From TradersList Editors
From TradersList Editors
Leverage historical data with machine learning to forecast closing prices and estimate errors, ensuring standardized, accurate predictions.
Leverage historical data with machine learning to forecast closing prices and estimate errors, ensuring standardized, accurate predictions.
Auto-Regressive Model Indicator: Machine Learning Price Forecasting with Error Bounds
Auto-Regressive Model Indicator: Machine Learning Price Forecasting with Error Bounds

by NinjaTrader_JoshG on NinjaTrader

by NinjaTrader_JoshG on NinjaTrader
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Auto-Regressive Model Indicator
Auto-Regressive Model Indicator
Auto-Regressive Model Indicator is a NinjaTrader indicator that uses historical data and machine learning to forecast closing prices. The algorithm learns relationships between current and past values with a decreasing learning rate for stability, provides error bounds and standardizes predictions by Z-scoring the time series.
Auto-Regressive Model Indicator is a NinjaTrader indicator that uses historical data and machine learning to forecast closing prices. The algorithm learns relationships between current and past values with a decreasing learning rate for stability, provides error bounds and standardizes predictions by Z-scoring the time series.

Get on NinjaTrader
This indicator is available on NinjaTrader charting platform. Signing up for a free account and downloading NinjaTrader software is required.

Get on NinjaTrader
This indicator is available on NinjaTrader charting platform. Signing up for a free account and downloading NinjaTrader software is required.
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Open Source
Listing has publically available source code that can be audited and modified as needed.
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Open Source
Listing has publically available source code that can be audited and modified as needed.
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Free Access
All features of this listing are fully free to use with no pricing tiers. Simply start using.
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Free Access
All features of this listing are fully free to use with no pricing tiers. Simply start using.
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Popular Topic
Concepts and theories mentioned in listing are popular topics in web search
🌐
Popular Topic
Concepts and theories mentioned in listing are popular topics in web search
Published by NinjaTrader_JoshG on NinjaTrader
Published by NinjaTrader_JoshG on NinjaTrader
Machine Learning Price Forecasting
The auto-regressive model learns statistical relationships between current and past closing prices to predict future values. Unlike traditional indicators that only describe what has already happened, this algorithm projects a forward-looking price estimate, giving traders a quantitative forecast to evaluate against their own analysis.
Decreasing Learning Rate for Stability
The algorithm uses a decreasing learning rate, meaning early data points have a stronger influence on initial calibration, while later adjustments become progressively smaller. This approach prevents overfitting to recent noise and creates a more stable, reliable prediction that balances historical patterns with recent price action.
Machine Learning Price Forecasting
The auto-regressive model learns statistical relationships between current and past closing prices to predict future values. Unlike traditional indicators that only describe what has already happened, this algorithm projects a forward-looking price estimate, giving traders a quantitative forecast to evaluate against their own analysis.
Decreasing Learning Rate for Stability
The algorithm uses a decreasing learning rate, meaning early data points have a stronger influence on initial calibration, while later adjustments become progressively smaller. This approach prevents overfitting to recent noise and creates a more stable, reliable prediction that balances historical patterns with recent price action.
Error Bounds and Z-Scoring
Each prediction comes with error bounds that quantify the model's uncertainty. The time series is standardized through Z-scoring - converting prices to standard deviations from the mean. This normalization allows the model to work consistently across different assets and price scales without manual parameter adjustments.
Using Forecasts as Confluence
Rather than trading the forecast blindly, experienced traders use the predicted price as one layer of confluence. If the model forecasts a price increase that aligns with a support bounce and bullish volume, the combined evidence strengthens the trade thesis. Divergence between forecast and actual price can also signal emerging trend changes.
Error Bounds and Z-Scoring
Each prediction comes with error bounds that quantify the model's uncertainty. The time series is standardized through Z-scoring - converting prices to standard deviations from the mean. This normalization allows the model to work consistently across different assets and price scales without manual parameter adjustments.
Using Forecasts as Confluence
Rather than trading the forecast blindly, experienced traders use the predicted price as one layer of confluence. If the model forecasts a price increase that aligns with a support bounce and bullish volume, the combined evidence strengthens the trade thesis. Divergence between forecast and actual price can also signal emerging trend changes.
Predicted closes using advanced learning model
Predicted closes using advanced learning model
The Auto-Regressive Model learns statistical relationships between current and past closing prices to forecast future values. The algorithm includes error bounds and Z-score normalization, providing quantitative predictions that work consistently across different assets.
The Auto-Regressive Model learns statistical relationships between current and past closing prices to forecast future values. The algorithm includes error bounds and Z-score normalization, providing quantitative predictions that work consistently across different assets.
Add forecasts to your confluence stack
Use predicted prices as one layer of analysis alongside support, resistance and momentum. Multiple confirming factors strengthen trade thesis.
Reference predicted price levels
The forecast provides a quantitative target to evaluate against your own analysis. Compare actual price to predictions for edge detection.
Set forecast-informed targets
Use the predicted price as a potential profit-taking zone. Error bounds quantify uncertainty around the central forecast.
Common Questions About Auto-Regressive Price Forecasting
Common Questions About Auto-Regressive Price Forecasting
Auto-regressive models learn statistical relationships between current and past prices to project future values. These questions address how the algorithm works, its limitations and how to use forecasts as confluence.
Auto-regressive models learn statistical relationships between current and past prices to project future values. These questions address how the algorithm works, its limitations and how to use forecasts as confluence.
How does the model learn to forecast prices?
The algorithm learns relationships between current and past closing prices using a decreasing learning rate. Early data points calibrate the model while later adjustments become smaller, preventing overfitting to recent noise.
What do the error bounds indicate?
Each prediction comes with error bounds that quantify the model's uncertainty. The time series is standardized through Z-scoring, allowing the model to work consistently across different assets and price scales.
Should I trade the forecast blindly?
No. Use the predicted price as one layer of confluence. If the model forecasts a price increase that aligns with a support bounce and bullish volume, the combined evidence strengthens the trade thesis.
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Web sentiment
Web sentiment
Traders on the web seem curious about the Auto-Regressive Model Indicator, as shown by its download numbers, but overall sentiment remains unclear due to a lack of explicit user feedback.
Traders on the web seem curious about the Auto-Regressive Model Indicator, as shown by its download numbers, but overall sentiment remains unclear due to a lack of explicit user feedback.
Source: Online Discussion
Source: Online Discussion
Source: Online Discussion
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TradersList Reviews (3)
TradersList Reviews (3)
TradersList Community has given this listing three stars, indicating decent trust and value for traders' toolkit. Make sure you understand functionality before using.
TradersList Community has given this listing three stars, indicating decent trust and value for traders' toolkit. Make sure you understand functionality before using.
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Source: TradersList Reviews
Source: TradersList Reviews

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