MMXM ICT [TradingFinder] Market Maker Model PO3 CHoCH/CSID + FVG🔵 Introduction
The MMXM Smart Money Reversal leverages key metrics such as SMT Divergence, Liquidity Sweep, HTF PD Array, Market Structure Shift (MSS) or (ChoCh), CISD, and Fair Value Gap (FVG) to identify critical turning points in the market. Designed for traders aiming to analyze the behavior of major market participants, this setup pinpoints strategic areas for making informed trading decisions.
The document introduces the MMXM model, a trading strategy that identifies market maker activity to predict price movements. The model operates across five distinct stages: original consolidation, price run, smart money reversal, accumulation/distribution, and completion. This systematic approach allows traders to differentiate between buyside and sellside curves, offering a structured framework for interpreting price action.
Market makers play a pivotal role in facilitating these movements by bridging liquidity gaps. They continuously quote bid (buy) and ask (sell) prices for assets, ensuring smooth trading conditions.
By maintaining liquidity, market makers prevent scenarios where buyers are left without sellers and vice versa, making their activity a cornerstone of the MMXM strategy.
SMT Divergence serves as the first signal of a potential trend reversal, arising from discrepancies between the movements of related assets or indices. This divergence is detected when two or more highly correlated assets or indices move in opposite directions, signaling a likely shift in market trends.
Liquidity Sweep occurs when the market targets liquidity in specific zones through false price movements. This process allows major market participants to execute their orders efficiently by collecting the necessary liquidity to enter or exit positions.
The HTF PD Array refers to premium and discount zones on higher timeframes. These zones highlight price levels where the market is in a premium (ideal for selling) or discount (ideal for buying). These areas are identified based on higher timeframe market behavior and guide traders toward lucrative opportunities.
Market Structure Shift (MSS), also referred to as ChoCh, indicates a change in market structure, often marked by breaking key support or resistance levels. This shift confirms the directional movement of the market, signaling the start of a new trend.
CISD (Change in State of Delivery) reflects a transition in price delivery mechanisms. Typically occurring after MSS, CISD confirms the continuation of price movement in the new direction.
Fair Value Gap (FVG) represents zones where price imbalance exists between buyers and sellers. These gaps often act as price targets for filling, offering traders opportunities for entry or exit.
By combining all these metrics, the Smart Money Reversal provides a comprehensive tool for analyzing market behavior and identifying key trading opportunities. It enables traders to anticipate the actions of major players and align their strategies accordingly.
MMBM :
MMSM :
🔵 How to Use
The Smart Money Reversal operates in two primary states: MMBM (Market Maker Buy Model) and MMSM (Market Maker Sell Model). Each state highlights critical structural changes in market trends, focusing on liquidity behavior and price reactions at key levels to offer precise and effective trading opportunities.
The MMXM model expands on this by identifying five distinct stages of market behavior: original consolidation, price run, smart money reversal, accumulation/distribution, and completion. These stages provide traders with a detailed roadmap for interpreting price action and anticipating market maker activity.
🟣 Market Maker Buy Model
In the MMBM state, the market transitions from a bearish trend to a bullish trend. Initially, SMT Divergence between related assets or indices reveals weaknesses in the bearish trend. Subsequently, a Liquidity Sweep collects liquidity from lower levels through false breakouts.
After this, the price reacts to discount zones identified in the HTF PD Array, where major market participants often execute buy orders. The market confirms the bullish trend with a Market Structure Shift (MSS) and a change in price delivery state (CISD). During this phase, an FVG emerges as a key trading opportunity. Traders can open long positions upon a pullback to this FVG zone, capitalizing on the bullish continuation.
🟣 Market Maker Sell Model
In the MMSM state, the market shifts from a bullish trend to a bearish trend. Here, SMT Divergence highlights weaknesses in the bullish trend. A Liquidity Sweep then gathers liquidity from higher levels.
The price reacts to premium zones identified in the HTF PD Array, where major sellers enter the market and reverse the price direction. A Market Structure Shift (MSS) and a change in delivery state (CISD) confirm the bearish trend. The FVG then acts as a target for the price. Traders can initiate short positions upon a pullback to this FVG zone, profiting from the bearish continuation.
Market makers actively bridge liquidity gaps throughout these stages, quoting continuous bid and ask prices for assets. This ensures that trades are executed seamlessly, even during periods of low market participation, and supports the structured progression of the MMXM model.
The price’s reaction to FVG zones in both states provides traders with opportunities to reduce risk and enhance precision. These pullbacks to FVG zones not only represent optimal entry points but also create avenues for maximizing returns with minimal risk.
🔵 Settings
Higher TimeFrame PD Array : Selects the timeframe for identifying premium/discount arrays on higher timeframes.
PD Array Period : Specifies the number of candles for identifying key swing points.
ATR Coefficient Threshold : Defines the threshold for acceptable volatility based on ATR.
Max Swing Back Method : Choose between analyzing all swings ("All") or a fixed number ("Custom").
Max Swing Back : Sets the maximum number of candles to consider for swing analysis (if "Custom" is selected).
Second Symbol for SMT : Specifies the second asset or index for detecting SMT divergence.
SMT Fractal Periods : Sets the number of candles required to identify SMT fractals.
FVG Validity Period : Defines the validity duration for FVG zones.
MSS Validity Period : Sets the validity duration for MSS zones.
FVG Filter : Activates filtering for FVG zones based on width.
FVG Filter Type : Selects the filtering level from "Very Aggressive" to "Very Defensive."
Mitigation Level FVG : Determines the level within the FVG zone (proximal, 50%, or distal) that price reacts to.
Demand FVG : Enables the display of demand FVG zones.
Supply FVG : Enables the display of supply FVG zones.
Zone Colors : Allows customization of colors for demand and supply FVG zones.
Bottom Line & Label : Enables or disables the SMT divergence line and label from the bottom.
Top Line & Label : Enables or disables the SMT divergence line and label from the top.
Show All HTF Levels : Displays all premium/discount levels on higher timeframes.
High/Low Levels : Activates the display of high/low levels.
Color Options : Customizes the colors for high/low lines and labels.
Show All MSS Levels : Enables display of all MSS zones.
High/Low MSS Levels : Activates the display of high/low MSS levels.
Color Options : Customizes the colors for MSS lines and labels.
🔵 Conclusion
The Smart Money Reversal model represents one of the most advanced tools for technical analysis, enabling traders to identify critical market turning points. By leveraging metrics such as SMT Divergence, Liquidity Sweep, HTF PD Array, MSS, CISD, and FVG, traders can predict future price movements with precision.
The price’s interaction with key zones such as PD Array and FVG, combined with pullbacks to imbalance areas, offers exceptional opportunities with favorable risk-to-reward ratios. This approach empowers traders to analyze the behavior of major market participants and adopt professional strategies for entry and exit.
By employing this analytical framework, traders can reduce errors, make more informed decisions, and capitalize on profitable opportunities. The Smart Money Reversal focuses on liquidity behavior and structural changes, making it an indispensable tool for financial market success.
Komut dosyalarını "curve" için ara
GOLDEN RSI by @thejamiulGOLDEN RSI thejamiul is a versatile Relative Strength Index (RSI)-based tool designed to provide enhanced visualization and additional insights into market trends and potential reversal points. This indicator improves upon the traditional RSI by integrating gradient fills for overbought/oversold zones and divergence detection features, making it an excellent choice for traders who seek precise and actionable signals.
Source of this indicator : This indicator is based on @TradingView original RSI indicator with a little bit of customisation to enhance overbought and oversold identification.
Key Features
1. Customizable RSI Settings:
RSI Length: Adjust the RSI calculation period to suit your trading style (default: 14).
Source Selection: Choose the price source (e.g., close, open, high, low) for RSI calculation.
2. Gradient-Filled RSI Zones:
Overbought Zone (80-100): Gradient fill with shades of green to indicate strong bullish conditions.
Oversold Zone (0-20): Gradient fill with shades of red to highlight strong bearish conditions.
3. Support and Resistance Levels:
Upper Band: 80
Middle Bands: 60 (bullish) and 40 (bearish)
Lower Band: 20
These levels help identify overbought, oversold, and neutral zones.
4. Divergence Detection:
Bullish Divergence: Detects lower lows in price with corresponding higher lows in RSI, signaling potential upward reversals.
Bearish Divergence: Detects higher highs in price with corresponding lower highs in RSI, indicating potential downward reversals.
Visual Indicators:
Bullish divergence is marked with green labels and line plots.
Bearish divergence is marked with red labels and line plots.
5. Alert Functionality:
Custom Alerts: Set up alerts for bullish or bearish divergences to stay notified of potential trading opportunities without constant chart monitoring.
6. Enhanced Chart Visualization:
RSI Plot: A smooth and visually appealing RSI curve.
Color Coding: Gradient and fills for better distinction of trading zones.
Pivot Labels: Clear identification of divergence points on the RSI plot.
Wave Smoother [WS]The Wave Smoother is a unique FIR filter built from the interaction of two trigonometric waves. A cosine carrier wave is modulated by a sine wave at half the carrier's period, creating smooth transitions and controlled undershoot. The Phase parameter (0° to 119°) adjusts the modulating wave's phase, affecting both response time and undershoot characteristics. At 30° phase the impulse response starts at 0.5 and exhibits gentle undershoot, providing balanced smoothing. Higher phase values reduce ramp-up time and increase undershoot - this undershoot in the impulse response creates overshooting behavior in the filter's output, which helps reduce lag and speed up the response. The default 70° phase setting provides maximum speed while maintaining stability, though practical settings can range from 30° to 70°. The filter's impulse response consists entirely of smooth curves, ensuring consistent behavior across all settings. This design offers traders flexible control over the smoothing-speed trade-off while maintaining reliable signal generation.
PIP Algorithm
# **Script Overview (For Non-Coders)**
1. **Purpose**
- The script tries to capture the essential “shape” of price movement by selecting a limited number of “key points” (anchors) from the latest bars.
- After selecting these anchors, it draws straight lines between them, effectively simplifying the price chart into a smaller set of points without losing major swings.
2. **How It Works, Step by Step**
1. We look back a certain number of bars (e.g., 50).
2. We start by drawing a straight line from the **oldest** bar in that range to the **newest** bar—just two points.
3. Next, we find the bar whose price is *farthest away* from that straight line. That becomes a new anchor point.
4. We “snap” (pin) the line to go exactly through that new anchor. Then we re-draw (re-interpolate) the entire line from the first anchor to the last, in segments.
5. We repeat the process (adding more anchors) until we reach the desired number of points. Each time, we choose the biggest gap between our line and the actual price, then re-draw the entire shape.
6. Finally, we connect these anchors on the chart with red lines, visually simplifying the price curve.
3. **Why It’s Useful**
- It highlights the most *important* bends or swings in the price over the chosen window.
- Instead of plotting every single bar, it condenses the information down to the “key turning points.”
4. **Key Takeaway**
- You’ll see a small number of red line segments connecting the **most significant** points in the price data.
- This is especially helpful if you want a simplified view of recent price action without minor fluctuations.
## **Detailed Logic Explanation**
# **Script Breakdown (For Coders)**
//@version=5
indicator(title="PIP Algorithm", overlay=true)
// 1. Inputs
length = input.int(50, title="Lookback Length")
num_points = input.int(5, title="Number of PIP Points (≥ 3)")
// 2. Helper Functions
// ---------------------------------------------------------------------
// reInterpSubrange(...):
// Given two “anchor” indices in `linesArr`, linearly interpolate
// the array values in between so that the subrange forms a straight line
// from linesArr to linesArr .
reInterpSubrange(linesArr, segmentLeft, segmentRight) =>
float leftVal = array.get(linesArr, segmentLeft)
float rightVal = array.get(linesArr, segmentRight)
int segmentLen = segmentRight - segmentLeft
if segmentLen > 1
for i = segmentLeft + 1 to segmentRight - 1
float ratio = (i - segmentLeft) / segmentLen
float interpVal = leftVal + (rightVal - leftVal) * ratio
array.set(linesArr, i, interpVal)
// reInterpolateAllSegments(...):
// For the entire “linesArr,” re-interpolate each subrange between
// consecutive breakpoints in `lineBreaksArr`.
// This ensures the line is globally correct after each new anchor insertion.
reInterpolateAllSegments(linesArr, lineBreaksArr) =>
array.sort(lineBreaksArr, order.asc)
for i = 0 to array.size(lineBreaksArr) - 2
int leftEdge = array.get(lineBreaksArr, i)
int rightEdge = array.get(lineBreaksArr, i + 1)
reInterpSubrange(linesArr, leftEdge, rightEdge)
// getMaxDistanceIndex(...):
// Return the index (bar) that is farthest from the current “linesArr.”
// We skip any indices already in `lineBreaksArr`.
getMaxDistanceIndex(linesArr, closeArr, lineBreaksArr) =>
float maxDist = -1.0
int maxIdx = -1
int sizeData = array.size(linesArr)
for i = 1 to sizeData - 2
bool isBreak = false
for b = 0 to array.size(lineBreaksArr) - 1
if i == array.get(lineBreaksArr, b)
isBreak := true
break
if not isBreak
float dist = math.abs(array.get(linesArr, i) - array.get(closeArr, i))
if dist > maxDist
maxDist := dist
maxIdx := i
maxIdx
// snapAndReinterpolate(...):
// "Snap" a chosen index to its actual close price, then re-interpolate the entire line again.
snapAndReinterpolate(linesArr, closeArr, lineBreaksArr, idxToSnap) =>
if idxToSnap >= 0
float snapVal = array.get(closeArr, idxToSnap)
array.set(linesArr, idxToSnap, snapVal)
reInterpolateAllSegments(linesArr, lineBreaksArr)
// 3. Global Arrays and Flags
// ---------------------------------------------------------------------
// We store final data globally, then use them outside the barstate.islast scope to draw lines.
var float finalCloseData = array.new_float()
var float finalLines = array.new_float()
var int finalLineBreaks = array.new_int()
var bool didCompute = false
var line pipLines = array.new_line()
// 4. Main Logic (Runs Once at the End of the Current Bar)
// ---------------------------------------------------------------------
if barstate.islast
// A) Prepare closeData in forward order (index 0 = oldest bar, index length-1 = newest)
float closeData = array.new_float()
for i = 0 to length - 1
array.push(closeData, close )
// B) Initialize linesArr with a simple linear interpolation from the first to the last point
float linesArr = array.new_float()
float firstClose = array.get(closeData, 0)
float lastClose = array.get(closeData, length - 1)
for i = 0 to length - 1
float ratio = (length > 1) ? (i / float(length - 1)) : 0.0
float val = firstClose + (lastClose - firstClose) * ratio
array.push(linesArr, val)
// C) Initialize lineBreaks with two anchors: 0 (oldest) and length-1 (newest)
int lineBreaks = array.new_int()
array.push(lineBreaks, 0)
array.push(lineBreaks, length - 1)
// D) Iteratively insert new breakpoints, always re-interpolating globally
int iterationsNeeded = math.max(num_points - 2, 0)
for _iteration = 1 to iterationsNeeded
// 1) Re-interpolate entire shape, so it's globally up to date
reInterpolateAllSegments(linesArr, lineBreaks)
// 2) Find the bar with the largest vertical distance to this line
int maxDistIdx = getMaxDistanceIndex(linesArr, closeData, lineBreaks)
if maxDistIdx == -1
break
// 3) Insert that bar index into lineBreaks and snap it
array.push(lineBreaks, maxDistIdx)
array.sort(lineBreaks, order.asc)
snapAndReinterpolate(linesArr, closeData, lineBreaks, maxDistIdx)
// E) Save results into global arrays for line drawing outside barstate.islast
array.clear(finalCloseData)
array.clear(finalLines)
array.clear(finalLineBreaks)
for i = 0 to array.size(closeData) - 1
array.push(finalCloseData, array.get(closeData, i))
array.push(finalLines, array.get(linesArr, i))
for b = 0 to array.size(lineBreaks) - 1
array.push(finalLineBreaks, array.get(lineBreaks, b))
didCompute := true
// 5. Drawing the Lines in Global Scope
// ---------------------------------------------------------------------
// We cannot create lines inside barstate.islast, so we do it outside.
array.clear(pipLines)
if didCompute
// Connect each pair of anchors with red lines
if array.size(finalLineBreaks) > 1
for i = 0 to array.size(finalLineBreaks) - 2
int idxLeft = array.get(finalLineBreaks, i)
int idxRight = array.get(finalLineBreaks, i + 1)
float x1 = bar_index - (length - 1) + idxLeft
float x2 = bar_index - (length - 1) + idxRight
float y1 = array.get(finalCloseData, idxLeft)
float y2 = array.get(finalCloseData, idxRight)
line ln = line.new(x1, y1, x2, y2, extend=extend.none)
line.set_color(ln, color.red)
line.set_width(ln, 2)
array.push(pipLines, ln)
1. **Data Collection**
- We collect the **most recent** `length` bars in `closeData`. Index 0 is the oldest bar in that window, index `length-1` is the newest bar.
2. **Initial Straight Line**
- We create an array called `linesArr` that starts as a simple linear interpolation from `closeData ` (the oldest bar’s close) to `closeData ` (the newest bar’s close).
3. **Line Breaks**
- We store “anchor points” in `lineBreaks`, initially ` `. These are the start and end of our segment.
4. **Global Re-Interpolation**
- Each time we want to add a new anchor, we **re-draw** (linear interpolation) for *every* subrange ` [lineBreaks , lineBreaks ]`, ensuring we have a globally consistent line.
- This avoids the “local subrange only” approach, which can cause clustering near existing anchors.
5. **Finding the Largest Distance**
- After re-drawing, we compute the vertical distance for each bar `i` that isn’t already a line break. The bar with the biggest distance from the line is chosen as the next anchor (`maxDistIdx`).
6. **Snapping and Re-Interpolate**
- We “snap” that bar’s line value to the actual close, i.e. `linesArr = closeData `. Then we globally re-draw all segments again.
7. **Repeat**
- We repeat these insertions until we have the desired number of points (`num_points`).
8. **Drawing**
- Finally, we connect each consecutive pair of anchor points (`lineBreaks`) with a `line.new(...)` call, coloring them red.
- We offset the line’s `x` coordinate so that the anchor at index 0 lines up with `bar_index - (length - 1)`, and the anchor at index `length-1` lines up with `bar_index` (the current bar).
**Result**:
You get a simplified representation of the price with a small set of line segments capturing the largest “jumps” or swings. By re-drawing the entire line after each insertion, the anchors tend to distribute more *evenly* across the data, mitigating the issue where anchors bunch up near each other.
Enjoy experimenting with different `length` and `num_points` to see how the simplified lines change!
Log Regression OscillatorThe Log Regression Oscillator transforms the logarithmic regression curves into an easy-to-interpret oscillator that displays potential cycle tops/bottoms.
🔶 USAGE
Calculating the logarithmic regression of long-term swings can help show future tops/bottoms. The relationship between previous swing points is calculated and projected further. The calculated levels are directly associated with swing points, which means every swing point will change the calculation. Importantly, all levels will be updated through all bars when a new swing is detected.
The "Log Regression Oscillator" transforms the calculated levels, where the top level is regarded as 100 and the bottom level as 0. The price values are displayed in between and calculated as a ratio between the top and bottom, resulting in a clear view of where the price is situated.
The main picture contains the Logarithmic Regression Alternative on the chart to compare with this published script.
Included are the levels 30 and 70. In the example of Bitcoin, previous cycles showed a similar pattern: the bullish parabolic was halfway when the oscillator passed the 30-level, and the top was very near when passing the 70-level.
🔹 Proactive
A "Proactive" option is included, which ensures immediate calculations of tentative unconfirmed swings.
Instead of waiting 300 bars for confirmation, the "Proactive" mode will display a gray-white dot (not confirmed swing) and add the unconfirmed Swing value to the calculation.
The above example shows that the "Calculated Values" of the potential future top and bottom are adjusted, including the provisional swing.
When the swing is confirmed, the calculations are again adjusted, showing a red dot (confirmed top swing) or a green dot (confirmed bottom swing).
🔹 Dashboard
When less than two swings are available (top/bottom), this will be shown in the dashboard.
The user can lower the "Threshold" value or switch to a lower timeframe.
🔹 Notes
Logarithmic regression is typically used to model situations where growth or decay accelerates rapidly at first and then slows over time, meaning some symbols/tickers will fit better than others.
Since the logarithmic regression depends on swing values, each new value will change the calculation. A well-fitted model could not fit anymore in the future.
Users have to check the validity of swings; for example, if the direction of swings is downwards, then the dataset is not fitted for logarithmic regression.
In the example above, the "Threshold" is lowered. However, the calculated levels are unreliable due to the swings, which do not fit the model well.
Here, the combination of downward bottom swings and price accelerates slower at first and faster recently, resulting in a non-fit for the logarithmic regression model.
Note the price value (white line) is bound to a limit of 150 (upwards) and -150 (down)
In short, logarithmic regression is best used when there are enough tops/bottoms, and all tops are around 100, and all bottoms around 0.
Also, note that this indicator has been developed for a daily (or higher) timeframe chart.
🔶 DETAILS
In mathematics, the dot product or scalar product is an algebraic operation that takes two equal-length sequences of numbers (arrays) and returns a single number, the sum of the products of the corresponding entries of the two sequences of numbers.
The usual way is to loop through both arrays and sum the products.
In this case, the two arrays are transformed into a matrix, wherein in one matrix, a single column is filled with the first array values, and in the second matrix, a single row is filled with the second array values.
After this, the function matrix.mult() returns a new matrix resulting from the product between the matrices m1 and m2.
Then, the matrix.eigenvalues() function transforms this matrix into an array, where the array.sum() function finally returns the sum of the array's elements, which is the dot product.
dot(x, y)=>
if x.size() > 1 and y.size() > 1
m1 = matrix.new()
m2 = matrix.new()
m1.add_col(m1.columns(), y)
m2.add_row(m2.rows (), x)
m1.mult (m2)
.eigenvalues()
.sum()
🔶 SETTINGS
Threshold: Period used for the swing detection, with higher values returning longer-term Swing Levels.
Proactive: Tentative Swings are included with this setting enabled.
Style: Color Settings
Dashboard: Toggle, "Location" and "Text Size"
Candled LWMA (Loacally Weighted MA)The Locally Weighted Moving Average (LWMA) is a type of moving average that emphasizes recent data points by assigning them higher weights compared to older values. Unlike the Simple Moving Average (SMA), which treats all data points equally, or the Exponential Moving Average (EMA), which uses a fixed weighting factor, the LWMA applies a linear weighting scheme. This means that the most recent prices contribute more significantly to the average, making the LWMA more responsive to price changes while retaining a smooth curve.
In trading, the LWMA is particularly useful for identifying trends and detecting price reversals with reduced lag. By giving more importance to the latest prices, it provides a clearer picture of the current market dynamics. Traders often use the LWMA in combination with other indicators to confirm trends or spot potential entry and exit points. The adjustable length parameter allows for fine-tuning the indicator to match different market conditions and trading styles. Its ability to adapt to recent price behavior makes it a valuable tool for both short-term and long-term traders.
N-Degree Moment-Based Adaptive Detection🙏🏻 N-Degree Moment-Based Adaptive Detection (NDMBAD) method is a generalization of MBAD since the horizontal line fit passing through the data's mean can be simply treated as zero-degree polynomial regression. We can extend the MBAD logic to higher-degree polynomial regression.
I don't think I need to talk a lot about the thing there; the logic is really the same as in MBAD, just hit the link above and read if you want. The only difference is now we can gather cumulants not only from the horizontal mean fit (degree = 0) but also from higher-order polynomial regression fit, including linear regression (degree = 1).
Why?
Simply because residuals from the 0-degree model don't contain trend information, and while in some cases that's exactly what you need, in other cases, you want to model your trend explicitly. Imagine your underlying process trends in a steady manner, and you want to control the extreme deviations from the process's core. If you're going to use 0-degree, you'll be treating this beautiful steady trend as a residual itself, which "constantly deviates from the process mean." It doesn't make much sense.
How?
First, if you set the length to 0, you will end up with the function incrementally applied to all your data starting from bar_index 0. This can be called the expanding window mode. That's the functionality I include in all my scripts lately (where it makes sense). As I said in the MBAD description, choosing length is a matter of doing business & applied use of my work, but I think I'm open to talk about it.
I don't see much sense in using degree > 1 though (still in research on it). If you have dem curves, you can use Fourier transform -> spectral filtering / harmonic regression (regression with Fourier terms). The job of a degree > 0 is to model the direction in data, and degree 1 gets it done. In mean reversion strategies, it means that you don't wanna put 0-degree polynomial regression (i.e., the mean) on non-stationary trending data in moving window mode because, this way, your residuals will be contaminated with the trend component.
By the way, you can send thanks to @aaron294c , he said like mane MBAD is dope, and it's gonna really complement his work, so I decided to drop NDMBAD now, gonna be more useful since it covers more types of data.
I wanned to call it N-Order Moment Adaptive Detection because it abbreviates to NOMAD, which sounds cool and suits me well, because when I perform as a fire dancer, nomad style is one of my outfits. Burning Man stuff vibe, you know. But the problem is degree and order really mean two different things in the polynomial context, so gotta stay right & precise—that's the priority.
∞
Quick scan for signal🙏🏻 Hey TV, this is QSFS, following:
^^ Quick scan for drift (QSFD)
^^ Quick scan for cycles (QSFC)
As mentioned before, ML trading is all about spotting any kind of non-randomness, and this metric (along with 2 previously posted) gonna help ya'll do it fast. This one will show you whether your time series possibly exhibits mean-reverting / consistent / noisy behavior, that can be later confirmed or denied by more sophisticated tools. This metric is O(n) in windowed mode and O(1) if calculated incrementally on each data update, so you can scan Ks of datasets w/o worrying about melting da ice.
^^ windowed mode
Now the post will be divided into several sections, and a couple of things I guess you’ve never seen or thought about in your life:
1) About Efficiency Ratios posted there on TV;
Some of you might say this is the Efficiency Ratio you’ve seen in Perry's book. Firstly, I can assure you that neither me nor Perry, just as X amount of quants all over the world and who knows who else, would say smth like, "I invented it," lol. This is just a thing you R&D when you need it. Secondly, I invite you (and mods & admin as well) to take a lil glimpse at the following screenshot:
^^ not cool...
So basically, all the Efficiency Ratios that were copypasted to our platform suffer the same bug: dudes don’t know how indexing works in Pine Script. I mean, it’s ok, I been doing the same mistakes as well, but loxx, cmon bro, you... If you guys ever read it, the lines 20 and 22 in da code are dedicated to you xD
2) About the metric;
This supports both moving window mode when Length > 0 and all-data expanding window mode when Length < 1, calculating incrementally from the very first data point in the series: O(n) on history, O(1) on live updates.
Now, why do I SQRT transform the result? This is a natural action since the metric (being a ratio in essence) is bounded between 0 and 1, so it can be modeled with a beta distribution. When you SQRT transform it, it still stays beta (think what happens when you apply a square root to 0.01 or 0.99), but it becomes symmetric around its typical value and starts to follow a bell-shaped curve. This can be easily checked with a normality test or by applying a set of percentiles and seeing the distances between them are almost equal.
Then I noticed that on different moving window sizes, the typical value of the metric seems to slide: higher window sizes lead to lower typical values across the moving windows. Turned out this can be modeled the same way confidence intervals are made. Lines 34 and 35 explain it all, I guess. You can see smth alike on an autocorrelogram. These two match the mean & mean + 1 stdev applied to the metric. This way, we’ve just magically received data to estimate alpha and beta parameters of the beta distribution using the method of moments. Having alpha and beta, we can now estimate everything further. Btw, there’s an alternative parameterization for beta distributions based on data length.
Now what you’ll see next is... u guys actually have no idea how deep and unrealistically minimalistic the underlying math principles are here.
I’m sure I’m not the only one in the universe who figured it out, but the thing is, it’s nowhere online or offline. By calculating higher-order moments & combining them, you can find natural adaptive thresholds that can later be used for anomaly detection/control applications for any data. No hardcoded thresholds, purely data-driven. Imma come back to this in one of the next drops, but the truest ones can already see it in this code. This way we get dem thresholds.
Your main thresholds are: basis, upper, and lower deviations. You can follow the common logic I’ve described in my previous scripts on how to use them. You just register an event when the metric goes higher/lower than a certain threshold based on what you’re looking for. Then you take the time series and confirm a certain behavior you were looking for by using an appropriate stat test. Or just run a certain strategy.
To avoid numerous triggers when the metric jitters around a threshold, you can follow this logic: forget about one threshold if touched, until another threshold is touched.
In general, when the metric gets higher than certain thresholds, like upper deviation, it means the signal is stronger than noise. You confirm it with a more sophisticated tool & run momentum strategies if drift is in place, or volatility strategies if there’s no drift in place. Otherwise, you confirm & run ~ mean-reverting strategies, regardless of whether there’s drift or not. Just don’t operate against the trend—hedge otherwise.
3) Flex;
Extension and limit thresholds based on distribution moments gonna be discussed properly later, but now you can see this:
^^ magic
Look at the thresholds—adaptive and dynamic. Do you see any optimizations? No ML, no DL, closed-form solution, but how? Just a formula based on a couple of variables? Maybe it’s just how the Universe works, but how can you know if you don’t understand how fundamentally numbers 3 and 15 are related to the normal distribution? Hm, why do they always say 3 sigmas but can’t say why? Maybe you can be different and say why?
This is the primordial power of statistical modeling.
4) Thanks;
I really wanna dedicate this to Charlotte de Witte & Marion Di Napoli, and their new track "Sanctum." It really gets you connected to the Source—I had it in my soul when I was doing all this ∞
DI Oscillator with Adjustments by DSPDI Oscillator with Adjustments by DSP – High-Volatility Commodity Trading Tool 📈💥
Maximize Your Trading Efficiency in volatile commodity markets with the DI Oscillator with Adjustments by DSP. This unique indicator combines the classic +DI and -DI (Directional Indicators) with advanced adjustments that help you identify key trends and reversals in highly volatile conditions.
Whether you're trading commodities, forex, or stocks, this tool is engineered to help you navigate price fluctuations and make timely, informed decisions. Let this powerful tool guide you through turbulent market conditions with ease!
Key Features:
Dynamic Background Color Shifts 🌈:
Green Background: Signals a strong uptrend where +DI is clearly above -DI, and the trend is supported by clear separation between the two indicators.
Red Background: Signals a strong downtrend where -DI is above +DI, indicating bearish pressure.
Violet Background: Shows a neutral or consolidating market where the +DI and -DI lines are closely interwoven, giving you a clear picture of sideways movement.
Buy and Sell Labels 📊:
Buy Signal: Automatically triggers when the background changes to green, indicating a potential entry point during a bullish trend.
Sell Signal: Automatically triggers when the background shifts from purple to red, indicating a bearish trend reversal.
Labels are positioned away from the bars, ensuring your chart remains uncluttered and easy to read.
Enhanced Adjustments for Volatile Markets ⚡:
Custom adjustments based on consecutive green or red bars (excluding “sandwiched” bars) provide you with more nuanced signals, improving the accuracy of trend detection in volatile conditions.
Horizontal Line Reference 📏:
Set a custom horizontal level to mark significant price levels that may act as resistance or support, helping you identify key price points in volatile market swings.
Separation Threshold 🧮:
A custom separation threshold defines when the +DI and -DI lines are far enough apart to confirm a strong trend. This is crucial for commodity markets that experience rapid price changes and fluctuations.
Visual Clarity ✨:
Both +DI and -DI lines are plotted clearly in green and red, respectively, with a dedicated background color system that makes trend shifts visually intuitive.
Why This Indicator Works for Volatile Commodities 🌍📊:
Commodity markets are notorious for their volatility, with prices often experiencing rapid and unpredictable movements. This indicator gives you clear visual cues about trend strength and reversals, enabling you to act quickly and confidently.
By adjusting the +DI based on consecutive green and red bars, this tool adapts to the specific price action in high-volatility conditions, helping you stay ahead of the curve.
The background color system ensures that you can visually track market trends at a glance, making it easier to make split-second decisions without missing opportunities.
How to Use:
Add the Indicator: Simply add the DI Oscillator with Adjustments by DSP to your TradingView chart.
Watch for Background Color Shifts: Stay alert for the background color to shift from violet to green (for buy) or purple to red (for sell), signaling potential trade opportunities.
Set Alerts: Receive notifications when background color changes, providing you with real-time alerts to keep track of market movements.
Interpret the DI Lines: Use the +DI and -DI lines to gauge trend strength and adjust your strategy accordingly.
Who Can Benefit:
Day Traders: Take advantage of quick trend reversals and high volatility in commodities markets, such as gold, oil, or agricultural products.
Swing Traders: Identify key trend shifts over longer periods, making it easier to enter or exit trades during major price movements.
Risk Managers: Use this tool’s visual cues to better understand price fluctuations and adjust your position sizes according to market conditions.
💡 Unlock Your Potential with the DI Oscillator 💡
For traders in high-volatility commodity markets, this indicator is a game-changer. It simplifies the complexity of trend analysis and gives you the actionable insights you need to make fast, profitable decisions. Whether you're trading gold, oil, or other volatile commodities, the DI Oscillator with Adjustments by DSP can help you navigate market chaos and make better-informed trades.
Don’t miss out — enhance your trading strategy today with this powerful tool and stay ahead in any market environment!
Silver Bullet ICT Strategy [TradingFinder] 10-11 AM NY Time +FVG🔵 Introduction
The ICT Silver Bullet trading strategy is a precise, time-based algorithmic approach that relies on Fair Value Gaps and Liquidity to identify high-probability trade setups. The strategy primarily focuses on the New York AM Session from 10:00 AM to 11:00 AM, leveraging heightened market activity within this critical window to capture short-term trading opportunities.
As an intraday strategy, it is most effective on lower timeframes, with ICT recommending a 15-minute chart or lower. While experienced traders often utilize 1-minute to 5-minute charts, beginners may find the 1-minute timeframe more manageable for applying this strategy.
This approach specifically targets quick trades, designed to take advantage of market movements within tight one-hour windows. By narrowing its focus, the Silver Bullet offers a streamlined and efficient method for traders to capitalize on liquidity shifts and price imbalances with precision.
In the fast-paced world of forex trading, the ability to identify market manipulation and false price movements is crucial for traders aiming to stay ahead of the curve. The Silver Bullet Indicator simplifies this process by integrating ICT principles such as liquidity traps, Order Blocks, and Fair Value Gaps (FVG).
These concepts form the foundation of a tool designed to mimic the strategies of institutional players, empowering traders to align their trades with the "smart money." By transforming complex market dynamics into actionable insights, the Silver Bullet Indicator provides a powerful framework for short-term trading success
Silver Bullet Bullish Setup :
Silver Bullet Bearish Setup :
🔵 How to Use
The Silver Bullet Indicator is a specialized tool that operates within the critical time windows of 9:00-10:00 and 10:00-11:00 in the forex market. Its design incorporates key principles from ICT (Inner Circle Trader) methodology, focusing on concepts such as liquidity traps, CISD Levels, Order Blocks, and Fair Value Gaps (FVG) to provide precise and actionable trade setups.
🟣 Bullish Setup
In a bullish setup, the indicator starts by marking the high and low of the session, serving as critical reference points for liquidity. A typical sequence involves a liquidity grab below the low, where the price manipulates retail traders into selling positions by breaching a key support level.
This movement is often orchestrated by smart money to accumulate buy orders. Following this liquidity grab, a market structure shift (MSS) occurs, signaled by the price breaking the CISD Level—a confirmation of bullish intent. The indicator then highlights an Order Block near the CISD Level, representing the zone where institutional buying is concentrated.
Additionally, it identifies a Fair Value Gap, which acts as a high-probability area for price retracement and trade entry. Traders can confidently take long positions when the price revisits these zones, targeting the next significant liquidity pool or resistance level.
Bullish Setup in CAPITALCOM:US100 :
🟣 Bearish Setup
Conversely, in a bearish setup, the price manipulates liquidity by creating a false breakout above the high of the session. This move entices retail traders into long positions, allowing institutional players to enter sell orders.
Once the price reverses direction and breaches the CISD Level to the downside, a change of character (CHOCH) becomes evident, confirming a bearish market structure. The indicator highlights an Order Block near this level, indicating the origin of the institutional sell orders, along with an associated FVG, which represents an imbalance zone likely to be revisited before the price continues downward.
By entering short positions when the price retraces to these levels, traders align their strategies with the anticipated continuation of bearish momentum, targeting nearby liquidity voids or support zones.
Bearish Setup in OANDA:XAUUSD :
🔵 Settings
Refine Order Block : Enables finer adjustments to Order Block levels for more accurate price responses.
Mitigation Level OB : Allows users to set specific reaction points within an Order Block, including: Proximal: Closest level to the current price. 50% OB: Midpoint of the Order Block. Distal: Farthest level from the current price.
FVG Filter : The Judas Swing indicator includes a filter for Fair Value Gap (FVG), allowing different filtering based on FVG width: FVG Filter Type: Can be set to "Very Aggressive," "Aggressive," "Defensive," or "Very Defensive." Higher defensiveness narrows the FVG width, focusing on narrower gaps.
Mitigation Level FVG : Like the Order Block, you can set price reaction levels for FVG with options such as Proximal, 50% OB, and Distal.
CISD : The Bar Back Check option enables traders to specify the number of past candles checked for identifying the CISD Level, enhancing CISD Level accuracy on the chart.
🔵 Conclusion
The Silver Bullet Indicator is a cutting-edge tool designed specifically for forex traders who aim to leverage market dynamics during critical liquidity windows. By focusing on the highly active 9:00-10:00 and 10:00-11:00 timeframes, the indicator simplifies complex market concepts such as liquidity traps, Order Blocks, Fair Value Gaps (FVG), and CISD Levels, transforming them into actionable insights.
What sets the Silver Bullet Indicator apart is its precision in detecting false breakouts and market structure shifts (MSS), enabling traders to align their strategies with institutional activity. The visual clarity of its signals, including color-coded zones and directional arrows, ensures that both novice and experienced traders can easily interpret and apply its findings in real-time.
By integrating ICT principles, the indicator empowers traders to identify high-probability entry and exit points, minimize risk, and optimize trade execution. Whether you are capturing short-term price movements or navigating complex market conditions, the Silver Bullet Indicator offers a robust framework to enhance your trading performance.
Ultimately, this tool is more than just an indicator; it is a strategic ally for traders who seek to decode the movements of smart money and capitalize on institutional strategies. With the Silver Bullet Indicator, traders can approach the market with greater confidence, precision, and profitability.
BarRange StrategyHello,
This is a long-only, volatility-based strategy that analyzes the range of the previous bar (high - low).
If the most recent bar’s range exceeds a threshold based on the last X bars, a trade is initiated.
You can customize the lookback period, threshold value, and exit type.
For exits, you can choose to exit after X bars or when the close price exceeds the previous bar’s high.
The strategy is designed for instruments with a long-term upward-sloping curves, such as ES1! or NQ1!. It may not perform well on other instruments.
Commissions are set to $2.50 per side ($5.00 per round trip).
Recommended timeframes are 1h and higher. With adjustments to the lookback period and threshold, it could potentially achieve similar results on lower timeframes as well.
Quick scan for drift🙏🏻
ML based algorading is all about detecting any kind of non-randomness & exploiting it, kinda speculative stuff, not my way, but still...
Drift is one of the patterns that can be exploited, because pure random walks & noise aint got no drift.
This is an efficient method to quickly scan tons of timeseries on the go & detect the ones with drift by simply checking wherther drift < -0.5 or drift > 0.5. The code can be further optimized both in general and for specific needs, but I left it like dat for clarity so you can understand how it works in a minute not in an hour
^^ proving 0.5 and -0.5 are natural limits with no need to optimize anything, we simply put the metric on random noise and see it sits in between -0.5 and 0.5
You can simply take this one and never check anything again if you require numerous live scans on the go. The metric is purely geometrical, no connection to stats, TSA, DSA or whatever. I've tested numerous formulas involving other scaling techniques, drift estimates etc (even made a recursive algo that had a great potential to be written about in a paper, but not this time I gues lol), this one has the highest info gain aka info content.
The timeseries filtered by this lil metric can be further analyzed & modelled with more sophisticated tools.
Live Long and Prosper
P.S.: there's no such thing as polynomial trend/drift, it's alwasy linear, these curves you see are just really long cycles
P.S.: does cheer still work on TV? @admin
Advanced Keltner Channel/Oscillator [MyTradingCoder]This indicator combines a traditional Keltner Channel overlay with an oscillator, providing a comprehensive view of price action, trend, and momentum. The core of this indicator is its advanced ATR calculation, which uses statistical methods to provide a more robust measure of volatility.
Starting with the overlay component, the center line is created using a biquad low-pass filter applied to the chosen price source. This provides a smoother representation of price than a simple moving average. The upper and lower channel lines are then calculated using the statistically derived ATR, with an additional set of mid-lines between the center and outer lines. This creates a more nuanced view of price action within the channel.
The color coding of the center line provides an immediate visual cue of the current price momentum. As the price moves up relative to the ATR, the line shifts towards the bullish color, and vice versa for downward moves. This color gradient allows for quick assessment of the current market sentiment.
The oscillator component transforms the channel into a different perspective. It takes the price's position within the channel and maps it to either a normalized -100 to +100 scale or displays it in price units, depending on your settings. This oscillator essentially shows where the current price is in relation to the channel boundaries.
The oscillator includes two key lines: the main oscillator line and a signal line. The main line represents the current position within the channel, smoothed by an exponential moving average (EMA). The signal line is a further smoothed version of the oscillator line. The interaction between these two lines can provide trading signals, similar to how MACD is often used.
When the oscillator line crosses above the signal line, it might indicate bullish momentum, especially if this occurs in the lower half of the oscillator range. Conversely, the oscillator line crossing below the signal line could signal bearish momentum, particularly if it happens in the upper half of the range.
The oscillator's position relative to its own range is also informative. Values near the top of the range (close to 100 if normalized) suggest that price is near the upper Keltner Channel band, indicating potential overbought conditions. Values near the bottom of the range (close to -100 if normalized) suggest proximity to the lower band, potentially indicating oversold conditions.
One of the strengths of this indicator is how the overlay and oscillator work together. For example, if the price is touching the upper band on the overlay, you'd see the oscillator at or near its maximum value. This confluence of signals can provide stronger evidence of overbought conditions. Similarly, the oscillator hitting extremes can draw your attention to price action at the channel boundaries on the overlay.
The mid-lines on both the overlay and oscillator provide additional nuance. On the overlay, price action between the mid-line and outer line might suggest strong but not extreme momentum. On the oscillator, this would correspond to readings in the outer quartiles of the range.
The customizable visual settings allow you to adjust the indicator to your preferences. The glow effects and color coding can make it easier to quickly interpret the current market conditions at a glance.
Overlay Component:
The overlay displays Keltner Channel bands dynamically adapting to market conditions, providing clear visual cues for potential trend reversals, breakouts, and overbought/oversold zones.
The center line is a biquad low-pass filter applied to the chosen price source.
Upper and lower channel lines are calculated using a statistically derived ATR.
Includes mid-lines between the center and outer channel lines.
Color-coded based on price movement relative to the ATR.
Oscillator Component:
The oscillator component complements the overlay, highlighting momentum and potential turning points.
Normalized values make it easy to compare across different assets and timeframes.
Signal line crossovers generate potential buy/sell signals.
Advanced ATR Calculation:
Uses a unique method to compute ATR, incorporating concepts like root mean square (RMS) and z-score clamping.
Provides both an average and mode-based ATR value.
Customizable Visual Settings:
Adjustable colors for bullish and bearish moves, oscillator lines, and channel components.
Options for line width, transparency, and glow effects.
Ability to display overlay, oscillator, or both simultaneously.
Flexible Parameters:
Customizable inputs for channel width multiplier, ATR period, smoothing factors, and oscillator settings.
Adjustable Q factor for the biquad filter.
Key Advantages:
Advanced ATR Calculation: Utilizes a statistical method to generate ATR, ensuring greater responsiveness and accuracy in volatile markets.
Overlay and Oscillator: Provides a comprehensive view of price action, combining trend and momentum analysis.
Customizable: Adjust settings to fine-tune the indicator to your specific needs and trading style.
Visually Appealing: Clear and concise design for easy interpretation.
The ATR (Average True Range) in this indicator is derived using a sophisticated statistical method that differs from the traditional ATR calculation. It begins by calculating the True Range (TR) as the difference between the high and low of each bar. Instead of a simple moving average, it computes the Root Mean Square (RMS) of the TR over the specified period, giving more weight to larger price movements. The indicator then calculates a Z-score by dividing the TR by the RMS, which standardizes the TR relative to recent volatility. This Z-score is clamped to a maximum value (10 in this case) to prevent extreme outliers from skewing the results, and then rounded to a specified number of decimal places (2 in this script).
These rounded Z-scores are collected in an array, keeping track of how many times each value occurs. From this array, two key values are derived: the mode, which is the most frequently occurring Z-score, and the average, which is the weighted average of all Z-scores. These values are then scaled back to price units by multiplying by the RMS.
Now, let's examine how these values are used in the indicator. For the Keltner Channel lines, the mid lines (top and bottom) use the mode of the ATR, representing the most common volatility state. The max lines (top and bottom) use the average of the ATR, incorporating all volatility states, including less common but larger moves. By using the mode for the mid lines and the average for the max lines, the indicator provides a nuanced view of volatility. The mid lines represent the "typical" market state, while the max lines account for less frequent but significant price movements.
For the color coding of the center line, the mode of the ATR is used to normalize the price movement. The script calculates the difference between the current price and the price 'degree' bars ago (default is 2), and then divides this difference by the mode of the ATR. The resulting value is passed through an arctangent function and scaled to a 0-1 range. This scaled value is used to create a color gradient between the bearish and bullish colors.
Using the mode of the ATR for this color coding ensures that the color changes are based on the most typical volatility state of the market. This means that the color will change more quickly in low volatility environments and more slowly in high volatility environments, providing a consistent visual representation of price momentum relative to current market conditions.
Using a good IIR (Infinite Impulse Response) low-pass filter, such as the biquad filter implemented in this indicator, offers significant advantages over simpler moving averages like the EMA (Exponential Moving Average) or other basic moving averages.
At its core, an EMA is indeed a simple, single-pole IIR filter, but it has limitations in terms of its frequency response and phase delay characteristics. The biquad filter, on the other hand, is a two-pole, two-zero filter that provides superior control over the frequency response curve. This allows for a much sharper cutoff between the passband and stopband, meaning it can more effectively separate the signal (in this case, the underlying price trend) from the noise (short-term price fluctuations).
The improved frequency response of a well-designed biquad filter means it can achieve a better balance between smoothness and responsiveness. While an EMA might need a longer period to sufficiently smooth out price noise, potentially leading to more lag, a biquad filter can achieve similar or better smoothing with less lag. This is crucial in financial markets where timely information is vital for making trading decisions.
Moreover, the biquad filter allows for independent control of the cutoff frequency and the Q factor. The Q factor, in particular, is a powerful parameter that affects the filter's resonance at the cutoff frequency. By adjusting the Q factor, users can fine-tune the filter's behavior to suit different market conditions or trading styles. This level of control is simply not available with basic moving averages.
Another advantage of the biquad filter is its superior phase response. In the context of financial data, this translates to more consistent lag across different frequency components of the price action. This can lead to more reliable signals, especially when it comes to identifying trend changes or price reversals.
The computational efficiency of biquad filters is also worth noting. Despite their more complex mathematical foundation, biquad filters can be implemented very efficiently, often requiring only a few operations per sample. This makes them suitable for real-time applications and high-frequency trading scenarios.
Furthermore, the use of a more sophisticated filter like the biquad can help in reducing false signals. The improved noise rejection capabilities mean that minor price fluctuations are less likely to cause unnecessary crossovers or indicator movements, potentially leading to fewer false breakouts or reversal signals.
In the specific context of a Keltner Channel, using a biquad filter for the center line can provide a more stable and reliable basis for the entire indicator. It can help in better defining the overall trend, which is crucial since the Keltner Channel is often used for trend-following strategies. The smoother, yet more responsive center line can lead to more accurate channel boundaries, potentially improving the reliability of overbought/oversold signals and breakout indications.
In conclusion, this advanced Keltner Channel indicator represents a significant evolution in technical analysis tools, combining the power of traditional Keltner Channels with modern statistical methods and signal processing techniques. By integrating a sophisticated ATR calculation, a biquad low-pass filter, and a complementary oscillator component, this indicator offers traders a comprehensive and nuanced view of market dynamics.
The indicator's strength lies in its ability to adapt to varying market conditions, providing clear visual cues for trend identification, momentum assessment, and potential reversal points. The use of statistically derived ATR values for channel construction and the implementation of a biquad filter for the center line result in a more responsive and accurate representation of price action compared to traditional methods.
Furthermore, the dual nature of this indicator – functioning as both an overlay and an oscillator – allows traders to simultaneously analyze price trends and momentum from different perspectives. This multifaceted approach can lead to more informed decision-making and potentially more reliable trading signals.
The high degree of customization available in the indicator's settings enables traders to fine-tune its performance to suit their specific trading styles and market preferences. From adjustable visual elements to flexible parameter inputs, users can optimize the indicator for various trading scenarios and time frames.
Ultimately, while no indicator can predict market movements with certainty, this advanced Keltner Channel provides traders with a powerful tool for market analysis. By offering a more sophisticated approach to measuring volatility, trend, and momentum, it equips traders with valuable insights to navigate the complex world of financial markets. As with any trading tool, it should be used in conjunction with other forms of analysis and within a well-defined risk management framework to maximize its potential benefits.
Hullinger Bands [AlgoAlpha]🎯 Introducing the Hullinger Bands Indicator ! 🎯
Maximize your trading precision with the Hullinger Bands , an advanced tool that combines the strengths of Hull Moving Averages and Bollinger Bands for a robust trading strategy. This indicator is designed to give traders clear and actionable signals, helping you identify trend changes and optimize entry and exit points with confidence.
✨ Key Features :
📊 Dual-Length Settings : Customize your main and TP signal lengths to fit your trading style.
🎯 Enhanced Band Accuracy : The indicator uses a modified standard deviation calculation for more reliable volatility measures.
🟢🔴 Color-Coded Signals : Easily spot bullish and bearish conditions with customizable color settings.
💡 Dynamic Alerts : Get notified for trend changes and TP signals with built-in alert conditions.
🚀 Quick Guide to Using Hullinger Bands
1. ⭐ Add the Indicator : Add the indicator to favorites by pressing the star icon. Adjust the settings to align with your trading preferences, such as length and multiplier values.
2. 🔍 Analyze Readings : Observe the color-coded bands for real-time insights into market conditions. When price is closer to the upper bands it suggests an overbought market and vice versa if price is closer to the lower bands. Price being above or below the basis can be a trend indicator.
3. 🔔 Set Alerts : Activate alerts for bullish/bearish trends and TP signals, ensuring you never miss a crucial market movement.
🔍 How It Works
The Hullinger Bands indicator calculates a central line (basis) using a simple moving average, while the upper and lower bands are derived from a modified standard deviation of price movements. Unlike the traditional Bollinger Bands, the standard deviation in the Hullinger bands uses the Hull Moving Average instead of the Simple Moving Average to calculate the average variance for standard deviation calculations, this give the modified standard deviation output "memory" and the bands can be observed expanding even after the price has started consolidating, this can identify when the trend has exhausted better as the distance between the price and the bands is more apparent. The color of the bands changes dynamically, based on the proximity of the closing price to the bands, providing instant visual cues for market sentiment. The indicator also plots TP signals when price crosses these bands, allowing traders to make informed decisions. Additionally, alerts are configured to notify you of crucial market shifts, ensuring you stay ahead of the curve.
Fear/Greed Zone Reversals [UAlgo]The "Fear/Greed Zone Reversals " indicator is a custom technical analysis tool designed for TradingView, aimed at identifying potential reversal points in the market based on sentiment zones characterized by fear and greed. This indicator utilizes a combination of moving averages, standard deviations, and price action to detect when the market transitions from extreme fear to greed or vice versa. By identifying these critical turning points, traders can gain insights into potential buy or sell opportunities.
🔶 Key Features
Customizable Moving Averages: The indicator allows users to select from various types of moving averages (SMA, EMA, WMA, VWMA, HMA) for both fear and greed zone calculations, enabling flexible adaptation to different trading strategies.
Fear Zone Settings:
Fear Source: Select the price data point (e.g., close, high, low) used for Fear Zone calculations.
Fear Period: This defines the lookback window for calculating the Fear Zone deviation.
Fear Stdev Period: This sets the period used to calculate the standard deviation of the Fear Zone deviation.
Greed Zone Settings:
Greed Source: Select the price data point (e.g., close, high, low) used for Greed Zone calculations.
Greed Period: This defines the lookback window for calculating the Greed Zone deviation.
Greed Stdev Period: This sets the period used to calculate the standard deviation of the Greed Zone deviation.
Alert Conditions: Integrated alert conditions notify traders in real-time when a reversal in the fear or greed zone is detected, allowing for timely decision-making.
🔶 Interpreting Indicator
Greed Zone: A Greed Zone is highlighted when the price deviates significantly above the chosen moving average. This suggests market sentiment might be leaning towards greed, potentially indicating a selling opportunity.
Fear Zone Reversal: A Fear Zone is highlighted when the price deviates significantly below the chosen moving average of the selected price source. This suggests market sentiment might be leaning towards fear, potentially indicating a buying opportunity. When the indicator identifies a reversal from a fear zone, it suggests that the market is transitioning from a period of intense selling pressure to a more neutral or potentially bullish state. This is typically indicated by an upward arrow (▲) on the chart, signaling a potential buy opportunity. The fear zone is characterized by high price volatility and overselling, making it a crucial point for traders to consider entering the market.
Greed Zone Reversal: Conversely, a Greed Zone is highlighted when the price deviates significantly above the chosen moving average. This suggests market sentiment might be leaning towards greed, potentially indicating a selling opportunity. When the indicator detects a reversal from a greed zone, it indicates that the market may be moving from an overbought condition back to a more neutral or bearish state. This is marked by a downward arrow (▼) on the chart, suggesting a potential sell opportunity. The greed zone is often associated with overconfidence and high buying activity, which can precede a market correction.
🔶 Why offer multiple moving average types?
By providing various moving average types (SMA, EMA, WMA, VWMA, HMA) , the indicator offers greater flexibility for traders to tailor the indicator to their specific trading strategies and market preferences. Different moving averages react differently to price data and can produce varying signals.
SMA (Simple Moving Average): Provides an equal weighting to all data points within the specified period.
EMA (Exponential Moving Average): Gives more weight to recent data points, making it more responsive to price changes.
WMA (Weighted Moving Average): Allows for custom weighting of data points, providing more flexibility in the calculation.
VWMA (Volume Weighted Moving Average): Considers both price and volume data, giving more weight to periods with higher trading volume.
HMA (Hull Moving Average): A combination of weighted moving averages designed to reduce lag and provide a smoother curve.
Offering multiple options allows traders to:
Experiment: Traders can try different moving averages to see which one produces the most accurate signals for their specific market.
Adapt to different market conditions: Different market conditions may require different moving average types. For example, a fast-moving market might benefit from a faster moving average like an EMA, while a slower-moving market might be better suited to a slower moving average like an SMA.
Personalize: Traders can choose the moving average that best aligns with their personal trading style and risk tolerance.
In essence, providing a variety of moving average types empowers traders to create a more personalized and effective trading experience.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Growth TrendThis powerful indicator plots the number of growth stocks in an uptrend, providing a comprehensive view of the market's overall direction. By applying a simple moving average, users can quickly gauge the trend and make informed trading decisions.
How does it work?
The script pulls tickers from the S & P 500 Growth ETF. It then plots the number of stocks from the ETF that are trending above a medium-term Moving Average, signaling an uptrend.
A moving average is applied to help understand the trend.
The background is shaded when 3 or more consecutive days are above (green) or below (red) the moving average.
Key Features:
Visual Trend Identification: The indicator shades the background green when three or more consecutive days are above the moving average, indicating a strong uptrend. Conversely, it shades red when three consecutive days are below the moving average, signaling a downtrend.
Breakout Insights: By tracking the trend, traders can identify when breakouts in growth stocks are more likely to occur or fail. This helps traders time their entries and exits more effectively.
Trend Strength Assessment: The indicator provides a quick visual assessment of the trend's strength, enabling traders to adjust their strategies accordingly.
Why is this indicator helpful?
Improved Trading Decisions: By understanding the overall trend and strength of growth stocks, traders can make more informed decisions about when to buy or sell.
Enhanced Risk Management: The indicator helps traders identify potential trend reversals, enabling them to adjust their positions and manage risk more effectively.
Market Insights: The Growth Stock Trend Indicator provides a valuable perspective on the market's overall direction, helping traders stay ahead of the curve.
By incorporating this indicator into their trading strategy, traders can gain a competitive edge and make more informed decisions in the growth stock market.
Cinnamon_Bear Indicators MA LibraryLibrary "Cinnamon_BearIndicatorsMALibrary"
This is a personal Library of the NON built-in PineScript Moving Average function used to code indicators
ma_dema(source, length)
Double Exponential Moving Average (DEMA)
Parameters:
source (simple float)
length (simple int)
Returns: A double level of smoothing helps to follow price movements more closely while still reducing noise compared to a single EMA.
ma_dsma(source, length)
Double Smoothed Moving Average (DSMA)
Parameters:
source (simple float)
length (simple int)
Returns: A double level of smoothing helps to follow price movements more closely while still reducing noise compared to a single SMA.
ma_tema(source, length)
Triple Exponential Moving Average (TEMA)
Parameters:
source (simple float)
length (simple int)
Returns: A Triple level of smoothing helps to follow price movements even more closely compared to a DEMA.
ma_vwema(source, length)
Volume-Weighted Exponential Moving Average (VWEMA)
Parameters:
source (simple float)
length (simple int)
Returns: The VWEMA weights based on volume and recent price, giving more weight to periods with higher trading volumes.
ma_hma(source, length)
Hull Moving Average (HMA)
Parameters:
source (simple float)
length (simple int)
Returns: The HMA formula combines the properties of the weighted moving average (WMA) and the exponential moving average (EMA) to achieve a smoother and more responsive curve.
ma_ehma(source, length)
Enhanced Moving Average (EHMA)
Parameters:
source (simple float)
length (simple int)
Returns: The EHMA is calculated similarly to the Hull Moving Average (HMA) but uses a different weighting factor to further improve responsiveness.
ma_trix(source, length)
Triple Exponential Moving Average (TRIX)
Parameters:
source (simple float)
length (simple int)
Returns: The TRIX is an oscillator that shows the percentage change of a triple EMA. It is designed to filter out minor price movements and display only the most significant trends. The TRIX is a momentum indicator that can help identify trends and buy or sell signals.
ma_lsma(source, length)
Linear Weighted Moving Average (LSMA)
Parameters:
source (simple float)
length (simple int)
Returns: A moving average that gives more weight to recent prices. It is calculated using a formula that assigns linear weights to prices, with the highest weight given to the most recent price and the lowest weight given to the furthest price in the series.
ma_wcma(source, length)
Weighted Cumulative Moving Average (WCMA)
Parameters:
source (simple float)
length (simple int)
Returns: A moving average that gives more weight to recent prices. Compared to a LSMA, the WCMA the weights of data increase linearly with time, so the most recent data has a greater weight compared to older data. This means that the contribution of the most recent data to the moving average is more significant.
ma_vidya(source, length)
Variable Index Dynamic Average (VIDYA)
Parameters:
source (simple float)
length (simple int)
Returns: It is an adaptive moving average that adjusts its momentum based on market volatility using the formula of Chande Momentum Oscillator (CMO) .
ma_zlma(source, length)
Zero-Lag Moving Average (ZLMA)
Parameters:
source (simple float)
length (simple int)
Returns: Its aims to minimize the lag typically associated with MA, designed to react more quickly to price changes.
ma_gma(source, length, power)
Generalized Moving Average (GMA)
Parameters:
source (simple float)
length (simple int)
power (simple int)
Returns: It is a moving average that uses a power parameter to adjust the weight of historical data. This allows the GMA to adapt to various styles of MA.
ma_tma(source, length)
Triangular Moving Average (TMA)
Parameters:
source (simple float)
length (simple int)
Returns: MA more sensitive to changes in recent data compared to the SMA, providing a moving average that better adapts to short-term price changes.
[Global Contraction Expansion Index SGM]Script Features
Dynamic Period Choice: The user can adjust the calculation period (period) for relative performance, allowing flexibility according to specific market analysis needs.
Sector Selection: The script takes into account different economic sectors through well-known ETFs like QQQ (technology), XLF (financial), XLY (consumer discretionary), XLV (healthcare), XLI (industrial) and XLE (energy). This diversification helps gain a general overview of economic health across different market segments.
Relative Performance Calculation: For each sector, the script calculates the relative performance using a simple moving average (SMA) of the price change over the specified period. This helps identify price trends adjusted for normal market fluctuations.
GCEI Index: The GCEI Index is calculated as the average of the relative performance of all sectors, multiplied by 100 to express it as a percentage. This provides an overall indicator of sectoral economic performance.
Crossover Signals: The script detects and marks points where the overall index (GCEI) crosses its own exponential moving average (emaGCEI), indicating potential changes in the overall trend of market performance.
Visualization: Results are visualized through graphs, where positive and negative regions are colored differently. Fills between the zero line and the index curves make it easy to see periods of contraction or expansion
When this index diverges from the SP500, it may be a sign that the technology sector is outperforming other sectors.
[SGM Forex Interest Rate]
Through an analysis of the interest rates of selected currencies, this indicator calculates the difference between these rates to provide an indication of the relative strength between the two currencies. This relative strength is graphically represented as an area with a specific color for better visualization.
The curves represent the values of the two selected currencies, while the colored area between them highlights the difference in strength. A horizontal line is also drawn at the zero level to help identify turning points or divergence points.
Interest rates play a crucial role in forex trading for several reasons:
Impact on capital flows: Interest rates affect international capital flows. Investors are attracted to currencies offering higher yields, which can lead to currency appreciation with higher interest rates.
Influence on monetary policy decisions: Central banks often adjust interest rates to achieve macroeconomic goals such as price stability, economic growth, and full employment. Announcements of interest rate changes or hints about the future direction of rates can have a significant impact on the foreign exchange market.
Determination of financing costs: Interest rates affect the financing costs of transactions in the foreign exchange market. Traders often borrow low-interest currencies to purchase higher-yielding currencies, which can lead to large movements in the market.
Economic Indicators: Interest rates are also key economic indicators. Changes in interest rates may reflect overall economic conditions and be interpreted as signs of economic growth or contraction.
Risk and volatility factors: Interest rate differences between two countries can create arbitrage opportunities, but also risks. Unexpected changes in interest rates or market expectations may result in increased volatility in the foreign exchange market.
In sum, interest rates are a fundamental part of the global financial landscape and are therefore crucial for forex traders, as they provide indications of economic trends, capital flows and trading opportunities.
SMA Angular Trends [Yosiet]This indicator uses two specific SMA configurations conditioned by an angular slope that is always repeated in trend markets, which are usually beneficial in swing or long-term strategies.
SETTINGS
- Fast Angle Threshold: Is the value in degrees for the condition of the fast sma
- Slow Angle Threshold: Is the value in degrees for the condition of the slow sma
- Linear Mode: When is active, it shows the sma curves only when the condition is satisfied. When is inactive, it shows color of the trends
HOW TO USE
This indicator it helps to see clearly the trends and the oppotunities to entry/exit in breakouts and retests
WHY THOSE SMAs
The SMAs are sma(7, low) and sma(30, high), those setups came from analyze several others indicators with machine learning searching for convergence points in 2018.
THOUGHTS
This indicator only pretends to help traders to take decissions with extra data confirmation
IMPROVEMENTS
You can comment your ideas and sugestions to improve this indicator
BAERMThe Bitcoin Auto-correlation Exchange Rate Model: A Novel Two Step Approach
THIS IS NOT FINANCIAL ADVICE. THIS ARTICLE IS FOR EDUCATIONAL AND ENTERTAINMENT PURPOSES ONLY.
If you enjoy this software and information, please consider contributing to my lightning address
Prelude
It has been previously established that the Bitcoin daily USD exchange rate series is extremely auto-correlated
In this article, we will utilise this fact to build a model for Bitcoin/USD exchange rate. But not a model for predicting the exchange rate, but rather a model to understand the fundamental reasons for the Bitcoin to have this exchange rate to begin with.
This is a model of sound money, scarcity and subjective value.
Introduction
Bitcoin, a decentralised peer to peer digital value exchange network, has experienced significant exchange rate fluctuations since its inception in 2009. In this article, we explore a two-step model that reasonably accurately captures both the fundamental drivers of Bitcoin’s value and the cyclical patterns of bull and bear markets. This model, whilst it can produce forecasts, is meant more of a way of understanding past exchange rate changes and understanding the fundamental values driving the ever increasing exchange rate. The forecasts from the model are to be considered inconclusive and speculative only.
Data preparation
To develop the BAERM, we used historical Bitcoin data from Coin Metrics, a leading provider of Bitcoin market data. The dataset includes daily USD exchange rates, block counts, and other relevant information. We pre-processed the data by performing the following steps:
Fixing date formats and setting the dataset’s time index
Generating cumulative sums for blocks and halving periods
Calculating daily rewards and total supply
Computing the log-transformed price
Step 1: Building the Base Model
To build the base model, we analysed data from the first two epochs (time periods between Bitcoin mining reward halvings) and regressed the logarithm of Bitcoin’s exchange rate on the mining reward and epoch. This base model captures the fundamental relationship between Bitcoin’s exchange rate, mining reward, and halving epoch.
where Yt represents the exchange rate at day t, Epochk is the kth epoch (for that t), and epsilont is the error term. The coefficients beta0, beta1, and beta2 are estimated using ordinary least squares regression.
Base Model Regression
We use ordinary least squares regression to estimate the coefficients for the betas in figure 2. In order to reduce the possibility of over-fitting and ensure there is sufficient out of sample for testing accuracy, the base model is only trained on the first two epochs. You will notice in the code we calculate the beta2 variable prior and call it “phaseplus”.
The code below shows the regression for the base model coefficients:
\# Run the regression
mask = df\ < 2 # we only want to use Epoch's 0 and 1 to estimate the coefficients for the base model
reg\_X = df.loc\ [mask, \ \].shift(1).iloc\
reg\_y = df.loc\ .iloc\
reg\_X = sm.add\_constant(reg\_X)
ols = sm.OLS(reg\_y, reg\_X).fit()
coefs = ols.params.values
print(coefs)
The result of this regression gives us the coefficients for the betas of the base model:
\
or in more human readable form: 0.029, 0.996869586, -0.00043. NB that for the auto-correlation/momentum beta, we did NOT round the significant figures at all. Since the momentum is so important in this model, we must use all available significant figures.
Fundamental Insights from the Base Model
Momentum effect: The term 0.997 Y suggests that the exchange rate of Bitcoin on a given day (Yi) is heavily influenced by the exchange rate on the previous day. This indicates a momentum effect, where the price of Bitcoin tends to follow its recent trend.
Momentum effect is a phenomenon observed in various financial markets, including stocks and other commodities. It implies that an asset’s price is more likely to continue moving in its current direction, either upwards or downwards, over the short term.
The momentum effect can be driven by several factors:
Behavioural biases: Investors may exhibit herding behaviour or be subject to cognitive biases such as confirmation bias, which could lead them to buy or sell assets based on recent trends, reinforcing the momentum.
Positive feedback loops: As more investors notice a trend and act on it, the trend may gain even more traction, leading to a self-reinforcing positive feedback loop. This can cause prices to continue moving in the same direction, further amplifying the momentum effect.
Technical analysis: Many traders use technical analysis to make investment decisions, which often involves studying historical exchange rate trends and chart patterns to predict future exchange rate movements. When a large number of traders follow similar strategies, their collective actions can create and reinforce exchange rate momentum.
Impact of halving events: In the Bitcoin network, new bitcoins are created as a reward to miners for validating transactions and adding new blocks to the blockchain. This reward is called the block reward, and it is halved approximately every four years, or every 210,000 blocks. This event is known as a halving.
The primary purpose of halving events is to control the supply of new bitcoins entering the market, ultimately leading to a capped supply of 21 million bitcoins. As the block reward decreases, the rate at which new bitcoins are created slows down, and this can have significant implications for the price of Bitcoin.
The term -0.0004*(50/(2^epochk) — (epochk+1)²) accounts for the impact of the halving events on the Bitcoin exchange rate. The model seems to suggest that the exchange rate of Bitcoin is influenced by a function of the number of halving events that have occurred.
Exponential decay and the decreasing impact of the halvings: The first part of this term, 50/(2^epochk), indicates that the impact of each subsequent halving event decays exponentially, implying that the influence of halving events on the Bitcoin exchange rate diminishes over time. This might be due to the decreasing marginal effect of each halving event on the overall Bitcoin supply as the block reward gets smaller and smaller.
This is antithetical to the wrong and popular stock to flow model, which suggests the opposite. Given the accuracy of the BAERM, this is yet another reason to question the S2F model, from a fundamental perspective.
The second part of the term, (epochk+1)², introduces a non-linear relationship between the halving events and the exchange rate. This non-linear aspect could reflect that the impact of halving events is not constant over time and may be influenced by various factors such as market dynamics, speculation, and changing market conditions.
The combination of these two terms is expressed by the graph of the model line (see figure 3), where it can be seen the step from each halving is decaying, and the step up from each halving event is given by a parabolic curve.
NB - The base model has been trained on the first two halving epochs and then seeded (i.e. the first lag point) with the oldest data available.
Constant term: The constant term 0.03 in the equation represents an inherent baseline level of growth in the Bitcoin exchange rate.
In any linear or linear-like model, the constant term, also known as the intercept or bias, represents the value of the dependent variable (in this case, the log-scaled Bitcoin USD exchange rate) when all the independent variables are set to zero.
The constant term indicates that even without considering the effects of the previous day’s exchange rate or halving events, there is a baseline growth in the exchange rate of Bitcoin. This baseline growth could be due to factors such as the network’s overall growth or increasing adoption, or changes in the market structure (more exchanges, changes to the regulatory environment, improved liquidity, more fiat on-ramps etc).
Base Model Regression Diagnostics
Below is a summary of the model generated by the OLS function
OLS Regression Results
\==============================================================================
Dep. Variable: logprice R-squared: 0.999
Model: OLS Adj. R-squared: 0.999
Method: Least Squares F-statistic: 2.041e+06
Date: Fri, 28 Apr 2023 Prob (F-statistic): 0.00
Time: 11:06:58 Log-Likelihood: 3001.6
No. Observations: 2182 AIC: -5997.
Df Residuals: 2179 BIC: -5980.
Df Model: 2
Covariance Type: nonrobust
\==============================================================================
coef std err t P>|t| \
\------------------------------------------------------------------------------
const 0.0292 0.009 3.081 0.002 0.011 0.048
logprice 0.9969 0.001 1012.724 0.000 0.995 0.999
phaseplus -0.0004 0.000 -2.239 0.025 -0.001 -5.3e-05
\==============================================================================
Omnibus: 674.771 Durbin-Watson: 1.901
Prob(Omnibus): 0.000 Jarque-Bera (JB): 24937.353
Skew: -0.765 Prob(JB): 0.00
Kurtosis: 19.491 Cond. No. 255.
\==============================================================================
Below we see some regression diagnostics along with the regression itself.
Diagnostics: We can see that the residuals are looking a little skewed and there is some heteroskedasticity within the residuals. The coefficient of determination, or r2 is very high, but that is to be expected given the momentum term. A better r2 is manually calculated by the sum square of the difference of the model to the untrained data. This can be achieved by the following code:
\# Calculate the out-of-sample R-squared
oos\_mask = df\ >= 2
oos\_actual = df.loc\
oos\_predicted = df.loc\
residuals\_oos = oos\_actual - oos\_predicted
SSR = np.sum(residuals\_oos \*\* 2)
SST = np.sum((oos\_actual - oos\_actual.mean()) \*\* 2)
R2\_oos = 1 - SSR/SST
print("Out-of-sample R-squared:", R2\_oos)
The result is: 0.84, which indicates a very close fit to the out of sample data for the base model, which goes some way to proving our fundamental assumption around subjective value and sound money to be accurate.
Step 2: Adding the Damping Function
Next, we incorporated a damping function to capture the cyclical nature of bull and bear markets. The optimal parameters for the damping function were determined by regressing on the residuals from the base model. The damping function enhances the model’s ability to identify and predict bull and bear cycles in the Bitcoin market. The addition of the damping function to the base model is expressed as the full model equation.
This brings me to the question — why? Why add the damping function to the base model, which is arguably already performing extremely well out of sample and providing valuable insights into the exchange rate movements of Bitcoin.
Fundamental reasoning behind the addition of a damping function:
Subjective Theory of Value: The cyclical component of the damping function, represented by the cosine function, can be thought of as capturing the periodic fluctuations in market sentiment. These fluctuations may arise from various factors, such as changes in investor risk appetite, macroeconomic conditions, or technological advancements. Mathematically, the cyclical component represents the frequency of these fluctuations, while the phase shift (α and β) allows for adjustments in the alignment of these cycles with historical data. This flexibility enables the damping function to account for the heterogeneity in market participants’ preferences and expectations, which is a key aspect of the subjective theory of value.
Time Preference and Market Cycles: The exponential decay component of the damping function, represented by the term e^(-0.0004t), can be linked to the concept of time preference and its impact on market dynamics. In financial markets, the discounting of future cash flows is a common practice, reflecting the time value of money and the inherent uncertainty of future events. The exponential decay in the damping function serves a similar purpose, diminishing the influence of past market cycles as time progresses. This decay term introduces a time-dependent weight to the cyclical component, capturing the dynamic nature of the Bitcoin market and the changing relevance of past events.
Interactions between Cyclical and Exponential Decay Components: The interplay between the cyclical and exponential decay components in the damping function captures the complex dynamics of the Bitcoin market. The damping function effectively models the attenuation of past cycles while also accounting for their periodic nature. This allows the model to adapt to changing market conditions and to provide accurate predictions even in the face of significant volatility or structural shifts.
Now we have the fundamental reasoning for the addition of the function, we can explore the actual implementation and look to other analogies for guidance —
Financial and physical analogies to the damping function:
Mathematical Aspects: The exponential decay component, e^(-0.0004t), attenuates the amplitude of the cyclical component over time. This attenuation factor is crucial in modelling the diminishing influence of past market cycles. The cyclical component, represented by the cosine function, accounts for the periodic nature of market cycles, with α determining the frequency of these cycles and β representing the phase shift. The constant term (+3) ensures that the function remains positive, which is important for practical applications, as the damping function is added to the rest of the model to obtain the final predictions.
Analogies to Existing Damping Functions: The damping function in the BAERM is similar to damped harmonic oscillators found in physics. In a damped harmonic oscillator, an object in motion experiences a restoring force proportional to its displacement from equilibrium and a damping force proportional to its velocity. The equation of motion for a damped harmonic oscillator is:
x’’(t) + 2γx’(t) + ω₀²x(t) = 0
where x(t) is the displacement, ω₀ is the natural frequency, and γ is the damping coefficient. The damping function in the BAERM shares similarities with the solution to this equation, which is typically a product of an exponential decay term and a sinusoidal term. The exponential decay term in the BAERM captures the attenuation of past market cycles, while the cosine term represents the periodic nature of these cycles.
Comparisons with Financial Models: In finance, damped oscillatory models have been applied to model interest rates, stock prices, and exchange rates. The famous Black-Scholes option pricing model, for instance, assumes that stock prices follow a geometric Brownian motion, which can exhibit oscillatory behavior under certain conditions. In fixed income markets, the Cox-Ingersoll-Ross (CIR) model for interest rates also incorporates mean reversion and stochastic volatility, leading to damped oscillatory dynamics.
By drawing on these analogies, we can better understand the technical aspects of the damping function in the BAERM and appreciate its effectiveness in modelling the complex dynamics of the Bitcoin market. The damping function captures both the periodic nature of market cycles and the attenuation of past events’ influence.
Conclusion
In this article, we explored the Bitcoin Auto-correlation Exchange Rate Model (BAERM), a novel 2-step linear regression model for understanding the Bitcoin USD exchange rate. We discussed the model’s components, their interpretations, and the fundamental insights they provide about Bitcoin exchange rate dynamics.
The BAERM’s ability to capture the fundamental properties of Bitcoin is particularly interesting. The framework underlying the model emphasises the importance of individuals’ subjective valuations and preferences in determining prices. The momentum term, which accounts for auto-correlation, is a testament to this idea, as it shows that historical price trends influence market participants’ expectations and valuations. This observation is consistent with the notion that the price of Bitcoin is determined by individuals’ preferences based on past information.
Furthermore, the BAERM incorporates the impact of Bitcoin’s supply dynamics on its price through the halving epoch terms. By acknowledging the significance of supply-side factors, the model reflects the principles of sound money. A limited supply of money, such as that of Bitcoin, maintains its value and purchasing power over time. The halving events, which reduce the block reward, play a crucial role in making Bitcoin increasingly scarce, thus reinforcing its attractiveness as a store of value and a medium of exchange.
The constant term in the model serves as the baseline for the model’s predictions and can be interpreted as an inherent value attributed to Bitcoin. This value emphasizes the significance of the underlying technology, network effects, and Bitcoin’s role as a medium of exchange, store of value, and unit of account. These aspects are all essential for a sound form of money, and the model’s ability to account for them further showcases its strength in capturing the fundamental properties of Bitcoin.
The BAERM offers a potential robust and well-founded methodology for understanding the Bitcoin USD exchange rate, taking into account the key factors that drive it from both supply and demand perspectives.
In conclusion, the Bitcoin Auto-correlation Exchange Rate Model provides a comprehensive fundamentally grounded and hopefully useful framework for understanding the Bitcoin USD exchange rate.
Signal Filter / Connectable [Azullian]The connectable signal filter is an intricate part of an indicator system designed to help test, visualize and build strategy configurations without coding. Like all connectable indicators , it interacts through the TradingView input source, which serves as a signal connector to link indicators to each other. All connectable indicators send signal weight to the next node in the system until it reaches either a connectable signal monitor, signal filter and/or strategy.
The connectable signal filter's function has several roles in the connectable system:
• Input hub: Connect indicators or daisy-chained indicators directly to the filter, manage connections in one place
• Modification: Modify incoming signals by applying smoothing, scaling, or modifiers
• Filtering: Set the trade direction and conditions a signal must adhere to to be passed through
• Visualization: When connected, the signal filter visualizes all incoming signal weights
Let's review the separate parts of this indicator.
█ INPUTS
We've provided 3 inputs for connecting indicators or chains (1→, 2→, 3→) which are all set to 'Close' by default.
An input has several controls:
• Enable disable: Toggle the entire input on or off
• Input: Connect indicators here, choose indicators with a compatible : Signal connector.
• G - Gain: Increase or reduce the strength of the incoming signal by a factor.
█ FILTER SIGNALS
The core of the signal filter , determine a signal direction with the signal mode and determine a threshold (TH).
• ¤ - Trade direction:
○ EL: Send Enter Long signals to the strategy
○ XL: Send Exit Long signals to the strategy
○ ES: Send Enter Short signals to the strategy
○ XS: Send Exit Short signals to the strategy
• TH - Threshold: Define how much weight is needed for a signal to be accepted and passed through to the connectable strategy .
■ VISUALS
• ☼: Brightness % : Set the opacity for the signal curves
• 🡓: ES Color : Set the color for the ES: Entry Short signal
• ⭳: XS Color : Set the color for the XS: Exit Short signal
• ⌥: Plot mode : Set the plotting mode
○ Signals IN: Show all signals
○ Signals OUT: Show only scoring signals
• 🡑: EL Color : Set the color for the EL: Enter Long signal
• ⭱: XL Color : Set the color for the XL: Exit Long signal
█ USAGE OF CONNECTABLE INDICATORS
■ Connectable chaining mechanism
Connectable indicators can be connected directly to the signal monitor, signal filter or strategy , or they can be daisy chained to each other while the last indicator in the chain connects to the signal monitor, signal filter or strategy. When using a signal filter you can chain the filter to the strategy input to make your chain complete.
• Direct chaining: Connect an indicator directly to the signal monitor, signal filter or strategy through the provided inputs (→).
• Daisy chaining: Connect indicators using the indicator input (→). The first in a daisy chain should have a flow (⌥) set to 'Indicator only'. Subsequent indicators use 'Both' to pass the previous weight. The final indicator connects to the signal monitor, signal filter, or strategy.
■ Set up the signal filter with a connectable indicator and strategy
Let's connect the MACD to a connectable signal filter and a strategy :
1. Load all relevant indicators
• Load MACD / Connectable
• Load Signal filter / Connectable
• Load Strategy / Connectable
2. Signal Filter: Connect the MACD to the Signal Filter
• Open the signal filter settings
• Choose one of the three input dropdowns (1→, 2→, 3→) and choose : MACD / Connectable: Signal Connector
• Toggle the enable box before the connected input to enable the incoming signal
3. Signal Filter: Update the filter settings if needed
• The default filter mode for the trading direction is SWING, and is compatible with the default settings in the strategy and indicators.
4. Signal Filter: Update the weight threshold settings if needed
• All connectable indicators load by default with a score of 6 for each direction (EL, XL, ES, XS)
• By default, weight threshold (TH) in the signal filter is set at 5. This allows each occurrence to score, as the default score in each / Connectable indicator is 6 and thus is 1 point above the threshold. Adjust to your liking.
5. Strategy: Connect the strategy to the signal filter in the strategy settings
• Select a strategy input → and select the Signal filter: Signal connector
6. Strategy: Enable filter compatible directions
• As the default setting of the signal filter has enabled EL (Enter Long), XL (Exit Long), ES (Enter Short) and XS (Exit short), the connectable strategy will receive all compatible directions.
Now that everything is connected, you'll notice green spikes in the signal filter representing long signals, and red spikes indicating short signals. Trades will also appear on the chart, complemented by a performance overview. Your journey is just beginning: delve into different scoring mechanisms, merge diverse connectable indicators, and craft unique chains. Instantly test your results and discover the potential of your configurations. Dive deep and enjoy the process!
█ BENEFITS
• Adaptable Modular Design: Arrange indicators in diverse structures via direct or daisy chaining, allowing tailored configurations to align with your analysis approach.
• Streamlined Backtesting: Simplify the iterative process of testing and adjusting combinations, facilitating a smoother exploration of potential setups.
• Intuitive Interface: Navigate TradingView with added ease. Integrate desired indicators, adjust settings, and establish alerts without delving into complex code.
• Signal Weight Precision: Leverage granular weight allocation among signals, offering a deeper layer of customization in strategy formulation.
• Advanced Signal Filtering: Define entry and exit conditions with more clarity, granting an added layer of strategy precision.
• Clear Visual Feedback: Distinct visual signals and cues enhance the readability of charts, promoting informed decision-making.
• Standardized Defaults: Indicators are equipped with universally recognized preset settings, ensuring consistency in initial setups across different types like momentum or volatility.
• Reliability: Our indicators are meticulously developed to prevent repainting. We strictly adhere to TradingView's coding conventions, ensuring our code is both performant and clean.
█ COMPATIBLE INDICATORS
Each indicator that incorporates our open-source 'azLibConnector' library and adheres to our conventions can be effortlessly integrated and used as detailed above.
For clarity and recognition within the TradingView platform, we append the suffix ' / Connectable' to every compatible indicator.
█ COMMON MISTAKES, CLARIFICATIONS AND TIPS
• Removing an indicator from a chain: Deleting a linked indicator and confirming the "remove study tree" alert will also remove all underlying indicators in the object tree. Before removing one, disconnect the adjacent indicators and move it to the object stack's bottom.
• Point systems: The azLibConnector provides 500 points for each direction (EL: Enter long, XL: Exit long, ES: Enter short, XS: Exit short) Remember this cap when devising a point structure.
• Flow misconfiguration: In daisy chains the first indicator should always have a flow (⌥) setting of 'indicator only' while other indicator should have a flow (⌥) setting of 'both'.
• Hide attributes: As connectable indicators send through quite some information you'll notice all the arguments are taking up some screenwidth and cause some visual clutter. You can disable arguments in Chart Settings / Status line.
• Layout and abbreviations: To maintain a consistent structure, we use abbreviations for each input. While this may initially seem complex, you'll quickly become familiar with them. Each abbreviation is also explained in the inline tooltips.
• Inputs: Connecting a connectable indicator directly to the strategy delivers the raw signal without a weight threshold, meaning every signal will trigger a trade.
█ A NOTE OF GRATITUDE
Through years of exploring TradingView and Pine Script, we've drawn immense inspiration from the community's knowledge and innovation. Thank you for being a constant source of motivation and insight.
█ RISK DISCLAIMER
Azullian's content, tools, scripts, articles, and educational offerings are presented purely for educational and informational uses. Please be aware that past performance should not be considered a predictor of future results.
Signal Monitor / Connectable [Azullian]The connectable signal monitor is a connectable tool to help test, visualize signal weights. Like all connectable indicators , it interacts through the TradingView input source, which serves as a signal connector to link indicators to each other. All connectable indicators send signal weight to the next node in the system until it reaches either a connectable signal monitor, signal filter and/or strategy.
Let's review the separate parts of this indicator.
█ INPUTS
We've provided 3 inputs for connecting indicators or chains (1→, 2→, 3→) which are all set to 'Close' by default.
An input has several controls:
• Enable disable: Toggle the entire input on or off
• Input: Connect indicators here, choose indicators with a compatible : Signal connector.
■ VISUALS
• ☼: Brightness % : Set the opacity for the signal curves
• 🡓: ES Color : Set the color for the ES: Entry Short signal
• ⭳: XS Color : Set the color for the XS: Exit Short signal
• ⌥: Plot mode : Set the plotting mode
○ Signals IN: Show all signals
○ Signals OUT: Show only scoring signals
• 🡑: EL Color : Set the color for the EL: Enter Long signal
• ⭱: XL Color : Set the color for the XL: Exit Long signal
█ USAGE OF CONNECTABLE INDICATORS
■ Connectable chaining mechanism
Connectable indicators can be connected directly to the signal monitor, signal filter or strategy , or they can be daisy chained to each other while the last indicator in the chain connects to the connectable signal monitor, signal filter or strategy . When using a signal filter or signal monitor you can chain the filter to the strategy input to make your chain complete.
• Direct chaining: Connect an indicator directly to the signal monitor, signal filter or strategy through the provided inputs (→).
• Daisy chaining: Connect indicators using the indicator input (→). The first in a daisy chain should have a flow (⌥) set to 'Indicator only'. Subsequent indicators use 'Both' to pass the previous weight. The final indicator connects to the signal monitor, signal filter, or strategy.
■ Set up the signal monitor with a connectable indicator and strategy
Let's connect the MACD to a connectable signal monitor :
1. Load all relevant indicators
• Load MACD / Connectable
• Load Signal monitor / Connectable
2. Signal Monitor: Connect the MACD to the Signal Monitor
• Open the signal monitor settings
• Choose one of the three input dropdowns (1→, 2→, 3→) and choose : MACD / Connectable: Signal Connector
• Toggle the enable box before the connected input to enable the incoming signal
Now that everything is connected, you'll notice green spikes in the signal monitor representing long signals, and red spikes indicating short signals.
█ BENEFITS
• Adaptable Modular Design: Arrange indicators in diverse structures via direct or daisy chaining, allowing tailored configurations to align with your analysis approach.
• Streamlined Backtesting: Simplify the iterative process of testing and adjusting combinations, facilitating a smoother exploration of potential setups.
• Intuitive Interface: Navigate TradingView with added ease. Integrate desired indicators, adjust settings, and establish alerts without delving into complex code.
• Signal Weight Precision: Leverage granular weight allocation among signals, offering a deeper layer of customization in strategy formulation.
• Advanced Signal Filtering: Define entry and exit conditions with more clarity, granting an added layer of strategy precision.
• Clear Visual Feedback: Distinct visual signals and cues enhance the readability of charts, promoting informed decision-making.
• Standardized Defaults: Indicators are equipped with universally recognized preset settings, ensuring consistency in initial setups across different types like momentum or volatility.
• Reliability: Our indicators are meticulously developed to prevent repainting. We strictly adhere to TradingView's coding conventions, ensuring our code is both performant and clean.
█ COMPATIBLE INDICATORS
Each indicator that incorporates our open-source 'azLibConnector' library and adheres to our conventions can be effortlessly integrated and used as detailed above.
For clarity and recognition within the TradingView platform, we append the suffix ' / Connectable' to every compatible indicator.
█ COMMON MISTAKES, CLARIFICATIONS AND TIPS
• Removing an indicator from a chain: Deleting a linked indicator and confirming the "remove study tree" alert will also remove all underlying indicators in the object tree. Before removing one, disconnect the adjacent indicators and move it to the object stack's bottom.
• Point systems: The azLibConnector provides 500 points for each direction (EL: Enter long, XL: Exit long, ES: Enter short, XS: Exit short) Remember this cap when devising a point structure.
• Flow misconfiguration: In daisy chains the first indicator should always have a flow (⌥) setting of 'indicator only' while other indicator should have a flow (⌥) setting of 'both'.
• Hide attributes: As connectable indicators send through quite some information you'll notice all the arguments are taking up some screenwidth and cause some visual clutter. You can disable arguments in Chart Settings / Status line.
• Layout and abbreviations: To maintain a consistent structure, we use abbreviations for each input. While this may initially seem complex, you'll quickly become familiar with them. Each abbreviation is also explained in the inline tooltips.
• Inputs: Connecting a connectable indicator directly to the strategy delivers the raw signal without a weight threshold, meaning every signal will trigger a trade.
█ A NOTE OF GRATITUDE
Through years of exploring TradingView and Pine Script, we've drawn immense inspiration from the community's knowledge and innovation. Thank you for being a constant source of motivation and insight.
█ RISK DISCLAIMER
Azullian's content, tools, scripts, articles, and educational offerings are presented purely for educational and informational uses. Please be aware that past performance should not be considered a predictor of future results.
