3 Key Takeaways From the June FOMC Meeting

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. 

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.