Trading Training Data

[IkigaiLabsETH]

I apologize for the confusion. You're absolutely right, and I'll provide a Node.js and TypeScript-based solution for adding knowledge about curated NFT collections on Ethereum to your AI agent. Here's a revised approach using TypeScript:

1. Data Retrieval and Filtering

First, let's set up the project and install necessary dependencies:

npm init -y
npm install axios dotenv typescript @types/node
npx tsc --init

Now, let's create a TypeScript file to fetch data from the Reservoir API:

// src/fetchData.ts
import axios from 'axios';
import dotenv from 'dotenv';

dotenv.config();

const API_KEY = process.env.RESERVOIR_API_KEY;
const BASE_URL = 'https://api.reservoir.tools';

interface Collection {
  id: string;
  name: string;
  totalSupply: number;
  floorAsk: {
    price: {
      amount: {
        native: number;
      };
    };
  };
  volume: {
    '1day': number;
  };
}

async function fetchCollections(): Promise<Collection[]> {
  try {
    const response = await axios.get(`${BASE_URL}/collections/v6`, {
      headers: {
        'accept': 'application/json',
        'x-api-key': API_KEY
      }
    });
    return response.data.collections;
  } catch (error) {
    console.error('Error fetching collections:', error);
    return [];
  }
}

export { fetchCollections, Collection };

2. Data Processing and Analysis

For data processing, we'll create a separate file:

// src/processData.ts
import { Collection } from './fetchData';

interface ProcessedCollection {
  name: string;
  totalSupply: number;
  floorPrice: number;
  volume24h: number;
}

function processCollectionData(collection: Collection): ProcessedCollection {
  return {
    name: collection.name,
    totalSupply: collection.totalSupply,
    floorPrice: collection.floorAsk.price.amount.native,
    volume24h: collection.volume['1day']
  };
}

export { processCollectionData, ProcessedCollection };

3. Data Preparation for Model Integration

For data preparation, we'll create another file:

// src/prepareData.ts
import { ProcessedCollection } from './processData';

function prepareCollectionData(collection: ProcessedCollection, maxLength: number = 2048): string {
  const summary = `Collection: ${collection.name}. Supply: ${collection.totalSupply}. Floor: ${collection.floorPrice} ETH. 24h Volume: ${collection.volume24h} ETH.`;
  return summary.slice(0, maxLength);
}

export { prepareCollectionData };

4. Model Inference

For the final inference step, we'll use a hypothetical custom model. In practice, you'd need to integrate with your specific model or service:

// src/runInference.ts
import axios from 'axios';

async function runInference(text: string): Promise<any> {
  // This is a placeholder. Replace with your actual model API call
  try {
    const response = await axios.post('https://your-model-api-endpoint.com/predict', {
      text: text
    });
    return response.data;
  } catch (error) {
    console.error('Error running inference:', error);
    return null;
  }
}

export { runInference };

Finally, let's create a main file to tie everything together:

// src/main.ts
import { fetchCollections } from './fetchData';
import { processCollectionData } from './processData';
import { prepareCollectionData } from './prepareData';
import { runInference } from './runInference';

async function main() {
  // Fetch data
  const collections = await fetchCollections();

  // Process data
  const processedCollections = collections.map(processCollectionData);

  // Prepare data
  const preparedData = processedCollections.map(collection => 
    prepareCollectionData(collection)
  );

  // Run inference
  for (const data of preparedData) {
    const result = await runInference(data);
    console.log('Inference result:', result);
  }
}

main().catch(console.error);

To run this, you'd compile the TypeScript files and then run the resulting JavaScript:

npx tsc
node dist/main.js

This TypeScript-based solution follows the same steps as before:

1. Fetches data from the Reservoir API
2. Processes and analyzes the data
3. Prepares the data to fit within model constraints
4. Runs inference on the prepared data

Remember to replace the placeholder model API in runInference.ts with your actual model integration. Also, you might need to adjust the data structures and processing steps based on your specific requirements and the exact format of the Reservoir API response.

///

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