Skills & Offers

Worked with git, TFS, SVN, CVS, SourceGear Vault Pro, Bugzilla, Jira to manage software codes, tasks and documentation.

Worked remotely with teams across different timezones (Asia, Europe, North America, Australia) and most of the tools have been used like Zoom, Slack, Teams, Skype, WebEx to communicate with stakeholders of different projects.

Financial Indicators : Breadth Indicators, Multi AMA, Dynamic Ind, Multi AMA - Quantile Osc, Vol Multi AMA - Quantile Osc.

Financial Indicators specified above been developed working with Quant experts in Investment Banks, Hedge Funds, etc.

C++,Python, OCaml, R, Wolfram Mathematica, TradeStation,MultiChart, ARIMA Models, Correlation and Covariance,Statistical Models, IQFeed API, Apache Kafka, TD ammeritrade and IB Brokers, Zacks Research.

Worked in Developing Custom Indicators, Oscillators, Dynamic Bars based on Ranges.

Worked on developing Low Frequency and Medium Frequency Trading apps.

Quantitative Analysis in Financial derivatives products (Equity, Interest Rates, Foreign Exchange).

Used QuantLib ,F#,Python and Winform for Cash Flow analysis,Commodity Trading applications.

Quantitative and Technical Analysis of Stocks.

Fundamental analysis was done and screener was built to select stocks (international market) by checking the Profit and Loss statements, Balance Sheet, C.

Cash flow statements and also worked on the past 10 to 20 years historical data including Foreign Investment, Institutional Investment, real time data using web sockets.

Analyzed using technologies like Scikit, NLP, Pandas, Numpy, d3.js, BeautifulSoup (Web scrappers), etc for having back testing scenarios, technical indicators like Stochastic

RSI, Bollingar Bands was customized and developed in python 3 to analyze the day trading scenarios like price breakout, bulk deal, etc.

Worked with Apache Paraquet and HDF5 format to store and process tick by tick data using Python, used ITCH file format and FIX protocol to process Raw based tick by tick data, filtering Nasdaq Dataset using AlgoSeek.

Worked with Plotly and Brokeh for displaying timeseries in browser and native environment.

Wavelets using pywt library in python was used to remove noisy signals into appropriate frequency signals.

Used CNNs (Nueral Network) for analyzing satellite images to convert into time series data based on object detection.

Have developed a TimeGAN for synthetic financial data.

Build a Deep RL for trading agent.

Apache MXNet was used to deploy Deep NNs and Apache Spark, Kafka, Flink were used to process and control producers and consumers.Deep Q-Learning on Stock analysis to find the optimal policy.