# AAPL Long Day Trade Equity Backtest

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!

Contents

## Prior Research

**Basics**

How to Trade Options Efficiently Mini-Series

**Backtesting Concepts**

Building a Research Framework

### AAPL – Apple Inc.

- AAPL Short Put 0 DTE Cash-Secured
- AAPL Short Put 45 DTE Cash-Secured
- AAPL Short Put 45 DTE Leveraged
- AAPL Long Day Trade

### AMZN – Amazon.com, Inc.

### BTC – Bitcoin

### C – Citigroup Inc.

### DIA – SPDR Dow Jones Industrial Average

- DIA Short Put 7 DTE Cash-Secured (coming soon)
- DIA Short Put 7 DTE Leveraged (coming soon)
- DIA Short Put 45 DTE Cash-Secured (coming soon)
- DIA Short Put 45 DTE Leveraged (coming soon)

### DIS – Walt Disney Co

### EEM – MSCI Emerging Markets Index

### GE – General Electric Company

### GLD – SPDR Gold Trust

### IWM – Russel 2000 Index

- IWM Short Put 7 DTE Cash-Secured
- IWM Short Put 7 DTE Leveraged
- IWM Short Put 45 DTE Cash-Secured
- IWM Short Put 45 DTE Leveraged
- IWM Long Day Trade

### MU – Micron Technology, Inc.

### QQQ – Nasdaq 100 Index

- QQQ Short Put 7 DTE Cash-Secured
- QQQ Short Put 7 DTE Leveraged
- QQQ Short Put 45 DTE Cash-Secured
- QQQ Short Put 45 DTE Leveraged

### SLV – iShares Silver Trust

- SLV Short Put 45 DTE Cash-Secured
- SLV Short Put 45 DTE Leveraged (coming soon)

### SPY – S&P 500 Index

- SPY Long Put 45 DTE Optimal Hedging
- SPY Long Call 45 DTE
- SPY Long Call 730 DTE LEAPS
- SPY Short Put 0 DTE Cash-Secured
- SPY Short Put 0 DTE Leveraged
- SPY Short Put 0, 7, 45 DTE Leveraged Comparison
- SPY Short Put 2-3 DTE M,W,F “BigERN Strategy” (guest post)
- SPY Short Put 7 DTE Cash-Secured (coming soon)
- SPY Short Put 7 DTE Leveraged
- SPY Short Put 45 DTE Cash-Secured
- SPY Short Put 45 DTE Leveraged
- SPY Short Put 45 DTE Leveraged binned by IVR (coming soon)
- SPY Short Vertical Put Spread 0 DTE (coming soon)
- SPY Short Vertical Put Spread 45 DTE
- SPY Short Call 0 DTE Cash-Secured
- SPY Short Call 0 DTE Leveraged
- SPY Short Call 45 DTE Cash-Secured
- SPY Short Call 45 DTE Leveraged
- SPY Short Straddle 45 DTE
- SPY Short Strangle 45 DTE
- SPY Short Iron Condor 45 DTE
- SPY Wheel 45DTE
- Making Money in Your Sleep: A Look at Overnight Returns
- A Bad Case of the Fridays: A Look at Daily Market Returns

### T – AT&T Inc.

### TLT – Barclays 20+ Yr Treasury Bond

### TSLA – Tesla, Inc.

### USO – United States Oil Fund

### VXX – S&P 500 VIX Short-Term Futures

- VXX Short Call 45 DTE Cash-Secured
- VXX Short Call 45 DTE Leveraged
- VXX Short Vertical Call Spread 45 DTE

### VZ – Verizon Communications Inc.

### Other

## Methodology

### Core Strategy

- Symbol AAPL
- Strategy Day Trade (positions are held for 24 hours or less, ignoring weekends
- Start Date 1993-01-04
- End Date 2020-04-17
- Positions opened 1
- Entry Days every trading day in which entry criteria is satisfied
- 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

### Commission

The following commission structure is used throughout the backtest:

- 0 USD, all in, per trade:
- to open
- to close

While the retail brokerage industry has moved to eliminate trade commissions, trade commissions were present and ranged on average from $4.95 to $19.99 per trade in the 1990s through early 2010s.

In practice strategy performance will be lower than what’s depicted due to elevated trading fees in the earlier years of the backtest.

### Slippage

Slippage is factored into all trade execution prices accordingly:

- In datasets where bid/ask values are present, midpoint price is selected and may result in fractions of a cent in certain calculations.
- In datasets where bid/ask values are NOT present, the depicted price is selected.

### Inflation

All values depicted are in nominal dollars. In other words, values shown are not adjusted for inflation.

In practice this may influence calculations that are anchored to a particular value in time such as the last “peak” when calculating drawdown days.

### Binning of Overnight Positions

For positions that are held overnight, performance is associated with the day in which the position is *closed*.

A position binned as “Monday overnight” is one that was opened at Friday’s closing bell and closed at Monday’s opening bell.

A position binned as “Monday price return” is one that was opened at Friday’s closing bell and closed at Monday’s closing bell.

A position binned as “Monday intraday” is one that was opened at Monday’s opening bell and closed at Monday’s closing bell.

The same mechanics hold true for positions opened mid week. For example, a position binned as “Wednesday price return” is one that was opened at Tuesday’s closing bell and closed at Wednesday’s closing bell.

### Fractional Shares

Capital is assumed to be 100% allocated at all times. In scenarios where a full share is not able to be purchased with remaining capital, a fractional share is purchased instead.

### Reinvesting Profits

The “All” strategies are calculated with returns compounded *daily*. That is, any profits are reinvested in the strategy the next trading day.

The “daily” strategies such as “Monday” or “Thursday” are calculated with returns compounded *monthly*. That is, any profits are reinvested in the strategy starting the first trading day of the next month.

### Calculating Strategy Statistics

I build all strategy performance statistics directly from the trade logs. Below is a breakdown on how I calculate each stat and the associated formula behind the calculation.

#### Win Rate

The percentage of trades that were profitable upon closure.

`( count of trades where P/L > 0 ) / count of all trades`

#### Average Win

The percentage of trades that were profitable upon closure.

`( sum of trade P/L values where P/L > 0 ) / count of trades where P/L > 0`

#### Best Win

Identify the largest value among the daily returns:

`MAX( daily return values )`

#### Lose Rate

The percentage of trades that were unprofitable upon closure.

`( count of trades where P/L < 0 ) / count of all trades`

#### Average Loss

The percentage of trades that were profitable upon closure.

`( sum of trade P/L values where P/L < 0 ) / count of trades where P/L < 0`

#### Worst Loss

Identify the smallest value among the daily returns:

`MIN( daily return values )`

#### Average Monthly Return

Identify the average monthly returns.

`AVERAGE( monthly return values )`

#### Best Monthly Return

Identify the largest value among the monthly returns.

`MAX( monthly return values )`

#### Worst Monthly Return

Identify the smallest value among the monthly returns.

`MIN( monthly return values )`

#### Max Drawdown

This measures the greatest peak-to-trough decline, described as a percentage of the portfolio’s end-of-day value (open positions / unrealized P/L is not factored into end-of-day P/L value).

`MIN( monthly drawdown values )`

#### Annual Volatility

The standard deviation of all the *monthly* returns are calculated then multiplied the by the square root of 12.

`STDEV.S( monthly return values ) * SQRT( 12 )`

#### Compound Annual Growth Rate

This measures the compounded annual rate of return, sometimes referred to as the geometric return. The following formula is used:

#### Sharpe Ratio

Total P/L alone is not enough to determine whether a strategy outperforms. To get the complete picture, volatility must be taken into account. By dividing the compound annual growth rate by the volatility we identify the risk-adjusted return, known as the Sharpe ratio.

`strategy CAGR / strategy volatility`

#### Total P/L

How much money is in the portfolio after the study? This stat answers that question and depicts it as a %

`( portfolio end value / portfolio start value ) - 1`

## 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

You'll need an active membership to view this content. Already a member? Log in.

### Annual Volatility

You'll need an active membership to view this content. Already a member? Log in.

### Sharpe Ratio

You'll need an active membership to view this content. Already a member? Log in.

### Max Drawdwn

You'll need an active membership to view this content. Already a member? Log in.

### Max Drawdown Duration

You'll need an active membership to view this content. Already a member? Log in.

### Monthly Returns

You'll need an active membership to view this content. Already a member? Log in.

### 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.

## Summary

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.

Thanks for reading!

Thoughts? Feedback? Dedications? Shoutouts? Leave a message in the comments below!