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EURUSDHello Traders! 👋 What are your thoughts on EURUSD? The EUR/USD pair has been ranging between key support and resistance zones since last week. At present, price is sitting right on a critical support level, as the market appears to be waiting for Fed Chair Jerome Powell's speech at Jackson Hole tomorrow. Our broader outlook remains bearish, but a clear break below support is needed to confirm downside continuation. If support breaks, the pair could head toward lower targets in the coming sessions. Avoid early short positions while price is still holding above support. Wait for a confirmed breakdown of the support level to validate the bearish scenario. Don’t forget to like and share your thoughts in the comments! ❤️
Short
by HAMED_AZ
20
USDJPY: Bearish Continuation is Highly Probable! Here is Why: It is essential that we apply multitimeframe technical analysis and there is no better example of why that is the case than the current USDJPY chart which, if analyzed properly, clearly points in the downward direction. Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis. ❤️ Please, support our work with like & comment! ❤️
Short
by UnitedSignals
1
EURJPY: Bearish Continuation & Short Trade EURJPY - Classic bearish setup - Our team expects bearish continuation SUGGESTED TRADE: Swing Trade Short EURJPY Entry Point - 172.05 Stop Loss - 172.21 Take Profit - 171.73 Our Risk - 1% Start protection of your profits from lower levels Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis. ❤️ Please, support our work with like & comment! ❤️
Short
by UnitedSignals
1
CHFJPY Technical Analysis! SELL! My dear friends, Please, find my technical outlook for CHFJPY below: The instrument tests an important psychological level 183.36 Bias - Bearish Technical Indicators: Supper Trend gives a precise Bearish signal, while Pivot Point HL predicts price changes and potential reversals in the market. Target - 182.73 About Used Indicators: Super-trend indicator is more useful in trending markets where there are clear uptrends and downtrends in price. Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis. ——————————— WISH YOU ALL LUCK
Short
by AnabelSignals
1
EURUSD Sellers In Panic! BUY! My dear friends, Please, find my technical outlook for EURUSD below: The price is coiling around a solid key level - 1.1639 Bias - Bullish Technical Indicators: Pivot Points High anticipates a potential price reversal. Super trend shows a clear buy, giving a perfect indicators' convergence. Goal - 1.1651 Safe Stop Loss - 1.1631 About Used Indicators: The pivot point itself is simply the average of the high, low and closing prices from the previous trading day. Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis. ——————————— WISH YOU ALL LUCK
Long
by AnabelSignals
1
Gold Holds Haven Status Amid Market UncertaintyXAUUSD has been in a 5-month consolidation phase since hitting its record high at $3,500. The current price action is respecting the lower edge of the range above $3,300, and testing resistance levels drawn from a Fibonacci extension of the $3,268 low, $3,408 high, and $3,311 pullback. • Resistance levels in focus: $3,350, $3,380, $3,400, and $3,450 • A confirmed breakout may extend to $3,780 and $4,000 • Downside risk: A close below $3,280 could open the path toward $3,260, $3,240, $3,130, and $2,900 The longer this consolidation lasts, the stronger the eventual breakout may be. The daily pattern may evolve into either an inverted head and shoulders (with an extended right shoulder) or a simple triangle formation. RSI across daily, weekly, and hourly charts remains neutral to slightly positive, unless invalidated by a breakdown. Written by Razan Hilal, CMT
FOREXCOM:XAUUSD
by FOREXcom
1
EURUSD Is Very Bullish! Buy! Take a look at our analysis for EURUSD. Time Frame: 12h Current Trend: Bullish Sentiment: Oversold (based on 7-period RSI) Forecast: Bullish The price is testing a key support 1.165. Current market trend & oversold RSI makes me think that buyers will push the price. I will anticipate a bullish movement at least to 1.184 level. P.S Overbought describes a period of time where there has been a significant and consistent upward move in price over a period of time without much pullback. Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis. Like and subscribe and comment my ideas if you enjoy them!
Long
by SignalProvider
1
EURNZD Is Going Down! Short! Please, check our technical outlook for EURNZD. Time Frame: 1D Current Trend: Bearish Sentiment: Overbought (based on 7-period RSI) Forecast: Bearish The market is trading around a solid horizontal structure 1.999. The above observations make me that the market will inevitably achieve 1.952 level. P.S Please, note that an oversold/overbought condition can last for a long time, and therefore being oversold/overbought doesn't mean a price rally will come soon, or at all. Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis. Like and subscribe and comment my ideas if you enjoy them!
Short
by SignalProvider
2
NZDCHF Will Grow! Long! Here is our detailed technical review for NZDCHF. Time Frame: 1D Current Trend: Bullish Sentiment: Oversold (based on 7-period RSI) Forecast: Bullish The market is testing a major horizontal structure 0.469. Taking into consideration the structure & trend analysis, I believe that the market will reach 0.479 level soon. P.S The term oversold refers to a condition where an asset has traded lower in price and has the potential for a price bounce. Overbought refers to market scenarios where the instrument is traded considerably higher than its fair value. Overvaluation is caused by market sentiments when there is positive news. Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis. Like and subscribe and comment my ideas if you enjoy them!
Long
by SignalProvider
1
USDCAD Top of the Channel issues short-term sell signal.The USDCAD pair has been trading within a Channel Up since the 1D RSI Bullish Divergence started on the June 16 bottom and right now the price is approaching its top (Higher Highs trend-line) yet again. With the 1D MA50 (blue trend-line) acting as Support, we expect as short-term pull-back (at least) as long as the 1D candles close within the pattern. Our Target is 1.37715. ------------------------------------------------------------------------------- ** Please LIKE 👍, FOLLOW ✅, SHARE 🙌 and COMMENT ✍ if you enjoy this idea! Also share your ideas and charts in the comments section below! This is best way to keep it relevant, support us, keep the content here free and allow the idea to reach as many people as possible. ** ------------------------------------------------------------------------------- Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis. 💸💸💸💸💸💸 👇 👇 👇 👇 👇 👇
Short
by TradingShot
AUD/JPY BULLISH BIAS RIGHT NOW| LONG Hello, Friends! AUD/JPY pair is trading in a local uptrend which know by looking at the previous 1W candle which is green. On the 1D timeframe the pair is going down. The pair is oversold because the price is close to the lower band of the BB indicator. So we are looking to buy the pair with the lower BB line acting as support. The next target is 96.919 area. Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis. ✅LIKE AND COMMENT MY IDEAS✅
Long
by EliteTradingSignals
1
GBP/CAD BEST PLACE TO SELL FROM|SHORT Hello, Friends! We are going short on the GBP/CAD with the target of 1.843 level, because the pair is overbought and will soon hit the resistance line above. We deduced the overbought condition from the price being near to the upper BB band. However, we should use low risk here because the 1W TF is green and gives us a counter-signal. Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis. ✅LIKE AND COMMENT MY IDEAS✅
Short
by EliteTradingSignals
1
EUR/USD Set to Test Key Support Level!Hello everyone, the EUR/USD chart currently shows a clear bearish structure. After failing to break the strong resistance at 1.17100, the market has reversed and is forming lower highs and lows. The key support level at 1.16100 is crucial to watch. If the price breaks this support, the pair may continue its strong downtrend, targeting 1.15300. News supporting the bearish trend: FOMC minutes: With a dovish FOMC minutes, the USD is likely to continue strengthening, putting pressure on EUR/USD. Initial jobless claims: Negative data from jobless claims reinforces the bullish trend of USD, pushing EUR/USD lower. Weak PMI: The weakening PMI indicators from the Eurozone increase the likelihood of EUR/USD continuing its downtrend. Strategy: Wait for a break below 1.16100 to enter a SELL trade, targeting 1.15300 as the next levels.
Short
by Rocky_trade_
11
What Are Autoregressive Models in Trading?What Are Autoregressive Models in Trading? Autoregressive (AR) models help traders analyse market movements by identifying statistical relationships in historical price data. These models assume that past values influence current prices, making them useful for spotting trends and price behaviour. This article explores “What is autoregression?”, how AR models function, their role in trading, and how traders apply them to market analysis. What Is an Autoregressive Model? Autoregressive (AR) models are statistical tools that can be used in numerous spheres, including market prices, weather, and traffic conditions. They analyse market movements by using past price data to understand current trends. The autoregressive definition refers to a model where each value in a time series depends on previous values plus an error term. The number of previous values considered is called the “lag order,” denoted as AR(p), where ‘p’ represents the number of lags. In an autoregressive model example, an AR(1) model looks at just the previous value to estimate the current one, while an AR(3) model considers the last three. In trading, the key idea is that if historical prices show a consistent pattern—whether trending or reverting to a mean—an AR model can help identify that structure. This approach differs from other time series models. Moving averages (MA) smooth out fluctuations by averaging past prices, while autoregressive integrated moving averages (ARIMA) combine both approaches and adjust for trends. AR models, however, focus purely on the statistical relationship between past and present values, making them particularly useful in markets where past behaviour has a clear influence on future movements. Traders use an autoregressive process to explore trends, momentum, and potential reversals in markets that exhibit persistent patterns. However, their effectiveness depends on market conditions and the assumption that past relationships remain relevant—something that isn’t always guaranteed, especially in volatile or news-driven environments. How Autoregressive Models Work in Trading Traders use AR models to examine how past prices influence current movements. An autoregressive model trading strategy often involves assessing whether an asset’s price exhibits momentum or mean reversion tendencies. For example, if an AR(1) model shows that today’s price is strongly influenced by yesterday’s price, it may suggest a continuation bias—meaning traders could expect trends to persist in the short term. In contrast, if an AR(2) or AR(3) model highlights a tendency for prices to move back toward an average after a few periods, it could indicate mean reversion. This is particularly relevant in range-bound markets where prices frequently return to support and resistance levels. The number of past values included in an AR model is a key decision. Too few lags might miss relevant patterns, while too many can add unnecessary complexity. Traders typically determine the appropriate lag length by evaluating past data and statistical criteria like the Akaike Information Criterion (AIC). AR models are more popular in markets where historical relationships hold for extended periods. It’s common to use autoregressive models for trading forex, equities, and commodities, especially in detecting short-term trends or cycles. While they aren’t predictive tools, they provide a structured way to analyse price behaviour, offering traders a statistical foundation for evaluating market movements. Stationarity and Its Role in AR Models For an autoregressive time series model to work, the data must be stationary. This means the statistical properties of the time series—such as its mean, variance, and autocorrelation—remain constant over time. If a dataset is non-stationary, meaning its trends, volatility, or relationships shift unpredictably, the AR model's analysis can become unreliable. Why Stationarity Matters The autoregressive model, meaning it assumes a consistent statistical structure, can struggle with shifting market conditions if stationarity is not ensured. If a time series is non-stationary, it might show an upward or downward drift, meaning price relationships aren’t consistent over time. This makes it difficult to analyse patterns. For example, a stock experiencing long-term growth won’t have a stable mean, which can distort AR-based analysis. Testing for Stationarity Traders often check for stationarity using statistical tests like the Augmented Dickey-Fuller (ADF) test. This test helps determine whether a time series has a unit root—a key characteristic of non-stationary data. If the test suggests a unit root is present, traders may need to adjust the data before using an AR model. Transforming Data to Stationarity When data is non-stationary, traders often apply transformations to stabilise it and convert it to an autoregressive model time series. Differencing is a common method, where they subtract the previous value from the current value to remove trends. Log transformations can also reduce the impact of volatility. Once stationarity is achieved, an AR model is believed to be more effective to analyse price movements. Using an Autoregressive Model in Practice Understanding how autoregressive models work is one thing—actually applying them in trading is another. These models are primarily used in quantitative strategies, where traders rely on statistical methods rather than gut feelings or news events. While AR models aren’t a complete trading strategy on their own, they can provide valuable insights when used correctly. Building an AR Model The first step in using an AR model is preparing the data. Traders typically start with a time series dataset—such as daily closing prices—and ensure it is stationary. If the data shows trends or changing volatility, they may apply differencing or log transformations to stabilise it. Once the data is ready, the next step is determining the lag order—how many past values should be included in an AR(p) model. This is done through statistical tests like the Akaike Information Criterion (AIC) or Partial Autocorrelation Function (PACF), which help identify how far back price movements remain relevant. For instance, an AR1 model considers only the previous price point, while an AR3 model incorporates the last three observations. Choosing too few lags might miss important relationships, while too many can overcomplicate the model. After selecting the lag order, traders fit the AR model using statistical software such as Python’s statsmodels or R’s forecast package. The model estimates how past prices influence current ones, producing a set of coefficients that define these relationships. The trader then analyses these results to determine if the model aligns with market behaviour. Applying AR Models to Trading Once built, an AR model provides insights into how past price behaviour influences future movement. For example: - If an AR(1) model shows a strong positive coefficient, it suggests that today’s price is closely linked to yesterday’s, reinforcing a short-term trend. - If an AR(2) or AR(3) model suggests a return toward a long-term mean, it may indicate a market where price cycles are present. Traders use these insights in different ways. Some apply AR models to analyse short-term market momentum, while others use them to examine mean-reverting assets like certain forex pairs or commodities. They can also compare AR-based analysis with other indicators like moving averages or Bollinger Bands to refine their decision-making process. Autoregressive models are also used in machine learning for time series forecasting, helping algorithms detect patterns in sequential data. In trading, autoregressive model machine learning techniques can refine models by dynamically adjusting lag parameters, improving adaptability to changing market conditions and reducing reliance on fixed assumptions. ARIMA: Extending AR Models While AR models work well on stationary data, many financial time series contain trends or seasonality that a basic AR model can’t handle. This is a scenario where Autoregressive Integrated Moving Average (ARIMA) models become useful. ARIMA combines AR components with moving averages (MA) and differencing (I for “integrated”) to account for non-stationary behaviour. For example, if a stock price has an upward drift, an AR model alone won’t be sufficient. An ARIMA model can first remove the trend through differencing, and then apply AR and MA components to analyse underlying patterns. This makes ARIMA more flexible for complex market environments. Challenges and Considerations When Using AR Models Autoregressive models can be useful for analysing price movements, but they come with limitations that traders should consider. Financial markets are complex, and historical price patterns don’t always repeat in the same way. Understanding where AR models fall short might help traders apply them more effectively. Overfitting and Choosing the Right Lag Order One of the biggest challenges in using AR models is selecting the right lag order. Including too many past values can lead to overfitting, where the model becomes overly sensitive to historical fluctuations that may not be relevant going forward. Overfitting can create misleading analysis, making the model seem accurate in hindsight but ineffective in real-time market conditions. Traders typically balance complexity with statistical tests like the Akaike Information Criterion (AIC) to determine an optimal lag length. Market Noise and Unexpected Events AR forecasting assumes that past price relationships remain relatively consistent. However, financial markets are influenced by a wide range of external factors—economic reports, central bank decisions, and geopolitical events—that models based purely on past prices cannot account for. A market that has historically followed a trend can abruptly reverse due to news or institutional flows, reducing the usefulness of AR-based analysis. Data Quality and Stationarity The reliability of an AR model depends on the quality of the data used. Non-stationary data, sudden regime changes, or structural shifts in the market can distort results. Traders often need to check for stationarity and adjust their approach when market conditions change, ensuring that their models remain relevant rather than assuming past relationships always hold. The Bottom Line Autoregressive models offer traders a statistical approach to analysing price movements, helping them identify trends and market behaviour based on historical data. While they are not standalone trading signals, they can be valuable when combined with other analytical tools. FAQ What Is an Autoregressive Model? An autoregressive (AR) model is a type of statistical model that analyses time series data by expressing a variable as a function of its past values. It assumes that past observations influence current values, making it useful for identifying patterns in sequential data. What Is an Autoregressive Model in Finance? In finance, AR models are used to analyse price movements by examining historical data. Traders apply them to identify trends, momentum, or mean-reverting behaviour in assets like stocks, forex, and commodities. AR models help quantify how past price changes relate to current movements. What Is an Autoregressive Model for Stock Analysis? AR models in stock analysis assess price patterns by using historical data to determine potential relationships between past and present values. They can highlight statistical trends but do not account for external market drivers like news or economic events. 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.
Education
by FXOpen
1
Is Gold About to Explode?Hello traders, today we can see gold making a strong recovery from the support level around 3,300 USD. If the price can break through the immediate resistance at 3,372 USD , gold could reach the target of 3,406 USD . The upward trend is supported by successive higher highs and lows, along with the EMA lines. News Supporting the Bullish Trend: The FOMC meeting minutes are dovish, jobless claims are higher than expected, and the PMI data is weaker than forecast, all indicating a weak economy. This could lead the Fed to maintain a loose monetary policy, weakening the USD. When the USD weakens, gold becomes more attractive, encouraging investors to turn to gold as a safe-haven asset, driving the price higher. Conclusion: Based on both fundamental and technical factors, XAUUSD is trending upwards, supported by dovish FOMC minutes, high jobless claims, and weak PMI data. The next targets are 3,372 USD and 3,406 USD. Strategy: Buy if the price breaks above 3,372 USD, with a target of 3,406 USD. Place a stop loss below 3,316 USD to protect capital.
OANDA:XAUUSDLong
by Rocky_trade_
10
SILVER (XAGUSD): Bearish Move From Trend Line I see a test of a strong trend line on Silver on an hourly time frame. A rapid growth stopped once the price approached that and a consolidation started. A bearish breakout of its support is a strong confirmation to sell. I think that the market will retrace to 37,54 ❤️Please, support my work with like, thank you!❤️ I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis.
TRADENATION:XAGUSDShort
by VasilyTrader
2
Quick Forex update. Waiting for the Powell speech.Let's have a quick technical look at the top FX pairs before the Jackson Hole Symposium. Let's dig in. TVC:DXY FX_IDC:AUDUSD FX_IDC:NZDUSD FX_IDC:USDJPY FX_IDC:USDCAD Let us know what you think in the comments below. Thank you. 75.2% of retail investor accounts lose money when trading CFDs with this provider. You should consider whether you understand how CFDs work and whether you can afford to take the high risk of losing your money. Past performance is not necessarily indicative of future results. The value of investments may fall as well as rise and the investor may not get back the amount initially invested. This content is not intended for nor applicable to residents of the UK. Cryptocurrency CFDs and spread bets are restricted in the UK for all retail clients.
MARKETSCOM:DOLLARINDEX
by Marketscom