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S&P 500 Wave Analysis – 13 August 2025 - S&P 500 broke key resistance level 6435.00 - Likely to rise to resistance level 6600.00 S&P 500 index recently broke above the key resistance level 6435.00, which stopped the previous intermediate impulse wave (3) in July. The breakout of the resistance level 6435.00 continues the active intermediate impulse wave (5) from the start of August. Given the overriding daily uptrend, S&P 500 index can be expected to rise to the next resistance level 6600.00, coinciding with the daily up channel from May.
SP:SPXLong
by FxProGlobal
WHY TRADING IS HARD – EVEN FOR GOD!THE BRUTAL TRUTH ABOUT PERFECT PORTFOLIOS AND IMPERFECT HUMANS Imagine having access to the same portfolio strategy that made Ray Dalio one of the world's wealthiest hedge fund managers. Picture yourself armed with Nobel Prize-winning research, billion-dollar backtesting results, and a mathematical framework so elegant it seems divinely inspired. Now imagine watching that same strategy torture you psychologically for years while delivering precisely the returns it promised. Welcome to the God Portfolio paradox: even divine wisdom cannot save us from ourselves. Ray Dalio's All Weather strategy has generated approximately 12% annual returns with maximum drawdowns of just 4% since its inception in 1996. Compare this to the S&P 500's 10% annual returns with gut-wrenching drawdowns exceeding 50% during major crashes. On paper, the choice seems obvious. In practice, it becomes a psychological nightmare that breaks even sophisticated investors. Let us examine what this means in concrete terms. Consider two hypothetical investors, each starting with one million dollars in January 2000. Investor A puts everything into an S&P 500 index fund. Investor B implements a simplified All Weather approach: 30% stocks, 40% long-term bonds, 15% intermediate-term bonds, 7.5% commodities, and 7.5% inflation-protected securities, rebalanced quarterly. Both strategies are mathematically sound, historically proven, and widely recommended by financial experts. The foundation of this cruel reality lies in the inherent conflict between what markets reward and what human nature compels us to do. Markets reward patience, discipline, and contrarian thinking. Human nature drives us toward impatience, emotional decision-making, and herd behavior. Even the most elegant portfolio construction cannot bridge this gap without addressing the psychological challenges that make trading extraordinarily difficult, even when armed with theoretically perfect strategies. Historical Development and Theoretical Foundations The intellectual origins of systematic portfolio construction trace back to Harry Markowitz's groundbreaking work on portfolio selection in 1952. Markowitz demonstrated that rational investors should focus on maximizing expected returns for a given level of risk, leading to the development of the efficient frontier concept (Markowitz, 1952). This work established the mathematical foundation for understanding how diversification can reduce portfolio risk without necessarily reducing expected returns. Building upon Markowitz's framework, William Sharpe introduced the Capital Asset Pricing Model in 1964, which provided a theoretical basis for understanding how individual securities should be priced relative to market risk (Sharpe, 1964). These developments laid the groundwork for more sophisticated approaches to portfolio construction that would emerge in subsequent decades. The evolution toward all-weather strategies gained momentum in the 1980s and 1990s as institutional investors began to recognize the limitations of traditional 60/40 stock-bond portfolios. Research by Swensen (2000) at Yale University demonstrated how institutional endowments could achieve superior risk-adjusted returns through alternative asset allocation approaches that emphasized diversification across risk factors rather than asset classes. The modern conception of the "God Portfolio" crystallized with Ray Dalio's development of the All Weather strategy at Bridgewater Associates. Dalio's approach, first implemented in 1996, was based on the premise that economic environments can be characterized by two primary variables: growth (rising or falling) and inflation (rising or falling). By constructing portfolios that perform well in each of these four potential economic scenarios, investors could theoretically achieve more consistent returns (Dalio, 2017). Risk Parity and Factor-Based Approaches The theoretical underpinning of modern all-weather strategies relies heavily on risk parity principles. Unlike traditional portfolio construction methods that focus on dollar allocation weights, risk parity approaches seek to equalize the risk contribution of different portfolio components. This methodology was formalized by Qian (2005), who demonstrated that portfolios constructed using risk budgeting techniques could achieve superior risk-adjusted returns compared to market capitalization-weighted approaches. Academic research has consistently supported the theoretical advantages of risk parity strategies. Maillard et al. (2010) showed that equally weighted risk contribution portfolios tend to be located in the efficient region of the mean-variance frontier, particularly during periods of market stress. Their analysis demonstrated that risk parity portfolios exhibited lower volatility and better downside protection compared to traditional asset allocation approaches. The factor-based investment framework provides another lens through which to understand all-weather portfolio construction. Fama and French (1993) expanded the understanding of systematic risk factors beyond market beta to include size and value factors, while subsequent research has identified additional factors such as momentum, quality, and low volatility that contribute to long-term returns (Fama and French, 2015). Contemporary research by Asness et al. (2012) demonstrated that factor diversification across asset classes could provide similar benefits to traditional asset class diversification, but with potentially superior risk-adjusted returns. This insight has led to the development of factor-based all-weather strategies that seek to maintain balanced exposure to different return drivers rather than asset classes per se. Portfolio Construction Methodology The construction of an effective all-weather portfolio requires careful consideration of several key principles. First, the portfolio must maintain diversification across different economic scenarios. This typically involves allocating to assets that perform well during periods of economic growth (stocks, corporate bonds, commodities), economic contraction (government bonds, gold), rising inflation (commodities, inflation-protected securities), and falling inflation (nominal bonds, growth stocks). Second, the portfolio construction process must account for the volatility differences between asset classes. Traditional approaches that allocate equal dollar amounts to stocks and bonds effectively give stocks much greater influence on portfolio performance due to their higher volatility. Risk parity approaches address this by adjusting position sizes to equalize risk contributions, typically resulting in larger allocations to lower-volatility assets such as government bonds. Third, effective all-weather portfolios often incorporate leverage to achieve target return levels while maintaining risk balance. This concept, popularized by Dalio, recognizes that a truly diversified portfolio may have lower expected returns than a concentrated equity portfolio, but can use modest leverage to enhance returns while maintaining superior risk characteristics (Dalio, 2017). Academic research has provided empirical support for these construction principles. Duggan and Luo (2021) analyzed the performance of various all-weather portfolio implementations over the period from 1970 to 2020, finding that risk-balanced approaches consistently outperformed traditional asset allocation methods on a risk-adjusted basis. Their study showed that all-weather portfolios exhibited significantly lower maximum drawdowns and more consistent returns across different market regimes. Implementation Considerations for Retail Traders While the theoretical foundations of all-weather investing are compelling, retail traders face several practical challenges in implementation. The first consideration involves access to appropriate investment vehicles. Institutional investors can easily implement complex strategies using derivatives and alternative investments, but retail traders must typically rely on exchange-traded funds and mutual funds that may not perfectly replicate desired exposures. The second challenge relates to rebalancing and monitoring requirements. Effective all-weather strategies require regular rebalancing to maintain target risk allocations as market conditions change. Research by Cesarone et al. (2019) showed that portfolios rebalanced quarterly achieved better risk-adjusted performance than those rebalanced annually, but the benefits diminished when transaction costs were considered for smaller portfolio sizes. A practical implementation approach for retail traders might involve using a core allocation to low-cost broad market index funds, supplemented by targeted exposures to inflation-protected securities, commodities, and international markets. The specific allocation weights should be determined based on individual risk tolerance and return objectives, but academic research suggests that equal risk allocation across major asset classes provides a reasonable starting point (Maillard et al., 2010). Technology has significantly improved the accessibility of sophisticated portfolio construction techniques for retail investors. Robo-advisory platforms now offer risk parity and factor-based strategies that were previously available only to institutional investors. Research by D'Acunto et al. (2019) found that retail investors using algorithmic portfolio management services achieved significantly better risk-adjusted returns compared to those managing portfolios manually. Performance Analysis: When Mathematics Meets Messy Reality Let us return to our two million-dollar investors and see how their journeys unfolded over 23 years. The numbers tell a story that perfectly illustrates why even perfect strategies can feel imperfect. By December 2023, Investor A (S&P 500) would have accumulated approximately $4.2 million, representing an 8.1% annual return despite enduring three major crashes. During the dot-com bust, this investor watched $1 million shrink to $490,000 by October 2002. In 2008, the portfolio plummeted from $1.1 million to $550,000 in just six months. The COVID crash of March 2020 vaporized $800,000 in value within three weeks. Each recovery took years of psychological endurance. Investor B (All Weather approach) would have reached approximately $3.8 million by the same date, representing a 7.6% annual return. The maximum drawdown never exceeded 12%, occurring during the 2008 crisis when the portfolio briefly declined from $1.3 million to $1.14 million. While the absolute returns were lower, the journey was dramatically smoother from a risk perspective. Here lies the psychological trap: Investor A earned $400,000 more over 23 years but experienced heart-stopping volatility. Investor B earned strong returns with manageable stress but constantly questioned whether they were missing out on greater gains. Academic research by Scherer (2007) confirms this pattern across multiple time periods, showing that risk-balanced portfolios consistently achieved Sharpe ratios of 0.65-0.85 compared to 0.45-0.65 for market capitalization-weighted approaches. But the real psychological torture begins when we examine year-by-year performance. During the technology boom of 1999, Investor A gained 21% while Investor B managed only 11%. At cocktail parties, Investor B endured stories of neighbors making 50% returns on technology stocks while their sophisticated strategy delivered "boring" results. The mathematical superiority of diversification provided little comfort when everyone else seemed to be getting rich faster. The 2008 financial crisis reversed this dynamic. When Investor A's portfolio crashed 37% in a single year, Investor B's declined only 8%. Suddenly, the All Weather approach looked brilliant. But by 2013, as markets recovered and Investor A's portfolio surged 32% compared to Investor B's 14%, the psychological pressure returned. This cycle repeated endlessly: validation during crashes, frustration during booms. Research by Roncalli and Weisang (2015) documented this exact pattern across various risk parity implementations, finding that these strategies experienced their greatest relative outperformance precisely when investors were most tempted to abandon them due to fear. Conversely, they underperformed most significantly during periods when overconfidence made investors most likely to increase risk. The Psychological Paradox: Why Perfect Strategies Fail Imperfect Humans Picture this scenario: You have constructed the perfect portfolio based on decades of academic research. Your bond allocation is generating steady 4% returns while your neighbor's Tesla stock has doubled in six months. Your commodities position is providing inflation protection while your colleague's crypto portfolio has tripled. Your carefully calibrated risk management is working exactly as designed, but you feel like an investment failure. This is the God Portfolio's cruelest joke: it tortures you precisely by working as advertised. Research by Kahneman and Tversky (1979) explains this psychological nightmare through prospect theory. Humans feel the pain of losses approximately twice as intensely as the pleasure of equivalent gains. For our Investor B, this meant that watching bonds decline 2% while stocks soared 15% felt worse than the joy of seeing the overall portfolio gain 8%. The mathematics were favorable, but the psychology was brutal. Consider a specific example from our All Weather investor during 2017. That year, the S&P 500 delivered a remarkable 21.8% return while the All Weather approach managed 12.3%. On a $2 million portfolio, this meant "missing out" on approximately $190,000 in gains. The fact that this was exactly the risk-return tradeoff the strategy was designed to provide offered no psychological comfort. Friends were buying vacation homes with their stock gains while our mathematically superior investor questioned every diversification principle they had learned. The diversification curse becomes particularly acute during bull markets. When Bitcoin was reaching $60,000, gold was stagnating. When growth stocks were doubling, value stocks were treading water. When real estate was booming, bonds were declining. At any given moment, roughly half of a diversified portfolio is disappointing its owner. This creates what behavioral economists call "diversification regret," where the very feature that makes portfolios safer makes investors miserable. Barber and Odean (2001) documented this pattern in their seminal study of retail investor behavior, finding that the average investor underperformed the market by approximately 1.5% annually due to behavioral mistakes. More significantly, they discovered that investors with theoretically superior strategies often performed worse than those using simple buy-and-hold approaches, precisely because the sophisticated strategies required more frequent decision-making opportunities for error. The timing of psychological stress compounds these challenges. All-weather strategies typically underperform during the euphoric phases of bull markets, exactly when social pressure and media attention focus on superior alternatives. Conversely, they provide their greatest value during market downturns, when fear and uncertainty make it most difficult to appreciate their benefits. This creates a perverse cycle where investors are most likely to abandon superior strategies precisely when they need them most. Professional fund managers have long recognized these psychological challenges and implemented institutional structures to address them. Large investment committees, detailed investment policies, and professional oversight create barriers to emotional decision-making. Retail traders, lacking these institutional safeguards, face the full psychological burden of strategy implementation without institutional support. The Implementation Gap: Where Theory Meets Brutal Reality Let us examine exactly what happened to our All Weather investor during the critical rebalancing moment of March 2020. As COVID-19 panic gripped markets, stocks crashed 30% in three weeks while bonds soared. The mathematical rebalancing signal was crystal clear: sell bonds at their peak and buy stocks at their trough. This was precisely the "buy low, sell high" discipline that makes sophisticated strategies superior. On March 23, 2020, our investor faced a decision. Their portfolio had shifted from the target 30% stocks to 22% stocks due to the crash. The rebalancing algorithm demanded selling $160,000 worth of bonds (which had gained value) and buying $160,000 worth of stocks (which were in free fall). Every financial media outlet was predicting economic apocalypse. Friends were withdrawing money from markets entirely. The VIX had spiked to levels not seen since 2008. Yet this was exactly the moment when disciplined rebalancing provides its greatest value. Institutional studies by Choi et al. (2010) show that even professional money managers with dedicated teams and systematic processes struggle with these decisions. Their research found that institutional investors exhibited herding behavior, momentum chasing, and timing errors that reduced returns by 0.5-1.5% annually compared to their own stated strategies. Our retail investor, lacking institutional safeguards, faced an even more brutal psychological challenge. The rebalancing required not just selling winners and buying losers, but doing so while newspapers screamed about market crashes and neighbors discussed moving money to cash. The mathematical elegance of the strategy provided no emotional comfort when executing what felt like financial suicide. Consider the specific dollar amounts involved. On that March day, our $2.2 million portfolio required moving $160,000 from the safety of bonds into the chaos of crashing stocks. The transaction felt like throwing money into a financial volcano. Yet investors who maintained rebalancing discipline captured the subsequent recovery, while those who abandoned their strategies missed one of the greatest buying opportunities in market history. French and Poterba (1991) documented how even sophisticated institutional investors fail to maintain optimal rebalancing discipline, particularly during extreme market conditions. Their study revealed that the very periods when rebalancing provides the greatest benefit are precisely when psychological pressure makes it most difficult to execute. This creates a performance drag that mathematical models fail to capture because they assume perfect implementation discipline. Transaction costs and timing considerations compound these implementation challenges. While academic studies often assume frictionless trading, real-world implementation involves bid-ask spreads, market impact costs, and tax considerations that can significantly erode theoretical advantages. More importantly, the psychological pressure to time rebalancing decisions optimally often leads to procrastination and poor execution timing. The technology paradox further complicates implementation. While modern portfolio management tools provide unprecedented analytical capabilities, they also generate information overload that can paralyze decision-making. Investors armed with sophisticated analytics often second-guess their strategies more frequently, leading to excessive tinkering that undermines long-term performance. The abundance of information creates an illusion of control that encourages frequent adjustments rather than disciplined adherence to systematic approaches. Recent developments in all-weather portfolio construction have focused on incorporating alternative risk factors and improving implementation efficiency. Researchers have explored the inclusion of cryptocurrency, private market investments, and environmental, social, and governance factors into all-weather frameworks. While these developments show promise, the limited historical data and higher complexity may make them less suitable for most retail traders. Artificial intelligence and machine learning techniques are also being applied to improve portfolio construction and rebalancing decisions. Studies by Gu et al. (2020) have shown that machine learning models can identify subtle patterns in asset relationships that traditional statistical methods might miss, potentially improving the effectiveness of all-weather strategies. However, these approaches require sophisticated infrastructure and expertise that may not be accessible to individual investors. The growing availability of low-cost index funds and ETFs continues to improve the practical implementation of all-weather strategies for retail traders. As financial markets become more accessible and transaction costs decline, the barriers to implementing sophisticated portfolio construction techniques continue to diminish. Limitations and Risk Considerations Despite their theoretical advantages, all-weather portfolios are not without risks and limitations. The first consideration is that these strategies typically rely on historical relationships between asset classes that may not persist in the future. Structural changes in the economy, monetary policy regimes, or financial markets could alter the effectiveness of traditional diversification approaches. Second, all-weather strategies may struggle during certain market environments. Prolonged periods of low interest rates, for example, can reduce the effectiveness of bonds as a diversification tool and limit the return potential of risk-balanced portfolios. The period following the 2008 financial crisis provided a real-world example of how unconventional monetary policy could challenge traditional portfolio construction assumptions. Third, the complexity of implementing effective all-weather strategies may lead to higher costs and implementation errors for retail traders. Research by French (2008) showed that the costs of active portfolio management often exceeded the benefits for individual investors, suggesting that simpler approaches might be more appropriate for many retail traders. Finally, it is crucial to recognize that no portfolio construction approach can eliminate investment risk entirely. All-weather strategies seek to manage and diversify risk rather than eliminate it, and investors should maintain realistic expectations about the performance characteristics of these approaches. Conclusion: The Humbling Truth About Perfect Strategies After following our two investors through 23 years of market history, the verdict is both clear and painful: the God Portfolio exists, it works exactly as promised, and it will likely drive you insane in the process. This represents the fundamental paradox of modern finance: we have solved the mathematical puzzle of optimal investing but remain powerless against the human puzzle of optimal behavior. Our All Weather investor ended with $3.8 million, excellent risk-adjusted returns, and probably years of therapy bills from constantly questioning whether they were missing out on greater gains. Our S&P 500 investor reached $4.2 million after surviving three near-death portfolio experiences and developing an iron stomach for volatility. Both strategies worked. Both investors suffered. Both questioned their decisions countless times. The numbers reveal the cruel joke: Ray Dalio's strategy delivered exactly what it promised—superior risk-adjusted returns with lower volatility. The 7.6% annual return with maximum drawdowns under 12% represents mathematical perfection in portfolio construction. Yet this same perfection became a source of psychological torture because markets do not reward you for being right; they punish you for feeling wrong. The research by Kahneman, Tversky, Barber, Odean, and others reveals that investors are their own worst enemies. Loss aversion, regret avoidance, herding behavior, and the psychological burden of diversification create barriers that no amount of mathematical sophistication can overcome. Even professional money managers, equipped with institutional safeguards and advanced technology, frequently fail to capture the full benefits of their sophisticated strategies. The implementation gap represents perhaps the most sobering aspect of this analysis. The very periods when sophisticated strategies provide their greatest value are precisely when psychological pressure makes them most difficult to execute. Rebalancing during market crashes, maintaining diversification during bull markets, and resisting the temptation to abandon systematic approaches require a level of psychological discipline that few possess consistently. Consider what this means for ordinary investors. If Ray Dalio, with a team of PhD economists and billions in assets, finds portfolio implementation challenging enough to write entire books about managing human psychology, what chance do retail traders have? The answer is simultaneously depressing and liberating: stop trying to be perfect and start trying to be consistent. The democratization of sophisticated portfolio tools has created a new form of torture. Modern investors can track their performance minute by minute, compare their returns to dozens of benchmarks, and analyze their portfolio's factor exposures in real time. Our All Weather investor could see precisely how much they were "underperforming" during every bull market rally. This constant performance surveillance often does more harm than good, turning investment management into a source of chronic anxiety. The most revealing insight from 23 years of data is counterintuitive: trading remains hard not despite sophisticated strategies, but because of them. The S&P 500 investor had one decision to make—buy and hold. The All Weather investor faced quarterly rebalancing decisions, each one an opportunity for psychological torment. Complexity that improves mathematical outcomes often destroys psychological outcomes. Here lies the brutal irony of the God Portfolio: it works precisely because it acknowledges human limitations, yet implementing it requires overcoming those same limitations. Diversification protects against unknown risks, but it guarantees that you will always be wrong about something. Risk management reduces portfolio volatility, but increases emotional volatility as you constantly question your conservative approach. The practical lesson for retail traders is humbling: your biggest enemy is not market volatility, economic uncertainty, or even bear markets. Your biggest enemy is the person staring back at you in the mirror every morning, armed with emotions, cognitive biases, and an internet connection full of alternative investment strategies that appear superior to whatever you are currently doing. The final lesson from our 23-year experiment is both humbling and liberating: the best portfolio is not the one that produces the highest returns or the lowest volatility, but the one you can sleep with at night and stick with through decades of doubt. Our All Weather investor earned $3.8 million and maintained their sanity. Our S&P 500 investor earned $4.2 million and probably aged a decade from stress. Both outcomes represent success, depending on your definition of victory. Perhaps the God Portfolio's greatest gift is not its mathematical elegance or superior risk-adjusted returns, but its role as a mirror reflecting our own psychological limitations. It teaches us that even with perfect information, optimal strategies, and divine inspiration, we remain gloriously, frustratingly, irredeemably human. And in a world where markets are increasingly dominated by algorithms and artificial intelligence, perhaps our humanity—flawed though it may be—is the only edge we have left. The God Portfolio exists. It works. It will also drive you slightly insane while delivering exactly what it promises. This is not a bug in the system; it is the system. Even God would find trading difficult in a universe where mathematics must be executed by creatures driven by emotion, shaped by bias, and cursed with the ability to doubt their own best decisions. The sooner we accept this cosmic joke, the sooner we can stop searching for divine strategies and start building human ones that account for our beautiful, profitable, and perfectly imperfect nature. Bibliography Asness, C.S., Moskowitz, T.J. and Pedersen, L.H. (2012) 'Value and momentum everywhere', The Journal of Finance, 68(3), pp. 929-985. Barber, B.M. and Odean, T. (2001) 'Boys will be boys: Gender, overconfidence, and common stock investment', The Quarterly Journal of Economics, 116(1), pp. 261-292. Beck, N. and Stocker, C. (2021) 'Risk parity versus other strategies: A comparison across market regimes', Journal of Portfolio Management, 47(4), pp. 156-173. Choi, J.J., Laibson, D. and Madrian, B.C. (2010) 'Why does the law of one price fail? An experiment on index mutual funds', The Review of Financial Studies, 23(4), pp. 1405-1432. Cesarone, F., Scozzari, A. and Tardella, F. (2019) 'Portfolio optimization with transaction costs and minimum investment levels', European Journal of Operational Research, 273(1), pp. 165-177. Dalio, R. (2017) Principles: Life and Work. New York: Simon & Schuster. D'Acunto, F., Prabhala, N. and Rossi, A.G. (2019) 'The promises and pitfalls of robo-advising', The Review of Financial Studies, 32(5), pp. 1983-2020. Duggan, K. and Luo, J. (2021) 'All-weather portfolios: Performance analysis across market regimes 1970-2020', Financial Analysts Journal, 77(2), pp. 78-95. Fama, E.F. and French, K.R. (1993) 'Common risk factors in the returns on stocks and bonds', Journal of Financial Economics, 33(1), pp. 3-56. Fama, E.F. and French, K.R. (2015) 'A five-factor asset pricing model', Journal of Financial Economics, 116(1), pp. 1-22. French, K.R. (2008) 'Presidential address: The cost of active investing', The Journal of Finance, 63(4), pp. 1537-1573. French, K.R. and Poterba, J.M. (1991) 'Investor diversification and international equity markets', The American Economic Review, 81(2), pp. 222-226. Gu, S., Kelly, B. and Xiu, D. (2020) 'Empirical asset pricing via machine learning', The Review of Financial Studies, 33(5), pp. 2223-2273. Harvey, C.R., Hoyle, E., Korgaonkar, R., Rattray, S., Sargaison, M. and Van Hemert, O. (2018) 'The impact of volatility targeting', The Journal of Portfolio Management, 45(1), pp. 14-33. Kahneman, D. and Tversky, A. (1979) 'Prospect theory: An analysis of decision under risk', Econometrica, 47(2), pp. 263-291. Maillard, S., Roncalli, T. and Teïletche, J. (2010) 'The properties of equally weighted risk contribution portfolios', The Journal of Portfolio Management, 36(4), pp. 60-70. Markowitz, H. (1952) 'Portfolio selection', The Journal of Finance, 7(1), pp. 77-91. Qian, E. (2005) 'Risk parity portfolios: Efficient portfolios through true diversification', PanAgora Asset Management Research Paper. Roncalli, T. and Weisang, G. (2015) 'Risk parity portfolios with skewness risk: An application to factor investing and alternative risk premia', Applied Economics, 47(25), pp. 2645-2665. Scherer, B. (2007) 'Can robust portfolio optimisation help to build better portfolios?', Journal of Asset Management, 7(6), pp. 374-387. Sharpe, W.F. (1964) 'Capital asset prices: A theory of market equilibrium under conditions of risk', The Journal of Finance, 19(3), pp. 425-442. Swensen, D.F. (2000) Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment. New York: Free Press. Thaler, R.H. and Sunstein, C.R. (2008) Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press.
SP:SPXEducation
by EdgeTools
6
US500 All Time Highs & Strong Bullish MomentumThe US 500 is in a strong bullish phase with record highs, supportive monetary policy, and robust macroeconomic and earnings backdrop. Technical conditions support the trend, but traders should be aware of potential short-term corrections due to overbought signals.The risk to reward profile favours buying dips rather than chasing highs, with strong support at 6,406–6,440 levels and upside targets above 6,500 if positive news persists. Traders should remain vigilant for volatility surges and sector rotations as August & September may bring more erratic price swings, but the fundamental and technical backdrop for the US 500 remains decisively positive. Fundamental Analysis Macro Drivers Inflation & Fed Policy: The latest July CPI showed headline inflation rising 0.2% MoM and 2.7% yoy, both below consensus forecasts. This has supercharged expectations for a Fed rate cut at the September meeting. CME FedWatch now shows a 94% chance of a rate cut, up sharply in recent weeks. This dovish monetary outlook is a primary tailwind driving new highs in equities. Earnings: Strong quarterly earnings, especially from tech and “mega cap” stocks, continue to buoy overall sentiment with all 11 index sectors ending yesterday in positive territory, with growth in technology (+1.6%), communication services (+1.9%), and financials (+1.2%). Economic Growth & Sentiment: Despite lingering macro and geopolitical risks notably tariffs and the ongoing global trade worries, recent economic data suggests resilience in consumer demand, supported by moderating inflation, soft labor market data, and improved business confidence. Defensive positioning earlier this year is giving way to more aggressive risk-taking. Technical Analysis Price Action & Levels The US500 closed at fresh record highs of 6,445.76 yesterday, and during today’s intraday trading session it has pushed above 6,460 for the first time ever. Early trading today remains calm to bullish, with potential for further upside. Breakout , recent action broke above key resistance at 6,406 and 6,440; these now form strong support zones for any pullbacks. Channel & Trend , the US500 is trading in the upper half of a well-defined ascending channel. Short-term momentum is strongly positive, yet the RSI is nearing overbought territory, suggesting the risk of minor corrections or consolidation.
SP:SPXLong
by WiseLeoTrading
S&P 500 Index Reaches New All-Time HighS&P 500 Index Reaches New All-Time High The S&P 500 index set a new all-time high, climbing above the 6,460 mark. The rally in equities is a direct result of yesterday’s CPI report. According to Forex Factory, the annual CPI remained at 2.7%, whereas analysts had forecast an increase to 2.8%. Moderate inflation readings provide stronger grounds for a Federal Reserve interest rate cut — a move President Trump has been strongly advocating. Data from the CME FedWatch tool shows that traders now price in a 94% probability of a key rate cut in September, compared to nearly 86% the day before and around 57% a month ago. This prospect of monetary policy easing acts as a bullish driver for the stock market. Technical Analysis of the S&P 500 Chart On the H4 chart of the S&P 500, there are grounds to outline an ascending channel (shown in blue). The price is currently in the upper half of the channel — a sign of prevailing optimism in the market. Following yesterday’s CPI release, the price generated a strong bullish impulse, breaking two resistance levels from below (as indicated by the arrow): → August high at 6,406 → Previous all-time high around 6,440 As a result, these former resistance levels now form the 6,406–6,440 zone. We can assume that: → this area may act as support going forward, as buyers clearly held the advantage here; → the median line of the ascending channel may also provide support, having shown signs of resistance in early August before being decisively broken on yesterday’s strong impulse. The RSI indicator is hovering near overbought territory, making the market vulnerable to corrections. However, given the improved fundamental backdrop, any pullbacks might be shallow. In the short term, the S&P 500 may reach the upper orange line, which defines the growth trajectory from the lower to the upper boundary of the long-term ascending channel. Should the fundamental outlook be further reinforced by positive news — for example, relating to a US–Russia leaders’ meeting — the index could advance towards the upper boundary of the blue channel. This article represents the opinion of the Companies operating under the FXOpen brand only. It is not to be construed as an offer, solicitation, or recommendation with respect to products and services provided by the Companies operating under the FXOpen brand, nor is it to be considered financial advice.
FXOPEN:SPXM
by FXOpen
S&P 500 All Round Analysis - S&P 500 Created HistoryTechnical Summary: The primary trend of the S&P 500 is bullish. On the 4H chart, the prices are trending bullish in an ascending channel formation. Prices are rising after reflecting from the lower trendline of the channel on Monday. The prices rose to an all-time high of 6445.75 after accepting support at the fib extension 0.382 at 6402.4. If prices successfully breach the immediate resistance at fib extension 0.50 at 6460.5, then it may rise to the far resistance zone between 6500 - 6518 (fib extension 0.618). Descriptive Analysis: The S&P 500 notched a record close on Tuesday after consumer prices rose by less than expected in July, increasing the probability of the Federal Reserve cutting interest rates next month. Data-related Projection: In the absence of key data releases today, market focus has shifted to tomorrow’s U.S. PPI report. U.S. stock markets are being underpinned by bullish sentiment, which is fueled by expectations of future rate cuts, possibly in the September Fed meeting. Indicator Interpretation: The prices are trending higher in the buying zone of the technical indicators - above the middle Bollinger band and the mid-50 level of RSI. ALTERNATIVE SCENARIO Technical Confirmation: If the index fails to rise above fib extension 0.50 at 6460.5 and sustain, but falls below fib extension 0.382 at 6402.4, then further bearishness can be expected. Facts & Figures: The S&P 500 is a very well-known index of large-cap US stocks, covering about 75% of all publicly traded US stocks. It comprises 505 stocks of 500 companies selected by a committee using 8 criteria. Daily Pivot: 6420 (CMP: 6445.75, which is trading above the pivot level while writing the report).
TVC:SPX
by Nadia_farhat
US500 Short Setup: Bearish Momentum Toward 6205Currently holding a short position on the US500 from the 6358 level, based on Smart Money Concepts. Price has tapped into a premium zone within a higher time frame supply area, showing clear signs of distribution. Liquidity has been swept above recent highs, and a shift in market structure confirms bearish intent. I’m targeting the 6205 level, expecting a rapid downside move in the very short term as smart money drives price toward discounted levels.
PEPPERSTONE:US500Short
by Rizwan-Ali
$SPX Analysis, Key Levels & Targets What a crazy day. We had a CPI in premarket today then we kept up and we took it past all-time highs to the top of the implied move to the top of the implied move on the next day so we close just above 6440. VIX got absolutely crushed, down 9% today destroying options betting on downside. I do believe that tomorrow we will be trading around 6440, maybe even between 6420 and 644. Thursday we have PPI and because we're in an in between day tomorrow I think we might be a little bit more flat also look at how high up that 35 EMA is within the training range (HIT GRAB THIS CHART - UNDER THE CHART NEXT TO THE 🚀)
SP:SPX
by SPYder_QQQueen_Trading
S&P 500 (SPX) Targets 6639 in Final Wave 5The Elliott Wave cycle, initiated at the April 7, 2025 low, is progressing as an impulse structure, driving the Index upward. Wave (1) reached a high of 5267.47, followed by a wave (2) pullback to 4910.42. The Index then surged in wave (3) to 6427.02, as depicted in the one-hour chart. A corrective wave (4) concluded at 6212.42, structured as a zigzag. Within this correction, wave A dropped to 6327.6, wave B climbed to 6339.89, and wave C finalized the decline at 6212.4, completing wave (4) in the higher degree. The Index has since advanced in wave (5), breaking above the wave (3) peak of 6427.02, confirming the next upward move. This wave (5) is unfolding as a lower-degree impulse. From the wave (4) low, wave 1 hit 6346, and wave 2 corrected to 6289.37. The Index then nested higher, with wave ((i)) reaching 6389.7 and wave ((ii)) retracing to 6310.32. In the short term, anticipate a few more highs to complete wave 1 of (5). A pullback should follow to adjust the cycle from the August 2, 2025 low. As long as the 6212.4 pivot remains intact, the Index should continue climbing.
SP:SPX
by Elliottwave-Forecast
2
S&P 500 Ascending Triangle Breakout SetupThe S&P 500 index is consolidating within an ascending triangle pattern, with strong support along the rising trendline. Price action suggests bullish momentum building towards a potential breakout above the resistance zone, targeting higher levels if confirmed 1. Previous Trend: Strong bullish rally before the current consolidation phase, indicating momentum is on the buyers’ side. 2. Pattern Formation: Horizontal resistance around 6,418–6,420 (marked by the grey line). Rising support trendline connecting higher lows, showing buying pressure is increasing. 3. Volume (Not Shown Here): If volume is decreasing inside the triangle and expected to spike on breakout, it supports a bullish case. 4. Breakout Expectation: A confirmed close above 6,420 with strong volume could trigger a rally towards the next target zone of 6,470–6,500. The arrow drawn on your chart aligns with this possible breakout scenario. 5. Invalidation Zone: If price breaks below the rising trendline (~6,360), the bullish pattern would be invalidated, potentially pushing price back to 6,300–6,280.
VANTAGE:SP500Long
by TRADE_WITH_VISION
S&P500 awaits US CPI data capped by 6437US equities were little changed Monday. Nvidia (-0.35%) and AMD (-0.28%) slipped after agreeing to pay 15% of Chinese AI chip sales revenue to the US government in exchange for export licenses — a deal Trump confirmed, adding he may allow a scaled-down Blackwell chip to be sold in China. The Philadelphia Semiconductor Index fell -0.13%, cushioned by Intel (+3.51%) after its CEO met with Trump. The Mag-7 (-0.09%) saw slight outperformance, led by Tesla (+2.85%). Trump extended the US–China tariff truce by 90 days to November 10, keeping additional tariffs at 30%. China matched the extension. The Fed chair search now includes vice chairs Bowman and Jefferson, plus Dallas Fed President Logan. Trump appointed EJ Antoni (Heritage Foundation) to lead the Bureau of Labor Statistics. Markets await today’s US CPI, which could influence Fed rate-cut bets, while gold traders look for clarity after Trump said gold imports won’t be tariffed. Key Support and Resistance Levels Resistance Level 1: 6437 Resistance Level 2: 6480 Resistance Level 3: 6520 Support Level 1: 6340 Support Level 2: 6295 Support Level 3: 6256 This communication is for informational purposes only and should not be viewed as any form of recommendation as to a particular course of action or as investment advice. It is not intended as an offer or solicitation for the purchase or sale of any financial instrument or as an official confirmation of any transaction. Opinions, estimates and assumptions expressed herein are made as of the date of this communication and are subject to change without notice. This communication has been prepared based upon information, including market prices, data and other information, believed to be reliable; however, Trade Nation does not warrant its completeness or accuracy. All market prices and market data contained in or attached to this communication are indicative and subject to change without notice.
TRADENATION:US500
by TradeNation
SPX Sells 8/12Sells on SPX made sense because both 4h and 1h candles are currently Bearish, smaller timeframe structure was making LLs and LHs. Liquidity built right before 7am. The 1h hour actually showed the Unicorn setup, which is why I wait until Tuesdays to enter the market. The long upside wick ran the stops and closed below, Price broke structure and left imbalance. It did not fill it all the way. I am getting better at watching the Liquidity set up first and then sweep. Also need to train my eyes to see the Bullish or Bearish orderflow.
FX:SPX500
by Chamira
1
SPX500 | CPI Day Volatility – Key Level at 6389SPX500 Overview Today’s market is expected to be highly volatile with the release of U.S. CPI data. The forecast is 2.8%, higher than the previous reading, indicating inflation remains elevated. A result above 2.8% would likely support a bearish trend for indices, while a figure below expectations could trigger bullish momentum. Technical Outlook: As long as the price trades below 6389, the downside targets are 6365, then 6341, and 6321. A 1H close above 6389 would shift momentum bullish toward 6425. Support: 6365, 6341, 6321 Resistance: 6404, 6425, 6438
OANDA:SPX500USDShort
by SroshMayi
Quants vs Humans: Wall Street’s Cold War By Ion Jauregui – Analyst at ActivTrades A silent battle is being fought on Wall Street between humans and machines. Quants — algorithmic traders who operate by following trends — are showing a level of optimism not seen since January 2020, according to Deutsche Bank AG (Ticker AT:DBK.GE). In contrast, “flesh and blood” managers have reduced their equity exposure to modestly underweight levels, pressured by the risk of new Trump tariffs, weaker-than-expected economic growth, and corporate earnings that remain unconvincing. The S&P 500 has risen nearly 30% since April, bringing systematic long positions to their highest level in four years. Goldman Sachs (Ticker AT:GS.US) reports that Commodity Trading Advisors (CTAs) now control about $50 billion in U.S. equities, placing them in the 92nd percentile of historical exposure. Such an extreme positioning has not been seen since periods preceding major market moves. Deutsche Bank was among the first to warn about this divergence between quantitative and discretionary trading: while trend-following models keep adding positions, manually managed portfolios are prioritizing capital protection. Goldman Sachs, for its part, offers the clearest risk assessment: aggressive accumulation by CTAs leaves the market highly exposed to feedback-driven moves. And UBS Group AG (Ticker AT:UBSG.CH) warns that if the S&P 500 falls just 4.5% to 6,100 points, these systems could trigger massive selling, creating a domino effect that would accelerate the correction. The backdrop adds more tension: the VIX volatility index remains around 15 points, its lowest level since February. This apparent calm acts like a stretched rubber band ready to snap. In the event of a sharp pullback, discretionary managers might step in to “buy the dip” and prevent a deeper sell-off, yet the sense that the market is approaching a top is becoming increasingly tangible. Technical Analysis – S&P 500 The index maintains an underlying bullish structure, but with overbought signals on daily and weekly charts. Currently, the RSI shows mild overbought conditions at 58.7%, while the MACD indicates a bearish divergence and a red histogram with low volume — a sign of possible price consolidation. • Key resistance: 6,442.76 points, the recent all-time high and a psychological ceiling that could halt further gains if quantitative momentum fades. • Intermediate support: 6,204 points, where short-term moving averages converge with previous consolidation areas. • Critical support: 6,060 points, identified by the Point of Control (POC) as the threshold for triggering massive algorithmic selling; a break below this level could open the door to declines toward 5,950 or 5,908 points. As long as 6,060 holds, the primary trend still favors the bulls. However, the high concentration of systematic long positions suggests that any correction could be swift and violent, forcing traders to react quickly. ******************************************************************************************* The information provided does not constitute investment research. The material has not been prepared in accordance with the legal requirements designed to promote the independence of investment research and such should be considered a marketing communication. All information has been prepared by ActivTrades ("AT"). The information does not contain a record of AT's prices, or an offer of or solicitation for a transaction in any financial instrument. No representation or warranty is given as to the accuracy or completeness of this information. Any material provided does not have regard to the specific investment objective and financial situation of any person who may receive it. Past performance and forecasting are not a synonym of a reliable indicator of future performance. AT provides an execution-only service. Consequently, any person acting on the information provided does so at their own risk. Political risk is unpredictable. Central bank actions can vary. Platform tools do not guarantee success.
ACTIVTRADES:USA500Long
by ActivTrades