BTC Long Day Trade Equity Backtest

5 min read

In this post we’ll take a look at the backtest results of opening one BTC long day-trade position each trading day from Sept 17 2014 through June 9 2020 and see if there are any discernible trends. We’ll also explore the profitable strategies to see if any outperform buy-and-hold BTC.

There are 15 backtests in this study evaluating over 6,200 BTC long day trade occurrences.

Let’s dive in!

Prior Research

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.

• AMZN Short Put 45 DTE Cash-Secured

BTC – Bitcoin

• BTC Long Day Trade

C – Citigroup Inc.

• C Short Put 45 DTE Cash-Secured

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

• DIS Short Put 0 DTE Cash-Secured
• DIS Short Put 45 DTE Cash-Secured

EEM – MSCI Emerging Markets Index

• EEM Short Put 45 DTE Cash-Secured
• EEM Short Put 45 DTE Leveraged

GE – General Electric Company

• GE Short Put 45 DTE Cash-Secured

GLD – SPDR Gold Trust

• GLD Short Put 45 DTE Cash-Secured
• GLD Short Put 45 DTE Leveraged

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.

• MU Short Put 45 DTE Cash-Secured

QQQ – Nasdaq 100 Index

• QQQ Short Put 7 DTE Cash-Secured (coming soon)
• QQQ Short Put 7 DTE Leveraged (coming soon)
• 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.

• T Short Put 45 DTE Cash-Secured
• T Short Put 45 DTE Leveraged

TLT – Barclays 20+ Yr Treasury Bond

• TLT Short Put 45 DTE Cash-Secured
• TLT Short Put 45 DTE Leveraged

TSLA – Tesla, Inc.

• TSLA Short Put 45 DTE Cash-Secured

USO – United States Oil Fund

• USO Short Put 45 DTE Cash-Secured
• USO Short Put 45 DTE Leveraged
• USO Short Call 45 DTE Cash-Secured

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.

• VZ Short Put 45 DTE Cash-Secured
• VZ Short Put 45 DTE Leveraged

Other

• Cumulative Income: Investing in Preferred Shares

Methodology

Core Strategy

• Symbol BTC
• Strategy Day Trade (positions are held for 24 hours or less, ignoring weekends
• Start Date 2014-09-07
• End Date 2020-06-09
• 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.

For example, an overnight position that is opened at the closing bell on Friday and closed Monday morning will have the trade performance binned as a “Monday” trade.

Similarly, a buy/hold position that is opened at the opening bell on Tuesday and closed Wednesday morning will have the trade performance binned as a “Wednesday” trade.

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.

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

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

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

Average Win

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

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

Lose Rate

Buy and hold outperformed with regard to lose rate vs trading overnight or intraday.

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 Monday exit had the lowest average loss magnitude.

Compound Annual Growth Rate

Buy and hold outperformed with regard to geometric return vs trading overnight or intraday.

Day trades with a Monday 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 Monday exit had the lowest volatility.

Sharpe Ratio

Buy and hold outperformed with regard to risk-adjusted returns vs trading overnight or intraday.

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

Max Drawdwn

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

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

Max Drawdown Duration

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

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

Monthly Returns

Buy and hold outperformed with regard to average monthly returns vs trading overnight or intraday.

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

Total P/L

Buy and hold outperformed with regard to total return vs trading overnight or intraday.

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

Overall

Profitability of various day trading strategies had mixed results.

Buy and hold outperformed over the duration of the backtest vs trading overnight or intraday.

Kurtosis

Daily returns were binned in 25 basis point (bp) increments and charted against a gaussian (normal) distribution. The distribution curve more accurately aligns with a laplace distribution vs a normal distribution.

Discussion

There are 168 hours in a week. Buying and holding bitcoin over the duration of the backtest yielded over a 25x return (2414.44%). By trading intraday on only Mondays and tying up capital for a mere 8 hours per week, one would have nearly quintupled their money (385.86%).

Said another way, 4.7% of the time in the market captured 16% of the buy-and-hold gains. That’s a 3.4x improvement in return per unit of time in the market (capital efficiency)!

If returns are reinvested (read: compounded) then participating only during intraday hours accounts for 24% of the time exposure and captures 84% of the gains for a 3.5x capital efficiency.

Summary

Opening positions Monday morning and closing them Monday evening improved capital efficiency by ~3.4x.

Opening positions Friday morning and closing them on Monday morning yielded the highest sharpe ratio among the day-trading strategies.

Thanks for reading!

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

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