ITI Faculty Roundup: Spring 2026 Trading Insights

The last few months gave traders a difficult mix: AI leadership, rate uncertainty, oil risk, currency pressure, momentum streaks, and off-hours venues. They also created traps for anyone trading a headline before checking the process.

Since ITI closed January by arguing for signal over noise, the faculty discussion has returned to one task: audit the quality of a signal before acting on it.

That lesson is simple, but easy to ignore. A trader can have the right theme, the right chart, and the right instrument, then still mishandle the trade because the signal was read outside its setting.

Momentum needs volatility context. Macro needs a transmission map. Technical levels need a decision plan. Off-hours markets need venue checks. Risk needs more than a default percentage.

The faculty notes cover different market problems, which is part of the value. Each problem points back to the same trader discipline: define the signal, define the setting, then decide whether the risk is worth taking.

What Changed Since the January Roundup

January’s signal-over-noise frame gave the spring material a useful starting point. From there, the discussion got more granular. The focus moved toward the mechanics that make a trade decision durable.

MACD-v gave momentum traders a better way to compare signals across volatility environments. AI, ghost GDP, and private credit treated high-growth stories as material for stress testing. The Hyperliquid oil weekend showed how an off-hours venue can matter when legacy benchmarks are closed, then paired that relevance with checks on depth, constraints, and confirmation.

The April ITI Insights work pushed that into execution. Risk became a system, liquidity came before direction, and the discussion of market traps used vulnerable positioning to explain why the first visible move can mislead.

That pattern carried across instruments. A trader might be looking at USD/JPY, SOXX, crude, an AI-linked equity basket, or an onchain commodity contract. The working questions stay the same: what kind of signal is this, and what can break it?

Momentum Needs Context

Momentum is where this gets concrete first. ITI’s MACD-v explainer and Alex Spiroglou’s MACD-v webinar both centered on a practical problem. A raw momentum reading can look strong because the move is strong, because volatility expanded, or because the indicator is being read outside its useful context.

MACD-v matters because it brings volatility into the momentum question. It makes it easier to ask whether the signal is comparable across assets and regimes.

For a serious trader, the workflow changes:

• Label the market state first: compression, expansion, trend, rotation, or post-event repricing.
• Read momentum as a volatility-adjusted measure.
• Decide what job the signal is doing: entry, management, exit, or watchlist priority.
• After the trade, ask whether momentum quality improved, stalled, or deteriorated after entry.

That is the line between indicator use and indicator process. The indicator can help, but the process decides whether the reading deserves position size.

Use MACD-v as the example, then apply the habit everywhere else. A breakout, moving average slope, relative strength reading, or volatility expansion signal all need a regime label.

Without that label, different market states get treated as the same problem.

Narratives Need a Risk Budget

The ITI piece on AI, ghost GDP, and private credit treated AI enthusiasm as a stress-test input. It asked what markets may be underpricing if AI-driven capital spending, private credit exposure, and measured growth are being interpreted too cleanly.

Michael Berman’s SOXX note gives the risk version of that problem. Narrative strength can hide process fragility when the plan has no clear loss limit. His example of an 18-day SOXX advance makes the point cleanly. A trader who keeps adding to a mean-reversion idea can be right about stretched conditions and still exhaust the account before the trade finally turns.

For traders, the process lesson is direct. Narrative intensity should trigger clearer risk architecture. Before adding exposure, write down three things:

• What would invalidate the thesis?
• What would confirm the stress case?
• How much adverse movement can the plan absorb before the original thesis no longer applies?

Marc Chandler’s May monthly linked oil, rates, currencies, equities, and policy uncertainty. That macro framing matters because asset classes interact when policy expectations, energy prices, and currency levels begin reinforcing each other. A trader may enter through a single instrument, yet the risk often arrives through a broader macro channel.

So slow the trade plan down when the narrative speeds up. Identify the core driver, the secondary transmission channel, and the position-size consequence.

If the story is AI capex, the secondary channel might be semiconductors, credit, power demand, rates, or equity concentration. If the story is oil, the secondary channel might be inflation expectations, FX, rates, or commodity-sensitive equities.

The immediate job is to know which path would damage the trade first, then size the position for that risk.

Read Liquidity Before Direction

Market structure made the first question more practical than “what moved?” Traders also had to ask whether the move was trustworthy enough to trade.

ITI’s piece on Hyperliquid’s oil weekend used weekend oil trading to show that an open venue can become important when the benchmark venue is closed. The distinction is narrow but important: an off-hours market can provide early information, and that information still needs checks on liquidity quality, rule constraints, and benchmark confirmation.

The same standard applies beyond onchain markets. Traders routinely confuse access with reliability. A price is visible, a chart is moving, and a quote is tradable. Before acting, check whether the venue is deep enough, balanced enough, and clean enough for the intended size.

The April ITI Insights pieces reinforced that standard in more familiar market settings. Read the liquidity and market-traps pieces together and the process is direct: first map where participation is concentrated, then judge whether the move created useful acceptance or only cleared vulnerable positioning.

Carol Harmer’s USD/JPY note was a useful reminder that technical analysis works best when levels define decisions. Treat a level as a decision boundary. If price rejects, accepts, accelerates, or fails around a level, the trader gets new information. If the trader enters before identifying the behavior that matters, the level is only a mark on the chart.

The Hyperliquid oil example and the USD/JPY note are different market problems, but they point to the same discipline. One deals with onchain commodity price discovery. The other deals with FX technical structure. Both ask the trader to respect the mechanism before acting on the move.

Before the next directional trade, write the liquidity sentence first.

• “Price swept prior-day high and accepted above it.”
• “Price swept prior-day high and immediately failed back inside range.”
• “Price is moving, but the move is happening away from a useful liquidity reference.”
• “Venue is open, but size should stay reduced until depth and confirmation improve.”

That one sentence can stop a weak trade before the order is entered.

Risk Is the Operating Layer

A recurring educational theme from the last few months was risk. ITI Insights argued that risk is a system built around more than position size. In this context, a fixed risk-per-trade rule is one control. It needs support from volatility, liquidity, correlation, and execution-quality checks.

Build the risk read in layers:

• Position risk: how much the trade can lose if the plan fails.
• Execution risk: spread, slippage, liquidity, venue quality, and time-of-day conditions.
• Portfolio risk: correlation, concentration, and overlapping drivers.
• Behavioral risk: the trader’s tendency to override rules during stress.
• Review risk: weak journals, vague screenshots, and incomplete post-trade diagnosis.

The discussion of retail trading’s professionalization described this from the education side. Retail traders have access to better tools, data, and platforms than prior generations, and the professional gap often shows up in standards. Better access still has to be converted into better decisions.

The companion piece on serious trading education set the standard for what that training should include: structure, risk, review, and feedback. The implication for training is straightforward: serious traders need a way to train decisions under pressure, not just collect more concepts.

Apply This to Your Own Trading

Run the same exercise on your own last 90 days of trades. Pull the trades, then classify them through four questions.

Q1: What regime did I think I was trading?

Mark each trade as trend, range, breakout, reversal, event, post-event, or liquidity sweep. If you cannot name the regime, the setup may have been too vague. Then compare that label with what happened.

Q2: Did I read liquidity before direction?

Identify where the likely orders were before entry. Mark the session high or low, prior day levels, swing points, volume areas, or obvious stops. Then ask whether your entry followed acceptance or chased a sweep.

Q3: Was size aligned with the risk state?

Review whether volatility, spread, correlation, event risk, and venue quality supported your size. If the trade depended on a fragile market, thin conditions, or a fast narrative, the size should reflect that lower-quality risk state.

Q4: What did the trade teach me about behavior?

Separate setup quality from execution behavior. A losing trade can still be well executed. A winning trade can still expose poor process if size, timing, or review discipline brok

This review should produce one operational change instead of a broad promise to “be more disciplined.” Pick the recurring error that did the most damage. It may be late entries after liquidity sweeps, oversizing during narrative spikes, ignoring volatility context, holding through invalidation, or treating a single indicator as enough evidence.

Then rewrite the rule in executable form. For example:

• “No momentum entry until I label volatility state and invalidation.”
• “A macro trade needs one primary driver and one secondary transmission channel.”
• “Off-hours execution needs a liquidity and venue-quality check.”
• “No size increase after a stop run unless acceptance confirms the move.”

Those rules can be reviewed. They turn the last few months of content into a repeatable training loop.

Here is the difference in a hypothetical trade log.

Before review:
“Bought semiconductor ETF after strong open. Momentum looked strong. Stopped out after reversal.”

After review:
“Regime: post-streak extension after a multi-session advance. Signal: raw momentum strong, volatility expanded. Liquidity: entry came after the opening drive and before acceptance above the first-hour high. Risk state: reduced quality because the trade depended on continuation after a crowded narrative move. Process fix: no full-size momentum entry after a multi-session extension unless price accepts above the first-hour range and stop distance fits the daily volatility state.”

The second version gives the trader a specific skill to train. It separates the signal, the liquidity condition, the risk state, and the rule change.

Apply the same format to the faculty notes:

• A USD/JPY level becomes useful only after the trader defines the behavior that matters at the level.
• A SOXX mean-reversion idea needs a clear loss limit before the next entry, especially after a long streak.
• A macro view from oil, rates, or currencies needs a transmission channel before it becomes a position.
• An off-hours commodity print needs a liquidity and venue-quality check before it influences size.

The Education Pathway From Here

Use the 90-day review as a placement diagnostic before choosing the next education step.

The most complete next step is ITI’s Master’s in Trading program. Choose that route when the review shows problems across several areas at once. Those areas may include regime classification, signal quality, execution, sizing, psychology, and post-trade feedback. A scattered error pattern usually calls for a full training architecture rather than another isolated concept.

For narrower skill development, use the short-course catalog when the review identifies one dominant weakness. The public short-course catalog includes Commodities & Derivatives, Algorithmic Trading & AI, Trading Psychology, and Portfolio & Risk Management. Those paths map naturally to the problems above:

• Commodities & Derivatives for traders focused on oil, futures, options, and volatility.
• Algorithmic Trading & AI for traders who need to understand model-driven markets, automation, and AI-related workflow.
• Trading Psychology for traders whose main gap is discipline, neutrality, and execution behavior.
• Portfolio & Risk Management for traders dealing with sizing, concentration, drawdowns, and cross-asset exposure.

For momentum-specific development, start with the MACD-v explainer and Alex Spiroglou’s webinar. The important habit is to treat momentum as part of a broader decision process: signal, volatility, liquidity, risk, and review.

Final Takeaway

The faculty and ITI-adjacent work since late January points to a professional standard: do the work between signal and size.

Normalize momentum. Stress-test narratives. Read liquidity before direction. Respect venue design. Define the decision around the level. Build risk as a layered operating system. Review behavior with the same seriousness as entries and exits.

The practical next step is to take the recent market education, translate it into decision rules, and make those rules visible in the trade journal.

For the full pathway, review ITI’s Master’s in Trading program. For a focused next step, compare the current short-course options in Courses and Certificates. Choose the course that addresses the weakest part of your trading process first.

Sources

• ITI – 2025: Year in Review, Putting Signal Over Noise: https://internationaltradinginstitute.com/blog/2025-year-in-review-putting-signal-over-noise/
• 0ITI – MACD-V Explained: A Smarter Momentum Indicator for Trading Strategies: https://internationaltradinginstitute.com/blog/macd-v-explained-a-smarter-momentum-indicator-for-trading-strategies/
• ITI – Beyond RSI and MACD: The Twice-Awarded Momentum Model for Modern Markets: https://internationaltradinginstitute.com/event/beyond-rsi-and-macd-the-twice-awarded-momentum-model-for-modern-markets/
• ITI – AI, Ghost GDP, and Private Credit: What Markets Haven’t Priced In: https://internationaltradinginstitute.com/blog/ai-ghost-gdp-private-credit-what-markets-havent-priced-in/
• ITI – Courses and Certificates: https://internationaltradinginstitute.com/courses-and-certificates/
• ITI – Master’s in Trading Program: https://internationaltradinginstitute.com/masters-in-trading-program/
• FXStreet / ITI Insights – Risk systems article: https://www.fxstreet.com/education/risk-is-a-system-not-a-percentage-202604221050
• FXStreet / ITI Insights – Liquidity Before Direction: https://www.fxstreet.com/education/liquidity-before-direction-202604080637
• FXStreet / ITI Insights – How Markets Trap Traders Before Moving: https://www.fxstreet.com/education/how-markets-trap-traders-before-moving-202604150755
• FXStreet / ITI Insights – What Defines Serious Trading Education?: https://www.fxstreet.com/education/what-defines-serious-trading-education-202603251055
• FXStreet / ITI Insights – The Quiet Professionalization of Retail Trading: https://www.fxstreet.com/education/the-quiet-professionalization-of-retail-trading-202603180932
• FXStreet / Carol Harmer – USD/JPY: What Can’t Go Up Goes Down: https://www.fxstreet.com/cryptocurrencies/news/usd-jpy-what-cant-go-up-goes-down-202604150611
• FXStreet / Michael Berman – How to Blow Your SOXX Off: https://www.fxstreet.com/analysis/how-to-blow-your-soxx-off-202604300613
• Marc to Market – May 2026 Monthly: https://www.marctomarket.com/2026/04/may-2026-monthly.html