Oil Price Reality: Are We Destined for New Highs, or Just Another False Alarm?

Every trader has had that moment where a seemingly perfect trade goes astray. 

You see a clean chart on the screen, showing a textbook candle pattern; it seems as though the market planets have aligned, and so you enthusiastically jump into your trade.

But before you even have time to indulge in a little self-praise at a job well done,  the market does the opposite of what you expected, and your stop loss is triggered.

This common scenario, which we have all unfortunately experienced, raises the question: What separates these “almost” trades from the truly higher-probability setups? 

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The State of Alignment

A high-probability setup isn’t necessarily a single signal or chart pattern. It is the coming together of several factors in a way that can potentially increase the likelihood of a successful trade. 

When combined, six interconnected layers can come together to form the full “anatomy” of a higher-probability trading setup:

• Context
• Structure
• Confluence
• Timing
• Management 
• Psychology

When more of these factors are in place, the greater the (potential) probability your trade will behave as expected.

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Market Context 

When we explore market context, we are looking at the underlying background conditions that may help some trading ideas thrive, and contribute to others failing.

Regime Awareness

Every trading strategy you choose to create has a natural set of market circumstances that could be an optimum trading environment for that particular trading approach.

For example:

• Trending regimes may favour momentum or breakout setups.
• Ranging regimes may suit mean-reversion or bounce systems.
• High-volatility regimes create opportunity but demand wider stops and quicker management.

Investing time considering the underlying market regime may help avoid the temptation to force a trending system into a sideways market.

Simply looking at the slope of a 50-period moving average or the width of a Bollinger Band can suggest what type of market is currently in play. 

Sentiment Alignment

If risk sentiment shifts towards a specific (or a group) of related assets, the technical picture is more likely to change to match that. 

For example, if the USD index is broadly strengthening as an underlying move, then looking for long trades in EURUSD setups may end up fighting headwinds.

Setting yourself some simple rules can help, as trading against a potential tidal wave of opposite price change in a related asset is not usually a strong foundation on which to base a trading decision.

Key Reference Zones

Context also means the location of the current price relative to levels or previous landmarks.

Some examples include:

• Weekly highs/lows
• Prior session ranges, e.g. the Asian high and low as we move into the European session
• Major “round” psychological numbers (e.g., 1.10, 1000)
  
  

A long trading setup into these areas of market importance may result in an overhead resistance, or a short trade into a potential area of support may reduce the probability of a continuation of that price move before the trade even starts.

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Market Structure 

Structure is the visual rhythm of price that you may see on the chart. It involves the sequences of trader impulses and corrections that end up defining the overall direction and the likelihood of continuation:

• Uptrend: Higher highs (HH) and higher lows (HL)
• Downtrend: Lower highs (LH) and lower lows (LL)
• Transition: Break in structure often followed by a retest of previous levels.

A pullback in an uptrend followed by renewed buying pressure over a previous price swing high point may well constitute a higher-probability buy than a random candle pattern in the middle of nowhere.

Compression and Expansion

Markets move through cycles of energy build-up and release. It is a reflection of the repositioning of asset holdings, subtle institutional accumulation, or a response to new information, and may all result in different, albeit temporary, broad price scenarios.

• Compression: Evidenced by a tightening range, declining ATR, smaller candles, and so suggesting a period of indecision or exhaustion of a previous price move, 
• Expansion: Evidenced by a sudden breakout, larger candle bodies, and a volume spike, is suggestive of a move that is now underway.
  
  

A breakout that clears a liquidity zone often runs further, as ‘trapped’ traders may further fuel the move as they scramble to reposition.

A setup aligned with such liquidity flows may carry a higher probability than one trading directly into it.

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Confluence 

Confluence is the art of layering independent evidence to create a whole story. Think of it as a type of “market forensics” — each piece of confirmation evidence may offer a “better hand’ or further positive alignment for your idea.

There are three noteworthy types of confluence:

1. Technical Confluence – Multiple technical tools agree with your trading idea:

• Moving average alignment (e.g., 20 EMA above 50 EMA) for a long trade
• A Fibonacci retracement level is lining up with a previously identified support level.
• Momentum is increasing on indicators such as the MACD.

2. Multi-Timeframe Confluence – Where a lower timeframe setup is consistent with a higher timeframe trend. If you have alignment of breakout evidence across multiple timeframes, any move will often be strengthened by different traders trading on different timeframes, all jumping into new trades together.

3. Volume Confluence – Any directional move, if supported by increasing volume, suggests higher levels of market participation. Whereas falling volume may be indicative of a lesser market enthusiasm for a particular price move.

Confluence is not about clutter on your chart. Adding indicators, e.g., three oscillators showing the same thing, may make your chart look like a work of art, but it offers little to your trading decision-making and may dilute action clarity.

Think of it this way: Confluence comes from having different dimensions of evidence and seeing them align. Price, time, momentum, and participation (which is evidenced by volume) can all contribute.

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Timing & Execution 

An alignment in context and structure can still fail to produce a desired outcome if your timing is not as it should be. Execution is where higher probability traders may separate themselves from hopeful ones.

Entry Timing

• Confirmation: Wait for the candle to close beyond the structure or level. Avoid the temptation to try to jump in early on a premature breakout wick before the candle is mature.
• Retests: If the price has retested and respected a breakout level, it may filter out some false breaks that we will often see.
• Then act: Be patient for the setup to complete. Talking yourself out of a trade for the sake of just one more candle” confirmation may, over time, erode potential as you are repeatedly late into trades.

Session & Liquidity Windows

Markets breathe differently throughout the day as one session rolls into another. Each session's characteristics may suit different strategies. 

For example:

• London Open: Often has a volatility surge; Range breaks may work well.
• New York Overlap: Often, we will see some continuation or reversal of morning trends.
• Asian Session: A quieter session where mean-reversion or range trading approaches may do well

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Trade Management 

Managing the position well after entry can turn probability into realised profit, or if mismanaged, can result in losses compounding or giving back unrealised profit to the market. 

Pre-defined Invalidation

Asking yourself before entry: “What would the market have to do to prove me wrong?” could be an approach worth trying. 

This facilitates stops to be placed logically rather than emotionally. If a trade idea moves against your original thinking, based on a change to a state of unalignment, then considering exit would seem logical. 

Scaling & Partial Exits

High-probability trade entries will still benefit from dynamic exit approaches that may involve partial position closes and adaptive trailing of your initial stop.

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Trader Psychology 

One of the most important and overlooked components of a higher-probability setup is you.

It is you who makes the choices to adopt these practices, and you who must battle the common trading “demons” of fear, impatience, and distorted expectation.

Let's be real, higher-probability trades are less common than many may lead you to believe. 

Many traders destroy their potential to develop any trading edge by taking frequent low-probability setups out of a desire to be “in the market.” 

It can take strength to be inactive for periods of time and exercise that patience for every box to be ticked in your plan before acting.

Measure “You” performance

Each trade you take becomes data and can provide invaluable feedback. You can only make a judgment of a planned strategy if you have followed it to the letter. 

Discipline in execution can be your greatest ally or enemy in determining whether you ultimately achieve positive trading outcomes.

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Bringing It All Together – The Setup Blueprint

Final Thoughts

Higher-probability setups are not found but are constructed methodically. 

A trader who understands the “higher-probability anatomy” is less likely to chase trades or feel the need to always be in the market. They will see merit in ticking all the right boxes and then taking decisive action when it is time to do so. 

It is now up to you to review what you have in place now, identify gaps that may exist, and commit to taking action!

Bitcoin has now outlasted the peak of all its previous four-year cycles.

For over a decade, every Bitcoin cycle has followed the same sequence: consolidation, breakout, mania, crash. Rinse and repeat.

Timeline-wise, we should be at the post-mania inflection point, waiting for the seemingly inevitable crash.

Yet unlike previous runs, this cycle never saw its “mania phase.” Instead, Bitcoin has spent the past year grinding sideways, touching new all-time highs without a euphoric blow-off top that defined previous cycles.

The fact that this euphoria period never materialised brings into question whether this cycle still has room to run, or has the market simply matured past the point of mania-driven peaks?

The Historical Four-Year Pattern

The traditional Bitcoin cycle was simple. Every four years, a halving event would reduce the block reward (amount of new Bitcoin being created) by half, creating a supply shock that triggered major bull markets.

The 2013 cycle, the 2017 cycle, and the 2021 cycle all followed this script. Each halving was followed by a 3-to 9-month growth period, then a full-on mania period, before topping out 12 to 18 months after the event.

Following the most recent halving in April 2024, Bitcoin experienced five months of sideways consolidation, then hinted at making its anticipated breakout into mania after the US election… but quickly returned to sideways consolidation for the next year.

We have seen new ATHs and the price has made some notable gains during the period, but the overall momentum has been much weaker.

This failure to repeat the frenzies of the past three cycles has brought into question how much influence the Bitcoin halving truly has on the market anymore.

No Longer a Supply Shock

In previous cycles, the halving created a situation where prices had to rise to clear the same dollar amount of miner expenses (who were now earning half the Bitcoin).

Bitcoin miners would simply not sell until the price reached a certain level, creating a supply shock that would drive prices higher.

Miners still do this today; however, the market’s maturation and the institutional adoption of Bitcoin have dampened the impact.

Selling off Bitcoin is no longer a balancing act where miners hold influence over price. The market has deep liquidity that can handle significant flows in either direction.

Institutional ETFs routinely purchase more Bitcoin in a single day than miners produce in a month. 

The supply reduction that once drove dramatic price movements is now easily absorbed by a market with institutional buyers providing constant demand.

If the Halving Isn't Driving Cycles, What Is?

The overriding narrative is that the Bitcoin cycle is now tied to the global liquidity cycle.

If you plot the Global M2 Money Supply versus Bitcoin on a year-on-year basis, you can see that every Bitcoin top has correlated with the peaks of Global M2 liquidity growth.

This isn't unique to Bitcoin. The Gold price has closely mirrored the rate of Global M2 expansion for decades.

When central banks flood the system with liquidity, capital tends to move into stores of value or high-risk assets. When they drain liquidity, those same assets tend to retreat.

However, this is a correlation; these relationships may change and should not be relied upon as indicators of future performance.

Is the Dollar Just Getting Weaker?

The U.S. Dollar Strength Index tells the other side of this liquidity story. Bitcoin versus the dollar year-on-year has been almost perfectly inversely correlated. 

Simply put, as fiat currencies lose purchasing power, “hard” assets like Bitcoin and Gold start to appreciate. Not because of improved fundamentals, but because the currencies they are paired against are simply worth less.

The Self-Fulfilling Prophecy

Beyond the charts and patterns, there is also the psychological notion that the four-year cycle persists precisely because people believe it will.

People have been conditioned by three complete cycles to expect Bitcoin to peak somewhere between 400 and 600 days after a halving.

This collective belief shapes behaviour: traders take profits, investors take fewer risks, and retail enthusiasm wanes. The prophecy fulfils itself.

When everyone believes Bitcoin should peak 18 months after a halving, the combined selling pressure can create exactly that outcome — regardless of whether the underlying driver still exists.

The current market weakness, with Bitcoin dropping over 20% from its October record high, occurred almost precisely at this 18-month mark.

Is This Cycle Built Different?

Despite this on-cue sell-off, this cycle still has the potential to break away from the historical four-year pattern. 

Increased ETF adoption by institutional investors has brought in higher quality and consistent ownership of Bitcoin.

Unlike retail traders, who often panic-sell during corrections, institutional holders tend to maintain their positions through volatility.

For example, Michael Saylor’s high-profile MicroStrategy fund has continued to purchase Bitcoin through market weakness. Recently reporting a purchase of 8,178 BTC at an average price of $102,171.

Another hard indicator that diverges from previous cycle peaks is the amount of Bitcoin being held on centralised exchanges.

The current amount of BTC on CEXs is unusually low. This pattern is generally seen closer to cycle lows, rather than peaks.

Other factors supporting the break of the four-year mould are coming out of the Whitehouse. 

A comprehensive regulatory framework through the CLARITY Act represents structural changes and boundaries for regulatory bodies that didn't exist in previous cycles.

And the move to establish a Strategic Bitcoin Reserve will see all government-held forfeited Bitcoin (approximately $30 billion worth) transferred into a government reserve, signalling Bitcoin as a strategic asset like Gold and oil.

Bitcoin Has Finally Grown Up

The four-year cycle has been a useful heuristic, but heuristics break down when conditions change. Institutional buyers, regulatory clarity, and strategic reserves represent genuinely new conditions historical patterns don’t account for.

At the same time, dismissing the cycle entirely would be premature. The self-fulfilling aspect means it retains predictive power even if the original cause has weakened. 

Market participants act on the pattern they've learned, and their actions create the pattern they expect.

Perhaps the real insight is that the Bitcoin market cycles never had just one cause. They were always the result of multiple overlapping forces — programmed scarcity, liquidity conditions, sentiment, self-reinforcing expectations.

The cycle shifts character as some forces strengthen and others weaken. But whether the forces have shifted enough to break the four-year trend is yet to be determined. 

The fundamental indicators show this cycle may have some life, but the psychological power of the four-year pattern could push it to another, predictable end.

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The decision to scale (increase the traded lot size of a specific EA) should be based on statistical evidence that indicates your EA has the potential to perform to certain expectations. 

Equal weight should be given to the decision to scale, as to the initial decision to deploy an EA. This guide provides an indicative approach on how to put together and action your scaling plan.

Before You Start Your Scaling Plan

Important: this should be an individual plan that is consistent with your personal trading objectives, your EA portfolio, and your personal financial situation (including account size).

We are going to use a starting lot of 0.10 per trade in the examples in this document —you want to adjust this based on your own risk tolerance.

Whatever your chosen lot size start point, EA scaling should be a pre-planned incremental approach, scaling stepwise based on performance metrics you are seeing in your live trading account.

You should also have assessed the current margin usage of your EA portfolio exposure to ensure that any scaling and related increased margin requirements are appropriate to the size of your account. 

Suggested Scaling Baseline Requirements

Scaling should only be performed when your EA is performing to what you deem to be a good standard. To make this judgment, you need to set some minimum performance standards.

The past performance of your EA is not a guarantee of future performance. If market conditions change, you must remain vigilant and continue to measure performance on an ongoing basis for every live EA you have.

You need to define the key metrics that are important to you. 

Two important metrics to include are:

• The number of trades: to provide some evidence of reliability 
• The period of time: to have had exposure to at least some variation in market conditions

Example of how you may lay your metrics out in a table:

Some may choose to include proximity to original expectations of other metrics, such as minimum win rate, average profit in winning trades, and average loss in those that go against you. 

It should only be after your metrics are met that lot scaling begins on any specific EA.

Lot Size Scaling Ladder

Below is an example of a performance-based scaling plan assuming a 0.10-lot baseline.

Again, this is indicative. It provides a framework with clear review dates and an approach that illustrates incremental scaling. You must still define a regime that is right for your specific trading objectives. 

Risk Guardrails

It is vital to keep an eye on your general account risks and have limits in place that guide your EA use.

Such limits must be constant across all stages of scaling and referenced beyond the risk of a single EA, but to your portfolio as a whole.:

Per-Trade Risk (Nominal)

Trade risk for any one trade should be seen in the context of account size and the dollar risk based on the risk parameters you have set for your EA. 

Specify a maximum percentage of the account balance — a $200 loss is more impactful on a $1000 account compared to a $10,000 account. 

Stick to what is right for you in terms of your tolerable risk level based on your trading objectives and financial situation. A common suggestion is a 1-2% risk of account equity per trade. 

Total Open Exposure

Specifying maximum exposure in the number of EAs open at any time and those that use the same asset class is important for overall portfolio risk management. 

There are tools you can use to monitor exposure risk generally, as well as those that can be used to indicate single asset exposure.

Margin Usage

It is always desirable that your set exit approaches and parameter levels are what your exits are based on. It should not be because your margin usage has meant you have moved into a margin call situation. 

Specify a minimum level to adhere to and make sure that your account is sufficiently funded. If volatility or slippage rises (e.g., news events or illiquid sessions), reduce lot size temporarily.

Scaling Psychology – Managing “Big Numbers”

As lot sizes rise, your emotions may respond accordingly when you see the larger dollar amounts that your EA is generating. 

If you are used to seeing an average profit of $100 and average loss of $50, and suddenly you are seeing significantly bigger numbers, it creates an emotional challenge where you may be tempted to do a “discretionary override”. 

Although there are situations, such as major market events, overexposure in a specific asset, or VPS or account system problems, where such intervention may be considered, generally this would distort the actual performance evaluation of your EA and is not encouraged (unless it is pre-planned).

The table below presents some of the generally accepted challenges and offers suggestions on how to manage them. 

Your Plan Into Action…

In practical terms, your scaling plan should have two components:

1. The key parameters for action on your chosen key metrics
2. Specified periodic review times to make your next scaling decision

This is not a race. Having systems in place facilitates creating the opportunity that scaling brings while still mitigating the risks.