Tuesday Reversal Strategy: Tested on Nasdaq 100 and S&P 500 Stocks

Turnaround Tuesday is an idea traders have discussed for decades: after a sharp Monday sell-off, the market may rebound on Tuesday or in the days that follow.

Dramatic examples followed Black Monday in 1987 and another Monday plunge in 1997. Most versions of the idea focus on a single index. In this test, we looked for the pattern across individual stocks instead.

The strategy combines three entry conditions, two exit rules, and a Position score formula that decides which stocks to buy when too many qualify.

Entry rules

The strategy evaluates three conditions at the close of the daily bar:

• The day of the week is Monday
• The stock closes more than 1% below its opening price: Close[0] < 0.99 × Open[0]
• The closing price is above the previous bar’s 200-day simple moving average: Close[0] > SMA(200)[1]

All three conditions must be true. The position opens at the next market open, on Tuesday, using a Market order.

The idea is to buy a stock that remains in a long-term uptrend but has just suffered a short-term decline. This is a mean reversion approach: looking for a rebound after temporary weakness.

Exit rules

The position closes when either condition occurs first:

• Today’s closing price is higher than the closing price two bars ago: Close[0] > Close[2]
• The position reaches the holding limit of 10 daily bars

Stop Loss and Profit Target are disabled. The first condition captures a rebound; the second limits how long the strategy waits for one.

Punteggio della posizione

The strategy can hold up to 10 positions. When more stocks meet the entry conditions than there are available slots, Position score ranks the candidates.

The formula is one minus the five-period ROC, or Rate of Change. A lower ROC produces a higher score, giving priority to qualifying stocks with the weakest recent performance.

The distinction matters: entry conditions decide whether a stock qualifies. Position score decides which qualifying stocks are selected. That second step can have a substantial effect on the backtest.

Nasdaq 100 backtest

The first test used a basket of Nasdaq 100 stocks, daily data, 20 years of history, and a commission of 0.1%.

Starting with $10,000, the backtest reported:

• Net profit: approximately $55,000
• Trades: approximately 5,900
• CAGR: almost 10%
• Profit factor: 1.35
• Win rate: 66%

The average winning trade made $54, while the average losing trade lost $79. The strategy won frequently, but its losses were larger than its wins—a typical mean reversion profile.

Average holding time was under three days, despite the 10-bar limit. Winners lasted about two days, while losers lasted almost five. Successful rebounds produced quick exits; unsuccessful trades stayed open longer and could reach the time limit.

Comparison with Buy & Hold

To compare the strategy with SPY, we first used normalization by dollar drawdown. StrategyQuant scaled the benchmark investment to approximately $6,300 so its historical dollar drawdown matched the strategy’s.

Under that normalization:

• Sharpe ratio: 1.02 for the strategy versus 0.65 for SPY
• CAGR / maximum drawdown: 0.76 versus 0.24
• Exposure: approximately 66% versus 100%

The report also showed a separate comparison with SPY scaled to match the strategy’s percentage drawdown. In that comparison, net profit was approximately $55,000 versus $11,500.

These are two different historical drawdown normalizations. The key takeaway is how returns compare relative to the drawdown taken to achieve them, rather than looking at profit alone.

The same strategy on S&P 500 stocks

Next, we changed the stock basket to the S&P 500. Everything else stayed the same: entry conditions, exits, Position score, maximum positions, and test period.

The result was approximately 8,000 trades and a CAGR of 15%—about five percentage points higher than on Nasdaq 100.

Why did it perform better? Position score is one possible explanation. On Nasdaq 100, the strategy selects up to 10 stocks from around 100. On the S&P 500, it selects up to 10 from around 500. The larger candidate pool may help, but that explanation needs its own test.

Why the strategy may work

Tuesday Reversal looks for short-term weakness within a long-term uptrend. The trend filter defines the eligible stocks, Position score prioritizes the weakest candidates, and the exit rules keep the focus on a relatively quick rebound.

The same rules produced profitable historical results on two different stock baskets without adjustments. That gives us a useful starting point for further research.

Download the complete strategy from the video description and test it yourself. Change the ranking formula, explore another stock basket, or rebuild the rules around your own ideas. Once you have finished testing and configuring it, you can run the strategy on AlgoCloud.

Watch the video below for the full AlgoWizard walkthrough and backtest analysis.