Trading strategies

Artificial Intelligence (AI) is no longer a futuristic concept; it is a rapidly evolving reality reshaping industries, including financial markets. For traders, understanding how AI impacts price action and adopting strategies to adapt to these changes are critical to staying competitive. This article aims to explore AI's current capabilities, its profound influence on price action, but also offer some thoughts on how traders can potentially thrive during current and future changes that may impact markets.

What is Artificial Intelligence? Artificial Intelligence refers to the ability of machines to simulate human intelligence and perform tasks such as learning, reasoning, problem-solving, and planning. AI can be broadly categorized into three types: Artificial Narrow Intelligence (ANI): Specialized AI systems designed to perform specific tasks (e.g., chatbots, fraud detection, and algorithmic trading).

Artificial General Intelligence (AGI): A hypothetical stage where AI matches human cognitive abilities, capable of learning and reasoning across diverse tasks. Artificial Superintelligence (ASI): An even more speculative stage where AI surpasses human intelligence in every way. Currently, ANI dominates the landscape and drives innovations across industries.

For financial markets, ANI forms the foundation for tools and algorithms that enhance trading efficiency, accuracy, and decision-making. What is Machine Learning? Machine learning (ML) is one of the most important technologies underpinning AI and its potential applications in the trading world and so is worth just a little more explanation.

In simple terms, it may enable machines to learn from data, identify patterns, and make predictions or decisions without requiring explicit programming for each scenario. Let’s look briefly at the key elements, types and applications of ML that may have trading relevance. Key Elements of Machine Learning Data: Machine learning relies on large datasets, such as historical market prices, trading volumes, and economic indicators.

Algorithms: These are mathematical rules and calculations used to analyse data and make predictions. They range from simple regressions to complex deep learning models. Feedback Loops: Feedback allows ML models to learn from successes and failures, continually improving their accuracy over time.

Types of Machine Learning Supervised Learning: Machines are trained using labeled datasets, such as identifying bullish or bearish patterns in historical data. Unsupervised Learning: Machines find hidden patterns or anomalies in unlabeled data, such as clustering similar market behaviors. Reinforcement Learning: Machines learn through interaction with an environment, receiving rewards or penalties for actions, making it particularly useful for dynamic trading environments.

Applications in Trading Machine learning drives key advancements in trading, including: Predicting price movements using historical and real-time data. Optimizing portfolio allocations. Detecting anomalies or potential fraud.

Automating decision-making processes based on market conditions. Understanding machine learning is essential because it forms the backbone of many AI-driven trading tools that are reshaping financial markets. Concepts like enhanced trend identification, predictive analytics, and scenario planning all stem from machine learning’s ability to process vast datasets and adapt to changing market conditions.

AI’s Current and Future Capabilities in Trading As the evolution of AI expands into most areas that impact on our world, trading is no exception, AI applications in the financial world span a wide spectrum of uses but most fall into three main categories. This comprise: Fraud Detection: Identifying irregularities in financial transactions. Predictive Analytics: Anticipating price movements based on historical patterns and real-time inputs.

Advanced Decision Support: Assisting traders by analyzing complex datasets and suggesting optimal actions. As ANI technology advances, it is expected to refine these capabilities further, enabling: Enhanced sales forecasting for financial products. Real-time risk management tools.

The development of more personalized trading recommendations. In the long term, these advancements are likely to create a trading environment driven by increasingly sophisticated AI systems. AI’s Impact on Price Action Price action—the study of historical price movements to predict future trends—is foundational to many trading strategies.

AI's integration into trading may begin reshaping this traditional paradigm in several potential ways: Enhanced Trend Identification AI’s speed and accuracy in identifying trends far outpace traditional methods: Faster Recognition: Algorithms can process vast datasets in real-time, detecting emerging trends before they are visible to manual analysis. Greater Accuracy: AI can filter out noise and focus on genuine market movements, providing more reliable insights. Predictive Analytics AI’s predictive capabilities extend traditional market forecasting: Forecasting: Using historical data and complex algorithms, AI predicts market shifts with varying confidence levels.

Scenario Analysis: Simulating multiple market conditions, AI helps traders prepare for diverse outcomes. Changing Trend Lifecycles AI-driven strategies could alter the nature and duration of market trends: Accelerated Trends: Rapid AI-driven trades may shorten the lifecycle of trends, making them more volatile and less predictable. Increased Volatility: High-speed trades based on AI predictions can lead to significant price swings in short timeframes.

Behavioural Impacts AI is likely to influence trader behaviour and market dynamics: Herding Behavior: Similar AI-driven insights can lead to collective actions, amplifying price movements. Strategy Diversification: To remain competitive, traders must develop diverse and creative strategies. Challenges and Risks While AI offers tremendous potential, it also introduces challenges traders must navigate: Increased Market Volatility AI’s speed and efficiency can exacerbate short-term market volatility.

Sudden price movements may trigger stop-losses more frequently, disrupting traditional risk management strategies. Flash Crashes Algorithmic trading can lead to flash crashes—sudden, sharp price declines caused by cascading AI-driven trades. These events create liquidity risks and potential financial losses.

Over-Reliance on AI Dependence on AI systems could lead traders to overlook market fundamentals, exposing them to algorithmic biases and failures. Reduced Effectiveness of Traditional Tools As AI reshapes market behaviour, traditional tools like moving averages may lose reliability, forcing traders to adopt more dynamic approaches. Ethical and Regulatory Concerns AI introduces challenges around transparency, data bias, and compliance with evolving regulations, requiring constant vigilance.

How to Adapt and Thrive To improve the chances of potential better outcomes in a new more AI-driven market, traders must adopt proactive strategies that embrace rather than push away likely changes in the traditional ways of looking at markets. These may include: Review and Refine Your Strategies Evaluate how AI might impact your existing methods, particularly those reliant on lagging indicators. Incorporate real-time data analysis tools to complement traditional approaches.

Action: Conduct stress tests on your strategies under simulated high-volatility scenarios to ensure resilience. Leverage AI for Competitive Advantage Explore AI-powered platforms for market analysis, trade recommendations, and risk management. Develop custom AI models tailored to your trading style.

Example: Use machine learning to identify unusual trading volumes across multiple markets, providing actionable insights into potential opportunities. Strengthen Risk Management Practices Adapt stop-loss levels dynamically based on real-time volatility metrics. Diversify portfolios to reduce exposure to single-market risks.

Action: Incorporate scenario analysis tools to prepare for unexpected market conditions, such as flash crashes or sudden policy changes. Stay Informed and Educated Keep up with advancements in AI and its applications in trading by attending webinars, reading industry reports, and engaging with experts. Experiment with AI tools in demo accounts to understand their capabilities and limitations.

Example: Test AI-based predictive analytics platforms to evaluate their effectiveness in your trading strategies. Harness Human Creativity and Judgment Combine AI-driven insights with personal market knowledge to develop hybrid strategies. Focus on areas where human intuition, creativity, and adaptability can complement AI’s analytical power.

Action: Use AI as a decision-support tool, relying on your judgment for execution and fine-tuning strategies. Conclusion AI is transforming financial markets, presenting both opportunities and challenges for traders. While its speed, accuracy, and predictive power can disrupt traditional methods, those who adapt their strategies and leverage AI’s potential stand to thrive.

By refining approaches, strengthening risk management, and staying informed, traders can navigate the complexities of AI-driven markets and position themselves for success. The future of trading is here. Embrace the change, adapt your strategies, and unlock the potential of AI to gain an edge in an increasingly competitive market.

Introduction: Understanding the Impact of Entry Errors Trade entry is a critical moment that is undoubtedly contributory to the success or failure of a trade (although exits remain an additional key component of course). Whilst many traders focus much energy and effort on entries, the importance of a well-planned and so called ‘high probability entry’ is often underestimated. Poor entries can put traders at an immediate disadvantage, increasing risk exposure, reducing profit potential, and fostering a cycle of emotional and often questionable decision-making at this critical point of any trade.

This article delves into the most common entry mistakes traders make, why these errors occur, and, more importantly, how to avoid them. Many of these are insidious but if remain unchecked can lead to disappointment in trading outcomes, and at worst, may result in significant trading losses if they are not addressed over time. Through developing a greater understanding of the psychological pitfalls, potential technical missteps, and strategic errors made behind poor entries, traders can take actionable steps to enhance their consistency and performance in the markets.

Whether you're a beginner or an experienced trader, mastering your trade entry process can have a profound impact on your long-term trading outcomes and ultimate success or otherwise. The great news is that many of these are not “hard” fixes. Although by no means an exhaustive list, and often connected, these TEN errors in our experience appear to be the most common, Use these areas covered below as a checklist, making notes on any aspect that may resonate you’re your behaviour and of course subsequently take appropriate action as needed. #1.

Chasing Price Implications: Chasing price happens when traders enter impulsively after a sharp price movement in a particular direction. This is often driven by FOMO (Fear of Missing Out), and typically results in buying at overextended levels where a trend is already very established and may have almost run its logical technical course. This often results in a trade reversing or at best price exhaustion and little or no positive outcome over time.

Price reversal will often, even with the appropriate risk management in place result in repeated losses. Solutions: Develop a disciplined approach by waiting for either retracements to logical support levels, with of course evidence either of a bounce upwards, or even a breach of a new key level, or previous swing high (or low if “going short”). Either of these approaches may result in achieving a more favourable entry.

Also many trading platforms, including MT4 and MT% GO Markets platforms can use notification alerts to identify when the price reaches these levels, which is a useful feature that may assist in making sure robust decision-making occurs on a consistent basis. Additionally pending orders may also be used as part of your effective entry toolbox, set with more “cold” logic rather than being driven by emotional excitement of price velocity that may often be short-lived. #2. Ignoring Market Context Implications: Ignoring the broader market environment leads to trades that contradict prevailing trends or key market conditions.

T his oversight often results in entering trades with low probability, increasing the likelihood of stops being triggered. For long-term success, aligning trades with the dominant market forces is not only logical but appears from any research performed to be generally higher probability of at least some period of time where it is more likely that price will move in your desired direction. Failure to do so on a regular basis, can leave traders feeling like they're always on the wrong side of the market.

Example: A trader shorts the S&P 500 during a small pullback, not realising the index is in a strong uptrend on the daily chart. The pullback ends, and the uptrend resumes, quickly hitting the stop-loss. Solutions: Perform a multi-timeframe analysis before entering a trade.

Use higher timeframes (e.g., daily if trading an hourly timeframe) to understand the broader trend and ensure the trade aligns with it. Incorporate trend-following tools like moving averages or trendlines to validate entries is of course a common method to help substantiate this approach. #3. Over-Leveraging Positions Implications: Over-leveraging magnifies both potential profits and losses, but the latter can have devastating consequences.

Even small adverse price movements can wipe out significant portions of an account, leading to margin calls (and so taking “exit control” away from the trader) or even complete account depletion. This often traps traders in a cycle of "chasing losses," further compounding mistakes. Solutions: Implement strict position sizing rules.

For example, risk no more than 1-2% of your account on a single trade by adjusting your position size relative to your stop-loss distance. Your maximum ‘Risk per trade’ should be based on your Tolerable risk % of Account size per trade (e,g, 1%) x Entry price to Stop-loss distance. #4. Entering Without a Stop-Loss Implications: Trading without a stop-loss exposes traders to uncontrolled risk.

It fosters a dangerous mindset of "hoping" the market will work in their favour, often leading to mounting losses. A single large loss can undo months of profitable trading, shaking both confidence and capital and so have longer term psychological implications such as loss aversion, which can further distort good decision-making. Solutions: Use stop-loss orders based on logical technical levels, such as below a recent swing low.

Although less pertinent to entry but equally important through the life of a trade is potential use of trailing stops can also help lock in profits as the price moves favourably, protecting against reversals and of course profit targets based on logical potential technical pause or reversal points. #5. Over-Reliance on Indicators Implications: Indicators are helpful tools but are often misused when relied upon as the sole basis for trade decisions. Many indicators are lagging by nature, meaning they reflect past price movements rather than anticipating future ones.

Blind reliance on indicators can lead to late or false entries, especially in trending or volatile markets. Price action and associated volume should be treated as the primary decision making points with indicators used for confluence, Example: A trader buys a stock because RSI indicates oversold conditions, but the stock continues to decline as the market remains in a strong downtrend. Solutions: Combine indicators with price action and market context.

For example, use RSI or MACD as confirmation for setups rather than primary signals. Always validate indicator signals with chart patterns, price range within a specific candle, and/or key levels of support/resistance. #6. Trading News Events Implications: News events often create sharp volatility, which can lead to slippage, widened spreads, and unexpected losses.

Trading without a structured plan during (and arguably before) such events exposes traders to heightened risk, especially in fast-moving markets. Examples: A trader enters a position before a Federal Reserve announcement, expecting dovish remarks. Instead, hawkish comments cause a rapid market reversal, leading to a significant loss.

It is worth noting that it doesn’t even have to be an adverse announcement to that which was expected to disappoint. If one believes, as is often cited, that everything that is known or expected is already “priced in” then even an expected number or news release can fail to provide a potentially profitable price move. Also of course, equally as dangerous to capital is not to be aware of significant market events at all.

To enter prior to these from a place of ignorance that they are even happening is potentially as damaging to capital.. Solution: Use a trading calendar to track upcoming high-impact news events. If trading news is part of your strategy, place pending orders above and below key levels to capitalise on breakouts while controlling risk. #7.

Trading Impatience Implications: Entering trades prematurely often leads to setups that fail or require larger stop-losses to accommodate unnecessary volatility. This behaviour stems from a need to "be in the market," and this “itchy trigger finger” which is in essence a compromise of discipline arguably can increase the likelihood of losses. Example: A trader buys a stock before confirmation of a breakout, only to see the price reverse and remain in a sideways trend for a prolonged period of time not only failing to see that specific trade do well but also arguably adds opportunity risk as that money invested could be in a trade that has indeed set up to confirm a change of sentiment, Solution: Establish clear entry criteria and wait for confirmation, such as a candle closing above resistance.

Articulate these clearly and unambiguously within your trading plan, #8. Misjudging Risk-Reward Ratios Implications: Poor risk-reward ratios undermine profitability. Even with a high win rate, losses can quickly outweigh gains if the potential reward doesn't justify the risk.

Either a failure to have defined acceptable levels articulated within your plan or ignoring (based on previous price action) potential pause or reversal points are the two main causes. Example: A trader risks $500 to make $200 on a trade. Over several trades, a few losses wipe out multiple winning trades.

Solutions: Ensure a minimum risk-reward ratio is stated for example 2:1 before entering. For instance, if risking $100, target a profit of at least $200 to maintain positive expectancy. #9. Over-Trading Implications: Over-trading leads to increased transaction costs, emotional exhaustion, and reduced focus on high-quality setups.

This is often driven by revenge trading or overconfidence after a winning streak. Example: A trader takes several trades in a single session after a loss, compounding mistakes and ending the day with a larger drawdown. Solutions: Set a daily trade limit and focus on quality over quantity.

Use a trading journal to reflect on your trades and identify patterns of over-trading. #10. Ignoring Correlation Between Assets Implications: Trading multiple correlated assets amplifies risk, as adverse moves in one asset can lead to simultaneous losses across others. Hence, even if say a 2% maximum risk is assigned to a single trade, if trades are highly correlated then that risk is multiplied potentially by the number of trades open.

Example: A trader goes long on EUR/JPY, AUDJPY and GBP/JPY and a sharp JPY rally causes losses in all three positions. Solutions: Use correlation matrices to assess relationships between instruments and diversify by trading uncorrelated assets. For instance, balance a forex position with a commodity trade.

Summary: Trade entry mistakes are often rooted in a combination of emotional decision-making, poor planning or preparation, and over-reliance on tools or strategies without proper context. By identifying these common errors and implementing structured solutions, traders can greatly enhance their ability to execute high-quality trades. The key to success lies in discipline, patience, and a willingness to adapt and learn from mistakes.

Start reviewing your entry process today, be honest with any of the above that may resonate with you (As awareness is always the first step in improvement) and give yourself the chance to potentially transform your trading outcomes over time.

Why you need to understand this market concept to improve your trading: Market Correlation For new traders and experienced traders, it can be daunting trying to find the best assets to trade. Whether it be equities, foreign exchange or indices, traders should be trying to have as many factors pointing in their favour as possible when entering a trade. These factors can include, the general trend of the individual asset, the price action at the time of entering the trade, candlestick patterns, use of technical indicators, among many others.

However, one thing that all traders should know about and understand is correlation. What is Correlation? Correlation is the pattern or relationship of how one asset performs relative to another asset.

In statistics, there are mathematical measures of correlation including covariance, correlation coefficients and other terms to describe the relationship of one asset to another. These methods can also be used to quantify asset correlations. A correlation between assets can be positive negative or uncorrelated.

Understanding which relationship between different assets can help provide some indication of the way in which an assets price will go. Below is a diagram that shows how the return of assets can be plotted against each other and the potential relationship. For example, imagine that there are two gold companies Gold company A Gold company B Assume that the price of their shares is perfectly, positively, correlated.

This means that when gold company A’s share price rises by 1% company B’s share price will also rise by 1%. This same price action will occur in reverse if the price of company A falls by 1%. Now in practice no two assets are perfectly correlated.

However, two or more assets may be very strongly correlated. Therefore, identifying how correlated certain assets are and how the price of one impact on the other can be a powerful tool. What creates correlation?

Strong correlation between assets usually occurs because the price of the different assets is material impacted by very similar factors. For instance, two companies in Australia may be more correlated than one company in Australia and one company in the USA. This is because geographically the Australian companies will be affected the local economic conditions.

This may include things such as inflation, taxation policies and other geographical specific conditions. Other factors that can influence the correlation include similarity of the assets or a company’s business operations, being in the same sector or a range of other factors. For example, see the correlation between the ‘Big 4’ banks in Australia below.

It can be seen due to how similar the businesses are and the conditions of which they operate in the pattern on returns are almost identical. Index correlation An important phenomenon to understand is the law of averages and big numbers. Essentially, if large companies are grouped together then they act as a good proxy for the overall market or a specific sector.

This essentially is what an ETF or and Index is. Therefore, as it represents how most individual companies are performing, most companies will be to a degree correlated to the overall market index or relevant sector index or ETF. Size matters Another important thing to understand about how correlation works is that smaller assets or companies will tend to correlate towards the performance of the major players within the sector.

For instance, in the technology sector, smaller technology company’s such as zoom will likely be correlated to larger companies such as Apple and Microsoft by virtue of being in the same sector. Correlations do not just occur in equities and are prevalent in FOREX and commodities. Correlation can be found between growth assets such as the Nasdaq Index which is a technology heavy Index and growth currencies such as the AUD or NZD.

Similarly, more stable assets such as the Dow Jones will likely be more correlated to commodities such as oil, they represent more stable industry and manufacturing sectors. How does it improve your trading? By simply being aware of the direction of the correlated assets, a trader is better able to trade with underlying trend and momentum.

This is vital when trying to optimise edge and improve trading accuracy. It can also equally show when a stock is underperforming or overperforming. For instance, if the general trend of a sector leader is trading 5% higher over a certain period, and a smaller company in the sector is trading at 10% higher it is outperforming the ‘sector’ and understanding why this occurs is an important step into deciphering what is driving price action.

Having a good understanding of how assets correlate can also help find potential trading opportunities earlier than others. This is because by following a sector it becomes easier to see which assets still may have room to shift their price. Ultimately, if a trader can develop their identification of patterns of correlation and the reasons for the relationships between different assets it can provide a trader with a much stronger and accurate edge.

Entries for longer-term stock investment approaches can be based on either long-term technical trends or more commonly, fundamental data related to a company’s current and projected performance. Despite the plethora of such suggestions, there is often a lack of clear guidance, or even a complete absence, of instructions on determining the timing of an exit from a long-term position. Logically, whether it’s a short-term technical entry or long-term fundamental entry, many of the “rules of the game” are similar, including the need for clear and unambiguous exit strategies seems paramount for consistently positive investment outcomes.

The approach originally used to make an entry decision can serve as a good starting point but there are other considerations that can potentially benefit outcomes. This article aims to briefly describe six potential exit approaches you could consider, providing some detail and examples as to how to action your chosen approach. Target Price Exit Strategy Setting Targets: Determine a fair value (and thus exit price target) by conducting in-depth fundamental analysis, utilizing metrics like Price-to-Earnings ratio (P/E), Cash flow, debt levels, book value, or longer-term technical levels.

On-going monitoring: Regularly track the price against this target. For example, if you calculate a fair value for a stock at $50, and it’s currently trading at $45, you might decide to sell once it reaches or exceeds $50. Other Considerations: Regularly review and adjust the target price, taking into account changes in fundamental factors impacting the relevant sector or market as a whole.

Ongoing Fundamental Awareness Ongoing Analysis: Continuously evaluate underlying fundamentals, such as earnings, balance sheets, cash flow, and management quality. Be vigilant not only when next company reporting dates are due but also for the often-unpredictable release of operational updates or changes in guidance. Trigger Points: Identify specific company indicators or information that would prompt an exit.

An example of this may be a sustained decline in revenue or mounting debt levels, particularly when beyond what was originally expected. Other Considerations: Implementing this strategy requires consistent research and a nuanced understanding of the particular business and industry factors influencing the investment. Having the optimum resources in place to be able to do this is vital and identifying these should be a primary goal of any fundamental investor.

Economic & Sector Changes On-going Analysis: Regularly review broader economic indicators like GDP growth, inflation, interest rates, or industry trends. Understand how such changes in these key data points may correlate with the asset price and establish exit criteria accordingly.For example, you may reconsider a position in a technology stock if there’s a widespread shift away from tech spending or growth concerns or regulatory changes that detrimentally affect the sector. Other Considerations: This strategy necessitates a broad understanding of economic cycles, industry dynamics, and how these elements interact with your particular investment holdings.

Additionally, it’s worth noting that appropriate resources should be in place to ascertain this as proactively as possible, or at worst in a timely manner. This may assist in preventing excess depreciation in asset price to the point where action is delayed and major capital damage has occurred. Dividend Targeted Approaches On-going Analysis: If part of your entry criteria and anticipated return from fundamental analysis-oriented trades is based on dividend yield to some degree, it is worthwhile to not only look at what is current but also perform ongoing evaluation of the reliability and/or growth of dividends.

Exit Criteria: Having established an expected return, it logically makes sense to have criteria in place to help decision making. For example a decrease in dividend yield below a certain threshold or a cut in dividends could be part of your potential exit plan for a specific investment. Other Considerations: As well as vigilance for the timing of company announcements where dividend changes are often announced, awareness of the yield of your current investment compared to others, and industry trends is required, as they could influence the sector and the market as a whole.

Time-Based Exits On-going Analysis: Often with time-based exits, there is alignment with a particular impending event. Examples of this type of event include a shift to EVs from petrol-fuelled cars or the impact on assets in the lead-up to an election. Either way, your investment time horizon needs to be reviewed should there be a change in circumstances and the rationale behind your initial thinking on entry.

Other Considerations: There is a discipline involved in exiting from a stock position that remains strong even after an event, or the impact of such, has passed. With a systematic approach to fundamental entries in place, it is legitimate to review whether other fundamental approach criteria are met and perhaps consider continuing to hold. Without this in place, or if no match with other approaches exists, logic would dictate that a planned exit is an exit, and you should action it as such, no matter how well this specific position has served you to date.

Portfolio Rebalancing On-going Analysis: Although not based on a specific entry approach, periodically evaluate your overall portfolio asset allocation is prudent. Reviewing whether the current holdings are still a fit with long-term investment aims and risk tolerance in current and ongoing market circumstances are appropriate rebalancing considerations. Rebalancing Exit Approach: Criteria for rebalancing should be pre-planned and clearly defined.

These may require consideration of multiple factors, such as an asset becoming an excessive portion of the portfolio on good performance, or changes in market or economic circumstances that threaten specific portions of the portfolio. Other Considerations: Continuous monitoring of the portfolio is required, and checking continuing congruence with desired asset allocation and your risk profile is vital. Rather than based on a specific entry approach, just to reinforce that the concept of rebalancing is one that is important across all of the approaches described above.

Summary Although they receive little “airplay” in comparison to technical approaches and exits, the exit strategies within a portfolio based on fundamental analysis entries are multifaceted, frequently interconnected, and equally important to master. Crafting a proficient exit system demands a comprehensive knowledge of each specific investment holding, and wider market and economic dynamics, in the context of your personal investment objectives, and risk tolerance. The need for a set of written system criteria for all actions, regular monitoring, thorough analysis, and disciplined adherence to predetermined exit criteria are essential.

Ideally, as traders, our aim is often to identify potential entries at the start of a new trend (so “first in the queue”) and exit at the end of that trend. Of course, we often will identify a price move where a trend may already be established and are therefore faced with the decision as to “join in” mid-trend (we hope) with the aim of catching the rest of a trend move. The concern of this approach is of course the fear of potentially entering just prior to that trend changing.

There are “clues” we can use, such as candle body/wick size and volume which may help, but also there is a group of indicators termed ‘oscillators’ which work on the idea that there are points in a price move which the underlying asset (be it a Forex pair or CFD) may be overbought (and hence a long trade could be deemed riskier), and oversold (where a short trade may be termed riskier). Although the Relative Strength Index (RSI) which we covered previous in an article (review "Adding the RSI to your entry or exit trading plan? "), is possibly a more commonly used oscillator for determining oversold and overbought situations, the stochastic although possibly seen as being slightly more complex, does appear to be frequently used by more experienced traders. This article aims to shed some light on how this indicator is used and what it may be showing you relative to price movement.

What is the stochastic trying to tell us? As with the RSI the Stochastic is an oscillator (whose value can theoretically lie between 0-100) which has identified key levels which may indicate whether a particular asset is overbought or oversold. A move into either of these two “zones” may suggest a trend change is more likely to be imminent.

The key levels are below 20 (oversold) and above 80 (overbought). See below a 30-minute chart for GBP/USD with the stochastic added using the default system settings (we have added horizontal lines from the drawing tools to make the key levels clearer. We will discuss settings later and the additional line but at a simple level, taking the blue line on the stochastic if it moves below 20, then you would be cautious and perhaps avoid entering a short trade (examples A and B), and perhaps avoid entering a long trade if it moves above 80 (see example C).

And the other dotted line? There are two lines that form the stochastic namely: %K (usually a solid line) – In this case blue as previously referenced above. %D (usually a dotted line) and is a moving average of %K (often set as an exponential) Slowing periods may also be set (default is 3). As a rule, the slower (bigger number the less “noisy” i.e. you will see less overbought and oversold conditions).

And how can it be used? a. As an additional entry criteria “tick” As referenced earlier, for entry, traders may use this as an additional tick (when other indicators may suggest entry) to make sure they do not enter a long trade on an overbought currency pair/CFD, or short trade on an oversold currency pair/CFD. b. As a warning to prepare for exit action in an open trade Though less commonly discussed, it would appear logical that if in a long trade for example and the Stochastic moves into an over-bought position this could be a warning to consider exit (more commonly used as a signal to tighten a trailing stop loss) c.

As a primary reversal signal Additionally, some traders may look to buy when moving out of an oversold situation when the EMA dotted line crosses the solid blue line. (and of course, the reverse when overbought). It would be rare to use this in isolation with no other indicators, using increasing volume, and candle change recognition would often be used also. The relatively fast default settings (5,3,3) may merit some review anyway but particularly in this case.

Which settings? As with any indicator you are in control of the settings and what you use for you is of course your choice. With the chart below, we have used the default 5,3,3 and added a 21,7, 7 to illustrate the difference of a less noisy set of perimeters.

In Summary Ultimately, and to finish, it is of course your choice as to which criteria you use for entry and exit. Remember, whatever these are for you, the key lessons of: a. specifically identifying how you are to use the criteria within your plan, b. the importance of forward-testing (as well as back-testing) of any system change, c. and of course, the discipline of following through are ALL critical whether you use the Stochastic, RSI or neither.

What is an Expert Advisor (EA)? Expert Advisors (EAs) are trading software that automatically run and trade based on their preprogrammed rules for initiating, managing, and exiting trades in the market. These automated trading systems are very popular among traders and are widely used on the Metatrader 4 and 5 platforms.

For most traders, EAs are primarily used for Forex, although they can be used on any market that’s available on the platform. These can be purchased prebuilt online from a developer or created to automate an existing strategy being used. There are many reasons why traders use them, and I will explain some of the main advantages and disadvantages.

Advantages of using an EA: Discipline - these programs are set to certain parameters and will manage your positions based on the programmed strategy. Using a set of yes/no triggers it will make trading decisions and act on them instantly without changing their decisions like humans would do. It will also manage risk based on your risk settings, so you do not overexpose your account.

Timesaving – there is only so much time a trader can look at the charts for trading opportunities before getting tired while the markets are open. An EA can monitor the charts 24 hours per day and open and close positions or even provide alerts which can save time. Emotionless – this plays a huge role in the decision making for traders.

When trading with real money traders tend to make emotional decisions and break their strategy from fear or greed. An EA removes this element and will stick to the original plan although manually intervention can still be done. Backtesting – you can backtest an EA to see whether the strategy has been profitable in the past on multiple markets.

Although these can give you confidence to use them, it’s important to keep in mind that past performance is not an indicator for future performance. Disadvantages of using an EA Technical failures – for an expert advisor to work, your platform needs to be open and running at all times which means if you experience technical issues such as a crash, software update, power outages, connection problems then this will effect the EA. Additional cost of VPS – this is a dedicated private server which allows you to remove some of the technical challenges when using an expert advisor.

There are benefits of lower latency and faster execution and also the peace of the mind that the EA is running on a private server which can be accessed from any location. It typically costs around A$30 per month to have this access. World events – an EA is programmed to trade based on technical parameters, which means should there be an unexpected world event or news announcement, this would have an impact on your trades as the the market moves in response to them.

Doesn’t teach how to trade – these are coded to trade certain parameters therefore unless you understand how to code, you can only watch. Although there are many EAs which make money for people who can’t trade, if they are unprofitable then it’s back to the drawing board; that could mean finding another EA or learning to trade. Here are example how an Expert Advisor looks running on MT4 platform: If you are interested to use an Expert Advisor and seeing how these can perform and the results, you can find them on MQL5.com.

This is the largest community for developers and signal providers to showcase their systems. You will find some for free and some that will need a monthly subscriptions to have access to them. You can run expert advisors on a GO Markets trading account.

If you need any help setting them up please contact our support team.