I’ve discussed many times on my timeline about Risk Management, bet sizing, and P&L implications. Today, I want to give some CONCRETE examples, as I know many of you struggle with that aspect of Trading. Yet, this is the FIRST milestone every aspiring trader should master before moving to the next step.
You're doomed to fail if you don’t understand the basic maths behind risk-reward, % winning trade, and randomness in the market. No matter how good you are, you WILL go broke if you don’t have a healthy system about the risks you take when you trade. Don’t believe me? let’s check that fact.
Example 1 - Joe, a classic broke retail trader
Joe thinks he is better than everyone else. After all, he just won 16 out of his last 20 trades and took his account from $1000 to 22,000 fucking American dollars! What a STUNNING performance from our friend Joe! Joe thinks he can do it again. But the $22,000 would transform this time into a shiny $484,000. Almost a half fucking Milly. Yeah, baby!
Joe's daydreams were not dreams anymore. He was about to fucking quit his job. “20 trades”. “That’s all I need”, he said with a little smile, sipping his overpriced Starbuck coffee on his way to work. The only problem in Joe’s plan is that he forgot he took too many risks on his first ten trades. He could have wiped his account in just a few losing trades. But as usual, money turns us into blind and evil creatures.
Let’s look at the below graph. Please read attentively, as this might be an “Aha moment” for many of you.
How to read this graph?
Firstly, let’s look at the assumptions used:
You start with a capital of $2,500
You win one trade out of two (50% winrate)
You always bet 30% of your Portfolio (compound effect)
Your average win size equals your average loss size
We look at the results after 20 trades
Secondly, we ran this simulation 50 times to reduce randomness. This is why you see 50 different curves on this chart. Think about this as you restarting this experiment every 20 trades with a capital of $2,500.
Finally, let’s draw some conclusions:
You will get broke 16% of the time (not too bad to be honest). I put the ‘broke’ threshold when the capital reaches $200 or less, as you won’t be able to take a trade with a micro lot on $NQ with less than $200.
There are 3 times where you end up above $10,000. I’m sure most of you are thinking, “Wow, that’s cool I have some chances to 4x my account”. Let’s look at it the other way. You have 96% of chance NOT to quadruple your account.
If you’re still thinking of these 3 chances, you’re a c**t.
The average ending capital across the 50 simulations is $2,385, or a 5% loss.
This doesn’t account for human error, and the actual results would look worst.
My goal is to educate you on the risks of poor Risk Management. Many discuss being undercapitalized. The real scourge is over-betting. If something is still not clear, please ask me, and I’ll try to reformulate it.
If you’d like to receive the Python code used, share this article and email me at retailcapital9@gmail.com.
Other examples
I’m adding a few additional charts with more trades and simulations to give you more depth of analysis.
Increasing the winrate from 40 to 50% changes everything (see the chart below)!
The difference between always betting the same amount vs. betting a fixed percentage of your Total Portfolio (compound effect)
Fixed amount
Compounded amount
Conclusion
Betting 5% of your initial portfolio over 200 trades vs. betting 5% of your current portfolio over 200 trades (compound effect).
If you have a STRONG edge, bet more.
If you’d like me to run some simulations for you, post in the comment section the following:
Starting capital
Winrate
Bet size
win to loss ratio
Number of trades
Number of simulations
I had a lot of fun writing this article. I know this is not the type of article that sells, but I don’t care. After all, I don’t know shit about fuck!
Stay safe,
- Retail
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Twitter: @itsonlymoney12
📩 retailcapital9@gmail.com
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Apex is a community of traders founded by Darrell Martin, a rancher and day trader who has built a thriving community of traders into over 30,000 members in over 150 countries since 2008.
I'm loving your articles!! I do have a question! how would you go about optimizing risk management for funded trader programs and making sure the risk of ruin is the least
Great work, been really closely tracking my statistics on various trades within my system so I can know which setups I want to size up for. I had been using a standard size for every trade which really doesn’t make sense. But I couldn’t justify arbitrarily setting size on the fly either. So really getting nerdy with the data is HUGE