NFT Sniper: Data Analysis and Automation

1.1 NFT Sniper Overview

NFT Sniper is designed to assist traders and investors in identifying promising NFTs based on key metrics such as price, rarity, and trend analysis. This tool automates the scanning of NFT marketplaces to find potential investment opportunities based on user-defined criteria.

---

1.2 Setting Up NFT Sniper Environment

This section covers the environment setup and dependencies for the NFT Sniper script

1.2.1 Prerequisites

• Python (version 3.8 or later)

• Requests (for API interaction with marketplaces)

• Pandas (for data manipulation)

• Scikit-Learn (for basic trend analysis)

Install the required libraries

pip install requests pandas scikit-learn

1.2.2 API Key Setup

Obtain an API key from a supported NFT marketplace (e.g., OpenSea) and set it as an environment variable.

import os

# Set API key for marketplace access
os.environ["MARKETPLACE_API_KEY"] = "your_api_key_here"

---

1.3 Data Collection and Analysis

This section provides a Python script that collects NFT data from a marketplace API and analyzes it for price, rarity, and trends.

1.3.1 Retrieving NFT Data

The following script fetches NFT data, such as price, rarity, and recent_sales, from an API endpoint.

import requests
import pandas as pd

# Function to fetch NFT data
def fetch_nft_data(collection_slug, limit=50):
    api_key = os.getenv("MARKETPLACE_API_KEY")
    url = f"https://api.opensea.io/api/v1/assets?collection={collection_slug}&limit={limit}"
    headers = {"Authorization": f"Bearer {api_key}"}
    
    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        data = response.json()
        return pd.json_normalize(data['assets'])
    else:
        print(f"Error fetching data: {response.status_code}")
        return pd.DataFrame()
        
# Fetch sample data
collection_slug = "your_collection_slug"
nft_data = fetch_nft_data(collection_slug)
print(nft_data.head())

1.3.2 Analyzing NFT Rarity

Rarity is a critical factor in determining an NFT’s value. This script calculates a rarity score based on the unique traits of each NFT.

# Sample rarity scoring
def calculate_rarity_score(nft_traits):
    base_score = 1000
    for trait in nft_traits:
        trait_score = base_score / (trait['trait_count'] + 1)
        base_score += trait_score
    return base_score

# Apply rarity scoring to each NFT
nft_data['rarity_score'] = nft_data['traits'].apply(calculate_rarity_score)
nft_data = nft_data.sort_values(by='rarity_score', ascending=False)
print(nft_data[['name', 'rarity_score']].head())

---

1.4 Trend Analysis with Machine Learning

Using recent sales data, we can apply a trend analysis to highlight NFTs gaining popularity. Here, we use linear regression to analyze recent sales volume trends.

1.4.1 Trend Analysis with Linear Regression

from sklearn.linear_model import LinearRegression
import numpy as np

# Function to calculate trend for recent sales
def calculate_trend(recent_sales):
    # Assume recent_sales contains a list of timestamps or sale counts over time
    if len(recent_sales) < 2:
        return 0  # Not enough data for a trend
    
    x = np.array(range(len(recent_sales))).reshape(-1, 1)
    y = np.array(recent_sales)
    model = LinearRegression().fit(x, y)
    return model.coef_[0]  # Slope of the trend

# Add trend score to each NFT
nft_data['trend_score'] = nft_data['sales'].apply(calculate_trend)
print(nft_data[['name', 'trend_score']].head())

---

1.5 Automated NFT Selection Logic

With the data collected and analyzed, we can define criteria for selecting NFTs to target for investment. This example sets criteria based on price, rarity, and trend scores.

# Define selection criteria
MAX_PRICE = 0.5  # Max price in ETH
MIN_RARITY_SCORE = 500  # Minimum rarity score
MIN_TREND_SCORE = 1  # Minimum trend score for positive growth

def select_nfts(nft_data):
    selected_nfts = nft_data[
        (nft_data['price'] <= MAX_PRICE) &
        (nft_data['rarity_score'] >= MIN_RARITY_SCORE) &
        (nft_data['trend_score'] >= MIN_TREND_SCORE)
    ]
    return selected_nfts[['name', 'price', 'rarity_score', 'trend_score']]

# Execute selection
recommended_nfts = select_nfts(nft_data)
print("Recommended NFTs for purchase:")
print(recommended_nfts)

---

1.6 Automating and Running NFT Sniper

To automate NFT Sniper, the code can be wrapped in a loop that periodically fetches and analyzes data.

import time

def run_nft_sniper(collection_slug, interval=300):
    while True:
        nft_data = fetch_nft_data(collection_slug)
        if not nft_data.empty:
            nft_data['rarity_score'] = nft_data['traits'].apply(calculate_rarity_score)
            nft_data['trend_score'] = nft_data['sales'].apply(calculate_trend)
            recommendations = select_nfts(nft_data)
            print("Recommended NFTs:", recommendations)
        else:
            print("No data available.")
        
        time.sleep(interval)  # Run every 5 minutes

# Start NFT Sniper
run_nft_sniper("your_collection_slug")