Arbitrage Crypto Project

A comprehensive cryptocurrency arbitrage and prediction program. This project leverages machine learning models to predict cryptocurrency prices for the following day across various exchange platforms, identifying arbitrage opportunities for financial gain.

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Final Project Deliverables

Core Requirements

1. Data Delivery: Collect and prepare data from multiple sources for analysis.
2. Backend ETL: Extract, transform, and load data to enable seamless processing.
3. Visualization: Develop intuitive visualizations to present findings effectively.
4. Presentation: Communicate insights and results through a well-structured presentation.
5. Slide Deck: Create a professional slide deck summarizing project outcomes.

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Tools and Technologies

• Machine Learning Frameworks: TensorFlow, scikit-learn
• APIs: CoinMarketCap, Binance, CoinLayer, CoinGecko
• Cryptocurrencies: BTC (Bitcoin), ETH (Ethereum), DOGE (Dogecoin), LTC (Litecoin), USDT (Tether), ADA (Cardano)

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Key Objectives

Presentation Goals

1. Current Arbitrage Opportunities: Identify real-time BTC arbitrage possibilities.
2. General Trends: Analyze and summarize trends for each cryptocurrency.
3. Price Predictions: Forecast price movements for five cryptocurrencies.

Web Application Features

• Arbitrage Explanation: Educate users on the concept of cryptocurrency arbitrage.
• Opportunity Identification: Showcase arbitrage opportunities for five cryptocurrencies across three exchanges.
• Machine Learning Predictions: Present ML-based buy/sell predictions and linear regression analyses using historical data.
• Market Analysis: Provide insights into current market trends and conditions.

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Workflow and Methodology

1. Data Collection: Leverage APIs from CoinMarketCap, Binance, and CoinLayer to retrieve cryptocurrency price data.
2. Data Wrangling & Cleaning: Assemble, clean, and preprocess the data to ensure quality and accuracy.
3. Exploratory Data Analysis (EDA): Analyze trends, identify patterns, and extract key insights.
4. Data Modeling: Use historical data to train and evaluate machine learning models (e.g., price prediction with >75% accuracy).
5. Insights & Visualization: Present findings through clear visualizations and storytelling.

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Project Execution Plan

1. Define Strategies and Metrics:

   • Gather historical price data for training and split into test and training sets.
   • Train and evaluate machine learning models for price prediction.
   • Develop arbitrage bots to compare real-time prices across exchanges and identify opportunities.
   • Metrics: Number of arbitrage opportunities, price differences, trend model accuracy (binary yes/no).

2. Data Sources:

   • CoinMarketCap: Comprehensive market data.
   • Binance: Cryptocurrency exchange data.
   • CoinLayer/CoinGecko: Supplementary market data.

3. Data Coverage:

   • Cryptocurrencies: BTC, ETH, DOGE, LTC, USDT, ADA
   • Symbols: ["BTCUSD", "ETHUSD", "DOGEUSD", "LTCUSD", "USDTUSD", "ADAUSD"]

4. Data Workflow:

   • Team Roles:
     • Reginald: CoinMarketCap
     • Lance: CoinLayer
     • Krista: Binance/CoinGecko
   • Steps:
     • Retrieve, clean, and integrate data.
     • Analyze trends and model predictions.
     • Acknowledge limitations and validate findings.
     • Present the story effectively.

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Insights and Deliverables

• Trends and Predictions: Demonstrate key insights derived from data.
• Arbitrage Opportunities: Highlight opportunities and associated metrics.
• Limitations: Address potential challenges and areas for future improvement.

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Additional Questions to Explore

• How has the popularity of cryptocurrencies evolved over time?
• What factors influence cryptocurrency price volatility and trends?