Innovative Investment Research

What We Do

A research company focused on developing and testing algorithmic trading strategies

At Innovative Investment Research, we specialize in technical analysis, the study of historical price and volume data to identify patterns and trends in market behavior, rather than examining company financials.

Develop and test algorithmic trading models using real-time data
Actively trade our strategies in our own portfolios
Use artificial intelligence to enhance our research
Work hands-on with real market datasets
Analyze how multiple strategies perform when combined
Backtest, validate, and review every trading system we study

Please note: We are a research company. We do not manage money or accept funds from outside sources.

Part-Time Jobs & Internships

We always have part-time internships and jobs available

Students collaborating on research at a shared workspace

A mentor reviewing market charts with a student

A student studying market data on a laptop

What You'll Do

Our part-time positions involve quantitative research, market analysis, and helping select stocks for the portfolios we trade ourselves. Interns work directly with our founder and research collaborators to study market behavior and test trading strategies.

In an internship you will:

Gain exposure to real algorithmic research projects
Learn to interpret data-driven trading models
See how a research workflow is structured end to end
Have opportunities to contribute to ongoing studies

Technical Analysis

Algorithmic Research

Market Analysis

Strategy Development

Data Analysis

Research Methods

Want to Join Us?

How to Apply

All you need is to be very computer savvy and have a serious interest in the stock market.

If you are interested in applying or want more details, please email a resume and/or a brief note telling us about yourself and your interest.

contact@innovativeir-ai.com

Who We Work With

Two professionals shaking hands in an office

Experienced Traders & Investors

We work with experienced traders, analysts, and interested investors. Our emphasis is on model testing and disciplined research rather than discretionary decision-making.

A small team reviewing research documents together

Undergraduate & MBA Students

We offer part-time jobs doing quantitative research and market analysis, plus internships for students who want to learn how analytical frameworks apply to real market data and how strategy design intersects with risk and market structure.

Students & the Curious

We offer internships and part-time jobs to University of Arizona and Pima Community College students, as well as anyone curious about investing, trading strategies, and data analysis. It is a chance to move beyond theory and gain hands-on experience.

What Is Algorithmic Trading?

Algorithmic trading is based on predefined rules, mathematical models, or AI-driven systems. Trading is executed by computer rather than manual discretion. It has clear structural advantages over pure discretionary trading, and AI increases the performance of algorithmic systems.

Research Initiatives

How we structure and validate our ongoing research

Assessing Historical Data
We analyze 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 system progresses through hypothesis, design, backtest, adaptation, and final validation.
Research Partners
We collaborate with independent researchers, traders, and student interns who want hands-on exposure to algorithmic trading methodologies, as well as testing theoretical models for improving investment results.

Areas We Study

The trading methods and technologies our research covers

Momentum & Trend Following

Strategies that seek to capture sustained market movement across multiple timeframes using moving averages, momentum, and breakout signals.

Mean Reversion

Research into conditions where prices temporarily diverge from equilibrium, using statistical tools to identify overextension.

Market Regimes & Risk

Regime classification, volatility measures, drawdown control, and risk-aware position sizing to understand when strategies perform.

Backtesting & Validation

Historical simulation, backtesting, and robustness checks with an emphasis on avoiding overfitting.

Statistical Arbitrage

Study of price relationships and divergences between related instruments across markets.

Machine Learning

How models can detect non-linear relationships across many variables that traditional rule-based systems may miss.

Sentiment Analysis

Using natural language processing to convert unstructured data such as news and social sentiment into research signals.

Execution Strategies

Volume- and time-weighted execution methods that break large orders into smaller pieces to reduce market impact.

Pioneers of Quantitative Investing

The researchers who established the foundations of algorithmic trading

Edward Thorp

Princeton/Newport Partners

A mathematics professor who applied quantitative methods to markets and launched what is widely considered one of the first quantitative hedge funds in 1969. His work on options pricing and statistical arbitrage predates much of modern systematic trading.

James Simons

Renaissance Technologies

A mathematician who founded Renaissance Technologies in 1982. Its Medallion Fund is widely reported to have averaged roughly 66% annual returns before fees between 1988 and 2018, demonstrating that systematic, data-driven models could be applied at scale.

David E. Shaw

D. E. Shaw & Co.

A computer scientist who founded D. E. Shaw & Co. in 1988 after leading automated trading work at Morgan Stanley. The firm helped establish the template for computation-driven quantitative investment research.