environment: The regulatory environment for cryptocurrencies can vary from country to country and can impact their value and legal status.

1. Assign weights to each factor: Once you have identified the risk factors, you need to assign weights to each factor based on their relative importance. For example, you may assign a higher weight to volatility than to adoption.
2. Score each factor: Next, you need to score each factor based on its current status. For example, you may score the volatility of a cryptocurrency based on its historical price fluctuations or its price volatility in the past month.
3. Calculate the overall risk score: Finally, you need to calculate the overall risk score for the cryptocurrency. This can be done by multiplying the score for each factor by its weight, summing the results, and dividing by the total weight. For example, if volatility has a weight of 0.4 and a score of 0.7, liquidity has a weight of 0.3 and a score of 0.8, and security has a weight of 0.3 and a score of 0.9, the overall risk score would be:

Overall risk score = (0.4 * 0.7) + (0.3 * 0.8) + (0.3 * 0.9) = 0.76

This is just a rough example of how you can calculate the risk score for a cryptocurrency.

Sample Code you can use :

To use the CryptoCompare APIs to fetch price and trade data and calculate the risk score, you will need to follow these steps:

1. Sign up for a free CryptoCompare API key at https://min-api.cryptocompare.com/.
2. Use the requests library in Python to make HTTP requests to the CryptoCompare API endpoints to retrieve historical price and trade data for the cryptocurrencies you want to analyze.
3. Use the data to calculate the risk score for each cryptocurrency based on the factors you have identified and assigned weights to.

Here is an example code that fetches historical price data for Bitcoin and Polygon using the CryptoCompare API and calculates their risk scores based on example risk factors and weights:

import requests
import pandas as pd

# Define risk factors and weights
risk_factors = ['Volatility', 'Liquidity', 'Security', 'Adoption', 'Regulatory Environment']
weights = [0.4, 0.2, 0.1, 0.2, 0.1]

# Fetch historical price data for Bitcoin
btc_url = 'https://min-api.cryptocompare.com/data/v2/histoday'
btc_params = {'fsym': 'BTC', 'tsym': 'USD', 'limit': 365}
btc_response = requests.get(btc_url, params=btc_params).json()
btc_data = pd.DataFrame(btc_response['Data']['Data'])
btc_data['time'] = pd.to_datetime(btc_data['time'], unit='s')
btc_data.set_index('time', inplace=True)

# Calculate volatility score for Bitcoin
btc_volatility_score = btc_data['high'].pct_change().std()

# Fetch historical price data for Polygon
poly_url = 'https://min-api.cryptocompare.com/data/v2/histoday'
poly_params = {'fsym': 'MATIC', 'tsym': 'USD', 'limit': 365}
poly_response = requests.get(poly_url, params=poly_params).json()
poly_data = pd.DataFrame(poly_response['Data']['Data'])
poly_data['time'] = pd.to_datetime(poly_data['time'], unit='s')
poly_data.set_index('time', inplace=True)

# Calculate volatility score for Polygon
poly_volatility_score = poly_data['high'].pct_change().std()

# Calculate risk scores
btc_risk_score = sum([w * s for w, s in zip(weights, [btc_volatility_score, 0.8, 0.7, 0.5, 0.6])])
poly_risk_score = sum([w * s for w, s in zip(weights, [poly_volatility_score, 0.7, 0.9, 0.6, 0.5])])

# Display risk scores
print('Bitcoin Risk Score:', btc_risk_score)
print('Polygon Risk Score:', poly_risk_score)