Connecting Experts with Podcasts. Building Influence. Creating Opportunities.

© Guests on Air powered by Podcast Guesting Pro. All rights reserved.

Ran Aroussi

Ran Aroussi

MUXI Founder and Open-Source Engineer Focused on Enterprise Operability for AI Agents

EntrepreneurArtificial IntelligenceFinanceAuthorLeadership
AboutTopics(6)QuestionsVideosEpisodes(6)Media(1)
MEMBER LOGIN

Ran Aroussi Podcast Episodes

Invest in You

Invest in You

How Coding Systems Drive Better Trades

March 6, 2026

GO TO APPLE PODCASTS

Listen

Your browser does not support audio playback.

About this episode

The world of trading can feel like a high-stakes psychological battlefield, but for Ran Aroussi, it’s a game of systems and logic. In this episode, Ran joins hosts Ivan and the team to discuss his transition from a 35-year coding and engineering background into the world of quantitative trading. Ran explains his ‘psychological reactionary’ style, which avoids deep fundamental analysis in favour of measuring price action and standard deviation. The group explores the burgeoning role of AI in market prediction, the necessity of emotional discipline, and why a simplified set of rules often beats a complex one.

KEY TAKEAWAYS

A background in engineering and coding provides a significant edge in trading by forcing an investor to view market data through a structured, systematic lens.

Successful trading doesn't always require complex math; sometimes, rules as simple as ‘buying after two down days’ can outperform broader indices like the S&P 500.

AI won't necessarily replace traders but will shift their role from manual execution to ‘orchestrating’ agents that can recognise patterns and predict support/resistance levels in real-time.

In the trading world, the ability to stick to a predefined system (discipline) is often more valuable than raw intelligence or the ability to predict the future.

A common mistake in backtesting is using the same data set repeatedly to find a ‘sweet spot’, which leads to strategies that work on past data but fail in live markets.

BEST MOMENTS

"I don't think we're great value investors. Coming from programming, you tend to type a few things and see a result."

"The markets are not efficient; otherwise, nobody would make money outside of investing in the S&P."

"My job is no longer typing; my job is to orchestrate and give the strategy and a set of rules and direction to the AI."

"If there's something that the market gives you, it's constant feedback. You need to know what's not working in order to know what to improve."

"I'd rather not lose 50% on a stock than gain 2,000% on a stock because I can't predict it. I'd rather make the easier bets."

Ran Aroussi

X @aroussi

Free book productionaibook.com

LI https://www.linkedin.com/in/aroussi/

Books by Fredrik Sandvall

Options Strategy: Build to Keep. Ready to Sell Tomorrow.

For founders and CEOs who want businesses that compound in value, stay under control, and are always sale-ready.

⁠https://amzn.to/4pwVhoi⁠

The Abyss of Finance: Trust, Leverage and the Cycles of Collapse

For investors and leaders who want to understand why financial systems break before they do.

⁠https://amzn.to/3YsRKfI⁠

About the Podcast

Invest in You is a global podcast where entrepreneur and investor Fredrik Sandvall shares practical ideas and conversations with founders, investors, and operators. Episodes focus on investing, entrepreneurship, and building long-term control and freedom.

Occasionally joined by his sons Ivan and Charlie, the show offers both experience-based insight and a next-generation perspective.

Listeners in 155+ countries tune in to think clearer, act smarter, and build with intent.

Community & Contact

Podcast community: https://bit.ly/4jxF94k

Fredrik Sandvall

LinkedIn: ⁠https://www.linkedin.com/in/sandvall/⁠

Facebook: ⁠https://www.facebook.com/FredrikinLondon/

Ran Aroussi Podcast Episodes

XTraw AI: Machine Learning and AI Applications

Interview with Ran Aroussi

XTraw AI: Machine Learning and AI Applications

Mar 2026

Scrum Master Toolbox Podcast: Agile storytelling from the trenches

When AI Decisions Go Wrong at Scale—And How to Prevent It With Ran Aroussi

Scrum Master Toolbox Podcast: Agile storytelling from the trenches

Feb 2026

Business of Tech: Daily 10-Minute IT Services Insights

Deploying Agentic AI at Scale: Infrastructure, Reliability, and Risk with Ran Aroussi

Business of Tech: Daily 10-Minute IT Services Insights

Feb 2026

AI for founders

Reality check every founder needs in 2026

AI for founders

Dec 2025

Software Development, Finance and AI

AI Writes Code, Engineers Build Systems (feat. Ran Aroussi)

Software Development, Finance and AI

Dec 2025

MEMBER LOGIN

Existing Podcast Guesting Pro clients only. Contact your Outreach Manager for access.

Latest episodes

XTraw AI: Machine Learning and AI Applications

Interview with Ran Aroussi

XTraw AI: Machine Learning and AI Applications

Scrum Master Toolbox Podcast: Agile storytelling from the trenches

When AI Decisions Go Wrong at Scale—And How to Prevent It With Ran Aroussi

Scrum Master Toolbox Podcast: Agile storytelling from the trenches

Business of Tech: Daily 10-Minute IT Services Insights

Deploying Agentic AI at Scale: Infrastructure, Reliability, and Risk with Ran Aroussi

Business of Tech: Daily 10-Minute IT Services Insights

View all episodes →

Key topics

Agent infrastructure is the next platform layer

Every major computing shift has needed a new layer: operating systems, the web, cloud, DevOps — and now, AgentOps. Ran explains how the next generation of value will come from tools that manage agent orchestration, communication, and observability. MUXI sits at this inflection point, showing how open, production-grade infrastructure will underpin the agentic AI ecosystem.

How transparency and design discipline keep AI systems human-aligned

As AI agents gain autonomy, governance can’t be an afterthought. Ran advocates for clear architecture — where decision paths are traceable, interactions are logged, and reasoning can be audited. He positions MUXI as a model for how openness and structure can coexist, keeping humans meaningfully in the loop while systems grow more capable.

AI is entering its enterprise era, and reliability is the new frontier

Enterprises have embraced AI experimentation, but few have built the controls, visibility, and accountability needed for real-world deployment. Ran believes this next phase requires infrastructure that treats agents like any other business-critical system — observable, auditable, and secure. MUXI was designed for that exact leap: turning AI prototypes into stable, governed, and scalable assets.

View all topics →