Financial systems have become too complex for the traditional tools and methods used to design and test them. Luckily, recent breakthroughs in AI and mathematics pave the way for a much-needed overhaul.

The trading systems generation engine is a novel AI technique for automatically exploring financial markets to identify unique edge cases in their behaviors. Aggregating uncovered insights into a quantitative and systematic trading system. Flexible modular architecture for solving a broad spectrum of quantitative trading tasks.

Developed particular engine core language for complex system interface encoding with mathematical precision. Automated mathematical techniques to synthesize logical circuits as a finite-space, event-driven model.

Generated trading agent design as a composition of:

• Blueprint of the logic circuit as a finite state space machine with events driven by signaling units.
• Signaling units transform input data streams through analytical units generated by appropriate subsystems.
• Market condition requirements.
• Parameters and specifications of trading instruments, order types, input data flows, etc.
• Description of all tunable parameters

Logic circuit engine technology for generating Event-Driven Finite State Machine models powered by In-House Language coding consisted of the following:

• States definition - a variable data structure that maintains the session's state and capital allocation in used trading instruments.
• Action definitions and handling logic are internal system events that trigger the state to change. Actions represent a powerful abstraction of how the system may respond to environmental updates or other internal state events (e.g., time change). Actions are described with a set of type definitions, validity constraints, and processing logic (e.g., generated by a signal synthesis engine).

Technological solutions inspired by genetic algorithms allow the encoding of highly complex logic as executable programs.

Some repeated patterns in market data can predict future events. That patterns are called signals and can be detected by performing mathematical transformations on financial time series. Applied transformations combined in a particular Signal-Analyses module.
Dedicated Engine for mining new Signal-Analyses modules automatically. Powered by an In-House formal Language coding scheme and evolutionary algorithm.

To tackle the challenges of trading systems from another angle, I developed multiple crucial components and methods focused on screening and amplifying particular market features that improved trading agents. Powered by techniques for calculating specific score values of trading instruments.

A Long/Short portfolio of available trading instruments can be viewed as a new Synthetic ETF. Integrated components for producing Synthetic ETFs open an opportunity to leverage the procedure of particular market feature amplifying. Moreover, it allows the retuning of Synthetic ETFs to extend the lifetime of the instrument.

Inspired by the reverse idea, a new component mining the imaginary set of trading sub-instruments, if available trading instruments are treated as Synthetic ETFs. Uncover a way for data enrichment and better module work.

Increased functionality of the Trading Instruments Module by integrating components:

• Financial time-series forecasting implementation,
• Mining and using Alternative data (enhanced by the NLP techniques).

Practical usage of Reinforcement Learning for generating analytical agents. Analytical agents architecture defines a sequence of Trading instrument data transformations to calculate a specific score value.

A Long/Short portfolio of available trading instruments can be viewed as a new Synthetic ETF. Integrated components for producing Synthetic ETFs open an opportunity to leverage the procedure of particular market feature amplifying. Moreover, it allows the retuning of Synthetic ETFs to extend the lifetime of the instrument.

Inspired by the reverse idea of the previous module, a new component mining the imaginary set of trading sub-instruments, if available trading instruments are treated as Synthetic ETFs. Uncover a way for data enrichment and better module work.

A model for generating nonlinear forecasts scenario of financial time series. Modeling thousands of forecast scenarios to compute forecast cloud statistical metrics. Enrichment of instrument data by exploring feature selection of forecast engine.

Enrichment of trading instruments by exploring, evaluating, and utilizing alternative/new data sources.

Delivered data tooling solutions for large-scale crawling and web scraping. Developed alternative data storing and distribution infrastructure modules.

Use of ML/NLP technologies for processing alternative text data. Exploring valuable trading signals and indicators value of a sentiment based analyses.

Essential Quantitative set of tools including the following:

• Combination of AI/ML/Quantitative systems for vital operations in creating trading strategies.
• Exploring the most suitable trading strategy parameters.
• Validating strategy solution.
• Risk management solution.
• Portfolio construction.

Exploring the best-suited parameters of the strategy component is a critical importance task.

Parameters optimization, powered by in-house numeric non-convex, non-smooth function minimization.

Evaluated parameters solution and related back-test results by computing OF/Constraints and inspected by a set of robustness protocols.

Delivered trading system with a good profile value of risks/performance metrics requires extensive inspection by a series of validation protocols.

A few of the verification protocols:

• Evaluating a chance of over-optimized results
• Multy OOS testing
• Estimation level of Robustness
• Sensitivity test
• Monkey test
• Synthetic data test

By performing various tests, generating a Risk Profile model for the Market, Trading Instruments, PnL/trades characteristics, and logical modules. By exploring various impacts on a system's stability and evaluating practical usage limits. The risk Profiles model aims to determine when results are not just underperformance but signal to stop using the strategy or even conduct risk avoidance protocols.

A solution for selecting and whitening a group of systematic strategies. Besides a solution, perform strategies replacement and update allocation parameters. Moreover, implemented an extra layer of portfolio risk management.

When designing advanced software, it's crucial to understand how it may behave. The initial requirement for flexible, scalable, modular design. Quantitative Machine system for exploring, developing, implementing, and running a broad specter of systematic strategies powered by innovative usage of AI/ML technologies for financial markets.

Four-tier architecture modular design framework developed in C/C++/C#/Python, with an extendable interface API and implementation of infrastructure modules as a core of the machine.

ZeroMQ is a practical and simple way to develop a multithreading, multiserver computation engine. On-demand scaling of engine power by linking additional processing nodes is practical in real-world tasks.

Levels of data request example:

The request for a time series data sample of a specific trading instrument with a starting and ending time mark. By utilizing a dictionary-like structure, reveal requested data specifications as a set of requested time series. In addition, provide some overlapping samples for verification protocols.

Level 0: Check in local memory; request the missing part if not available. Update the latest request time, and store it in memory for future use. Many practical tasks are driven by a back-test repeatedly utilizing the same market data.

Level 1: Check the hard-drive memory of a shared folder; request the missing part if not available. Check the requested data for integrity:

• Requested data market events check (like stock spit event). If detected, refresh all stored data.
• No spikes in price data (if any, verify with alternative data source and fix if possible)
• No gaps (if any, fill with related tool)

Level 2: Check the data server; update missing parts if requested from Machine servers.

Level 3: Check in data feeds.

Core infrastructure module.

Highly accurate trading simulation by considering BID/ASK price, order execution latency, fees, dividends, overnight swaps, margin requirements, and other aspects.

Data preprocessing routines and intelligent workflow optimization boost the computational performance to the edge of what is possible.

A complete trading solution for running fully automated trading live.
Easy-to-use interface for switching from back-testing to live trading.

Trade Execution System made for optimally reproducing the target portfolio by managing the order flow to the linked brokerage account.

Moreover, flexible execution styles, monitoring tools, and risk-management technologies power smooth and robust operations.

Framework-integrated subsystems keep the intake of steady and secured work of all components. Rapid response to unexpected changes in workflow, executing emergency protocols with an effort to avoid negative effects while signaling the problem. In case of critical events, implement emergency switching to the reserve system.