Tag: exposed

A few days ago, while I was analyzing charts at my desk, I got this in my inbox. A message that made me feel like a complete idiot when it comes to trading. It contained the real profit and loss days of a random trader from the internet, but the winning days were 97%. I couldn’t believe what I was seeing. It was like this guy had found the holy grail of trading, the ultimate secret. It was like a green unicorn vomited all over the chart so much that a little bit of blood appeared.

This unknown trader’s profit loss chart was shared with me by one of my Patreon supporters, and we quickly came to 2 conclusions about what strategy he might be using.

But I wasn’t satisfied. If the profit loss days were real, why I’m not using the same strategy? Who wouldn’t want months and months of green days? I definitely do. But what even is the strategy this guy is using? But then I remembered, in my many years of trading experience, I have never seen a professional trader get high winning days like this. So something might be fishy in all this. I must find the truth!

To find out the strategy that gives 97% winning days, I planned around 10 experiments. My goal in these tests will be to end up with green profitable days like that unknown trader.

Test 1.

I sat at my desk again and started gathering the best possible ways I could achieve a high-winning day like this. But then I realized, “Oh wait! I have already tested many strategies 100 times. I can use that data to see if I can get 97% winning days.”

The highest win rate in my testing was around 60%, with a 1.5 to 1 Reward Risk Ratio.

So, I created my own stock market simulator that took random trades with a 60% probability of winning.

In the first test, I wanted to see how the profit and loss days would look like if I took 1 trade a day with a 60% win rate strategy. But the results of the first test were disappointing.

But then, in hindsight, I realized how stupid I was. If I take 1 trade a day with a 60% win rate strategy, of course, the profit days will also be around 60%. I must have been overexcited in the first test.

Test 2.

So I lowered my excitement and modified the trading simulator to take 5 trades a day with a 60% win rate and a 1.5 to 1 Reward Risk Ratio.

But the results I got were still not satisfying. The winning days were around 91%, which is good, I guess. But it’s still not what the unknown trader from the internet was getting. I still wanted to see what would get 97% winning days.

Test 3.

But then I remembered that when I was trading on smaller timeframes many years ago, I sometimes used to aim for 10 trades a day. So, in the next test, I increased the maximum number of trades in a day to 10 and hoped it would be the answer.

But this only got me to 93%. There is still 4% missing. But then I noticed that my results only had a winning streak of 1 month, while a random internet guy had 3 straight months of green days. My results are unacceptable!

Test 4.

So, in the next test, I increased the maximum number of trades in a day to 20.

1st month was all green, 2nd month was all green, 3rd month was also all green! Yes! This worked! But who am I kidding?

20 trades a day with a 60% win rate for a year straight? That’s simply not possible! A 60% win rate with a 1.5 to 1 Reward risk ratio is achieved in a good trending market. But in my 10,000 hours of live trading experience, I have never seen the market stay good every single day for an entire year straight!

So, I bet the random internet guy is definitely not using a 60% win rate strategy. But since his profit loss days were real, getting 97% and 3 months straight winning days is possible. So there must be a way I haven’t tested yet!

Test 5.

I must try to use a strategy that has a much higher win rate. But, the easiest way to increase the win rate of a strategy is to reduce the reward-risk ratio. So, in the next test, I used a 0.5 to 1 reward-risk ratio. Even the break even win rate with this ratio is around 67%. So, the probability of strategies getting a 75% win rate is not that rare. I think I even did a backtest a while back where M-A-C-D and 200 E-M-A got around a 75% win rate with a 0.5 to 1 reward-risk ratio.

When I ran the experiment this time, I set max trades in a day to 10. To be honest, I still think finding 10 good trades every single day for a year straight is rare. But it still didn’t work! This looks more like the first test where only 1 trade a day was taken.

Test 6.

But then I set the maximum trades a day to 30. This is completely unrealistic. A trader will most likely never get 75% probability setups continuously for 30 trades in a single day.

If the result of this test is not 97% winning days, I bet that the random trader is doing something completely different.

I was happy to see that the result came back negative. The trader is indeed using something else. But I’m scratching my head at this point because I don’t see a safe way to achieve 97% winning days. And yet, a random trader I saw on the internet is doing it.

Test 7.

So, I’m going to have to break some rules and use the forbidden strategies.

The first one is averaging the losses while day trading. This is when someone takes another trade in the same direction instead of booking losses at stop-loss.

The second one is martingale. This is when someone doubles the position size of the next trade after making a loss. They keep doubling the position size until a trade is won.

These are some of the worst trading strategies a trader can use. Even ChatGPT says this strategies are stupidly risky. If they actually give the 97% winning days I’m looking for, the conclusion will not be something I had hoped for.

But then I realized I would have to modify my stock market simulator to test averaging. So, I created a price simulation and then took the trades in one direction. I used a 0.5 to 1 reward-risk ratio so that the win rate would be high. But I also wanted to see if random trades with averaging can give 97% of winning days. So, I set the probability of the price moving in one direction to 50%. This makes the 0.5 to 1 reward risk ratio have a break even 67% win rate.

If this break even win rate can result in 3 months of winning days, that random guy is basically a new trader and is probably gambling. There is no profitable strategy. It’s just probability, giving an illusion of a profitable strategy.

Previously, this number showed the win rate of the strategy. But for averaging, I changed it to show the probability of the price moving in the entry direction.

But then I realized, “Wait a minute, one cannot average the losses forever. If the price keeps moving in the loss direction, at some point, the trader will run out of account balance required to take the next trade.” So, I set the max averaging limit to 5.

But then I looked at the profit loss days of the unknown trader, and realized that if he is averaging, he is probably only doing that after making a loss, kind of like revenge trading. If the first trade is won, he books the profit and stops trading for the day. If he loses the first trade, he keeps averaging the loss until the loss becomes zero. Then, he stops trading for the day.

So, in my experiment, I used the same rules.

And to my surprise, it actually got 97% winning days, 4 months of green days straight. And it was all gambling. The entry direction was random, the win rate of the strategy was break even, and yet, this gave 97% winning days. My green unicorn threw up more than his.

But hold on a minute. Did I just see gambling or random trades make a profit for 4 months straight and get a 97% win rate?

So, I exported the account balance of the simulation and saw this! Even though there were green days and a high win rate, the profit graph was actually moving in a downward direction.

That was not the case with the random trader I saw on the internet.

Test 8.

So, in the next test, I set the probability of the price moving in the entry direction to 55%. This is like averaging the loss near support resistance, where the price is more likely to move in the entry direction. But when I thought about it more, I realized this is most likely not possible in reality. I mean, averaging loss on stock market indices, such as the S&P500, is good because that price is more likely to move up in the long term. Many people, including me, do that because it works. But I don’t think averaging losses near support resistance or with any day-trading strategy will work.

Still, I wanted to see what the daily profit loss data would be like. So, I ran the next experiment with a 55% probability of the price moving in the entry direction.

After running the test, I couldn’t believe what I was seeing. There were 99% winning days, and the profit graph actually moved up!

But even after many years of trading, I have yet to see a data-backed day trading strategy where averaging works in the long run.

So, this data is, unfortunately, unrealistic for day trading. But since averaging loss or dollar cost averaging works on higher timeframe stock market indices, this is probably what the long-term data looks like for those strategies. A slow and steady rise as the stock market moves up in the long run.

Test 9.

But I still wanted to find what this random internet trader was using. Since this was not it, the next test I did was martingale with a 0.5 to 1 reward risk ratio, 75% win rate, and max trades per day set to 10.

But this only gave me around 86% winning days. The profit graph went in the upward direction though. But then the curiosity got me. From previous experiments, I know that the martingale strategy is bad in the long run. Here, it made a profit in a year. But I wanted to see after how many trades it would blow up the account.

So, I set the maximum trades per day to 100. But even after 26000 trades, the martingale strategy didn’t lose money. It turned $100 into more than $5000.

But then I thought maybe I set the win rate too high. There is no way the market is going to stay the same or good for 26000 trades. So there is no way the win rate is going to consistently stay good for 26000 trades in a row. It can drop to break even and even lower. So, I set the win rate to just about break even, which is 70 percent. With this around 3 percent edge, I ran the experiment again.

But it was still making money. I ran the test again and again and again. But the results always ended up in profit. But I know for a fact that Martingale will blow up the account when the big losing streak starts. So it appears that because the win rate is so high, the probability of a big losing streak has been reduced by a huge amount.

So, I modified the simulation to run 100,000 martingale trades 10,000 times. That’s more trades than I can count using my fingers.

But the results showed that out of 10,000 runs, only 10 lost the entire capital. It means only 0.1% of the martingale runs blew up the account even after taking 100,000 trades with a 70% win rate and 0.5 to 1 reward-risk ratio.

I kind of lost my mind after looking at this number. I was not expecting martingale to be successful 99.9% of the time. But then I remembered I’m a bit stupid. I forgot to track how many runs couldn’t take the next martingale trade because the account balance was insufficient.

So, I ran the code again with this modification. It showed that around 45% of runs couldn’t continue the martingale strategy. They lost so much money that the account balance was not enough to take the next martingale trade.

Based on this data, I saw that if a trader uses martingale even with a high win rate, there is around a 45% chance they will lose at least half of their account. Since they can’t continue, they will have to reset the martingale strategy. Then, they again will have around a 45% chance of losing half of that account size. That sounds risky to me.

I don’t think most traders can handle a 50% loss on their hard-earned money.

But I don’t think the random trader I saw on the internet is using a normal martingale strategy.

Test 10.

So, after gathering my last 2 brain cells, I spent more time analyzing the profit loss days. I was about to give up, but then it hit me. All this time, I was going in the wrong direction. The strategy to get around 97% winning days and multiple months of green days was something I already learned many years ago as a beginner trader. I bet most beginner traders already know about this as well.

While analyzing the profit loss days, I realized that the random internet trader was not just using a low reward-risk ratio. He was using a very, very low reward-risk ratio. If he was risking around 1% of the account, the reward would be ranging between 0 and 0.2% of the account.

But most profits were near 0.1% of the account.

So, in the next experiment, I simulated trades where the reward-risk ratio was ranging between 0 and 0.1

I set the max trades per day to 1 and the probability of the price going in the entry direction to 55%. This is like trading near support resistance or only taking long trades on higher timeframes in the stock market.

And it actually worked! I more consistently got 97 to 98% winning days. There were multiple months of green days. And I even saw the profit graph moving up more consistently. I ran the test again with more trades, and the profit graph went more in the upward direction.

After spending weeks writing code and analyzing things, I found the 97% winning days strategy.

Here’s how it goes.

Step 1. Only take trades where the probability of price moving in the entry direction is higher than break even. This includes support resistance areas, buying good stocks, or the stock market index on higher timeframes.

Step 2. Once the trade is taken, use a very low reward-risk ratio, such as 0.1 to 1. But try to close the trade at break even if the price shows strong signs of moving towards the stop-loss.

I have data that says the way I draw support resistance works around 60% of the time with a 1 to 1 reward-risk ratio. So, a 0.1 to 1 reward risk and all these rules should most likely work with that.

If I have bought a good stock, I can also take short trades on it using derivatives such as options. This turns the long stock positions into a low reward-risk ratio long trade. Since a good stock has a higher probability of moving up in the long run, there will most likely be high winning days. If the price doesn’t move up quickly, the short options trades will generate some small profits in the meantime. This is a classic options strategy. I think in Part 2 of “How I Made 100% Profit in a Year, Stock Market Series,” I called this method a better way of generating more consistent income.

After spending an entire week figuring out how a random person I saw on the internet is getting 97% winning days, will I be using this high win-rate strategy?

I actually did use it when I was new to the stock market. But since the reward-risk ratio is so low, the max yearly returns of the strategy on average is around 20%. I think I was averaging around 10% profit in a year. So, I stopped using it as the main strategy.

I mean, I made around 25 to 50% profit from a few trades last year by simply buying and exiting when I saw signs of buying and exiting. I like that kind of higher profit potential.

On the other hand, I also use low reward-risk ratios when I don’t find the market good for higher reward strategies. So, it also depends on the market.

If I see that this 0.1 to 1 reward-risk ratio strategy might be more useful in the current market, I might start using it like before.

Thanks to the Patreon supporters for making this video possible, especially by sending the high-winning days screenshot and giving the martingale idea.

If you liked the video, see how I made 100% profit in a year, what I’m trading right now, and more by supporting Trading Rush on Patreon. Thanks for watching!