AAPL Long Day Trade Equity Backtest

4 min read

In this post we’ll take a look at the backtest results of opening one AAPL long day trade position each trading day from Jan 4 1993 through April 17 2020 and see if there are any discernible trends. We’ll also explore the profitable strategies to see if any outperform buy-and-hold AAPL.

There are 15 backtests in this study evaluating over 27,400 AAPL long day trade equity occurrences.

Let’s dive in!

Summary

Trading overnight outperformed with regard to total return vs intraday or buy/hold.

Exiting day trades on Monday outperformed relative to other days of the week.

Methodology

Strategy Details

• Symbol: AAPL
• Strategy: Day Trade (positions are held for 24 hours or less, ignoring weekends)
• Days Till Expiration: N/A
• Start Date: 1993-01-04
• End Date: 2020-04-17
• Positions opened per trade: 1
• Entry Days: daily
• Timing: 9:00am ET and/or 4:00pm ET
• Strategies
  • Overnight (open position at market close and close at market open)
  • Intraday (open position at market open and close at market close)
  • Buy/Hold (open position at market open and close the following market open)
• Trade Entry
  • Mondays
  • Tuesdays
  • Wednesdays
  • Thursdays
  • Fridays
• Trade Exit
  • Mondays
  • Tuesdays
  • Wednesdays
  • Thursdays
  • Fridays

Assumptions

• Margin requirements are always satisfied
• Margin calls never occur
• Margin requirement for all positions is 30%

Mechanics

• Prices are in USD
• Prices are nominal (not adjusted for inflation)
• Commission to open or close positions is 0.00 USD
• Slippage is calculated according to the slippage table
• For comprehensive details, visit the methodology page

Results

Win Rate

Trading overnight outperformed with regard to win rate vs intraday or buy/hold.

Day trades with a Monday exit yielded the greatest win rate.

Average Win

Buy and hold outperformed with regard to average win magnitude vs overnight or intraday.

Day trades exited on Wednesday had the greatest average win magnitude.

Lose Rate

Trading overnight outperformed with regard to lose rate vs intraday or buy/hold.

Exiting day trades on Monday had a significantly lower loss rate. Exiting on Friday underperformed across the board.

Average Loss

Trading overnight outperformed with regard to lose rate vs intraday or buy/hold.

Monday, which had the lowest rate of losses per above section, experienced losses with the greatest average magnitude. Similarly, Friday, which had the highest rate of losses, experienced losses with the smallest average magnitude.

Compound Annual Growth Rate

Annual Volatility

Sharpe Ratio

Max Drawdwn

Max Drawdown Duration

Monthly Returns

Total P/L

Trading overnight outperformed with regard to total return vs intraday or buy/hold.

Exiting day trades on Monday outperformed relative to other days of the week.

Overall

Profitability of various day trading strategies had mixed results.

Day trading overnight outperformed over the duration of the backtest.

Kurtosis

Daily returns were binned in 25 basis point (bp) increments and charted against a gaussian (normal) distribution. As mentioned in the SPY day trading posts here and here, the stock market distribution curve more accurately aligns with a laplace distribution vs a normal distribution.

Below is a zoomed-in view of the left tail. This highlights the high rate of what-would-be 4-sigma and rarer events if modeled against a normal distribution of returns.

Discussion

Wow! What a performance difference when holding positions overnight vs intraday or buy / hold.

Before we dump our existing strategy and start trading overnight positions on AAPL, the first question we need to ask is: “if there’s such a great opportunity here, why hasn’t it been arbitraged away?”

Unfortunately, the short answer is that it already has. Let’s shift the strategy start date from Jan 1993 to Jan 2007. This time frame aligns with the other research on this site, correlates with the introduction of options trading, and allows us to see if the opportunity was arbitraged away in the last 13 years.

Outperformance remains material but fades until buy/hold pulls ahead early 2020.

There’s an inflection point starting around 2016 that portrays overnight beginning to decline and, although hard to tell from the chart alone, intraday having a comeback. Let’s again adjust the backtest start date and look from Jan 2016.

Overnight positions actually underperform all strategies and the strategy spends a material amount of time in negative P/L territory. Of course, had someone picked up on overnight trading in 1993 and executed they would still be very far ahead. However, anyone implementing an active strategy will (or at least should) be periodically evaluating its effectiveness over benchmarks like buy/hold. It is around the 2016-2017 time frame that trading AAPL overnight lost its edge.

At this point in time it appears a simple buy/hold strategy outperforms. I’ll chalk up the blip of intraday outperformance in 2020 to timing luck. The disconnected markets we experienced in March is an outlier event / artifact that made the intraday strategy “lucky” in avoiding the steep overnight losses that occurred during this narrow window.

The main takeaway here is to think critically about what appears to be outperforming strategies or ideas. Not unlike the world of IT security, finding and exploiting trading strategies is no different than finding and exploiting bugs in software: the strategy works until enough people exploit it, then it doesn’t. The markets become efficient / the software vendor becomes aware and the bug patched.