INVITE-ONLY SCRIPT
Neural Network Synthesis: Trend and Valuation [QuantraSystems]
Updated
Neural Network Synthesis - Trend and Valuation
Introduction
The Neural Network Synthesis (๐๐๐ฎ๐๐ท๐ฝ๐ฑ) indicator is an innovative technical analysis tool which leverages neural network concepts to synthesize market trend and valuation insights.
This indicator uses a bespoke neural network model to process various technical indicator inputs, providing an improved view of market momentum and perceived value.
Legend
The main visual component of the ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicator is the Neural Synthesis Line, which dynamically oscillates within the valuation chart, categorizing market conditions as both under or overvalued and trending up or down.
The synthesis line coloring can be set to trend analysis or valuation modes, which can be reflected in the bar coloring.
The sine wave valuation chart oscillates around a central, volatility normalized โfair valueโ line, visually conveying the natural rhythm and cyclical nature of asset markets.
The positioning of the sine wave in relation to the central line can help traders to visualize transitions from one market phase to another - such as from an undervalued phase to fair value or an overvalued phase.
Case Study 1
The asset in question experiences a sharp, inefficient move upwards. Such movements suggest an overextension of price, and mean reversion is typically expected.
Here, a short position was initiated, but only after the Neural Synthesis line confirmed a negative trend - to mitigate the risk of shorting into a continuing uptrend.
Two take-profit levels were set:
Although mean-reversion trades are typically closed when price returns to the mean, under circumstances of extreme overextension price often overcorrects from an overbought condition to an oversold condition.
Case Study 2
In the above study, the ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicator is applied to the 1 Week Bitcoin chart in order to inform long term investment decisions.
Note - It is prudent to pay close attention to any change in trend conditions when the market is in an accumulation/distribution phase, as this can increase the likelihood of a full-cycle market peak forming.
In summary, the ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicator is also an effective tool for long term investing, especially for assets like Bitcoin which exhibit prolonged bull and bear cycles.
Special Note
It is prudent to note that because markets often undergo phases of extreme speculation, an asset's price can remain over or undervalued for long periods of time, defying mean-reversion expectations. In these scenarios it is important to use other forms of analysis in confluence, such as the trending component of the ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicator to help inform trading decisions.
A special feature of Quantraโs indicators is that they are probabilistically built - therefore they work well as confluence and can easily be stacked to increase signal accuracy.
Example Settings
As used above.
Swing Trading
Long Term Investing
Methodology
The ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicator draws upon the foundational principles of Neural Networks, particularly the concept of using a network of โneuronsโ (in this case, various technical indicators). It uses their outputs as features, preprocesses this input data, runs an activation function and in the following creates a dynamic output.
The following features/inputs are used as โneuronsโ:
These base indicators were chosen for their diverse methodologies for capturing market momentum, volatility and trend strength - mirroring how neurons in a Neural Network capture and process varied aspects of the input data.
Preprocessing:
Each technical indicatorโs output is normalized to remove bias. Normalization is a standard practice to preprocess data for Neural Networks, to scale input data and allow the model to train more effectively.
Activation Function:
The hyperbolic tangent function serves as the activation function for the neurons. In general, for complete neural networks, activation functions introduce non-linear properties to the models and enable them to learn complex patterns. The tanh() function specifically maps the inputs to a range between -1 and 1.
Dynamic Smoothing:
The composite signal is dynamically smoothed using the Arnaud Legoux Moving Average, which adjusts faster to recent price changes - enhancing the indicator's responsiveness. It mimics the learning rate in neural networks - in this case for the output in a single layer approach - which controls how much new information influences the model, or in this case, our output.
Signal Processing:
The signal line also undergoes processing to adapt to the selected assets volatility. This step ensures the indicatorโs flexibility across assets which exhibit different behaviors - similar to how a Neural Network adjusts to various data distributions.
Notes:
Introduction
The Neural Network Synthesis (๐๐๐ฎ๐๐ท๐ฝ๐ฑ) indicator is an innovative technical analysis tool which leverages neural network concepts to synthesize market trend and valuation insights.
This indicator uses a bespoke neural network model to process various technical indicator inputs, providing an improved view of market momentum and perceived value.
Legend
The main visual component of the ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicator is the Neural Synthesis Line, which dynamically oscillates within the valuation chart, categorizing market conditions as both under or overvalued and trending up or down.
The synthesis line coloring can be set to trend analysis or valuation modes, which can be reflected in the bar coloring.
The sine wave valuation chart oscillates around a central, volatility normalized โfair valueโ line, visually conveying the natural rhythm and cyclical nature of asset markets.
The positioning of the sine wave in relation to the central line can help traders to visualize transitions from one market phase to another - such as from an undervalued phase to fair value or an overvalued phase.
Case Study 1
The asset in question experiences a sharp, inefficient move upwards. Such movements suggest an overextension of price, and mean reversion is typically expected.
Here, a short position was initiated, but only after the Neural Synthesis line confirmed a negative trend - to mitigate the risk of shorting into a continuing uptrend.
Two take-profit levels were set:
- The midline or โfair valueโ line.
- The lower boundary of the ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicators valuation chart.
Although mean-reversion trades are typically closed when price returns to the mean, under circumstances of extreme overextension price often overcorrects from an overbought condition to an oversold condition.
Case Study 2
In the above study, the ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicator is applied to the 1 Week Bitcoin chart in order to inform long term investment decisions.
- Accumulation Zones - Investors can choose to dollar cost average (DCA) into long term positions when the ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicates undervaluation
- Distribution Zones - Conversely, when overvalued conditions are indicated, investors are able to incrementally sell holdings expecting the market peak to form around the distribution phase.
Note - It is prudent to pay close attention to any change in trend conditions when the market is in an accumulation/distribution phase, as this can increase the likelihood of a full-cycle market peak forming.
In summary, the ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicator is also an effective tool for long term investing, especially for assets like Bitcoin which exhibit prolonged bull and bear cycles.
Special Note
It is prudent to note that because markets often undergo phases of extreme speculation, an asset's price can remain over or undervalued for long periods of time, defying mean-reversion expectations. In these scenarios it is important to use other forms of analysis in confluence, such as the trending component of the ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicator to help inform trading decisions.
A special feature of Quantraโs indicators is that they are probabilistically built - therefore they work well as confluence and can easily be stacked to increase signal accuracy.
Example Settings
As used above.
Swing Trading
- Smooth Length = 150
- Timeframe = 12h
Long Term Investing
- Smooth Length = 30
- Timeframe = 1W
Methodology
The ๐๐๐ฎ๐๐ท๐ฝ๐ฑ indicator draws upon the foundational principles of Neural Networks, particularly the concept of using a network of โneuronsโ (in this case, various technical indicators). It uses their outputs as features, preprocesses this input data, runs an activation function and in the following creates a dynamic output.
The following features/inputs are used as โneuronsโ:
- Relative Strength Index (RSI)
- Moving Average Convergence-Divergence (MACD)
- Bollinger Bands
- Stochastic Momentum
- Average True Range (ATR)
These base indicators were chosen for their diverse methodologies for capturing market momentum, volatility and trend strength - mirroring how neurons in a Neural Network capture and process varied aspects of the input data.
Preprocessing:
Each technical indicatorโs output is normalized to remove bias. Normalization is a standard practice to preprocess data for Neural Networks, to scale input data and allow the model to train more effectively.
Activation Function:
The hyperbolic tangent function serves as the activation function for the neurons. In general, for complete neural networks, activation functions introduce non-linear properties to the models and enable them to learn complex patterns. The tanh() function specifically maps the inputs to a range between -1 and 1.
Dynamic Smoothing:
The composite signal is dynamically smoothed using the Arnaud Legoux Moving Average, which adjusts faster to recent price changes - enhancing the indicator's responsiveness. It mimics the learning rate in neural networks - in this case for the output in a single layer approach - which controls how much new information influences the model, or in this case, our output.
Signal Processing:
The signal line also undergoes processing to adapt to the selected assets volatility. This step ensures the indicatorโs flexibility across assets which exhibit different behaviors - similar to how a Neural Network adjusts to various data distributions.
Notes:
- While the indicator synthesizes complex market information using methods inspired by neural networks, it is important to note that it does not engage in predictive modeling through the use of backpropagation. Instead, it applies methodologies of neural networks for real-time market analysis that is both dynamic and adaptable to changing market conditions.
Release Notes
Added 'Dynamic' capabilities.Added 'Compressed Signal Mode'.
Release Notes
- Cleaned up input menu for more uniform structure.
- Added signal table - Note: the prior signal is the previous signal before a change in valuation occurred.
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Author's instructions
โณGet access to our Indicators: https://www.quantra.ai
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No statements or claims aim to be financial advice,
neither are any signals from us or our indicators.
Want to learn Trading, Investing or system building?
Join the Community! ๐
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neither are any signals from us or our indicators.
Want to learn Trading, Investing or system building?
Join the Community! ๐
discord.gg/FMZDM3bZ9T
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.