Quant Researcher | Algorithmic Trading Developer | Systematic Trading Research

I am an independent quant researcher focused on systematic trading research, market regimes, risk management, backtesting, and algorithmic decision-making.

My work combines programming, quantitative thinking, trading experience, and systems design. I am especially interested in building trading research frameworks that are explainable, monitorable, and robust across changing market conditions.

Preferred name: Amir Location: Remote Email: bs.amir.heydar@gmail.com CV: Download CV

• Quantitative trading research
• Systematic strategy development
• Market regime analysis
• Trend-following logic
• Risk management
• Backtesting and strategy validation
• Edge monitoring
• Decision-making under uncertainty
• Python / MQL trading research infrastructure
• Antifragile and convex trading system design

A Python/MQL quantitative research framework for hypothesis-based systematic trading decisions.

The core idea behind Decision Alpha Lab is that a trading strategy should not be tested only as a black-box system. Every trading idea has underlying assumptions about market behavior, trend structure, volatility, regimes, risk, liquidity, and payoff asymmetry.

This project focuses on separating those assumptions, testing them independently, and only then converting validated ideas into trading logic.

Main research questions include:

• Why does a trading edge work?
• Is the edge still valid during a deep drawdown?
• Has the market regime changed?
• Did the assumptions behind the system break down?
• Is the drawdown within expected behavior?
• Should the system continue, pause, or be redesigned?

The goal is to build trading systems that are not only historically profitable, but also explainable, measurable, and monitorable.

• Python
• MQL4 / MQL5
• MetaTrader 4 / MetaTrader 5
• Backtesting
• Strategy validation
• Market regime research
• Trend-following systems
• Volatility and fat-tail behavior
• Risk management
• Historical data workflows

• Python research tooling
• MQL Expert Advisors and indicators
• Git / GitHub
• TypeScript basics
• Algorithmic problem-solving
• ERP-style process logic
• Business systems and automation thinking

• decision-alpha-lab Python/MQL research framework for systematic trading, market regimes, risk control, and edge monitoring.

• breakout-micro-following MQL5 breakout-following research system focused on market behavior, execution logic, and structured entries.

• pullback-trading MQL5 pullback trading research system focused on rule-based entries, order logic, and chart-based validation.

• micro-structure MQL5 market microstructure research tools for imbalance, regime behavior, and execution hypotheses.

• volatility-following MQL5 volatility and fat-tail research tools for regime-aware trading systems.

• divergence MQL5 divergence-based trading research system with session, candle, entry, and order modules.

• CLS63 Experimental MQL5 trading system for hook-based entries, risk-controlled execution, and visual review.

• Gartal-Inc Software and business-system repository showing product thinking, systems design, and applied development.

• Aura Application/software repository showing general development and project-building experience.

• next Web/software repository showing development practice outside trading systems.

My research approach is based on the idea that markets are not fully predictable, so trading systems should not rely on fragile prediction or over-optimized patterns.

Instead, I focus on:

• Robustness over overfitting
• Risk-first strategy design
• Limited downside and open-ended upside
• Optionality and convex payoff structures
• Market regime awareness
• Hypothesis-first research
• Monitoring the assumptions behind an edge
• Separating signal quality from execution and risk logic

The goal is not to claim certainty in markets. The goal is to design decision frameworks that can survive uncertainty, identify when assumptions change, and reduce emotional decision-making.

I started programming around the age of 12 with languages such as C++ and Python. Over time, I became deeply interested in technology, systems, mathematics, probability, statistics, and algorithmic problem-solving.

I later applied systems thinking in real business environments, including ERP-style process logic, marketing systems, customer acquisition workflows, and repeatable growth systems.

Around five years ago, I began trading with a research-first mindset. I built early backtesting tools in Python, tested rule-based and indicator-driven models, and then moved into MQL4/MQL5 development, where I built and tested Expert Advisors, indicators, and trading tools.

My current focus is building stronger research infrastructure around systematic trading, market regimes, risk management, and hypothesis-based alpha validation.

I am open to remote opportunities in:

• Quant Researcher
• Quantitative Researcher
• Algorithmic Trading Researcher
• Systematic Trading Researcher
• Quant Developer / Research Engineer
• Trading Systems Developer
• Python Research Developer
• MQL4 / MQL5 Developer
• Financial Data Analyst

Email: bs.amir.heydar@gmail.com GitHub: github.com/bsAmirHeydar CV: Amir Hosein Heydar — Quant Researcher CV