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Japan 225: Breakout Watch as Tariff Cut Fuels RallyThe Japan 225 contract is ripping higher today on a trade deal being reached between the U.S. and Japan, including the apparent lowering of auto tariffs to 15%. The price has already bounced strongly from uptrend support established in May, putting a potential retest of the June 30 high of 40854 on the cards. With momentum indicators perking up—pointing to building bullish momentum—the case for a topside break is improving. If the price can break and hold above the June 30 high, watch for a potential run towards 41000—a level that saw plenty of action back in 2024. It looms as a decent level to build setups around. If the price breaks and holds above 41000, consider establishing longs with a tight stop beneath the level for protection. 41600 and 42480 screen as potential targets, depending on desired risk-reward. While the setup could be flipped if the price stalls beneath 41000, just how much downside would be forthcoming given the current mood is questionable. Good luck! DS
FOREXCOM:JP225Long
by FOREXcom
Bullish bounce?US Dollar Index (DXY) is falling towards the pivot which aligns with the 78.6% Fibonacci retracement and could bounce to the 1st resistance. Pivot: 97.00 1st Support: 96.48 1st Resistance: 97.77 Risk Warning: Trading Forex and CFDs carries a high level of risk to your capital and you should only trade with money you can afford to lose. Trading Forex and CFDs may not be suitable for all investors, so please ensure that you fully understand the risks involved and seek independent advice if necessary. Disclaimer: The above opinions given constitute general market commentary, and do not constitute the opinion or advice of IC Markets or any form of personal or investment advice. Any opinions, news, research, analyses, prices, other information, or links to third-party sites contained on this website are provided on an "as-is" basis, are intended only to be informative, is not an advice nor a recommendation, nor research, or a record of our trading prices, or an offer of, or solicitation for a transaction in any financial instrument and thus should not be treated as such. The information provided does not involve any specific investment objectives, financial situation and needs of any specific person who may receive it. Please be aware, that past performance is not a reliable indicator of future performance and/or results. Past Performance or Forward-looking scenarios based upon the reasonable beliefs of the third-party provider are not a guarantee of future performance. Actual results may differ materially from those anticipated in forward-looking or past performance statements. IC Markets makes no representation or warranty and assumes no liability as to the accuracy or completeness of the information provided, nor any loss arising from any investment based on a recommendation, forecast or any information supplied by any third-party.
TVC:DXYLong
by ICmarkets
USNAS100 Eyes 23350 Ahead of Powell & Earnings StormUSNAS100 The price maintains bullish momentum, as outlined in the previous idea, with a clean rejection from the 23280 zone. Today, volatility is expected to increase ahead of Jerome Powell’s speech and a wave of key earnings reports. As long as price holds above 23140, the outlook remains bullish toward 23230 and 23350. A short-term correction toward 23045 is possible if we get a 1H close below 23140. Resistance: 23230 · 23350 · 23510 Support: 23045 · 22920
OANDA:NAS100USDLong
by SroshMayi
1
China A50 Breakout: 14% Opportunity Amid Summer Lull?Markets are quiet, but the China A50 is heating up. A bullish inverse head and shoulders pattern has formed, backed by stimulus hopes. Target upside is 14% with a strong risk-reward setup. Are you ready to trade it, or still on holiday? This content is not directed to residents of the EU or UK. Any opinions, news, research, analyses, prices or other information contained on this website is provided as general market commentary and does not constitute investment advice. ThinkMarkets will not accept liability for any loss or damage including, without limitation, to any loss of profit which may arise directly or indirectly from use of or reliance on such information.
THINKMARKETS:CHINA50Long
by ThinkMarkets
#NIFTY Intraday Support and Resistance Levels - 23/07/2025Nifty is expected to open with a gap-up today, continuing its sideways movement within a tight range. There are no significant changes in key levels from the previous session, indicating a consolidative phase in the market. Price action near these levels will be crucial for intraday direction. On the upside, a bullish continuation can be expected if Nifty sustains above 25,050–25,100. This zone has been acting as a breakout point, and a sustained move above can lead to an intraday rally toward 25,150, 25,200, and 25,250+. Further strength will be confirmed if Nifty crosses 25,250, opening the possibility to test 25,350, 25,400, and even 25,450+ levels. On the downside, if Nifty breaks below 24,950, it may trigger a short setup with potential downside targets of 24,850, 24,800, and 24,750-. This breakdown would indicate weakness, especially if it comes with volume.
NSE:NIFTY
by TradZoo
US30 - What I believe will happen todayConsidering today is now Wednesday and Wednesday's have a consistency of being one of those off days. Primarily because usually there are Fed speeches on this day that manipulate the market. With that said, I'm expecting price action to play out how I plotted the arrows on the chart. I believe there is still some further upside for today but then I believe we might get that intraday correction around 11:am - noon EST. Let's see how today plays out. Trade safe
BLACKBULL:US30
by Michael_Harding
The Empirical Validity of Technical Indicators and StrategiesThis article critically examines the empirical evidence concerning the effectiveness of technical indicators and trading strategies. While traditional finance theory, notably the Efficient Market Hypothesis (EMH), has long argued that technical analysis should be futile, a large body of academic research both historical and contemporary presents a more nuanced view. We explore key findings, address methodological limitations, assess institutional use cases, and discuss the impact of transaction costs, market efficiency, and adaptive behavior in financial markets. 1. Introduction Technical analysis (TA) remains one of the most controversial subjects in financial economics. Defined as the study of past market prices and volumes to forecast future price movements, TA is used by a wide spectrum of market participants, from individual retail traders to institutional investors. According to the EMH (Fama, 1970), asset prices reflect all available information, and hence, any predictable pattern should be arbitraged away instantly. Nonetheless, technical analysis remains in widespread use, and empirical evidence suggests that it may offer predictive value under certain conditions. 2. Early Empirical Evidence The foundational work by Brock, Lakonishok, and LeBaron (1992) demonstrated that simple trading rules such as moving average crossovers could yield statistically significant profits using historical DJIA data spanning from 1897 to 1986. Importantly, the authors employed bootstrapping methods to validate their findings against the null of no serial correlation, thus countering the argument of data mining. Gencay (1998) employed non-linear models to analyze the forecasting power of technical rules and confirmed that short-term predictive signals exist, particularly in high-frequency data. However, these early works often omitted transaction costs, thus overestimating potential returns. 3. Momentum and Mean Reversion Strategies Momentum strategies, as formalized by Jegadeesh and Titman (1993), have shown persistent profitability across time and geographies. Their approach—buying stocks that have outperformed in the past 3–12 months and shorting underperformers—challenges the EMH by exploiting behavioral biases and investor herding. Rouwenhorst (1998) confirmed that momentum exists even in emerging markets, suggesting a global phenomenon. Conversely, mean reversion strategies, including RSI-based systems and Bollinger Bands, often exploit temporary price dislocations. Short-horizon contrarian strategies have been analyzed by Chan et al. (1996), but their profitability is inconsistent and highly sensitive to costs, timing, and liquidity. 4. Institutional Use of Technical Analysis Contrary to the belief that TA is primarily a retail tool, it is also utilized—though selectively—by institutional investors: Hedge Funds: Many quantitative hedge funds incorporate technical indicators within multi-factor models or machine learning algorithms. According to research by Neely et al. (2014), trend-following strategies remain a staple among CTAs (Commodity Trading Advisors), particularly in futures markets. These strategies often rely on moving averages, breakout signals, and momentum filters. Market Makers: Although market makers are primarily driven by order flow and arbitrage opportunities, they may use TA to model liquidity zones and anticipate stop-hunting behavior. Order book analytics and technical levels (e.g., pivot points, Fibonacci retracements) can inform automated liquidity provision. Pension Funds and Asset Managers: While these institutions rarely rely on TA alone, they may use it as part of tactical asset allocation. For instance, TA may serve as a signal overlay in timing equity exposure or in identifying risk-off regimes. According to a CFA Institute survey (2016), over 20% of institutional investors incorporate some form of technical analysis in their decision-making process. 5. Adaptive Markets and Conditional Validity Lo (2004) introduced the Adaptive Markets Hypothesis (AMH), arguing that market efficiency is not a binary state but evolves with the learning behavior of market participants. In this framework, technical strategies may work intermittently, depending on the ecological dynamics of the market. Neely, Weller, and Ulrich (2009) found technical rules in the FX market to be periodically profitable, especially during central bank interventions or volatility spikes—conditions under which behavioral biases and structural inefficiencies tend to rise. More recent studies (e.g., Moskowitz et al., 2012; Baltas & Kosowski, 2020) show that momentum and trend-following strategies continue to deliver long-term Sharpe ratios above 1 in diversified portfolios, particularly when combined with risk-adjusted scaling techniques. 6. The Role of Transaction Costs Transaction costs represent a critical variable that substantially alters the net profitability of technical strategies. These include: Explicit Costs: Commissions, fees, and spreads. Implicit Costs: Market impact, slippage, and opportunity cost. While early studies often neglected these elements, modern research integrates them through realistic backtesting frameworks. For example, De Prado (2018) emphasizes that naive backtesting without cost modeling and slippage assumptions leads to a high incidence of false positives. Baltas and Kosowski (2020) show that even after accounting for bid-ask spreads and market impact models, trend-following strategies remain profitable, particularly in futures and FX markets where costs are lower. Conversely, high-frequency mean-reversion strategies often become unprofitable once these frictions are accounted for. The impact of transaction costs also differs by asset class: Equities: Higher costs due to wider spreads, especially in small caps. Futures: Lower costs and higher leverage make them more suitable for technical strategies. FX: Extremely low spreads, but high competition and adverse selection risks. 7. Meta-Analyses and Recent Surveys Park and Irwin’s (2007) meta-analysis of 95 studies found that 56% reported significant profitability from technical analysis. However, profitability rates dropped when transaction costs were included. More recent work by Han, Yang, and Zhou (2021) extended this review with data up to 2020 and found that profitability was regime-dependent: TA performed better in volatile or trending environments and worse in stable, low-volatility markets. Other contributions include behavioral explanations. Barberis and Thaler (2003) suggest that TA may capture collective investor behavior, such as overreaction and underreaction, thereby acting as a proxy for sentiment. 8. Limitations and Challenges Several methodological issues plague empirical research in technical analysis: Overfitting: Using too many parameters increases the likelihood of in-sample success but out-of-sample failure. Survivorship Bias: Excluding delisted or bankrupt stocks leads to inflated backtest performance. Look-Ahead Bias: Using information not available at the time of trade leads to unrealistic results. Robust strategy development now mandates walk-forward testing, Monte Carlo simulations, and realistic assumptions on order execution. The growing field of machine learning in finance has heightened these risks, as complex models are more prone to fitting noise rather than signal (Bailey et al., 2014). 9. Conclusion Technical analysis occupies a contested but persistent role in finance. The empirical evidence is mixed but suggests that technical strategies can be profitable under certain market conditions and when costs are minimized. Institutional investors have increasingly integrated TA within quantitative and hybrid frameworks, reflecting its conditional usefulness. While TA does not provide a universal arbitrage opportunity, it can serve as a valuable tool when applied adaptively, with sound risk management and rigorous testing. Its success ultimately depends on context, execution discipline, and integration within a broader investment philosophy. References Bailey, D. H., Borwein, J. M., Lopez de Prado, M., & Zhu, Q. J. (2014). "The Probability of Backtest Overfitting." *Journal of Computational Finance*, 20(4), 39–69. Baltas, N., & Kosowski, R. (2020). "Trend-Following, Risk-Parity and the Influence of Correlations." *Journal of Financial Economics*, 138(2), 349–368. Barberis, N., & Thaler, R. (2003). "A Survey of Behavioral Finance." *Handbook of the Economics of Finance*, 1, 1053–1128. Brock, W., Lakonishok, J., & LeBaron, B. (1992). "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns." Journal of Finance, 47(5), 1731–1764. Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (1996). "Momentum Strategies." Journal of Finance, 51(5), 1681–1713. De Prado, M. L. (2018). Advances in Financial Machine Learning, Wiley. Fama, E. F. (1970). "Efficient Capital Markets: A Review of Theory and Empirical Work." Journal of Finance, 25(2), 383–417. Gencay, R. (1998). "The Predictability of Security Returns with Simple Technical Trading Rules." Journal of Empirical Finance, 5(4), 347–359. Han, Y., Yang, K., & Zhou, G. (2021). "Technical Analysis in the Era of Big Data." *Review of Financial Studies*, 34(9), 4354–4397. Jegadeesh, N., & Titman, S. (1993). "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." *Journal of Finance*, 48(1), 65–91. Lo, A. W. (2004). "The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective." *Journal of Portfolio Management*, 30(5), 15–29. Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). "Time Series Momentum." *Journal of Financial Economics*, 104(2), 228–250. Neely, C. J., Weller, P. A., & Ulrich, J. M. (2009). "The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market." *Journal of Financial and Quantitative Analysis*, 44(2), 467–488. Neely, C. J., Rapach, D. E., Tu, J., & Zhou, G. (2014). "Forecasting the Equity Risk Premium: The Role of Technical Indicators." *Management Science*, 60(7), 1772–1791. Park, C. H., & Irwin, S. H. (2007). "What Do We Know About the Profitability of Technical Analysis?" *Journal of Economic Surveys*, 21(4), 786–826. Rouwenhorst, K. G. (1998). "International Momentum Strategies." *Journal of Finance*, 53(1), 267–284. Zhu, Y., & Zhou, G. (2009). "Technical Analysis: An Asset Allocation Perspective on the Use of Moving Averages." *Journal of Financial Economics*, 92(3), 519–544.
SP:SPXEducation
by EdgeTools
USDX at a crucial price point, which will decide trendThe next few candles for USDX price will be extremely important to understand where the dollar index is headed. Based on that, we'll be able to choose 'which' Forex pairs we can trade, and more importantly 'how' we can trade them. If price starts to follow the green line, and takes out 99.000, we'll be in an uptrend. If price goes back to the red line and eventually goes below 96.400, we'll be in a downtrend. Hopefully price won't consolidate too much, and trend in a particular direction.
PEPPERSTONE:USDX
by Nikladon
6
DXY – The Trap Is Set. The Drop Is Coming.Wave structure complete. Retail thinks we’re going up. I know where it’s really going. This is GreenFire Execution, not prediction. 🧠 Final liquidity hunt possibly toward 98.76 or even 101.40, then lights out. Targeting the big liquidity pocket at 95.215 That’s where legends buy while the crowd panics. 📐 Elliott Wave | Wedge Mastery | Market Psychology If you understand this chart — you don’t need signals. You need silence and execution. #DXYSetup | #Wave5Ready | #SmartMoney | #ForexMillionaire | #TradingViewElite
TVC:DXYShort
by Greenfireforex