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.
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💸💸💸💸💸💸
👇 👇 👇 👇 👇 👇
M-forex
BITCOIN BULLS ARE GAINING STRENGTH|LONG
BITCOIN SIGNAL
Trade Direction: long
Entry Level: 113,384.53
Target Level: 119,504.33
Stop Loss: 109,334.66
RISK PROFILE
Risk level: medium
Suggested risk: 1%
Timeframe: 1D
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CAD/JPY BUYERS WILL DOMINATE THE MARKET|LONG
CAD/JPY SIGNAL
Trade Direction: long
Entry Level: 106.501
Target Level: 108.027
Stop Loss: 105.476
RISK PROFILE
Risk level: medium
Suggested risk: 1%
Timeframe: 1D
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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.
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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.
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GBP/USD (1H timeframe).GBP/USD (1H timeframe) with Ichimoku cloud and target zones drawn.
From what I see on my chart:
Current price: ~1.3467
First target (TP1): around 1.3400 – 1.3410 zone (first red "TARGET POINT")
Second target (TP2): around 1.3280 – 1.3290 zone (lower red "TARGET POINT")
📌 So my chart is showing a bearish setup with:
TP1 ≈ 1.3400
TP2 ≈ 1.3280
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.
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.
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.
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
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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.
EURCHF: Bullish Forecast & Bullish Scenario
Our strategy, polished by years of trial and error has helped us identify what seems to be a great trading opportunity and we are here to share it with you as the time is ripe for us to buy EURCHF.
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GOLD: Long Signal Explained
GOLD
- Classic bullish pattern
- Our team expects retracement
SUGGESTED TRADE:
Swing Trade
Buy GOLD
Entry - 3340.2
Stop - 3337.6
Take - 3345.2
Our Risk - 1%
Start protection of your profits from lower levels
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GBPCHF Massive Long! BUY!
My dear subscribers,
My technical analysis for GBPCHF is below:
The price is coiling around a solid key level - 1.0833
Bias - Bullish
Technical Indicators: Pivot Points High anticipates a potential price reversal.
Super trend shows a clear buy, giving a perfect indicators' convergence.
Goal - 1.0870
About Used Indicators:
By the very nature of the supertrend indicator, it offers firm support and resistance levels for traders to enter and exit trades. Additionally, it also provides signals for setting stop losses
Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis.
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WISH YOU ALL LUCK
USDCAD A Fall Expected! SELL!
My dear friends,
My technical analysis for USDCAD is below:
The market is trading on 1.3881 pivot level.
Bias - Bearish
Technical Indicators: Both Super Trend & Pivot HL indicate a highly probable Bearish continuation.
Target - 1.3873
Recommended Stop Loss - 1.3886
About Used Indicators:
A pivot point is a technical analysis indicator, or calculations, used to determine the overall trend of the market over different time frames.
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
CADCHF: Pullback Will Continue 🇨🇦🇨🇭
There is a high chance that CADCHF will go up from the underlined
support cluster.
The price formed an ascending triangle pattern on that on an hourly time frame
during the Asian session.
Goal - 0.5814
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XAUUSDHello Traders! 👋
What are your thoughts on GOLD?
Gold remains trapped in a tight consolidation range between key support and resistance zones.
In the short term, the ongoing decline is expected to extend toward the support area, where a bullish reaction may occur.
As long as price stays within this range, the optimal strategy is to buy near support and sell near resistance.
A clear breakout above resistance or below support is needed to confirm the next directional move
Don’t forget to like and share your thoughts in the comments! ❤️
GBPJPY H4 | Falling towards 50% Fibonacci supportBased on the H4 chart analysis, we could see the price fall to the buy entry, which is a pullback support that aligns with the 50% Fibonacci retracement and could bounce to the take profit.
Buy entry is at 197.51, which is a pullback support that lines up with the 50% Fibonacci retracement.
Stop loss is at 196.36, which is a pullback support that is slightly above the 78.6% Fibonacci retracement.
Take profit is at 198.91, which is a pullback resistance.
High Risk Investment Warning
Trading Forex/CFDs on margin carries a high level of risk and may not be suitable for all investors. Leverage can work against you.
Stratos Markets Limited (tradu.com ):
CFDs are complex instruments and come with a high risk of losing money rapidly due to leverage. 65% 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.
Stratos Europe Ltd (tradu.com ):
CFDs are complex instruments and come with a high risk of losing money rapidly due to leverage. 66% 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.
Stratos Global LLC (tradu.com ):
Losses can exceed deposits.
Please be advised that the information presented on TradingView is provided to Tradu (‘Company’, ‘we’) by a third-party provider (‘TFA Global Pte Ltd’). Please be reminded that you are solely responsible for the trading decisions on your account. There is a very high degree of risk involved in trading. Any information and/or content is intended entirely for research, educational and informational purposes only and does not constitute investment or consultation advice or investment strategy. The information is not tailored to the investment needs of any specific person and therefore does not involve a consideration of any of the investment objectives, financial situation or needs of any viewer that may receive it. Kindly also note that past performance is not a reliable indicator of future results. Actual results may differ materially from those anticipated in forward-looking or past performance statements. We assume no liability as to the accuracy or completeness of any of the information and/or content provided herein and the Company cannot be held responsible for any omission, mistake nor for any loss or damage including without limitation to any loss of profit which may arise from reliance on any information supplied by TFA Global Pte Ltd.
The speaker(s) is neither an employee, agent nor representative of Tradu and is therefore acting independently. The opinions given are their own, constitute general market commentary, and do not constitute the opinion or advice of Tradu or any form of personal or investment advice. Tradu neither endorses nor guarantees offerings of third-party speakers, nor is Tradu responsible for the content, veracity or opinions of third-party speakers, presenters or participants.
AUDUSD H4 | Bullish reversalThe Aussie (AUD/USD) is falling towards the buy entry, which is a pullback support and could bounce from this level to the upside.
Buy entry is at 0.6423, which is a pullback support.
Stop loss is at 0.6361, which acts as a swing low support that lines up with the 138.2% Fibonacci extension.
Take profit is at 0.6481, which is an overlap resistance.
High Risk Investment Warning
Trading Forex/CFDs on margin carries a high level of risk and may not be suitable for all investors. Leverage can work against you.
Stratos Markets Limited (tradu.com ):
CFDs are complex instruments and come with a high risk of losing money rapidly due to leverage. 65% 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.
Stratos Europe Ltd (tradu.com ):
CFDs are complex instruments and come with a high risk of losing money rapidly due to leverage. 66% 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.
Stratos Global LLC (tradu.com ):
Losses can exceed deposits.
Please be advised that the information presented on TradingView is provided to Tradu (‘Company’, ‘we’) by a third-party provider (‘TFA Global Pte Ltd’). Please be reminded that you are solely responsible for the trading decisions on your account. There is a very high degree of risk involved in trading. Any information and/or content is intended entirely for research, educational and informational purposes only and does not constitute investment or consultation advice or investment strategy. The information is not tailored to the investment needs of any specific person and therefore does not involve a consideration of any of the investment objectives, financial situation or needs of any viewer that may receive it. Kindly also note that past performance is not a reliable indicator of future results. Actual results may differ materially from those anticipated in forward-looking or past performance statements. We assume no liability as to the accuracy or completeness of any of the information and/or content provided herein and the Company cannot be held responsible for any omission, mistake nor for any loss or damage including without limitation to any loss of profit which may arise from reliance on any information supplied by TFA Global Pte Ltd.
The speaker(s) is neither an employee, agent nor representative of Tradu and is therefore acting independently. The opinions given are their own, constitute general market commentary, and do not constitute the opinion or advice of Tradu or any form of personal or investment advice. Tradu neither endorses nor guarantees offerings of third-party speakers, nor is Tradu responsible for the content, veracity or opinions of third-party speakers, presenters or participants.
XAUUSD | Gold awaits news from the FedThe market is waiting for the Federal Reserve to release its report. The minutes of the latest meeting of the Federal Open Market Committee (FOMC) will be released and investors will analyze carefully for signals on upcoming monetary policy.
Financial markets are relatively quiet this week ahead of the annual Jackson Hole conference organized by the Kansas City branch of the Federal Reserve, which begins on Thursday.
Fed Chairman Jerome Powell will speak on Friday morning, expected to provide an update on monetary policy, including a possible new hint that the Fed could cut interest rates as early as September.