The Professional Stock Trading Approach

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Hi everyone, in this video we look at the Risk Reward element in trading, and how it plays a vital role in any strategy.

First letâ€™s simplify the concept.

Itâ€™s widely accepted that in order to increase reward, there is often a need to increase risk, and whilst this simplistic correlation is often correct, a shrewd trader (or investor) can skew the theory, thereby minimising risk and maximising reward.

The closer we can target our trades within this area, the better.

There is however a sad statistic which shows that many new traders trade within this area, with high risk and low reward. We discover shortly how trader psychology can play a significant role in this regard.

Letâ€™s place this concept on to a typical trade example and see how we can skew the ratios into our favour.

First, we have the novice high risk, low reward trader.

A trade is placed against a stock price of $10. The trader chooses not to place a stop loss order, believing that the price will eventually increase to $50.

The trader has therefore accepted to take all of the risk, therefore if price falls to zero, the trader would suffer a 100% loss.

At this stage there is no major concern, the trader has in fact become an investor by taking a buy and hold stance, and should price reach the $50 there is the prospect of seeing a 400% return, or a 1 to 4 risk reward ratio. That would be a good proposition, however the reality is that the novice trader would likely sell a position early, perhaps at $12, with the desire of being right and locking in a profit at the earliest opportunity.

Conversely the novice trader also has a desire not to be wrong, therefore refusing to sell whilst price continues a decline. In this scenario, the trader has turned the risk reward upside down, meaning he is risking $10 to win $2, or as a ratio, risking 5 to return 1.

Remember the mantra of cutting losses short and letting your winners run? Well, here we let the losers run, and cut the winners short, an approach which will likely lead to significant losses in the long run.

As a trader, what we want to achieve is; limited risk, and unlimited reward.

Letâ€™s move from theory to a more practical approach, which is also aligned to my personal strategy.

Being a trader more often than not relies on technical analysis, and I personally look for consolidation breakouts on the weekly chart. And for those interested in such an approach, members have access to our bespoke scanning software which I use to find such set-ups.

Such a set up provides me with the foundation for favourable risk reward. For example, the consolidation area offers rationale to suggest there is an area of support. If, price should break support, we cut our losses. We therefore now have our maximum â€˜riskâ€™ portion established.

Letâ€™s assume our breakout candle rises a few percentage points above consolidation, and to keep the maths simple we call the difference between the current price and the bottom of support, 10%.

This now becomes our area of risk. We define this area, or percentage, as 1R.

Over the following few weeks price continued its momentum, increasing by 20%.

Knowing the value of 1R, we can determine that we have gained a total of 2R, providing a risk reward ratio of 1 to 2, therefore risking 1 to return 2.

Now letâ€™s look what would have happened if we allowed the breakout candle to extend further before taking a position. This could make our new risk area 15% instead of 10 %, meaning 15% now becomes 1R.

We see the same price increase, but from the new height of the extended candle, providing a 15% return.

The 15% increase equals 1R, which equates to the same value of risk. Therefore in this scenario we are risking 1R to return 1R, a risk reward ratio of 1 to 1.

Can you see how the same stop loss rationale, yet a relatively small increase in the breakout bar from our first example, can have such a significant impact on return.

In our first example we achieved a 200% return on risk, whereas our second example with a slightly more extended breakout bar, saw a 100% return on risk. A small change in breakout price, yet a huge variation in returnâ€¦.

To emphasise the impact further, we can see price action from a typical chart.

We see the consolidation, and on closer examination we can see two inflection points creating resistance, followed by a breakout bar.

If we assume a stop loss position amongst consolidation at $49, against a buy point of $51, we can calculate the stop loss risk to be 4%.

If however we waited for the following week for confirmation, we would have entered at a higher price of 52.25, resulting in a stop loss distance of 6%. This equates to an extra 50% of risk, and as we find out shortly, such a variation can have a significant impact on returns in the long run.

Staying with this example for a moment, we established that the first breakout trade has a risk of 4%, therefore 1R equals 4%. The second breakout bar has a risk of 6%, therefore 1R = 6%.

If we assume the target price to be $57, we can determine the risk reward profiles for each trade.

The first trade has a 1 to 2.5 risk reward ratio, whereas the second trade has a 1 to 1.33 ratio.

Before we look at how the equity profiles differ for each of these strategies, over a large number of trades, itâ€™s important to point out that the risk reward element of trading is only part of the equation. The win rate is equally important, and we look at their relationship shortly.

For the purposes of our example here, we assume both strategies to have a 50% win rate.

We run the initial simulation based on the second trade, which saw the later candle at a slightly higher price.

In the simulator, we enter the win rate of 50%, and in our risk reward section, we enter $1000 risk and $1333 reward. This provides us with the 1 to 1.33 ratio.

We run the simulator, and after almost 1000 trades we can see the profit accumulated to over $185,000, which we see in this equity curve.

In our first trade which saw the price at the initial breakout candle, we achieved a 1 to 2.5 ratio. If we now put these figures into the simulator, we can see how much the strategies vary.

We again use a 50% win rate, and the 1 to 2.5 risk reward ratio.

This time we see a significantly higher return of $740,000, which again we see in the equity curve.

To quickly recap, the first breakout bar trade had a 1 to 2.5 risk reward ratio, and returned $740,000 over 1000 trades.

The second candle bar had a 1 to 1.33 risk reward ratio, and returned a considerably less $185,000 over the same 1000 trades. Small price change yet vastly different returnsâ€¦

It is again important to remind ourselves, that the win rate is also key to these returns.

With our 50% win rate used in both examples, we are assuming that the first bar has the same probability of hitting the stop loss point as the higher second bar. This is arguably not realistic, however, if the rationale of the initial stop loss is solid, then there is also an argument to suggest that the chances of a losing trade are in fact equal.

I look for the first breakout bar, and this bar must have enough momentum to suggest a continuation, but must also not be too extended that it skews our risk reward ratio.

Our scanner places a weighted score against variable breakout metrics, thereby filtering for momentum aligned with managed risk.

The key take away here is that a small variance in risk can have a huge impact on profit. Look after the risk and the profit will look after itselfâ€¦

One of my previous videoâ€™s by Van Tharp is another great resource to learn more about risk reward and expectancy.

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