The key to successful trading is risk management and avoiding overtrading. Traders must first recognise their risks to control them. Traders who do not employ risk-management measures will struggle to make a profit.
When entering any new trade, traders must know where to stop and how much equity they have in their accounts. The difference between their entry and stop-loss prices indicates the amount of capital to risk on each transaction.
Status Quo Bias and Risk Aversion
What would you do if you won $1 million and a casino offered you the option of keeping the money or flipping a coin on a double-or-nothing bet?
There is ample factual evidence that you would refuse the bet and not lose sleep in “showing” that most individuals would be hesitant to risk away a guaranteed thing. This result rings true even to those of us who have never had the opportunity to conduct our behavioural finance study.
In other words, most individuals believe that how things are now is preferable to how things will be if conditions change. Additionally, even when the risk/reward ratio is favourable, most people have little interest in taking risks.
As a result, it is remarkable that when Khaneman changed the conditions of this inquiry but not the conclusion, most respondents reacted substantially differently than one might predict. In their preliminary research, they proposed two scenarios:
- You have a one hundred per cent chance of winning $3,000,
- There is an eighty per cent chance of earning $4,000 and a twenty per cent chance of getting nothing.
Even though they had a reasonable possibility of winning much more, 80% of respondents picked the $3,000 prize. This finding is consistent with traditional assumptions and is a typical example of status quo bias and risk aversion.
The individuals were then posed with a different scenario, and the findings opposed typical assumptions.
- You have a 100% probability of losing $3,000
- An 80% risk of losing $4,000 with a 20% chance of breaking even.
92% of respondents picked the second option—risking $4,000 or attempting to break even—even though they could lock in a far lesser loss ($3,000) and were highly likely (4:1) to lose significantly more by taking the bet.
The most striking result of this experiment is that despite the language used in Scenario 2 implying that it is a different circumstance from Scenario 1, it is identical regarding the risk taken.
Both scenarios give a decision between a guaranteed thing and a chance. On the surface, the instances appear distinct since one provides a high probability of winning while the other offers, at most, a low possibility of breaking even. Yet, the mathematical expectation of betting in both situations is $3,200 ($4,000 x 80%).
In the first scenario, when the gambler is likely to be paid, the majority was more concerned with avoiding the 20% possibility of receiving nothing. But, in the second scenario, in which one is likely to be penalised for gambling, all but 8% of respondents elected to put themselves in danger, rejecting the status quo and risking an unknown outcome with possible questionable gain.
Misconceptions of Taking Losses
The Khaneman/Tversky experiment found that, contrary to popular belief, people dislike accepting losses rather than taking risks. Furthermore, the dread of losing tends to outweigh the ability to think sensibly. Why were 92% of Scenario 2 respondents willing to place a bad bet? It appears they were solely concerned with the possibility of a negative consequence and were prepared to do anything, even something dumb, to prevent it.
Every time you make a transaction as a trader, you will be presented with this problem. Prospect Theory causes traders to sell their gains too soon, fulfilling their desire to be correct and hold their losers too long, expecting not to suffer a loss and be proven wrong.
You have entered the domain of illogical thinking if you have convinced yourself that realising a loss signifies failure or that you must make a profit since a definite thing is always preferable to an unpredictable future.
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The 2% Rule
Setting strict loss boundaries and adhering to them is the most effective strategy to safeguard your wealth through risk management. One prominent strategy is the 2% Rule, which states that you should never risk more than 2% of your account value (Table 1).
For example, if you trade a $50,000 account with a 2% risk management stop loss, you might risk up to $1,000 on each transaction. The overwhelming beauty of this rule is that if you rigorously follow it, you would have to make hundreds of consecutive 2% losing transactions to lose all of your money. Even for a novice trader, this is quite unlikely.
The 2% Rule also structures your trading selections, as seen in Table 2. For example, if you have $50,000 in your account and wish to buy 5 Canadian Dollar futures, the 2% Rule says you may risk no more than 20 ticks on the deal (5 contracts x $10/tick x 20 ticks = $1,000). You could only buy two contracts if you wished to use a more lenient 50-tick stop. Similarly, you could risk no more than five ticks to acquire a larger stake, say 20 contracts.
Employing a stop-loss threshold with a defined risk/reward ratio can also assist you in determining exit points on lucrative transactions. For example, assuming a 2:1 risk/reward ratio, if you risk 20 ticks on 5 Canadian Dollar futures, you should aim to gain 40 points, earning $2,000 if the market goes your way and losing $1,000 if stopped out.
Let’s compare risk management using the 2% Rule to an even stricter 1% One. Table 3 depicts what your account might look like following a five-day losing run, which an aggressive trader may experience in a single day. Take note of how the fixed % rule decreases the amount of each successive trade, which slows the decay of your account during a losing streak.
While trading futures, account losses are unavoidable. Therefore, if you go on a losing run, you must rebuild your earnings to return to where you started. The amount you have to gain will be more than you have to lose. Table 4 indicates the percentage increase required to compensate for a corresponding loss.
Proper Position Size
Choosing the appropriate position size before entering a trade can significantly influence your trading performance. Firstly, position size can be changed to represent the risk of the transaction. Second, having stringent position size limitations can assist in reducing losses that are most likely to occur when you start trading.
To establish the appropriate position size, you must know two things: (1) where the stop will be placed and (2) the percentage or dollar amount of your account that you are willing to risk on the trade.
The first step is to set your stop-loss order for the transaction. Stops should not be set at arbitrary levels. A stop should be put at a rational level, alerting you if you are incorrect about the trade’s direction. Additionally, avoid placing a stop where typical market fluctuations might readily trigger it.
Next, assess your account size and determine if your stop-loss dollar amount corresponds to the percentage you can afford to lose per transaction. Novice traders should risk no more than 1%-3% of their account on a single trade, and as shown in Table 3, 1% risk on a $50,000 account is $500.
You know how risky the transaction is when you have a stop level. For example, if your stop is 50 ticks from the entry price and each tick is worth $10, your total risk for one contract is $500; if each tick is worth $5, you might have a position size of two contracts while keeping your risk at $500.
System-Based vs Discretionary Trading
Something is appealing about creating an automatic trading system that forces you to behave in a certain way every time market action triggers a signal. The advantage is that it is significantly simpler for certain traders to maintain trading discipline when there is no discretion in decision-making. The disadvantage is that to reap the full benefits of a lucrative method, the trader must follow the system’s recommendations precisely.
Creating trading systems, on the other hand, is a huge endeavour. While some traders can use off-the-shelf software applications to back-test historical data, many encounter difficulties. Furthermore, everyone appears to be looking for the Holy Grail of trading systems—one that is so powerful that it is 100% failsafe while remaining so hidden that it has yet to be found.
Another area for improvement in building a trading system is that market data is frequently tainted with errors. Thirdly, and most importantly, many developers optimise their systems to the point that they are no longer predictive. Optimisation happens when a developer deliberately or unknowingly “fits” market data to support a specific conclusion.
Discretionary traders, like their coder counterparts, must obey rules. The difference is that discretionary traders implement those guidelines based on their subjective assessment of each scenario. There may be substantial variations in how the trader’s approach is applied from one deal to the next; this is not to say that a discretionary trader can avoid discipline lapses. Instead, because the trader’s decision-making process is subjective, discipline must always be at the forefront.
Setting Realistic Expectations
You would be prudent to consider the following aspects while focusing on reasonable goals rather than how much your bank account may grow:
Your experience level
Which markets do you trade in?
Your trading account’s size
Expenses for operations
Your tolerance for risk
How much leverage do you employ
Whether you trade full-time or part-time
It’s difficult to comprehend why anyone would believe that becoming a qualified professional trader is a fast procedure.
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