🔮 Price Action Fractal Forecasts [AlgoAlpha] - Unleash the Power of Historical Patterns! 🌌✨
Dive into the future with AlgoAlpha's Price Action Fractal Forecasts! This innovative indicator utilizes the mesmerizing complexity of fractals to predict future price movements, offering traders a unique edge in the market. By analyzing historical price action and identifying repeating patterns, this tool forecasts future price trends, providing visually engaging and actionable insights.
Key Features:
🔄 Flexible Data Series Selection: Choose your preferred data series for precise analysis.
🕰 Flexible Training and Reference Data Windows: Customize the length of training data and reference periods to match your trading style.
📈 Custom Forecast Length: Adjust the forecast horizon to suit your strategic objectives.
🌈 Customizable Visual Elements: Tailor the colors of forecast deviation cones, data reference areas, and more for optimal chart readability.
🔄 Anticipatory and Repetitive Forecast Modes: Select between anticipating future trends or identifying repetitive patterns for forecasts.
🔎 Enhanced Similarity Search: Leverages correlation metrics to find the most similar historical data segments.
📊 Forecast Deviation Cone: Visualize potential price range deviations with adjustable multipliers.
🚀 Quick Guide to Maximizing Your Trading with Price Action Fractal Forecasts:
🛠 Add the Indicator: Search for "Price Action Fractal Forecasts" in TradingView's Indicators & Strategies. Customize settings according to your trading strategy.
📊 Strategic Forecasting: Monitor the forecast deviation cone and forecast directional changes for insights into potential future price movements.
🔔 Alerts for Swift Action: Set up notifications based on forecast changes to stay ahead of market movements without constant monitoring.
Behind the Magic: How It Works The core of the Price Action Fractal Forecasts lies in its ability to compare current market behavior with historical data to unearth similar patterns. It first establishes a training data window to analyze historical prices. Within this window, it then defines a reference length to identify the most recent price action that will serve as the basis for comparison. The indicator searches through the historical data within the training window to find segments that closely match the recent price action in the reference period.
Depending on whether you choose the anticipatory or repetitive forecast mode, the indicator either looks ahead to predict future prices based on past outcomes following similar patterns or focuses on the repeating patterns within the reference period itself for forecasts. The forecast's direction can be configured to reflect the mean average of forecasted prices or the end-point relative to the start-point of the forecast, offering flexibility in how forecasts are interpreted.
To enhance the comprehensiveness and visualization, the indicator features a forecast deviation cone. This cone represents the potential range of price movements, providing a visual cue for volatility and uncertainty in the forecasted prices. The intensity of this cone can be adjusted to suit individual preferences, offering a visual guide to the level of risk and uncertainty associated with the forecasted price path.
Embrace the fractal magic of markets with AlgoAlpha's Price Action Fractal Forecasts and transform your trading today! 🌟🚀
เอกสารเผยแพร่
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updated the default settings to be less stressful on the script memory, to reduce timeouts and loading time
this is really nice script, i am a 7 and 30 sma pair trader and this code help me to read their curves, Thank you very much, please bring more updates and similar content like this. i am using 30,7 ,7 settings for it is this ok? or i need improvement.
AlgoAlpha
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@Crypto_Popat, yeah the settings seems good, we're glad you love it and we'll keep doing our best to make quality indicators 🤝
priz1714
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Hello Nice script thanks for sharing its giving error please make modification for line # 28 to 39 as
c.clear()
index.clear()
// Extract the call to ta.correlation from the scope
correlation = ta.correlation(src, src[leng], len)
for i = leng to len - leng
c.push(correlation)
index.push(i)
c1 = c.indexof(c.max())
similarBarsBack = index.get(c1<0 ? 0 : c1)
AlgoAlpha
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@priz1714, Thank you for pointing that out, we've updated the code. Enjoy!
priz1714
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@AlgoAlpha, Hello, Can you please update the best parameters after this change as after this change all results are changed drastically Thanks.
AlgoAlpha
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@priz1714, We just realised that the update actually breaks it, the original was the correct version. The current version doesn't pick the most correlated area it just pick the data that's outside of the training data.
priz1714
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@AlgoAlpha, Means I discard this correction and use previous version only and best settings is 400/15/120 right?
AlgoAlpha
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@priz1714, That's right. You can change the settings to suite your preferences but the default settings are fine.
priz1714
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@AlgoAlpha, Great then please change the code as original there not be any confusion for all traders, Very Nice script Thanks Again.