US dollar drops as the economy shrinks by 0.9% for the last quarter.
GO Markets
30/8/2024
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US economic data revealed last night shows that the country’s GDP has shrunk by 0.9%, although some are remaining positive that a recession may still be avoided. Despite the worrying figures, Federal Reserve Chair, Jerome Powell, outlined his belief that due to low unemployment figures of 3.6% and a strong market for jobs with 11 million job openings that there may be a 'soft landing'. Joe Biden commented, “It’s no surprise that the economy is slowing down as the Federal Reserve acts to bring down inflation.” More US CPI data is expected to be announced tonight.
In response to the GDP figure, the US indices had another green day with the Dow Jones, the Nasdaq, and the S&P500 all rising 1.03%, 1.08%, and 1.21% respectively. In terms of share price movement, Meta’s stock price dipped 5.22% as it posted its first-ever quarterly drop in revenue, signaling how interest rate hikes have been impacting growth companies. The data also followed through to the Australian market with the yield on 3-year government bonds falling to 3.1%.
The ASX200 also continued its momentum for the week as it pushed higher again on Thursday. Brent Crude Oil had a mixed day ending the day flat at $107.58. Gold continued to bounce off its support zone and climbed up 1.25% and Natural Gas fell 4.66% as it continues to pull back from its recent highs dropping 4.66%.
FOREX and Cryptocurrency The USD dropped sharply as the GDP figures were announced. It recovered briefly, before selling back down, closing towards the lowest price of the day, a total drop of 0.28%. Bitcoin and Ethereum also gained momentum as money continued to flow back into risk assets, with the latter jumping to its highest level since the middle of July.
ETHUSD closed at $1726 and Bitcoin at $23,860.
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GO Markets
Os artigos são elaborados por analistas e colaboradores da GO Markets e baseiam-se na sua análise independente ou em experiências pessoais. As opiniões, pontos de vista ou estilos de negociação expressos são próprios dos autores e não devem ser considerados como representativos ou partilhados pela GO Markets. Qualquer conselho fornecido é de natureza “geral” e não leva em conta os seus objetivos, situação financeira ou necessidades pessoais. Antes de agir com base em qualquer conselho, considere se ele é apropriado para os seus objetivos, situação financeira e necessidades. Se o conselho estiver relacionado à aquisição de um produto financeiro específico, você deve obter a nossa Declaração de Divulgação (Disclosure Statement - DS) e outros documentos legais disponíveis no nosso site antes de tomar qualquer decisão.
The “Magnificent Seven” technology companies are expected to invest a combined $385 billion into AI by the end of 2025.Each of the Seven is trying to carve out its own territory in the AI landscape.Microsoft is positioning itself as the platform leader. Nvidia dominates the underlying AI infra. Google leads in research. Meta is building open-source tech. Amazon – AI agents. Apple — on-device integration. And Tesla pioneering autonomous vehicles and robots.But with these enormous sums pouring into AI, is this a winner-take-all game? Or will each of the Mag Seven be able to thrive in the AI future?[caption id="attachment_712288" align="aligncenter" width="554"]
The “Big 4” tech companies' AI spending alone is forecast at $364 billion.[/caption]
Microsoft: The AI Everywhere Strategy
Microsoft has made one of the biggest bets on AI out of the Mag Seven — adopting the philosophy that AI should be everywhere.Through its deep partnership with OpenAI, of which it is a 49% shareholder, the company has integrated GPT-5 across its entire ecosystem.Key initiatives:
GPT-5 integration across consumer, enterprise, and developer tools through Microsoft 365 Copilot, GitHub Copilot, and Azure AI Foundry
Azure AI Foundry for unified AI development platform with model router technology
Copilot ecosystem spanning productivity, coding, and enterprise applications with real-time model selection
$100 billion projected AI infrastructure spending for 2025
Microsoft’s centerpiece is Copilot, which can now detect whether a prompt requires advanced reasoning and route to GPT-5's deeper reasoning model. This (theoretically) means high-quality AI outputs become invisible infrastructure rather than a skill users need to learn.However, this all-in bet on OpenAI does come with some risks. It is putting all its eggs in OpenAI's basket, tying its future success to a single partnership.[caption id="attachment_712289" align="aligncenter" width="530"]
Elon Musk warned that "OpenAI is going to eat Microsoft alive"[/caption]
Google: The Research Strategy
Google’s approach is to fund research to build the most intelligent models possible. This research-first strategy creates a pipeline from scientific discovery to commercial products — what it hopes will give it an edge in the AI race.Key initiatives:
Over 4 million developers building with Gemini 2.5 Pro and Flash
Ironwood TPU offering 3,600 times better performance compared to Google’s first TPU
AI search overviews reaching 2 billion monthly users across Google Search
DeepMind breakthroughs: AlphaEvolve for algorithm discovery, Aeneas for ancient text interpretation, AlphaQubit for quantum error detection, and AI co-scientist systems
Google’s AI research branch, DeepMind, brings together two of the world's leading AI research labs — Google Brain and DeepMind — the former having invented the Transformer architecture that underpins almost all modern large language models. The bet is that breakthrough research in areas like quantum computing, protein folding, and mathematical reasoning will translate into a competitive advantage for Google.
Today, we're introducing AlphaEarth Foundations from @GoogleDeepMind , an AI model that functions like a virtual satellite which helps scientists make informed decisions on critical issues like food security, deforestation, and water resources. AlphaEarth Foundations provides a… pic.twitter.com/L1rk2Z5DKk
Meta has made a somewhat contrarian bet in its approach to AI: giving away their tech for free. The company's Llama 4 models, including recently released Scout and Maverick, are the first natively multi-modal open-weight models available.Key initiatives:
Llama 4 Scout and Maverick - first open-weight natively multi-modal models
AI Studio that enables the creation of hundreds of thousands of AI characters
$65-72 billion projected AI infrastructure spending for 2025
This open-source strategy directly challenges the closed-source big players like GPT and Claude. By making AI models freely available, Meta is essentially commoditizing what competitors are trying to monetize. Meta's bet is that if AI models become commoditized, the real value will be in the infrastructure that sits on top. Meta's social platforms and massive user base give it a natural advantage if this eventuates.Meta's recent quarter was also "the best example to date of AI having a tangible impact on revenue and earnings growth at scale," according to tech analyst Gene Munster. [caption id="attachment_712301" align="aligncenter" width="996"]
H1 relative performance of the Magnificent Seven stocks. Source: KoyFin, Finimize[/caption]However, it hasn’t been all smooth sailing for Meta. Their most anticipated release, Llama Behemoth, has all but been scrapped due to performance issues. And Meta is now rumored to be developing a closed-source Behemoth alternative, despite their open-source mantra.
Amazon: The AI Agent Strategy
Amazon’s strategy is to build the infrastructure for AI that can take actions — booking meetings, processing orders, managing workflows, and integrating with enterprise systems. Rather than building the best AI model, Amazon has focused its efforts on becoming the platform where all AI models live.Key initiatives:
Amazon Bedrock offering 100+ foundation models from leading AI companies, including OpenAI models.
$100 million additional investment in AWS Generative AI Innovation Center for agentic AI development
Amazon Bedrock AgentCore enabling deployment and scaling of AI agents with enterprise-grade security
$118 billion projected AI infrastructure spending for 2025
The goal is to become the “orchestrator” that lets companies mix and match the best models for different tasks. Amazon’s AgentCore will provide the underlying memory management, identity controls, and tool integration needed for these companies to deploy AI agents safely at scale.This approach offers flexibility, but does carry some risks. Amazon is essentially positioning itself as the middleman for AI. If AI models become commoditized or if companies prefer direct relationships with AI providers, Amazon's systems could become redundant.
Nvidia: The Infra Strategy
Nvidia is the one selling the shovels for the AI gold rush. While others in the Mag Seven battle to build the best AI models and applications, Nvidia provides the fundamental computing infrastructure that makes all their efforts possible. This hardware-first strategy means Nvidia wins regardless of which company ultimately dominates. As AI advances and models get larger, demand for Nvidia's chips only increases.Key initiatives:
Blackwell architecture achieving $11 billion in Q2 2025 revenue, the fastest product ramp in company history
New chip roadmap: Blackwell Ultra (H2 2025), Vera Rubin (H2 2026), Rubin Ultra (H2 2027)
Data center revenue reaching $35.6 billion in Q2, representing 91% of total company sales
Manufacturing scale-up with 350 plants producing 1.5 million components for Blackwell chips
With an announced product roadmap of Blackwell Ultra (2025), Vera Rubin (2026), and Rubin Ultra (2027), Nvidia has created a system where the AI industry must continuously upgrade to Nvidia’s newest tech to stay competitive.This also means that Nvidia, unlike the others in the Mag Seven, has almost no direct AI spending — it is the one selling, not buying.However, Nvidia is not indestructible. The company recently halted its H20 chip production after the Chinese government effectively blocked the chip, which was intended as a workaround to U.S. export controls.
Apple: The On-Device Strategy
Apple's AI strategy is focused on privacy, integration, and user experience. Apple Intelligence, the AI system built into iOS, uses on-device processing and Private Cloud Compute to help ensure user data is protected when using AI.Key initiatives:
Apple Intelligence with multi-model on-device processing and Private Cloud Compute
Enhanced Siri with natural language understanding and ChatGPT integration for complex queries
Direct developer access to on-device foundation models, enabling offline AI capabilities
$10-11 billion projected AI infrastructure spending for 2025
The drawback of this on-device approach is that it requires powerful hardware from the user's end. Apple Intelligence can only run on devices with a minimum of 8GB RAM, creating a powerful upgrade cycle for Apple but excluding many existing users.
Tesla: The Robo Strategy
Tesla's AI strategy focuses on two moonshot applications: Full Self-Driving vehicles and humanoid robots.This is the 'AI in the physical world' play. While others in the Mag Seven are focused on the digital side of AI, Tesla is building machines that use AI for physical operations.[caption id="attachment_712292" align="aligncenter" width="537"]
Tesla’s Optimus robot replicating human tasks[/caption]Key initiatives:
Plans for 5,000-10,000 Optimus robots in 2025, scaling to 50,000 in 2026
Robotaxi service targeting availability to half the U.S. population by EOY 2025
AI6 chip development with Samsung for unified training across vehicles, robots, and data centers
$5 billion projected AI infrastructure spending for 2025
This play is exponentially harder to develop than digital AI, and the markets have reflected low confidence that Tesla can pull it off. TSLA has been the worst-performing Mag Seven stock of 2025, down 18.37% in H1 2025.However, if Tesla’s strategy is successful, it could be far more valuable than other AI plays. Robots and autonomous vehicles could perform actual labor worth trillions of dollars annually.
The $385 billion Question
The Mag Seven are starting to see real revenue come in from their AI investments. But they're pouring that money (and more) back into AI, betting that the boom is just getting started.The platform players like Microsoft and Amazon are betting on becoming essential infrastructure. Nvidia’s play is to sell the underlying hardware to everyone. Google and Meta compete on capability and access. While Apple and Tesla target specific use cases.The $385 billion question is which of the Magnificent Seven has bet the right way? Or will a new player rise and usurp the long-standing tech giants altogether?You can access all Magnificent Seven stocks and thousands of other Share CFDs on GO Markets.
The ASX 200 closed out the 2025 financial year on a high, reaching a new intra-month peak of 8,592 in June and within touching distance of the all-time record. The index delivered a 1.4% total return for the month, rounding off a strong final quarter with a 9.5% return and locking in a full-year gain of 13.8% — its best performance since 2021.This strong finish all came down to the postponement of the Liberation Day tariffs. From the April 7 lows through to the end of the financial year, the ASX followed the rest of the world. Mid-cap stocks were the standout performers, beating both large and small caps as investors sought growth opportunities away from the extremes of the market. Among the sectors, Industrials outperformed Resources, benefiting from more stable earnings and supportive macroeconomic trends tied to infrastructure and logistics.But the clear winner was Financials, which contributed an incredible 921 basis points to the overall index return. CBA was clearly the leader here, dominating everything with 457 basis points on its own. Westpac, NAB, and others also played a role, but nothing even remotely close to CBA. The Industrials and Consumer Discretionary sectors made meaningful contributions, adding 176 and 153 basis points, respectively. While Materials, Healthcare, and Energy all lagged, each detracting around 45 to 49 basis points. Looking at the final quarter of the financial year, Financials were by far the biggest player again, adding 524 basis points — more than half the quarter’s total return of 9.5%. Apart from a slight drag from the Materials sector, all other parts of the market made positive contributions. Real Estate, Technology, and Consumer Discretionary followed behind as key drivers. Once again, CBA was the largest individual contributor, adding 243 basis points in the quarter, while NAB, WBC, and Macquarie Group added a combined 384 basis points. On the other side of the ledger, key underperformers included BHP, CSL, Rio Tinto, Treasury Wine Estates, and IDP Education, which all weighed on quarterly performance.One of the most defining features of the 2025 financial year was the dominance of price momentum as a market driver — something we as traders must be aware of. Momentum strategies far outpaced more traditional, fundamental-based approaches such as Growth, Value, and Quality. The most effective signal was a nine-month momentum measure (less the most recent month), which delivered a 31.2% long-short return. The more commonly used 12-month price momentum factor was also highly effective, returning 23.6%. By contrast, short-term reversals buying last month’s losers and selling last month’s winners was the worst-performing approach, with a negative 16.4% return. Compared to the rest of the world, the Australian market was one of the strongest trades for momentum globally, well ahead of both the US and Europe, despite its relatively slow overall performance.Note: these strategies are prone to reversal, and in the early days of the new financial year, there has been a notable shift away from momentum-based trading to other areas. Now is probably too early to say whether this marks a sustained change, but it cannot be ignored, and caution is always advised.The second big story of FY26 will be CBA. CBA’s growing influence was a key story of FY25. Its weight in the index rose by an average of 2.1 percentage points across the year, reaching an average of 11.5% by June. That helped push the spread between the Financials and Resources sectors to 15.8 percentage points — the widest gap since 2018. Despite the strong cash returns, market valuations are eye-watering; at one point during June, CBA became the world’s most expensive bank on price metrics. The forward price-to-earnings multiple now sits at 18.9 times. This is well above the long-term average of 14.7 and higher than the 10-year benchmark of 16.1. Meanwhile, the dividend yield has slipped to 3.4%, down from the historical average of 4.4%. Earnings momentum remains soft, with FY25 growth estimates still tracking at 1.4%, and FY26 forecast at a moderate 5.4%. This suggests that recent gains have come more from expanding valuation multiples than from actual earnings upgrades, making the August reporting date a catalyst day for it and, by its size, the market as a whole.On the macro front, attention now turns to the Reserve Bank of Australia. The central bank cut the cash rate by 25 basis points to 3.6% at its July meeting. Recent commentary from the RBA has taken on a more dovish tone, with benign inflation data and ongoing global uncertainty expected to outweigh the strength of the labour market. The RBA appears to be steering toward a neutral policy stance, and markets will be watching for further signals on how that shift will be managed. Recent economic data has been mixed. May retail sales were weaker than expected, while broader household spending indicators held up slightly better. Building approvals saw a smaller-than-hoped-for bounce, employment remains strong, but productivity is low. Inflation is now at a 3-year low and falling; all this points to underlying support from the RBA’s easing bias both now and into the first half of FY26.As we move into FY26, the key questions are:
Can fundamentals wrestle back control over momentum?
Will earnings growth catch up to price to justify valuations?
How will policy decisions from the RBA and other central banks shape investor sentiment in an ever-volatile world?
While the early signs suggest a possible rotation, the jury is still out on whether this marks a new phase for the Australian market or just a brief pause in the rally that defined FY25.
While recent data has shown core inflation moderating, core PCE is on track to average below target at just 1.6% annualised over the past three months.Federal Reserve Chair Jerome Powell made clear that concerns about future inflation, especially from tariffs, remain top of mind.“If you just look backwards at the data, that’s what you would say… but we have to be forward-looking,” Powell said. “We expect a meaningful amount of inflation to arrive in the coming months, and we have to take that into account.”While the economy remains strong enough to buy time, policymakers are closely monitoring how tariff-related costs evolve before shifting policy. Powell also stated that without these forward-looking risks, rates would likely already be closer to the neutral rate, which is a full 100 basis points from current levels.
2. The Unemployment Rate anchor
Powell repeatedly cited the 4.2% unemployment rate during the press conference, mentioning it six times as the primary reason for keeping rates in restrictive territory. At this level, employment is ahead of the neutral rate.“The U.S. economy is in solid shape… job creation is at a healthy level,” Powell added that real wages are rising and participation remains relatively strong. He did, however, acknowledge that uncertainty around tariffs remains a constraint on future employment intentions.If not for a decline in labour force participation in May, the unemployment rate would already be closer to 4.6%. Couple this with the continuing jobless claims ticking up and hiring rates subdued, risks are building around labour market softening.
3. Autumn Meetings are Live
While avoiding firm forward guidance, Powell hinted at a timeline:“It could come quickly. It could not come quickly… We feel like the right thing to do is to be where we are… and just learn more.”This suggests the Fed will remain on hold through the July meeting, using the summer to assess incoming data, particularly whether tariffs meaningfully push inflation higher. If those effects prove limited and unemployment begins to rise, the stage could be set for a rate cut in September.
Artificial intelligence stocks have begun to waver slightly, experiencing a selloff period in the first week of this month. The Nasdaq has fallen approximately 2%, wiping out around $500 billion in market value from top technology companies.
Palantir Technologies dropped nearly 8% despite beating Wall Street estimates and issuing strong guidance, highlighting growing investor concerns about stretched valuations in the AI sector.
Nvidia shares also fell roughly 4%, while the broader selloff extended to Asian markets, which experienced some of their sharpest declines since April.
Wall Street executives, including Morgan Stanley CEO Ted Pick and Goldman Sachs CEO David Solomon, warned of potential 10-20% drawdowns in equity markets over the coming year.
And Michael Burry, famous for predicting the 2008 housing crisis, recently revealed his $1.1 billion bet against both Nvidia and Palantir, further pushing the narrative that the AI rally may be overextended.
As we near 2026, the sentiment around AI is seemingly starting to shift, with investors beginning to seek evidence of tangible returns on the massive investments flowing into AI, rather than simply betting on future potential.
However, despite the recent turbulence, many are simply characterising this pullback as "healthy" profit-taking rather than a fundamental reassessment of AI's value.
Supreme Court Raises Doubts About Trump’s Tariffs
The US Supreme Court heard arguments overnight on the legality of President Donald Trump's "liberation day" tariffs, with judges from both sides of the political spectrum expressing scepticism about the presidential authority being claimed.
Trump has relied on a 1970s-era emergency law, the International Emergency Economic Powers Act (IEEPA), to impose sweeping tariffs on goods imported into the US.
At the centre of the case are two core questions: whether the IEEPA authorises these sweeping tariffs, and if so, whether Trump’s implementation is constitutional.
Chief Justice John Roberts and Justice Amy Coney Barrett indicated they may be inclined to strike down or curb the majority of the tariffs, while Justice Brett Kavanaugh questioned why no president before Trump had used this authority.
Prediction markets saw the probability of the court upholding the tariffs drop from 40% to 25% after the hearing.
Polymarket odds on Supreme Court upholding Trump's tariffs
The US government has collected $151 billion from customs duties in the second half of 2025 alone, a nearly 300% increase over the same period in 2024.
Should the court rule against the tariffs, potential refunds could reach approximately $100 billion.
The court has not indicated a date on which it will issue its final ruling, though the Trump administration has requested an expedited decision.
Shutdown Becomes Longest in US History
The US government shutdown entered its 36th day today, officially becoming the longest in history. It surpasses the previous 35-day record set during Trump's first term from December 2018 to January 2019.
The Senate has failed 14 times to advance spending legislation, falling short of the 60-vote supermajority by five votes in the most recent vote.
So far, approximately 670,000 federal employees have been furloughed, and 730,000 are currently working without pay. Over 1.3 million active-duty military personnel and 750,000 National Guard and reserve personnel are also working unpaid.
SNAP food stamp benefits ran out of funding on November 1 — something 42 million Americans rely on weekly. However, the Trump administration has committed to partial payments to subsidise the benefits, though delivery could take several weeks.
Flight disruptions have affected 3.2 million passengers, with staffing shortages hitting more than half of the nation's 30 major airports. Nearly 80% of New York's air traffic controllers are absent.
From a market perspective, each week of shutdown reduces GDP by approximately 0.1%. The Congressional Budget Office estimates the total cost of the shutdown will be between $7 billion and $14 billion, with the higher figure assuming an eight-week duration.
Consumer spending could drop by $30 billion if the eight-week duration is reached, according to White House economists, with potential GDP impacts of up to 2 percentage points total.
You've been using a 30-pip trailing stop for as long as you can remember. It feels professional, manageable and relatively safe.
But during volatile sessions, you see your winners get stopped out prematurely, while low-volatility winners drift back and hit stops that are relatively too tight.
Same 30 pips, different market contexts, but inconsistent in the protection of profit and overall results.
The Fixed-Pip Fallacy?
Traders gravitate toward fixed pip trailing stops because they feel concrete and calculable. The approach is easy to execute, readily automated through platforms like MetaTrader, and aligns with how most people naturally think about profit and loss.
But this simplicity masks a fundamental problem.
A twenty-five pip move in EURUSD during the London open represents an entirely different market event than the same move during the Asian session. The context matters, yet the fixed-pip approach treats them identically.
This becomes even more problematic when you consider different currency pairs. GBPJPY might have an average true range of thirty pips on an hourly chart, while EURGBP shows only ten. The same trailing stop applied to both instruments ignores the reality that volatility varies dramatically across pairs.
Timeframe introduces yet another layer of complexity. Take AUDUSD as an example: a ten-pip move on a four-hour chart barely registers as meaningful price action, but on a five-minute chart it represents a significant swing. The fixed-pip method treats these scenarios as equivalent.
The natural response might be to use something more sophisticated, like an ATR multiple. This accounts for your chosen timeframe, the instrument's normal volatility, and even session differences. But it brings its own complications.
When do you measure the ATR? Do you use the value at entry, knowing it might be distorted by sessional effects? Or do you make it dynamic, which becomes far more complex to implement in practice?
Perhaps there's another way forward that doesn't rely on abstract measures of volatility but instead responds directly to the movement of price in relation to the trade you're actually in—accounting for your lot size and the profit you've already captured.
Maximum Give Back: The Percentage Approach
Instead of asking "how do I protect profit after fifty pips," ask "how do I protect profit after giving back a certain percentage of open gains."
Consider a maximum give-back threshold of 40%. When your trade is up one hundred pips, the trailing stop activates if price retraces forty pips from peak, locking in a minimum of sixty pips.
But when that same trade reaches two hundred fifty pips of profit, the stop adjusts, and now it activates at a one-hundred-pip pullback, securing at least one hundred fifty pips. The stop distance scales naturally with the magnitude of the win you're sitting on.
This creates a logical asymmetry that fixed pip approaches miss entirely. Small winners receive tighter protection. Big winners get room to breathe.
The approach adapts automatically to what the market is actually giving you in real time, without requiring you to predict anything in advance.
You don't need to maintain a reference table where EURUSD gets thirty pips and GBPJPY gets sixty. You don't need different standards for different instruments at all.
The same 40% logic works whether the average true range is high or low, whether volatility is expanding or contracting. It survives regime changes without requiring recalibration because it's responding to the trade itself rather than to abstract measures of what the instrument normally does.
The market tells you how much it's willing to move in your direction, and you protect that information proportionally. Nothing more complicated than that.
Key Parameters to Specify in Your System:
Maximum Give Back Percent: 30-50% is typical, but is dependent on how much profit retracement you can tolerate.
Minimum Profit to Activate: In dollar amount or an ATR multiple form entry. This prevents premature exits on tiny winners, e.g., if it has moved 5 pips at 40% that would mean you are only locking in a 3-pip profit.
Update Frequency: Potentially every bar. More frequent, but there may be issues if there is a limited ability to look at the market (if using some sort of automation, this could be programmed).
Is Maximum Giveback Always the Optimum Trail?
As with many approaches, results can be highly dependent on underlying market conditions. It is important to be balanced.
The table below summarises some observations when maximum giveback has been used as part of automated exits.
The major difference isn’t likely to be an increased win rate. It is about keeping more of your runners during high-volatility price moves rather than donating them back to the market.
It may not always be the best approach, as different strategies often merit different exit approaches.
There are two obvious scenarios where fixed pips may still be worth consideration.
Very short-term scalping (sub-20 pip targets)
News trading, where you want instant hard stops
Integrating Maximum Giveback With Your System
You may have other complementary exit filters in place that you already use. Remember, the ideal is often a combination of exits, with whichever is triggered first.
There is no reason why this approach will not work well with approaches such as set stops, take profits and partial closes (where you simply use maximum Giveback in the remainder as well as time-based exits.
Final Thoughts
To use fixed-pip trailing stops irrespective of instrument pricing, volatility, timeframe, and sessional considerations is the trading equivalent of wearing the same jacket in summer and winter.
Maximum Give Back trailing adjusts to the ‘market weather’. It won't make bad trades good, but it will stop you from cutting your best trades short just because your stop was designed for average conditions.
The market doesn't trade in averages but has specific likely moves dependent on context. Your exits should not be average either.
Multi-Timeframe (MTF) analysis is not just about checking the trend on the daily before trading on the hourly; ideally, it involves examining and aligning context, structure, and timing so that every trade is placed with purpose.
When done correctly, MTF analysis can filter market noise, may help with timing of entry, and assist you in trading with the trending “tide,” not against it.
Why Multi-Timeframe Analysis Matters
Every setup exists within a larger market story, and that story may often define the probability of a successful trade outcome.
Single-timeframe trading leads to the trading equivalent of tunnel vision, where the series of candles in front of you dominate your thinking, even though the broader trend might be shifting.
The most common reason traders may struggle is a false confidence based on a belief they are applying MTF analysis, but in truth, it’s often an ad-hoc, glance, not a structured process.
When signals conflict, doubt creeps in, and traders hesitate, entering too late or exiting too early.
A systematic MTF process restores clarity, allowing you to execute with more conviction and consistency, potentially offering improved trading outcomes and providing some objective evidence as to how well your system is working.
Building Your Timeframe Hierarchy
Like many effective trading approaches, the foundation of a good MTF framework lies in simplicity. The more complex an approach, the less likely it is to be followed fully and the more likely it may impede a potential opportunity.
Three timeframes are usually enough to capture the full picture without cluttering up your chart’s technical picture with enough information to avoid potential contradiction in action.
Each timeframe tells a different part of the story — you want the whole book, not just a single chapter.
Scalpers might work on H1-M15-M5, while longer-term traders might prefer H4-H1-H15.
The key is consistency in approach to build a critical mass of trades that can provide evidence for evaluation.
When all three timeframes align, the probability of at least an initial move in your desired direction may increase.
An MTF breakout will attract traders whose preference for primary timeframe may be M15 AND hourly, AND 4-hourly, so increasing potential momentum in the move simply because more traders are looking at the same breakout than if it occurred on a single timeframe only.
Applying MTF Analysis
A robust system is built on clear, unambiguous statements within your trading plan.
Ideally, you should define what each timeframe contributes to your decision-making process:
Trend confirmed
Structure validated
Entry trigger aligned
Risk parameters clear
When you enter on a lower timeframe, you are gaining some conviction from the higher one. Use the lower timeframe for fine-tuning and risk control, but if the higher timeframe flips direction, your bias must flip too.
Your original trading idea can be questioned and a decision made accordingly as to whether it is a good decision to stay in the trade or, as a minimum action, trail a stop loss to lock in any gains made to date.
Putting MTF into Action
So, if the goal is to embed MTF logic into your trade decisions, some step-by-step guidance may be useful on how to make this happen
1. Define Your Timeframe Stack
Decide which three timeframes form your trading style-aligned approach.
The key here is that as a starting point, you must “plant your flag” in one set, stick to it and measure to see how well or otherwise it works.
Through doing this, you can refine based on evidence in the future.
One tip I have heard some traders suggest is that the middle timeframe should be at least two times your primary timeframe, and the slowest timeframe at least four times.
2. Build and Use a Checklist
Codify your MTF logic into a repeatable routine of questions to ask, particularly in the early stages of implementing this as you develop your new habit.
Your checklist might include:
Is the higher-timeframe trend aligned?
Is the structure supportive?
Do I have a valid trigger?
Is risk clearly defined?
This turns MTF from a concept into a practical set of steps that are clear and easy to action.
3. Consider Integrating MTF Into Open Trade Management
MTF isn’t just for entries; it can also be used as part of your exit decision-making.
If your higher timeframe begins showing early signs of reversal, that’s a prompt to exit altogether, scale out through a partial close or tighten stops.
By managing trades through the same multi-timeframe approach that you used to enter, you maintain logical consistency across the entire lifecycle of the trade.
Final Action
Start small. Choose one instrument, one timeframe set, and one strategy to apply it to.
Observe the clarity it adds to your decisions and outcomes. Once you see a positive impact, you have evidence that it may be worth rolling out across other trading strategies you use in your portfolio.
Final Thought
Multi-Timeframe Analysis is not a trading strategy on its own. It is a worthwhile consideration in ALL strategies.
It offers a wider lens through which you see the market’s true structure and potential strength of conviction.
Through aligning context, structure, and execution, you move from chasing an individual group of candles to trading with a more robust support for a decision.