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Spiral Guide Algorithm [V2]
ที่อัปเดต:
Spiral Guide Algorithm (SGA)
At its core, the Spiral Guide Algorithm is a low noise, un-bounded, oscillating cycle indicator designed to capture state change within a non-Gaussian distribution.
The idea, design, and application of the Spiral Guide Algorithm is rooted in first principles from four core areas of study, and in application proves to be an excellent trend following tool.
The Spiral Guide Algorithm produces and visualizes three principal components. Below we will cover each of those areas, as well as, how to apply this algorithm in trade analysis.
Principal Components
1. SGA Signal = waveform fundamental signal line
2. SGA Filtered Signal= finite impulse response filter of the SGA signal
3. SGA Histogram= delta between SGA signal and SGA filtered signal
Theory of Operation
1. Digital Signal Processing (DSP)
a. The SGA applies a DSP technique used in wireless transmission that decomposes a waveform into discrete components and then quantifies the interaction between each of those components.
2. Complex Systems Theory
a. In complex systems the tail often wags the dog and so SGA focuses not on the average of the distribution, but on the edges.
3. Game Theory
a. Positive feedback drives large changes from historical extremes, so targeting points of extreme oscillation offers the best chance of capturing large changes in the distribution.
4. Auction Theory
a. We know the auction process cycles between two phases:
i. At value
ii. Discovering value
b. SGA is designed to capture much of the “value discovery” phase between two “at value” areas.
Derivatives
1. When the SGA signal line is above 0 the time frame is up-trending
2. When the SGA signal line is below 0 the time frame is down-trending
3. A fundamental time frame shift is occurring when SGA signal line crosses 0
4. The ratio of SGA signal line time above 0 vs time below 0 will expose the current time frame bias (long, short, flat)
Application
1. Trades are signaled when the SGA signal line crosses the SGA filtered signal, and the trade is confirmed when the SGA filtered signal changes state.
2. Trades can be entered when the SGA signal line crosses 0.
3. SGA should align with the following before entering a trade: Structure, Cycles, Fractals.
4. The histogram is used for detecting divergence.
When and Where
1. Due to the large number of sample sets needed to calculate the SGA signal line, the SGA is designed for intraday charting.
2. Monitor multiple time frames around entry and exit time frame to satisfy the fractal requirements. As a rule, a 3-5x fractal above and below the entry and exit time frame is needed to align cycles.
3. For example:
a. Tick data or 1 second
b. 1 min
c. 5 min (entry/exit)
d. 15 min
4. This algorithm sees success in markets that are not mean reversion biased.
a. Trending markets with high volatility provide the best results.
At its core, the Spiral Guide Algorithm is a low noise, un-bounded, oscillating cycle indicator designed to capture state change within a non-Gaussian distribution.
The idea, design, and application of the Spiral Guide Algorithm is rooted in first principles from four core areas of study, and in application proves to be an excellent trend following tool.
The Spiral Guide Algorithm produces and visualizes three principal components. Below we will cover each of those areas, as well as, how to apply this algorithm in trade analysis.
Principal Components
1. SGA Signal = waveform fundamental signal line
2. SGA Filtered Signal= finite impulse response filter of the SGA signal
3. SGA Histogram= delta between SGA signal and SGA filtered signal
Theory of Operation
1. Digital Signal Processing (DSP)
a. The SGA applies a DSP technique used in wireless transmission that decomposes a waveform into discrete components and then quantifies the interaction between each of those components.
2. Complex Systems Theory
a. In complex systems the tail often wags the dog and so SGA focuses not on the average of the distribution, but on the edges.
3. Game Theory
a. Positive feedback drives large changes from historical extremes, so targeting points of extreme oscillation offers the best chance of capturing large changes in the distribution.
4. Auction Theory
a. We know the auction process cycles between two phases:
i. At value
ii. Discovering value
b. SGA is designed to capture much of the “value discovery” phase between two “at value” areas.
Derivatives
1. When the SGA signal line is above 0 the time frame is up-trending
2. When the SGA signal line is below 0 the time frame is down-trending
3. A fundamental time frame shift is occurring when SGA signal line crosses 0
4. The ratio of SGA signal line time above 0 vs time below 0 will expose the current time frame bias (long, short, flat)
Application
1. Trades are signaled when the SGA signal line crosses the SGA filtered signal, and the trade is confirmed when the SGA filtered signal changes state.
2. Trades can be entered when the SGA signal line crosses 0.
3. SGA should align with the following before entering a trade: Structure, Cycles, Fractals.
4. The histogram is used for detecting divergence.
When and Where
1. Due to the large number of sample sets needed to calculate the SGA signal line, the SGA is designed for intraday charting.
2. Monitor multiple time frames around entry and exit time frame to satisfy the fractal requirements. As a rule, a 3-5x fractal above and below the entry and exit time frame is needed to align cycles.
3. For example:
a. Tick data or 1 second
b. 1 min
c. 5 min (entry/exit)
d. 15 min
4. This algorithm sees success in markets that are not mean reversion biased.
a. Trending markets with high volatility provide the best results.
เอกสารเผยแพร่
#Added Alerts for Each Tradable Condition of the SGAเอกสารเผยแพร่
Added functionality to allow reference timeframe to be modified; allowing for multi-timeframe analysis on a single chart via stacked SGA indicatorsเอกสารเผยแพร่
Added multi-timeframe functionality เอกสารเผยแพร่
#Refactored most of the code#Added more functionality to adjust SGA indicator:
- Enable/disable SGA signal lines
- Enable/disable SGA histogram
- Divergence detection both positive and negative
- Redesigned histogram calculation to reduce false signals and increase tradeability
- SGA/Hist Max/Min enable/disable
- Long/Short Coloring
- SGA Fast
- SGA Very Fast
เอกสารเผยแพร่
Added SGA filter control Corrected alert print output
เอกสารเผยแพร่
Fixed scaling issue with OTFsเอกสารเผยแพร่
- Improved Settings MenuAdded OTF Heat MapAdded New Alert FunctionsRe-factored Core RoutinesAdded Signal Stat BoxAdded Histogram Stat Box
เอกสารเผยแพร่
Build 3.1 Hot Fix 1- Corrected Alert Logic
- Updated Alert Menu
เอกสารเผยแพร่
- updated script to include only user requested features
- improved loading times
- LTS SGA Version
เอกสารเผยแพร่
- Refactored most of of the code base
- Updated to Pinescript V5
- Updated Menu Section
- Added Liner Regression Channels
- Added SGA Prime Indicator
- Updated Alert Functionality
เอกสารเผยแพร่
- Fixed SGA Prime issue caused by TV backend
เอกสารเผยแพร่
- Added Strategy Visualizations
เอกสารเผยแพร่
- Added SGA Trend Confirm Tool
- Removed Stat Boxes
เอกสารเผยแพร่
- Updated SGA Trend Confirm
เอกสารเผยแพร่
- Added SGA Prime plot label
เอกสารเผยแพร่
- Added SGA Prime Strategy Settings & Alerts
เอกสารเผยแพร่
- Added Trend Confirm Plots
[*} Updated Trend Confirm Function
เอกสารเผยแพร่
- Corrected Data Input Formula
เอกสารเผยแพร่
- Corrected bug in TC
เอกสารเผยแพร่
- Added SGA TC Color Change Plots
เอกสารเผยแพร่
- Corrected Trend Confirm Plots
- Added Rev Control
เอกสารเผยแพร่
- Added Trend Confirm Alerts
- Removed SGA Crossing Zero Exit Filter
- Added Bot ID Selection In Settings
- Corrected Alerts "side":"exit" problem
เอกสารเผยแพร่
- Updated TC Plot to Show When No Bias Present
เอกสารเผยแพร่
Updated: - Re-organized menu structure
- Added SGA Prime forced alert
เอกสารเผยแพร่
Update:- Unknown TV bug; reverting to build 1.2
เอกสารเผยแพร่
- Removed unused plots due to hitting limit
- Re-organized menu structure
- Added SGA Prime forced alert
เอกสารเผยแพร่
- Updated SGA Color Alerts
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