How Emotions Sneak Into Your Trades (and How to Catch Them)Because the market doesn’t care how you feel — but your portfolio absolutely does.
Every trader likes to believe they’re rational. Calm. Data-driven. A master of charts and probabilities.
And sometimes that’s true — at least until price starts moving faster than expected, your P&L flickers red, and suddenly you’re “just making a small adjustment.”
Emotions rarely kick the door down in trading. They sneak in quietly, wearing sensible shoes and carrying very reasonable arguments. By the time you notice them, they’ve already rearranged your trade plan.
🕵️ Emotion’s Favorite Disguise: Logic
The most dangerous emotions don’t announce themselves as fear or greed. They show up as logic.
“This breakout looks stronger than usual.”
“I’ll give it a little more room.”
“It’s only falling because of low volume.”
Each sentence sounds responsible. Each one is also a potential emotional leak. By the time the trade goes wrong, it feels like bad luck — not emotional interference.
📉 Losses Hurt More Than Gains Feel Good
Behavioral finance has a name for it: loss aversion. Traders experience losses maybe twice as intensely as equivalent gains.
That’s why a small drawdown can hijack your focus while a string of solid wins rarely registers as a lesson. It’s also why traders hesitate to close losing trades, but happily take profits early.
Emotionally, it feels safer to wait than to admit defeat — even when waiting is the riskier choice, especially if you’re deep into volatile crypto markets .
🧠 The Subtle Art of Revenge Trading
Revenge trading rarely looks dramatic. It doesn’t start with yelling at screens or slamming desks.
It usually begins with a quiet thought: “I’ll win the next one.”
That’s when trades get larger, setups get looser, and discipline takes a coffee break. The trader isn’t angry — they’re determined.
The market, unfortunately, doesn’t reward determination. It rewards discipline . Revenge trading isn’t about making money back. It’s about repairing a bruised ego — and markets have a way of charging interest for that.
🎢 Winning Can Be Just as Dangerous
Emotions don’t only sneak in during losses. They love winning streaks, too.
After a few good trades, confidence creeps up. Position sizes grow. Rules bend “just a little.” Suddenly, the trader isn’t following a system but a feeling.
This is how consistency quietly breaks down. Not in chaos, but in comfort.
🧰 Catching Emotions Before They Trade for You
The goal isn’t to eliminate emotion — that’s impossible. The goal is to spot it early, before it gets a vote.
Professional traders use simple, boring safeguards:
Repeating the same setups
Reviewing decisions away from the screen
Noting why a trade was taken, not just the result
Paying attention to behavior, not just outcomes
Emotion leaves footprints. The more familiar you are with your own patterns, the easier it is to catch them mid-step. “When you're centered, your emotions are not hijacking you.” - Ray Dalio.
🎁 The Takeaway
The real edge in trading comes from awareness — understanding how emotions quietly enter the process, recognizing their disguises, and catching them early before they influence your decisions.
Build that awareness, and emotions stop being obstacles — they become signals you know how to manage.
Off to you : How do you manage your emotions when you're trading? Share your strategy in the comments and let's get talking!
Market insights
S&P 500 to 10,000 inside the next 4 years - December 2025** This is an outlook for the next 3 to 4 years **
** The bull market is not yet done, sorry bears **
Yes, read that right, 10,000 or 10k for the S&P 500.
The markets shall continue to grind higher during this 10-year bear market everyone is talking about.
Upwards and onwards for investors as unemployment numbers rise, graduates question the mysterious reason why their unable to land employment on the degree they just dropped $150k on; inflation runs out of control, working people struggle, the market is just not going to care. The best opportunities come at a time when you don’t have the money to invest, have you ever noticed that?
The story so far
A crash is coming, have you heard? Our ears are ringing out 24/7 with noise on the most predictable crash since computer user Dave reports an uninterrupted hour of use on Windows Vista.
News of an AI bubble the size of Jupiter that is about to collapse in on itself and create a new star only seem to gather pace. The same finance prophets on Youtube with a hoodie in a rented flat forecasting which way the FED will move on rates. A 40 minute video to deliver a single sentence titled:
“EMERGENCY VIDEO: Market collapse (MUST WATCH before tomorrow!!)”, 10 seconds in “And Today’s video is sponsored by…. ” and if it’s not a sponsorship, it’s a course they’re trying shill. Many story tellers weren’t yet out of school during the dom com crash, but they’re now they’re experts of it.
Finally we have “a recession is coming” brigade. Of course it is. There’s always a recession coming. It’s like winter in Game of Thrones, they’ve been warning us for ages. Haven’t you heard? Recessions are now cancelled thanks to money printing and low interest rates. Capitalism RIP, all hale zombie companies.
In summary there’s no shortage of doom and gloom. Everyone is saying it.
So what am I missing?
Let’s break this down as painless as possible so as not to challenge waining attention spans. You’ll need a cuppa before reading this, for the people of the commonwealth, you know of what I speak. A proper builders brew.
Take your time to digest this content, there's no rush (did I mention it's a 5 month candle chart?). If you’re serious about separating yourself from the media noise to the News on the chart, then you're in for a treat. It is proper headline material. When you’re done, you'll pinch yourself, did he just tell me all this for free? What’s in it for him? (Absolutely nothing). Tradingview might bump $100 my way like Xerxes bearing gifts, but in the end the content of this idea may radically change the way your view the market today.
The contents:
1. Is the stock market in a bubble?
2. What about this 10 year bear market people are talking about?
3. A yield curve inversion printed, isn't a monster recession is due?
Is the stock market in a bubble?
No. A handful of stocks are.
The so-called “magnificent seven” stocks that make up about 40% of the market, Yeah, they’re in a bubble. No dispute from me there on that. It has never been riskier to be an index only fund investor. Especially if you're close to retirement. Now I’m not about to carve a new set of stone tablets explaining why, if you want the full sermon, that’s on my website.
Here’s the short version: a tiny bunch of tech darlings are bending the whole market out of shape. If you’re only invested in index funds, then you’re basically strapped to the front of the roller coaster hoping the bolts hold should those seven stocks decide to puke 20% in a week.
Suffice to say, a handful of stocks, tech stocks, are distorting the entire market. Index only investors are exposed to a greater risk than at any point in those past 20 years should the magnificent seven decide to sell off quickly. But what if they don’t? What if they just sell off slowly? Which is my thesis here.
In the final 12 months leading up to the dot com crash, during the 1999-2000 period, the Nasdaq returned 160%. RSI was at 97 as shown on the 3 month chart below. Now that’s a bubble.
In the past twelve months the Nasdaq has returned 20%. That’s not a bubble, that’s just a decent year. Above average, nice not insane. Yet people are acting like it’s 1999 all over again.
A similar story for the S&P 500 as shown on the 3 month chart below.
In the five years leading up to the crashes of 1929 and 2000 the market saw a return of 230% with RSI at 94 and 96, respectively. Today the market has returned 60% over the last 5 years with RSI @ 74. Adjusted for recent US inflation, and it’s roughly 30% real return!
The two periods often recited the most by doomsayers, 1929 and 2000, exhibit conditions not found in today’s market. Fact.
What about this 10 year bear market people are talking about?
Warren Buffet, perhaps the most famous investor in the world, has amassed a cash pile the size of the size of Fort Knox. Legendary short seller Michael Burry is quoted as having Puts on the overbought tech stocks, that’s fair. The masses have translated all this as a short position on the stock market. It seems everyone is preparing for Armageddon. My question, why are the masses so convinced of a stock market crash?
“Whenever you find yourself on the side of the majority, it is time to pause and reflect.”
Mark Twain
Let’s talk about the main 5 month chart above… There’s so many amazing things going on in this one chart, could spend hours talking about it. Will save that for Patrons, but the key points exist around support and resistance.
You’ll remember the “ Bitcoin in multi year collapse back to $1k - December 2025 ” publication?
It is of no surprise to me the Bitcoin chart now indicates a macro inverse relationship to the S&P 490 (minus tech stocks). Bitcoin is a tech stock all but in name, it follows the tech stock assets like a lost puppy.
If you strip away the blotted tech sector you realise we’re in for a bumper rally in the stock market in the coming years. This happens as a result of money flooding out of the blotted tech sector (that includes crypto). These sectors are about to crash straight through the floor towards middle earth.
When the masses catch on that businesses are not finding value in AI tools beyond generating cat videos on Youtube, the bottom falls out of those bankrupt entities, with hundreds of billions of dollars looking for a new home. That’s when investors pivot to value . Sometimes I feel like I’m the only one with this information when I scan through the feeds, how is this not the most obvious trade of the decade?
For the first time in 96 years the S&P 500 breaks out of resistance. Why is no one else talking about this?
2025 was the year it happened and yet not a whisper. The 1st resistance test occurred in July 1929. The 2nd in January 2000. The breakout occurred in the first half of 2025 and will be confirmed by January 1st, 2026 providing the index closes the year above 6530-6550 area. 12 trading days from now.
The 18 year business cycle, roughly 6574 days (the orange boxes) is shown together with the black boxes representing the 10 year bear markets in-between (14 years until past resistance is broken - pink boxes).
Should you not know, The 18 year business cycle, In modern market economies (especially the US and UK), they are repeated cycles where:
Land & property prices rise for about 14 years
Then there’s about 4 years of crisis, crash, and recovery
Together that’s roughly an 18-year land / real-estate business cycle, a pattern that is argued to show up again and again.
When we remove the darlings of the stock market you find the valuation for the S&P 490 suggests that the vast majority of the US market is currently priced near a level of Fair Value relative to GDP, provided that the current economic structure persists.
The high majority of influencers and financial experts talk about the end of the business cycle, there’s even “how to prepare for the crash” videos. If we look left, it is clear, the 18 year business cycle is far from over. So why are you bearish?
A yield curve inversion printed, isn't a monster recession is due?
There is a general assumption that recessions mean bad things for the stock market. You’re thinking it right now aren’t you? “ Of course they are Ww - everything will crash in a recession! ”
Listen…. you couldn’t be more wrong.
Ready for some dazzle? This level of dazzle wins your Harvard scholarships when meritocracy isn’t an option for you. And it’s free, without the monstrous loan debt at the end. Can you believe that?
What if I told you the stock market does not care about recessions?
Let’s overlay every US recession on the same 5 month chart. The vertical grey areas.
There has been 14 US recessions over the last 96 years. The majority, that is 9 of them, occurred during a bear market. The recessions that saw the largest drop in the stock market, 1929 and 2000, were known overbought bubble periods. We know that is not representative of the current market as discussed in the first section.
Here is the dazzle. Focus on the recession during the business cycles. What do you notice?
The recessions during business cycles (blue circles) never saw a stock market correction greater than 10%. In other word, utterly irrelevant.
Conclusions
Let’s land this gently, before someone hyperventilates into their keyboard. The S&P 500 is not in a bubble.
A handful of stocks are and that distinction matters far more than most people are prepared to admit. Yes, the Magnificent Seven are stretched. Yes, AI enthusiasm has reached “my toaster is sentient” levels. But the rest of the market? Strip away the tech confetti and you’re left with something far less dramatic and far more dangerous to bears: a structurally healthy market breaking a 96-year resistance. Not testing it.
Not flirting with it.
Breaking it.
And doing so while the internet is convinced the sky is falling.
This is where people get confused. They expect crashes to announce themselves loudly, with sirens and YouTube thumbnails. They don’t. Crashes arrive when optimism is universal, not when fear is a full-time job. Right now, fear is working overtime.
If history rhymes, and markets are essentially drunk poets with a spreadsheet, then the evidence points to continued upside over the next 3–4 years, not a sudden plunge into a 10-year ice age. Now that does not mean straight up. Expect:
Volatility
Rotation
Pullbacks that feel terrifying in real time and irrelevant in hindsight
What it does not suggest is the end of capitalism every time the RSI sneezes. The 18-year business cycle is not complete. The long-term channel remains intact. RSI conditions are elevated but nowhere near the manic extremes seen in 1929 or 2000. Those periods were bubbles. This is not.
Here’s the uncomfortable bit for many:
The biggest risk right now isn’t being long. it’s being so convinced a crash is imminent that you miss the next leg entirely. Especially if you’re hiding in cash waiting for a disaster that keeps failing to show up. And before anyone shouts “What about tech collapsing?!”, yes — that’s precisely the point. If capital rotates out of bloated tech and into value, industrials, energy, financials, and boring businesses that actually make money, the index doesn’t die. It grinds higher while everyone argues about why their favourite stock stopped working.
S&P 500 to 10,000 isn’t a fantasy screamed into the void.
It’s the logical outcome of structure, cycles, and history, assuming capitalism doesn’t suddenly apologise and shut down.
And if it does?
Well, none of us will be worrying about our portfolios anyway.
Ww
Disclaimer
===================================
This is not financial advice.
It is not a signal, a promise, or a guarantee that markets will behave politely while you feel clever. Markets can remain irrational longer than you can remain solvent, especially if you’re trading leverage, emotion, or YouTube confidence.
This outlook is based on historical price behaviour, long-term cycles, and observable market structure. If those conditions change, the thesis changes. Blind loyalty to an idea after the data disagrees isn’t conviction, it’s just stubbornness in a nicer font.
If you’re looking for certainty, reassurance, or someone to blame later, this will disappoint you.
If you’re looking for probabilities, context, and a framework that doesn’t rely on shouting “CRASH” every six months, you're welcome. Ww
SP500 Price Update – Clean Clear ExplanationSP500 Price has broken down from the rising channel, signalling a shift in short-term market structure. After multiple rejections near the upper channel resistance, sellers stepped in aggressively, pushing price below key support.
🔹 Current Structure
Breakdown below mid-range support (~6770)
Failed bullish continuation inside the channel
Momentum turning bearish
🔹 Key Zones
Resistance: 6830 – 6885
Immediate Support: 6720 – 6700
Major Target: 6600 – 6580 (Strong demand zone)
🔹 Expectation
A minor pullback / retest toward the breakdown zone is possible, followed by continuation lower toward 6600 if sellers maintain control.
If you find it helpful please and comments for this post and share thanks.
US500: Beware of a Possible DowntrendUS500: Beware of a Possible Downtrend
US500 found a strong resistance area near 6920, as we can see the price reacting quickly. There is a high possibility that we will have a strong resistance area near the top of the structure, which could push the price back down to the bottom of a larger corrective pattern, with support/target 6500 - 6550.
I don't like to sell indices in these extremely bullish conditions, but you can take this as a possible downside scenario that this move could happen, and it could help you in your trading.
If you want to sell indices, I would only advise you to buy PUT options, considering that the risk is more limited compared to CFD trading.
The price could fall to 6700 and 6500, created by year-end profits.
If the price moves above 6920, this analysis will be invalid.
You may find more details in the chart!
Thank you and Good Luck!
PS: Please support with a like or comment if you find this analysis useful for your trading day
The Retail Trend-Following MythThe Illusion of Simple Profits: A Quantitative Analysis of Moving Average Trend Following Strategies and the Gap Between Retail Mythology and Institutional Reality
The proliferation of retail trading education has created a widespread belief that trend following through moving average crossover systems represents a reliable path to consistent profits. This study challenges that assumption through empirical analysis of over 50,000 backtested strategy configurations across multiple asset classes. Our findings reveal that the simplified trend following approaches promoted in retail trading circles fail to generate statistically significant risk-adjusted returns after accounting for realistic transaction costs.
More critically, we demonstrate that what retail traders understand as trend following bears little resemblance to the sophisticated quantitative approaches employed by institutional trend followers who have historically captured crisis alpha. This paper bridges the gap between retail mythology and institutional reality, providing both a cautionary analysis and a roadmap toward more rigorous trend following methodologies.
1. Introduction
Every year, millions of aspiring traders encounter some variation of the same promise: draw two lines on a chart, wait for them to cross, and watch the profits roll in. The golden cross strategy, where a 50-day moving average crosses above a 200-day moving average to signal a buy, has achieved almost mythological status in retail trading education. YouTube tutorials, trading courses, and social media influencers present these systems as the democratization of Wall Street wisdom, finally making the secrets of the wealthy accessible to ordinary people.
But here is an uncomfortable question that rarely gets asked: if these strategies are so effective and so simple, why do professional trend followers employ entirely different methods? Why do firms like AQR Capital Management, Man AHL, and Winton Group invest millions in research infrastructure when a few moving averages would apparently suffice?
This study was designed to answer that question empirically. We constructed a comprehensive testing framework spanning eight major asset classes, six moving average calculation methods, and multiple strategy configurations including both long-only and long-short implementations. The results paint a sobering picture for anyone who believed that profitable trading could be reduced to watching two lines cross.
Figure 1 displays the distribution of Sharpe ratios across all tested strategy configurations, separated by asset class. The box plots show the median performance (horizontal line), interquartile range (box), and outliers (individual points).
What immediately strikes the eye is how many configurations cluster around or below zero. A Sharpe ratio of zero means the strategy performed no better than holding cash. The wide spread of outcomes, particularly visible in the currency pairs, suggests that any apparent success in trend following may be attributable to luck rather than skill. Notice how even the best performing asset, SPY, shows a median Sharpe ratio barely above 0.3, which institutional investors would consider inadequate for a standalone strategy.
2. Methodology and Data
Our analysis employed daily price data from 2010 through 2024 for the following instruments: SPY representing US equities, GLD for gold, USO for crude oil, SLV for silver, and currency ETFs FXE, FXB, FXY, and FXA representing EUR/USD, GBP/USD, USD/JPY, and AUD/USD respectively. This fourteen-year period encompasses multiple market regimes including the post-financial crisis bull market, the 2015-2016 commodity crash, the COVID-19 volatility event, and the 2022 inflation-driven correction.
We tested six moving average types: Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), Hull Moving Average (HMA), Double Exponential Moving Average (DEMA), and Triple Exponential Moving Average (TEMA). Fast period parameters ranged from 5 to 50 days while slow period parameters ranged from 20 to 200 days, constrained such that the fast period was always shorter than the slow period.
Critically, each configuration was tested in two modes. The long-only mode, which is what most retail traders employ, takes a long position when the trend signal is bullish and exits to cash when bearish. The long-short mode, more common among professional trend followers, takes a long position when bullish and a short position when bearish, maintaining constant market exposure in one direction or the other.
Transaction costs were set at 10 basis points per trade, which is generous compared to what many retail brokers actually charge when accounting for bid-ask spreads, particularly in less liquid instruments. Position changes from long to short incur double the transaction cost since both a sale and a purchase occur.
Figure 2 compares the performance distributions of different strategy modes. Each box represents thousands of backtested configurations. The striking finding here is that long-short strategies, which are theoretically capable of profiting in both rising and falling markets, show worse average performance than their long-only counterparts in most cases. This contradicts the intuition that being able to profit from downtrends should improve overall returns. The explanation lies in the persistence of the equity risk premium during our sample period, combined with the whipsaw costs incurred when strategies repeatedly flip between long and short positions during trendless markets.
3. The Retail Trader Illusion
Before presenting our quantitative findings in detail, it is worth examining what retail traders typically believe about trend following and why those beliefs are so persistent despite limited evidence.
The standard retail narrative goes something like this: markets trend because of herding behavior among participants. Once a trend begins, it tends to continue because traders observe price movement and pile in, creating self-fulfilling momentum. Moving averages smooth out noise and reveal the underlying trend direction. When a faster moving average crosses above a slower one, it confirms that recent price action is stronger than historical price action, signaling the beginning of a new uptrend. The reverse signals a downtrend.
This narrative contains elements of truth but dangerously oversimplifies the challenge. What it omits is far more important than what it includes.
First, it ignores the distinction between trending and mean-reverting market regimes. Research by Hurst, Ooi, and Pedersen (2017) demonstrates that trend following strategies have historically made most of their returns during relatively brief crisis periods while suffering extended drawdowns during calm markets. The 2008 financial crisis was extremely profitable for trend followers. The 2009 to 2019 period was largely a grind. Retail traders who expect consistent monthly returns from trend following will be disappointed and likely abandon the approach precisely when they should be persisting.
Second, the simple crossover story ignores the profound impact of parameter selection. Our analysis tested thousands of parameter combinations. The difference between the best and worst performing parameter sets within the same asset class often exceeded 2 Sharpe ratio points. This creates a severe multiple testing problem. When you test enough combinations, some will appear profitable by chance alone. The probability that the specific combination you choose going forward will perform as well as the historical backtest suggests is remarkably low.
Figure 3 presents a heatmap showing average Sharpe ratios for each combination of moving average type and asset class. Darker blue colors indicate better performance while red indicates worse performance. The pattern is immediately revealing. There is no single moving average type that dominates across all assets. EMA works reasonably for SPY but poorly for currencies. HMA shows promise in gold but disappoints in crude oil. This inconsistency suggests that any apparent edge from a particular MA type may be spurious, resulting from data mining rather than a genuine economic effect. A truly robust strategy should show more consistency across markets.
Third and most importantly, the retail narrative treats trend following as a complete strategy when it is actually just a signal generation method. Professional trend followers embed their signals within comprehensive systems that include volatility scaling, correlation-based position sizing, portfolio construction optimization, and dynamic leverage management. The signal is perhaps ten percent of the system. The retail trader who implements only that ten percent is like someone who buys a car engine and wonders why it does not drive.
4. What Professionals Actually Do
To understand the gap between retail and institutional trend following, we must examine what professional systematic traders actually implement. The following section introduces several key concepts with their mathematical foundations.
4.1 Volatility-Adjusted Position Sizing
Retail traders typically allocate fixed percentages of capital to each trade. Professional trend followers normalize position sizes by volatility so that each position contributes approximately equal risk to the portfolio. The standard approach uses the formula:
Position Size = (Target Risk) / (Instrument Volatility x Price)
Where target risk is often expressed as a fraction of portfolio equity and volatility is typically measured as the annualized standard deviation of returns over a recent lookback period, commonly 20 to 60 days. This approach, documented extensively by Carver (2015), ensures that a position in a highly volatile instrument like crude oil does not dominate the portfolio simply because it moves more.
The mathematical expression for the number of contracts or shares to hold becomes:
N = (k x E) / (sigma x P x M)
Where N is the number of contracts, k is the target risk as a percentage of equity, E is total equity, sigma is the annualized volatility, P is the price, and M is the contract multiplier. This seemingly simple formula has profound implications. It means position sizes change daily as volatility evolves, automatically reducing exposure during turbulent periods and increasing it during calm periods.
4.2 The Time Series Momentum Factor
Academic research by Moskowitz, Ooi, and Pedersen (2012) formalized trend following as time series momentum, distinct from the cross-sectional momentum studied in equity markets. The signal for instrument i at time t is calculated as:
Signal(i,t) = r(i,t-12,t) / sigma(i,t)
Where r(i,t-12,t) is the cumulative return over the past 12 months and sigma(i,t) is the annualized volatility. This creates a standardized momentum measure that can be compared across instruments with very different volatility characteristics.
The position in each instrument is then:
Position(i,t) = Signal(i,t) x (Target Volatility / sigma(i,t))
This double normalization by volatility, once in the signal and once in the position size, is crucial. It prevents the strategy from making large bets simply because an instrument has been moving a lot recently.
4.3 Exponentially Weighted Moving Average Crossover with Trend Strength
A more sophisticated approach to moving average signals incorporates trend strength rather than simple direction. The trend strength measure advocated by Baz et al. (2015) is:
TSMOM = (EWMA_fast - EWMA_slow) / sigma
Where EWMA represents the exponentially weighted moving average with different half-lives and sigma is recent volatility. Rather than generating binary signals, this approach creates a continuous signal that ranges from strongly negative to strongly positive. Positions are scaled proportionally:
Position = sign(TSMOM) x min(|TSMOM|, cap) x base_position
The cap parameter prevents extreme positions when the signal is exceptionally strong, which often occurs during bubbles or crashes when trend followers are most vulnerable to reversals.
4.4 Correlation-Based Portfolio Construction
Perhaps the most significant difference between retail and institutional trend following is portfolio construction. Retail traders typically divide capital equally among instruments or allocate based on conviction. Professionals optimize allocations to account for correlations between positions.
The mean-variance optimization framework determines weights w to maximize:
w'mu - (lambda/2) x w'Sigma w
Subject to constraints on total exposure, sector concentration, and other risk limits. Here mu is the vector of expected returns based on trend signals, Sigma is the covariance matrix of instrument returns, and lambda is a risk aversion parameter.
More advanced implementations use hierarchical risk parity as developed by Lopez de Prado (2016), which clusters instruments by correlation structure and allocates risk equally across clusters rather than instruments. This prevents highly correlated positions from dominating the portfolio.
4.5 Regression-Based Trend Detection: The Statistical Foundation
The most sophisticated trend following approaches employed by quantitative hedge funds move beyond simple price averaging entirely. Instead, they treat trend detection as a statistical inference problem, asking not merely whether prices are rising or falling, but whether the observed price movement represents a statistically significant trend or merely random walk behavior.
The regression-based trend model, implemented by firms such as Winton Group and Man AHL, represents the gold standard in this domain. Rather than smoothing prices through moving averages, this approach fits a linear regression model to price data over a rolling window, extracting both the slope coefficient and its statistical significance.
The mathematical foundation begins with the standard linear regression model:
P(t) = alpha + beta x t + epsilon(t)
Where P(t) represents the price at time t, alpha is the intercept term, beta is the slope coefficient representing the trend strength, t is the time index, and epsilon(t) is the error term assumed to be independently and identically distributed with mean zero and variance sigma squared.
For a rolling window of length L ending at time T, we observe prices P(T-L+1), P(T-L+2), ..., P(T). The ordinary least squares estimator for the slope coefficient is:
beta_hat = sum((t - t_bar) x (P(t) - P_bar)) / sum((t - t_bar)^2)
Where t_bar = (1/L) x sum(t) and P_bar = (1/L) x sum(P(t)) represent the sample means of the time index and prices respectively, with both summations running from t = T-L+1 to t = T.
The numerator represents the covariance between time and price, while the denominator is the variance of the time index. This formulation makes intuitive sense: if prices consistently increase over time, the covariance will be positive, producing a positive slope estimate.
However, extracting the slope alone is insufficient. A positive slope could arise from random walk behavior with an upward drift, or it could represent a genuine trend. To distinguish between these cases, we must assess the statistical significance of the slope coefficient.
The standard error of the slope estimator is:
SE(beta_hat) = sqrt(MSE / sum((t - t_bar)^2))
Where MSE, the mean squared error, is calculated as:
MSE = (1/(L-2)) x sum((P(t) - alpha_hat - beta_hat x t)^2)
The t-statistic for testing the null hypothesis that beta equals zero is:
t_stat = beta_hat / SE(beta_hat)
Under the null hypothesis of no trend, this statistic follows a t-distribution with L-2 degrees of freedom. A large absolute t-statistic indicates that the observed slope is unlikely to have occurred by chance, providing evidence for a genuine trend.
The signal generation mechanism then becomes:
Signal(t) = sign(beta_hat) x min(|t_stat| / t_critical, 1)
Where t_critical is the critical value from the t-distribution at the desired significance level, typically 1.96 for a two-tailed test at the five percent level. This formulation creates a continuous signal that ranges from -1 to +1, with magnitude proportional to both trend strength and statistical confidence.
The position sizing formula incorporates both the slope and its significance:
Position(t) = (beta_hat / sigma_returns) x (|t_stat| / t_critical) x (Target_Volatility / sigma_instrument)
This triple normalization is crucial. The first term, beta_hat / sigma_returns, standardizes the slope by recent return volatility, preventing the strategy from taking large positions simply because prices have been moving rapidly. The second term, |t_stat| / t_critical, scales the position by statistical confidence, reducing exposure when trends are weak or statistically insignificant. The third term, Target_Volatility / sigma_instrument, ensures that each position contributes equal risk to the portfolio regardless of the instrument's inherent volatility.
The multi-horizon ensemble extension, which significantly improves robustness, runs parallel regressions across multiple lookback windows. Common choices include 20, 60, 120, and 252 trading days, corresponding roughly to one month, one quarter, six months, and one year. The final signal becomes a weighted average:
Signal_ensemble(t) = sum(w_i x Signal_i(t))
Where w_i represents the weight assigned to horizon i, typically determined through out-of-sample optimization or equal weighting. Research by Hurst, Ooi, and Pedersen (2017) demonstrates that ensemble approaches reduce the variance of returns by approximately 30 percent compared to single-horizon implementations while maintaining similar mean returns.
The computational efficiency of this approach in modern trading platforms stems from the recursive updating property of linear regression. When moving from window ending at time T to time T+1, we can update the regression statistics without recalculating from scratch:
beta_hat_new = beta_hat_old + delta_beta
Where delta_beta can be computed efficiently using only the new data point and the previous regression statistics. This makes the approach computationally tractable even when applied to hundreds of instruments with multiple lookback windows.
The superiority of regression-based trend detection over moving averages becomes apparent when examining performance during regime transitions. Moving averages, being backward-looking by construction, always lag price movements. A regression model, by explicitly modeling the relationship between time and price, can detect trend changes more rapidly, particularly when combined with significance testing that filters out noise.
Empirical evidence from institutional implementations suggests Sharpe ratio improvements of 0.2 to 0.4 points compared to equivalent moving average systems. However, this improvement comes at the cost of increased complexity and the requirement for statistical software infrastructure that most retail traders lack.
Figure 4 plots Sharpe ratios against Sortino ratios for all strategy configurations. The Sortino ratio, which measures risk-adjusted returns using only downside deviation rather than total volatility, provides insight into whether strategies achieve returns through consistent positive performance or through occasional large gains offset by frequent small losses. Points clustering along the diagonal indicate balanced risk profiles, while points above the diagonal suggest strategies with favorable upside capture relative to downside exposure. The wide scatter in this plot further reinforces the lack of a robust edge in simple moving average systems.
Figures 5a through 5i present heatmaps showing average Sharpe ratios for each combination of fast and slow moving average types, separately for each asset class. These visualizations reveal the extreme parameter sensitivity that plagues retail trend following. Notice how performance varies dramatically across MA type combinations even within the same asset. For SPY, EMA paired with SMA shows reasonable performance, but EMA paired with HMA produces substantially worse results. This inconsistency across what should be similar smoothing methods suggests that any apparent edges are fragile and unlikely to persist out of sample.
Figure 6 shows average Sharpe ratios for different combinations of fast and slow moving average periods. The horizontal axis shows the fast period in days while the vertical axis shows the slow period. Each cell represents the average performance across all assets and MA types for that specific period combination. Notice the inconsistent pattern. There is no clear sweet spot where performance is reliably strong. Some period combinations that work well in certain market conditions fail completely in others. This lack of a robust optimal parameter region is a warning sign that the apparent edges we observe may be artifacts of our specific sample period rather than persistent market inefficiencies.
5. Empirical Results
Our research produced sobering results for the retail trend following thesis. Across 51,840 unique strategy configurations, the mean Sharpe ratio was 0.18 with a standard deviation of 0.42. Only 23 percent of configurations produced Sharpe ratios above 0.5, which is generally considered the minimum threshold for a viable strategy. A mere 8 percent exceeded 1.0.
Figure 7 presents the optimal parameter combination identified for each asset class through our grid search optimization. While these numbers may appear attractive in isolation, they must be interpreted with extreme caution. These are in-sample optimized results, meaning we selected the best performing parameters after observing all the data. The probability that these exact parameters will produce similar results going forward is low. Academic research consistently shows that out-of-sample performance degrades by 50 percent or more compared to in-sample optimization (Moskowitz, Ooi, and Pedersen, 2012).
The asset class breakdown reveals further challenges. Equity index trend following in SPY produced the most consistent results, with a best Sharpe ratio of 0.87 for the dual moving average long-only strategy using EMA with 10 and 75 day periods. Currency pairs performed substantially worse, with best Sharpe ratios ranging from 0.31 to 0.52. Commodities fell in between, with gold showing 0.68 and crude oil at 0.54.
These results align with the academic literature. Moskowitz, Ooi, and Pedersen (2012) document significant time series momentum profits in equity index futures but weaker effects in currencies. The explanation likely relates to central bank intervention in currency markets, which can abruptly reverse trends, and the generally higher efficiency of currency markets where large institutional participants dominate.
Figure 8 compares the performance distributions of different moving average calculation methods. Each box plot represents thousands of configurations using that specific MA type. The most striking finding is the absence of a clearly superior method. Simple Moving Average, the most basic calculation, performs comparably to sophisticated alternatives like Hull Moving Average or Triple Exponential Moving Average. This undermines the popular belief that exotic MA types provide meaningful edges. In fact, more complex calculations introduce additional parameters that create more opportunities for overfitting.
The long-short versus long-only comparison yielded counterintuitive results. Conventional wisdom suggests that long-short strategies should outperform because they can profit in both directions. Our data shows the opposite in most cases. The long-short configurations produced mean Sharpe ratios of 0.12 compared to 0.24 for long-only. This approximately fifty percent reduction reflects two factors: the persistent upward drift in equity markets during our sample period, and the transaction costs incurred when strategies flip between long and short positions during trendless periods.
Figure 9 plots each strategy configuration by its maximum drawdown on the horizontal axis and its compound annual growth rate on the vertical axis. Each dot represents one backtested configuration, color-coded by asset class. The ideal positions would be in the upper right, showing high returns with shallow drawdowns. Instead, we observe a cloud of points with no clear relationship between risk and return at the strategy level. Many configurations that achieved high returns also suffered devastating drawdowns exceeding fifty percent. Conversely, strategies with modest drawdowns rarely exceeded single-digit annual returns. This lack of a favorable risk-return tradeoff suggests that trend following, as implemented in these simple forms, does not offer a free lunch.
6. Statistical Significance Testing
To address the multiple testing problem inherent in evaluating thousands of strategy configurations, we applied rigorous statistical tests. One-way ANOVA comparing Sharpe ratios across MA types produced an F-statistic of 2.34 with a p-value of 0.038. While technically significant at the five percent level, the effect size is tiny, explaining less than one percent of variance in outcomes. This suggests that MA type selection, despite the emphasis it receives in retail education, contributes almost nothing to strategy performance.
The non-parametric Kruskal-Wallis test, which makes no assumptions about the distribution of returns, confirmed this finding with an H-statistic of 11.2 and p-value of 0.047. Pairwise t-tests with Bonferroni correction for multiple comparisons found no statistically significant differences between any specific pair of MA types after adjustment.
Figures 10a through 10f break down performance by both strategy mode and asset class, allowing us to examine whether long-short strategies outperform long-only in any specific market. The answer is predominantly negative. Only in crude oil does the long-short approach show a meaningful advantage, likely reflecting the extended downtrend in oil prices during 2014-2016 and the COVID crash in 2020. For equities and currencies, long-only strategies dominate. This finding should give pause to retail traders who believe that adding short selling capability automatically improves their systems.
Figure 11 displays the twenty best-performing parameter combinations for the SPY equity index, ranked by Sharpe ratio. What immediately stands out is the diversity of configurations that achieved similar performance levels. The top entry uses EMA with periods 10 and 75, but configurations using SMA with periods 15 and 100, or WMA with periods 20 and 150, also appear in the top tier. This parameter space flatness, where many different combinations produce comparable results, is actually a positive sign. It suggests that the strategy may be somewhat robust to parameter selection, at least within certain ranges. However, the fact that the best Sharpe ratio barely exceeds 0.9, and that this represents in-sample optimization, means that out-of-sample performance will likely degrade substantially.
Figures 12a through 12e compare strategy performance across the four currency pairs tested: EUR/USD, GBP/USD, USD/JPY, and AUD/USD. The results are uniformly disappointing. No currency pair produced a best Sharpe ratio above 0.6, and the median performance across all configurations hovers near zero. This aligns with academic research showing that currency markets, being highly efficient and dominated by large institutional participants, offer fewer exploitable trends than equity or commodity markets (Moskowitz, Ooi, and Pedersen, 2012). The frequent intervention by central banks, which can abruptly reverse currency trends, further complicates trend following in this asset class. Retail traders who attempt to apply equity market trend following techniques directly to currencies without understanding these structural differences are likely to experience frustration.
Figures 13a through 13c examine performance in the three commodity instruments: gold, crude oil, and silver. Gold shows the strongest results, with a best Sharpe ratio of 0.68, while crude oil and silver both cluster around 0.5. The superior performance in gold may relate to its dual role as both a commodity and a monetary asset, creating more persistent trends than pure industrial commodities. However, even gold's best configuration falls short of what institutional investors would consider acceptable for a standalone strategy. The wide dispersion of outcomes within each commodity, visible in the heatmaps, further emphasizes the parameter sensitivity problem that plagues these approaches.
Figure 14 presents a detailed sensitivity analysis showing how strategy performance varies with the choice of fast and slow moving average periods for the SPY equity index. The subplots display the mean Sharpe ratio, with error bars showing one standard deviation, for different period choices. The fast period sensitivity shows performance peaking around 10 to 15 days, then declining as the period increases. The slow period sensitivity reveals a more complex pattern, with local optima around 75 and 150 days. However, the error bars are substantial, indicating high variance in outcomes. This uncertainty in optimal parameter selection is precisely why institutional traders employ ensemble methods rather than attempting to identify a single best configuration.
Figures 15a through 15c display histograms showing the distribution of key performance metrics across all strategy configurations. The Sharpe ratio distribution reveals a roughly normal shape centered slightly above zero, with a long tail extending to positive values. The maximum drawdown distribution shows that a substantial fraction of configurations experienced drawdowns exceeding 30 percent, with some exceeding 50 percent. The win rate distribution clusters around 45 to 55 percent, indicating that most configurations are only slightly better than random. These distributions collectively paint a picture of strategies that occasionally produce attractive risk-adjusted returns but more often produce mediocre or negative results, with significant tail risk in the form of large drawdowns.
7. Alternative Professional Trend Following Methodologies
Beyond regression-based approaches, institutional trend followers employ several other sophisticated techniques that bear little resemblance to retail moving average systems. Understanding these methods provides insight into the true complexity of professional trend following.
The Hodrick-Prescott filter, originally developed for macroeconomic time series analysis (Hodrick and Prescott, 1997), decomposes price series into trend and cyclical components through a penalized least squares optimization. The trend component T(t) minimizes:
sum((P(t) - T(t))^2) + lambda x sum((T(t+1) - T(t)) - (T(t) - T(t-1)))^2
Where lambda is a smoothing parameter, typically set to 129,600 for daily data. The first term penalizes deviations from the observed price, while the second term penalizes changes in the trend's growth rate, creating a smooth trend estimate. Trend following signals are generated when the filtered trend changes direction, with position sizes scaled by the magnitude of the trend acceleration. This approach, while computationally intensive, produces smoother signals than moving averages and reduces false breakouts during choppy markets.
Donchian channel breakouts, while conceptually simple, become sophisticated when implemented as multi-horizon ensembles with volatility scaling. Rather than using fixed 20-day or 55-day channels as retail traders do, professional implementations simultaneously monitor breakouts across 20, 50, 100, and 200-day channels. Signals are weighted by the channel width relative to recent volatility, with wider channels relative to volatility producing stronger signals. The ensemble signal becomes:
Signal = sum(w_i x (P(t) - Channel_Low_i) / (Channel_High_i - Channel_Low_i))
Where w_i are horizon-specific weights optimized through walk-forward analysis. This multi-timeframe approach captures trends operating at different scales simultaneously, a crucial advantage over single-horizon methods.
Ehlers filters, developed specifically for trading applications (Ehlers, 2001), use advanced digital signal processing techniques to extract trends while minimizing lag. The Super Smoother filter, for example, applies a two-pole Butterworth filter with adaptive cutoff frequency based on market volatility. The mathematical formulation involves complex frequency domain transformations that are beyond the scope of this paper, but the key insight is that these filters are designed to respond quickly to genuine trend changes while filtering out noise, achieving a better trade-off between responsiveness and stability than traditional moving averages.
The CUSUM drift detector provides a statistical framework for identifying regime changes (Page, 1954). The cumulative sum statistic is calculated as:
S(t) = max(0, S(t-1) + (r(t) - k))
Where r(t) is the return at time t and k is a drift parameter, typically set to half the expected return during a trend. When S(t) exceeds a threshold h, a trend is declared. This approach has the advantage of providing explicit statistical control over false positive rates, unlike moving average crossovers which have no such theoretical foundation.
Each of these methods addresses specific weaknesses in simple moving average approaches. Regression-based methods provide statistical significance testing. HP filters produce smoother trends. Donchian ensembles capture multi-scale trends. Ehlers filters minimize lag. CUSUM detectors provide statistical rigor. Professional implementations typically combine multiple methods, weighting their signals based on recent performance and market regime indicators.
Figure 16 conceptually illustrates the difference between retail and professional trend following. The retail approach, represented by a simple moving average crossover, produces binary signals with no statistical foundation and consists of merely four steps: price data, MA calculation, crossover detection, and trade execution. The professional approach incorporates seven distinct processing stages: multi-asset data ingestion, multiple parallel signal generators (regression-based, multi-horizon ensemble, and DSP filters), statistical significance testing and signal aggregation, volatility scaling and dynamic position sizing, correlation-based portfolio construction, risk limits and drawdown controls, and finally trade execution. The key insight is that professional trend following is not merely a more sophisticated version of retail trend following, but an entirely different approach that happens to share the same name.
8. The Path Forward
If simple moving average strategies fail to deliver consistent risk-adjusted returns, what alternatives exist for traders seeking systematic trend following approaches?
The first step is accepting that profitable trend following requires substantially more infrastructure than drawing two lines on a chart. The successful systematic trading firms operate research teams, maintain massive databases of historical prices, and continuously refine their models. They accept that any given strategy may underperform for years while maintaining confidence in the long-term statistical edge.
For individual traders without institutional resources, several paths remain viable. The first is specialization. Rather than attempting to trade multiple asset classes with a single methodology, focus on deep understanding of one market. The inefficiencies that persist today are subtle and require expertise to exploit.
The second is ensemble approaches. Rather than selecting one MA type and one parameter combination, implement multiple variations and combine their signals. This diversification across methodologies reduces the variance of outcomes and the dependence on any single backtest.
The third is incorporation of additional factors. Pure price trend is just one source of potential edge. Professional trend followers combine momentum signals with carry, the interest rate differential across currencies, with value measures, and with volatility signals. Academic research by Hurst, Ooi, and Pedersen (2017) demonstrates that multi-factor approaches produce more stable returns than any single factor in isolation.
The fourth and perhaps most important path is realistic expectation setting. Even the most successful trend following funds experience extended drawdowns and periods of underperformance. The AQR Managed Futures Strategy Fund, one of the largest trend following vehicles available to retail investors, lost money in 2009, 2010, 2011, 2012, 2016, 2017, 2018, and 2021. Seven losing years out of thirteen. Yet the strategy remains viable because the winning years, particularly 2008 and 2022, produced exceptional returns that more than compensated.
9. Conclusion
This study systematically evaluated over fifty thousand configurations of moving average trend following strategies across multiple asset classes, MA types, and trading modes. The results conclusively demonstrate that the simple approaches promoted in retail trading education fail to produce reliable risk-adjusted returns after accounting for transaction costs and multiple testing biases.
The gap between what retail traders believe about trend following and what professional systematic traders actually implement is vast. Retail approaches treat the entry signal as the complete system. Professional approaches treat the signal as merely one component within a sophisticated framework encompassing position sizing, portfolio construction, risk management, and execution optimization.
This does not mean that trend following is without merit. Academic research documents persistent time series momentum across asset classes over multi-decade periods. Crisis alpha, the tendency of trend followers to profit during market dislocations, provides genuine diversification benefits for portfolios otherwise exposed to equity risk. The strategy has a legitimate economic basis in the behavioral tendencies of market participants to underreact to information initially and overreact subsequently.
However, capturing this edge requires moving beyond the oversimplified frameworks that dominate retail education. It requires accepting that profitable trading is difficult, that edges are small and unstable, and that consistent success demands continuous adaptation and rigorous analysis.
The trader who approaches markets with humility, armed with statistical tools rather than certainty, stands a far better chance than one who believes two moving average lines hold the secret to wealth. No evidence, no trade. That principle, applied ruthlessly to every strategy and every assumption, separates the survivors from the casualties in the long game of systematic trading.
References
Baz, J., Granger, N., Harvey, C.R., Le Roux, N. and Rattray, S. (2015) 'Dissecting Investment Strategies in the Cross Section and Time Series', Working Paper, Man AHL.
Carver, R. (2015) Systematic Trading: A Unique New Method for Designing Trading and Investing Systems. Petersfield: Harriman House.
Ehlers, J.F. (2001) Rocket Science for Traders: Digital Signal Processing Applications. New York: John Wiley and Sons.
Hodrick, R.J. and Prescott, E.C. (1997) 'Postwar U.S. Business Cycles: An Empirical Investigation', Journal of Money, Credit and Banking, 29(1), pp. 1-16.
Hurst, B., Ooi, Y.H. and Pedersen, L.H. (2017) 'A Century of Evidence on Trend-Following Investing', Journal of Portfolio Management, 44(1), pp. 15-29.
Lopez de Prado, M. (2016) 'Building Diversified Portfolios that Outperform Out of Sample', Journal of Portfolio Management, 42(4), pp. 59-69.
Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', Journal of Financial Economics, 104(2), pp. 228-250.
Page, E.S. (1954) 'Continuous Inspection Schemes', Biometrika, 41(1/2), pp. 100-115.
From QE to QT. Reading the Fed’s Cycle from the ChartQuantitative Easing (QE) is when the Federal Reserve buys large amounts of Treasuries and mortgage‑backed securities to expand its balance sheet, inject liquidity, and push interest rates lower across the curve.
Quantitative Tightening (QT) is the opposite: the Fed allows its bond holdings to roll off or sells securities, shrinking the balance sheet and tightening financial conditions.
QE near zero rates
Historically the Fed has only launched QE when the policy rate was pinned near zero and conventional rate cuts were basically exhausted, as in 2008–2014 and again in 2020–2022.
QT at elevated rates
By contrast, QT has been used only once the Fed had already hiked rates to clearly positive, “elevated” levels and wanted to normalize the balance sheet from those earlier QE waves.
What ending QT in December could imply
QT effectively ended around 1 December, it suggests the Fed may feel comfortable pausing balance‑sheet tightening while rates are still high, opening the door later to cuts if growth or markets weaken.
In that setting, the market could start to price a shift from outright restriction toward neutrality, which often coincides with more two‑sided volatility in risk assets.
Echoes of the QT1 → QE3 window
The period after QT1 and before QE3 saw rates come off their highs and then a major shock (COVID-18 crysis) that helped justify easier policy again.
A similar path is plausible here: a “black swan” type event in the coming year could hit growth or credit, force a rapid drop in rates, and trigger a new QE‑style response that would rhyme with the QT1‑to‑QE3 sequence your chart visually captures.
S&P 500 index at PRZ — Next Bullish Rally!!!In general, I place significant importance on the S&P 500 index( SP:SPX ), especially over the past month, because of its strong correlation with the crypto market, particularly Bitcoin( BINANCE:BTCUSDT ). When sharp movements occur in the S&P 500 index, we often see a mirrored effect in the crypto market and Bitcoin’s chart.
Currently, the S&P 500 index is moving near the support zone($6,776_$6,712) and the Potential Reversal Zone(PRZ) , and it appears to have successfully broken the upper line of the descending channel, which is a positive sign for a continued bullish trend in the coming days.
From an Elliott Wave perspective, it seems that the S&P 500 has completed a zigzag correction(ABC/5-3-5) within the descending channel, and we can expect an upward wave towards the resistance zone($6,853_$6,823).
I expect that the S&P 500 will begin to rise again from the Potential Reversal Zone(PRZ) and could climb at least up to $6,816. If it breaks resistance zone($6,853_$6,823), we can expect even more gains, which can also positively impact the broader markets.
What’s your outlook on the S&P 500 index and the U.S. stock market?
First Target: $6,816
Second Target: $6,834
Stop Loss(SL): $6,739(Worst)
Note: During U.S. trading hours, market volatility and emotions tend to increase. Please make sure to apply strict risk and capital management.
💡 Please respect each other's opinions and express agreement or disagreement politely.
📌S&P 500 Index Analyze (SPX500USD), 1-hour time frame.
🛑 Always set a Stop Loss(SL) for every position you open.
✅ This is just my idea; I’d love to see your thoughts too!
🔥 If you find it helpful, please BOOST this post and share it with your friends.
SP 500 abc decline has ENDED wave a=c to a .382 pullback 3 UPThe chart posted is the sp 500 I am calling the decline as Over and wave 3 up to start waves and c are equal and the drop was .382 > I now look for the santa rally to start in wave 3 up it should be .618 of wave 1 wave 3 should now see 6996 plus or minus 5. best of trades WAVETIMER I am NOW long calls at 75 %
S&P500 Will it have a big correction in 2026 back to 5500?The S&500 (SPX) has been trading within a massive 16-year Channel Up since the 2008 U.S. Housing Crisis. Within this pattern it has been repeating various shorter fractals as you can see on this chart it is one that truly stands out.
That's the necessity of the market to correct back to its 1W MA200 (orange trend-line) every time it reaches a Top after an exhaustion rally. With the 1W RSI on a Lower Highs Bearish Divergence (against the price's Higher Highs), there is no better time to consider a market top, thus a strong correction, especially after such a non-stop exhaustion rally since the April 2025 Low.
Based on the 1W MA200 trajectory, we make a fair estimate that contact can be achieved around the 5500 level, which will be our next long-term buy on stocks. Alternatively, if the 1W RSI approaches the 30.00 oversold level, without the index touching 5500, it will be a good idea to Buy regardless of the price.
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More upside again for SPX500USDHi traders,
Last week SPX500USD started the correction down to fill the Weekly bullish FVG just as I've predicted.
Next week we could see the correction finish inside the bullish Weekly/ Daily FVG (confluence 38.2 fib retrace) and after it could go up again.
Let's see what the market does and react.
Trade idea: Wait for the correction down to finish. After a change in orderflow to bullish you could trade longs.
This shared post is only my point of view on what could be the next move in this pair based on my technical analysis.
But I react and trade on what I see in the chart, not what I've predicted or expect.
Manage your emotions, trade your edge!
Eduwave
Session Structure on SPX — Reading Pullbacks Inside the Range
Session Structure — Price Action Framework (SPX 5-Minute Example)
“This structure is automatically mapped by my private Session Structure tool, but shown manually here to comply with TradingView’s public posting rules.”
This indicator is designed for price action traders who want a clean, objective way to visualise session structure, support/resistance, and measured pullbacks — without signals or clutter.
What the indicator does
Automatically plots a session range box from a user-defined start time
Calculates internal pullback levels (33% / 50% / 66%)
Provides a simple framework for assessing:
Where price is accepting
Where it is being rejected
Where risk can be defined cleanly using candles + structure
The indicator does not generate trade signals — it simply provides clear reference levels so traders can focus on reading price.
Chart context (image example)
Instrument: SPX
Timeframe: 5-minute
Session range begins at the user-defined session start
Custom EMAs enabled (20 & 200 shown)
After the first few candles, a range begins to form. Once the initial range is established, price starts trading sideways, repeatedly finding resistance between the 50% and 66% pullback levels.
This zone quickly becomes a clearly defined resistance area.
Reading the price action
After the first 6 candles, price stalls and begins to rotate
Multiple candles fail to close above the 50–66% pullback zone
Bar 10 closes below the low of Bar 5, signalling weakness
Bar 11 then pulls back into the same 50–66% zone and rejects again
At this point:
Structure is clear
Resistance is respected
Risk is well defined
This creates a high-quality price-action short setup, using candles and structure — not indicators.
Trade management example (illustrative)
Entry:
Conservative: on the close of Bar 13
Aggressive: inside the 50–66% pullback zone once rejection is clear
Stop loss:
Above the high of Bar 2 (also above the 200 EMA)
Outcome:
1:1 reached by Bar 15
1:2 reached by Bar 17
The indicator doesn’t tell you when to enter — it simply makes the structure obvious so you can make those decisions confidently.
Why this works
Price is reacting to measured levels, not arbitrary lines
Support and resistance are defined by the session itself
Candles confirm acceptance or rejection at known areas
Risk-to-reward can be assessed before entering
Additional features
Custom session start time and timezones (DST-aware)
Adjustable range size (number of candles)
Range box can be:
Fixed
Extended
Continuous (full-day structure tracking)
Custom EMA lengths
ATR HUD (bottom-right) to help with:
Scalp sizing
Stop-loss context
Close-above / close-below EMA tracking for bias awareness
Optional alerts for:
Range breaks
Pullback level interactions
Designed primarily for 5-minute charts, but adaptable to other intraday timeframes.
SPX500-Patience Mode Waiting for a Valid Break Above 6,820As long as price remains below the 6,820 area, we are not buyers.
At the same time, we are not sellers either, since no clear trend reversal has occurred yet.
For now, there is no additional trade setup to focus on.
We will wait for a clean break above resistance, and only then start looking for long entry triggers.
TikTok Becomes a US Company, Stock Market Favors It!
The news of TikTok's US joint venture boosted the stock market. With over two billion users having already downloaded the once-unknown Chinese short-video sharing app, this impressive number may well save the app from a ban in the US. According to a CNBC report on Friday, TikTok is already banned in Canada and was temporarily banned in the US due to national security concerns. Now, it will continue its US operations through a joint venture with Oracle and other companies.
The app is practically a staple for Generation Z and has popularized viral social media trends such as dance challenges, lip-syncing, and role-playing skits. But its parent company, ByteDance, is a Chinese company and must comply with Beijing's infamous 2017 National Intelligence Law, which forces Chinese companies to assist the government in safeguarding national security. Article 7 of the law states, "All organizations and citizens shall, in accordance with the law, support, assist, and cooperate with national intelligence work, and shall keep confidential any secrets of national intelligence work that they become aware of." This 1984-esque language alarmed the US and Canada, both of which have banned ByteDance from operating in their countries. But Trump, a businessman, managed to delay the US ban and brokered an agreement that ultimately led to the formation of the US joint venture, which included Texas tech giant Oracle, California private equity firm Silver Lake, Abu Dhabi investment firm MGX, and several other investors.
The new investment partner will own 50% of what is now called TikTok USDS Joint Venture LLC. Oracle surged on the news, fueling Friday's stock market rally, with both its stock and Bitcoin rising. The stock's rise may have influenced the subsequent rise in BTC prices, although this connection may be purely coincidental.
FAQ ⚡ Why did TikTok's stock rise after the news?
TikTok's stock rose after the announcement of the US joint venture, easing regulatory concerns and boosting risk sentiment.
What changes have occurred to TikTok's status in the US?
TikTok will continue to operate in the US through the new joint venture, involving Oracle and other US investors.
Why is TikTok at risk of being banned?
US officials cited national security concerns related to China's intelligence law, which regulates ByteDance.
Did the TikTok deal directly drive the stock market rise?
It's not a direct impact, but the rise in stocks may have improved overall market sentiment and drawn into the Bitcoin market.
The SPX500 and Bitcoin are going to crash.Why?
This is a messy chart, but the economy is simple.
Japan's interest rate hike and the U.S. interest rate drop mean a decline is inevitable,
especially with high margin debt in equities.
Everything is on the verge of a major mid-term crash. This is not financial advice.
SPX...time to buySPX 500 is in a clear upwards channel and has broken the last bit of resistance (white trendline line shown) - this is a clear confirmation that the next target will be the next resistance zone to the upside shown above (this is a great buy trade opportunity) - time to buy the SPX 500 now
SP500 Price Update – Clean & Clear ExplanationSP500 highlighting key supply & demand zones, current price structure, and a bullish continuation bias Price recently reacted from a strong demand zone around 6,760–6,720, forming a higher low and indicating buyer strength. The market is currently consolidating just above this demand area, suggesting accumulation before the next directional move.
A bullish scenario is projected price is expected to push higher toward the 6,880–6,900 resistance zone a successful breakout and retest could open the path toward the upper supply zone near 6,950–6,980 the projected move aligns with prior liquidity highs and unfilled imbalance areas
A deeper pullback into the lower demand zone could offer a better risk-to-reward long opportunity the structure favours buy-the-dip setups as long as price holds above demand, with momentum expected to resume to the upside toward higher liquidity levels.
If you find it helpful please like and comments for this post and share thanks.
SPX500 | Bulls Target 6888 as Futures Tick HigherSPX500 – Technical Overview
S&P 500 futures opened the week in positive territory, rising around 0.3% as traders cautiously regain confidence after last week’s tech-driven turbulence.
While early strength is encouraging, markets are still testing whether this momentum can sustain into a broader risk-on trend.
Technical Analysis
SPX500 continues to show bullish momentum, with price pushing toward 6888.
A break and 1H close above 6888 is required to confirm continuation toward a new all-time high, with the next major target at: → 6918
However, if price closes a 1H candle below 6852, bearish pressure may return, triggering a corrective move toward: → 6815 → 6771
The zone between 6852 and 6888 remains the key intraday decision area.
Pivot Line: 6852
Support: 6815 · 6771
Resistance: 6888 · 6918
Caution: Cash Levels Among Fund Managers Are at Record LowsAccording to the latest Global Fund Manager Survey conducted by Bank of America, the percentage of cash held by fund managers has fallen to 3.3%, the lowest level since 1999. In terms of asset allocation, historically low cash levels among managers have often coincided with peaks in equity markets. Conversely, periods when cash levels reached elevated zones were frequently precursors to major market bottoms and to the end of bear markets.
At a time when S&P 500 valuations are in an overextended bullish zone, this new historical low in cash holdings among managers therefore constitutes a signal of caution. Sooner or later, cash levels are likely to rebound, which would translate into downward pressure on equity markets. This reflects the basic principle of asset allocation between cash, equities, and bonds, with capital flowing from one reservoir to another. It is the fundamental mechanism of asset allocation: the reservoirs represented by cash, equities, and bonds fill and empty at the expense of one another.
This signal is all the more significant because such a low level of cash implies that managers are already heavily invested. In other words, the vast majority of available capital has already been allocated to equities. In this environment, the pool of marginal buyers shrinks considerably, making the market more vulnerable to any negative shock: macroeconomic disappointment, a rise in long-term interest rates, geopolitical tensions, or even simple profit-taking.
Moreover, historically low cash levels reflect an extreme bullish consensus. Financial markets, however, tend to move against overly established consensuses. When everyone is positioned in the same direction, the risk-reward balance deteriorates. In such cases, the market does not necessarily need a major negative catalyst to correct; the mere absence of positive news can sometimes be enough to trigger a consolidation.
It is also important to recall that the rise in the S&P 500 has been accompanied by an extreme concentration of performance in a limited number of stocks, mainly related to technology and artificial intelligence. In such an environment, a simple portfolio rebalancing or sector rotation can amplify downward moves.
Finally, the gradual return of cash typically does not occur without pain for equity markets. It is often accompanied by a phase of increased volatility, or even a correction, allowing a healthier balance to be restored between valuations, positioning, and economic prospects.
In summary, this historically low level of cash among fund managers is not a signal of an imminent crash, but it clearly calls for caution, more rigorous risk management, and greater selectivity within the S&P 500, in an environment where optimism appears to be largely priced in.
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SPX500 Faces Bearish Pressure Below Key Pivot 6838SPX500 – Technical Overview
The market reversed back into the pivot zone after dropping nearly 400 points.
For now, the structure remains bearish while price trades below 6838, with continuation expected toward:
→ 6815
→ 6772
A shift toward a bullish correction requires a 1H candle close above 6838, which would open the path toward:
→ 6863
→ (extended resistance) 6888
Momentum will remain sensitive as markets digest recent macro data and volatility stays elevated.
Pivot Line: 6838
Support: 6815 · 6775 · 6735
Resistance: 6862 · 6888
S&P 500 "tech wreck": Double top breakdown targets 6,500?The S&P 500 has sliced through its 50-day moving average and paused by 50% Fibonacci retracement following a fourth straight day of losses. With a confirmed double top at 6,930 and momentum shifting bearish, we’re eyeing a move back to the range lows at 6,500.
In this video, we break down the impact of the "tech wreck" and Fed Governor Waller’s mixed signals on 2026 rate cuts, which have triggered a risk-off sentiment. Then, we outline a short setup selling the bounce into 6,765–6,800, targeting the November 21 lows.
Key drivers
Technical Breakdown : The index has broken below its 50-day moving average and the 50% Fibonacci retracement (6,725), confirming bearish momentum from a double top structure.
"Tech Wreck" & Macro : High-value tech stocks and crypto sold off sharply yesterday, exacerbated by Fed Governor Waller’s caution on "hurrying up" rate cuts despite inflation risks.
Range Structure : The S&P 500 is trading within a rectangular range between ~6,930 (highs) and 6,500–6,520 (lows). We are currently in the middle of this range with a downside bias.
Short Setup : We are looking to sell a retracement to the 38.2% Fib / prior low (~6,765–6,800) rather than chasing the breakdown due to the 4-hour RSI shift.
Trade Plan : Entry around 6,765, stop loss above 6,830 (23.6% Fib), targets at 6,600 and ultimately 6,500. Risk/reward is favourable at 1.7+.
Are you selling the tech sell-off or waiting for support? Share your levels in the comments and follow for more technical swing setups.
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