Market news & insights

So for the first time in over four years the Reserve Bank of Australia (RBA) has cut the official cash rate by 25 basis points to 4.1%. They fired the gun they've loaded it for the possibility of more but are they blanks? Let's drive into what was said in the statement, what outlook was given and what this means for the Australian dollar and your trading going forward.

First – the action. The RBA made its first move in over a year 15 months to be exact, cutting the cash rate by 25 basis points to 4.1% at its February meeting. This marks the first rate reduction since November 2020, and marks what looks to be the other side of the “Table Top” mountain effect for the cash rate as we had discussed when the hike cycle began.

While the cut itself was widely expected - the RBA went full hawk in explaining what the outlook and rate cutting cycle will look like particularly on expectations of an extended easing cycle. To really highlight this point, have a look at the AUD when RBA Chair Michele Bullock was speaking on this point during her press conference. The short reversal is telling.

The crux of the whole position is - the RBA acknowledges progress on inflation but remains cautious about declaring victory. And that is a fair position to hold, core inflation is running at 3.2% and headline at 2.4%. Yet the bank is forecasting headline inflation to flare to 3.7% come December with core (trimmed mean) at 2.7% unchanged for TWO AND A HALF YEARS!

It reflects a delicate balancing act—while inflation is tracking lower, the labour market remains unexpectedly tight. The central bank wants (needs) more evidence before committing to further cuts, making it clear that markets are getting ahead of themselves hence the moves in the AUD. It begs the question: Why Cut Rates Now?

The Inflation-Labor Market Trade-Off Clearly the RBA’s decision to ease policy was largely driven by better-than-expected inflation data in Q4 2024. Disinflation has been happening faster than anticipated, which gave the central bank confidence to remove some policy restrictiveness, which it has always said it would as soon as it thought it could. But inflation is only one side of the equation.

The flip side is the labour market – which is hot and running hotter than expected. Unemployment remains at 4.2%, below the RBA’s estimated full employment level of 4.5%. While job vacancies have eased slightly, demand for workers remains strong, which could keep wage pressures elevated.

Caveat – the wage price index the day after the RBA meeting came in at 3.2% meaning in real wages terms wages growth is 0. But – it still presents a risk: easing too much too soon could reignite inflation, particularly in wage-sensitive sectors like services. Hence the hawkish stance.

As mentioned inflation is projected to settle at 2.7%—still above the 2.5% midpoint of its target range—while unemployment is expected to remain relatively low. This suggests that the RBA is not entirely convinced that inflation will continue declining without further policy restraint. The central bank is effectively saying: "We’ll cut where possible as we don’t want to leave policy restrictive any longer than necessary, but we’re not ready to call this the start of a full easing cycle." Is this just new aged jawboning?

Moderating the Market We feel we are now entering a new phase – upside jawboning a deliberate ploy to temper market expectations and more importantly – the consumer. The concern is that easing policy could trigger a rebound in spending, asset prices, and broader economic activity—creating inflationary pressures that could undo recent progress. This is why Governor Michele Bullock took a firm stance in her press conference, directly challenging the market’s expectation of multiple rate cuts and described the market’s pricing—which holds three additional cuts in 2025—as " far too confident." In short the Board’s message is clear: February’s cut does not automatically signal a sustained easing cycle.

The Board remains data-dependent and will only consider further rate reductions if inflation risks subside and the labour market shows definitive signs of cooling. What’s the biggest threat to the RBA? The Federal Election and Fiscal Policy Never underestimate a government and spending money – which makes fiscal policy risk number 1.

With a federal election due by May 17, government spending could play a significant role in shaping the economic outlook. If fiscal stimulus is ramped up, through cost-of-living relief measures or infrastructure spending, it will add upward pressure to inflation, thus reducing the urgency for further rate cuts. The RBA has explicitly stated that its forecasts do not assume any additional election-driven spending, meaning any surprises on this front could alter the rate outlook and the 3.7% headline figure could be worse.

Consumer and Housing Market Reactions Another key factor is how households respond to this rate cut. If consumer confidence rebounds strongly and household spending picks up, this may also signal a reassessment. Housing both prices and construction activity will be other critical indicators.

A surge in property market activity, driven by lower borrowing costs, could create renewed inflationary pressures, forcing the RBA to hold back on further cuts. And let’s be honest this has happened every easy cycle. Global Economic, Geopolitics The broader global economic landscape also plays a role.

If central banks in major economies, such as the US Federal Reserve, move more aggressively on rate cuts, this could influence the RBA’s decision-making. A more dovish global monetary environment could ease financial conditions in Australia, allowing the RBA to be more patient in its approach. Counter this with trade tariffs, trade wars and tit-for-tact reactions that increase inflation may lead to not only a long pause but also the risk of hikes.

Crystal ball time For now, the market has a cut fully priced in by the July meeting but the risk of a delay is growing. The RBA’s cautious approach suggests it wants more time to assess how inflation, employment, and economic activity evolve before making another move, suggesting September is a more likely month for the next cut, all things being equal. Ultimately, the path forward remains highly uncertain.

This means the central bank is unlikely to move quickly, and expectations of a rapid series of rate cuts may need to be revised. Hence as traders the AUD weakness may now have found a floor as cuts are not going to be as forthcoming. In short: while this cut marks the beginning of policy easing, it’s far from a signal that the RBA is on an aggressive cutting path.

The data will dictate the next steps, and for now, the Board remains firmly in watch-and-wait mode.

The financial markets are evolving rapidly, driven by increased data availability, computational advancements, and sophisticated trading strategies. Traders—both institutional and retail—are turning to artificial intelligence (AI) and, more specifically, Machine Learning (ML) to gain an edge in the markets. At its core, Machine Learning is a subset of AI that enables systems to learn from data and make predictions or decisions without being explicitly programmed.

This is a fundamental shift from traditional rule-based trading systems, which rely on static conditions and predefined rules. Instead, ML-powered strategies can adapt, refine, and improve their decision-making process over time, allowing traders to respond more dynamically to ever-changing market conditions. In this article we attempt to unravel not only what ML is but why is ML is likely to become such a dominant force in trading, how you might get ML to work for you, AND even if you are likely to sit on the sidelines what it all may mean for discretionary traders and how it could change the way process move.

Why Now? The Perfect Storm for ML Adoption Several key developments in technology and financial markets have contributed to the widespread adoption of machine learning in trading. I have identified the primary four factors which include: Explosion of Market Data Financial markets generate enormous amounts of data every second, including price movements, order book data, trading volumes, macroeconomic reports, earnings releases, news articles, and even sentiment indicators from social media.

Historically, traders have relied upon basic statistical models or simple technical indicators to analyse this data. However, with ML, traders can now process and extract insights from vast amounts of structured and unstructured data far beyond human capabilities. For example, natural language processing (NLP), a branch of AI, can scan financial news sources, social media platforms like Twitter, and earnings call transcripts in real time to gauge market sentiment.

This allows traders to make more data-driven decisions, predicting how a specific news event might impact stock prices before traditional market participants react. Advancements in Computing Power The ability to leverage ML in trading was once limited by hardware constraints. However, the rise of cloud computing, GPU (graphics processing units) – which may be better than CPUs for acceleration of data pattern matching, and quantum computing research has dramatically increased the speed and efficiency of processing large datasets.

In practical terms, this means that ML models can be trained and executed in real-time, allowing traders (or trading algos) to make split-second decisions based on rapidly evolving market conditions. For instance, hedge funds and proprietary trading firms now run ML-driven models that execute high-frequency trades (HFT) at lightning-fast speeds. These models can analyse thousands of data points within milliseconds to determine the most optimal trade execution strategy.

Algorithmic Trading Dominance Institutional trading desks and hedge funds increasingly depend on sophisticated algorithms to identify patterns, predict price movements, and execute trades with precision. Machine learning adds an additional layer of intelligence, allowing these strategies to evolve and optimize continuously. For example, ML-powered quantitative trading strategies can adjust trading parameters dynamically, responding to shifts in volatility, changes in liquidity conditions, or sudden macroeconomic shocks (such as Federal Reserve rate decisions).

This gives firms a huge competitive advantage over traders using fixed-rule systems. Retail Trader Accessibility This is where it may become more relevant to you or I in a trading context! Machine learning is no longer limited to large institutions with deep pockets.

AI-powered tools and trading platforms are making ML-driven strategies more accessible to retail traders. Many brokers and third-party developers now offer plug-and-play ML models that traders can integrate into their trading systems, even without a deep understanding of coding or data science. For instance, platforms like MetaTrader 5, along with the help of those who know programming language, allow traders to build and test ML-based strategies, This democratisation of technology ensures that even independent traders and not just the big players can begin to utilise the leverage in decision making associated with AI-driven system development potential.

How is Machine Learning Used in Trading? Having covered why ML is a NOW issue in trading, let's explore in more detail how it can be used in trading so you can begin to understand its full potential. Machine learning is transforming trading strategies in several significant ways, enabling traders to gain insights, optimise trade execution, and react more dynamically to market movements and changes in sentiment.

Identifying Patterns That Humans Might Miss One of the most valuable aspects of machine learning is its ability to detect hidden patterns and relationships that may not be immediately obvious to human traders and traditional forms of technical analysis and standard indicators. Some key applications include: Detecting correlations between price, volume, sentiment, and macroeconomic indicators that are too complex for traditional analysis. Recognizing trading patterns such as mean reversion, momentum shifts, and breakout signals.

Using sentiment analysis on financial news, social media, and earnings reports to anticipate potential price movements. To give a potential example, an ML model can analyse Bitcoin price action, news sentiment, and trading volume to determine whether a sudden spike in tweets mentioning Bitcoin is more likely to trigger a short-term rally or a market dump. Optimising Trading Strategies for Higher Accuracy Machine learning doesn’t just help traders recognise patterns—it actively refines and optimizes trading strategies by learning from past market conditions and improving decision-making processes.

Reducing False Signals: ML models apply probability techniques in an attempt to limit the occurrence of false positives. This is particularly useful for traders whose strategies may struggle with whipsaws in volatile markets. Refining Trade Entry and Exit Points: Instead of rigid rules, ML systems dynamically adjust trade timing based on changing volatility, volume, and market sentiment.

Automating Risk Management: ML-powered risk models optimize stop-loss levels and position sizing based on the current market environment (and the likelihood that this may change) For example, a forex trader might use an ML system that widens stop-losses during high-volatility events like FOMC rate decisions and tightens them when price action is stable. Adapting to Changing Market Conditions Unlike traditional strategies, ML models dynamically adjust parameters in response to market shifts. Regime Detection: ML identifies when markets switch from trending to ranging, adjusting trading strategies accordingly.

Adaptive Position Sizing: Models automatically increase or decrease trade size based on real-time risk assessments. Feature Selection: ML continuously selects the most relevant technical indicators based on current market behaviour. For instance, an ML-driven strategy might rely on moving averages during a trend, but switch to RSI and Bollinger Bands when markets consolidate.

How Machine Learning Works in applying trading – A process model Machine learning follows a structured four-stage process when applied to trading. This process ensures that trading models are built, refined, and continuously improved to enhance the chances of profitability and appropriate adaptability. Should you dive into the world of ML this would provide an appropriate framework for you to follow.

Let’s break down each stage with examples of how traders and institutions can, and do, apply these concepts in real-world markets. Recognising Patterns – Collecting and Analysing Market Data The foundation of any machine learning model is data collection. Without accurate and comprehensive data, ML models cannot learn effectively.

In trading, this involves gathering historical and real-time market data, such as: Price action (open, high, low, close, and volume) Order book data (bids, asks, and execution flow) Macroeconomic indicators (inflation rates, GDP data, central bank decisions) News and sentiment analysis (financial news articles, earnings reports, and social media sentiment) Let’s give an example to help clarify how this could work. A hedge fund using ML might aggregate 10 years of historical price data from multiple asset classes (stocks, forex, crypto, commodities) along with real-time social media sentiment data from Twitter and Reddit. The model scans for correlations between news sentiment and asset price movements, allowing it to predict how a stock may react to a particular news headline before the broader market does.

Using Additional Factors – Feature Selection and Confluence Indicators Once raw data is collected, the next step is to identify the most relevant factors (also called features) that contribute to successful trading decisions. Feature selection helps filter out unnecessary noise and focus on variables that strongly influence price action. ML models use statistical techniques to evaluate which features matter most, including: Standard Technical indicators: Moving Averages, RSI, Bollinger Bands, MACD, etc.

Order flow dynamics: Imbalance between buyers and sellers at key price levels. Volatility measures: ATR (Average True Range) and historical volatility. Sentiment indicators: Word frequency analysis from news articles.

Again, here is an example to help illustrate this approach. Suppose a trader is building an ML model to predict breakout trades in the S&P 500 index. Initially, the model considers 100 different features, including volume, volatility, RSI divergence, Bollinger Bands, earnings reports, and Federal Reserve announcements.

After running a feature selection process, the model identifies that only five key factors have predictive power—for instance, breakouts are most reliable when combined with a sudden surge in trading volume, an increase in open interest, and a bullish sentiment score from recent news headlines. By narrowing down the list of variables, the ML system focuses only on high-probability signals, reducing false positives and improving accuracy. Testing and Adjusting Probabilities – Training the Model Once relevant features are identified, the next step is to train the ML model.

Training involves feeding historical data into the model, allowing it to learn how different market conditions impact trade outcomes. This phase involves: Backtesting: Running the model on past data to see how well it would have performed historically. Cross-validation: Splitting data into multiple sets to prevent overfitting (where the model memorizes past data instead of generalizing patterns).

Probability adjustments: Refining the model by increasing the weight of more reliable signals and reducing the impact of weaker ones. As an example, a forex trader using ML wants to develop a model that predicts trend reversals in EUR/USD. Initially, the model has an accuracy of 55%, which is slightly better than random chance.

However, after adjusting the model’s probability weighting, the trader discovers that reversal trades are significantly more reliable when price is near a key Fibonacci retracement level AND volatility is low. After refining these inputs, the model’s accuracy improves to 68%, making it a potentially more viable trading tool. This stage is crucial because many ML models fail when they are over-optimized for past data but don’t perform well in real-time markets.

The goal is to find patterns that repeat across different time periods and market conditions. One of the challenges of this of course is to determine what constitutes a reasonable amount of past data and how this differs depending on the timeframe under investigation. Programming and Evaluating Results – Testing in Live Markets Once an ML model has been trained and optimized, the final step is deploying it in real-time trading.

This process involves simulated (demo) trading, forward-testing, and continuous performance monitoring. At this stage, traders must ensure: The model performs well in real-time data streams, not just historical backtesting. It adapts to changing market conditions rather than being reliant on past patterns.

Risk management is incorporated so that even if predictions fail, drawdowns remain controlled. AND is consistently monitored to quickly identify and potential intervene on changing performance. For example, a hedge fund may develop an ML model to trade Bitcoin breakout patterns.

In backtests, the model had a 72% win rate. However, once deployed in live markets, it struggles due to sudden changes in Bitcoin’s liquidity conditions and large institutional order flows. To fix this, the fund integrates real-time order book analysis, allowing the model to detect large buy/sell orders from major players.

After this adjustment, the model stabilizes and achieves consistent profitability in live trading. Many traders assume that once an ML model is built, it will work indefinitely. Just to reinforce the need for consistent monitoring, remember markets are constantly evolving.

The most successful machine learning models are those that are continuously monitored, retrained, and optimised based on the impact of new data on previously developed systems. What Machine Learning may mean for market price action for all traders. The growing influence of machine learning in trading is reshaping how markets behave.

Whether choosing to be an active participant or simply a discretionary trader it is essential to give some thinking about how market prices, and the movement of such could be impacted through a proliferation of ML driven strategies and automated models. Here are FOUR key ways ML may already be altering price action dynamics: Smoother Trends with Fewer Pullbacks Historically, market trends have often experienced frequent retracements, with price pulling back before resuming its primary direction. However, as ML-powered trading models become more dominant, trends are becoming smoother and more sustained.

This is because ML-driven trend-following strategies can identify high-probability trend continuations and execute trades that reinforce directional movement. For example, large hedge funds using ML-driven strategies may enter scaling positions, gradually increasing exposure instead of making single large trades. This reduces erratic price movements and contributes to more gradual, extended trends.

Faster Breakouts & Fewer False Signals One of the biggest frustrations for traders is entering a breakout trade, only for price to quickly reverse—a phenomenon known as a false breakout or "fakeout." Machine learning is improving breakout trading strategies by identifying breakout strength indicators, such as volume surges, volatility expansions, and order flow imbalances. For instance, ML models analysing Bitcoin price action may detect that breakouts with a 30% increase in trading volume have a significantly higher chance of success compared to breakouts without volume confirmation. As a result, traders using ML-based breakout models filter out weak breakouts and focus only on those with strong supporting evidence.

Increased Stop-Loss Hunting & Engineered Liquidity Grabs As ML-powered algorithms become more sophisticated, they are increasingly able to predict where retail traders and traditional algorithmic strategies place stop-loss orders. This has led to a rise in engineered liquidity grabs, where price briefly spikes below key support levels (or above resistance levels) to trigger stop-loss orders before reversing in the intended direction. For example, an ML-driven institutional trading desk might analyse order book data and recognize that a high concentration of stop-loss orders sits just below a key support level.

The algorithm may execute a series of aggressive sell orders to trigger those stops, temporarily pushing the price lower. Once the stop losses are triggered, the algorithm quickly reverses its position and buys back at a lower price, capitalising on the forced liquidation of retail traders. More Algorithmic Whipsaws in Low-Liquidity Zones As ML-powered trading strategies become more widespread, low-liquidity markets are experiencing an increase in whipsaws and rapid price reversals.

This is because ML algorithms are constantly competing with one another, leading to aggressive, short-term volatility spikes when multiple models react to the same data simultaneously. For example, in markets with thin liquidity—such as exotic forex pairs or small-cap stocks—ML-driven strategies might detect an inefficiency and rush to exploit it. However, because multiple trading models recognize the same opportunity at the same time, prices can experience violent, rapid movements as algorithms aggressively adjust their positions.

This has made it increasingly challenging for manual traders to navigate low-liquidity environments without getting stopped out by unexpected reversals. Final Thoughts: Machine Learning as a Continuous Process There are two key takeaways I want you to get from this article. Firstly, machine learning is here to stay and is only likely to proliferate further impacting on strategy developed but at the CORE of trading – will impact on the traditional way we see asset prices move.

Even if not an active part of ML in how you decide to trade you need to keep abreast of what is happening in this world and the potential changes to traditional technical analysis techniques that may necessitate a review of how YOU trade now. Secondly, machine learning in trading is not a “set-it-and-forget-it” system. Rather, it is a continuous learning process where models must be refined, adapted, and improved based on real-time data and evolving market conditions.

Those who do embrace this are likely to fall very short of its potential. There are NO SHORT CUTS in the process described nor in the need for continuous and thorough performance measurement and evaluation. Traders and institutions that effectively integrate ML into their strategies gain a significant edge by leveraging data-driven decision-making, automation, and adaptive learning.

While ML does not guarantee success, it reduces human bias, improves accuracy, and enhances trading efficiency, making it one of the most powerful tools for modern market participants.

We will keep on returning to the thematics of 2025 every single time they appear. Already two out of the five themes for this year have been the biggest movers of markets, and we expect this thing to continue throughout. Make no mistake, as in indices and FX traders, we're going to have to accept that volatility is going to grow from this point, something that we should embrace.

Clearly Washington and Beijing are going to be the main impactors geopolitically, While the US dollar, crude and gold will be the markets most likely to see movement that will attract us as traders. Which brings me to thematic 1 of 2025 nationalism. It is going to be the theme that keeps on giving this year, because clearly the president sees tariffs and the nationalisation of the US economy as the future of US growth and economic management.

We have waited to comment over what might transpire in markets around tariffs but is it now being official and with the US over the weekend announced that tariffs on imports from Canada, Mexico, and China will take effect on Tuesday, February 4, only for this to be held off for 30 days for Mexico and Canada due to ‘constructive changes from sovereign nations’. We need to highlight something every telling in what was originally proposed in the tariffs. “Imports from Mexico and Canada (excluding energy products) will face a 25% tariff, while Canadian energy products and all imports from China will be subject to a 10% tariff.” We can’t highlight enough how much crude is going to be a pawn in all trade war mongering going forward. The fact that the administration excluded Canadian energy products from the 25% tariff does show that the President is aware that adversely impacting one of the biggest consumption products in the US will be highly detrimental to POTUS personal standing.

It is something the administration is aware of and will do whatever it can to mitigate a core product increasing in price. However this also shows its hand – the retaliation from China is a 10% tariffs on US LNG coal and crude products. This hits the US where it hurts most, most will forget the US is the largest exporter of oil on the planet.

It just gets blurred when we look at OPEC as a whole and the fact that this cartel produces 21 to 23% of the world's global oil supplies. China doesn't have to play by the same rules as the US. It can get around its tariffs by going to nations such as Venezuela, something most in the West can't do.

Leaving the US with less trade revenue, a core product that appears more expensive to Chinese consumers that will inevitably look to other options and the dream of using tariffs to fund personal income tax cuts in tatters. But it's more than that too, in the words of the late great Milton Friedman: ‘tariffs are tax on the consumer not on the sovereign.” The upside risks to inflation in the coming months are growing by the day. The Fed has signed as much and now so has the market.

Which brings us to gold, the astronomical rise that we have seen in the price of gold over the last three to four weeks shows very clearly the inflation risk markets now see. The question we keep getting is how high can gold actually go? And add to this the chart shows that gold has actually broken through most resistance levels and is now trading in uncharted levels and making new highs daily.

It's not hard to see why, should tariffs on Canada and Mexico persist beyond a week or two, food and energy prices in headline inflation data could be affected relatively quickly. Indicators like daily retail gas prices and weekly food prices may provide early signals of the impact on headline CPI for February. Next Monday’s inflation report may actually be the first reading to show the effects of proposed tariffs and explain almost exclusively the capital that is flowing into gold.

For those out there looking to estimate how much tariffs would impact US CPI. Here is a previous estimate from the market around a 25% tariff on Canadian oil. A 25% tariff could push headline CPI up by approximately 0.2 percentage points alone with the broader tariffs pushing the upper-bound estimate by as much as 0.8 percentage points.

Approximately 10% of all US consumer spending involves imported goods, either directly or indirectly. With 25% of total imports facing a 25% tariff and approximately 17% subject to a 10% tariff, the potential inflationary impact is notable. Add to this the indirect effects we have already seen such as retaliatory tariffs or further trade tensions, add uncertainty.

These could and will push inflation expectations higher, posing a greater inflationary risk. Secondly, prolonged tariffs might also slow growth and employment - something indices traders will be very very aware of and considering the valuation of the S&P 500 and the like this is a concern. In short semantic 1 of nationalism is becoming an absolute core player in 2025 and opportunities and risks are presenting themselves readily.

As traders we need to be ready to take every opportunity that we can.

Introduction The ability to recognise and effectively use chart patterns is often considered a fundamental skill in technical trading. Traders across all levels, from beginners to institutional professionals, study recurring price formations in an attempt to predict future market movements. However, the actual reliability of these patterns is frequently debated.

Most traders are aware of terms like 'bullish flag,' 'double top,' or 'ascending triangle,' but what do these formations truly indicate in terms of statistical success rates and practical trading strategies? More importantly, how do we use them effectively rather than treating them as standalone signals? Key Principles: Why Reliability Matters in Trading Understanding the probability of a price move based on historical occurrences is essential for making strategic decisions.

Theoretically, at least, there are three considerations worth outlining when considering this as a topic. Risk Management Traders should be able to set more accurate stop-loss and take-profit levels by understanding the likelihood of pattern success. This helps reduce emotional decision-making and provides better-defined risk-reward ratios.

Confidence in Trade Execution If traders have quantified probabilities, they can trust their system instead of second-guessing trades. A data-driven approach particularly one that has demonstrated some evidence of success in live trading helps build system confidence and so maintain discipline, in multiple market conditions, Strategy Optimisation Patterns should not be used in isolation. They must be tested against various timeframes, market conditions, and confluence factors.

Not only with commonly used lagging indicators but also candle structure and trading volume. Optimizing trading strategies involves identifying weaknesses in pattern success rates. Reliability of Bullish and Bearish Patterns Historically, many authors have suggested potential reliability:scores of various patterns, We have summarised these and relevant ranges of such, in the following two tables, a) Bullish Patterns and Reliability Scores Pattern Type Description Reliability (%) Double Bottom A reversal pattern indicating a potential upward move after a downtrend. 60-75% Breakout (Bullish) Price moves above a resistance level with increased volume. 70-90% Head and Shoulders (Inverse) A reversal pattern indicating a potential upward move. 70-80% Bullish Flag A continuation pattern indicating consolidation before the uptrend resumes. 65-75% Ascending Triangle A continuation pattern indicating a potential upward move after consolidation. 50-60% Cup and Handle A continuation pattern indicating a potential upward move after a consolidation period. 60-70% Moving Average Crossover (Bullish) A shorter-term moving average crosses above a longer-term moving average. 55-65% b) Bearish Patterns and Reliability Scores Pattern Type Description Reliability (%) Double Top A reversal pattern indicating a potential downward move after an uptrend. 60-75% Breakout (Bearish) Price moves below a support level with increased volume. 50-70% Head and Shoulders A reversal pattern indicating a potential downward move. 70-80% Bearish Flag A continuation pattern indicating a brief consolidation before the downtrend resumes. 65-75% Descending Triangle A continuation pattern indicating a potential downward move after consolidation. 50-70% Bearish Divergence Price makes a higher high while an oscillator makes a lower high. 50-60% Moving Average Crossover (Bearish) A shorter-term moving average crosses below a longer-term moving average. 55-65% Potential Flaws in Generalised Reliability Figures However, despite theoretical benefits, to focus solely on the reliability of chart patterns would logically be an error.

There are potential flaws in doing this and we would suggest these are threefold. 1. Lack of Context These figures often (unless measured specifically) will not account for market conditions (trending vs. ranging markets). Different timeframes, direction, and instrument volatility can produce vastly different probabilities. 2.

Absence of Trade Management Factors Intra-trade movements (retracements, consolidations) impact the final success rate of a pattern, as well as candle structure and trading volume as previously mentioned. Exit criteria matter just as much, if not more, than entry probabilities. Without a clear context of what exit has been used in such probability calculations, to be frank, such numbers verge on the almost meaningless. 3.

The Role of Confluence A chart pattern alone is not enough. Other factors should confirm reliability, such as: Volume Key support/resistance levels or zones Market sentiment indicators Moving Toward Higher Probability Entries & Exits There is no doubt, that one of the biggest mistakes traders make is focusing too much on entry setups while neglecting to balance this with as much attention on trade exits. While choosing the right entry is important, arguably it is the exit strategy that ultimately determines profitability.

The Reality of Chart Patterns in Trading Many traders enter the market with the assumption that recognizing chart patterns is enough to become profitable. They rely on historical probabilities and assume that a pattern’s past success rate will repeat itself in the future. However, as we’ve explored, trading is not that simple.

The true edge in trading does not come from pattern recognition alone. It is worth emphasizing that despite reservations related to the probabilities, for the reasons expressed earlier, one still shouldn’t dismiss these as completely irrelevant. Of course, entry remains important.

As a potentially more fruitful approach, one would suggest that effective use of this information comes from understanding when and how to use a pattern effectively within a broader context. A pattern might work 70% of the time in theory, but what happens if: The market conditions change? The volume doesn’t confirm the breakout?

A key resistance level invalidates the move? The trader manages the trade poorly, leading to an early exit? This is why trading success is not about blindly trusting probabilities—it is about using real-world, data-driven insights to determine when a pattern has the highest probability of success.

Key Lessons for Traders Moving Forward So how do we balance this? Perhaps a consistent reminder of some basic truths. Probabilities Are Not Absolute Patterns do not have fixed success rates.

Their effectiveness depends on market conditions, timeframe, volatility, and confluence factors. A double top on a 5-minute chart in a choppy market is not the same as a double top on a weekly chart in a trending market. Entry is Important, But Exit is Crucial Trade exits, risk management, and stop placement ultimately define profitability—not just how good an entry looks.

Dynamic exits, such as volatility-based trailing stops, often outperform rigid take-profit targets. A Trading System Must Evolve with, and be Responsive to, Market Conditions No system works forever. The best traders consistently refine their strategies based on new data and performance insights.

Journaling and backtesting allow traders to identify patterns that work best in their preferred market. Technology & Automation Can Improve Consistency in decision making Algorithmic backtesting can help traders quantify pattern reliability under different conditions. Using tools like MetaTrader Strategy Tester, or even basic journaling and meaningful evaluation can uncover insights that an overview analysis might miss.

Final Thought: The Path to Becoming a Data-Driven Trader So how do we summarise this in practical terms? Perhaps it is right to emphasise that the transition from an average trader to a successful one is not about memorising patterns but about developing a systematic approach to trading. A data-driven trader does not ask, 'Does this pattern work?' Instead, they ask, 'When does this pattern work best, and how can I optimize my strategy around it?' The difference is mindset - and mindset is what separates profitable traders from those who struggle.

As we sit here and watch our overseas central bank counterparts move on interest rates. Our central bank gave us a new term, to explain why rate cuts are a long way off in their thinking. This term “sustainably” – that is “sustainably back to target”, “sustainable path”, and a hundred other zingers that basically point out that the central bank doesn’t think we are returning to the target band of its inflation mandate.

Yet despite this language and rhetoric, the movement in the market is – muted, bordering disobedient. The movement in the AUD has been strong as seen in the chart below. But that is basically down to the news out of China, (which we will come to later) and the US Fed finally pushing the trigger.

But domestically - the interbank and bond markets see rate cuts much sooner than the Board does, and if you look at the differentials between the RBA and the rest, there is a strong argument that the uptick in the AUD should be more than has occurred. What are we missing? So, what is it that the market sees that the RBA is missing?

Or more importantly – what does the RBA see that the market isn’t taking as seriously? First – we need to really drill into the August monthly inflation read, because there is some reasonable dispute between Board and Market. The headline monthly inflation rate fell to 2.7% and marks the first time in the post inflation era that Australia’s inflation has been back in the target 2% to 3% band.

Couple this with its decline from 3.5% in July and 4.0% in June. Thus, maybe the market has a point as it marks the lowest annual inflation rate since August 2021 and a sharp contrast to the 8.8% peak in December 2022. Which is why a lot are crowing about this chart from the ABS.

This may seem like a positive sign that inflation is under control and is ‘returning’ to a sustainable level, under the hood of the headlines, the data tells a different story. For example: The monthly index recorded a 0.24% decline between July and August, after a ‘no change’ from June to July. This decline is mainly down to a 0.58% increase in prices last August falling out of the 12-month calculation, so that is a one of and would be transitory and not sustainable.

We are also about to see another technicality happen this month when a similar 0.58% increase from September 2022 drops out. Even if we see a modest 0.2% rise between August and September, the headline inflation rate will likely fall further, potentially reaching 2.3% by September. This is a ‘seller beware’ issue for traders, bears will make a lot of noise about this but the RBA has made it clear here, it’s not for moving.

Next example: the August inflation drop is largely attributed to temporary relief measures. The whopping 14.6% decline in electricity prices in August was a direct result of the federal government’s $300 energy relief measure. The Queensland and WA state governments threw in $1000 and $400 respectively adding further downside in energy inflation.

Interestingly enough – since this has been pointed out the government has stated it might make the subsidy ‘semi-permanent’ again this is artificial and something the Governor has stated is transient. Finally August saw a 3.1% fall in petrol prices due to lower global oil prices – something that is likely to hold true for most of September but the increase tensions in the Middle East over the past week and China properly stimulating itself for the first time in the post COVID world coupled with the approaching Northern hemisphere winter that 3.1% reduction will be quickly returned. These highlight why the RBA never really pays much attention to headlines month-to-month quarter to quarter as it bounces around randomly.

And AUD traders in particular would be prudent to remember this. Stuck like a fly in a honey pot The catch with chasing the headline inflation figure is that although it may be back within the RBA’s target band the critical trimmed mean inflation rate, which excludes the most extreme price movers, is not. The trimmed mean rose by 3.4% over the year to August, down from 3.8% in July and 4.1% in June.

Now some will argue that is close to the band, but it’s still significantly above the RBA’s target range midpoint of 2.5% which is seen as the magical ‘sustainable’ point the RBA needs. For more context if we collate the first two months of the September quarter, the calculated annual underlying inflation rate sits at 3.6% even further away from the band and mid-point. The RBA’s most recent Statement of Monetary Policy forecast expects this to ease to 3.5% by the December quarter.

A full 1% above the midpoint illustrating just how stubborn inflation has been to budge. It’s even more of a headache when you look at where the stickiness sits Have a look at service-based inflation of education, health and financial services – these are all over 5% year on year. Then have a look at the housing.

Rent increases are still sitting at 6.8%, New dwellings 5.1%. These five things make up more than a third of the total CPI basket. There is nothing sustainable about these figures.

RBA versus the Market The RBA has been a pain to point out the issues of ‘purchasing power’, that long term issue of cost compounding on themselves and making essentials unattainable in the long run. This old adage is running through our heads: “short term pain for long term gain” thus from our views interest rates are staying on hold for the rest of 2024 as the RBA seems determined to make the inflation rate fall further before acting. Yet you wouldn't know it judging by the perception in the market - it is still pricing in a near enough to 75% chance of a rate cut at the December meeting.

How is that conclusion being reached? If we take what has been stated by the RBA as ‘baseline’ there is next to no information the RBA sees between November and December that would justify a cut especially if they do not cut in November. The only piece of additional information is the September quarter GDP figures (due first full week of December).

If that was to register a contraction and a recession is on the cards then maybe. That’s the only data that could trigger the RBA this year – but considering Government spending in this quarter is so large, the consumer will have had to really bottomed out and retail sales while poor are not that bad. With this in mind there is a real justification for the AUD to be higher than it currently is.

Each time we see another piece of data that is weak but not weak enough should be an upside mover for the currency. We are not normally ones to fight the market as the trend is your friend, and we are not considering the AUD has moved some 3.8% in September alone. It's more – we think the upside has more to go as the market realises it under-pricing a more hawkish RBA and it isn’t going to deliver Australian debtholders a Christmas present.

Will June be the turning point? The market thinks it is – and its reaction to the CPI data not only signalled how it will trade in the coming months. It also showed that traders are primed to rotate to even more bullish positions.

Because from the market’s perspective September is now more than live its near enough to a lock for the Fed to cut rates. The Data: Core CPI (excluding food and fuel) saw an anaemic rise of just 0.065% month on month falling well short of the 0.2% consensus. Key component of the slowdown was in owners’ equivalent rent (OER) to 0.28% month on month, with primary rents decreasing to 0.26%.

This is the first real piece of data that is shown shelter is finally bucking its upward trend and moving to the lower side. But it wasn't the only good news in the data. Core services prices, excluding rents and OER, dropped by 0.07% month on month.

Its lowest level in over 12 months. Hotel prices declined by 2%, while airfares saw a significant 5% drop. Recreation services decreased by 0.1%, while personal services rose by 0.9% month on month, driven by a 4% spike in legal services.

Core goods prices dipped by 0.12% month on month, with used car prices down 1.5% and new car prices falling 0.2%. Apparel prices crept up 0.1%, but furniture prices fell 0.2%. Overall the core CPI P data should give the Federal Reserve that bit more confidence that it's returning to target and that its rate cut cycle can begin sooner than probably forecasted.

Moving to headline CPI for the first time in more than four years monthly inflation dropped down 0.1% month on month and eased to just 3% year on year a 12-month low which also happens to be the three year low. There's a pretty good reasonable explanation for this we saw a 3.8% slide in petrol pricing which held inflation back and offset the 0.2% increase in food and shelter. So it's not completely clear cut The Fed can cut rates in September.

But we digress because the market is suggesting it will treasury yields tumbled off the back of this you only have to look at the three year and the 10 year to understand that right cuts not only are they coming but are coming in waves. It really dropped into further FX trading with the US dollar once again under real pressure. The US dollar Is something that we are still wary of there is definite downside in certain pairs.

AUDUSD, NZDUSD have pretty clear reasons to see the appreciation bullishness that has been transpiring. But we're not as confident in EURUSD GBPUSD or even USDCAD. The other trading reactions of note With the CPI slowdown being more pronounced than anticipated, especially in shelter prices, which saw a 0.27% increase in owners' equivalent rent, akin to pre-pandemic levels.

It does also put first quarter CPI into the box of being a blip rather than the trend. We think one of the more interesting trading movements off the back of the June CPI was the rotation out of mega caps and into small caps. One of the reasons the S&P 500 and the NASDAQ hoping so strong since October last year Has been on the premise the Fed would cut rates in 2024-2025.

However realistically has been driven by the magnificent 7 particularly NVIDIA, Apple, Meta and Microsoft. So much so that it's actually papered over issues in industrials another non tech exposed sectors that have move no idea the level that the tech sector has. There's a big question that’s been building what happens post the first rate cut?

Its been driving the bullishness in equities but once it becomes actual what next? There's been somewhat of an answer in the trading post the CPI data which showed the S&P 500 and the NASDAQ declining well the Russell 2000 jumped. The suggestion here is the market is looking to move into even more bullish positions in equities but also to cash in on the anticipated first rate cut that has been developed since October 2023.

We think that is a trade we need to be mindful of because what may appear to be good news with regards to the right cut cycle beginning is probably already priced in. Multiples in the US markets are high multiples in US tech stocks are incredibly high. Which reminds us of the old market adage buy the rumour sell the fact.

And maybe just maybe the trading post the June CPI data gave us a massive insight into what could happen in either the September or November Fed meeting months. But returning to the data we can't go past the other economic data that may have been missed that again muddy the calls for a September rate cut. The Labor Department reported that weekly jobless claims fell to 222,000 a further 17,000 decrease from the previous week.

Its the lowest level since June 1. Continuing claims dropped to 1.85 million. This suggests that although last week's nonfarm payrolls was much lower than expected the overall employment market is still firm.

Employment has been cited by the likes of San Francisco president Mary Daly as a reason to cut sooner rather than later. However if employment was to remain solid it could actually say a reverse of the trading that we've seen from the CPI data. It's why we remain vigilant and once again why we think the USD is a tricky denominator, and only have confidence in pairs that have clear central bank differentials as we explained last week.

We now await the data of the next two months heading into September 18 to confirm all these views.