# A Bad Case of the Fridays: A Look at Daily Market Returns

Day trading – is there an optimal way to do it? At a high level the data suggests you’re better off avoiding intraday exposure and instead trading overnight (or shorting intraday, if your risk profile can tolerate it). But what about specific days of the week? If you were to only trade on, say, Tuesdays, how would you do compared to only Fridays or Mondays? Is there a material difference or are all weekdays essentially the same?

Let’s find out!

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 SPY
- Strategy Day Trade (positions held for 24 hours or less, ignoring weekends
- Start Date 1993-01-29
- End Date 2020-01-03
- 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 whatâ€™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.

Wednesday scored highest and Thursday scored lowest.

### Average Win

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

Tuesday scored highest and Wednesday and Friday tied for lowest.

### Best Win

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

Monday scored highest and Thursday and Friday tied for lowest.

### Lose Rate

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

Wednesday scored highest and Thursday and Friday tied for lowest.

### Average Loss

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

Tuesday scored highest and Monday scored lowest.

### Worst Loss

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### Compound Annual Growth Rate

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### Annual Volatility

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### Sharpe Ratio

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### Total P/L

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

Tuesday scored highest and Friday scored lowest.

### Overall

Profitability of various days had mixed results.

Tuesday scored highest and Friday scored lowest.

## Discussion

Walking into this study I expected overnight weekend performance (long trades opened at Fridays close and closed on “Monday”) to have the worst return profile. It turns out such a strategy yields the third greatest risk-adjusted and total return across all strategies. Perhaps the “weekend risk” is overblown, or even a false narrative.

These results assume a long strategy is implemented. Short strategies have their place and appear to shine on Fridays.

The *why* behind the daily performance profiles is anyone’s guess. My $.02 is that people are taking profits or otherwise cashing out on Friday in order to have some spending money for the weekend. Intraday Monday is the second worst performing strategy. Perhaps people overspent during the weekend and need to sell more positions than initially planned.

## Summary

Opening positions Monday at or around the closing bell and closing them first thing Tuesday morning yielded the greatest risk-adjusted and total return, followed by opening a position Monday at the opening bell and closing Tuesday at the opening bell.

Opening positions first thing Friday morning and closing at or around the closing bell yielded the lowest risk-adjusted and total return.

Thanks for reading!

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

Jeff Jewell

February 14, 2020 @ 8:54 am

Great study, Spintwig! When I trade my 2 trading day to expiration strategy, I have noticed that premiums are significantly higher for contracts over the weekend. My database shows that there is an edge for trades opened on Thursday close and allowed to expire on Monday. Sounds like you and I are thinking along the same lines these days!

Nice to see that our results are indicating the same thing also. Have a great weekend!

spintwig.com

February 16, 2020 @ 6:17 pm

Thanks Jeff! It sounds like the uncertainty of the weekend translates into a higher IV for late-in-the week options. Great to hear our independent research is agreeing!

Erick

February 14, 2020 @ 5:03 pm

This is the way

spintwig.com

February 16, 2020 @ 6:24 pm

I don’t have a Disney+ subscription yet ðŸ™‚

JEI

February 14, 2020 @ 7:16 pm

Noice. So buy Monday at the close and sell Friday at the open to avoid the worst intradays, and you could buy at close on Friday and sell at close on Monday as well.

This is really interesting! Thanks!

spintwig.com

February 16, 2020 @ 6:23 pm

That could work!

You’re welcome. Thanks for raising the question / idea last week.