Mehedi Hasan Ahmed

Business Analyst

Dhaka, Dhaka, Bangladesh2 yrs 9 mos experience

Most Likely To Switch

Key Highlights

Expert in quantitative analysis of financial instruments.
Proficient in Python for data analysis and visualization.
Strong foundation in financial principles and risk management.

Stackforce AI infers this person is a Fintech professional with strong quantitative analysis and data management skills.

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Skills

Core Skills

Quantitative AnalyticsFinancial Market AnalysisAlgorithmic TradingData Analysis

Other Skills

Analytical SkillsArtificial Neural NetworksAsset ManagementBacktestingBacktesting and SimulationCascading Style Sheets (CSS)Cash Flow AnalysisCollaborationComputational Fluid Dynamics (CFD)Convolutional Neural Networks (CNN)DV01 valuationData Analysis and InterpretationData AnalyticsData CleaningData Collection

About

I am Mehedi Hasan Ahmed, a Quant Analyst with a unique background in Naval Architecture, now immersed in the finance domain while concurrently pursuing an MBA. My core responsibilities revolve around analyzing diverse financial instruments such as stocks, options, futures, and bonds using Python-based quantitative methodologies. On a daily basis, I utilize essential Python libraries including pandas, numpy, Talib, and scikit-learn to perform trend analysis, correlation assessments, statistical measurements, and future projections. My expertise extends to data visualization where I employ tools like matplotlib, plotly, and seaborn to craft a range of charts including bar, line, pie, and scatter plots tailored to specific project needs. Proficient in database management, I have hands-on experience with PostgreSQL, MongoDB, and influxDB, effectively storing and retrieving financial time series data. My strong foundation in financial and statistical knowledge, coupled with a passion for CFA principles, enables me to navigate complex financial statements, ratios, derivatives, economics, and corporate finance with ease. Collaborating closely with my team under the guidance of the CTO, I demonstrate leadership skills when tasked with addressing intricate financial challenges, creating dynamic Excel dashboards, or overseeing data scraping projects. I am driven by a desire to seek new opportunities that allow me to broaden my expertise across diverse industries and cultures, continually enhancing my skill set to deliver impactful contributions to challenging projects.

Experience

Lr global bangladesh

Quantitative Analyst

May 2024 – Present · 1 yr 10 mos · Dhaka, Bangladesh · On-site

Automate collection, validation, and processing of DSE equities' financial and corporate data (balance sheets, income statements, EPS, dividends, NAV, etc)
Manage real-time streaming of DSE OHLCV data, ensuring data integrity and timeliness
Make weekly and monthly reports on the NAV performance of six Mutual Funds managed by the AMC
Automate stock valuation in financial models by dynamic formulation and other data analytic tools

Data CollectionData ValidationData ProcessingFinancial ReportingQuantitative AnalyticsFinancial Market Analysis

Anchorblock technology

2 roles

Quantitative Analyst

Promoted

Jun 2023 – May 2024 · 11 mos · Dhaka, Bangladesh

Conducted comprehensive research on commodities trading in the Asia Pacific region, collecting and analyzing data on gold and oil futures, assessing data availability, and evaluating opportunities for arbitrage trading.
Managed data collection efforts for cryptocurrencies, Forex, and Asia Pacific region futures commodities data, ensuring data quality, organization, and availability for analysis.
Demonstrated expertise in applying Principal Component Analysis (PCA) to analyze risk and factor models, including clustering analysis for asset correlation and market impact assessments.
Evaluated and compared the performance of various models, including AutoTS, ETNA, and Prophet, for stocks and commodities, analyzing benchmark results and making recommendations for model selection and optimization.
Automated scraping of contract data was achieved for 15-second updates using Selenium with Chromedriver. The data collected through this process was stored on a MongoDB server running on AWS. A comprehensive pipeline was developed to utilize the stored data from the database as required, ensuring efficient and timely access to the latest contract information.
Built a Python library named sdrpy, specifically designed to facilitate the retrieval, analysis, and visualization of data from MongoDB containing live SDR (Swap Data Repository) data. This library was developed with a strong emphasis on robust filtering capabilities and versatile querying functions, enabling users to efficiently extract and manipulate trade data for detailed analysis and visualization.
Calculated risk metrics like DV01 from cash flow schedules of various financial derivatives bonds using QuantLib in Python. The work involved analyzing different financial derivatives, including rates, forex, and swaps from the DTCC, ensuring precise and accurate risk assessment for these complex financial instruments.

Data AnalysisRisk AssessmentData CollectionData Quality ManagementPrincipal Component AnalysisModel Evaluation+3

Quantitative Analyst

Mar 2023 – May 2023 · 2 mos · Dhaka, Bangladesh

During my time at Anchorblock Technology, I have gained valuable experience as a Quantitative Analyst. Here are the key tasks and responsibilities I undertook during my four-month journey:
Conducted detailed analysis of share market fluctuations, considering factors such as supply-demand dynamics, public sentiment, news events, and financial summaries.
Explored technical analysis concepts and implemented various indicators like SMA, EMA, MACD, and Bollinger Bands on OHLCV data of cryptocurrencies to create algorithmic trading strategies.
Utilized version control with Git and collaborated with team members on projects involving MPL finance and CCTX for Binance.
Demonstrated proficiency in backtesting using tools like Zipline and VNPY, including data ingestion from Quandl, custom CSV files, and large-scale competition datasets.
Applied machine learning models, including Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors, Decision Trees, Random Forests, Extra Trees, AdaBoost, Support Vector Machines, Gradient Boosting, and MLP, to predict target variables and improve trading strategies.
Contributed to research projects such as Project Optimizer, where I optimized trading strategies using technical indicators and performed machine learning-based predictions for asset allocation.
Completed relevant courses and certifications, including Google Python Crash Course, Kaggle Pandas Mini Course, Kaggle Data Cleaning Mini Course, and Kaggle Data Visualization Mini Course.