This is one of the most important trading videos you will ever watch! Because you will see tested data that shows whether you will become a profitable trader or not!
Over the years, I have tested many different trading strategies 100 times on the Trading Rush Channel.
I didn’t just test them in one market, but in multiple market conditions, from good to bad.
In total, we tested more than 10,000 trades!
That is a lot of trades!
But I have also given all tested strategies a Trading score and rated them in different categories, such as win rate, ease of use, reliability, consistency of profits, and quality of trades.
All this helps us find the best trading strategy out there!
But when i plotted this tested data using charts, I found something really important and interesting!
It showed the reality of trading and what actually decides if you will make it in trading or not!
So in this video, we will go through seven really important data points, that will greatly impact your probability of becoming a successful trader according to data.

If you have been trading for a while, there is a good chance you have tried many strategies.
And there is an even better chance that most of them did not work.
So, you ditched them, found new ones, and the cycle kept going.
In our trading journey, we experience that the majority of trading strategies don’t work.
Some of them give around a breakeven win rate, and only a tiny percentage actually make money.
But here is what the data from over 10,000 trades actually shows, and it is going to change everything you knew about why strategies work.
You see, on the Trading Rush Channel, I tested strategies in mostly good, extremely good, and extremely bad market conditions.
And almost all strategies were trend trading strategies.
Here, the first chart shows what happened to strategies in the mostly good, trending market.
This is the market where the price is mostly trending with bigger pullbacks, but also has range choppiness in between.
In this market condition, around 78% of strategies made a profit.
Only thirteen percent actually lost money.
And around 9% were sitting near the breakeven point.
This data proves something really important.
Take any strategy you have ever used, the ones you deleted, the ones you gave up on, and the ones we all thought didn’t work.
If those strategies were trend trading strategies, there is a really high chance the strategy was working and making money all along.
The strategy was never broken, but something else was the problem, which you will see in a moment.
The second chart shows what happened to the strategies in extremely good market conditions.
This is where the price was moving strongly in one direction and the range-choppy markets were filtered out.
Around 87% of strategies made a profit, and only 12% lost money.
This also includes strategies that didn’t give enough trading opportunities.
But even counting those as bad results, only 12% lost.
Most of them made a really nice profit.
But things get really interesting in the extremely bad market conditions.
Around 75% of strategies lost money, and 23% were around the breakeven point.
Only a single strategy out of everything managed to make a good profit.
This is important to understand because it really shows what is actually happening!
For example, imagine you are standing at a train station.
When the train slows down and stops, some people get out of the train and some people get in.
Then, the train starts moving again, picks up speed, and before you know it, it is flying down the tracks.
Now, at that point, it doesn’t matter what is inside that train: luggage, a bicycle, a confused tourist, or a broken umbrella.
Everything inside is moving at full speed, not because of any special ability, but simply because the train is carrying it forward.
But now, imagine that this station is the last stop on the route.
The train slows down, some people get out, and some people get in, but this time, nothing happens.
Everything and everyone who got on the train is now just sitting in a stationary train, going absolutely nowhere.
The ten thousand trades tested data is showing the same thing!
A trending market is like a moving train: the price moves in one direction, then slows down a little at the train station where people get in and out, and then the price continues to move in the trend direction.
Strategies work not because they are special, but because the trend is doing all the heavy lifting.
When the price train comes to the last station and stops moving, becoming choppy, slow, ranging, or pretty much flat, most trend trading strategies in this market will simply lose money.
They lose not because they don’t work, but simply because the train itself has stopped moving.
That is why the data shows most strategies winning in good trending markets and most strategies losing money in bad market conditions.
But the thing is, when we are beginner traders, we have no experience identifying the good and bad market conditions.
Everything looks the same to us.
Our trading experience looks something like the third chart, where most trading strategies lose money and we think they simply don’t work.
This is also the point where many traders simply quit trading.
But once we gain enough live trading experience to identify the good and bad markets, we learn to simply avoid trading when market conditions get bad.
As a result, the profit graph starts moving in an upward direction overall.
This is when we start finding strategies that actually work.
But it was all an illusion. All these strategies always used to work.
It just didn’t work before because we lacked enough experience to filter out the bad market conditions.
So, the data from more than 10,000 tested trades says that if you really want to become profitable in trading, don’t focus on the strategies.
Focus on identifying the good and bad market conditions first, because those are the main things that greatly impact your probability of success.

When we are beginner traders, we have a win rate number planted in our heads.
We see a trading guru get a 60% or 70% win rate, and that is what we think we should be getting.
Trading gurus show us how they win 8 out of 10 trades and make big profits.
But even after following all the rules, we only manage to get a breakeven, or just above a breakeven, win rate.
We think that we are just not good enough.
The trading guru must have a secret strategy that makes him win 8 out of 10 trades with a big reward risk ratio.
So, we keep switching our strategy and chasing that high win rate, but we still never manage to get win rates like those trading gurus.
But if we look at the data, you will see the reality of trading gurus and how they actually trade.
This chart shows the win rate data of 10,000 trades.
On the x-axis, we see the win rate score a strategy has achieved, and on the y-axis, we see the number of strategies that achieved that particular win rate score.
Basically, if you see a tall mountain near the left side of the chart, it means a lot of strategies got a really low win rate.
But if you see a tall mountain on the right side, it means many strategies got a high win rate.
The different colored mountains you are seeing represent different market conditions.
The red one represents the extremely bad market win rate.
The blue one represents the mostly good market win rate.
And the green mountain represents the extremely good market win rate.
In my testing series, I used a 1.5 to 1 reward-to-risk ratio.
The breakeven win rate for this ratio is 40%, which is represented by a win rate score of 4 on this chart.
Basically, this yellow line is the breakeven point.
All strategies below that point made a loss, and all strategies on the right-hand side of that point made a profit.
As you can see, in the extremely bad market, not only did most strategies make a loss, but the median point, which is represented by this red line, is also below the yellow breakeven point.
But you can also see that some part of this red mountain is on the right side of the yellow line. It basically means that even though there were a few strategies that managed to make a profit even in an extremely bad market, the median line shows that the true middle point of all this data is on the losing side.
It means that although there may be profit in the short term, there is a high chance that the long-term profit in an extremely bad market will be negative.
The win rate will drop below the breakeven point.
But then, if you look at the green mountain, which shows the extremely good trending market, you will see that its median point is far away from the yellow breakeven win rate.
The thing is, this median point could have been even higher, as there were some strategies that achieved a really high win rate.
However, if you notice, the x-axis shows the win rate score and not the actual win rate, because in an extremely good market, some breakout-like strategies managed to get a really high, unrealistic win rate.
They achieved this by spamming trades back-to-back or by getting lucky.
So, their win rate score had to be reduced.
Since this win rate category in the Trading Rush score accounts for spammy and lucky trades, this win rate data becomes more reliable than looking at the spammy win rates.
In fact, after filtering the lucky ones out, the data is saying that most trading strategies in an extremely good trending market will get a win rate that is around the 60% mark with a 1.5 to 1 reward-to-risk ratio.
Remember that the breakeven win rate with this ratio is 40 percent.
So, this 60% median is 20 points above the breakeven win rate, which is actually a good thing for us.
But if you pay attention to the blue mountain, which represents the mostly good trending market, you will notice that its median line is only slightly above the breakeven point.
In fact, it is only 5% above the breakeven point.
This proves two very important things.
The first is that if you see a random trading guru, claiming to win 8 out of 10 trades in a row consistently, or get a really high win rate with a high reward-to-risk ratio, remember that he is most likely lying and showing you a handpicked setup.
The data from more than 10,000 trades says that most people can realistically only achieve a profitable win rate that is 5 to 10 percent point higher than the breakeven win rate.
Basically, if you find a strategy that is making money, but only has a 5% to 10% point higher win rate than the breakeven, do not throw that strategy away, in hopes of finding some unrealistic strategy that you saw a trading guru use.
The second thing this data proves is that trading in the right market conditions is really important if we want to make good money in trading.
For example, you can see how far away the green mountain is from the breakeven point, and even so far ahead of the blue mountain.
But the thing is, extremely bad market conditions, like ranging slow choppy, are more frequent.
So, the majority of good traders will end up taking trades in mostly good markets which is made up of little bit of bad in between.
Taking trades only in the extremely good market is really rare. And one has to be really good at trading.
But imagine having enough trading experience to filter out the extremely bad markets as much as possible, so you only end up trading in these two good market conditions.
Imagine what that trading style would look like.
I would say that kind of trader, for the majority of the time, would do nothing and patiently wait for the right trading opportunity.
That is exactly why it is said that patience in trading is one of the most important things.
Patience and enough experience to avoid bad market conditions are what will make you really profitable in trading, according to the data.

At some point in our trading journey, we find someone who is trading on charts that look like this.
There are many indicators on the chart and they are confirming ten different things.
Sometimes the idea makes sense: if an indicator has a disadvantage, then using another indicator to fix it sounds like a good idea.
I am sure we all do this at some point.
But all we do is end up creating a messy-looking chart.
Instead of finding good trades, we just end up finding confusion.
So the question is, does adding more indicators to the chart or using a really difficult, confusing strategy actually increase the win rate?
This is what the 10,000 trades’ data says.
On this chart, the x-axis shows how easy a strategy was to use.
And the y-axis shows the number of strategies at that easy-to-use level.
Basically, strategies on the left-hand side were difficult and confusing, and the ones on the right were easy to use.
The tall mountain near the right-hand side shows that the most popular strategies were simple and easy to follow.
They were not extremely simple like some strategies, but simple enough for all market conditions.
We only see one mountain this time because the other mountains are overlapping each other.
However, there were also some strategies that were messy.
They had too many rules or required multiple indicators to filter things.
To see if these difficult strategies actually increase the win rate or not, we will compare this easy-to-use data with the win rate data.
This is what it looks like.
In this chart, the x-axis shows the win rate score, and the y-axis shows how easy the strategy was to use.
Basically, if a strategy was difficult but got a higher win rate, it would appear in the right bottom corner of this chart.
If the strategy was difficult but still got a lower win rate, then it would appear on the bottom-left of the chart.
But as you can see, the strategies are all over the place.
Not only did the easy-to-use strategies get a high win rate, but the difficult ones also did.
On the other hand, in other market conditions, not only did the easy-to-use strategies get a lower win rate, but the difficult ones also did.
So, the data says that adding multiple indicators to the chart or using confusing and messy strategies with too many rules is probably not going to increase your win rate.
The biggest thing that will impact your win rate is the market condition itself.
Think of it like this. There are two cars. One is a basic, reliable sedan with no fancy features, and the other one is loaded with every fancy gadget available.
It has a heads-up display, automatic lane correction, and autopilot with 3D sensors everywhere and a hundred different cameras.
It has a big screen for every possible feature.
Now, imagine both cars are driving on a road that is full of potholes.
When they drive, the fancy gadget car has no ability to fix the potholes. The road is still the road.
The extra complexity of the second car does absolutely nothing to solve the actual problem, which is the condition of the road.
Here, the fancy car and the simple sedan will experience the same outcome and the same success rate in crossing the road.
In trading, the market conditions are the road.
If the market conditions are bad, it’s better to slow down.
It’s better to take fewer trades with lower risk, or simply avoid trading in the garbage market conditions.
The best move is to adapt to the market and find a different route to success.
But if the market conditions are good and the road looks smooth, then it’s better to increase our speed by taking as many trades as possible in these good market conditions.
That’s why it is said that adapting to the market is one of the most important skills you can have in your trading journey.

Imagine you have two friends.
One of them talks a lot and gives a lot of advice throughout the day.
Some of his recommendations are right, but most of them are bad.
Then there is the other friend who talks less. Butwhen he opens his mouth, his advice is more accurate.
One of these friends is worth listening to more than the other when it comes to advice.
One of them is simply more reliable than the other.
Trading strategies are like these two friends.
Some give many entry points and end up winning in the short term on a particular chart or time frame.
But at other times, even if the market conditions are the same, they become completely unreliable because they simply managed to win previously by spamming.
But then there are strategies that give fewer entry points.
But when they talk, they are more reliable.
It’s like one friend is Warren Buffett and the other one is some random trading guru with ten Ferraris in the background.
We all would definitely pay attention to what Mr. Buffett has to say.
This is exactly what this reliability data is showing.
The X-axis shows the reliability scores the strategies received, and the Y-axis shows how many strategies received those reliability scores.
Here, if you see a mountain on the left side, it means more strategies were unreliable.
But if you see tall mountains on the right-hand side, it means more strategies were reliable.
But as you can see, this time we see multiple mountains, even in the same market condition.
The blue represents the mostly good market condition, but this time it has two mountains.
This happened because fewer strategies reliably gave entry points without spamming or getting lucky.
If you use them in different stocks, Forex, or crypto, the win rate will have a higher probability of staying similar as long as the market condition is also similar.
But then, there were some strategies that managed to make money this time.
But if you try them on different stocks, Forex, or crypto, they can give a different win rate even if the market condition is mostly good and trending.
That is because those strategies gave entries in a spammy way, like talking too much, and won by getting lucky this time.
You can see a similar pattern in the extremely good market conditions as well.
Since some strategies got a high win rate by spamming trades, they received a lower score in the reliability category, while others got a higher score.
That is why we see two mountains.
But in the extremely bad market, almost all strategies sucked, and so they received a lower score, except for one strategy.
This one managed to make a good enough profit without spamming trades and received a higher reliability score.
But that is rare.
The tall red mountain and its median line are far away from this more reliable strategy.
But the data is basically saying that although you will find working, profitable strategies, some talk more and might look good in the short term.
However, fewer strategies are actually reliable across different charts and timeframes, even if the market conditions are the same.

Every strategy gives an entry point with its own logic.
In a trend, some strategies try to give an entry point near the end of a pullback, like when the price starts moving back in the trend direction.
But some strategies give an entry point at every small reversal, like at every attempt of the price moving back in the trend direction.
In certain market conditions, this can lead to multiple back-to-back losses before finally winning a trade.
Here, it is much better to use a strategy that uses a slow-reacting logic and only gives an entry point with a higher probability of winning.
Basically, some strategies give entries that do not really make sense in those market conditions.
They kind of look random.
But some strategies are more logical.
That is exactly what the quality of trades data is measuring.
The x-axis shows the quality score a strategy received, and the y-axis shows the number of strategies that received that quality score.
In extremely bad markets, when the price is choppy and messy, taking trades with most strategies doesn’t really make sense.
Here, the quality of those trades was much lower, except for one strategy.
We will get to that strategy in the next chapter.
But the interesting thing is that, in the mostly trending market, you can once again see two mountains.
This is because even though there were profitable strategies, many of them gave a lower-quality setup.
It’s similar to getting an entry point at every small reversal.
On the other hand, fewer strategies gave higher-quality entries.
Like one proper entry at the end of the pullback.
But things get even more interesting.
The same strategies that had a lower quality score in the mostly good market, became better strategies in the extremely good market.
That is because, in the extremely good market, when the price is in a really strong trend, the pullbacks are often much smaller.
The price moves in the opposite direction a little bit and then quickly returns to the trend direction.
Here, quick-reacting strategies that were bad before in the mostly good market, actually performed better and achieved a higher win rate.
Not only that, but the strategies that performed really well during bigger pullbacks in the mostly good market, can give a relatively low win rate in the extremely good market, simply because there are not enough bigger pullbacks.
The number of trading opportunities can also drop as a result.
So, the data from more than 10,000 trades, shows us that the logic that makes a strategy work in one market condition, can fail in a better market condition, and vice versa.
So, instead of only using one best trading strategy, we should have multiple strategies for different market conditions.
When we want to take a trade, we first analyze what kind of market it is.
Then, we simply use the strategy that works in that market condition.
On the other hand, if we only use one strategy, we might fail in the long run.

Speaking of failures, do you know how, in extremely bad market conditions, one strategy achieved a good win rate and made some nice profits, while the rest of them sucked?
That strategy was the weighted moving average crossover strategy.
We buy when the 50-period weighted moving average, crosses above the 200-period weighted average.
The stop loss goes below the crossover, and the profit target is 1.5 times the stop-loss distance.
One of the reasons it made profits is that it didn’t spam trades like some other strategies.
But this strategy can’t be completely full proof, right?
Even though it made good profits in extremely good markets, in mostly trending markets, and even in extremely bad markets, it has to lose somewhere, right?
There has to be some weakness where it would fail, right?
To find out, I wrote some code this time and tested many trades with this weighted moving average strategy in different charts and time frames, and this is what I found.
Number 1. In extremely good trending markets, this strategy gave a win rate of around 60 percent with a 1.5-to-1 reward risk ratio.
Remember that the breakeven win rate for this ratio is 40 percent.
So this win rate of around 60 percent is really high.
Number two, in extremely strong uptrending markets, if you take both long and short trades with this strategy, then the win rate drops significantly.
It dropped from around 60 percent to only 45 percent.
In some scenarios, it even dropped below 40 percent.
That makes sense, because if there is a strong uptrend going on, and you take both long and short trades, the long trades will have a really high win rate and the short trades will suck completely.
So, overall, your win rate will drop.
For example, in the same extremely strong uptrend, when I only took short trades, the win rate dropped to around 30 percent.
Number 3. In a mostly trending market, that also had a decent amount of range and slow movement in between, when I only took long trades with this strategy, I got around a 46 to 47 percent win rate.
The interesting thing is that when I flipped the direction and only took short trades in this mostly trending and slow market, I still got around a 47 percent win rate.
Then, when I took both long and short trades, in the mostly trending and slow market, I still got around a 47 percent win rate.
But in a narrow range, where the price was not making wider swings, the moving averages were too slow to react and change direction.
Because of that, they end up giving crossovers when the price is about to reverse back.
In these narrow-range market conditions, I got a wide range of win rates from 30 percent to around 40 percent. But most win rates stayed on the lower side.
Basically, when there is a narrow range occurring, this moving average crossover strategy completely sucks.
On the other hand, in wider ranges like this with big swings, it performed better.
But when I didn’t account for any trend or range, I got a random win rate that was ranging anywhere from around 30 to 45 percent.
Basically, this weighted moving average crossover strategy worked when the market was extremely trending, mostly trending, slowly trending, or in a wide range.
It doesn’t work when the range is too narrow or extremely flat.
It sucks when you take trades against the trend.
And it gives a random win rate if we don’t account for market conditions and trade randomly.

But one thing is clear in all the tests we have done in different market conditions.
In the more than 10,000 trades of data we have seen, the thing that had the biggest impact on the win rate, reliability, quality of trades, and consistency of profits was the market condition itself.
Since the data says around 75 to 80 percent of strategies make money in trending markets, we really shouldn’t focus on finding the best strategy.
Instead, if we really want to succeed in trading, we should focus on getting better at identifying good and bad market conditions, because that skill alone can make you a profitable trader.
If you have a perfect strategy, it can still lose money if you don’t know when not to trade.
But if we are good at identifying different market conditions, we can make good profits in the long run even with an average strategy, because We can simply adapt our strategy and rules when the market conditions change.

Even in my own trading journey, learning when not to trade is one of the main things that has helped me survive for 9 to 10 years now.
To see how I made 100% profit in a year and to see live trade setups that made profits in the live market in the long run, support Trading Rush on Patreon.
It’s basically my current strategies applied in the live market and proof of if they work or not.
The link is in the description.
Thanks for watching.