Is the Bitcoin Four-Year Cycle Over?

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.

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 is designed to be more adaptive to regime changes than fixed-pip stops, potentially requiring less manual recalibration as 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 could help 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.