KANISHK
DHIMAN

My journey didn't start with a computer science textbook — it started with curiosity about how systems work, whether that system was a piece of software or a financial market.
In mid-2023, I began trading financial markets independently, learning technical analysis through manual chart reading and discretionary decision-making. There was no shortcut — just hours of staring at candles, slowly building market intuition before any systematic approach existed.
At the same time, I was working through foundational programming concepts, slowly seeing a pattern: everything I did manually in markets could eventually be quantified, automated, and systematized. That realization connects everything I've built since.
The Systematic Shift
My trading evolved from pure discretion to systematic strategy. I moved deep into ICT and Smart Money Concepts — Fair Value Gaps, order blocks, liquidity sweeps, premium/discount theory. This wasn't just a trading evolution; it was the moment I started thinking like an engineer about markets.
I officially began my B.Tech in Computer Science with AI/ML specialization at Bennett University in July 2024, and almost immediately started building — an AI Crop Recommendation System (Smart India Hackathon finalist) and an Adaptive PDF Quiz Generator using a fine-tuned T5 transformer model. My first real full-stack-plus-ML builds: idea to deployed system, not idea to Jupyter notebook.
Funded Trading & Validation
I became a Funded Trader at 5%ers.com, managing real allocated capital — not a demo account — using ML-informed systematic strategies. Over five months, I achieved an 80% win rate.
This mattered because it was proof that systematic thinking actually works when real money is on the line. It also taught me more about discipline, risk management, and performing under pressure than any classroom ever could.
Deep Technical Building Phase
My most technically intensive year. I built an ICT Price Action ML system using LightGBM to predict Fair Value Gap fills with 96.5% accuracy — literally systematizing the exact concepts I'd been trading manually for two years.
Followed by an ensemble trading system combining Temporal Fusion Transformers, CNNs, Graph Neural Networks, and PPO reinforcement learning. In parallel: Odus (AI Linux assistant via Gemini Vision), FinChat & LocalLens (RAG document intelligence with Armaan Choudhary), and PrismNet (edge AI — 6.3x inference speedup on object detection).
Execution Mode
Transitioning into my 5th semester at Bennett University while actively pursuing freelance AI automation work, reaching out to companies for internship and engineering opportunities, and exploring how to turn two years of trading knowledge into educational content for other students navigating the same path.
Building this portfolio as part of that broader push — to have one place that actually represents the depth of what I've built, rather than scattered GitHub repos and a static PDF resume.
Technical Skills
WORK EXPERIENCE
FUNDED TRADER
5%ERS.COM
Managed $50,000 in allocated capital using systematic ML-driven trading strategies. Achieved an 80% win rate over a 5-month period and maintained a 1.8 Sharpe ratio. Built and deployed a production trading infrastructure with sub-100ms signal latency.
INDEPENDENT TRADER
& ML DEVELOPER
Developed ensemble ML trading models combining TFTs, CNNs, GNNs, and PPO reinforcement learning. Backtested strategies across 3 years of data. Processed over 1 million daily market data points using Polars and deployed containerized inference APIs on AWS.
AI AUTOMATION
ENGINEER (FREELANCE)
Built a multi-agent AI workflow automating lead qualification, processing 15,000+ leads/month. Reduced manual work by 85% through GPT-4 classification and Claude personalization. Implemented event-driven architecture using Celery and RabbitMQ.
ML TRADING SYSTEM
Ensemble Deep Learning for Systematic Market Prediction
Discretionary trading relies on human intuition, which doesn't scale and is inconsistent under pressure. Existing quantitative systems often use single-model approaches that fail to capture the multi-dimensional nature of financial markets — price action, temporal dependencies, and inter-asset relationships are treated in isolation.

BENNETT UNIVERSITY
B.Tech Computer Science
Specialization in AI & Machine LearningCurrently pursuing my undergraduate degree with a core focus on artificial intelligence, machine learning systems, and deep learning architectures. Building foundational knowledge in data structures, algorithms, and advanced OOP while actively applying them to real-world AI projects.

UNIVERSITY OF MARYLAND
Data Science and Business Analytics
Professional Capstone & AnalyticsCompleted a comprehensive professional certification in Data Science during my gap year. This period was heavily focused on building a systematic understanding of data, statistical analysis, and predictive modeling — which eventually laid the groundwork for my transition into ML-based algorithmic trading.
KEY ACHIEVEMENTS
SMART INDIA HACKATHON 2024 FINALIST
AgriTech crop recommendation solution, ranked among top teams nationwide, featuring a Progressive Web App with voice interface and multilingual support for farmers.
FUNDED TRADER STATUS
Earned the right to manage $50,000 in live capital through a rigorous evaluation process at 5%ers.com, then sustained an 80% win rate over 5 months.
6.3X INFERENCE SPEEDUP
Compressed an RT-DETR object detection model by 49.7% in size while improving frame rate from 7 to 47.49 FPS, with negligible accuracy loss.
CERTIFICATES
Continuous learning across AI/ML, software engineering, and data science.
Accelerated CS Fundamentals
University of Illinois
Google IT Support
Intro to Arm Architecture
Arm Education
Google Cybersecurity
Software Engineering
NPTEL
Accelerated CS Fundamentals
University of Illinois
Google IT Support
Intro to Arm Architecture
Arm Education
Google Cybersecurity
Software Engineering
NPTEL
Accelerated CS Fundamentals
University of Illinois
Google IT Support
Intro to Arm Architecture
Arm Education
Google Cybersecurity
Software Engineering
NPTEL
Information Design Technologies
Infosys Springboard
MATLAB Fundamentals
MathWorks
Linear Algebra with MATLAB
MathWorks
MATLAB Programming Techniques
MathWorks
Deep Learning
Infosys Springboard
Information Design Technologies
Infosys Springboard
MATLAB Fundamentals
MathWorks
Linear Algebra with MATLAB
MathWorks
MATLAB Programming Techniques
MathWorks
Deep Learning
Infosys Springboard
Information Design Technologies
Infosys Springboard
MATLAB Fundamentals
MathWorks
Linear Algebra with MATLAB
MathWorks
MATLAB Programming Techniques
MathWorks
Deep Learning
Infosys Springboard
Automation Testing
Infosys Springboard
Performance Testing
Infosys Springboard
ML using Python
Infosys Springboard
Data Structures & Algorithms
Infosys Springboard
Automation Testing
Infosys Springboard
Performance Testing
Infosys Springboard
ML using Python
Infosys Springboard
Data Structures & Algorithms
Infosys Springboard
Automation Testing
Infosys Springboard
Performance Testing
Infosys Springboard
ML using Python
Infosys Springboard
Data Structures & Algorithms
Infosys Springboard