Trading strategies
Explore practical techniques to help you plan, analyse and improve your trades.
Our library of trading strategy articles is designed to help you strengthen your market approach. Discover how different strategies can be applied across asset classes, and how to adapt to changing market conditions.


Most traders understand EA portfolio balance through the lens of traditional risk management — controlling position sizes, diversifying currency pairs, or limiting exposure per trade.
But in automated trading, balance is about deliberately constructing a portfolio where different strategies complement each other, measuring their collective performance, and actively managing the mix based on those measurements.
The goal is to create a “book” of EAs that can help diversify performance over time, even when individual strategies hit rough patches.
A diversified mix of EAs across timeframes and assets can, in some cases, reduce reliance on any single strategy. This approach reduces dependency on any single EA’s performance, smooths your overall equity curve, and builds resilience across changing market conditions.
It’s about running the right mix, identifying gaps in your coverage, and viewing your automated trading operation as an integrated whole rather than a collection of independent systems.
Basic Evaluation Metrics – Your Start Point

Temporal (timeframe) Balancing
When combined, a timeframe balance (even on the same model and instrument) can help flatten equity swings.
For example, a losing phase in a fast-acting M15 EA can often coincide with a profitable run in an H4 trend model.
Combining this with some market regime and sessional analysis can be beneficial.

Asset Balance: Managing Systemic Correlation Risk
Running five different EAs on USDJPY might feel diversified if each uses different entry logic, even though they share the same systemic market driver.
But in an EA context, correlation measurement is not necessarily between prices, but between EA returns (equity changes) relating to specific strategies in specific market conditions.
Two EAs on the same symbol might use completely different logic and thus have near-zero correlation.
Conversely, two EAs on a different symbol may feel as though they should offer some balance, but if highly correlated in specific market conditions may not achieve your balancing aim.

In practical terms, the next step is to take this measurement and map it to potential actionable interventions.

For example, if you have a EURUSD Trend EA and a GBPUSD Breakout EA with a correlation of 0.85, they are behaving like twins in performance related to specific market circumstances. And so you may want to limit exposure to some degree if you are finding that there are many relationships like this.
However, if your gold mean reversion EA correlates 0.25 compared to the rest of your book, this may offer some balance through reducing portfolio drawdown overlap.
Directional and Sentiment Balance
Markets are commonly described as risk-on or risk-off. This bias at any particular time is very likely to impact EA performance, dependent on how well balanced you are to deal with each scenario.
You may have heard the old market cliché of “up the staircase and down the elevator shaft” to describe how prices may move in alternative directions. It does appear that optimisation for each direction, rather than EAs that trade long and short, may offer better outcomes as two separate EAs rather than one catch-all.

Market Regime and Volatility Balance
Trend and volatility states can have a profound impact on price action, whether as part of a discretionary or EA trading system. Much of this has a direct relationship to time of day, including the nature of individual sessions.
We have a market regime filter that incorporates trend and volatility factors in many EAs to account for this. This can be mapped and tested on a backtest and in a live environment to give evidence of strategy suitability for specific market conditions.
For example, mean reversion strategies may work well in the Asian session but less so in strongly trending markets and the higher volatility of the early part of the US session.
As part of balancing, you are asking questions as to whether you actually have EA strategies suited to different market regimes in place, or are you using these together to optimise book performance?
The table below summarises such an approach of regime vs market mapping:

Multi-Level Analysis: From Composition to Interaction
Once your book is structured, the challenge is to turn it into something workable. An additional layer of refinement that turns theory and measurement into something meaningful in action is where any difference will be made.
This “closing the circle” is based on evidence and a true understanding of how your EAs are behaving together. It is the step that takes you to the point where automation can begin to move to the next level.
Mapping relationships with robust and detailed performance evaluation will take time to provide evidence that these are actually making a difference in meeting balancing aims.
To really excel, you should have systems in place that allow ongoing evaluation of the approaches you are using and advise of refinements that may improve things over time.

What Next? – Implementing Balance in Practice
Theory must ultimately translate into an executable EA book. A plan of action with landmarks to show progress and maintain motivation is crucial in this approach.
Defining classification tags, setting risk weights, and building monitoring dashboards are all worth consideration.
Advanced EA traders could also consider a supervisory ‘Sentinel’ EA, or ‘mothership’ approach, to enable or disable EAs dynamically based on underlying market metrics and external information integrated into EA coding decision-making.
Final Thoughts
A balanced EA portfolio is not generated by accident; it is well-thought-out, evidence-based and a continuously developing architecture. It is designed to offer improved risk management across your EA portfolio and improved trading outcomes.
Your process begins with mapping your existing strategies by number, asset, and timeframe, then expands into analysing correlations, directional bias, and volatility regimes.
When you reach the stage where one EA’s drawdown is another’s opportunity, you are no longer simply trading models but managing a system of EA systems. To finish, ask yourself the question, “Could this approach contribute to improved outcomes over time?”. If your answer is “yes,” then your mission is clear.
If you are interested in learning more about adding EAs to your trading toolbox, join the new GO EA Programme (coming soon) by contacting [email protected].


In previous articles we have discussed in detail the merits of a trading journal in offering evidence for both: a. How well you are following a trading plan? b. How well your trading system is serving you? (assuming you are already following a trading plan) We have also outlined the importance of “closing the circle” and making sure you review journal data and action plan to make any amendments that would be of benefit.
If you are in the position that you have “jumped in” and made a trading a journal a reality in your trading, next level journaling aims to increase the quality of information, where you can optimise those things you are doing well and work on those things that need improvement. This, in essence, is all to do with asking the right questions of the information you have, so you can continue to make evidence-based judgements as to what type of trading suits you best. The reality is that no two traders are the same (even if using a similar system).
Your challenge is to find YOUR best approach that works for YOU. And subsequently, mirror this on an ongoing basis. Here are THREE potentially “game-changing” questions you could ask of your journal data which may give clues about “best fit” behaviour for you as an individual. #1 Which trading direction works for me?
There is no doubt that some traders have results that seem to be better going “long” and others trading “short”. The other possible outcome, of course, is that it doesn’t matter, and you perform equally as well irrespective of direction. Measuring the results of long versus short trades will give you this answer.
Let’s assume there is a noticeable difference. After obtaining this evidence your choices are twofold. The root cause of this may either be: a.
You have a simple aptitude for trading in a specific direction and so can mirror this with all future trading. b. It may be that your system works well for going in one direction and needs adjustment with the other. In this case, provided you are not comfortable sticking to (a) above then of course you have the evidence to refine that part of your system that appears to require adjustment. #2 Which timeframe works for me?
Similarly, we can look at whether specific timeframes work better for you as an individual trader. Questions about optimum timeframes are some of the most frequent that we receive on both ‘Inner Circle’ and the ‘First Steps courses. We have written about this topic before, the conclusion being that it is your individual circumstances that are most likely to dictate which timeframe works best for you.
Again, the power of a journal is that you can easily come to an answer, and so mirror that going forward (of course, this is dependent on you recording this as part of your journal process). #3 Which trading vehicle suits my trading style? Many of you reading this may be trading multiple vehicles e.g. Forex, Index CFDs, Share CFDs, commodities, options.
There are obvious differences not only in how these various instruments are priced but also influencing factors on how they move. Using a similar approach to the above, you can easily identify which vehicles are working for you. As with exploring trading direction the reason for this could be your characteristics as a trade or the robustness of your system in trading different vehicles.
So, the choices are the same - you can allocate a larger proportion (or even all) of your capital into trading the vehicle that produces better results or of course review and tweak the system for those vehicles with less desirable results. OK, so these are your three starting questions, that may help you find a trading style that is best fit for you. However, before we finish, it is worth offering a couple of additional pieces of guidance when doing an exercise such as this. a.
You need a critical mass of trades to make the data meaningful. (there is little evidence that can be gained from a couple of trades in any category). There is no definitive number to what this may be but logically perhaps 15-20 will suffice in the first instance. b. Compare like with like.
To make things meaningful you need to reduce the number of actors that may skew your results. As a start point it would make sense to: i. remove any trades where you clearly didn’t follow your plan, ii. Unless analysing #3 above it would seem logical to compare within one trading vehicle e.g. just your forex trades.
Finally, we would love to hear your feedback on journaling and how it has/has not worked for you (or even problems) you have had getting started. Drop a line a [email protected] with any feedback you would like to share.


A trading edge is a certain approach or special system techniques that, in theory, gives a trader some type of advantage over other market participants, hence making a trader more likely to achieve positive trading results. Many are cynical about the objective of creating a trading edge, despite the plethora of articles and books on various trading techniques. According to them because many traders may learn and apply this same information, the chances of it providing an edge for any individual trader are limited at best, if not non-existent.
Although logically, on the surface, this may seem like a reasonable critique, in much the same way as searching for the “holy grail’, this statement is more than questionable for reasons: a. The assumptions underpinning this thinking are essentially flawed. b. There are traders (although perhaps in the minority) who create positive trading outcomes on a consistent, sustainable basis.
This is indicative of the definition of a trading “edge”. Let’s look at these in more detail. Challenging flawed assumptions Although it is correct that many trading techniques are written about and taught, in reality why most of these do not work are either because: a.
System issues – Most people fail to develop a comprehensive, sufficiently specific system that facilitate consistency in action when entering or more commonly exiting a trade. If this is crucial in order to implement any technique, then it is the absence of this rather than the technique that is a major impacting factor. b. Behavioural issues – Even with the above in place, it is commonly recognised that many traders fail to follow through on such systems.
We have written about this in other articles extensively and it likely most traders have discipline issue when trading in the “heat of the action”. Again, a failure to execute is a major contributing factor rather than any technique. c. A failure to measure and adapt a system as an individual trader – Again this is a common theme in the articles we publish.
Any business, including your “trading business” is best served through formal measurement (e.g., in a journal as well as the “accountancy” information). It is only through this that we can identify: i. How well or otherwise you are following your system ii.
Whether some components of your system would benefit from some amendment to better suit you as an individual trader. So, if most traders suffer from any or all of the above, then the assumptions that all traders have a robust system that as required for an edge is essentially incorrect. And successful traders?
We have suggested previously that in any field, those who succeed do the things that most people do not like/fail to do. The three issues covered in the previous section are more commonly NOT embraced and adhered to by most traders, and it appears as though these are common characteristics by those consistently successful traders that we aspire to be. The reasons for traders not to embrace these are many, but it boils down to a basics e.g., required education or failure to take trading seriously enough, or invest the effort to do the “hard yards” (it is human nature to look for short cuts).
Arguably therefore, even without looking a special trading technique “a” versus technique “b”, if accepting that the three components discussed above are beneficial, is part of what can make a successful trader. Actioning ALL of these is what most traders don’t do and making these happen could give you an advantage over other market participants - this is your possible trading edge. And finally The result of actioning the above in total, and with reference to the third component of trading measurement is you will be able to begin to objectively compare system versus system.
It is quite simple. In summary, Is it possible to create a “trading edge” and give you a potential advantage over other market participants? Well the very fact that most traders don’t do what they need to, as we have discussed above, could theoretically give you that “edge”.
This is your starting point and then take it to the next system versus system testing level.


In a previous article we addressed the concept of cognitive trading biases as a barrier to potential successful implementation of a trading plan in the heat of the action you “press the button” on entry or exit action. This article discussed these biases - “loss aversion” which you can read here ( click to read ). In this article we examine another common cognitive trading bias, termed minimalisation bias.
Trading biases revisited People have inbuilt set of belief and value developed outside the trading context but when the trader interacts with the market, these individual natural ways of thinking and feeling become part of decision-making. Some of these natural in-built responses may not serve you well and are termed ‘cognitive biases’ which may take over from your written and planned ‘trading system’ and become the major influence on your market behaviour. Recognising that these exist and developing awareness of whether one or some of them are part of your trading psychology is the first stage in addressing any bias.
The aim of this series is to help explain what they are, and you are able to make the judgement on your market interaction. What is a minimalization bias? Logically, good decisions in any context (including trading of course) are based on having complete and accurate information, to enable us to process this, and subsequently take appropriate action.
In a trading context, we have access to not only information relating to market sentiment, and tools (indicators) that can help us make sense of this, but also resources that may indicate terms of increased risk e.g. economic data release dates and times. Ideally, the way we use this information both for entry and exit should be specifically articulated within a trading plan which acts as a guiding light for action. In simple terms, many plans will have a set of criteria, or checklist, that if all can be ticked off as present, then act e.g. trade entry can be taken.
With a minimalization bias, the trader basis their decisions on small amounts of usually incomplete information, or in other words, act when all of the criteria have NOT been met. What happens with a minimalisation bias? This bias often leads to premature entry and exit before a full set of signals are confirmed.
Common examples of this may include low trading volumes, not keeping an eye an eye on the economic data release, attempting to predict the next price move often seen when acting on immature candles or bars, or before there is confirmation of a breakthrough a key price point. Commonly, such errors originate from time pressures, poor charting techniques, a lack of specificity in trading instructions within a plan or a lack of, or skipping looking at, appropriate resources to help inform decisions. When in an open trade we may see action (e.g. exit) without substantial evidence of a weakening price, retracements often used as exits rather than clear reversal signs.
The impact of this is limiting the profit potential of a specific trade. Trying to ‘bottom pick’ at the market (if looking for a long trade) may also be a problem in more severe cases, where the investor believes the price had stopped going down on a slow down on the drop rather than waiting for a clear reversal signal. Remember, an exit signal is not necessarily a reason to trade in the opposite direction.
Overtrading due to poor entries, followed by rapid exits may also be a symptom. What you can do if you think you may have a minimalisation bias? If this resonates with you, then the purpose of this article is fulfilled, as recognising and “owing” that there is something that needs to be addressed.
It is the VITAL first step in making a change. Obviously, there are steps you can take to address this (and you MUST). Here are some suggestions: a.
You have a complete trading plan that articulates trading actions both for entry and exit. The more specific these are, the less likely you are to stray. Make sure EVERY one of your criteria is crystal clear. b.
Record and review in your journal how you are feeling as you trade and the market circumstances during your decision-making. It would be rare that this bias is present in every trade. Through recording this information, you may be able to see common thread as to when this bias raises its ugly head.
Armed with this information you will then be able to either avoid trading in certain circumstances, or simply “checking yourself” a little more rigorously. Sometimes the very process of formally recording what you are doing helps in doing the right thing more consistently. c. Re-align with your trading plan prior to every trading session, remind yourself prior to looking at the market what your key criteria for action are. e.
Take regular breaks from the market during any session, particularly when trading shorter timeframes, to re-align with purpose and plan and avoid over-emotional trading. f. If you are in a position where you are finding information difficult to access, then simply ASK. There are many out there with those resources not only at hand but also how to get that information efficiently.
Finally, as we finished when we discussed “loss aversion” as you work on this please be gentle on yourself in terms of your development. Biases by nature are usually deeply ingrained and will take some work to address.


Warning: Turn your sensitivity meter down a little. This is a no sugar-coating, tell-it-how-it-is article (but rest assured it comes from a nurturing place). All over the globe, trading gurus attempt to sell their wares (software, the ‘holy grail’ of trade set ups etc) using retrospective charting examples.
Such powerful visual “evidence” is often used to persuade prospective FX clients that this vehicle is ‘easy’ to make profit with. With little work, little time, or whatever marketing buttons they are using to press to get a response. So, hours of energy invested, often cash is exchanged and yet more often than not, with an off the shelf system in place (often just an entry system which we know is never going to offer a complete trading solution) traders are left feeling more than a little disappointed that such “guaranteed, easy riches” are not showing up in their trading account.
On an individual level we see similar. Much airplay is given to the merits of back-testing and yet as with the aforementioned guru approach, you can just about find examples, if you look hard enough, of chart examples that mean this “next new indicator thing” is now the answer to replenish your now depleted finds. So, what happens, we have a system change, and yet results still often fall short of expectations.
There are 3 common dangers of the retrospective approach to creating (if you haven’t a trading plan already) or altering an existing plan that are worth highlighting. #1 – Overstating the function of back-testing. Let us be completely blunt. The purpose of back-testing is NOT, nor should ever be viewed as evidence that a trading plan, based on what ever system you are exploring, will work for you in the reality of live trading.
Back-testing does not generally consider: a. The impact of economic data releases and revisions, b. The political and general climate both globally and specifically in the countries that currency pairs relate to, c.
Individual investor behaviour re. timeframes, time of day that they trade, nor their ability (or otherwise) to act or inaction on a change of sentiment, d. Unplanned events such as escalating conflict (or the threat of such), e. The relationship and impact of other financial instruments of FX pairs e.g. equity and bond markets, commodities So, why back-test at all if the evidence could be so flawed?
The answer is simple, back-testing creates evidence, not that a system will definitely work for you as a trader, but ONLY as evidence that a forward (or prospective) test may be worthwhile. So, the bottom line is the function of back-testing is to justify the time and effort to prospectively test. It is after such a prospective test that system changes can be made/developed. #2 – Failure to gather a critical mass of evidence There are two issues here. a.
What constitutes enough evidence to move to the next stage of system testing. Quite often traders will make decisions on a limited amount of data e.g. one timeframe and one currency pair, over the last couple of months on which to make system decisions. Now you have read this it may seem obvious and may not need pointing out (but we will anyway) why this is insufficient information on which to base a “cross the board’ entry and exit system. b.
The second issue here is one of selective evidence gathering. A natural human response when excited by an idea is search for evidence to back up that idea. The potential danger with this is that we often tend in this search, to ignore information that refutes our idea. #3 – The reason behind doing this may not be that your system is failing rather it could be a YOU issue.
System skipping is common amongst many traders and is invariably motivated by results that are not as desired. Here is the danger. As much of what goes into creating trader results (some would suggest up to 80%) is due to behavioural issues (we have waxed lyrical about trading discipline previously) unless you: a.
Have a trading plan that is specific, measurable and comprehensive AND b. Follow it religiously ‘to the letter” then you are not really in a position to make a judgement on whether system could serve you well or is likely not to produce desired results. AND to add to this, as such behavioural issues have not been either acknowledged or addressed whatever system (based or retrospective charts or not) is more likely to produce equally disappointing results.
So, before you start on the journey of altering a system you should logically make every effort to have, follow and measure the impact of any system before you even consider changing it (or looking into what you may change it to). This MUST be your #1 priority before going down any path of system alterations. So there you have it.
You have a choice to take action of course on what you have read, If so, your missions going forward are: a. Make sure you have a comprehensive plan that you follow. Then, and only then, should you begin to explore further development including the use of retrospective charts (or back-testing) b.
Recognise the SOLE PURPOSE of back-testing is to create evidence that a forward (or prospective) live test is justified. c. Make sure you are basing any potential system change on a enough “balanced” data.


What is a dividend? A dividend is a payment made by a company to its shareholders to give back some of its profits or return. Dividends are most often paid to shareholders, annually, semi-annual, or quarterly.
Non annual dividends that are paid periodically are known as interim dividends. Companies can also pay dividends at their discretion, and these are known as special dividends. Companies that issue dividends are usually very mature and stable businesses with steady cash flow.
Index funds, or ETF’s will often also pay dividends from as they receive dividends from their underlying holdings. In Australia, well-known companies that issues consistent dividends include ‘Big 4’ banks, BHP, Rio Tinto Wesfarmers, and Qantas just to name a few. In the USA, the big banks such as JP Morgan and other mature company’s such as Walmart and Coke Cola.
Important Terms Dividend Yield - The dividend yield is the total value of all dividends paid in the year divided by the share price. Alternatively, it can be thought of as the dividend return on the market value of the share. Ex-Dividend Date – This is the date in which a holder of stock must possess the stock to receive the dividend payment.
Dividend Payment date – This is the date in which the payment is made. Do Dividends even matter? There are theories that suggest dividends don’t really provide any benefit for holders as they are just eating into the overall Compound Annual Growth Rate of the price.
This is because once a dividend is paid the share price should adjust to account for the payment that has been made to the holder. For example, company A has a share price of $100 and issues a $1 dividend. Therefore, after the payment date, the price should in theory drop down to $99.
Consequently, those who oppose dividends as opposed to the being paid a dividend it a holder of a top performing share could just sell a certain number of their units to in some respects pay themselves a ‘dividend’. On the other hand, companies that pay dividends generally allow the holder to participate in what is known as a ‘reinvestment plan’. This is a scheme in which the company allows holders to reinvest their dividends back into the company’s shares and use the payment to purchase more of those shares allowing for compounding.
These schemes often operate without needing to pay commission and sometimes the shares are discounted. The reinvestment plan also removes certain tax liabilities. For instance, look below at an example of theoretical share that trades.
Price = $10.00 Number of shares at inception = 1000 Total Investment = $10,000.00 Annual Dividend growth =1% Annual share price growth = 1% Time period = 10 years Below is the same share but with a change in the timeframe of 10 to 20 years. This highlights how important having as much time in the market as possible can make a huge difference to the overall returns of a reinvestment strategy/portfolio. The return for 10 years with reinvestment is around 1.32 times the amount for without reinvestment.
Having the same investment for an extra 10 years will yield a return a result 2.35 times better than if the dividends are aid in cash. Can you live off dividends? Dividends payments have created an ideal or goal in which traders and investors strive for is to ‘live off’ their dividends.
Creating a portfolio that is heavily weighted towards dividend stocks can be a way in which to have a periodic income to supplement a pension or salary. This process involves developing a large enough portfolio that can provide these periodic dividends to a level that will cover the cost-of-living requirements. Choosing high quality, high yielding investments can provide this outcome for those who are savvy.
Below is a list of ETF’s and ASX Listed Stocks with the highest recent Dividend Yields? List of ETF Code Company Price Yield Gross DRP 1yr Return IVV Ishares S&P 500 ETF $37.63 16.67% 16.67% Yes -10.40% IHVV Ishares S&P 500 Aud Hedged ETF $37.06 14.93% 14.93% No -16.90% HACK Betashares Global Cybersecurity ETF $7.57 8.99% 8.99% No -23.30% SLF SPDR S&P/ASX 200 Listed Property Fund $11.28 7.45% 7.52% No -16.01% VAS Vanguard Australian Shares INDEX ETF $91.89 6.92% 8.86% Yes -2.18% ILC Ishares S&P/ASX 20 ETF $28.95 6.67% 9.35% Yes +2.77% STW SPDR S&P/ASX 200 Fund $67.10 6.43% 8.42% Yes -1.19% A200 Betashares Australia 200 ETF $123.01 6.35% 8.35% Yes -0.98% IOZ Ishares Core S&P/ASX 200 ETF $29.87 5.96% 8.06% Yes -0.53% VHY Vanguard Australian Shares High Yield ETF $69.87 5.93% 8.31% Yes +5.46% SFY SPDR S&P/ASX 50 Fund $65.77 5.78% 8.01% Yes +1.78% VSO Vanguard MSCI Australian Small Companies INDEX ETF $64.70 5.54% 6.32% Yes -10.81% MVA Vaneck Australian Property ETF $21.20 5.14% 5.25% Yes -13.43% List of ASX Stocks Code Company Price Yield Gross DRP 1yr Return TER Terracom Ltd $0.99 20.20% 24.53% No +360.46% CRN Coronado Global Resources Inc $2.125 19.72% 19.72% No +40.26% MFG Magellan Financial Group Ltd $9.35 19.14% 25.46% No -53.25% YAL Yancoal Australia Ltd $6.53 18.85% 18.85% No +123.63% ACL Australian Clinical Labs Ltd $3.065 17.29% 24.70% Yes -43.24% NHC New Hope Corporation Ltd $6.67 12.89% 18.42% No +177.92% SIQ Smartgroup Corporation Ltd $5.41 12.20% 17.43% No -25.48% TAH Tabcorp Holdings Ltd $1.115 11.66% 16.66% Yes +13.99% BFL BSP Financial Group Ltd $4.80 11.36% 11.36% No +12.41% GRR Grange Resources Ltd $1.07 11.21% 16.02% No +30.49% LFS Latitude Group Holdings Ltd $1.42 11.06% 15.79% Yes -31.73% The final word Ultimately dividend portfolios can be a great step in achieving financial security and freedom and is also a great way to diversify a portfolio or trading strategy.


Many traders early on in their trading journey may jump into trading without knowing if their system or edge can be profitable. The most important metric that a trader should measure their system on is by using expected value. This essentially wors out the average return that the system will return for every trade that it makes, considering both winning trades and losing trades.
The formular for the expected value is written below. Expected Value = (Probability of winning trade X Average Winning Trade Value) – (Probability of a Losing trade X Average Loss) For example, Trader A - Wins 40% of their trades - Loses 60% of their trades - Average win = $20 - Average Loss = $10 Therefore, Expected Value = (0.4x20) – (0.6x10) = $2 This means over the long run the system will return $2.00 per trade made. This relationship describes any trading strategy or edge’s average performance per trade.
Therefore, by determining the expected value a trader can see how effective their edge will be excluding slippage and transaction costs in the long term. Risk and Return The relationship also shows that a strategy does not need to necessarily win every single trade to be profitable. The rule of risk and reward is that they are inversely correlated.
This means that the more a trader is willing to risk, whether it be size or distance to a stop loss the higher potential reward. Alternatively, the less risk a trader takes the lower potential reward. It doesn’t matter which type of trader you are often different personality types will gravitate to either more frequent winning and smaller winnings or larger winnings, but a smaller number of wins.
In fact, a trader may only need to be profitable on 20% of their trades if they can ensure that their average winning trades are more profitable by a factor of 5:1. A strategy that wins more frequently may only need a smaller average win vs its average loss. When testing a system, it is important that there is sufficient data to ensure the inputs for the above formula is accurate.
This means using data from various time periods and potentially across a range of markets to measure the Expected Value of the system. See below for the required a=Average Winning trade/Average Loss trade per Average win rate for a breakeven trading system. Ultimately it is vital that when assessing the performance of a trading strategy or edge to be able to measure the profitability of the system.
The best way to do this is by using expected value. Profitable trading strategies can be made with either a high win rate and low average W/L ratio or a low winning strategy with a high W/L ratio.