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Maddrix_club III (strategy)
Maddrix club III (STRATEGY) is an advanced trading algorithm that goes long and short in spite of the market condition. It aims to capture large moves. It is the fruit of over a thousand hours of work, trials, errors, research, etc.
The STUDY version is also posted.
There are 5 main variables to adjust the indicator:
Framework length – Use this to establish the environment. Shorter length = volatile environment. Longer length = steadier.
Framework multiplier – Use this to adjust the environment to the asset. Lower value for less volatile price action and higher value for very volatile price action.
Search timeframe – Use this to lookback for a fractal . In theory, the longer the lookback the more reliable the fractal recognition.
Trigger line – Use this to adjust the trigger level. In theory, the lower the trigger the more signals you get.
Threshold – Use this to filter the signals. The higher the threshold, the less signal you get.
The best way to go about changing the settings, is to start off the default value. I’d do a monthly check-in playing with few of them and readjusting based on results from the exchange.
They are different, as this one can show you the equity curve.
It works on many markets as long as there is sufficient activity to create patterns and repetition.
You will notice a slight difference between the strategy calculation and the algo calculation (built in the algo) - this is because the exits are not exactly at the same levels. I left this on purpose, so that you can see that even with a little slippage, overall this is positive.
Very good results have been observed on the 15 min time frame though it technically works on all timeframe (5 min for example, 1 hour also). On the example above ETHUSDT – 15 min, the back test shows consistency in the results for the last 2 years. The theory behind it is based on probabilities, human emotions, and repetition in market patterns.
Results have also shown great adaptability, meaning the total profits don’t change considerably when we play with settings. This is very good because even if you try to fine tune a set of settings to the past, the probability that it keeps working in the future is rather high.
Very important note: the calculations DO NOT take into account any fee or slippage that you always experience on the exchanges.
The indicators only uses real time data, therefore it can’t repaint.
There are absolutely no guarantees about this algorithm and past results are not indicative of future performance.
Fees, slippage and API delay: for any algorithm you will use (from me or others), please keep in mind that fees add up, slippage and delay creates differences between algo theory and reality. We can put in place systems to circumvent that, but we will always have them.
The STUDY version is also posted.
There are 5 main variables to adjust the indicator:
Framework length – Use this to establish the environment. Shorter length = volatile environment. Longer length = steadier.
Framework multiplier – Use this to adjust the environment to the asset. Lower value for less volatile price action and higher value for very volatile price action.
Search timeframe – Use this to lookback for a fractal . In theory, the longer the lookback the more reliable the fractal recognition.
Trigger line – Use this to adjust the trigger level. In theory, the lower the trigger the more signals you get.
Threshold – Use this to filter the signals. The higher the threshold, the less signal you get.
The best way to go about changing the settings, is to start off the default value. I’d do a monthly check-in playing with few of them and readjusting based on results from the exchange.
They are different, as this one can show you the equity curve.
It works on many markets as long as there is sufficient activity to create patterns and repetition.
You will notice a slight difference between the strategy calculation and the algo calculation (built in the algo) - this is because the exits are not exactly at the same levels. I left this on purpose, so that you can see that even with a little slippage, overall this is positive.
Very good results have been observed on the 15 min time frame though it technically works on all timeframe (5 min for example, 1 hour also). On the example above ETHUSDT – 15 min, the back test shows consistency in the results for the last 2 years. The theory behind it is based on probabilities, human emotions, and repetition in market patterns.
Results have also shown great adaptability, meaning the total profits don’t change considerably when we play with settings. This is very good because even if you try to fine tune a set of settings to the past, the probability that it keeps working in the future is rather high.
Very important note: the calculations DO NOT take into account any fee or slippage that you always experience on the exchanges.
The indicators only uses real time data, therefore it can’t repaint.
There are absolutely no guarantees about this algorithm and past results are not indicative of future performance.
Fees, slippage and API delay: for any algorithm you will use (from me or others), please keep in mind that fees add up, slippage and delay creates differences between algo theory and reality. We can put in place systems to circumvent that, but we will always have them.
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Instrukcje autora
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