Alqama Ansari

Associate Consultant

Ahmedabad, Gujarat, India0 mo experience

AI EnabledAI ML Practitioner

Key Highlights

Engineered a full-stack quantitative trading engine.
Achieved 11.5% alpha with an Information Ratio of 2.08.
Applied advanced reinforcement learning for optimal trade execution.

Stackforce AI infers this person is a Fintech professional with expertise in quantitative trading and machine learning.

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Skills

Core Skills

Machine LearningData VisualizationData AnalyticsCustomer Segmentation

Other Skills

Algorithmic TradingApache SparkBack-testingBacktestingBlack-Scholes ModelBloomberg TerminalC++CVXPYComputer Vision (OpenCV)Data PreprocessingDeep LearningDeep Q-Networks (DQN)DerivativesDockerEconometrics

About

As a recent engineering graduate, I'm driven by the challenge of solving complex financial problems through technology. My focus is on engineering robust solutions in quantitative trading, from strategy conception and back-testing to live algorithmic execution. I thrive on turning theory into practice. I engineered a full-stack quantitative trading engine from the ground up, which I designed and validated over eight unique strategies, ultimately achieving an 11.5% alpha with an Information Ratio of 2.08 in a live paper trading environment. My work also extends to applying advanced reinforcement learning (Deep Q-Networks) and Reinforcement Deep Markov Model(RDMM) to tackle the optimal trade execution problem, significantly reducing market impact and transaction costs. My core competencies include: 🔹 Quantitative Strategy & Back-testing 🔹 Algorithmic Trading & Reinforcement Learning 🔹 Financial Modeling & Risk Management 🔹 C++ & Python (NumPy, Pandas, PyTorch) I am actively seeking a full-time role as a Quantitative Researcher, Quant Trader, or Quantitative Developer where I can apply my analytical and technical skills to create value in the financial markets.

Experience

0 mo

Total Experience

Average Tenure

Current Experience

Emerging five

AIML Intern

Jan 2025 – Apr 2025 · 3 mos · Ahmedabad, Gujarat, India · On-site

Engineered a high-performance, real-time brand logo identification system using a YOLOv8 object detection pipeline, achieving 95% accuracy and providing critical data for market analysis.
Key Contributions:
🔹 Developed and launched an interactive Streamlit dashboard equipped with confidence-scoring algorithms. This tool enabled stakeholders to perform advertisement exposure analytics and directly measure campaign ROI.
🔹 Devised and implemented a scalable framework to calculate logo display percentages across more than 50 distinct marketing campaigns, establishing a foundational system for benchmarking campaign performance.

YOLOv8Streamlitobject detectionMachine LearningData Visualization

Ibm-csrbox

Data Analytics Intern

Jun 2024 – Aug 2024 · 2 mos · Remote

Spearheaded the development of a customer segmentation model using k-means clustering and Principal Component Analysis (PCA). This model directly improved the targeting of marketing campaigns by 25%, leading to more effective outreach.
Key Contributions:
🔹 Executed a comprehensive data preprocessing pipeline that enhanced model stability by reducing input noise by 15%.
🔹 Led sprint-based analytics delivery within a 5-member quantitative team, consistently maintaining 100% adherence to rigorous data quality standards and ensuring the reliability of our insights.

k-means clusteringPrincipal Component Analysisdata preprocessingData AnalyticsCustomer Segmentation