Innovative Investment Research
Developing algorithmic investing systems through proven methodologies and cutting-edge research
Technical and Algorithmic Trading Research
Innovative Investment Research is a company where traders, analysts, and others work with us to explore and develop traders, analysts, and students join with us to explore algorithmic and strategies. These strategies include trend-following, momentum, statistical arbitrage, mean-reversion, hedged, and other advanced technical trading methods.
What We Do:
- Develop and test trading models using real-time data
- Actively trade these strategies in our own portfolios
- Use artificial intelligence to enhance our research
- Hands-on algorithmic trading with real datasets
- Analysis of combining multiple strategies in trading
- Trading system backtesting, validation. and optimization
Core Areas of Algorithmic Trading Research
Momentum & Trend Systems
We study momentum and trend-following strategies that seek to capture sustained market movement across multiple timeframes. Research focuses on signal generation, trend filters, regime alignment, and risk controls designed to adapt to changing market behavior.
Reversal & Mean Reversion
This research area explores conditions where prices temporarily diverge from equilibrium. We analyze overextension, volatility contraction, support/resistance behavior, and statistical tendencies that may signal short-term reversals.
Market Regimes & Risk
Markets behave differently in trending, volatile, and range-bound environments. We research regime classification, volatility measures, drawdown control, and risk-aware position sizing to better understand when strategies perform — and when they don’t.
Backtesting & Validation
We emphasize rigorous testing through historical simulation, walk-forward analysis, and robustness checks. This research area focuses on avoiding overfitting and understanding how strategies behave across different market cycles.
Research Initiatives
Assessing Historical Data
Analyze extended historical data across multiple market regimes to understand how algorithmic strategies behave during trending, volatile, and range-bound conditions. Emphasis is placed on robustness, repeatability, and regime awareness rather than short-term outcomes.
Testing Trading Systems
Our research includes momentum systems, trend filters, reversal models, and risk-aware frameworks. Each study progresses through hypothesis design, backtesting, walk-forward validation, and review.
Research Partners
We collaborate with independent researchers, stock traders and student interns who want hands-on exposure to advanced trading methodologies and algorithmic systems. Our research concentrates on developing and testing sophisticated theoretical models for improving investment results.
Who We Work With
Experienced Traders & Investors
We work with experienced traders, analysts, and interested investors. Our emphasis is on model testing, and disciplined research over personal discretionary decision-making regarding inestments.
Undergraduate & MBA Students
Part-time Jobs
We offer part-time jobs doing quantitative research, market analysis, and selecting stocks for and trading in our own portfolios.
Internships
We offer internships tp students who want to learn more about all aspects of investing including how to apply analytical frameworks to real market data, develop, research discipline, and explore how strategy design intersects with risk and market structure.
In an internship students will:
Gain exposure to real-world algorithmic research projects Experience interpreting data-driven trading models Insight into how professional research workflows are structured Opportunities to contribute to ongoing studies
Undergraduate Students and Others Who Are Interested
We offer internships and part time jobs to – University of Arizona and Pima Community College students as well as other people who are curious about investing in the stock market, trading strategies, and data analysis. This is an opportunity to move beyond theory and gain hands-on experience exploring how trading strategies are researched, tested, and evaluated.
Research Discipline
Our work emphasizes documented assumptions, repeatable methods, and careful validation. We prioritize understanding behavior and limitations over optimization or short-term results.
Open Methodology
We focus on explaining how frameworks behave, why signals change, and where models may fail—encouraging critical thinking and informed discussion.
Responsible Exploration
Research is conducted with an emphasis on risk awareness, ethical use of data, and educational intent. This is a learning-driven environment, not a trading service.