Dutch Algotrading
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The TRUE Winning Strategy Identified - 6 Month forward test results
🕑 Added 2024-08-30 08:10:54 +0000 UTCPrelude
This post contains all the configs, compose files, strategies, freqtrade user files AND ALL TRADING DATABASES WITH THE TRADES of the bots that I used for analyzing and of the video. For your reference and further analysis if you like to do that.
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In this post I will present you the forward testing results of NINE different trading strategies that I have tested under real cryptocurrency market circumstances.
So if you want to know how some of the best trading strategies perform real live trading conditions, then you cannot miss this video!
Introduction
Hello everybody, and welcome to another video where I dive into algorithmic trading strategies that have the potential to attain live changing money for you.
This time I want to present you with the forward testing results of nine of best performing strategies that I have found so far.
In other words, this is the “proof of the pudding”, the “walk the talk” and the “show me the money” phase where you test a newly found trading strategy, that has been performing well in backtests, and now put it to the test in real world trading to see if it really does what it promised to do.
I do not want to lose money and probably neither do you, so this is a crucial phase in choosing a trading strategy for real live money trading.
Setup
If you have read my earlier posts then you probably know that I like to search for the best trading algorithms that have the highest probability to earn wealth in the future as well.
My main tool for backtesting strategies is the backtesting engine you can find in the Freqtrade trading bot. It is relatively simple to use if you put some effort in learning how to use this bot. And it also provides with lots of helpful information about the performance of a trading strategy.
All the trading strategies that I use therefore also has to be programmed into code so that I can automatically test on multiple assets, on multiple timeframes and with a dataset that spends many years of backtesting data.
By testing a trading strategy this way I can attain multiple advantages like:
I can simulate the most realistic backtest for a trading strategy.
I can prevent the human psychology factor
Also, by changing a trading strategy into a hardcoded algorithm I can prevent that I deviate from my trading strategy while testing
And most important of all, after extensive backtests, I can automatically start with the forward test of multiple strategies at the same time. Which spreads my risks and saves me loads of time too.
The main reasons for concern in these tests are:
Errors in the code’s logic,
Programmed lookahead-bias or
strange, but nonetheless real, market circumstances in the dataset that might causes some results to be overly optimistic or instead make a good trading idea perform terrible.
This is why, after backtesting, you should always forward test a strategy in the real market as well. To see if it really performs this well (or bad) and to get realistic expectations for the future.
Be very aware however that all trading strategies can lose their edge in the market at some point in time. So maintenance of your trading strategy is always recommended after you start to use it in trading.
Technical setup of the bot
In the following slides I show you some of the technical details I have set up on my trading bots to let them test in the real market.
I have configured nine separate trading bot instances. And each bot uses its own trading strategy. The strategies I use are the best performing strategies from the backtests I have done over the last couple of years.
You can find the complete list of trading strategies I tested in the Strategy League that I have published on my website dutch algotrading dot com.
Many of these trading strategies results are also tested and presented in my video’s so if you want to get more details about these well performing trading algorithms, then check out my channel and the strategies playlist there.
Master Swing Trading with the Bollinger Bands Trend Indicator! (youtube.com)
More on which strategies I tested after a moment.
Each bot is allowed to only have three trades open at the same time.
I use a start amount of 1000 USDT with a tradeable balance ratio of 99%
All the best trading algorithms that I have found so far only do spot trading. So shorting is not used in this test.
Also, since I live in the Netherlands, Bybit is the only best option to do this kind of test. No Binance allowed here.
In this test I use the following pairs. These pairs are selected on their marketcap and also on their trading volume over the last year on the Bybit exchange. I wanted to use pairs with liquidity. To get into and out of the market without the least amount of spread. So I created a script to query the exchange on these criteria and got these to use in the test.
"BTC/USDT", "ETH/USDT", "SOL/USDT", "XRP/USDT", "MNT/USDT", "ARB/USDT", "DOGE/USDT", "SUI/USDT", "MATIC/USDT", "AVAX/USDT", "APT/USDT", "LINK/USDT", "PEPE/USDT", "LTC/USDT", "ADA/USDT", "APEX/USDT", "DOT/USDT", "CTC/USDT", "OP/USDT", "BONK/USDT", "SHIB/USDT", "BLUR/USDT", "DYDX/USDT", "BNB/USDT"
Again, all strategies are run with the Freqtrade trading bot.
Some of these bots are configured to be a Linux service. And some are using Docker. Mainly because the algo’s that are run on Docker use deprecated Freqtrade functions, so I had to use a lower software version of the bot here.
The contest and market circumstances
Besides the technical setup and the strategies there is also the broader context of the crypto market environment in which we should judge the results of the forward tests. For that I will use the Crypto total market cap chart on Tradingview, since I trade more than Bitcoin alone with these tests.
https://www.tradingview.com/chart/NmxZSutP/?symbol=TOTAL
I started forward testing trading strategies in March 2024 up until May. So some strategies run longer then others.
By a stroke of coincidence, the first few launched at the peak of the run in 2024, and others on the exact day that another peak was reached in the total crypto market cap.
Overall, the 2024 market has a bearish undertone for the most part. With lower highs and lower lows.
The most important event was the Bitcoin halving event on April 20th. This might trigger the next major bull run in a couple of months. But the periods around earlier halvings have been mostly characterized by sideways action, followed by a supply shock. Followed bij a major bull run.
I think that this time, the total market probably anticipated this move a little bit prematurely and caused a peak before the event. And now we are stuck in a somewhat sideways movement up until today.
So you now know the market circumstances in which the forward tests happened. No major bull or bear trends, but a lot of sideways action with a bearish undertone.
And this context may or may not suit the trading strategy that is tested. Some will thrive in sideways markets and some will only perform best if there are long upward or downward trends.
So this information is something to keep in mind when you see the testresults of these strategies. And it is also something to consider when you yourself test out your own strategy or a 3rd party trading algo.
Strategies and timeframes
So what about the strategies themselves?
Let me now reveal the nine algorithmic trading strategies that I used for these tests.
In random order they are:
ClucMay72018 on the 5 minute timeframe
Obelisk_Ichimoku_ZEMA_v1 on the 5 minute timeframe
BB_RPB_TSL_BI on the 5 minute timeframe
SMAOffsetProtectOptV1 on the 30 minute timeframe
ElliotV8_original_ichiv3_NoTSL on the 5 minute timeframe
NFI5MOHO_WIP on the 5 minute timeframe
MacheteV8b on the 15 minute timeframe
NASOSv4 on the 5 minute timeframe
NASOSv5 on the 5 minute timeframe
Results
I will first present the results of the 7 strategies from the worst to the best. Then I will show you the results of the worst performing trading strategy and finally the strategy that performed the best.
ElliotV8_original_ichiv3_NoTSL
The first strategy I'd quickly like to discuss is the ElliotV8 Ichimoku. In the strategy League this strategy currently ranks within the top 12 of best performers. With high gains, a good win rate and moderate drawdown.
In a forward test however, it performs as one of the worst. At the moment is experiences a loss of 48 USDT.
The fact that is still has a 53 percent win rate with, in total 67 trades, does not help here. This strategy has a profit factor of just 0.79. Which means that on average, it loses money while trading.
And you can see in the Period Breakdown table on the left left, in the profit column, that over all the months it ran, it lost money. So this algo never managed to make any profit in the forward test.
In the cumulative profit chart you see that in the beginning it started positively. But the current market circumstances are not favorable for this algorithm. Why this is the case has to be examined more in depth and trade by trade. Which I will not do in this video.
It's possible that more bullish markets with long parabolic runs fit this strategy better because in backtests it performed well and that should come from somewhere. But for this to find out, I have to test longer and wait for the next bullrun.
For this moment, this is the result of the forward test.
Let me continue with the next trading strategy.
ClucMay72018
Unfortunately, the results of the backtest over a long period of trading data proved to give a different view then what actually was its real time performance.
Until now this algorithm has a negative result of 27 USDT with 60 wins over 35 losses. Giving it a 61% win rate.
In total there were 98 trades where APEX and PEPE were the coins with the majority of trades.
It’s good to know that the bot started with a losing month but eventually also made some positive months. But as you can see in the period breakdown table. A couple of losing trades can really spread havoc on your balance.
This is also clearly visible in the cumulative profit chart in this final overview.
NFI5MOHO
The NFI5 MOHO, in other words an alternate version of the “Nostalgia for infinity” trading algorithm, is the first that has a modest profit over the forward testing period.
What is remarkable here is that this trading algorithm currently holds the top position in the strategy league. But it does not rank highest in the forward test.
Between May and August it managed to attain a profit of 115 USDT. With the help of 34 wins over 6 losses, that’s a 85 percent win rate. Really nice work here I must say!
These numbers give us a expectancy ratio of 2.92 and a profit factor of 3.62.
Also the bot was started in may so it had less time to acquire this amount, which makes it extra special.
June was the only month where this strategy did not perform great, with a minor negative result. But in July it managed to had 10 percent profit over the month. Not bad I must say…
PEPE was again the best coin to trade with here.
Overall you can see at the equity curve in the cumulative profit chart that there was a drawdown at the start of august. Which is not a surprise, since last august’s start was very turbulent and has the last lower low over this bearish period.
A really good performance so far and this makes me curious on how well this trading algorithm would perform if the bull market really started off.
NASOSv5 mod3
Anyway, the next trading strategy with even better results and that started at the same time as the NFI discussed just now, is called the NASOS version 5.
This strategy is also currently one of the best trading algo’s I have found so far. And it stands at a proud number three position in the Strategy League.
Here an unbeatable 15 wins over zero losses gives it a 100% win rate. Not too shabby.
It also manages to attain a profit of 156 USDT in the same period as the NFI5. Mainly because of PEPE again.
From day one it only has green months and as time progressed it managed to gain a higher profit percentage over the months.
But besides these positive things there also hides a potential problem.
Because 15 trades over 4 months is pretty low for a 5 minute timeframe. While others have at least 40 on the 5 minute timeframe.
It could therefore be that these trades are just luck and this is no well balanced trading strategy where more risks are taken but also more profits can be attained.
So keep this in mind when you want to investigate this algo on your own setup.
SMAOffsetProtectOptV1
Moving forward, we come at the SMAOffsetProtectOptV1 trading algorithm.
This forward test started in March so it’s logical that a higher ROI has been gained here.
In total this strategy attained 170 USDT on a 30 minute timeframe, over 27 wins and only 4 losses. The drawdown experienced during this period was slightly over 9 percent.
And PEPE was again the largest contributor to this profit.
The expectancy rate is 5.32 and profit factor 1.82.
You can see in the period breakdown that there were two months that were outliers. One with a loss and another without any trades for some reason.
Also in April this year the account balance was momentarily under water, but recovered fast and decisively.
So, In short for the first strategy that operates on a higher timeframe, these are numbers that an avid trader can be proud of.
NASOSv4
Now the next trading strategy that I forward tested is the NASOS version 4 on the 5 minute timeframe.
This algorithm has the second place on the strategy league and in this list is also is placed at a well deserved third spot.
With a slightly higher profit of that of the SMAOffset strategy, 171 USDT, it attained this ROI over a much shorter period of time. Since I started to test this algorithm at the end of May. So well done here…
Until so far this algo has a win rate of 95 percent, caused by 39 wins over only 2 losses. That’s also truly admirable here. The drawdown also is slightly over 5 percent too until now.
The expectancy ratio is 4.18 and the profit factor is 4.11.
What’s funny is that PEPE is also the best performer here.
Over the months that this algo has been running it only made green numbers each month, with a 6.39 percent overall gain over the month of June.
The cumulative profit curve also shows that after some periods where the line stays flat, sudden increases in profits are gained. And in that turbulent period of August, this bot remained calm and actually did not trade at all. Only to catch new trades after the dip happened.
No financial advice, but if I had to choose an algo for my trading bot at this time, then you’ll know which one I would keep my eye on.
BB_RPB_TSL_BI
But it’s not over yet because I still have three more strategies to discuss here.
The next winning algorithm in my backtest is the Bollinger Real pullback.
This 5 minute strategy attained a 182 USDT ROI with 44 winning trades over 2 losers. That’s again an almost 90 percent win rate here. The drawdown is even better. Only 3.35 percent.
Now to be honest, this strategy was started in march so it had a headstart over the NASOS v4 algorithm. But nonetheless the numbers speak for themselves in this slide.
This time MNT/USDT was the biggest gainer for this algo. But PEPE was close behind with a third spot.
Only in the first month of testing there were negative numbers, but soon after that the profit exploded over the month of march and managed to stay positive over the other months as well.
Which is also clearly shown in the cumulative profit curve of this screen here.
Worst performer
So now we come at the moment where we find out which algo is the worst and which one is the best performer.
Besides good performing trading algo’s there also has to be one that completely fails miserably. Unfortunately this time it is a trading strategy that fails to meet its high expectations…
It was the first trading algorithm that I found that managed to create over a million USDT from 1000 dollars in a backtest. At the time I got those results I was very anxious to test this in real live. Not only because of the high expectations but also because the video where I presented my results got some negative comments as well. Without adding any suggestions to improve the test or explain some of the results. Giving positive criticism so to say.
Anyway, now that I tested this algo and got its real time results it is time to expose the real impostor of this list.
And unfortunately this is the MacheteV8b trading algorithm.
Now to start of with some possible explanations on why this algo performs so badly.
To run this strategy code I had to downgrade the backtesting software to an older version to make some of the deprecated functions run again. That’s why you also do not see some of the information you saw earlier. It just wasn’t available at that point.
In the Performance list with the used pairs you can also see that some coins, like Binance, do in fact respond good to the algorithm. But most others do not. To the point where heavy losses are made.
But the reason that has the highest possibility of causing these losses are hidden in the close reason of most of the trades.
Because every time a loss occurs, it has the close reason “trailing stop loss”. This could be caused by either the build_in trailing stop loss or the optional but available custom stop loss in this algorithmic code.
But since I configured the code to not use the build in trailing_stop_loss is suspect the custom stop loss.
And for some reason this was not detected in the backtest of the strategy.
Custom stop loss should be taken into consideration with backtesting, according to the developers of the bot. See custom stoploss & backtest · Issue #4357 · freqtrade/freqtrade (github.com)
And it is hard to believe this is the real issue. But still it is strange to see that most of the time this trailing stop is triggered and causes a losing trade.
My final thought is that it is another bug in the strategies code or in the software itself. But this should be investigated more thoroughly in the future.
For now it is just a slow bleed to the bottom of your trading account as you can see here.
But to stay positive, we learned a very good lesson here today.
Because if we did not do these forward tests, then we would certainly found this out with real money and thus with real losses.
Best performer
And with this valuable lesson learned we’ve come to the final and best result of this forward test.
Which cryptocurrency trading algorithm has the highest, and above all - proven, potential to make gains in the market?
Until it loses it’s edge ofcourse…
And it is the Obelisk_Ichimoku_ZEMA_v1 algorithm!
Started in March, this algorithm made an ROI over all trades of 311 Tether.
Made with a total of 129 trades on the 5 minute timeframe. With 84 wins and 45 losing trades, that gives it a 65 % win rate.
And an experienced drawdown of around 9 percent.
All recognizable numbers so far.
The algo has an expectancy of 2.42 , so basically the amount you stand to gain (or lose) for each dollar of risk. And a profit factor of 1.6 which is a quantitative evaluation of a strategy’s ability to generate profits relative to losses.
Again Pepe has the best overall performance.
The period breakdown shows a good start of this forward test with an immediate 11 percent profit in the first month. Only the last month has some weaker numbers.
From the Cumulative profit plot you see that this strategy has regular trades and is very active. Most of the trades end up with a profit. And that’s logical considering its results so far.
The performance of this trading algorithm, under the circumstances the market is currently in is truly admirable. With this algo I am again curious to see what the performance would be if the bull market really takes off.
You still have to remember that this is a test without real stakes.
But you cannot get any closer or get more realistic results then this if you want to simulate a strategies performance in the market.
Conclusion and goodbye
And with this reveal we come at the end of this post. 
I hope you learned something valuable from this forward test. Like the importance of it and its uniqueness on Youtube as far as I can tell.
Also it adds valuable insight to which cryptocurrency trading algorithms to investigate further and which to abandon.
Thank you very much for being a supporter and I’ll see you in the next post. Because I will continue to search for the best algorithmic trading strategy for crypto.
Goodbye!
Comments
Dutch Algotrading
I decommissioned them and will run new ones next year. But results might be skewed because of the bull market.
Jaziz V
Hi, this is truly a great post. It would be really helpful to have an update on how these strategies have performed since the original post. Thank you for all the work you do.
Alex
Indeed, great post! Thanks!
Dutch Algotrading
Thanks. And thank you too for being a supporter!
Claudio
Thank you, this type of content is great including the files and configs that you upload. it has been great help to me
