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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


If you would like to know more or discuss our use cases in detail