I’ve spent the last month fully focused on improving my algorithmic trading framework. And here’s what I’ve achieved…
If you want to try this setup yourself, I’m using IC Trading ⮕ https://www.ictrading.com/?camp=86158 *

▬ Links ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
GitHub Project ⮕ https://github.com/s-stolz/algotrader
My Website ⮕ https://www.tradingnerd.io/
How I get interest on Crypto: Nexo ⮕ https://nexo.sjv.io/4eQZvM *

*Affiliate Link

Timestamps
00: 00 Intro
00: 22 Previous State & Issues
00: 52 Fixing the Laggy Chart
01: 38 Chart Performance Demo
03: 08 cTrader Token Lifecycle Automation
04: 02 Introducing Shared Libraries
05: 10 Live Indicator Updates
06: 07 Indicator Demo (Live Updates)
07: 07 How I Achieved Instant Loading (TimescaleDB)
08: 18 Wrap Up

#algotrading

In this video, I go through 30 days of improvements to my algorithmic trading framework. I fixed major charting issues, including lag from tick-based candle calculations and unnecessary full re-renders, and switched to a more efficient event-driven approach using candle updates.
I also added automatic token refreshing for the cTrader Open API, introduced shared Python libraries to simplify data access across services, and implemented live indicator updates via the indicator API and Redis streams.
Finally, I improved data loading performance by properly using TimescaleDB features like hypertables and continuous aggregates, enabling near-instant chart loading even with millions of candles.
This is part of my series where I build a full algorithmic trading system from scratch.