Intelligent Mimicry: A Data-Driven Options Strategy for Private Investors

Options trading has become an increasingly accessible asset class for retail investors, thanks to platforms like Robinhood, Thinkorswim, and Webull. But while access has increased, information asymmetry remains steep. Individual traders find themselves at a severe disadvantage compared to institutional investors, hedge funds, and algorithmic trading desks that operate with cutting-edge data infrastructure and market-moving capital. The average trader is left reacting to the market—often too late.

Rather than attempt to out-analyze or out-predict Wall Street, there is a more pragmatic path for private investors: smart mimicry. By carefully tracking unusual options activity—specifically trades with volume far exceeding normal behavior—and using automated tools to replicate this behavior, it’s possible to "surf the waves" created by institutional players without trying to create them.

This paper presents a streamlined, rule-based options trading strategy designed to give private investors a data-driven edge by identifying and reacting to massive volume surges. The method is simple in principle but powerful in its implications: follow the smart money by letting data reveal conviction, sentiment, and market intent in near-real time.

Institutional investors operate in an entirely different universe than retail traders. They use high-frequency algorithms, co-located servers to reduce latency, and teams of analysts interpreting macroeconomic signals, earnings models, and even satellite imagery of retail parking lots. Moreover, they often have access to order flow, allowing them to see and act upon market trends milliseconds before the average trader even refreshes their browser.

Retail traders, by contrast, are often emotionally driven and react to delayed information—such as social media hype, lagging indicators, or CNBC headlines. Research has shown that institutional traders consistently outperform retail traders, particularly in derivatives markets like options, where complexity magnifies both opportunity and risk (Barber, Lee, Liu, & Odean, 2009).

Given this, the most sustainable advantage for an individual trader may not lie in predicting where the market is going, but in recognizing where significant actors are placing large bets—and following that data with discipline and speed.

At the heart of this strategy lies a simple thesis: unusually large volume in specific options contracts often reflects institutional positioning or rebalancing. These trades are rarely random—they’re executed with conviction and backed by deeper research, insider insights, or algorithmic signals.

Step 1: Define the Option Universe — The first filter focuses on call options expiring within the next four weeks. This short-dated window captures high-sentiment trades, speculative bets, and earnings-related plays—contracts most likely to reflect aggressive directional positioning.

For each expiration date, 11 contracts are monitored: 5 strike prices in-the-money (ITM), 1 at-the-money (ATM), and 5 out-of-the-money (OTM). This forms a balanced band around the current price, allowing you to track the most relevant activity while excluding deep, illiquid options.

Step 2: Establish Baseline Metrics — For each contract, calculate the 24-hour average volume, monitor the current trade price and implied volatility, and refresh data via API every 30 seconds. These real-time snapshots give context to every incoming trade.

Step 3: Detect and React to Unusual Volume — A trigger is activated when any single trade meets the following criteria: trade size is ≥25x the 24-hour average volume, occurs on a monitored strike, and direction is known. Once triggered, the system replicates the trade, logs sentiment, and builds broader sentiment maps.

Step 4: Build a Real-Time Sentiment Engine — By aggregating trade flow over rolling windows (e.g., 15 minutes, 1 hour, 2 hours), the system identifies sustained trends, compares bullish vs bearish positioning, and detects one-sided activity (e.g., only sells). For example, if over two hours only sells occur, the system generates a bearish signal prompting a potential put entry.

There are several key reasons why this strategy holds water:

a. Volume Reflects Conviction — Large trades, especially those 25x average volume, are unlikely to be made lightly. They suggest deep confidence, insider insight, or systematic model output.

b. Filtering Noise with Thresholds — Many "unusual options activity" systems create noise by flagging every deviation. The 25x threshold eliminates most false positives, surfacing only trades with real potential to move the underlying asset.

c. Aggregated Sentiment Beats Intuition — Retail traders often go against the grain based on feeling or misread signals. Multi-hour sell trends without bullish flow reflect market psychology more than any headline.

d. Reaction, Not Prediction — Unlike predictive systems that rely on assumptions or models prone to overfitting, this strategy waits for confirmed signals before acting.

e. Automation Removes Emotion — Automating trade detection and response eliminates human bias and fatigue from decision-making.

No strategy is without risk, and this approach is no exception:

a. Execution Lag and Slippage — A 30-second refresh interval may result in reacting too late to market shifts. Market makers or HFTs could move prices before the system executes.

b. False Signals in High-Volatility Environments — During earnings or macro events, volume spikes can mislead. A hedge can appear similar to directional conviction.

c. Lack of Context — Volume does not reveal intent. A large sell might be part of a covered call or hedge, not a bearish outlook.

d. Broker/API Constraints — Real-time data and execution speed are key. Not all brokers offer affordable or reliable APIs to support this.

e. Overfitting the Threshold — The 25x volume rule may need adjustment depending on stock liquidity. A static threshold may not always be optimal.

This options strategy isn’t about reinventing the wheel—it’s about riding the momentum generated by those who already know where the wheel is rolling. By focusing on high-volume activity, automating detection, and mimicking behavior only when conviction is clear, private investors can sidestep emotional pitfalls and align themselves with market movers.

It’s a method grounded in discipline and data—not speculation. With careful implementation and risk controls, this “smart mimicry” approach offers a compelling framework for navigating the complex world of options trading with clarity, confidence, and consistency.