Current stage: active research, system development, and live-market learning. This is an engineering and education project, not a performance claim or financial advice.
Core direction
Turn discretionary trading decisions into explicit systems.
My focus is not on predicting every market move. It is on classifying market state, defining when a playbook is authorized, identifying the evidence required for execution, controlling risk, and preserving enough data to evaluate whether the process actually works.
The same engineering discipline I use in software development applies here: define the model, isolate the decision rules, test one controlled change at a time, preserve failures, and keep a known-good baseline.
Trading systems
Futures Trading Operating System
A structured framework for market classification, directional authority, playbook selection, entry permission, risk management, trade management, and post-session review.
Indicators
Kit Decision Engine
An evolving decision-support indicator built around market state, EMA authority, balance versus expansion, structure, value, continuation, and execution context.
Software
Trading tools and applications
Utilities, dashboards, research tools, data workflows, backtesting support, and other software that emerge from problems I encounter while trading and studying markets.
What I plan to publish
A durable trading knowledge base instead of scattered notes.
The dedicated Futures Trading space will organize the work into practical material that can be revisited and improved over time.
Research
Market structure & execution
Notes on trend, balance, expansion, liquidity, acceptance, continuation, pullbacks, structural invalidation, and the execution models I am testing.
Learning
Quant Finance
My progression through probability, statistics, market microstructure, time-series analysis, backtesting, risk, derivatives, and quantitative trading research.
Media
Articles & videos
Trading-system breakdowns, chart studies, development logs, indicator walkthroughs, tool demonstrations, lessons, and experiments published from the work itself.
Public / private boundary
Share the learning without publishing everything.
The public layer will contain educational material, selected research, tools, demonstrations, and documented lessons. Raw account information, credentials, private trade records, proprietary source code, and internal research that creates a real competitive advantage stay private by default.
This keeps the public material useful without turning the project into a real-time signal service or exposing sensitive trading infrastructure.
Dedicated project workspace established
The Futures Trading Lab now has its own private GitHub control repository for the Trading OS, KDE source, research, backtesting, Quant Finance learning, applications, and evidence. The planned public hub remains futures.akildigital.com. Until that standalone surface is ready, this page is the public project record.