Our Technologies
Our R&D process
Our R&D process based on Project management approach with multistage analysis and decision making processes
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Knowledge base
Publications regarding Scope of the Project
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Techniques & Technologies
Our projects based on advanced Fintech techniques & technologies
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Techniques & Technologies
Our techniques & technologies
Investigating of ideas, analysis of opportunities, selection of quantitative methods, data mining and data science, prototyping of algorithmic trading systems Data Science software R | | Python | | Prototyping software Special software and packages for prototyping: Wealthlab | WealthScript | TradingView |Pine | AmiBroker | AFL | QuantStrat | R | SIT | R |…
Integration process include setup of and testing infrastructure including servers, hosting, trading platforms and risk management tools. Ultraticks can design, setup and maintain trading infrastructure depending of the requirements. We have experience of operating Equinex, Beeks, , UltraFXVPS, NYCservers, Interserver, FXVM, ForexVPS etc. Trading platforms: MT4/5, Multicharts, NinjaTrader, TradeStation, TradingView trading platforms.
Programming of algorithmic trading systems and special trading and analytical software for financial markets MT4/5 |MQL |, Multicharts | PowerLanguage | pdf | C# | NinjaTrader | NinjaScript | TradeStation | EasyLanguage | ProRealTime | ProBuilder | PTMC | C# |…
Risk analysis and optimization
Risk analysis, construction of trading portfolios, development of hedging models Rebalancing of trading portfolios
Performance management
Analysis and optimization of logic and performance metrics of algorithmic trading systems
Experience in maintain of algorithmic trading process: 1. Setup of trading settings, monitoring of trading process 2. Accounts opening 3. Execution optimization 2. Reporting: drop coping, CTA reporting etc
Algorithmic trading system development cycle
Stage I. Analysis and Planning
Description of the Trading Idea
Formalized describing of the Trading System concept: type of the trading system, description of trading logic concept and signal logic model (algorithm), target markets and trading instruments, target investors.
Preliminary analysis
Analysis of opportunities and requirements to get the trading system started: world global and market tendencies, market inefficiencies and and excess of liquidity, benchmarks of performance and risk metrics, availability of technologies, target investors profile, competition level.
Design of research, development, testing, optimization and trading plans.
Stage II. Resources Definition
Tecnical Infrastructure
Analysis of technical requirements: required execution metrics (intensity of trading, tick to trade latency), technology providers and trading venues (exchanges, brokers), integration scheme, hardware (servers), hosting, software (platforms).
Investments & Cost analysis
Analysis of investments, setup and maintenance costs required for trading system run and operate: min deposit, min ticket size, trading commission fees, cost of technical infrastructure (hardware, hosting, software), management cost.
Market Data specification
Analysis of composition, format, period, volume, sources and cost of data required for design of the logic's model and further backtesting and optimization of performance metrics of the trading system.
Stage II. Prototyping
Methodology Design
Design of the Methodology which includes trading logic concept, target performance metrics, scheme (model) of signal logic, execution logic, risk management scheme, optimization scheme, etc.
Market Data Mining
Purchasing, loading, collecting of Market data, processing (synchronization, normalization, aggregation etc) of the Data, organization of Data storage.
Development of the Prototype of a signal and execution logic in prototyping environment: Excel, R, Python, Wealthlab, Amibroker, TradingView (depending on trading system type)
Stage III. Backtesting and optimization
Optimization plan
Determine of fitness functions, conditions and parameters, selection of optimization method, setup of insample and outsample periods for initial data.
Backtesting & Optimization
Optimization and robust testing of trading system's parameters to get stable target performance metrics of the trading system.
Risk Control Model Design
Selection of risk control methods, position sizing, hedging schemes for the algorithmic trading system.
Stage IV. Implementation
Architecture Design
Disign of archtecture of the algorithmic trading system, definition of all architectural modules along with its relations and data flow scheme including external and third party modules to be used
Programming of signal and execution logic due to designed Prototype and selected trading software (Integrated script of the platform (MQL, Easylanguage etc) or programming language (Python, C#, C++ etc), design of technical documentation.
Setup of trading infrastructure due to the technical requirements: hardware, hosting, trading and risk management software, integration to venues, performance testing, technical reporting.
Stage IV. Integration and Testing
Delivery of the infrastructure (hardware, hosting and software) to end users (quantitative analysts, traders and risk managers), providing with user documentation.
Programming of signal and execution logic due to designed Prototype and selected trading software (Integrated script of the platform (MQL, Easylanguage etc) or programming language (Python, C#, C++ etc), design of technical documentation.
Implementartion & Integration
Setup of trading infrastructure due to the technical requirements: hardware, hosting, trading and risk management software, integration to venues, performance testing, technical reporting.
Stage V. Operating and Maintenance
Risk management
Operating the trading sytem due to the Trading plan and Risk model: raising of ticket size to scheduled one, optimization of risk and execution schemes, design of portfolios of trading systems
Operating & Maintenance
System run in live mode, collecting of trading statistics, final comparison with performance and execution metrics got at the Backtesting stage, technical assistance
Consequently testing of the trading system in demo, paper and live regimes to compare actual metrics with Backtesting benchmark to make sure the system is ready for production
Knowledge base
Larry Harris  |  2003
Trading and Exchanges: Market Microstructure for Practitioners
This book is about trading, the people who trade securities and contracts, the marketplaces where they trade, and the rules that govern it. Readers will learn about investors, brokers, dealers, arbitrageurs, retail traders, day traders, rogue traders, and gamblers; exchanges, boards of trade, dealer networks, ECNs (electronic communications networks), crossing markets, and pink sheets. Also covered in this text are single price auctions, open outcry auctions, and brokered markets limit orders, market orders, and stop orders. Finally, the author covers the areas of program trades, block trades, and short trades, price priority, time precedence, public order precedence, and display precedence, insider trading, scalping, and bluffing, and investing, speculating, and gambling.
Andrew Kumiega and Ben Van Vliet  |  2008
A Software Development Methodology for Research and Prototyping in Financial Markets
The objective of this paper is to develop a standardized methodology for software development in the very unique industry and culture of financial markets. The prototyping process we present allows the development team to deliver for review and comment intermediate-level models based upon clearly defined customer requirements. This spreadsheet development methodology is presented within a larger business context, that of trading system development, the subject of an upcoming book by the authors of this paper.
Ernest P. Chan  |  2009
Quantitative Trading: How to Build Your Own Algorithmic Trading Business
While institutional traders continue to implement quantitative (or algorithmic) trading, many independent traders have wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, Dr. Ernest Chan, a respected independent trader and consultant, will show you how. Whether you're an independent "retail" trader looking to start your own quantitative trading business or an individual who aspires to work as a quantitative trader at a major financial institution, this practical guide contains the information you need to succeed.
Rishi K. Narang  |  2009
Inside the Black Box: A Simple Guide to Quantitative and High Frequency Tradingle
rn this updated edition of his bestselling book, Rishi K Narang offers in a straightforward, nontechnical style—supplemented by real-world examples and informative anecdotes—a reliable resource takes you on a detailed tour through the black box. He skillfully sheds light upon the work that quants do, lifting the veil of mystery around quantitative trading and allowing anyone interested in doing so to understand quants and their strategies. This new edition includes information on High Frequency Trading.
Ernest P. Chan  |  2013
Algorithmic Trading: Winning Strategies and Their Rationale
Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies,which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers.
Alvaro Cartea, Sebastian Jaimunga, Jose Penalva  |  2015
Algorithmic and High-Frequency Trading (Mathematics, Finance and Risk)
The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency.
Dilip Kumar, Nishant Bhatia  |  2015
Strategies, Tips & Tricks for Algo Trading
Have you wondered why professional traders consistently make money whereas retail investors struggle to remain profitable and solvent? How do professionals trade? What are their secrets? What strategies do they use? The book covers various aspects of algo trading and how they can be applied to professional trading.
Ernest P. Chan  |  2017
Machine Trading: Deploying Computer Algorithms to Conquer the Markets
Machine Trading is a practical guide to building your algorithmic trading business. Written by a recognized trader with major institution expertise, this book provides step-by-step instruction on quantitative trading and the latest technologies available even outside the Wall Street sphere. You'll discover the latest platforms that are becoming increasingly easy to use, gain access to new markets, and learn new quantitative strategies that are applicable to stocks, options, futures, currencies, and even bitcoins.
Kevin J. Davey  |  2018
Introduction To Algo Trading: How Retail Traders Can Successfully Compete With Professional Traders
Are you interested in algorithmic trading, but unsure how to get started? Join best selling author and champion futures trader Kevin J. Davey as he introduces you to the world of retail algorithmic trading. In this book, you will find out if algo trading is for you, while learning the advantages and disadvantages involved. You will also learn how to start algo trading on your own, how to select a trading platform and what is needed to develop simple trading strategies. Finally you will learn important tips for successful algo trading, along with a roadmap of next steps to take.
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