BTC Long Day Trade Equity Backtest

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!
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 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.
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


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
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Annual Volatility
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Sharpe Ratio
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Max Drawdwn
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Max Drawdown Duration
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Monthly Returns
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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.

[/mepr-active rules="9006" unauth="both"]
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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June 12, 2020 @ 8:11 am
I am so glad I stumbled across this site while researching how to properly do backtests for my options trading strategies. Thank you!
June 12, 2020 @ 3:56 pm
Hi Jason – happy to hear it’s helpful.
June 14, 2020 @ 3:47 pm
I like the reduced risk by limiting the trading time to 8 hours/week. Good job
August 14, 2020 @ 4:29 am
Hi,
1) with these Strategies, where you close and open a new position intermediately, what will be the difference if you just buy and hold, without selling?
2) in regulated brokers it’s not possible to buy bitcoin (only bitcoin futures, where I think that results will be similar to this strategy) and GBTC
3) When you close a position, in this backtest, it can be a profit or loss, correct?
4) it’s possible to back-test a longer holding period, like 1 week or 1 month? and GBTC?
thank you very much and keep with your great research!
August 17, 2020 @ 12:31 am
Hey Uri —
1) I’m not sure I understand the question. Perhaps restate differently?
2) This is true, and agree the results should be similar.
3) Correct.
4) It is. Is the idea to stratify results by week of month or month of year?
You’re welcome! Appreciate the kind words.
August 17, 2020 @ 5:07 am
Thanks for your answer!
In question 1, I mean, that this strategy will be similar to big and never sell?
what is the advantage of close a position and buy again intermediately?
5) I think that this back-test show a good performance, because it include a big bull market in bitcoin,
if you make this back-test from 2018 to 2020, I think that you will get very different results…
August 18, 2020 @ 12:06 am
Correct, should be similar to buy and never sell.
There is no advantage if the intent is to buy/hold. It’s strictly an administrative activity to measure daily returns. For example, if someone wanted to “buy and hold” only on Tuesdays (granted, this isn’t an actual buy and hold), exiting and reopening the following day does nothing more than allow measuring of the previous day’s performance.
5) It’s quite possible. The same thing happened on the AAPL equity study: https://spintwig.com/aapl-long-day-trade-equity-backtest/#Discussion
The outperforming strategy started underperforming in 2016 onward.
October 25, 2020 @ 12:35 pm
I do not believe you are able to trade BTCUSD commission free anywhere (with the exception of derivatives which obviously complicates everything) so do you think it is likely the backtest results are not very representative when assuming 0% commissions? It would be interesting to see the results assuming the fee structure of a major BTC fiat exchange like Coinbase Pro.
Cheers
October 25, 2020 @ 11:17 pm
That’s a good point.
According to Coinbase Pro the trade fee is a % of the notional traded – 0.5% for amounts <= $10k. https://help.coinbase.com/en/pro/trading-and-funding/trading-rules-and-fees/fees
That's a 1% drag every week! I didn't realize commissions are that high. I guess it's apparent that I don't trade BTC so my assumption is a poor one in this case 🙂
The relative performance of the different strategies stands but implementation is impractical due to costs. I'll look into reproducing this study with the trade fees intact and will be sure to include them in other BTC studies.
Good call!
November 6, 2020 @ 6:52 am
Hi, how will be the results if instead of backtest, BTC, Long Day Trade, you do backtest, BTC, Long MONTH Trade, that means open a position the first of the month, and close it at the end of the month, and then open a new long position…
November 6, 2020 @ 8:35 am
Great question.
Not sure on performance. However, it would be quite susceptible to timing luck. For example, we could open a position on the 1st and close on the 30th, or we could open on 15th and close on 14th. There are essentially 30 variations of this (ignoring Feb), one for each day of the month. Each will have a unique result.
Since BTC only has about 6 years of trading history, that’s only 6 “January” trades. The observation count would be too low to derive defensible stats.