If you’ve been following my journey at Digital Jivan, you know I’m a big believer in the intersection of trading and building. As a discretionary trader here in India, one of the biggest hurdles I faced wasn’t actually the chart patterns or the volatility of the Nifty—it was the sheer boredom of data entry. For years, I struggled to keep a consistent trading journal. After a long day of screen time, the last thing I wanted to do was manually type entry prices, exit times, and P&L figures into a spreadsheet.
But as any professional will tell you: if you don’t measure it, you can’t improve it. So, I decided to put on my builder hat and automate the friction out of the process. Today, I want to share exactly how I use a simple automation stack to manage my journal without losing the human touch that discretionary trading requires.
My workflow starts with a Python script that connects to my broker’s API (in my case, Zerodha’s Kite Connect). Every evening at around 4:00 PM, once the Indian markets have cooled down, my script kicks in. It fetches all the executed orders for the day, calculates the net P&L after brokerage and taxes, and formats them into a clean data object. This data is then pushed directly into a Notion database via the Notion API.
The beauty of this isn’t just in the time saved; it’s in the accuracy. Manual entry is prone to ‘fat-finger’ errors or, worse, the psychological urge to ‘forget’ to log a particularly embarrassing revenge trade. The automation ensures that every single trade—the good, the bad, and the ugly—is sitting there waiting for me when I log into Notion.
However, automation only handles the ‘What.’ As a discretionary trader, the ‘Why’ is far more important. My Notion setup includes a dedicated section for each automated trade entry where I have to manually answer three questions: What was my emotional state? Did I follow my setup? And what did I learn? Because the boring data entry (price, time, quantity) is already done for me, I actually find the energy to do this qualitative analysis. The friction is gone, leaving only the high-value work.
Since implementing this, my review process has transformed. Every weekend, I can filter my Notion gallery to see trades where I marked my emotion as ‘anxious’ versus trades where I felt ‘calm,’ and see the direct correlation with my P&L. It has helped me identify that most of my losses happen on Tuesday afternoons when I’m prone to overtrading. I wouldn’t have found that pattern if I were still struggling with manual spreadsheets.
For my fellow traders and builders in India, I highly recommend looking into API-based journaling. Whether you use Python, No-code tools like Zapier, or even simple Google Apps Script, removing the manual labor from your journal is the best investment you can make in your trading career. It allows you to stop being a data entry clerk and start being a disciplined performer. If you’re interested in the specific code I use, feel free to reach out—I’m always happy to help a fellow builder.