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Anjanesh Sharma

Building for the "Pro": Bridging the Feature Gap for High-Volume Traders

Ethnographic study of "Hyper-Traders" to identify critical feature gaps driving user migration to competitors.

Project Overview

Role

Senior UX Researcher

Timeline

Nov 2023 

Methods

Ethnographic Interviews, Competitive Feature Analysis & Journey Mapping

Impact

Defined Roadmap for "Pro" Features

Business Context

A disproportionate share of brokerage revenue was generated by a small segment of high-frequency derivatives traders  “Champions” and “Hyper Traders.”

While beginner acquisition was growing, advanced traders were increasingly multi-homing or switching to competitors such as Zerodha due to perceived feature gaps.

For this segment:

  • 70–150+ trades per day was common

Upstox Trade

  • Execution speed directly impacted profitability

  • Risk management controls were non-negotiable

  • Minor friction translated to capital risk

The business needed to understand:

  • Which feature gaps were hygiene vs differentiators

  • Whether switching was emotional, economic, or structural

  • How to retain users as they matured into higher-value segments

The Strategic Question

Why were high-frequency, profitable traders using the platform for analysis but executing trades elsewhere?

Research Approach

I conducted deep-dive interviews with advanced traders to map their full workflow:

  1. Market analysis (Indicators, TradingView usage)

  2. Decision validation (Risk rules, position sizing)

  3. Execution (Order placement, bulk exits)

  4. Post-trade discipline (Loss recovery behavior)
     

Segments Studied:

  • “Champions” (Disciplined, profitable traders)

  • “Hyper Traders” (High-frequency, aggressive scalpers)

Research Approach

Key Insight 1: Speed Was Interaction Architecture, Not Infrastructure

I conducted deep-dive interviews with advanced traders to map their full workflow:

  1. Market analysis (Indicators, TradingView usage)

  2. Decision validation (Risk rules, position sizing)

  3. Execution (Order placement, bulk exits)

  4. Post-trade discipline (Loss recovery behavior)
     

Segments Studied:

  • “Champions” (Disciplined, profitable traders)

  • “Hyper Traders” (High-frequency, aggressive scalpers)

Traders perceived the platform as “slower” than competitors — not due to technical latency, but due to workflow inefficiencies.

Examples:

  • Inability to “Exit All” positions instantly

  • Multi-step order closing during volatility

  • Quantity restrictions during peak trading windows

    Upstox Trade

For scalpers trading 800–2000 quantities, every extra click was interpreted as risk.

Insight:
In high-frequency trading, interaction steps are perceived as latency.

Key Insight 2: Advanced Traders Wanted Enforcement, Not Freedom

Advanced traders are disciplined. They set strict rules for themselves. They were frustrated that Upstox didn't help them enforce these rules.

  • Missing Tool: "Kill Switch" (A feature to lock the account after a set loss).

  • User Behavior: Traders wanted the platform to act as a "Supervisor" to prevent emotional overtrading.

Key Insight 3: The "Glitch" Trust Cost

Trust isn't just about security; it's about accuracy.

  • The Pain Point: Users reported "phantom" margin errors where the system said they exhausted funds despite having capital. This forced them to call support during market hours—a fatal friction for day traders.

  • The Chart Issue: Discrepancies between Upstox charts and TradingView charts (e.g., Volume indicators) forced users to analyze on third-party apps, breaking their flow.

Strategic Recommendations

I synthesized these findings into a "Pro-Trader Feature Specification" document for the product team:

  1. Prioritize "Bulk Actions": Immediate development of "Exit All" and "Basket Orders" to match competitor parity. This was identified as the #1 retention lever.

  2. Build "Trader Wellness" Tools: Implement a Kill Switch and Trailing Stop Loss. Positioning these not just as features, but as "Risk Management Essentials."

  3. Fix the "Data Trust" Layer: Audit and align Chart Indicators (Volume, RSI) to match TradingView 1:1. Eliminate the margin reset glitches.

The Impact: Strategic Alignment

This study reframed advanced feature development from competitive imitation to revenue defense.

Outcomes:

  • “Exit All” and Basket Order fast-tracked on roadmap

  • Organizational shift toward retaining maturing users

  • Recognition that feature parity for power users is a hygiene factor, not a luxury

It shifted focus from acquisition-only growth to lifecycle retention strategy.

Reflection

"For power users, 'Good Enough' is not enough. I learned that feature parity (like 'Exit All') isn't just a 'nice-to-have'—it's a basic hygiene factor for retention in the high-frequency trading space."

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