ALGORITHMIC TRADING
Our AI enabled algorithmic trading
strategies
enables more sophisticated automated trading,
better risk management, and improved
execution
Key Figures from our use case deployment
ENHANCED PREDICTIONOur AI-driven trading algorithms provide market predictions with high accuracy
RISK ADJUSTED PERFORMANCEOur models secure high
Sharpe Ratio above 3.0
consistently
(a ratio higher than 1 is
considered good)
REDUCED LATENCYOur high-performance computing
solutions and advanced data processing
result in faster trade execution
Business Challenge
- Inadequate prediction of market conditions: Traditional trading methods and static algorithms failed to adapt to dynamic market conditions, leading to suboptimal trade execution and missed opportunities
- High latency: Delays in trade execution due to slow decision-making and information processing negatively impacted the client's ability to capitalize on short-lived market opportunities
- Limited risk management: Traditional trading strategies often lacked the capacity to dynamically adjust risk exposure, leading to market shock vulnerability and financial losses
- Scalability issues: Difficulties in scaling existing trading infrastructure, and therefore hindering growth
Our Use Cases in Algorithmic Trading are in the following Areas:
- Market Prediction
- High-frequency Trading (HFT)
- Algorithmic Trading Strategies
- Risk Management
- Execution Optimization
- Sentiment Analysis
- Smart Order Routing
- Performance Analytics
The benefits of using AI enabled algorithmic trading
- Enhanced prediction: Our AI-driven trading algorithms provide market predictions with high accuracy
- Risk adjusted performance: Our models secured consistently high Sharpe Ratio above 3.0 on average, while a ratio above 1 is considered good as per industry benchmark
- Reduced latency and scalability: Implementing high-performance computing solutions and advanced data processing techniques resulted in faster trade execution, also scalability
Finarb’s Machine Learning Methods for Algorithmic Trading
- Reinforcement learning and deep learning techniques to develop adaptive trading algorithms that can learn from market data, optimize decision-making, and respond to changing market conditions
- Time-series analysis: We use advanced time-series analysis methods, such as ARIMA and GARCH models, to forecast market trends and volatility, enabling more informed trading decisions
- Sentiment analysis: NLP techniques to analyze unstructured data, such as news articles to gauge market sentiment and identify potential trading signals
- High-frequency data processing: To handle the large volumes of high-frequency data generated by financial markets, ensuring fast trade execution and reduced latency
- Back-testing and validation: Rigorous back-testing of our AI-driven trading algorithms and strategies using historical market data to evaluate performance, identify potential issues, and fine-tune parameters