Every systematic trader spends their days hunting for patterns. Reliable, repeatable patterns that show up often enough to build a strategy around. And among all the questions that keep traders up at night, here's a big one: Does buying the S&P 500 (SPY) and Nasdaq (QQQ) after they crash actually make sense?

We know the long-term story. Both indices climb over time. That's not up for debate. But we also know they've taken some truly savage beatings along the way. The 2008 financial meltdown. The COVID-19 panic in March 2020. The 2022 selloff after Russia invaded Ukraine. Anyone holding these indices through those periods watched their portfolio get hammered, often in a matter of weeks.

So here's the real question worth asking: Would you have been better off waiting for those major drops before jumping in? Could you have avoided the worst losses while still capturing the eventual recovery?

Starting Point: What Happens If You Just Hold

Let's turn this idea into something testable. We need rules, historical data, and a way to measure whether this approach has actually worked in the past. For this analysis, we're using data from Nasdaq futures (@NQ) and S&P 500 futures (@ES), covering the period from 2008 through today.

First step is establishing a baseline. What would have happened with a straightforward buy-and-hold approach? To simulate this, we enter a long position at the market open and hold it for exactly 10 consecutive trading sessions, then repeat the process continuously. This gives us a benchmark that looks a lot like traditional buy-and-hold, which we can compare against the more sophisticated strategies coming later. All of them will use the same 10-session holding period, keeping things apples-to-apples.

Strategy #1: The Baseline

input: n_bars(10);

if sessionlastbar then buy next bar market;

if barssinceentry > n_bars then setexitonclose;

Figure 1 shows the equity lines for this baseline strategy. Left chart is Nasdaq futures (@NQ), right chart is S&P 500 futures (@ES).

The results are what you'd expect from markets that have climbed substantially over the past decade and a half. Just holding on would have generated serious profits. But look at that maximum drawdown. You would have had to stomach more than $71,000 in losses on the S&P 500 and nearly $126,000 on the Nasdaq during the worst periods. That's the price of admission for those gains.

Testing a Three-Day Pullback Filter

Now let's add a condition. Instead of buying all the time, we'll only enter the market after the daily closing price has dropped for three consecutive sessions. Once we're in, we exit after 10 sessions, same as before.

Strategy #2: Waiting for Three Down Days

input: n_bars(10);

if close < close[1] and close[1] < close[2] and close[2] < close[3] then buy next bar market;

if barssinceentry > n_bars then setexitonclose;

Figure 2 shows the equity lines using a 3-day pullback entry filter. Left chart is Nasdaq futures (@NQ), right chart is S&P 500 futures (@ES).

This didn't help. The equity curves look choppier than the baseline, and the performance metrics tell a disappointing story. Net profit dropped significantly on both markets. The average trade value on the Nasdaq actually declined. Sure, the maximum drawdown improved slightly on the S&P 500, but the Nasdaq drawdown barely budged. Overall, this filter made things worse, not better.

Adding a Moving Average for More Selective Entries

Time to refine the approach. Instead of requiring three consecutive down days, let's dial it back to two. But we'll add another filter: the most recent close must be below the 10-day moving average, which represents roughly two trading weeks. The thinking here is that we want to catch more meaningful pullbacks, not just minor three-day hiccups. This should make our entries more selective and hopefully more profitable.

Strategy #3: Pullback Plus Moving Average

input: n_bars(10);

if close < close[1] and close[1] < close[2] and close < Average(c,10) then buy next bar market;

if barssinceentry > n_bars then setexitonclose;

Figure 3 shows equity lines with more selective entry conditions combining pullbacks and a moving average filter. Left chart is Nasdaq futures (@NQ), right chart is S&P 500 futures (@ES).

Now we're getting somewhere. This strategy captured most of the original net profit from the baseline, but with some meaningful improvements. The maximum drawdown decreased on both instruments. More importantly, the average trade value jumped by roughly 50% on both the S&P 500 and Nasdaq. We're trading less frequently but making more per trade, which is exactly what you want from a selective strategy.

The Momentum Filter: Waiting for Signs of Recovery

There's one more test worth running. Everything so far has focused on identifying good pullback conditions. But there's another piece to the puzzle when you're buying dips: figuring out when the market is actually starting to turn around.

To tackle this, we added an entry condition based on momentum. The idea is simple: don't take any long positions until prices show signs of moving back up. After testing different lookback periods for the momentum indicator, we settled on eight days. That setting, along with several other strong alternatives nearby, confirmed that this filter actually adds value. The rule: the 8-day momentum must be greater than zero, signaling that the market is beginning to rebound.

Figure 4 shows the optimization results for the momentum indicator across different lookback periods.

Strategy #4: Adding the Momentum Confirmation

input: n_bars(10),m_bars(8);

if close < close[1] and close[1] < close[2] and close < Average(c,10) and momentum(c,m_bars) > 0 then buy next bar market;

if barssinceentry > n_bars then setexitonclose;

Figure 5 shows equity lines for the momentum-filtered strategy. Left chart is Nasdaq futures (@NQ), right chart is S&P 500 futures (@ES).

The results match what we saw during optimization. Total profits decreased, which makes sense because we're trading far less frequently. But look at the trade quality. The average trade value more than doubled compared to earlier strategies. Even more impressive, the maximum drawdown dropped to about one-third of the baseline strategy. The equity curves are dramatically smoother and more stable. You're making less money overall, but you're taking much less risk to get there.

What This All Means

Based on these tests, hunting for market bottoms as entry points in the S&P 500 and Nasdaq is a genuinely interesting approach, but only if you have proper risk management in place.

That's exactly what's missing from the strategies shown here, which makes them too risky for actual trading. But that wasn't the point. The goal was to answer a specific question: In indices that generally trend upward most of the time, does waiting for a significant decline give you a better opportunity to go long?

The answer, as we've seen, is yes. With the right filters, you can capture solid returns while dramatically reducing your exposure to brutal drawdowns. But this approach hasn't worked consistently across all strategies we tested. Simple pullback filters actually made things worse. Developing an effective crash-buying strategy turns out to be surprisingly complex.

The hardest part? Identifying and quantifying what actually constitutes a market crash. That remains one of the toughest challenges for any systematic trader. You need multiple filters working together: pullback depth, moving average context, and momentum confirmation of the recovery. Miss any one of those pieces and your results suffer.

The bottom line is that buying crashes can work, but it's not as simple as just waiting for a few down days. You need a systematic approach that balances selectivity with opportunity, and you absolutely need robust risk management before putting real money on the line.

Until next time, happy trading!