About the Client
This project is an internal one, aimed at creating a product to assist traders in making better-informed decisions regarding bitcoin trades. The client’s focus is on leveraging artificial intelligence and blockchain technology to capitalize on the potential benefits of cryptocurrencies. The client’s industry and size are not specified in the case study.
The challenge our client faced was the constant fluctuations in the price of bitcoin, which made it difficult for potential investors to determine the right time to enter a particular trade position. They needed a solution that could help them understand price fluctuations, predict future movements, and quantify risks and potential profits.
Specific pain points and obstacles:
- Lack of reliable tools for predicting bitcoin price movements and making informed trading decisions.
- Difficulty in integrating diverse data sources and machine learning models to create accurate price predictions.
- Inability to maximize portfolio profit and hedge investment risks with existing trading strategies.
The SmartCat team addressed the problem by developing a comprehensive solution powered by sentiment analysis, machine learning algorithms, and trading strategies.
Strategies and tactics used:
- Training, testing, and fine-tuning machine learning models with diverse data to predict bitcoin prices and movements.
- Integrating the outputs from various machine learning models into existing trading strategies to maximize portfolio profit.
- Focusing on simple and reliable trading strategies that work effectively with the generated signals.
The unique value brought by our company
Our solution provided a reliable estimate of how the trading system performs with the generated signals compared to a baseline strategy. By incorporating sentiment analysis, machine learning algorithms, and trading strategies, we offered a comprehensive tool for better-informed trading decisions.
The outcomes of our solution can be summarized into qualitative and quantitative results.
- Qualitative results: A dashboard that summarizes signals such as positive and negative scores in tweets, sentiment metrics correlated with price, and predictions from classification and regression models.
- Quantitative results: A quantitative report on returns when applying our signals to the trader. Backtesting our trading strategy showed an average improvement of 16.5% in returns compared to a simple trading strategy used as a baseline.
The project utilized the following technologies and tools:
- Python: Programming language used for data analysis, machine learning, and backend development.
- Airflow: Platform used for scheduling and managing workflows.
- Superset: Data visualization and exploration tool for creating dashboards and reports.