# IWM Long Day Trade Equity Backtest

In this post we’ll take a look at the backtest results of opening one IWM long day trade position each trading day from Jan 3 2007 through May 8 2020 and see if there are any discernible trends. We’ll also explore the profitable strategies to see if any outperform buy-and-hold IWM.

There are 15 backtests in this study evaluating over 13,400 IWM 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 IWM
- Strategy Day Trade (positions are held for 24 hours or less, ignoring weekends
- Start Date 2007-01-03
- End Date 2020-05-08
- 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 Friday exit yielded the greatest win rate.

### Average Win

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

Day trades with a Tuesday exit had the greatest average win magnitude.

### Lose Rate

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

Day trades with a Friday exit had the lowest loss rate.

### Average Loss

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

Day trades with a Tuesday exit had the lowest average loss magnitude.

### Compound Annual Growth Rate

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

Day trades with a Friday exit outperformed exiting on other days.

### Annual Volatility

Trading overnight outperformed with regard to annual volatility vs intraday or buy/hold.

Day trades with a Thursday exit had the lowest volatility.

### Sharpe Ratio

Trading overnight outperformed with regard to risk-adjusted returns vs intraday or buy/hold.

Day trades with a Tuesday exit had the highest sharpe ratio.

### Max Drawdwn

Overnight outperformed with regard to max drawdown vs intraday or buy/hold.

Day trades with a Tuesday exit had the softest max drawdown.

### Max Drawdown Duration

Overnight outperformed with regard to max drawdown days vs intraday or buy/hold.

7 of 15 strategies failed to reach a new high after their largest drawdown

### Monthly Returns

Overnight outperformed with regard to average monthly returns vs intraday or buy/hold.

Day trades with a Tuesday exit had the greatest average monthly returns.

### Total P/L

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

Day trades with a Tuesday exit had the greatest total P/L.

### 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.

## Discussion

Opening a position at the Monday morning bell and closing it at the Tuesday morning bell **outperforms a total return buy/hold strategy by 1.6% with nearly 86% less time in the market**.

The mantra of buy and hold investors is: “time in the market beats timing the market.” It appears there may be some exceptions.

The challenge is that the exception works until it doesn’t. Meanwhile, it’s not possible to discern when it’s no longer working until relative losses have been discovered by backtesting / looking in the rearview mirror.

We can actually see this exception in action. Take a look at the Tuesday buy/hold strategy on the equity curve graph in the section above. It experiences massive outperformance but then stalls early 2014, trending flat-to-down for the next 6 years.

Around the time of the stall the Tuesday overnight strategy begins to trend upward.

## Summary

Systematically day trading is an unprofitable strategy.

Systematically trading overnight outperforms buy/hold on both a risk-adjusted and total return approach.

Thanks for reading!

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