BECOME
A QUANT.
Ten minutes, one real strategy, twenty-seven years of real S&P 500 data. By the end of this page you will know what a quant is, why software beats emotions, and how to run your first strategy without watching a chart ever again.
How to learn quantitative investing with real market data
In one line: learn by testing. Pick one well-known rule, run it on decades of real market history, and read four numbers before you study the theory behind them. This page does exactly that with the 200-day moving average on the S&P 500, on real data, with no sign-in.
The four numbers: growth a year (CAGR), the worst drop from a peak (drawdown), the Sharpe ratio (how much return for the bumps along the way) and the number of trades.
Updated October 27, 2026
A five-step path
- Learn to read a backtest. Start with the four numbers above. Step one below shows them for the S&P 500.
- Test one simple, well-known rule on real data. The 200-day moving average is a good first one: one rule, decades of research behind it. Steps two to five below walk through it.
- Check the data behind the test. It needs the companies that later went under or were bought, or the results look better than the market really was, and company numbers should count only from the day they were filed. In QuantGPT's market-wide backtests, companies that later delisted are in from 1998 (12,521 of 12,841, or 98%) and company numbers are joined by filing date; before 1998 the data holds only companies that survived.
- Stress the rule. Count the trades, add a buffer against false alarms, think about trading costs, and look at years the rule was not tuned on. Step five below adds the buffer.
- Decide where it runs. Run it as a screener and place the trades yourself, or run your own code on QuantGPT Cloud with QuantGPT Pro, at your own broker. Where it runs, below, has both.
Ways to learn quant investing, compared
| ROUTE | GOOD FOR | CODE NEEDED | MARKET DATA |
|---|---|---|---|
| Books and online courses | The theory, the math, career preparation | Often, for the exercises | Usually you bring your own |
| Python courses for trading | Learning to write strategies as code, step by step | Yes | Depends on the course |
| Code-first platforms | Writing your own algorithms, for example in Python or C# on QuantConnect | Yes | Included |
| Plain English with your AI (QuantGPT) | Testing documented strategies by asking Claude or ChatGPT | No | Decades of US stock history; companies that later delisted are in it from 1998 |
The rest of this page is the worked example: one rule, from idea to result, on real data.
What is a quant
No hunches, no headlines, no 3am chart staring. An idea becomes criteria, criteria become a program, the program runs somewhere reliable and executes through a brokerage API. That is the entire job description.
How you become one
Today QuantGPT covers the idea, the criteria and the test on real data. QuantGPT Cloud hosts your program, on QuantGPT Pro. Execution happens at your broker, on your keys, at your direction. The trade is yours.
Step one: meet the market
This is the S&P 500, real data, 1999 to 2026, from the QuantGPT warehouse. Ten thousand dollars riding it the whole way becomes about sixty-one thousand. The index is a great machine. It is also a machine that occasionally sets your money on fire: twice in this window it cut an account nearly in half or worse.
Step two: the idea
What if you only rode the index while it was healthy? One of the oldest findings in the research: compare the price to its own 200-day moving average. Above the line, the market spends most of its time going up. Below the line is where the catastrophes live: the crashes, the panics, the year-long bleeds.
Step three: see the regimes
Paint the same history by which side of the line the market is on. Green: above the average. Red: below it. Nearly all of the misery, 2000 to 2003, 2008 to 2009, the 2020 crash, the 2022 bleed, happens below the average, in red. The idea works. Armed with this knowledge, take this observation, validated by data, and USE IT to build a trading system.
Step four: why humans fail at this
Simple rule, right? Except executing it by hand means watching the close every single day for twenty-seven years. And the naive version of this rule, sell the moment price dips below the line, buy the moment it pokes above, would have flipped your account 196 times. Every triangle below is a trade the naive rule demands. Most are whipsaws: false alarms that cost money, patience, and sleep. This is where humans break: they hesitate, they revenge-trade, they stop following the rule the week before it matters most. Software has none of those feelings.
Step five: build the quant
So we tune the rule to trade less and mean it more: go long only when the close is 1% or more above the 200-day average; go flat only when it closes 3% or more below. The buffer kills the whipsaws: 42 trades in 27 years instead of 196. And flat does not mean idle: while out of the market the cash earns the going money market rate, the real 3-month Treasury bill yield of each day, straight from the Fed's own data. Here is that machine against buy-and-hold, on the same real data:
THERE YOU GO. THAT WAS A QUANT STRATEGY, AND YOU JUST UNDERSTOOD EVERY PIECE OF IT.
Step six: same rule, faster index
One more move. The rule you just built is a crash filter, and a crash filter is worth the most on the index that crashes hardest: the Nasdaq 100. Same signal, same 42 trades, same money market while flat, but the long leg holds NDX instead of the S&P. Riding the Nasdaq raw meant an 83% drawdown in the dot-com bust, a hole almost nobody holds through. Behind the filter it kept most of the rocket, dodged most of the crater, and finished ahead of buy-and-hold on both indexes:
Where it runs
A strategy is useless on a laptop that sleeps. So the program goes on a machine that is always on, always watching the close, with no emotions. Then it points at your brokerage API with your own keys, and the trades land in your account. Two ways to get there: run it on QuantGPT Cloud with QuantGPT Pro, or on your own machine, against your own brokerage account for U.S.-listed stocks and ETFs. Or run any strategy as a plain screener in the terminal, see what it would own today, and place the trades yourself. Your call, always.
On QuantGPT Pro: QuantGPT Cloud hosts your own code and sends its orders to your own brokerage account, under your keys, using only your broker's own order types. We never decide a trade. A 30-day paper window is there if you want it.
More than one play
Trend is the simplest quant strategy, not the only one. The same loop, idea, criteria, program, applies to every documented edge:
Low P/E plus real profitability: the classic value screen, run as a machine.
Buy strength, rebalance monthly, let winners compound. The most documented edge in the literature.
Margins, balance sheets, earnings growth: own only companies that clear the bar.
The three steps
From the Strategy Library, or build your own criteria in the terminal and test them on real data.
On QuantGPT Cloud with QuantGPT Pro, or download your criteria and run them on your own machine. Both are yours.
Your own brokerage account, for U.S.-listed stocks and ETFs. Your account, your keys, your trades.
This page is the written companion to chapter S1 of QuantGPT University, "What a quant is". Members play the lesson, open the 200-day rule in the terminal and take the three-question check there.
Start your 3-day trial Chapter S1 in University Everything on Learn
Questions
What is the best way to learn quantitative investing?
Learn by testing. Run one well-known rule on real market history, read what happened, then study why it worked or failed. The theory sticks once you have seen it in numbers.
Do I need to know Python to learn quant investing?
No, not to start. Python matters if you want to build your own systems or work as a quant developer. To learn how strategies behave, you can test them in plain English with an AI assistant connected to a backtest engine, and learn code later.
Where can I get real market data for learning?
FRED, the Federal Reserve's database, has free economic data. For stock backtests, the data needs the companies that later delisted and company numbers dated by when they were filed, or results look better than they were. The lesson on this page runs on the QuantGPT warehouse with no sign-in.
What is a good first quant strategy to study?
A trend filter such as the 200-day moving average. It has one rule and decades of published research, and it shows the main trade-offs clearly: a smaller worst drop against growth, and how often it trades.
How long does it take to learn quantitative investing?
You can follow one complete strategy from idea to result in a single sitting, as this page does. Getting good at research, testing and risk takes many ideas tested over months.
Can I learn quant investing for free?
Yes, much of it. Books, free data such as FRED and QuantGPT's three lessons, which play without an account, cost nothing. The QuantGPT terminal starts with a 3-day free trial of QuantGPT or QuantGPT Pro, with a card up front.