After executing over 50 day trades using the discussed algorithm, it’s time to review the outcomes and explore potential areas for enhancement. The premarket day trading strategy I’ve employed has consistently outperformed the market, even when factoring in fees and taxes. Despite the tax advantages of holding the S&P 500, my approach has generated superior returns, even at the highest income tax bracket. This trend has persisted for the majority of the past 2 months, and I intend to sustain this positive momentum. I won’t delve into the specifics of the algorithm, as it’s already linked above. Let’s focus on analyzing the results and identifying areas for self-improvement.

Since I began implementing the real-time strategy, the S&P 500 has demonstrated robust growth, presenting a formidable challenge to any trading approach. From November 9 onwards, it has surged by 12.2% in price. Factoring in taxes, this gain would remain at 12.2% if you held onto the investment, or potentially drop to 9.8% if you sold and faced the maximum long-term capital gains tax rate of 20%.

Let’s consider the scenario of 0% tax, which reflects the standard philosophy of investors and serves as the strictest benchmark for comparison. To match the returns of the S&P 500 with a 37% short-term income tax rate, my strategy would have needed to yield a return of 19.4% starting from November 9.

In actuality, my strategy has yielded approximately a 27% gain since that date, surpassing the market by roughly 14.8% before taxes. Even at the highest income bracket (which I’m not in), the gain would still be 17%, outpacing the S&P 500 by 4.8% after taxes.

I’m thankful for developing a disciplined, purely algorithmic trading strategy that has performed well thus far. Additionally, I’m pleased to note that my trades typically last a median time of 4 minutes, minimizing my exposure to the market and keeping margin fees low as I scale up.

However, I’m not here to boast or revel. Let’s delve into further results:

- I took a similar number of positions in both directions, with 30 long trades and 26 short trades. Despite an accuracy of around 57%, which may seem modest, it’s comparable to many hedge funds. Accuracy was hindered by short trades, which had a 50% accuracy rate.

- As mentioned earlier, it’s crucial to set a profit-loss ratio in your favor due to the potential for random chance. I’ve been using a 2:1 ratio for most trades and have recently introduced a dynamic trailing stop loss to allow profits to run even further when possible.

- The average return from each trade was roughly half a percent, respectable for a day trader but potentially higher if I had automated the closing of trades sooner. Unfortunately, Fidelity’s caching issue delays trade closure, prompting consideration of switching to a broker with an actual API like Interactive Brokers. However, higher trading fees with Interactive Brokers may offset profits.

- Diversification, while challenging due to Fidelity’s cache refresh delay, has proven beneficial. It has prevented overexposure to a single stock and allowed profits to accrue from the best-performing stock(s) of the day.

- The median time exposed to the market was just 4 minutes per trade. Typically, I initiated positions seconds after 9:30 and closed them before 9:35. Exceptions occurred, such as during a shortened holiday session when I held Salesforce for the entire day, yielding only a 0.1% profit.

Now, let’s discuss market exposure further, as I believe it’s a critical aspect of my strategy and investing in general.

Over the span of 2 months and 56 trades, I’ve spent a mere 14 hours with capital actively invested in the market. During the remaining time, my available cash has been earning a risk-free 5% return in Fidelity’s money market. This experience challenges the common belief that timing the market is impossible. While certainly more challenging than a passive S&P 500 investment strategy, it’s evident that active trading can be profitable and serve as a robust means to avoid unexpected intraday losses.

Thus far, our focus has been on time exposure rather than monetary exposure. Engaging in trades during the most volatile minutes of the day entails risk, especially considering the substantial amounts involved. In fact, analyzing each trade as an individual risk revealed that my cumulative exposure exceeded my net worth by approximately $100k. Such a significant financial commitment underscores the importance of risk management, despite the methodical and data-driven nature of trading. While not akin to gambling, unless one considers meticulous strategy testing akin to choosing a game to play, risk remains a pertinent consideration.

To maintain profitability while mitigating risk, it’s essential to leverage broader market dynamics to inform decision-making. Let’s conclude by exploring how I could utilize these dynamics to make more informed trading choices.

The market is currently experiencing bullish sentiments, with some attributing it to new highs and the anticipation of decreasing inflation. November saw exceptional performance, with a 9% increase, marking the best November since 2020 (which saw an 11% rise) and the best overall month since July 2022 (which saw a 6.5% increase). Following the typical pattern, a Santa Claus rally ensued, propelling prices above previous highs. The subsequent cooldown into the new year appeared reasonable. From a technical standpoint, the market is indeed demonstrating remarkable strength.

There’s a valid concern regarding the ongoing layoffs and acquisition shortcomings in big tech companies. For instance, Adobe’s decision to not acquire Figma and Microsoft’s recent layoffs in its gaming division indicate a trend that could impact economic stimulation. However, Wall Street tends to view leaner companies favorably, often celebrating such measures. Despite the layoffs, tech giants can still experience financial success, as seen with your former employer’s record month following your layoff in 2022.

Moving forward, it’s crucial for my trades to better align with the market dynamics. Over the past two months, there have been approximately twice as many “long” days for the S&P 500 as “short” days. Improving accuracy may involve buying on days when the S&P is trending upward, as short positions may struggle against the broader market momentum. Although attempts were made to implement this feature early on, the use of a data provider with a delay hindered timely decision-making. With access to real-time premarket prices through Fidelity, I can now incorporate this aspect into my strategy more effectively.

Additionally, diversifying by sector could help capture broader market movements. Studies have shown that a stock’s price is significantly influenced by its sector’s overall movement. While I’ve occasionally targeted specific sectors, such as retail or crypto mining, based on daily observations, my strategy doesn’t currently account for sector correlations. Integrating sector rankings from sources like investors.com or Zacks could enhance my strategy by identifying stocks within sectors that confirm market trends, rather than solely relying on individual stock trends.

It’s been quite the journey since I first started backtesting my initial trading algorithm, and I’m grateful for the opportunity to reflect on what has ultimately proven successful. While this strategy may not be sustainable indefinitely, as few market-beating strategies are, the fact that I’ve been consistently profitable for the past couple of months is certainly a success worth acknowledging. Moving forward, I plan to delve into deeper analysis and implement a more sophisticated diversification approach to build upon my current strategy.

Thank you for joining me on this journey!