# Play-to-Earn Optimizer: Earnings Optimization

## 1.1 P2E Optimizer Overview

{% hint style="info" %}
P2E Optimizer module enables users and developers to analyze and optimize earning potential in blockchain-based games that support P2E mechanics. Through data analysis and algorithmic prediction, the module helps identify in-game activities and strategies that yield the highest returns, providing users with real-time insights and analytics.
{% endhint %}

***

## 1.2 Code for Earnings Optimization

### 1.2.1 Setup: Connect to Game API and Blockchain

```python
import requests
from web3 import Web3
import pandas as pd

# Connect to Ethereum node
web3 = Web3(Web3.HTTPProvider("https://mainnet.infura.io/v3/YOUR_INFURA_PROJECT_ID"))

# Example game API endpoint
game_api_url = "https://api.examplegame.com/gameplay"

# Function to retrieve gameplay data
def get_gameplay_data(player_id):
    response = requests.get(f"{game_api_url}/players/{player_id}/data")
    if response.status_code == 200:
        return response.json()
    else:
        print("Error retrieving gameplay data.")
        return None
```

### 1.2.2 Earnings Calculation Script

```python
def calculate_earnings(data):
    # Extract relevant gameplay data
    in_game_currency = data.get("in_game_currency", 0)
    assets_staked = data.get("assets_staked", 0)
    tasks_completed = data.get("tasks_completed", 0)

    # Define earnings model (example weights for different activities)
    earnings = (in_game_currency * 0.4) + (assets_staked * 0.3) + (tasks_completed * 0.3)
    return earnings

# Sample data retrieval and calculation
player_id = "12345"
game_data = get_gameplay_data(player_id)
if game_data:
    earnings = calculate_earnings(game_data)
    print(f"Estimated Earnings for Player {player_id}: {earnings} tokens")
```

### 1.2.3 Price Data Analysis for Earning Optimization

```python
# Define API endpoint for price data (e.g., Coingecko)
price_api_url = "https://api.coingecko.com/api/v3/simple/price"

# Function to retrieve token price
def get_token_price(token_id):
    response = requests.get(f"{price_api_url}?ids={token_id}&vs_currencies=usd")
    if response.status_code == 200:
        return response.json().get(token_id, {}).get("usd", 0)
    else:
        print("Error retrieving token price.")
        return None

# Adjust earnings based on token price
token_id = "example-token"
token_price = get_token_price(token_id)
adjusted_earnings = earnings * token_price
print(f"Adjusted Earnings in USD: ${adjusted_earnings:.2f}")
```

### 1.2.4 Historical Data Analysis for Optimized Strategy

```python
def analyze_historical_data(historical_data):
    # Load data into a DataFrame for analysis
    df = pd.DataFrame(historical_data)
    # Calculate average earnings per activity type
    avg_earnings = df.groupby("activity_type")["earnings"].mean()
    print("Average Earnings per Activity Type:")
    print(avg_earnings)
    # Identify top-earning activity
    top_activity = avg_earnings.idxmax()
    print(f"Optimal activity for earnings: {top_activity}")
    return top_activity

# Sample historical data analysis
historical_data = [
    {"activity_type": "staking", "earnings": 50},
    {"activity_type": "task_completion", "earnings": 30},
    {"activity_type": "trading", "earnings": 45},
    # Additional data here...
]
optimal_activity = analyze_historical_data(historical_data)
```

***

## 1.3 API Endpoints for P2E Optimizer

### 1.3.1 GET /players/{player\_id}/earnings

```bash
curl -X GET "https://api.capsurelabs.com/players/12345/earnings" \
  -H "Authorization: Bearer <token>"
```

### 1.3.2 POST /analytics/price-trend

```bash
curl -X POST "https://api.capsurelabs.com/analytics/price-trend" \
  -H "Authorization: Bearer <token>" \
  -d '{"token_id": "example-token"}'
```
