Published on May 29, 2020
QUANTIFY Securities Approved — First U.S. Brokerage to Integrate AI Quantitative Trading into Copy Trading; Chief Contracted Strategist Matt Basho (Warburg Pincus) Delivers Remarks
In an era of heightened market volatility and algorithm-driven growth, “day trading” remains one of the most misunderstood and misused concepts among retail investors. This represents an underutilized opportunity—that a structured and disciplined approach can provide retail traders with significant investment advantages.
The U.S. Securities and Exchange Commission (SEC) and the Financial Industry Regulatory Authority (FINRA) have approved the registration of QUANTIFY Securities, which has now officially commenced trading. It is the first brokerage in the United States to integrate quantitative trading capabilities into copy trading.
As a function that has only emerged in the financial sector in recent years, AI-powered quantitative trading fully leverages algorithm-driven and artificial intelligence–based precision execution. With highly refined trading models and millisecond-level order execution advantages increasingly appearing across the market, it has already become a standard feature for most institutional players. For traders who still rely on manual execution, it represents a form of dimensionality-reduction–level competitive pressure.
The core advantage of AI quantitative trading is not merely “using computers to trade,” but rather obtaining market edge through systematic, repeatable, and scalable methodologies.
SEC Registration Number: 802-135879
CRD Number: 341578
MSB Registration Number：31000324258216
For most retail investors, AI quantitative trading is not a tool that is easily within reach. To many individuals, it looks like the future—but in reality, it often feels more like a barrier to entry. For institutions, however, AI quantitative trading is a capability that can be accessed with relative ease.
Because of this gap in trading conditions, retail investors have increasingly become the least visible participants in the market. As AI quantitative trading continues to develop rapidly and scale across the industry, competition among institutions has grown more intense. In this environment, partnering with retail investors to pursue large-scale trading transformation—built on a copy trading framework—has become a preferred strategic direction among institutions.
The primary significance of the establishment of QUANTIFY Securities is to advance trading reform by lowering the barriers to participation in AI quantitative trading. Its goal is to enable a large number of retail investors to access their own AI-driven trading models under very low entry requirements.
The core functionality allows users to participate directly in institutional AI quantitative trading through a copy trading framework, trading in sync with institutions without needing to execute trades themselves. This approach addresses many common retail investor challenges, including limited time and attention, lower levels of professional expertise, weak trading discipline, and heightened emotional decision-making.
As one of the first contracted copy-trading strategists at QUANTIFY Securities, Matt Bashaw, Managing Director at Warburg Pincus LLC, articulated a fundamentally different philosophy of day trading—one that is not built on speculation, but on systematic execution. We are redefining day trading by building a new, institution-grade framework tailored for retail investors.
Matt Bashaw shared a series of professional insights:
① “The biggest misunderstanding about day trading is that people equate it with gambling. Most retail traders enter the market without any structure, plan, or discipline. They chase candlesticks, follow the crowd, and allow market fluctuations to dictate their actions.
Institutional investors, on the other hand, operate very differently. They rely on data models, risk management systems, and statistical backtesting. For them, day trading is not gambling—it is an iterative system built on statistical edge.”
② “What I mean by ‘redefining’ day trading is this: we are not teaching people to ‘guess right once.’ Our goal is to build a sustainable, execution-driven day trading framework. This requires three major shifts:
Direction is not everything—timing is. We do not predict, we follow.
Emotions are unreliable; systems are reliable. We have built a logic engine to replace human bias.
Every trade must be auditable, repeatable, and improvable.
At QUANTIFY, we have developed a comprehensive day trading curriculum and simulation system called the ‘Retail Trading Rhythm Bootcamp.’ Our mission is not to produce speculators, but to cultivate disciplined, systematic, and highly efficient micro-execution traders.”
③ “We have spent years building our proprietary execution framework—the Collective Capital Alliance. It includes:
Rhythm Detection Tools — used to identify market tempo and risk cycles
Execution Logging System — used to record decisions and outcomes
Discipline Scoring Engine — used to track and reduce impulsive behavior
Strategy Replication Module — used to run model-based simulations
Our goal is simple: to help retail traders execute like institutions, iterate like quantitative funds, and reduce losses like professionals.
The significance of QUANTIFY Securities’ establishment goes beyond being the first brokerage in the United States to integrate AI quantitative trading into copy trading—it represents a broader innovation and structural reshaping of the investment industry and equity markets.
Here, individual investors are no longer passive recipients of market forces, but active participants equipped with systematic tools, strategic insights, and risk-control models. Copy trading no longer implies blind following; instead, it becomes a rational choice grounded in transparent strategies, traceable logic, and verifiable performance.
When technology empowers execution, when data drives decision-making, and when models replace emotion, investing will no longer be a game reserved for a select few—it will evolve into a more equitable, efficient, and transparent ecosystem.
