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That is the purpose of this research.

I recently updated a systematic intraday options study designed to participate in meaningful market movement without requiring a directional forecast at the time of entry.

The study covered January 2, 2020, through July 28, 2026, and included 1,638 hypothetical trades.

The modeled results were significant:

These results provide meaningful evidence that the underlying market behavior may support this type of systematic options structure.

A profit factor of 2.88 indicates that modeled gross profits were nearly three times modeled gross losses. The study also produced a favorable relationship between cumulative profitability, average trade, and historical closed-trade drawdown.

Just as importantly, the research included more than six years of data and over 1,600 observations across multiple market environments.

What the Study Was Designed to Measure

The study was built from historical Nasdaq futures data and used a theoretical options-pricing framework to estimate the behavior of the corresponding options position.

This approach is useful because it allows the researcher to isolate and evaluate the central economic question:

The modeled results indicate that it may have.

The study was not intended to reproduce every historical option quote or claim that every modeled price would have been available as a live fill.

Instead, it was designed to determine whether the underlying market effect was sufficiently strong and persistent to justify the next level of research.

That distinction is important, but it does not diminish the value of the study.

Model-based testing is a standard and practical way to evaluate an idea before purchasing specialized datasets, building a historical option-chain engine, or committing capital to forward testing.

Why the Results Are Encouraging

Several features of the study support continued development.

A Large Historical Sample

The study included 1,638 trades from January 2020 through July 2026.

This period contained several distinct market environments, including:

• The 2020 pandemic shock
• Elevated volatility during 2022
• Lower-volatility expansion periods
• Strong directional markets
• Range-bound sessions
• Rapid intraday reversals
• Changing interest-rate and liquidity conditions

A concept that remains profitable across varied environments is generally more interesting than one derived from a short or narrowly selected sample.

A Strong Modeled Profit Factor

The 2.88 profit factor indicates that the modeled gross profits were substantially larger than the modeled gross losses.

Profit factor alone does not establish tradeability, but it provides a useful measure of the potential margin available for execution costs, pricing differences, and model refinement.

A concept with only a small theoretical edge may disappear when more conservative assumptions are applied.

A wider modeled advantage provides a more substantial foundation for additional validation.

More Than Directional Accuracy

The concept was designed around the magnitude of market movement rather than the need to forecast whether the market would rise or fall.

That changes the research question.

Instead of attempting to predict direction, the study evaluates whether realized movement can exceed the combined effects of premium, time decay, and other option-pricing variables.

This is a different form of edge and may complement traditional directional trading systems.

Favorable Risk Characteristics

The maximum closed-trade drawdown was approximately $27,000, compared with more than $1.25 million in cumulative modeled profit.

That relationship is notable, although future testing should also examine:

• Intraday mark-to-market drawdown
• Consecutive losses
• Volatility-regime sensitivity
• Changes in position size
• Product-specific execution costs
• The effect of wider bid-ask spreads

The current drawdown profile suggests that these questions are worth investigating further.

The Role of Theoretical Option Pricing

The study used a theoretical pricing model to estimate option values from historical underlying-market data.

A pricing model incorporates variables such as:

• Underlying market price
• Strike price
• Time to expiration
• Expected volatility
• Interest rates
• Contract specifications

This provides a structured method for estimating how an option position may have responded as the market moved throughout each session.

The primary advantage is research efficiency.

A researcher can test the core logic, compare variations, evaluate different entry and exit structures, and determine whether the concept has sufficient potential before acquiring more expensive or complex historical option data.

The theoretical model therefore serves as a research bridge between the underlying market hypothesis and a full quote-level implementation.

The Next Stage: Historical Option-Chain Validation

The logical next step is to rebuild the study using actual historical option quotes.

This would allow the strategy to select the relevant contracts available on each historical date and use the quoted market prices near the intended entry and exit times.

For each session, the process would ideally capture:

• The applicable expiration
• The selected strike or strikes
• The entry bid and offer
• The exit bid and offer
• The contract multiplier
• Strike-specific implied volatility
• Quote size and liquidity
• Commissions and exchange fees

A conservative historical simulation could assume entry at the offer and exit at the bid.

That would directly incorporate the quoted spread into the backtest and provide a more execution-focused estimate of performance.

This additional validation would not invalidate or replace the existing study.

It would refine it.

The current results identify and quantify the potential market effect. Historical option-chain data can then measure how efficiently that effect could have been captured through the selected trading instrument.

Product Selection Matters

A Nasdaq-based options concept can potentially be expressed through several related products.

Possible choices include:

Each product has different characteristics, including:

• Contract multiplier
• Strike spacing
• Expiration schedule
• Settlement method
• Trading hours
• Liquidity
• Bid-ask spread
• Exercise style
• Tax treatment

The next-stage test should select one product and apply it consistently throughout the historical sample.

That ensures the performance reflects the characteristics of an actual tradable market rather than a mixture of different instruments.

Variables That Historical Quotes Can Clarify

Historical option-chain data would allow several important components to be measured more precisely.

Bid-Ask Spreads

Theoretical pricing generally produces an estimated fair value.

Actual markets provide a bid and an offer.

The difference between those prices represents a real execution cost, particularly when a position contains multiple option legs.

Quote-level testing can measure how much of the theoretical edge remains after applying those spreads consistently.

Implied Volatility

Option values respond not only to movement in the underlying market but also to changes in implied volatility.

A large move can increase the value of an option position, while a simultaneous decline in implied volatility may offset part of that benefit.

Historical quotes make it possible to capture those interactions directly rather than estimate them solely through model assumptions.

Strike Selection

The nearest at-the-money strike can change as the underlying market moves.

A quote-level backtest can define a consistent selection rule and identify the exact contracts that would have been available at each entry.

This removes ambiguity and makes the test easier to reproduce.

Liquidity

Historical bids and offers can reveal whether a contract was actively quoted and whether spreads were reasonably stable.

For larger position sizes, quote size and market depth also become relevant.

A strategy can have a strong theoretical edge while still requiring careful capacity analysis.

Fees and Slippage

A comprehensive study should include:

• Brokerage commissions
• Exchange fees
• Regulatory fees
• Bid-ask spread
• Additional slippage assumptions
• Treatment of missing or abnormal quotes

Because the current modeled average trade is relatively substantial, the concept may have room to absorb realistic costs. Historical quote analysis will allow that question to be measured directly.

A Layered Research Process

A systematic options strategy can be developed through a series of increasingly precise stages.

The Equity Curve Represents the Research Potential

An equity curve is the visible summary of a much broader research process.

In this case, the curve reflects:

• A defined and repeatable concept
• More than six years of underlying-market history
• Over 1,600 modeled trades
• A consistent pricing framework
• A systematic entry and exit process
• A strong theoretical performance profile

That is valuable information.

The curve does not need to be viewed as either definitive proof or something to dismiss because it is model-based.

It should be viewed for what it is: evidence that the concept has demonstrated enough historical potential to justify deeper, quote-level validation.