12-ICT Mentorship Core Content - Month 2 - No Fear Of Losing

Last modified by Drunk Monkey on 2022-09-02 11:06
1 00:00:33,330 --> 00:00:42,810 ICT: Okay folks, welcome back. This is the fourth installment of month two of the ICT mentorship, where we specifically talking about why losing on trades
2 00:00:42,810 --> 00:00:57,510 will affect your profitability. What trading with fear of taking losses actually does to your trading. Mustaine concerned about taking a loss promotes fear based
3 00:00:57,600 --> 00:01:13,980 decision making. Equity that is managed by traders that cannot take a loss can't profit long term. Losing is inevitable, fear based decision making keeps focus
4 00:01:14,220 --> 00:01:32,880 on the adverse. Finally, fear based decision making fosters trade paralysis, or inability to execute efficiently. Now, why profits are achievable despite taking
5 00:01:32,880 --> 00:01:45,720 reasonable losses. The professional equity manager understands that losses are costs of doing business. Using sound equity management and high probability
6 00:01:45,720 --> 00:02:00,300 setups yield handsome percent returns. Trading scenarios that encourage potential three to one reward ratios provide initial foundation. And finally,
7 00:02:00,450 --> 00:02:06,720 defining trade setups to frame five to one reward to risk or more efficiently cover losses.
8 00:02:15,540 --> 00:02:27,060 Give us we're gonna give a brief overview on framing a trade just for the context of this discussion. Looking at this sample size of data as it relates to
9 00:02:27,300 --> 00:02:41,790 price action, we're gonna be referring to a specific concept known as market setup and framing the risk to reward multiples. Obviously, we're going to use a
10 00:02:41,820 --> 00:02:50,610 standard in my repertoire the bullish order block. As we see here, the market returns to a previous institutional area of buying noted by the down candle
11 00:02:50,670 --> 00:03:06,090 prior to the previous rally higher. by noting the down candle or the bullish order block high to open price defines the fair value gap or most probable
12 00:03:06,120 --> 00:03:06,720 support.
13 00:03:14,669 --> 00:03:26,069 Now specifically inside of that retracement into the order block, there's a mean threshold and hypothetical long entry on the secondary bullish order block. What
14 00:03:26,069 --> 00:03:35,489 I'm going to refer to is this down candle here, the middle of that candle wherever you using that as a means threshold. In other words, we don't want to
15 00:03:35,489 --> 00:03:51,149 see that violated on a closing basis. By using 20 pips as the trade stop loss, easily frames reward multiples of three to one reward to risk and five to one
16 00:03:51,179 --> 00:04:03,449 reward the risk or even higher. Nutrient, oh old high 20 pips above it gives us a nice objective above where price would be retreating to.
17 00:04:11,430 --> 00:04:20,370 Now having a simple trade idea, based on the things that we taught in September, on what to focus on, or what you should be focusing on right now, in price
18 00:04:20,370 --> 00:04:33,510 action, let's take a look at some things regarding those setups and how we can frame good reward multiples, how we can frame the ideas and justify why taking
19 00:04:33,510 --> 00:04:44,640 losing trades doesn't really or shouldn't have that much of an impact on your long term profitability. We're going to assume that we're using a hypothetical
20 00:04:45,180 --> 00:04:58,890 account size of $5,000. And we're gonna start with a low accuracy rate of 30%. That means that you're losing 70% of the time when we're looking for trades that
21 00:04:58,920 --> 00:05:08,370 are rewarded risk ratio of three to one, that means we're hoping to make or willing to hold on to a trade to pay out $3 gained for every $1 that we risk.
22 00:05:10,680 --> 00:05:24,420 We're risking on each trade 1% of our $5,000 account. Because we're risking 1%, and we're looking for a yield of three to one reward to risk, our average wind
23 00:05:24,720 --> 00:05:41,040 win trade is should be $150. And our average loss should be $50 or 1%. We're gonna be focusing on a sample set of 10 trades. And we're gonna say that 30% of
24 00:05:41,040 --> 00:05:58,560 those 10 trades are winners. And obviously, 70% would be losing trades. Out of those 10 trades, we're assuming that three wins in 10 trades, and seven losses
25 00:05:58,740 --> 00:06:18,480 in 10 trades. The average profit, again is $150. And the average loss again, is $50. A subtotal for the three wins at an average profit of $150, would bring us
26 00:06:18,480 --> 00:06:31,530 to a $450 winning basis on the three trades out of 10 that were winners. And the subtotal for the losses would equate to 350 hours, or seven times 50 hours of an
27 00:06:31,530 --> 00:06:45,720 average loss. Even in this low accuracy rate, with a multiple three to one, you still can marginally eke out a net positive profit. It's not much. And you look
28 00:06:45,720 --> 00:06:55,650 at that, it doesn't seem like anyone really would be terribly excited about that. But if you were doing 10 trades over the course of a month, and you netted
29 00:06:55,650 --> 00:07:06,690 a 2% return, I can tell you, that is an absolutely amazing return for managed funds. So if you're not going to be trading your own capital, or if you're
30 00:07:06,720 --> 00:07:17,850 aspiring to be a trader that manages other people's money, so in 2%, while that's not terribly impressive, in the grand scheme of things 2% compounded over
31 00:07:18,090 --> 00:07:24,000 the course of a calendar year, 2% per month, that it's an astronomical return for managed funds.
32 00:07:30,270 --> 00:07:38,460 Let's assume for a moment now we're going to start focusing on reward to risk multiples of five to one, that means we're trying to make $5 for every $1 that
33 00:07:38,460 --> 00:07:48,420 we risk. And we're keeping the same sample set of looking at 10 trades. And we're still looking at the accuracy rate of 30%. Now one thing that's changed
34 00:07:48,420 --> 00:08:00,060 now is reframing trades to have a multiple of five to one reward risk. Suddenly, our three winning trades out of 10 sample set, the average profit becomes 250
35 00:08:00,060 --> 00:08:12,540 hours or three wins at 250 hours average brings us a subtotal of $750. The seven losses in the sample set of 10 trades, average loss is $50. That still leaves us
36 00:08:12,540 --> 00:08:28,290 at a subtotal of $350 $750 minus $350 gives us a net profit of $400 or a 8% return. Now again, if we're looking at 10 trades over the course of one calendar
37 00:08:28,290 --> 00:08:40,800 month, to see results like this with a very, very low accuracy rate of 30% still brings us an 8% return, that's a wonderful return for a monthly, great
38 00:08:45,870 --> 00:08:55,200 now we're going to take a look at having a low accuracy rate of 30% with the reward to risk multiple of five to one, and now we're going to be risking 2% of
39 00:08:55,200 --> 00:09:08,460 our account. So now the average win jumps to $500. And the average loss jumps to $100. Again, keeping accuracy at a low 30% accuracy. That means we're losing 70%
40 00:09:08,460 --> 00:09:18,840 of our trades. Out of a sample set of 10 trades over the course of a calendar month, three wins at 2% risk per trade multiple of five to one ratio to reward
41 00:09:18,870 --> 00:09:28,560 I'm sorry reward the risk. Our average profit jumps to 500 hours if our three winning trades at 500 hours average profit. This gives us a subtotal of $1,500
42 00:09:29,100 --> 00:09:40,920 or seven losing trades at an average loss of $100 or 2% of our equity. The subtotal would obviously be set on and off. Now the average loss and average
43 00:09:40,920 --> 00:09:53,970 profit would increase as the equity increases or drops. But for these examples, we're looking at the sample size data and a sample set of 10 trades. So the
44 00:09:53,970 --> 00:10:05,220 details are mentioned here with a very hypothetical basis but with a subtotal all Under the three winds of 15 hours, and this seven losses subtotal 700, that
45 00:10:05,220 --> 00:10:18,420 will give us a net gain of $750 or 15% return, again, crazy returns with just a very low accuracy. Now think about this for a moment, when you first got into
46 00:10:18,420 --> 00:10:30,330 trading, you're wanting to get 90% accuracy or 100% accuracy or 90% accuracy, you can still make ridiculous returns with having very low accuracy. Okay, you
47 00:10:30,330 --> 00:10:39,720 don't need high accuracy, you need the framing of the reward to risk multiples in your favor. And we didn't really go crazy with our risky that we're only
48 00:10:39,720 --> 00:10:52,140 doing 2% Maximum portrayed. Right, so now we're gonna look at an accuracy increase to 40% Nothing's changed outside of the previous example here. So now
49 00:10:52,140 --> 00:11:03,630 we're gonna say 40% of a sample set of 10 trades, four of the 10 trades are winning trades. average profit per trade still at $500 or four trades at 500
50 00:11:03,630 --> 00:11:13,710 hours average profit brings us a subtotal $2,000, our six losing trades out of the 10 average losses still remains at $100 per loss, six of them would give us
51 00:11:13,710 --> 00:11:28,020 a subtotal $600. That would give us a net profit of $1,400, which would be again, that's a 28% return with just a 10% increase in accuracy to a factor of
52 00:11:28,050 --> 00:11:40,860 2%. For risk, and reward the risk ratio again, framing on a model of five to one. Now we're going to look at an increase in our accuracy. Say we've been
53 00:11:40,860 --> 00:11:49,830 trading for a while we know our trading model a little bit more intimately. We know what we're trading, we know how to frame our trades, we've learned
54 00:11:49,830 --> 00:12:01,170 patience, we've been able to stick to our rules and our parameters about our reward the risk framing, we know how to reduce our risk. While we're in a trade
55 00:12:01,560 --> 00:12:10,350 and accuracy increases by default, we're gonna say we jumped to a 50 basis. In other words, half our trades are winners and half our trades are losers. On a
56 00:12:10,350 --> 00:12:22,320 sample set of 10 trades, the average Wednesday's at 500, the average loss stays at $100.05 wins at an average profit of five. And this brings us to a subtotal
57 00:12:22,320 --> 00:12:35,640 $2,500. While five losses other than 10 simple set trades, average loss is $100, or a subtotal of $500. So $2,500 minus $500 loss on five losing trades, because
58 00:12:35,640 --> 00:12:51,000 it's a net profit of $2,000 or a 40% return on 10 trades. The factor of just increasing a 5050 hit rate will reward the risk five, the one with a risk per
59 00:12:51,000 --> 00:13:03,390 trade 2%. The only thing we're doing is framing our trade around a little bit more success. In other words, our ability to read price action, look how fast
60 00:13:03,420 --> 00:13:09,180 our multiples jump up. And we haven't increased the number of trades, we haven't increased the risk portrayed either.
61 00:13:14,789 --> 00:13:26,009 accuracy rate of 50%, or reward to risk model stays at five to one. But we're going to lower our risk per trade to 1%. That means the average win drops back
62 00:13:26,009 --> 00:13:36,809 down to $2 per win. And the average loss is down to $50 per win. Our hit rate we're going to say is 5050. Still, that means five winning trades at a 10.
63 00:13:37,769 --> 00:13:47,729 average profit is 20 to 150 hours, and five wins at 250 hours brings us a subtotal of 12 150 hours. And in five losing trades out of that sample set of 10
64 00:13:47,729 --> 00:13:56,759 trades, average loss being 1% of the $5,000 account, or 50 hours. In this case, five losing trades with an average loss of 50 hours gives us a subtotal of
65 00:13:56,759 --> 00:14:10,229 $2,250. So $1,250 of the five wins. Minus the subtotal of $250 on the five losing trades gives us a net profit of $1,000. Now, I want you to take a look at
66 00:14:10,229 --> 00:14:18,719 this for a minute. Okay, think about this for a minute. You only have to be right half the time. Or the other way of saying it is you can afford to be wrong
67 00:14:18,719 --> 00:14:29,729 half the time. You're looking for trades that pan out five to one, and you're risking 1% of your account. Now, think back to a moment when you first started
68 00:14:29,729 --> 00:14:40,949 learning about trading, and you felt that you had to put big risk on. We're not talking about 2%, which is the industry standard here. We're talking about 1% 1%
69 00:14:41,669 --> 00:14:56,969 makes millionaires if you look at the 1% risk portrayed and the accuracy rate of only 50%. This by itself is exactly what everyone would dream of as 3d return
70 00:14:57,539 --> 00:15:09,839 20% per month if you can Get 10 trades per month, half of them be wrong, but framed all of them on five to one reward risk. With 1% risk only, your rate of
71 00:15:09,839 --> 00:15:25,469 return is 20%. With only 1% at risk. This is optimal trading goals. This is exactly what you should be aspiring to do. You're not trading a lot. You're not
72 00:15:25,559 --> 00:15:35,279 demanding a high rate of success or accuracy. You're not pushing the limits on your risk. You're keeping it at a low, you're doing half the industry standard
73 00:15:35,279 --> 00:15:46,409 in terms of risk for portrayed usually it's 2% maximum. Okay, well, we're doing 1%. Let me ask you a question. What if you were to drop that risk portrayed down
74 00:15:46,409 --> 00:15:57,719 to a half a percent? Would you be upset with 10% return per month? My question would be, why would you be upset with that? Now imagine if we were to consider
75 00:15:58,169 --> 00:16:13,289 what was 2% per month, with 30% accuracy 1% risk per trade with three to one reward risk model, on our first example, that's exactly what large funds look to
76 00:16:13,289 --> 00:16:22,559 do for their clients over the calendar year, they're looking for one to 2% per month. And if they can compound that over the course of a year, they can give
77 00:16:22,559 --> 00:16:33,149 their investors a 2025 to 20% Return on the year. And believe me, there are millions and millions of dollars sitting out there that would love for someone
78 00:16:33,149 --> 00:16:41,129 to be able to do that for them. So you don't need to have these astronomical rates of return per month to manage other people's money. Believe me, they will
79 00:16:41,129 --> 00:16:53,249 go crazy if you give them 1%, one and a half percent 2% per month. And you only need to do three to one reward risk to do that, with 1%. If you do 1% here, and
80 00:16:53,249 --> 00:17:05,339 you have a 50% chance of being accurate, and you frame your trades around five to one, look how easy it is to get into a really high in yield for the month
81 00:17:05,969 --> 00:17:15,929 20%. You don't have to train every single month if you're managing our money or other people's money. So this is an optimal goal because it gives you the
82 00:17:15,929 --> 00:17:27,269 cushion to do basically half a year of trading. There are some months in the year that you don't really want to be trading. So if you can do a multiple of
83 00:17:27,269 --> 00:17:35,459 five to one, and yield really handsome results. And I'm not saying that everyone's going to get 20% returns or higher every single month. But this
84 00:17:35,459 --> 00:17:45,179 should be a good trading goal for you to frame your trades around, were expecting only half your trades to be accurate. Training on Find the one reward
85 00:17:45,179 --> 00:17:58,799 to risk keeping your risk low 1%. By doing this, it gives you the optimal objectives. It gives you low hanging fruit, it doesn't force performance. And it
86 00:17:58,799 --> 00:18:03,119 gives you an opportunity to relax and actually enjoy the process of trading.
87 00:18:04,470 --> 00:18:15,060 There is no fear that's justified in taking losses, they are all part of this business, it's all part of the game. It's all part of your job. As an equity
88 00:18:15,060 --> 00:18:23,940 manager, you're going to weather losses, you're going to assume you're going to assume losing trades. That's all cost of doing business. No one goes through
89 00:18:23,940 --> 00:18:31,740 their career without taking losses, you're going to have lots of them. If you trade for a long time, if you had a column of all your wins and all your losses,
90 00:18:31,980 --> 00:18:42,870 your losses are going to be very, very long in the list. But it does not dampen or it does not remove the profitability factor that's still available to traders
91 00:18:42,870 --> 00:18:52,530 that know how to frame the trades with good multiples of reward to risk keeping risk managed and defined. And thinking about how they're going to trade with
92 00:18:52,530 --> 00:19:02,370 these parameters. If we use the example, we showed in the beginning of this video, with a 20 pip stop, all you have to do is take well what's 1% of $5,000
93 00:19:03,630 --> 00:19:15,300 It's 50 hours. So if you had a 20 pip stop, you divide that by $50. And I'll give you your dollar per pip leverage. And that's what you would use for your
94 00:19:15,300 --> 00:19:25,740 trade. And that would give you all of these numbers that you see here. Now again, we can only speak in terms of hypothetical, but it's a rule or general
95 00:19:25,830 --> 00:19:37,290 principle that you're going to build on as a trader. highlighting the fact that you don't need high accuracy I did not show 60% accuracy. I didn't show 70%
96 00:19:37,290 --> 00:19:47,130 accuracy. I didn't show 80 or 90, none of that's necessary. But as time goes on, and you grow in your proficiency and your your understanding about price action
97 00:19:47,130 --> 00:19:59,970 and you as the trader, by default, your accuracy rate will increase and you'll never demand or need for it to be higher than 5050. So until the next discussion
98 00:20:00,240 --> 00:20:03,330 Next teaching I wish you good luck and good trading