Skip to content

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

Our AI-driven trading algorithms provide market predictions with high accuracy

ENHANCED PREDICTION

Our models secure high Sharpe Ratio above 3.0 consistently (a ratio higher than 1 is considered good)

RISK ADJUSTED PERFORMANCE

Our high-performance computing solutions and advanced data processing result in faster trade execution

REDUCED LATENCY

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

SCHEDULE A MEETING FOR PoC

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

CONTACT US FOR POC