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Know About Quantitative Developer
  • Last Updated : 30 Jan, 2020

Who is a Quantitative Developer? What does the Quantitative Developer do? How do quants program a financial product? Do these questions sound familiar to you, or do you find it different? Whether it is familiar or unique here, we are going to give you a competent thought fire on how do quantitative developers do code the complex financial models and derivative pricings?.

This article is all about Quants, quantitative researchers, quantitative developers and quantitative analysts who develop crafty solutions to trade in the financial markets to strike strong earnings.

Who is a Quant Developer?

A quant is a computer programmer who develops financial modeling solutions to quantitative finance and quantitative trading industry. Quantitative developers would have profound knowledge of applied mathematics, statistical models, advance finance concepts, data structures, algorithms, and scientific computing.

Quantitative developers are more in demand across investment banks, hedge fund companies, asset management companies, trade brokerage firms, and financial securities firms.

Quants provide simple solutions to more complex trading problems through deep quantitative analysis with mathematics computations. Quantitative developers create mathematical models with the key components of trading such as price and volume of the stock.

These are about the quantitative developers, coders who are familiar with Java, C#, C++ could foray into investment banks as Quants. Whereas coders who are sound with R, Python, Matlab can catch up with the hedge fund firms and brokerage firms.

Prerequisites for Implementing Quantitative Models

Any computer science engineer is highly competent to enter into the quantitative finance and trading field. Where computers and electronic transaction plays a vital role in today’s scenario and stock exchange is no getaway. Along with the skills of a computer engineer, there are some industry-ready skills a quant should be sound of. They are highly proficient in database management systems, statistical analysis software like Matlab, SAS, R, S-Plus, advanced spreadsheet skills, data structures, algorithms, and coding in C++ more prominently or Python, Java, SQL.

So if you have a thirst for numbers and curious to develop financial models then Quantitative finance is a way to go option. The implementation of Quantitative models is a broad area like algorithmic strategies, quantitative financial models also have numerous strategies that do have to be computed with analyzation of past data, current and anticipated future data.

Programming Languages in developing Quant Models

Advanced C++ concepts are of prime importance for developing financial instruments and derivative pricing, whereas Java, Python can be utilized for algorithmic high-frequency trading purposes.

Knowledge of advanced statistical analysis software like Matlab, R, SAS would be a vital plus. If you got the courage and passion for trading, provability and financial models one could become a self-taught quantitative developer. You just need to have a ton of practice on the algorithms and financial numerical models.

Tools For Quants: Let’s see about Quant software tools that help in creating a financial model or trading strategy. Quant software for Derivatives pricing in financial products and Management of complex Risks are:

  • Quant Lib: These are library functions are written in C++ exclusively for derivative pricing.
  • JQuantLib: Same as Quantlib but written in Java
  • OpenGamma: For analyzing operational risk and market risk
  • Maygard: For evaluation of stock and price movements
  • Quantcode: For financial modeling
  • Rosetta code: To practice on quantitative programming.
  • Nvidia Computational Finance Tools: For financial modeling and graphical simulation purposes.

Software for Trading Purposes

  • Quantopian: For hedge fund backtesting algorithmic trading strategies. Written in python also used with brokerage agencies for paper trading. They conduct algorithmic trading contests too.
  • Quantconnect: Forex algorithmic trade engine used for backtesting.
  • Quantmod To develop, test and deploy trade engines.
  • Quandl: Provides data sets and models for deploying in quantity.

Financial Models in Practice: Let’s look into what are the most familiar mathematical and statistical models that are being deployed on quantitative development.

  • Sum of the Parts Model: This model is computed by taking multiple discounted cash flow models and adding them finally.
  • Consolidation Model: Here multiple business units are consolidated to one model.
  • Budget Model: Focusing the income stream the budget models are framed.
  • Forecasting Model: Predicting the future expense with the financial planning and analysis.
  • Option Pricing Model: This is a straight mathematical model based on the binomial tree and Black-Scholes.
  • Three Statement Model: The three important financial statements income statement, balance sheet statement, cash flow statement is formulated in Excel macros.
  • Discounted Cash Flow (DCF) Model: This is computed along with three primary statements along with net present value and future values.
  • Merger Model (M&A): This is the merger and acquisition model computed with the consolidation of primary states of merging companies.
  • Initial Public Offering (IPO) Model: With a thorough company analysis the IPO models are constructed.
  • Leveraged Buyout (LBO) Model: Based on debt schedules this model is constructed.

Quants are considered to be quite tough as they involve high-risk management and financial product modeling that requires a deep analysis of the economic spread of the demographics. But it’s a challenging career if you got the passion towards mathematics models, excel macros, and programming it’s a way to go option.

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