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XAUUSD Breakout Loading! Explosive Move Incoming?🚨 Market Situation TVC:GOLD Gold is currently trading inside a tight compression (triangle structure), with price approaching a key descending resistance trendline. After multiple higher lows, buyers are slowly building pressure — a classic sign of a potential breakout setup. 💡 Compression = Expansion coming soon 🎯 Key Levels (Must Watch) 🔴 Resistance Levels: → 4726 (First resistance) → 4805 (Second resistance / major target) 🟢 Support Zone: → 4387 (Key structural support) 🚀 Trade Idea 👉 Break above trendline → Expect bullish expansion toward 4726 & 4805 👉 Rejection from resistance → Possible short-term pullback ⏳ Wait for breakout confirmation — avoid false moves ⚠️ Disclaimer This is for educational purposes only. Not financial advice. Always manage your risk. #XAUUSD #GoldTrading #ForexTrading #PriceAction #TechnicalAnalysis #TradingView #BreakoutSetup 🔥 If this setup helped you: 👍 Like 💬 Comment your bias (Breakout or Rejection?) ⭐ Follow for more high-probability setups Charts Don’t Lie — Traders Don’t Quit. 📉🔥
FX:XAUUSDLong
by KABHI_TA_TRADING
16
EURUSD is showing signs of a bullish trend breakdown💱 EURUSD – Bearish Breakdown Opportunity 🔻📉 EURUSD is showing signs of a bullish trend breakdown on the 1-hour timeframe ⏱️ and is now reacting from a key supply zone around 1.15200 — potential short setup in play. 💡 Trade Setup: 🔹 Entry Zone: 1.15200 (Supply Area) 🎯 Target 1: 1.15000 🎯 Target 2: 1.14800 🎯 Target 3: 1.14500 ⚠️ Risk Management First! Use proper lot sizing, set your stop loss, and avoid emotional trading. Consistency comes from discipline 💼 📊 Wait for confirmation (bearish structure / rejection) before entering. Trade what you see, not what you feel! 👍 Like | 💬 Comment | 🔁 Share Follow for more professional forex setups and market insights 📊📉
Short
by ExpertTraderASK
6
EURUSD Short: Bearish Channel Holds - Price Target 1.1470 DemandHello traders! Here’s my technical outlook based on the current EURUSD (1H) chart structure. EURUSD moved from a consolidation range into a descending channel, forming lower highs and confirming bearish pressure. After a brief range, price was rejected from a pivot high and continued lower. Currently, price is trading below the 1.1530 supply zone and moving toward the 1.1470 demand zone while staying inside the descending channel. As long as EURUSD remains below 1.1530 and respects the channel, the bearish bias remains valid. A move lower toward 1.1470 is the main scenario. If price breaks above 1.1530, the bearish setup weakens and a consolidation phase may follow. Manage your risk!
Short
by heniitrading
20
Lingrid | GOLD Weekly Market Analysis: Rebound Under PressureOANDA:XAUUSD has staged a technical rebound from key support levels following a sharp correction, yet the broader market structure remains under significant pressure from rising real yields and a resurgent US dollar. While the recent escalation in US-Iran tensions initially sparked safe-haven flows, the precious metals complex has largely retreated. Technically, the chart reveals a price action is currently coiling within a potential rising wedge pattern situated inside the overarching major bearish channel. Following the decisive breakdown from the previous triangle, the recent bounce appears to be a corrective retracement rather than a trend reversal. The chart shows a possible bearish continuation from the apex of this current wedge formation, with a downside target near 4,230. Immediate resistance looms near the 4,700 level, where the pattern's upper trendline converges with previous support-turned-resistance, offering a high-probability zone for bearish entries. Fundamental catalysts this week align with this bearish technical outlook. The economic calendar features high-impact US data, including ADP Non-Farm Employment, Core Retail Sales, and ISM Manufacturing PMI. Strong readings in these reports would likely reinforce the Federal Reserve's hawkish stance, boosting the dollar and pressuring gold further. We should watch for a breakdown below as confirmation to target the 4,230 support zone, while a sustained move above 4,700 would be required to invalidate the immediate bearish thesis. Traders, if you like this idea, please leave your thoughts in the comments. I look forward to reading your ideas!
OANDA:XAUUSDShort
by Lingrid
23
GBP/CAD – Triangle Rejection & Bearish ContinuationGBP/CAD is currently showing a clear rejection from a strong supply zone combined with a failed triangle breakout, indicating growing bearish pressure. Price attempted to push higher multiple times but sellers defended the resistance area aggressively. The structure shows lower highs forming after rejection, suggesting the bullish momentum is weakening. With price now moving below the ascending trendline, the market is likely preparing for a bearish continuation toward lower demand zones. If momentum increases, we could see a strong downside move toward the next psychological support levels. FX:GBPCAD 🔎 Technical Overview : • Ascending triangle structure broken • Multiple rejections from strong supply zone • Bearish momentum building • Lower highs forming after rejection • Downside targets clearly defined As long as price stays below the key supply zone, the bearish outlook remains valid. A strong breakout above resistance could invalidate the bearish scenario. 👍 Support This Analysis If you found this idea useful: 👍 Like this analysis 💬 Drop your view in the comments – Bullish or Bearish? 🔔 Follow for more high-probability setups Your support helps grow and share valuable trading insights 🚀 ⚠️ Disclaimer This content is for educational purposes only
Short
by AM_AlphaTrading
12
#GBPCHF: Bearish Move Is Likely To Continue! ✴️ The GBPCHF pair recently established a new two-day low at 1.0300. Subsequently, the price exhibited a bullish trend, reaching the significant resistance level of 1.06500. Upon touching this point, the price initiated a reversal on the one-day timeframe. ✴️ We anticipate a continued downward trajectory, targeting 0.9800. This movement suggests a potential market dominance by CHF, as investors seek a more secure currency option. ✴️ The optimal entry point is identified at the current trading price of 1.055, with a stop-loss positioned above 1.075. We advise implementing precise risk management strategies and refraining from utilising large lot sizes. 🥇 Entry Criteria 🔺Sell Entry At: 1.055-1.065 🔺Stop Loss At: 1.080 🔺Take Profit At:0.9800 Like And Comment ❤️🏆 Team Setupsfx_ #forex #forextrading #gbpchf #gbpchfsell #smc #smctrading #ict #icttrading #smartmoneyconept #daytrading #swingtrading #swing #longterm #gbpchfswing #gbp #chf
Short
by Setupsfx_
8
What 14 Million Tests Say About MACDThis paper is the fourth in a series. The first study tested RSI across 26 million configurations and found zero Bonferroni-significant results. The second documented a small but genuine Turn of the Month anomaly, with 21 significant results from 385 tests. The third examined VWAP and produced the strongest findings so far: 150,546 Bonferroni-significant results concentrated in mean reversion, with short signal edge of 0.89 percentage points, roughly six times transaction costs. MACD was always going to be next. The Moving Average Convergence Divergence indicator is the second-most popular technical analysis tool after RSI, present on virtually every charting platform, explained in every trading course, and defended with religious fervor. It has also been defended, occasionally, in the academic literature. Brock, Lakonishok and DeLoach (1992) found some evidence for moving average rules on the Dow Jones Industrial Average. That paper has been cited over three thousand times and forms the intellectual scaffolding for every MACD apologist who claims institutional backing. We tested MACD more thoroughly than it has been tested before. 14,310,400 parameter configurations across eight distinct strategies, 20 assets spanning six categories, 436 valid parameter pairs, eight signal periods, and 26 holding periods. The same Welch t-test framework with Bonferroni correction used for RSI, Turn of the Month, and VWAP. The results are unambiguous. MACD generates 3,235 Bonferroni-significant results from over 14 million tests, of which 2,844 concentrate in a single strategy type, histogram divergence, on the short side. The overall mean long edge is +0.054 percentage points and the mean short edge is +0.018 percentage points. Both are economically negligible, falling well below transaction costs. The standard 12/26/9 setup, the configuration taught in every introductory course, produces zero Bonferroni-significant results. The line crossover, the most commonly used MACD signal, also produces zero. MACD joins RSI in the category of indicators that fail systematic testing. Unlike RSI, which produced absolutely nothing, MACD shows a faint statistical pulse in divergence signals. That pulse is too weak and too concentrated to constitute a trading strategy, but it exists. Whether it constitutes a genuine edge or a statistical artifact at the boundary of detection is a question the data cannot definitively resolve. 1. Introduction Gerald Appel introduced the MACD indicator in 1979 as a tool for identifying changes in trend momentum (Appel, 1979). The calculation is straightforward: take the difference between a fast exponential moving average and a slow exponential moving average of price, then smooth that difference with a third exponential moving average called the signal line. The difference between the MACD line and the signal line forms the histogram. Appel recommended the 12/26/9 parameterization, meaning 12-period and 26-period EMAs with a 9-period signal smoothing. That combination has since become one of the most widely recognized parameter sets in all of technical analysis. The indicator spread quickly through the retail community. By the 1990s, MACD was included as a default indicator on nearly every charting platform. Murphy (1999) dedicated an entire chapter to it in his standard reference on technical analysis. Elder (1993) featured it prominently in his trading methodology. Pring (2002) described it as one of the most reliable momentum indicators available to traders. These endorsements were based on observation, pattern recognition, and discretionary experience, not systematic statistical testing. The academic treatment of MACD has been more cautious. Brock, Lakonishok and DeLoach (1992) tested simple moving average rules on the Dow Jones Industrial Average from 1897 to 1986 and found some evidence of predictive content in moving average signals. Their results applied to simple moving average crossovers, not specifically to MACD, but the paper became the most frequently cited academic justification for MACD-type strategies. Sullivan, Timmermann and White (1999) subsequently re-examined these results using the bootstrap reality check methodology and found that after correcting for data snooping, the evidence weakened substantially. Lo, Mamaysky and Wang (2000) applied computational pattern recognition to technical analysis and concluded that some technical patterns contain genuine information, while cautioning that the magnitude was economically small after costs. Park and Irwin (2007) surveyed 95 studies of technical analysis profitability and concluded that the evidence was mixed, with earlier studies tending to find profitability and later studies, using more rigorous methodology, finding less. The academic literature on MACD specifically, as distinct from simple moving average rules, is thin. Most quantitative studies lump MACD into broader examinations of moving average rules without testing its specific strategy variants independently. This gap leaves the retail community relying on decades-old endorsements from practitioners rather than on direct statistical evidence. The present study fills that gap with what we believe is the most comprehensive systematic test of MACD strategies in the available literature: 14,310,400 parameter configurations tested with Bonferroni correction across a diversified asset universe. 2. What MACD measures MACD is built from three exponential moving averages. The MACD line equals the fast EMA minus the slow EMA: MACD line = EMA(close, fast period) minus EMA(close, slow period) The signal line smooths the MACD line: Signal line = EMA(MACD line, signal period) The histogram equals the difference: Histogram = MACD line minus Signal line When the fast EMA exceeds the slow EMA, the MACD line is positive, indicating that recent prices are higher than prices over the longer lookback. When the MACD line exceeds the signal line, the histogram is positive, indicating that the trend is accelerating. Traders interpret these relationships as momentum signals. The standard 12/26/9 parameterization uses alpha equal to 2/(n+1) for each EMA, giving the fast EMA a smoothing factor of approximately 0.154, the slow EMA approximately 0.074, and the signal line approximately 0.20. These are not derived from any optimization or theoretical framework. Appel (1979) selected them based on his visual examination of stock charts. The combination stuck because it was published first, not because it was demonstrated to be optimal. At its core, MACD measures the difference between two lagged representations of the same price series. Both the fast and slow EMAs are functions of past closing prices, weighted by geometrically declining coefficients. The MACD line captures the difference in how quickly these two representations respond to recent price changes. When price moves sharply, the fast EMA reacts first, creating a gap that the MACD line registers. The signal line further smooths this gap. The critical question is whether this double-differenced, triple-smoothed transformation of price adds information that the original price series does not contain. Moving averages are linear combinations of past prices. Taking the difference of two moving averages produces another linear combination of past prices. Smoothing that difference with a third moving average produces yet another linear combination. At every step, the input is price, and the output is a weighted average of past prices. No new information enters the calculation at any stage. This is the same structural problem documented in our RSI study. RSI computes a ratio of recent up moves to recent down moves, which is a nonlinear transformation of price, and still produces zero Bonferroni-significant results. MACD performs a purely linear transformation, which is theoretically even less likely to extract latent information. The question is whether the empirical evidence agrees with this theoretical expectation. 3. Common MACD strategies Retail MACD education centers on eight interpretive frameworks. Some are taught in every introductory course. Others appear in more advanced material. All eight are represented in our test battery. The line crossover interprets the MACD line crossing above the signal line as a buy signal and crossing below as a sell signal. This is the canonical MACD trade and the first strategy every tutorial teaches. The zero-line cross interprets the MACD line crossing above zero as bullish, meaning the fast EMA has overtaken the slow EMA, and crossing below zero as bearish. The histogram direction change interprets the histogram crossing from negative to positive as a buy signal and positive to negative as a sell signal. Because the histogram equals MACD line minus signal line, this is equivalent to the line crossover with one bar less lag. The histogram divergence compares price action with histogram behavior over a lookback window. When price makes a new low but the histogram does not, this is interpreted as bullish divergence. When price makes a new high but the histogram does not, bearish divergence. The price-MACD divergence is similar but compares price against the MACD line itself rather than the histogram. Price making a new low while the MACD line does not suggests weakening downward momentum. The histogram momentum strategy looks for three or more consecutive bars of increasing or decreasing histogram value, interpreting sustained directional movement in the histogram as acceleration in the underlying trend. The above-zero filtered crossover restricts the standard line crossover to signals occurring when the MACD line is above zero for longs and below zero for shorts. The logic is that a crossover above the zero line represents a stronger confirmation because it occurs within an established trend rather than against one. The histogram peak-trough reversal identifies local extrema in the histogram and enters when the histogram reverses from a peak or trough. A trough followed by a rising bar generates a long signal. A peak followed by a falling bar generates a short signal. None of these strategies has a theoretical derivation. They are all interpretive frameworks applied to a derived indicator, based on the assumption that various patterns in the MACD-signal-histogram relationship predict future price direction. The divergence strategies have the weakest theoretical basis of all, requiring price and momentum to disagree in a specific way and treating the disagreement as predictive. Whether any of them contain information is an empirical question. 4. Data and methodology 4.1 Asset universe We constructed a universe of 20 ETFs across six categories covering the major liquid segments of global financial markets. US large cap equities comprised SPY, QQQ, IWM, DIA, and VTI, providing exposure from the S&P 500 to the total market. Sector ETFs included XLF, XLK, XLE, XLV, and XLU, spanning financials, technology, energy, healthcare, and utilities. International equities included EFA and EEM, covering developed and emerging markets. Fixed income comprised TLT, IEF, and AGG, spanning long-duration treasuries, intermediate treasuries, and the aggregate bond market. Commodities included GLD, SLV, and USO, covering gold, silver, and oil. Real assets comprised VNQ and DJP, covering REITs and a broad commodity index. All data is daily, sourced from TwelveData with Tiingo as fallback, covering approximately 5,000 trading days per asset. 4.2 Parameter grid We tested the full practical range of MACD parameters: Fast period: 2 through 20 in steps of 1, giving 19 values. Slow period: 10 through 60 in steps of 2, giving 26 values. Valid parameter pairs require the slow period to exceed the fast period by at least 5, producing 436 valid combinations. Signal period: 3, 5, 7, 9, 12, 15, 18, and 21, giving 8 values. Holding period: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 25, 30, 40, 50, 60, 75, 90, 120, 150, 180, and 252 trading days, giving 26 values from one day to one full year. The total configuration space is 8 strategies multiplied by 20 assets multiplied by 436 parameter pairs multiplied by 8 signal periods multiplied by 26 holding periods, equalling 14,510,080 target tests. After filtering for minimum data length and minimum signal counts, 14,310,400 valid test results remained. 4.3 Strategy implementation For each asset and parameter combination, MACD is calculated once using pandas ewm with adjust=False, which produces the standard exponential moving average with alpha equal to 2/(n+1). All eight strategies generate their signals from this single MACD calculation. Forward returns are computed as close-to-close returns over the specified holding period. This avoids look-ahead bias: the signal fires at bar t, and the return is measured from the close of bar t to the close of bar t plus the holding period. 4.4 Statistical framework Edge equals the mean forward return following a signal minus the mean forward return over all bars in the same asset sample. This baseline-adjusted approach ensures that a strategy in a rising market does not receive credit for beta. Significance is assessed using Welch's t-test for unequal variances. A minimum of 10 signals per configuration is required. Given 14,310,400 valid tests, the Bonferroni-corrected significance threshold at alpha equal to 0.05 is 3.49 times ten to the negative ninth power. Surviving this threshold requires a result so extreme that it would occur by chance fewer than once in 300 million tries under the null hypothesis. 5. Results 5.1 Aggregate findings Figure 1 presents the overall picture, and it is a quiet one. Mean edges cluster near zero for most strategies. No strategy produces mean edge exceeding 0.35 percentage points on either side. Histogram divergence and price-MACD divergence are the only strategies with visible positive bars on the long side. The annotations beneath each strategy name show Bonferroni-significant test counts, and most read zero. Across all 14,310,400 tests, mean long edge is +0.054 percentage points and mean short edge is +0.018 percentage points. Both figures are below the 0.10 to 0.15 percentage points required to cover round-trip transaction costs for liquid ETFs. Even if these edges were statistically robust, they would not survive implementation. Figure 2 decomposes by asset category and shows that the weak aggregate results are not masking strong effects in subgroups. US equities show the most positive numbers: long edge +0.209 percentage points and short edge +0.128 percentage points. But even these figures are marginal relative to transaction costs and do not achieve significance after Bonferroni correction in the vast majority of configurations. 5.2 Statistical significance Figure 3 maps the parameter space. The heatmap is dominated by colors near zero, with no clearly defined region of strong positive or negative edge. The standard 12/26 position sits in an unremarkable part of the landscape, neither at a local optimum nor at a local minimum. There is no hidden sweet spot that traders are missing. The parameter space is uniformly mediocre. Figure 4 examines whether edge develops at longer holding horizons, as it did for VWAP mean reversion. The answer is no. Long and short edges show no consistent monotonic pattern across holding periods. Some strategies show mild positive edge at very long holding periods above 120 days, but the effects are small, inconsistent across strategies, and could plausibly reflect equity risk premium rather than MACD signal content. Figure 5 shows the p-value distribution. Under the null hypothesis, p-values distribute uniformly. The observed distribution is close to uniform, with a mild excess at low values that is much less pronounced than the sharp spike documented in the VWAP study. The excess represents the 391 long-side and 2,844 short-side Bonferroni-significant results. These are not zero, which distinguishes MACD from RSI, but they are not the kind of departure from uniformity that characterizes a robust indicator. 5.3 Results by strategy Histogram divergence is the only strategy with a meaningful number of significant results. Of 1,788,800 histogram divergence tests, 255 long signals and 2,844 short signals survive Bonferroni correction. Mean long edge is +0.323 percentage points, mean short edge is +0.102 percentage points. The long edge is above transaction costs. The short edge is not. The mechanism behind histogram divergence is that price makes a new low over a lookback window while the MACD histogram does not. This is interpreted as weakening selling pressure. The data suggests this interpretation has some validity: when selling pressure weakens as measured by the histogram failing to confirm a price low, subsequent long returns are modestly above baseline. The effect is real in the statistical sense of surviving strict correction. Whether it is robust enough to build a strategy around is another question: 255 Bonferroni-significant long results from 1.79 million tests is a survival rate of 0.014 percent. Price-MACD divergence produces 136 Bonferroni-significant long results with mean long edge of +0.347 percentage points. This is structurally similar to histogram divergence but uses the MACD line itself rather than the histogram. The slightly higher mean edge and lower survival count suggest a less consistent but occasionally stronger signal. Line crossover produces zero Bonferroni-significant results from 1,788,800 tests. The canonical MACD trade, the one drawn first on every tutorial chart, has no statistical support. Zero-line cross produces zero Bonferroni-significant results. The idea that the fast EMA overtaking the slow EMA predicts future returns is not supported. Histogram direction change produces zero Bonferroni-significant results. This is equivalent to the line crossover with slightly different timing, and the data treats them identically: both fail. Histogram momentum produces zero Bonferroni-significant results. Three consecutive bars of histogram acceleration predict nothing. Above-zero filtered crossover produces zero Bonferroni-significant results. Adding a trend filter to a crossover that does not work does not produce a crossover that works. Histogram peak-trough reversal produces zero Bonferroni-significant results. Identifying local extrema in the histogram and trading the reversal generates random outcomes. Figure 7 presents the complete numerical summary. Six of eight strategies produce zero Bonferroni-significant results. The two that produce significant results, histogram divergence and price-MACD divergence, account for a total of 3,235 significant tests from 14.3 million, a rate of 0.023 percent. For comparison, VWAP mean reversion alone produced 100,765 significant results from 1.68 million tests, a rate of 6.0 percent. 5.4 Results by asset category US equities show the strongest effects: long edge of +0.209 percentage points and short edge of +0.128 percentage points. This is consistent with US equities being the most liquid, most traded, and most studied market, where any residual signal is most likely to appear. International equities show modest long edge of +0.028 percentage points but more interesting short edge of +0.193 percentage points. Sector ETFs show positive long edge of +0.134 percentage points with short edge near zero. Commodities show negative edge on both sides: long negative 0.175 percentage points and short negative 0.180 percentage points. This means MACD signals applied to commodity ETFs actively destroy value on both the long and the short side. The commodity result is consistent with the lack of volume-based microstructure dynamics in commodity ETFs, where price movements are driven by supply-demand fundamentals rather than by institutional execution patterns. Fixed income shows mildly negative edge on both sides. Real assets show negligible edge in both directions. 5.5 The standard 12/26/9 setup Figure 8 isolates the question every MACD user wants answered: is the standard setup at least better than the average parameter combination? From 4,160 tests using exactly the 12/26/9 parameterization, mean long edge is +0.060 percentage points and mean short edge is negative 0.012 percentage points. Bonferroni-significant results: zero on both sides. The violin plots show the 12/26/9 distribution sitting squarely in the middle of the full parameter distribution. It is not worse than average. It is not better than average. Appel's original recommendation, selected from visual chart examination in 1979, performs almost exactly at the median of the parameter space. There is no hidden better combination waiting to be discovered either: the best mean long edge across all parameter combinations is +0.656 percentage points (price-MACD divergence, fast=20, slow=26, signal=3), which sounds like progress until you realize it is a mean across 20 assets and 26 holding periods and that the best-looking aggregate can easily emerge from random variation when 3,488 parameter triplets are evaluated. 6. Comparison with previous studies Four indicators, 46 million tests, one consistent framework. The scoreboard: RSI: zero Bonferroni-significant results from 26 million tests. The most popular technical indicator generates random output. Turn of the Month: 21 Bonferroni-significant results from 385 tests. A small test universe with a real effect driven by identifiable institutional flow cycles. VWAP: 150,546 Bonferroni-significant results from 5.8 million tests. Mean reversion short signals deliver 0.89 percentage points of edge, roughly six times transaction costs. A genuine, economically meaningful finding with a clear microstructure mechanism. MACD: 3,235 Bonferroni-significant results from 14.3 million tests. Concentrated in histogram divergence with mean long edge of +0.323 percentage points. Marginal relative to transaction costs. No strategy variant approaches the economic significance of VWAP mean reversion. The pattern that emerges across these four studies is now well established. Price-only transformations, RSI and MACD, fail. Both indicators take closing prices, apply mathematical operations, and return a derived series that contains no information beyond what the original price series already carried. The EMAs in MACD are linear weighted averages of past prices. Their differences are linear combinations of linear combinations. No transformation in this chain can create information that the input did not contain. Volume-incorporating indicators, specifically VWAP, succeed. VWAP integrates volume with price and thereby accesses information about market participation that price alone does not capture. The institutional execution patterns around VWAP create genuine supply-demand dynamics that a price-only indicator cannot detect. Calendar effects, Turn of the Month, succeed because they proxy for institutional flow patterns anchored to economic payment cycles. The information comes from the calendar, not from price. The lesson is consistent: indicators predict when they access information beyond the price series. Indicators that merely transform price cannot create information the price did not already contain. This is not a philosophical position. It is a consistent empirical finding across 46 million tests. 7. Why MACD fails and why divergence partially survives 7.1 The mathematical constraint MACD is a linear function of past prices. The fast EMA, the slow EMA, the MACD line, the signal line, and the histogram are all linear combinations of the same closing price series with different weighting schemes. In the framework of Lo, Mamaysky and Wang (2000), the question is whether the specific weighting scheme applied by MACD isolates information that raw price data obscures. For linear filters applied to the same underlying series, the answer is generally no. A linear filter can redistribute information across frequencies but cannot create it. The EMA emphasizes recent prices relative to older prices. The MACD line computes the difference between two such emphases. The result is a signal that is correlated with recent price momentum but carries no independent information about future returns. This is different from VWAP, which multiplies price by volume before averaging. That multiplication introduces a genuinely new data dimension. MACD multiplies price by nothing except predetermined decay weights that are functions of the chosen period parameters. 7.2 Why divergence is different Histogram divergence and price-MACD divergence are structurally different from the other six strategies because they compare two separate quantities: price level and an oscillator derived from price. When price makes a new low but the histogram does not confirm that low, the signal is detecting a nonlinear discrepancy between raw price and the smoothed momentum measure. This is a second-order relationship that goes beyond the simple linear filtering of the crossover strategies. The divergence detection involves comparing local extrema in two different series, a comparison that introduces a nonlinearity the other MACD strategies lack. Price can make new lows while smoothed momentum improves because the rate of decline is slowing even as the level continues to fall. This is economically interpretable: weakening selling pressure before a price reversal. The divergence signals survive Bonferroni correction, but barely. 255 long and 2,844 short results from 3.58 million divergence tests is a survival rate of 0.087 percent. For context, VWAP mean reversion produces a survival rate of 6.0 percent, roughly 70 times higher. The divergence effect is real in the statistical sense but sits at the edge of what can be confidently distinguished from multiple testing artifacts at this scale. 7.3 What Brock, Lakonishok and DeLoach actually found The most cited academic defense of MACD-type strategies is Brock, Lakonishok and DeLoach (1992), who tested simple moving average crossover rules on the DJIA from 1897 to 1986. Their results showed buy signals following moving average crossovers generated higher returns than sell signals. Three qualifications limit the applicability of those results to modern MACD trading. First, the study tested simple moving averages, not exponential moving averages with a signal line. MACD's triple-EMA structure is a more complex filter than anything Brock and colleagues examined. Second, the study used a single asset, the DJIA, over a single period. Sullivan, Timmermann and White (1999) applied the bootstrap reality check to an expanded set of technical trading rules and found that after correcting for the full universe of rules that could have been tested, the significance of the best-performing rule was substantially reduced. Third, the study period ended in 1986. Markets in 1986 were structurally different from markets in 2026: spreads were wider, execution was slower, algorithmic trading was non-existent, and information dissemination operated on fundamentally different timescales. A moving average rule that exploited slow information diffusion in 1960 would face a radically different environment today. Our results on 14.3 million tests across 20 assets and 40 years of more recent data find no support for MACD crossover strategies. This is not inconsistent with Brock and colleagues finding weak support for simple moving average rules on one asset ending in 1986. It simply means that any signal that may have existed has been arbitraged away, or that it was specific to the asset, period, and methodology of the original study. 8. Implications For traders using MACD crossover: 1,788,800 configurations tested. Zero Bonferroni-significant results. This is the same outcome as VWAP crossover and the same outcome as RSI in general. Crossover strategies applied to momentum oscillators do not predict future returns. The consistency of this null result across three independent indicators tested in three separate studies suggests that the finding is not specific to any one indicator. It is a property of crossover signals derived from price transformations. For traders using MACD divergence: the data shows a statistically real but economically marginal effect. Histogram divergence long signals average +0.323 percentage points, which exceeds transaction costs but only modestly. The effect concentrates in specific assets and parameter combinations rather than being broadly available across the universe. A trader who restricts MACD usage exclusively to divergence signals on US equities with carefully selected parameters might extract a small positive edge. Whether that edge justifies the complexity of implementation relative to alternatives such as VWAP mean reversion, which delivers nearly three times the edge with 70 times the significant configurations, is a question of opportunity cost. For systematic strategy developers: MACD does not belong in the toolbox as a primary signal generator. The 14.3 million tests produce no evidence of consistent, economically meaningful prediction from any MACD strategy variant. This does not preclude MACD as a filter or auxiliary input in a broader system, but the data does not support building a strategy around MACD signals. Resources allocated to MACD-based strategy development would generate substantially higher expected returns if redirected to VWAP mean reversion or calendar-based approaches. For trading educators: the standard MACD curriculum, centered on line crossovers and the 12/26/9 setup, teaches strategies that have been tested across 14 million configurations and produce nothing. If MACD must remain in curricula for completeness, the honest presentation would acknowledge that crossover signals have no statistical support, that the 12/26/9 setup is no better than the median parameter combination, and that the only variant with any empirical basis is divergence, which even then produces marginal edge. Teaching the standard MACD crossover as a viable trading strategy is, at this point, not supported by the evidence. 9. Limitations Several constraints bound the conclusions. First, this study tests daily data only. MACD behavior on intraday timeframes was not examined. The VWAP study showed that timeframe matters substantially, and it is possible that MACD performs differently on 4-hour or weekly data, though there is no theoretical reason to expect it would. Second, we tested MACD in isolation. Some practitioners combine MACD with other indicators, support and resistance levels, or fundamental filters. These combinations were not evaluated. It is possible that MACD contributes useful information as one component of a multi-factor system even though it fails as a standalone signal, though the same could be said of any random variable. Third, the divergence lookback was fixed at 14 bars. Variable lookback periods for divergence detection might produce different results. Fourth, execution was assumed at bar close. Real execution involves slippage that would further reduce the already-marginal edge values. Fifth, this study does not test whether MACD had predictive value in earlier historical periods that has since been arbitraged away. It is possible that MACD signals were informative in the 1980s and 1990s when markets were less efficient and algorithmic trading did not exist. That hypothesis is not testable with the data horizon available. References Appel, G. (1979). The Moving Average Convergence-Divergence Trading Method. Signalert Corporation, Great Neck, NY. Appel, G. (2005). Technical Analysis: Power Tools for Active Investors. Financial Times/Prentice Hall, Upper Saddle River, NJ. Brock, W., Lakonishok, J. and DeLoach, S. (1992). Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance, 47(5), pp. 1731-1764. Elder, A. (1993). Trading for a Living: Psychology, Trading Tactics, Money Management. John Wiley and Sons, New York. Lo, A.W., Mamaysky, H. and Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. Journal of Finance, 55(4), pp. 1705-1765. Murphy, J.J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance, New York. Park, C.H. and Irwin, S.H. (2007). What do we know about the profitability of technical analysis? Journal of Economic Surveys, 21(4), pp. 786-826. Pring, M.J. (2002). Technical Analysis Explained. 4th edn. McGraw-Hill, New York. Sullivan, R., Timmermann, A. and White, H. (1999). Data-snooping, technical trading rule performance, and the bootstrap. Journal of Finance, 54(5), pp. 1647-1691.
CME_MINI:ES1!Education
by EdgeTools
31
SILVER(XAGUSD): Intraday Smart Trading Setup, Risky But Worth It✴️ Silver has recently reached levels of 74. Additionally, an analysis of the DXY suggests a possible bearish reversal after the index touched the 101 mark. This DXY movement is a significant factor supporting a sustained bullish trend for Silver. ✴️ However, given our intraday trading strategy, only a slight depreciation of the USD is required to propel silver prices towards our target. Other fundamental economic indicators, such as Non-Farm Payrolls (NFP) and additional data releases, could also influence the metals market. ✴️We encourage you to like, comment, and follow our work to support the continued sharing of such analyses. We wish you successful and secure trading. Sincerely, Team Setupsfx #xagusd #xag #silver #silvertrading #xagtrading #xagusdtrade #smartmoneyconcept #smc #smctrading #smcstrategy #icttrading #ict #daydtrading #forex #forexmarket #intradaytrading #smartsystem #metalmarket #silvermarket #goldmarket
OANDA:XAGUSDLong
by Setupsfx_
19
XAUUSD Rejection at 4,550 Signals Downside Risk To 4,390Hello traders! Here’s my technical outlook based on the current XAUUSD (4H) chart structure. Price moved inside an ascending channel before a rejection at the top signaled a shift. A break below channel support confirmed bearish structure. Currently, gold is trading below the 4,550 resistance (seller zone) and near the 4,390 support (buyer zone), forming a triangle compression. As long as price remains below the 4,550 resistance, the bearish bias stays valid. A rejection from this area could push price toward the 4,390 support (TP1) as the next downside target. Please share this idea with your friends and click Boost 🚀
FOREXCOM:XAUUSDShort
by LegionQ8
30
WTI Oil Preparing for Next Big Direction MoveThe chart illustrates WTI crude oil trading in a consolidation phase following a strong bullish rally 📊. Price has repeatedly tested the resistance zone around 101, forming multiple rejection wicks that highlight strong selling pressure at this level. Despite this, the ascending trendline continues to act as dynamic support, maintaining the overall bullish structure. Currently, price is approaching a key breakout zone, making the next move crucial 👀. A successful breakout above resistance could trigger strong bullish momentum, pushing price toward the target of 111.71 🚀. However, if price breaks below the trendline, it would signal weakness, leading to a decline toward the first bearish target at 91.95, with further downside potential extending to 86.52 if selling pressure intensifies. If you found this XAUUSD analysis helpful, don’t forget to LIKE 👍 and COMMENT 💬!
TVC:USOIL
by Dynamic_Trade_Strategies
4
Nike Stock Way Off Record Ahead of Earnings. What to Know.There is a particular kind of corporate humbling that comes not from collapse but from irrelevance creeping in at the edges. Nike NYSE:NKE is still the world's largest sportswear brand, still moving tens of billions in revenue, still on the feet of more athletes than any other company on earth. And yet at around $50 a share, nine-year lows, off 70% from its 2021 peak, the stock is asking a question the company has to answer on Tuesday: So how’s that reset going? (insert "well we’re waiting.gif") The earnings calendar rolls on with Nike’s earnings day next . 📉 How Did We Get Here? Nike's November 2021 high of $178 looks almost fictional from today's vantage point. The shares are lower by more than 20% this year alone and have spent the past several months hovering near levels last seen when athleisure was still a novelty. The company is in the middle of what it calls a business reset , refocusing on wholesale partnerships after a years-long push toward selling direct to consumers that did not deliver the margins management had anticipated. Wholesale means selling through retailers like Foot Locker rather than exclusively through Nike's own stores and app. The retreat is sensible but the execution has been choppy, and the market has been keeping score. 🌍 Three Problems, One Earnings Call Investors heading into Tuesday's report are watching three specific pressure points. Europe is slowing. Consumer confidence across the continent has softened, and discretionary spending, the kind that goes on premium trainers, tends to be the first casualty when household budgets tighten. The US wholesale business, the very channel Nike is trying to reinvigorate, remains unhealthy. Rebuilding retailer relationships after deprioritizing them takes time, and the inventory and shelf-space dynamics are still normalizing. All the while profits are slumping . Outside the tariff woes from a year ago , China continues to disappoint. The post-pandemic recovery that Nike and many other consumer brands were counting on has been more stubborn than anticipated, with local competitors gaining ground and consumer sentiment remaining soft. The market is expecting revenue of roughly $11.1 billion for the quarter, flat year on year, an improvement on the 9.3% decline recorded in the same period last year. Earnings per share, meaning profit divided across all outstanding shares, is expected to land at 29 cents. Last quarter Nike beat on both revenue and earnings, so the bar exists, even if it is not set particularly high. 👟 The Cool Factor Problem Beyond the financials, Nike has a cultural challenge that balance sheets struggle to capture. The brand that defined athletic aspiration for four decades is working harder than usual to stay relevant. Its answer, at least partly, is a sneaker called Mind, a shoe focused on mindfulness. Whether a mindfulness sneaker moves the needle on coolness is a question the market will eventually answer, but it is the kind of product that signals a company actively searching for its next identity. 🆕 New Balance Is Having a Moment While Nike searches, New Balance is sprinting. The 120-year-old brand, once synonymous with sensible footwear for people who prioritize comfort over style, grew sales 19% last year and 180% since 2020. It has raised average prices by roughly 30% over the past five years, proving consumers will pay a premium for a brand they believe in. It opened 80 new stores last year, aggressively rebuilding physical retail presence at the exact moment Nike was stepping back from it. New Balance expects to cross $10 billion in sales this year, putting it roughly $2 billion behind Nike's quarterly revenue run rate. That gap is closing, and it is closing fast. 🎯 What to Watch Tuesday The headline numbers are important, for sure, but the language around China and wholesale recovery will tell the more important story. So grab your two shares and listen for whether management sounds like a team executing a plan or a team revising one. At $50, Nike is cheap relative to its own history. Cheap and done falling are different things, though. Off to you : What’s your outlook for Nike? Share your views in the comments!
NYSE:NKE
by TradingView
4
Ethereum formed a fake bearish H&S, creating a bear trapBased on the bullish CH on the chart and the formation of higher lows, the structure is bullish on the hourly timeframes. The market maker has created a wide ranging zone to hunt liquidity. With each drop, it gives traders the impression that sellers have stepped in, but the structure remains bullish on the hourly. A bearish head and shoulders pattern is also visible (marked in light purple), but in our view, such patterns are ineffective in a bullish structure and mainly serve as liquidity traps. There is also a small liquidity pool above, where retail traders place their stops behind the wicks, leading to liquidity buildup. We have marked the zone where strong, real sell orders are located. As long as the demand zone holds, price can move toward the targets and the marked area. With all that said, don’t forget risk management it's more important than correctly predicting market direction. If you would like us to analyze a coin or altcoin for you, first like this post, then comment the name of your altcoin below. What is your opinion about Ethereum?
by iMoneyTeam
8
EUR/GBP Daily Outlook: Rebound Building from DemandOn EUR/GBP, my short-term view is constructive, but still balanced on the broader daily structure. Price has reacted cleanly from the 0.8610–0.8640 demand and is now pushing higher, forming higher lows. As long as this area holds, I see room toward 0.8745/0.8760. However, I do not consider this a full trend reversal yet, as a strong supply zone sits above at 0.8780–0.8800, extending toward 0.8823, where sellers could re-enter. From a COT perspective, the picture is mixed. Euro positioning remains slightly net long but is losing momentum, with longs being reduced. Sterling is still net short overall, but recent data shows short covering and some rebuilding in longs. This shift suggests GBP is becoming less bearish at the margin, which limits bullish conviction on EUR/GBP, especially into resistance. Seasonality has supported EUR/GBP throughout March, with most datasets pointing higher. This aligns with the current rebound, but being at month-end, I treat it as a secondary factor rather than a trigger. Retail sentiment is neutral, with a 50/50 split, offering no real edge. I rely more on structure and institutional positioning here. Overall, EUR/GBP can continue higher while holding above 0.8638, targeting 0.8745/0.8760, and potentially testing 0.8780–0.8800. That zone will be the key decision point. Rejection there keeps the market in a range; a clean break would shift the structure more bullish. My bias remains neutral to mildly bullish in the short term: upside is possible, but not yet a high-conviction breakout scenario.
Short
by EdgeTradingJourney
5
Bitcoin showing strong market structureBitcoin is currently showing strong market structure after holding key support zones and maintaining higher price levels. The recent price action suggests that BTC is not weak — instead, it is building momentum for a potential bullish continuation. Bitcoin correction phase, Bitcoin is consolidating in a tight range, which usually indicates accumulation by smart money. This type of price behaviour often comes before a strong breakout move. Tecnically Bitcoin has respected major support zones showing that buyers are active and defending the market. This confirms demand is still strong Higher Highs & Higher Lows market structure is bullish on higher timeframes. BTC continues to form higher lows, which is a key sign of an uptrend Liquidity Build-Up Price is ranging below resistance creating liquidity above. markets often move toward liquidity meaning a breakout to the upside is likely. You may find more details in the chart, Trade wisely best of luck buddies. Ps; Support with like and comments for better analysis Thanks for Supporting.
Long
by MR_GOLD_12
6
USDJPY Bullish Channel Setup The chart shows a clear bullish trend on USDJPY moving inside an ascending channel. Price has been respecting the channel boundaries with higher highs and higher lows, supported by the Ichimoku cloud acting as dynamic support. Recently, price reached the upper zone and is showing a potential pullback. The expected move is a retracement towards the channel support and the marked target zone around 159.200, where price may find support before continuing the uptrend.
Short
by Alpha_Trade_Scope
6
AUDUSD:Strong USD & Risk-Off Sentiment Pressure Aussie Lower!Hey Traders, in today's trading session we are monitoring AUDUSD for a selling opportunity around the 0.69000 zone. AUDUSD is trading in a downtrend and currently is in a correction phase in which price is approaching the trendline resistance around the 0.69000 support and resistance area. From the macro side, the US Dollar remains strongly bid across the board, supported by safe-haven flows as Middle East tensions continue to fuel risk aversion in global markets. The sharp surge in oil prices and rising US Treasury yields are keeping the market in a risk-off environment, which is negative for risk-sensitive currencies like the Australian Dollar. In addition, the Australian Dollar is facing extra pressure from slower China demand concerns and weaker global growth sentiment, both of which typically weigh on commodity-linked currencies. With traders also positioning ahead of this week’s major US data releases, including ISM and Nonfarm Payrolls, the USD could stay supported and continue pushing AUDUSD lower. As long as price remains below the 0.69000 resistance zone, the bearish trend structure remains valid, and we anticipate a continuation move toward lower support levels. Trade safe, Joe.
Short
by JoeChampion
3
BTCUSDT – 4H Outlook: New Consolidation RangeHello traders, Bitcoin is showing signs of structural weakness after failing to hold the Weekly Open. This rejection suggests the market is entering a "New Range" phase, with the potential for a deeper correction if bulls can't reclaim key pivots 💡Trade Sentiment The close below the Weekly Open is a bearish signal in the short term. We are likely looking at two main scenarios: Range Bound: Price chops between $66k and $67.8k to build liquidity. A Rejection: A weak move into the $67.3k area followed by a sharp rejection, confirming a move toward the $63k-$64k 🔑 Key Levels to Watch Resistance: $67,300 - $67,865 - $71,359 – $72,000 Support: $65,979 - $64,000 – $63,000
Short
by VIPCryptoVault
10
XAU/USD Selling Pressure Big Move IncomingGold is trading inside a descending channel, showing strong bearish 📉 pressure after multiple rejections at resistance. A clear head and shoulders pattern has already played out, followed by confirmed bearish 🔥 breakouts, strengthening downside momentum. Price continues forming lower highs and lower lows, showing weak bullish strength and strong seller control. Geopolitically 📌, market sentiment is also influenced by close Strait of Hormuz tensions, and ongoing decline in Iran negotiations with USA and Israel across the Middle East, increasing uncertainty and volatility in energy-linked assets, indirectly impacting gold flows. A key support level is at 4460 — if price breaks below this zone, a smooth bearish 📉 continuation is expected. Retests of broken support acting as resistance may provide ideal sell opportunities. Overall structure favors sellers, with next major target around 4271 🎯 if momentum continues. If you found this XAUUSD analysis helpful, don’t forget to LIKE 👍 and COMMENT 💬!
FX:XAUUSDShort
by Dynamic_Trade_Strategies
24
SPX: Structure Shifting Below HighsAfter a strong multi-month expansion, price transitioned into compression beneath prior highs. The structure has now weakened, with price breaking below the rising 50-day moving average. Key level: – 6,800: prior support → now under pressure The broader regime is no longer stable. Conditions have shifted from expansion to conditional. As long as price remains below the 50 SMA, downside continuation risk increases. Informational analysis. Not investment advice.
SP:SPX
by RegimeAtlas
5
Bitcoin may spike and liquidate $13B in short positionsThis analysis is an update to the one you can see in the Related Releases section: First of all, you should know that, based on data shared by crypto statistics sites over the past few days, there is about $13 billion in short positions and only $3 billion in long positions! This liquidity makes hunting and capturing positions attractive for market makers. It appears we have a diametric pattern, where wave F has extended, causing the price to drop further. Now, by holding the green zone, Bitcoin could gradually conclude wave F and enter wave G, which is a bullish wave. The red zone, where the main orders are located, has not been reached yet, so we do not have a strong and reliable drop. Currently, if Bitcoin can hold the green zone, it could move toward the targets marked on the chart, especially considering that Tether dominance is at a resistance level and the structure of altcoins is strong and bullish on hourly timeframes. Bitcoin has been volatile due to market sentiment, the conflict in the Middle East, and other factors, making trading in these areas challenging. This means proper risk and capital management is essential. If you have a coin or altcoin you want analyzed, first hit the like button and then comment its name so I can review it for you. Do you also think BITCOIN is bullish?
by behdark
12
Bitcoin (BTC) | Expecting Rejection From Current Retest Zone2 ways we can look at this chart here: some might say that we had recently an MSB on lower zones and this is a trend reversal, whereas others might say that we are retesting the previous break of structure zone. Now both ways of thinking are correct, and that's why the current test zone is important for us. If all goes as we plan and we see the rejection, we could catch a good short position from here, where if buyers manage to break out from here, then we might be looking for upside momentum. As of now, we are expecting the rejection so that's what we are going to look and wait for, a proper MSB on smaller timeframes so no rush here! Swallow Academy
Short
by SwallowAcademy
5