Algorithmic Trading

Problem Statement:

Traditional manual trading is not only time-consuming but also prone to emotional biases and can't capture the minute market inefficiencies that might arise in split seconds. Additionally, the dynamic and fast-paced nature of modern financial markets demands tools that can instantly react to market data and news.


We developed an algorithmic trading system that uses quantitative models and strategies to identify trading opportunities. Leveraging real-time market data feeds, our algorithms make swift buy/sell decisions, ensuring timely execution and optimizing profit margins.


  • Real-time Sentiment Scoring: Continuous evaluation of financial sentiments from multiple sources.
  • Interactive Dashboards: Visualization tools for a quick assessment of the market mood.
  • Alert System: Notification system for drastic sentiment shifts.
  • Historical Sentiment Analysis: Archive and review past sentiments and correlate with market movements.

Use Cases:

  • Investment Strategy: Tailor investment strategies based on prevailing market sentiment.
  • Risk Management: Adjust financial portfolios in response to sudden negative sentiment spikes.
  • Market Prediction: Anticipate market movements by correlating sentiment with historical market behavior.

Data Science Specific Points:

  • Data Collection: Implemented web scraping tools to collect data from financial news websites, blogs, and social media platforms. Utilized APIs, where available, especially for platforms like Twitter.
  • Data Analysis: Text data underwent preprocessing (tokenization, stemming, etc.) followed by sentiment classification using deep learning models and natural language processing techniques.
  • Results: The system successfully identified sentiment trends in correlation with market movements, achieving an 87% accuracy rate in sentiment classification, leading to enhanced market prediction capabilities for our users.

Technologies Used:

  • Programming Languages: Python, SQL
  • Frameworks: TensorFlow, NLTK, spaCy
  • Tools: Elasticsearch, Kibana, Web Scraping tools (BeautifulSoup, Scrapy)
  • Databases: Google Cloud Databases