Why Quant Trading Platforms Are Designed to Handle Market Crashes
February has been a down month for a lot of markets across the board. If we look at the S&P, for example, we’re sitting at a 5% pullback for the month of February. Even worse, Bitcoin is down 20% just in this month alone.
The purpose of this video is not to highlight the crashes going on in financial markets, but more importantly, to highlight how market-neutral trading strategies can perform during these periods. In fact, I’m going to go over nine different trading strategies and review their performance in February compared to the S&P 500, Bitcoin, and other traditional assets that have taken a hit.
Enough rambling – let’s look at the data.
This is Nurp’s MyFXBook, a third-party verifier that tracks trading performance. As you can see, this account is connected to several of their different strategies.
Dalio Bot Results During the Market Crash and What They Show About Quant Trading
First up: Dalio Bot.
Let’s look at the Dalio bot. If you’re not familiar, I’ve been tracking a $9,000 Dalio bot account that is now sitting at $116,000. If you’re curious about that journey and how it’s performed, check out my earlier videos.
Looking at Nurp’s Dalio bot: since it started on July 30th, 2024, it has achieved a 37% gain. But for the purpose of this video, I want to focus only on how it performed in February.
Using custom dates, we see that in February, the Dalio bot produced a 2% gain. It experienced a maximum drawdown of just 1.19% and delivered a total profit of $1,100. Scrolling down, it took 148 trades with a profit factor of 4.07.
If you’re unfamiliar with “profit factor” – it’s the ratio of total gains to total losses. A profit factor of 4 means that for every dollar it lost, it gained four. That’s solid.
Quant Trading Software Performance Review: Fed Strategy Results
Next up: Fed Bot.
This is one of Nurp’s most well-known strategies. The account has done 24% since inception, but again, we’re just focused on February.
For the month, Fed Bot returned 2.38%, with a maximum drawdown of 1.19%. It generated $2,391 in total profit from an account size of $12,000. It executed 267 trades with a profit factor of 2.24—slightly outperforming the Dalio bot.
Combining Quant Trading Strategies: Fed and Gold Strategy Performance
Now, Fed + Gold.
This setup is interesting because it mirrors what I’ve done with my own account—combining the Fed strategy with the Gold Digger strategy. Since doing that, I’ve seen a dramatic improvement in performance and account growth.
The combined Fed + Gold account returned 5.27% in February with a drawdown of 3.48%. It generated $2,928 in profit on an account size of $58,467. It took 259 trades, had a profit factor of 2.06, and showed an impressive win rate of 88%, meaning it lost only 12% of its trades.
Quant Trading Platform Performance During Market Volatility: Yellen Strategy Results
Yellen Strategy.
The Yellen account has done 188% since 2023. In February, it posted a 1.73% gain—making it the lowest performer in this lineup. It experienced a 1.22% drawdown and earned $933 in profit. During the month, it placed 493 trades with a profit factor of 2.0 and a win rate of 82%.
Quant Trading Strategy Combination: Dalio and Gold Strategy Results
Dalio + Gold.
This is the exact configuration I’m currently running. While it’s been performing well overall, this particular instance didn’t shine in February. It posted a 1.70% gain but had a higher drawdown at 4.29%. The total profit was $1,162 across 74 trades. The profit factor was 1.45, and the win rate was 73%, meaning 27% of trades were losses—higher than I expected, especially compared to my personal results.
Gold Digger Strategy and High Growth Quant Trading Platforms
Now, let’s look at Gold Digger on its own.
This software is an absolute beast. Since 2023, it has achieved a staggering 592% gain. But it hasn’t come without volatility. At one point, the Gold Digger account experienced a 49% maximum drawdown. That’s nearly a 50% haircut.
Fortunately, since it’s software, nothing was manually closed out during that dip, and the account bounced back. However, if this had been managed by a human, the emotional pressure likely would’ve caused someone to panic and take the loss.
