Lessons from Building a Quant Fund: My Journey from Startup to Institutional-Grade Investing
Published: January 5, 2026 | Category: Entrepreneurship | Reading Time: 18 minutes
Key Takeaways
- Building a quantitative fund requires excellence across multiple domains including technology, finance, operations, compliance, and business development simultaneously
- The biggest challenges are often non-technical such as fundraising, regulatory compliance, operational infrastructure, and building trust with investors
- Technology alone does not guarantee success; the best trading systems in the world are worthless without proper risk management, capital, and execution capabilities
- Institutional investors evaluate operational due diligence as rigorously as investment due diligence; failing to build robust operations will limit your ability to scale
- The journey from startup to institutional-grade operation requires constant evolution of systems, processes, and team capabilities as assets under management grow
- Persistence and continuous learning are essential because the path is long, the challenges are numerous, and success requires adapting to constant change
Introduction: The Road Less Traveled
Building a quantitative investment fund from scratch is one of the most challenging entrepreneurial endeavors one can undertake. It requires mastering the intersection of advanced technology, sophisticated finance, rigorous operations, complex regulations, and competitive fundraising. Most who attempt this journey fail. Those who succeed do so through a combination of skill, persistence, timing, and often a fair amount of luck.
Over the past two decades, I have built Savanti Investments from concept to reality, developing proprietary AI trading platforms including QuantAI, SavantTrade, and QuantLLM, and launching one of the first tokenized equities funds on a US-regulated ATS exchange. This journey has been filled with victories and setbacks, moments of clarity and periods of confusion, vindication and humility in equal measure.
In this article, I want to share the lessons I have learned along the way. My goal is not to present a polished success story but rather to offer honest insights into what it actually takes to build an institutional-grade quantitative investment operation. Whether you are considering starting your own fund, working at an existing one, or simply curious about the inner workings of the quant finance world, these lessons are drawn from real experience in the trenches.
The Genesis: From Idea to First Trade
Finding Your Edge
Every successful quant fund begins with an edge, a systematic way to generate returns that the market has not fully recognized or competed away. Finding this edge is perhaps the most critical and difficult step in the entire journey.
When I started exploring quantitative strategies, I spent years researching academic literature, testing hypotheses, and experiencing countless failures before identifying approaches that showed promise. The key insight was that true edges often exist not in simple price patterns but in the intersection of multiple information sources and the application of advanced technology to process them faster or better than competitors.
My background in AI and machine learning provided a foundation for building systems that could identify complex patterns across multiple data sources. But having technical skills is just the beginning. Converting those skills into a tradeable edge requires deep understanding of markets, transaction costs, execution dynamics, and the competitive landscape.
Lesson Learned: Your edge must be substantial enough to survive transaction costs, slippage, and market impact. Many academically interesting patterns disappear when you account for real-world trading frictions. Be ruthlessly honest in evaluating whether your edge is real and sustainable.
The First Trade
I remember the first live trade our system executed. After years of research, development, backtesting, and paper trading, watching real capital move based on our algorithms was both exhilarating and terrifying. The trade worked, generating a small profit. But more important than the outcome was the validation that our systems functioned correctly in live markets.
The transition from backtesting to live trading revealed countless issues that do not appear in historical simulations. Data feeds behave differently in real-time. Orders do not always fill at expected prices. System latencies matter in ways that backtests cannot capture. The first months of live trading were as much about fixing technical issues as they were about generating returns.
Lesson Learned: Paper trading and backtesting, no matter how thorough, cannot fully prepare you for live trading. Start with small capital and expect to discover and fix numerous issues. The goal of early live trading is validation and learning, not profit maximization.
Building the Technology Stack
The Build Versus Buy Decision
One of the first major decisions any quant fund faces is whether to build proprietary technology or leverage existing platforms. There are strong arguments on both sides.
Building proprietary systems provides maximum flexibility and can become a competitive advantage. But it requires significant upfront investment and ongoing maintenance. Buying or licensing existing platforms reduces development time but may limit customization and create dependency on vendors.
At Savanti, we made the strategic decision to build our core systems including QuantAI, SavantTrade, and QuantLLM in-house. This decision was driven by the belief that our technology would be a key differentiator, and we wanted full control over our capabilities. The investment was substantial, requiring years of development and iteration.
Lesson Learned: There is no universally correct answer to build versus buy. The decision should be based on your specific edge, your technical capabilities, your capital constraints, and your long-term vision. Whatever you choose, commit fully and execute excellently.
Scaling Technology Infrastructure
As our strategies grew in complexity and our assets under management increased, our technology requirements expanded dramatically. What worked for a $1 million portfolio does not work for $100 million. What works for one strategy does not work for ten.
Scaling challenges appeared in every area. Data infrastructure needed to handle larger volumes and more sources. Research systems needed more computing power for increasingly complex models. Execution systems needed lower latency and higher throughput. Risk systems needed real-time monitoring of larger portfolios. Everything needed better redundancy and disaster recovery.
We went through multiple generations of infrastructure, each time rebuilding systems to handle the next order of magnitude of scale. These rebuilds were painful and expensive, but necessary for continued growth.
Lesson Learned: Build systems with scalability in mind from the beginning, but do not over-engineer for scale you have not yet achieved. Plan for growth while being pragmatic about current needs. Expect to rebuild systems multiple times as you scale.
The Importance of Data
In quantitative finance, data is everything. The quality, breadth, and timeliness of your data directly determines the potential of your strategies.
We invested heavily in data infrastructure, building systems to ingest, clean, store, and process diverse data sources. Alternative data including satellite imagery, web scraping, sentiment analysis, and other non-traditional sources became increasingly important as traditional data was commoditized.
Data quality issues caused some of our most painful mistakes. Corrupted data led to incorrect signals. Survivorship bias in historical data produced overly optimistic backtests. Look-ahead bias in feature engineering created strategies that could not be replicated in live trading. Each mistake taught us the importance of rigorous data validation.
Lesson Learned: Invest heavily in data infrastructure and quality control. Every hour spent on data cleaning and validation will save multiple hours of debugging and real money from avoided mistakes. Treat data as a strategic asset.
Navigating Regulatory Complexity
Understanding the Regulatory Landscape
The financial services industry is heavily regulated, and for good reason. Regulations protect investors, maintain market integrity, and ensure systemic stability. However, navigating this regulatory landscape is one of the most challenging aspects of building a fund.
In the United States, investment funds face oversight from multiple regulators including the Securities and Exchange Commission (SEC), the Commodity Futures Trading Commission (CFTC), the Financial Industry Regulatory Authority (FINRA), and state regulators. Each has different requirements, forms, and examinations.
We spent significant time and resources understanding our regulatory obligations and building compliant operations. This required engaging experienced legal counsel, compliance consultants, and building internal expertise.
Lesson Learned: Do not underestimate regulatory complexity. Engage qualified legal counsel early. Build compliance into your operations from the beginning rather than retrofitting later. View compliance as a competitive advantage, not just a cost.
Choosing the Right Fund Structure
The legal structure of your fund has profound implications for taxation, investor access, regulatory requirements, and operational complexity. Common structures include hedge funds using limited partnership or LLC structures, registered investment advisers using separately managed accounts, and newer structures like our tokenized fund on a regulated ATS.
Each structure involves tradeoffs. Traditional hedge fund structures provide flexibility but limit investor access. Registered structures provide broader access but come with more regulatory requirements. Tokenized structures offer innovation but require navigating emerging regulatory frameworks.
Our decision to launch a tokenized equities fund on a US-regulated ATS was driven by the potential to offer fractional ownership, improved liquidity, and operational efficiency through blockchain technology. This was a pioneering effort that required extensive work with regulators and service providers to structure appropriately.
Lesson Learned: Choose your fund structure carefully based on your strategy, target investors, and long-term vision. Understand the tradeoffs and implications before committing. Be willing to innovate but do so within appropriate regulatory frameworks.
Ongoing Compliance Operations
Regulatory compliance is not a one-time effort but an ongoing operational requirement. Regular filings, examinations, recordkeeping, and policy updates are continuous obligations.
We built compliance systems and processes to handle these requirements efficiently. This included compliance calendars to track filing deadlines, automated systems for trade surveillance and best execution monitoring, policies and procedures documentation, and training programs for all team members.
Regulatory examinations can be stressful, but thorough preparation makes them manageable. We maintained documentation and records that could satisfy examiner inquiries, and we viewed examinations as opportunities to validate and improve our compliance operations.
Lesson Learned: Build robust compliance operations from the start. Document everything. Maintain policies and procedures. Prepare for examinations continuously rather than scrambling when they occur. Compliance is not optional; make it a strength.
Operational Excellence
Building Institutional-Grade Operations
Institutional investors conduct extensive operational due diligence before investing in a fund. They want assurance that their capital will be managed with proper controls, accurate accounting, and robust infrastructure. Failing operational due diligence will prevent you from accessing institutional capital, regardless of how good your returns are.
Building institutional-grade operations required developing comprehensive policies and procedures covering all operational aspects. It meant implementing proper segregation of duties and access controls. We engaged reputable service providers for administration, custody, and audit. We built disaster recovery and business continuity plans. We created comprehensive investor reporting capabilities.
The operational build was as significant an investment as our technology development. But it was essential for credibility with institutional investors.
Lesson Learned: Operations are as important as investment performance for building a successful fund. Institutional investors will scrutinize your operations thoroughly. Invest in building proper operational infrastructure from early stages.
The Critical Role of Service Providers
No fund operates in isolation. Success requires relationships with various service providers including prime brokers who provide financing, securities lending, and execution services, fund administrators who handle NAV calculation, investor services, and accounting, custodians who safeguard assets, auditors who provide independent verification, legal counsel who advise on regulations and structures, and technology vendors who provide data, infrastructure, and tools.
Selecting the right service providers was critical. We evaluated providers based on their capabilities, reliability, reputation, and alignment with our needs. Building strong relationships with service providers created operational resilience and often provided valuable advice and connections.
Lesson Learned: Choose service providers carefully and invest in the relationships. Good service providers are partners in your success. They can provide insights, introductions, and support that extend beyond their core services.
Risk Management Infrastructure
Risk management is not just about investment risk; it encompasses operational risk, technology risk, counterparty risk, and regulatory risk. Building comprehensive risk management infrastructure was a major undertaking.
Our risk systems include real-time portfolio monitoring and alerts, position limits and automatic risk reduction triggers, scenario analysis and stress testing, counterparty exposure tracking, operational risk assessment and mitigation, and cybersecurity measures and monitoring.
Risk management is as much about culture as technology. We built a culture where risk awareness permeated all decisions, from investment sizing to system architecture to vendor selection.
Lesson Learned: Build comprehensive risk management that covers all risk types, not just investment risk. Create a risk-aware culture throughout your organization. Risk management should be proactive, not reactive.
Raising Capital
The Fundraising Challenge
Having great technology and a proven track record does not automatically attract capital. Fundraising is a distinct skill that requires its own strategy, resources, and persistence.
Institutional fundraising is a long, relationship-driven process. From first meeting to investment can take months or even years. Investors need to understand your strategy, evaluate your team, conduct extensive due diligence, and fit you into their portfolio allocation. Competition for allocations is intense, with established funds and emerging managers all competing for limited investment dollars.
We learned that fundraising success requires clear articulation of your edge and why it is sustainable, professional materials including pitch decks, DDQs, and fact sheets, patience and persistence through long sales cycles, building relationships before you need capital, understanding investor needs and fitting into their allocation frameworks, and being responsive and transparent throughout due diligence.
Lesson Learned: Start fundraising earlier than you think you need to. Build relationships before you need capital. Be prepared for a long process with many rejections. Fundraising is a skill that improves with practice.
Different Investor Types
Different investor types have different needs, processes, and allocation sizes. Understanding these differences helps target your fundraising efforts effectively.
High Net Worth Individuals and Family Offices often have more flexible mandates and faster decision processes, but allocations tend to be smaller. They may value personal relationships and direct access to portfolio managers.
Funds of Funds provide access to multiple underlying investors but add a layer of fees and may have specific strategy requirements. They can be good early partners for emerging managers.
Institutional Investors including pensions, endowments, and sovereign wealth funds offer larger allocations but have extensive due diligence requirements and long decision timelines. They typically require institutional-grade operations.
Seed Investors provide early capital in exchange for favorable economics or ownership stakes. They can be valuable partners but involve giving up economics permanently.
We pursued a diversified approach to fundraising, building relationships across investor types while targeting those best aligned with our strategy and stage of development.
Lesson Learned: Understand the different investor types and their needs. Target investors appropriate for your stage and capabilities. Diversify your investor base to reduce concentration risk.
Building Track Record
Investors want to see performance history before committing capital. But you need capital to generate track record. This chicken-and-egg problem is one of the toughest challenges for emerging managers.
We addressed this by starting with proprietary capital and generating a track record before seeking outside investment. We maintained audited performance records from the earliest days. We were transparent about what was live trading versus paper trading or backtesting.
Building a meaningful track record takes time. Investors typically want to see at least two to three years of live performance, ideally through different market conditions. Patience and persistence are required.
Lesson Learned: Start building your track record as early as possible, even with small amounts of capital. Maintain rigorous documentation of performance. Be honest about the nature and limitations of your track record.
Team Building and Culture
Hiring for Quantitative Finance
Building a team for a quant fund presents unique hiring challenges. The talent pool for people with both quantitative skills and financial knowledge is limited and competitive. Major technology companies, established hedge funds, and other quant firms all compete for the same candidates.
We learned that successful hiring requires clear articulation of what makes your firm attractive, including interesting problems, ownership of outcomes, and competitive compensation. It involves looking beyond traditional backgrounds to find talented people who can grow. Creating a strong culture attracts people who value mission and environment. Investing in developing team members rather than only hiring experienced talent is essential.
The best hires were often people with strong fundamentals and growth mindsets rather than those with specific experience. Intellectual curiosity, work ethic, and cultural fit proved more important than existing knowledge, which can be taught.
Lesson Learned: Hiring is one of the most important and difficult aspects of building a fund. Compete on culture and opportunity, not just compensation. Look for fundamentals and growth potential, not just existing skills.
Building Culture
Culture shapes how decisions are made, how people collaborate, how challenges are addressed, and ultimately whether talented people want to work with you. Building the right culture was a deliberate effort.
The culture we built emphasizes intellectual honesty where we question assumptions, acknowledge mistakes, and follow evidence over ego. We focus on long-term thinking, making decisions for sustainable success rather than short-term gains. Continuous learning is central since markets evolve and we must evolve with them. Collaboration matters because complex problems require diverse perspectives. Excellence is expected since we set high standards and hold ourselves accountable.
Culture is not set once and forgotten. It requires constant reinforcement through actions, decisions, and communication.
Lesson Learned: Culture is a strategic asset. Build it deliberately and reinforce it consistently. The right culture attracts talent, improves decisions, and enables long-term success.
Managing Through Challenges
Every fund goes through difficult periods including drawdowns, personnel departures, operational issues, and external challenges. How you manage through these periods defines your organization.
We experienced all of these challenges and more. The lessons learned include communicating transparently with investors and team members during difficult periods, staying focused on long-term objectives rather than overreacting to short-term setbacks, using challenges as opportunities to improve systems and processes, maintaining confidence in your approach while remaining open to necessary adaptations, and taking care of people since how you treat team members during tough times is remembered.
Lesson Learned: Challenges are inevitable. Your response to challenges matters more than avoiding them. Build resilience into your organization and maintain perspective during difficult periods.
Evolution and Growth
Scaling Strategies
As assets under management grow, strategies must evolve. Capacity constraints become real. Market impact increases. What worked at $10 million may not work at $100 million.
We addressed scaling challenges by developing new strategies to diversify and add capacity, adapting existing strategies to trade more efficiently at scale, expanding into new markets and asset classes, and investing in better execution infrastructure to reduce market impact.
Growth also brought increased complexity. More strategies meant more systems, more risk monitoring, and more coordination. We built infrastructure to handle this complexity while maintaining quality and control.
Lesson Learned: Plan for scale from the beginning but adapt as you grow. Capacity constraints are real and must be addressed proactively. Growth brings complexity that requires organizational evolution.
Innovation and Adaptation
Markets evolve continuously. Strategies that work today may not work tomorrow. Competitors learn and adapt. New data sources and technologies emerge. Successful funds must innovate continuously.
We maintained a research program focused on developing new strategies and improving existing ones. We stayed current with advances in AI and machine learning. We explored new data sources and technologies. We were willing to retire strategies that no longer worked.
Innovation requires balancing exploitation of current advantages with exploration of new possibilities. Too much focus on current strategies leaves you vulnerable to obsolescence. Too much focus on research leaves you without profits to fund the research.
Lesson Learned: Build continuous innovation into your organization. Markets evolve and you must evolve with them. Balance running current strategies with developing future ones.
Building for the Long Term
The most successful funds are built for long-term sustainability, not short-term gains. This means making decisions that may reduce near-term returns but build lasting advantages.
We invested in infrastructure, processes, and people beyond what was required for immediate needs. We built relationships with investors, service providers, and partners for the long term. We prioritized sustainability over maximum growth.
Long-term orientation also means managing through cycles. Markets have good years and bad years. Strategies have periods of outperformance and underperformance. Building reserves, managing leverage conservatively, and maintaining investor trust through cycles enables long-term success.
Lesson Learned: Build for the long term. Make decisions that create sustainable advantages. Manage through cycles with patience and perspective. Short-term optimization often undermines long-term success.
Conclusion: The Entrepreneurial Journey
Building a quantitative fund is a marathon, not a sprint. It requires excellence across multiple domains, persistence through numerous challenges, and continuous evolution as markets and opportunities change. The journey is demanding but rewarding for those who commit to it fully.
Looking back over two decades of building Savanti Investments, I am proud of what we have accomplished, from developing AI trading platforms that represent the state of the art to launching innovative tokenized fund structures. But I am equally aware of how much remains to be done and how much we continue to learn.
For those considering this path, I offer encouragement along with honesty about the challenges. The opportunity to build something meaningful, to solve complex problems, and to create value for investors and team members is tremendously fulfilling. But success requires realistic expectations, substantial resources, and unwavering commitment.
The lessons I have shared here are drawn from real experience, including both successes and failures. I hope they provide useful guidance for your own journey, whether you are starting a fund, working at one, or simply seeking to understand this fascinating industry.
The quantitative investment field continues to evolve rapidly, with new technologies, new data sources, and new opportunities emerging constantly. Those who combine technical excellence with business acumen, who build robust organizations with strong cultures, and who maintain the persistence to work through inevitable challenges will continue to find success.
The journey continues.
Frequently Asked Questions
How much capital do you need to start a quant fund?
The capital required depends significantly on your strategy and structure. Some strategies require substantial capital to be viable due to minimum position sizes or diversification requirements, while others can start smaller. Beyond trading capital, you need operational capital for technology, legal, compliance, and administrative costs, which can easily reach $500,000 to $1 million annually for even small operations. Many successful quant funds started with proprietary capital from founders before raising outside investment. The realistic minimum to start a credible institutional fund is typically several million dollars, though some start with less and grow organically.
What background is best for starting a quant fund?
Successful quant fund founders come from diverse backgrounds including academia with expertise in mathematics, statistics, or computer science, technology companies with experience in machine learning and software engineering, trading firms with market knowledge and risk management experience, and investment banks with financial and operational expertise. The common thread is deep expertise in at least one relevant area combined with the ability to learn and build capabilities in others. Most successful founders either have diverse backgrounds themselves or partner with people who complement their skills.
How long does it take to raise institutional capital?
The timeline for institutional fundraising varies widely but is typically measured in years rather than months. Building relationships with institutional investors often begins long before you are ready to accept capital. The due diligence process for a single institutional investor can take six to twelve months from first meeting to investment. Emerging managers typically need at least two to three years of track record before most institutional investors will consider them. Realistic expectations for an emerging manager would be three to five years from founding to meaningful institutional capital.
What are the biggest mistakes you see new quant funds make?
The most common mistakes include underestimating operational requirements and costs, focusing too heavily on strategy and technology while neglecting business fundamentals, overfitting strategies to historical data resulting in strategies that fail in live trading, underestimating the time and effort required for fundraising, neglecting risk management until problems occur, hiring for skills alone without considering cultural fit, and growing too quickly before infrastructure can support it. Most of these mistakes stem from an overly narrow focus on the quantitative aspects while neglecting the business, operational, and human elements that are equally important for success.
Is it still possible to start a successful quant fund, or is the field too competitive?
While competition has certainly increased, new successful quant funds continue to emerge. The key is finding genuine edge rather than competing on well-known strategies. Opportunities exist in applying new technologies like advanced AI and machine learning, processing new data sources before they become commoditized, serving underserved market segments or strategies, building superior operational efficiency, and combining quantitative and qualitative approaches in novel ways. The barriers to entry have increased, but so have the tools and infrastructure available to new entrants. Success requires realistic assessment of where you can genuinely compete and disciplined execution on building sustainable advantages.
About the Author
Braxton Tulin is the Founder, CEO & CIO of Savanti Investments and CEO & CMO of Convirtio. With 20+ years of experience in AI, blockchain, quantitative finance, and digital marketing, he has built proprietary AI trading platforms including QuantAI, SavantTrade, and QuantLLM, and launched one of the first tokenized equities funds on a US-regulated ATS exchange. He holds executive education from MIT Sloan School of Management and is a member of the Blockchain Council and Young Entrepreneur Council.
Investment Disclaimer
The information provided in this article is for educational and informational purposes only and should not be construed as investment advice, financial advice, trading advice, or any other type of advice. Nothing contained herein constitutes a solicitation, recommendation, endorsement, or offer to buy or sell any securities or other financial instruments.
Past performance is not indicative of future results. All investments involve risk, including the possible loss of principal. The strategies and investments discussed may not be suitable for all investors. Before making any investment decision, you should consult with a qualified financial advisor and conduct your own research and due diligence.
The author and associated entities may hold positions in securities or assets mentioned in this article. The views expressed are solely those of the author and do not necessarily reflect the views of any affiliated organizations.
This article discusses the author’s personal experiences and opinions regarding building a quantitative investment fund. Individual experiences will vary based on numerous factors including strategy, market conditions, capital, regulatory environment, and other circumstances. The experiences described should not be taken as guarantees of similar outcomes.
Starting an investment fund involves significant risks including the risk of total loss of capital, regulatory penalties, and other adverse outcomes. Anyone considering starting a fund should consult with qualified legal, tax, and financial advisors before proceeding.
