Gilad Bar-Ilan is the CEO and co-founder of Crowd Wisdom Trading, with extensive experience across proprietary trading, equities, options, futures, foreign exchange, product management, software development, and financial technology. Gilad began his career as a day trader with remote U.S. proprietary trading firms before joining an Israeli proprietary trading firm and becoming deeply involved in options trading on the Tel Aviv Stock Exchange. He later worked as a product manager with Israeli technology startups, combining his trading experience with the ability to transform complex technical concepts into working financial products. In this episode, Gilad joins us for an in-depth conversation about crowd intelligence, financial sentiment, alternative data, artificial intelligence, and how thousands of market opinions can be turned into structured, actionable trading signals. Gilad explains how Crowd Wisdom Trading analyzes thousands of hours of financial content from YouTube and other online sources. Using large language models and task-specific AI agents, the platform identifies which assets traders are discussing, distinguishes between short-term and long-term views, and extracts concrete information such as direction, entry prices, targets, stop levels, and time horizons. We discuss why identifying positive or negative sentiment is not enough. A general opinion about a stock may provide limited value, while a specific trading plan containing an entry, target, stop, and time horizon can be measured, evaluated, and compared with other predictions. The conversation explores the challenge of separating useful information from noise. Gilad explains why aggregating every market opinion does not automatically create intelligence and why the quality, experience, and track record of the contributors matter. Instead of relying on the general public, his approach focuses on building a professional crowd of experienced traders with relevant market expertise. Gilad connects this framework to Philip Tetlock’s research on superforecasters. Just as groups of skilled forecasters can outperform ordinary prediction groups, Gilad believes that combining the structured opinions of successful traders can produce a stronger market view than following any single analyst or commentator. We also examine how raw information becomes actionable signals. Gilad explains why replacing unstructured noise with a larger collection of filtered opinions still leaves traders with too many decisions. The next step is therefore to rank opportunities by factors such as risk and reward, narrow the list, and present a manageable selection of potential trades. The discussion then moves to execution and simplicity. While collecting, classifying, and aggregating data may require complex technological infrastructure, Gilad argues that the final trading plan should remain clear and understandable, with a defined entry, stop, target, and method for measuring results. Gilad shares the entrepreneurial journey behind Crowd Wisdom Trading and explains how the emergence of ChatGPT, large language models, and AI agents made it possible to build a product he had wanted throughout his trading career. What began as a question about why traders should follow one financial commentator when technology could analyze thousands quickly developed into a scalable platform for extracting collective market intelligence. We also explore Gilad’s experience across equities, options, futures, and foreign exchange. He explains why risk management matters more than the specific financial instrument being traded and why traders must always expect that something will eventually break. Whether the disruption is a financial crisis, natural disaster, pandemic, or unexpected market event, survival depends on controlling risk and preparing multiple contingency plans. Finally, we discuss the future of discretionary and retail trading. Gilad expects professional tools that were once available mainly through institutional environments to become increasingly accessible on retail traders’ mobile devices. At the same time, access to more tools will not guarantee success. Traders will need to specialize, remain adaptable, understand their own decision-making, and learn how to convert expanding volumes of data into useful signals. A practical and detailed conversation on crowd intelligence, artificial intelligence, financial sentiment, alternative data, signal construction, trading psychology, risk management, and the future of technology-assisted decision-making in financial markets. *DISCLAIMER* The information shared on this podcast is for educational and informational purposes only and reflects the personal opinions of the hosts and guests at the time of recording. Nothing in this podcast constitutes financial, investment, legal, tax, or trading advice, and nothing should be interpreted as a recommendation to buy, sell, or hold any security, cryptocurrency, derivative, or financial product. Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.
