Moving Average Cross Probability [AlgoAlpha]Moving Average Cross Probability 📈✨
The Moving Average Cross Probability by AlgoAlpha calculates the probability of a cross-over or cross-under between the fast and slow values of a user defined Moving Average type before it happens, allowing users to benefit by front running the market.
✨ Key Features:
📊 Probability Histogram: Displays the Probability of MA cross in the form of a histogram.
🔄 Data Table: Displays forecast information for quick analysis.
🎨 Customizable MAs: Choose from various moving averages and customize their length.
🚀 How to Use:
🛠 Add Indicator: Add the indicator to favorites, and customize the settings to suite your trading style.
📊 Analyze Market: Watch the indicator to look for trend shifts early or for trend continuations.
🔔 Set Alerts: Get notified of bullish/bearish points.
✨ How It Works:
The Moving Average Cross Probability Indicator by AlgoAlpha determines the probability by looking at a probable range of values that the price can take in the next bar and finds out what percentage of those possibilities result in the user defined moving average crossing each other. This is done by first using the HMA to predict what the next price value will be, a standard deviation based range is then calculated. The range is divided by the user defined resolution and is split into multiple levels, each of these levels represent a possible value for price in the next bar. These possible predicted values are used to calculate the possible MA values for both the fast and slow MAs that may occur in the next bar and are then compared to see how many of those possible MA results end up crossing each other.
Stay ahead of the market with the Moving Average Cross Probability Indicator AlgoAlpha! 📈💡
Forecast
Median Analyst ConsensusThe Median Analyst Consensus Indicator provides an unbiased, easy-to-interpret view of market sentiment by leveraging TradingView's comprehensive financial data library. This tool displays the median 12-month price target and the percentage difference from the current price directly on your charts.
Key Features
1. Accurate Market Sentiment: By consolidating analyst ratings and price targets from multiple reputable sources like Bloomberg, Refinitiv (formerly Thomson Reuters), S&P Capital IQ, and Morningstar, this indicator displays the median analyst consensus. Using the median ensures outlier ratings don't skew the overall sentiment, providing a more robust representation.
2. Simplicity at a Glance: View the median 12-month price target and percentage difference from the current price directly on your chart. No need to juggle multiple reports - key insights are surfaced within your normal trading workflow.
3. Data-Driven Transparency: If no analyst data is available for a particular asset, the indicator will not display, ensuring you only see reliable information. The number of contributing analysts is also shown for context.
Why the Median?
The median is favored over the mean to minimize the impact of outlier ratings that could distort the consensus view. By taking the middle value across all analyst projections, the median provides a more stable, outlier-resistant measure of market sentiment.
Powered by TradingView Data
This indicator taps into TradingView's financial data library, which aggregates analyst ratings, estimates, and recommendations from leading institutional data providers. TradingView sources this data from firms like FactSet, Bloomberg, Refinitiv, S&P Capital IQ, and Morningstar, ensuring a comprehensive and trusted view of analyst sentiment.
The library provides variables like:
syminfo.recommendations_buy
syminfo.recommendations_sell
syminfo.target_price_median
syminfo.recommendations_buy_strong
syminfo.recommendations_sell_strong
The indicator calculates and displays the median of these analyst inputs.
Usage
The indicator displays:
The median 12-month price target across analysts
The percentage difference between the price target and current price
The number of contributing analyst estimates
If no analyst data is available, the indicator does not display, ensuring full transparency.
The Median Analyst Consensus Indicator provides an unbiased, easy-to-interpret view of market sentiment by leveraging TradingView's comprehensive financial data library. This tool offers a new perspective on potential trade opportunities directly on your charts.
Disclaimer
While the data is sourced from reputable providers, analyst forecasts should not be construed as investment recommendations. This indicator aims to synthesize market opinions, but investment decisions are solely your responsibility. As with any analytical tool, you should conduct your own research and risk assessments before executing any trades.
Nasan Moving Average with ForecastThe "Nasan Moving Average with Forecast" indicator is a technical analysis forecasting tool that combines the principles of historical data analysis and random walk theory. It calculates a customized moving average (Nasan Moving Average) by integrating price data and statistical measures and projects future price points by generating forecast values within calculated volatility bounds, creating a dynamic and insightful visualization of potential market movements. This indicator to blend past market behavior with probabilistic future trends to enhance forecasting.
Input Parameters:
len: Differencing length (default 21, Use a minimum of 5 and for lower time frames less than 15 min use values between 300 -3000)
len1: Correction Factor Length 1 (default 21, this determines the length of the MA you want , eg. 10 MA, 50 MA, 100 MA, )
len2: Correction Factor Length 2 (default 9, this works best if it is ~ </=1/2 of len1 )
len3: Smoothing Length (default 5, I would not change this and only use if I want to introduce lag where you want to use it for cross over strategies).
forecast_points: Number of points to forecast (default 30).
m: Multiplier for standard deviation (default 2.5).
bl: Block length for calculating max/min values (default 100).
use_calculated_max_min: Boolean to decide whether to use calculated max/min values.
Nasan Moving Average Calculation:
Calculates the simple moving average (mean) and standard deviation (sd) of the typical price (hlc3).
Computes intermediate variables (a, b, c, etc.) based on log transformation and cumulative sum.
Applies weighted moving averages (wma) to these intermediate variables to smooth them and derive the final value c6.
Plots c6 as the Nasan Moving Average if the bar is confirmed. To learn more see Nasan Moving Average.
Forecast Points Calculation:
Calculates maximum (max_val) and minimum (min_val) values for the forecast, either using a fixed value or based on standard deviation and a multiplier.
Initializes an array to store forecast values and creates polyline objects for plotting.
If the current bar is one of the last three bars and confirmed:
Clears and reinitializes the polyline.
Initializes the first forecast value from the cumulative sum c.
Generates subsequent forecast values using a random value within the range .
Updates the forecast array and plots the forecast points as an orange curved polyline.
Plotting Max/Min Values:
Plots max_val and min_val as green and red lines, respectively, to indicate the bounds of the forecast range.
Components of the Forecasting Model
Historical Dependence:
Nasan Moving Average Calculation: The script calculates a custom moving average (c6) that incorporates historical price data (hlc3), standard deviations (sd), and weighted moving averages (wma). This part of the code processes historical data to create a smoothed representation of the price trend.
Max/Min Value Calculation: The maximum (max_val) and minimum (min_val) values for the forecast can be calculated based on the historical standard deviation of a transformed variable b over a block length (bl). This introduces historical volatility into the bounds for the forecast.
Random Walk Model:
Random Value Generation: Within the forecast points calculation, a random value (random_val) is generated for each forecast point within the range . This random value introduces stochasticity into the model, characteristic of a random walk process.
Cumulative Sum for Forecasting: The script uses a cumulative sum (prev_f + random_val) to generate the next forecast point (next_f). This is a typical approach in random walk models where each new point is based on the previous point plus some random noise.
Explanation of the Forecast Model
Random Walk Characteristics: Each new forecast point is generated by adding a random value to the previous point, making the model a random walk with drift, where the drift is influenced by historical correction factors (c1, c4).
Historical and Statistical Dependence: The bounds of the random values and the initial conditions are derived from historical data, ensuring that the forecast respects historical volatility and trends.
The forecasting model in the script is a hybrid approach: It uses a random walk to generate future points, characterized by adding random values to the previous forecasted value.
The historical and statistical dependence is incorporated through initial conditions, scaling factors, and bounds derived from historical price data and its statistical properties.
This combination ensures that the forecasts are not purely stochastic but are grounded in historical price behavior, making the model more robust and potentially more accurate in reflecting market conditions.
Price Reversal Probability + Price Forecast [TradeDots]The TradeDots Price Reversal Probability + Price Forecast Indicator helps traders discern market direction and identify potential trading opportunities.
📝 HOW IT WORKS
The indicator provides two types of reversal signals:
Bullish Reversal: Marked with a green label, indicating an expected upward market reversal.
Bearish Reversal: Marked with a red label, indicating an expected downward market reversal.
⭐️ Computation
This tool identifies significant reversal patterns using a mathematical model on a designated window of candlesticks to calculate price action changes. It incorporates candlestick data and price indicators, such as the Open, Close, High, Low of candlesticks and Average True Range (ATR), to detect similar occurrences in real-time.
Potential market turning points are marked with reversal labels and percentage changes , calculated using pivot high or low price data from the last reversal patterns of the opposite side.
For example, a green label on the chart indicates a bullish reversal pattern, showing the market is expected to reverse upward. However, signals are based on historical price actions and are not 100% accurate. If the price breaks down from the bullish reversal pivot low, the original signal will turn half transparent until the next reversal pattern is detected.
The algorithm groups consecutive bullish reversal patterns until a bearish reversal pattern appears. The last bullish label occurrence indicates the maximum number of bullish patterns required to confirm a reversal in the group. This information is stored to apply Bayesian statistical models and probability models to generate market insights.
⭐️ Statistical Analysis
Reversal signals are categorized into bullish and bearish groups, with each group storing consecutive reversal signals.
In the indicator table, each new reversal is labeled sequentially (e.g., "🟢 #1" for the first bullish reversal after a bearish signal). The number increases for each new signal on the same side and resets when a reversal signal on the opposite side appears.
The indicator provides two forecasts: the probability of reversal and the expected price change if the pattern is successful or unsuccessful.
⭐️ Probability of Reversal
By counting the number of consecutive reversal patterns on one side before a reversal pattern on the opposite side appears, we can calculate the probability of reversal of each signal throughout the entire price action history.
Using Bayes’ Theorem, the probability increases with each consecutive pattern. The values are displayed in the first two columns of the indicator table, with the current condition highlighted in orange.
⭐️ Price Forecast
The price forecast uses the pivot point of the last reversal pattern of the opposite side as a reference for calculating the percentage change.
For example, for a group of bullish patterns, the pivot high of the most recent bearish pattern is taken. A percentage is calculated with the pivot low of all bullish patterns in the same group. Repeating this model throughout the entire historical price action patterns gives the average price percentage difference between all bearish and bullish patterns.
Whenever a new reversal pattern is detected, a price can be forecasted using the percentage difference from the statistical model. The target price is calculated and displayed in the third and fourth columns of the indicator table.
Assisting Traders To Make Data-Informed Trading Decisions
All included features in this indicator:
Labeling of bullish and bearish reversal patterns
Success probability of each reversal pattern
Price targets of each reversal pattern
Visual aid for pattern confirmation
More (check the changelog below for current features)
🛠️ HOW TO USE
⭐️ Reversal Signals
There are two types of reversal signals identified by the algorithm that detects reversal patterns using price action analysis with candlestick data and price indicators. When the price breaks out from the labeled pivot, the label will turn half transparent.
Bullish reversal signals: Labeled in green. The number represents the price of the candlestick "low," and the percentage value indicates the price difference from the previous bearish reversal pattern's candlestick "high."
Bearish reversal signals: Labeled in red. The number represents the price of the candlestick "high," and the percentage value indicates the price difference from the previous bullish reversal pattern's candlestick "low."
⭐️ Probability Table
The probability table shows the likelihood of reversal for each number of occurrences of bullish and bearish reversal signals, displayed in the first two columns.
It also shows the target prices for both bullish and bearish conditions for each number of reversal patterns.
⭐️ Price Targets
By combining the probability of reversal and the price forecast, price targets for new reversal patterns are calculated. These insights help traders align their strategies with price action analysis and statistics by simply observing the candlestick chart in real-time.
Bullish Price Target: The average percentage price and probability that the next bearish reversal signal might hit.
Bearish Price Target: The average percentage price and probability that the next bullish reversal signal might hit.
⭐️ Market Trend Panel
The market trend panel is a small table that indicates the market trend using a 200 Exponential Moving Average (EMA) alongside reversal signals. A bullish reversal pattern above the moving average indicates a "bullish" market, while a bearish reversal pattern below it indicates a "bearish" market. If the price fluctuates around the moving average, it is identified as "choppy."
The panel also shows the risk and reward for each trade by taking the closing bullish and bearish targets from the most recent reversal pattern's price reference. Lastly, it displays the probability of reversal, consistent with the number highlighted in the probability table.
⭐️ Other Visual Aid
Other visual aids visualize the market trend and potential direction for users on the candlestick chart.
Background colors reflect the current market trend (green = bullish, red = bearish, blue = choppy).
A white plotted line represents the moving average for categorizing market trends.
❗️LIMITATIONS
Price targets represent only the mean of percentage differences. Therefore, the price could reverse before hitting either side of the price target.
When the market is in extreme price action or a new market pattern, the price targets may not be forecasted accurately and might move out of the model's range.
This model works best for assets with less price variation and a near-Gaussian distribution in returns. It may be less accurate for assets with random price movements.
CONCLUSION
This indicator uses fundamental statistics and mathematical models to generate reversal probabilities and price forecasts. It does not have the ability to predict the future with certainty. Traders should combine this indicator with other confirmation strategies to make informed investment decisions.
See Author's instructions below to get instant access to this indicator.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
NOTES*
Calculations are based on historical data and do not guarantee future results.
Reversals exceeding ten consecutive occurrences are rare (likely <1% of total occurrences).
Users SHOULD NOT blindly follow the price targets as their trading strategy.
If you encounter a timeout with this indicator, reapply it to your chart.
LDPM Crossover Scanner AddonThe LDPM Crossover Scanner is designed to be used in conjunction with the Liquidity Dependent Price Movement Algorithm and is included with LDPM access.
The LDPM Crossover Scanner displays the LDPM status for up to 10 equity's. When conditions are bearish, per LDPM, the equity will light up on the scanner; otherwise, the equity will not light up.
When used in aggregate, this becomes a particularly useful way to measure up-coming market moves (especially when the crossover scanner showcases equities with significant beta to the chart's underlying!).
Johnny's Trend Lines, Supports and ResistancesInspired and based on ismailcarlik's Trend Lines, Supports and Resistances.
Additions include an overall upgrade to Pinescript v5, changes in the way resistance and support levels are calculated, improved visual queues, and additional customization options.
This indicator is meticulously crafted to provide traders with visual tools for identifying trend lines, support, and resistance levels, enhancing the decision-making process in trading activities.
Features and Functionality
Trend Lines: The indicator allows users to enable or disable trend lines, adjust the number of points to check for establishing a trend, and set parameters for trend validation, including the maximum violation and exceptions for the last bars.
Support and Resistance: It offers tools to identify and visualize key support and resistance levels based on recent pivot points. This includes adjustable parameters for the maximum violations allowed and the exclusion of recent bars from the analysis.
Pivot Points: Users can define the pivot length for calculating highs and lows, which helps in marking significant pivot points that are instrumental in trend analysis.
Alerts and Notifications: The indicator is equipped with customizable alerts for trend line breaches and pivot point formations, which can be set to trigger at different frequencies based on user preference.
How It Works
Input Flexibility: Users can adjust various settings like the length of trend lines and pivot points, enabling or disabling specific features like marking pivots, and managing alert settings directly from the indicator’s input panel.
Dynamic Analysis: By analyzing the price action relative to the calculated trend lines and pivot points, the indicator dynamically identifies potential trend reversals, continuations, and significant price levels.
Visualization: It plots trend lines and marks support and resistance levels directly on the chart, with options to extend these lines and add labels for better clarity. Violated trend lines can be visually differentiated by changing their style and width.
Practical Application
Trend Line Strategy: Traders can use the trend lines to determine the strength of the current market trend and to spot potential reversal points.
Support and Resistance Strategy: By marking where the price has historically faced resistance or found support, traders can plan entry and exit points, set stop-loss orders, or identify breakout opportunities.
Pivot Points Strategy: Pivot points serve as vital indicators for intraday trading or long-term trend analysis, providing insights into potential support and resistance levels.
Customization and Alerts
Custom Alerts: Traders can set alerts for when the price crosses trend lines or when new support or resistance levels are formed, helping them stay informed of critical market movements without having to continuously monitor the charts.
Visual Customization: Users can personalize the appearance of trend lines and labels, choosing from a variety of colors and styles to match their chart setup or preferences.
"Johnny's Trend Lines, Supports and Resistances" is an essential tool for traders who rely on technical analysis, offering detailed insights and real-time updates on market conditions, trend strength, and potential price barriers.
Pivot Profit Target [Mxwll]Introducing the Pivot Profit Target!
This script identifies recent pivot highs/lows and calculates the expected minimum distance for the next pivot, which acts as an approximate profit target.
The image above details the indicator's output.
The image above shows a table consisting of projection statistics.
How to use
The Pivot Profit Targets can be used to approximate a profit target for your trade.
Identify where your entry is relative to the most recent pivot, and assess whether the minimum expected distance for the most recent pivot has been exceeded. Treat the zones as an approximation.
If your trade aligns with the most recent pivot - treat the minimum expected distance zone as a potential profit target area. Of course, price might stop short or continue beyond the projection area!
That's it! Just a short and sweet script; thank you!
Edge AI Forecast [Edge Terminal]This indicator inputs the previous 150 closing prices in a simple two-layer neural network, normalizes the network inputs using a sigmoid function, uses a feedforward calculation to send it to the second layer, shows the MSE loss curve and uses both automatic and manual backpropagation (user input) to find the most likely forecast values and uses the analog forecasting algorithm to adjust and optimize the data furthermore to display potential prices on the chart.
Here's how it works:
The idea behind this script is to train a simple neural network to predict the future x values based on the sample data. For this, we use 2 types of data, Price and Volume.
The thinking behind this is that price alone can’t be used in this case because it doesn’t provide enough meaningful pattern data for the network but price and volume together can change the game. We’re planning to use more different data sets and expand on this in the future.
To avoid a bad mix of results, we technically have two neural networks, each processing a different data type, one for volume data and one for price data.
The actual prediction is decided by the way price and volume of the closing price relate to each other. Basically, the network passes the price and volume and finds the best relation between the two data set outputs and predicts where the price could be based on the upcoming volume of the latest candle.
The network adjusts the weights and biases using optimization algorithms like gradient descent to minimize the difference between the predicted and actual stock prices, typically measured by a loss function, (in this case, mean squared error) which you can see using the error rate bubble.
This is a good measure to see how well the network is performing and the idea is to adjust the settings inputs such as learning rate, epochs and data source to get the lowest possible error rate. That’s when you’re getting the most accurate prediction results.
For each data set, we use a multi-layer network. In a multi-layer neural network, the outputs of neurons in one layer serve as inputs to neurons in the next layer. Initially, the input layer of the neural network receives the historical data. Each input neuron represents a feature, such as previous stock prices and trading volumes over a specific period.
The hidden layers perform feature extraction and transformation through a series of weighted connections and activation functions. Each neuron in a hidden layer computes a weighted sum of the inputs from the previous layer, applies an activation function to the sum, and passes the result to the next layer using the feedforward (activation) function.
For extraction, we use a normalization function. This function takes a value or data (such as bar price) and divides it up by max scale which is the highest possible value of the bar. The idea is to take a normalized number, which is either below 1 or under 2 for simple use in the neural network layers.
For the activation, after computing the weighted sum, the neuron applies an activation function a(x). To introduce non-linearity into the model to pass it to the next layer. We use sigmoid activation functions in this case. The main reason we use sigmoid function is because the resulting number is between 0 to 1 and is better for models where we have to predict the probability as an output.
The final output of the network is passed as an input to the analog forecasting function. This is an algorithm commonly used in weather prediction systems. In this case, this is used to make predictions by comparing current values and assuming the patterns might repeat in the future.
There are many different ways to build an analog forecasting function but in our case, we’re used similarity measurement model:
X, as the current situation or set of current variables.
Y, as the outcome or variable of interest.
Si as the historical situations or patterns, where i ranges from 1 to n.
Vi as the vector of variables describing historical situation Si.
Oi as the outcome associated with historical situation Si.
First, we define a similarity measure sim(X,Vi) that quantifies the similarity between the current situation X and historical situation Si based on their respective variables Vi.
Then we select the K most similar historical situations (KNN Machine learning) based on the similarity measure sim(X,Vi). We denote the rest of the selected historical situations as {Si1, Si2,...Sik).
Then we examine the outcomes associated with the selected historical situations {Oi1, Oi2,...,Oik}.
Then we use the outcomes of the selected historical situations to forecast the future outcome Y^ using weighted averaging.
Finally, the output value of the analog forecasting is standardized using a standardization function which is the opposite of the normalization function. This function takes a normalized number and turns it back to its original value by multiplying it by the max scale (highest value of the bar). This function is used when the final number is produced by the network output at the end of the analog forecasting to turn the final value back into a price so it can be displayed on the chart with PineScript.
Settings:
Data source: Source of the neural network's input data.
Sample Bars: How many historical bars do you want to input into the neural network
Prediction Bars: How many bars you want the script to forecast
Show Training Rate: This shows the neural network's error rate for the optimization phase
Learning Rate: how many times you want the script to change the model in response to the estimated error (automatic)
Epochs: the network cycle or how many times you want to run the data through the network from the first layer to the last one.
Usage:
The sample bars input determines the number of historical bars to be used as a reference for the network. You need to change the Epochs and Learning Rate inputs for each asset and chart timeframe to get the lowest error rate.
On the surface, the highest possible epoch and learning rate should produce the most effective results but that's not always the case.
If the epochs rate is too high, there is a chance we face overfitting. Essentially, you might be over processing good data which can make it useless.
On the other hand, if the learning rate is too high, the network may overshoot the optimal solution and diverge. This is almost like the same issue I mentioned above with a high epoch rate.
Access:
It took over 4 months to develop this script and we’re constantly improving it so it took a lot of manpower to develop this script. Also when it comes to neural networks, Pine Script isn’t the most optimal language to build a neural network in, so we had to resort to a few proprietary mathematical formulas to ensure this runs smoothly without giving out an error for overprocessing, specially when you have multiple neural networks with many layers.
The optimization done to make this script run on Pine Script is basically state of the art and because of this, we would like to keep the code closed source at the moment.
On the other hand we don’t want to publish the code publicly as we want to keep the trading edge this script gives us in a closed loop, for our own small group of members so we have to keep the code closed. We only accept invites from expert traders who understand how this script and algo trading works and the type of edge it provides.
Additionally, at the moment we don’t want to share the code as some of the parts of this network, specifically the way we hand the data from neural network output into the analog method formula are proprietary code and we’d like to keep it that way.
You can contact us for access and if we believe this works for your trading case, we will provide you with access.
RSI AcceleratorThe Relative Strength Index (RSI) is like a fitness tracker for the underlying time series. It measures how overbought or oversold an asset is, which is kinda like saying how tired or energized it is.
When the RSI goes too high, it suggests the asset might be tired and due for a rest, so it could be a sign it's gonna drop. On the flip side, when the RSI goes too low, it's like the asset is pumped up and ready to go, so it might be a sign it's gonna bounce back up. Basically, it helps traders figure out if a stock is worn out or revved up, which can be handy for making decisions about buying or selling.
The RSI Accelerator takes the difference between a short-term RSI(5) and a longer-term RSI(14) to detect short-term movements. When the short-term RSI rises more than the long-term RSI, it typically refers to a short-term upside acceleration.
The conditions of the signals through the RSI Accelerator are as follows:
* A bullish signal is generated whenever the Accelerator surpasses -20 after having been below it.
* A bearish signal is generated whenever the Accelerator breaks 20 after having been above it.
Momentum Ghost Machine [ChartPrime]Momentum Ghost Machine (ChartPrime) is designed to be the next generation in momentum/rate of change analysis. This indicator utilizes the properties of one of our favorite filters to create a more accurate and stable momentum oscillator by using a high quality filtered delayed signal to do the momentum comparison.
Traditional momentum/roc uses the raw price data to compare current price to previous price to generate a directional oscillator. This leaves the oscillator prone to false readings and noisy outputs that leave traders unsure of the real likelihood of a future movement. One way to mitigate this issue would be to use some sort of moving average. Unfortunately, this can only go so far because simple moving average algorithms result in a poor reconstruction of the actual shape of the underlying signal.
The windowed sinc low pass filter is a linear phase filter, meaning that it doesn't change the shape or size of the original signal when applied. This results in a faithful reconstruction of the original signal, but without the "high frequency noise". Just like any filter, the process of applying it requires that we have "future" samples resulting in a time delay for real time applications. Fortunately this is a great thing in the context of a momentum oscillator because we need some representation of past price data to compare the current price data to. By using an ideal low pass filter to generate this delayed signal we can super charge the momentum oscillator and fix the majority of issues its predecessors had.
This indicator has a few extra features that other momentum/roc indicators dont have. One major yet simple improvement is the inclusion of a moving average to help gauge the rate of change of this indicator. Since we included a moving average, we thought it would only be appropriate to add a histogram to help visualize the relationship between the signal and its average. To go further with this we have also included linear extrapolation to further help you predict the momentum and direction of this oscillator. Included with this extrapolation we have also added the histogram in the extrapolation to further enhance its visual interpretation. Finally, the inclusion of a candle coloring feature really drives how the utility of the Momentum Machine .
There are three distinct options when using the candle coloring feature: Direct, MA, and Both. With direct the candles will be colored based on the indicators direction and polarity. When it is above zero and moving up, it displays a green color. When it is above zero and moving down it will display a light green color. Conversely, when the indicator is below zero and moving down it displays a red color, and when it it moving up and below zero it will display a light red color. MA coloring will color the candles just like a MACD. If the signal is above its MA and moving up it will display a green color, and when it is above its MA and moving down it will display a light green color.
When the signal is below its MA and moving down it will display a red color, and when its below its ma and moving up it will display a light red color. Both combines the two into a single color scheme providing you with the best of both worlds. If the indicator is above zero it will display the MA colors with a slight twist. When the indicator is moving down and is below its MA it will display a lighter color than before, and when it is below zero and is above its MA it will display a darker color color.
Length of 50 with a smoothing of 100
Length of 50 with a smoothing of 25
By default, the indicator is set to a momentum length of 50, with a post smoothing of 2. We have chosen the longer period for the momentum length to highlight the performance of this indicator compared to its ancestors. A major point to consider with this indicator is that you can only achieve so much smoothing for a chosen delay. This is because more data is required to produce a smoother signal at a specified length. Once you have selected your desired momentum length you can then select your desired momentum smoothing . This is made possible by the use of the windowed sinc low pass algorithm because it includes a frequency cutoff argument. This means that you can have as little or as much smoothing as you please without impacting the period of the indicator. In the provided examples above this paragraph is a visual representation of what is going on under the hood of this indicator. The blue line is the filtered signal being compared to the current closing price. As you can see, the filtered signal is very smooth and accurately represents the underlying price action without noise.
We hope that users can find the same utility as we did in this indicator and that it levels up your analysis utilizing the momentum oscillator or rate of change.
Enjoy
Machine Learning: Multiple Logistic Regression
Multiple Logistic Regression Indicator
The Logistic Regression Indicator for TradingView is a versatile tool that employs multiple logistic regression based on various technical indicators to generate potential buy and sell signals. By utilizing key indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend, the indicator aims to provide a systematic approach to decision-making in financial markets.
How It Works:
Technical Indicators:
The script uses multiple technical indicators such as RSI, CCI, DMI, Aroon, EMA, and SuperTrend as input variables for the logistic regression model.
These indicators are normalized to create categorical variables, providing a consistent scale for the model.
Logistic Regression:
The logistic regression function is applied to the normalized input variables (x1 to x6) with user-defined coefficients (b0 to b6).
The logistic regression model predicts the probability of a binary outcome, with values closer to 1 indicating a bullish signal and values closer to 0 indicating a bearish signal.
Loss Function (Cross-Entropy Loss):
The cross-entropy loss function is calculated to quantify the difference between the predicted probability and the actual outcome.
The goal is to minimize this loss, which essentially measures the model's accuracy.
// Error Function (cross-entropy loss)
loss(y, p) =>
-y * math.log(p) - (1 - y) * math.log(1 - p)
// y - depended variable
// p - multiple logistic regression
Gradient Descent:
Gradient descent is an optimization algorithm used to minimize the loss function by adjusting the weights of the logistic regression model.
The script iteratively updates the weights (b1 to b6) based on the negative gradient of the loss function with respect to each weight.
// Adjusting model weights using gradient descent
b1 -= lr * (p + loss) * x1
b2 -= lr * (p + loss) * x2
b3 -= lr * (p + loss) * x3
b4 -= lr * (p + loss) * x4
b5 -= lr * (p + loss) * x5
b6 -= lr * (p + loss) * x6
// lr - learning rate or step of learning
// p - multiple logistic regression
// x_n - variables
Learning Rate:
The learning rate (lr) determines the step size in the weight adjustment process. It prevents the algorithm from overshooting the minimum of the loss function.
Users can set the learning rate to control the speed and stability of the optimization process.
Visualization:
The script visualizes the output of the logistic regression model by coloring the SMA.
Arrows are plotted at crossover and crossunder points, indicating potential buy and sell signals.
Lables are showing logistic regression values from 1 to 0 above and below bars
Table Display:
A table is displayed on the chart, providing real-time information about the input variables, their values, and the learned coefficients.
This allows traders to monitor the model's interpretation of the technical indicators and observe how the coefficients change over time.
How to Use:
Parameter Adjustment:
Users can adjust the length of technical indicators (rsi_length, cci_length, etc.) and the Z score length based on their preference and market characteristics.
Set the initial values for the regression coefficients (b0 to b6) and the learning rate (lr) according to your trading strategy.
Signal Interpretation:
Buy signals are indicated by an upward arrow (▲), and sell signals are indicated by a downward arrow (▼).
The color-coded SMA provides a visual representation of the logistic regression output by color.
Table Information:
Monitor the table for real-time information on the input variables, their values, and the learned coefficients.
Keep an eye on the learning rate to ensure a balance between model adjustment speed and stability.
Backtesting and Validation:
Before using the script in live trading, conduct thorough backtesting to evaluate its performance under different market conditions.
Validate the model against historical data to ensure its reliability.
MACD Based Price Forecasting [LuxAlgo]The MACD Based Price Forecasting tool is an innovative price forecasting method based on signals generated by the MACD indicator.
The forecast includes an area which can help traders determine the area where price can develop after a MACD signal.
🔶 USAGE
The forecast returned by the tool allows users to obtain a general picture of how price tends to progress after a specific MACD signal. The forecast is constructed based on percentiles of previous price progressions done after a specific MACD signal is generated.
Users can change which condition is used to generate MACD signals from the "Trend Determination" dropdown menu, with "MACD" determining trends based on whether the MACD is positive (uptrend) or negative (downtrend) and "MACD-Signal" determining trends based on the position of the MACD relative to its signal line, with an MACD above the signal line indicating an uptrend, else a downtrend.
Users can introduce bias to the forecast by changing the "Average Percentage" setting, with values above 50% introducing bullish bias, and below bearish bias.
It can be possible for the forecast to highlight potential reversals depending on the selected forecasting horizon as long as reversals can be observed on trends detected by the MACD.
🔹 Forecasting Area
The forecasting area can help visualize the area that will likely contain price after a specific signal. The area width is based on the "Top/Bottom Percentiles" settings, with a higher "Top Percentile" value returning a higher top bound and a lower "Bottom Percentile" value returning a lower bottom bound.
These areas can also serve as potential support/resistance areas.
🔶 SETTINGS
Fast Length: Fast length of the moving average used to compute the MACD
Slow Length: Slow length of the moving average used to compute the MACD
Signal Length: Length of the MACD moving average.
Trend Determination: Method used to determine a trend direction from the MACD.
🔹 Forecast
Maximum Memory: Determines the maximum amount of prices recorded at each steps succeeding a signal. Lower values will return forecasts with a higher degree of variability.
Forecasting Length: Forecasting horizon in bars, this value only serves as a limit of the forecasting horizon and might not be reached depending on user selected MACD settings.
Top Percentile: Percentile value used to determine the upper bound of the forecasting area.
Average Percentile: Percentile value used to determine the forecast.
Lower Percentile: Percentile value used to determine the lower bound of the forecasting area.
Session breakThis indicator will show future lines before each session start. It will only show London session and US session start.
You can change the color of the lines and time as per day light savings.
ATH Gain PotentialThe indicator quantifies the relative position of a symbol's current closing price in relation to its historical all-time high (ATH).
By evaluating the ratio between the ATH and the present closing price, it provides an analytical framework to estimate the potential gains that could accrue if the symbol were to revert to its ATH from a specified reference point. The ratio serves as a quantitative measure for assessing the distance between the current market value and the symbol's historical peak, enabling investors to gauge the prospective profitability of a return to the ATH.
GARCH Volatility Estimation - The Quant ScienceThe GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model is a statistical model used to forecast the volatility of a financial asset. This model takes into account the fluctuations in volatility over time, recognizing that volatility can vary in a heteroskedastic (i.e., non-constant variance) manner and can be influenced by past events.
The general formula of the GARCH model is:
σ²(t) = ω + α * ε²(t-1) + β * σ²(t-1)
where:
σ²(t) is the conditional variance at time t (i.e., squared volatility)
ω is the constant term (intercept) representing the baseline level of volatility
α is the coefficient representing the impact of the squared lagged error term on the conditional variance
ε²(t-1) is the squared lagged error term at the previous time period
β is the coefficient representing the impact of the lagged conditional variance on the current conditional variance
In the context of financial forecasting, the GARCH model is used to estimate the future volatility of the asset.
HOW TO USE
This quantitative indicator is capable of estimating the probable future movements of volatility. When the GARCH increases in value, it means that the volatility of the asset will likely increase as well, and vice versa. The indicator displays the relationship of the GARCH (bright red) with the trend of historical volatility (dark red).
USER INTERFACE
Alpha: select the starting value of Alpha (default value is 0.10).
Beta: select the starting value of Beta (default value is 0.80).
Lenght: select the period for calculating values within the model such as EMA (Exponential Moving Average) and Historical Volatility (default set to 20).
Forecasting: select the forecasting period, the number of bars you want to visualize data ahead (default set to 30).
Design: customize the indicator with your preferred color and choose from different types of charts, managing the design settings.
Forecast: PastFluxDelta PredictionThe theory is that time periods and the conditions during these periods repeat themselves. Especially if it is the same day of the week in the past, there is a high probability that price fluctuations will roughly repeat themselves.
Eternal return (or eternal recurrence) is a philosophical concept which states that time repeats itself in an infinite loop, and that exactly the same events will continue to occur in exactly the same way, over and over again, for eternity.
History does repeat itself.
The stock market is a manifest example.
Chief market strategist at Miller Tabak + Co. Matt Maley pointed out the strong resemblance between the stock market recently and that in the past.
Various scientific studies and articles show that there could be something to this theory
Most of the investors are ignoring the parallels between stocks today and "heady" years 1929, 1999 and 2007…
Post Labor Day sees investors returning to the S&P 500 near all-time highs and some dark economic shadows lurking …
So how should we regard these inescapable results?
Nietzsche said we should embrace them, accept them, and love them. Once they stop, expect them to start again.
But remember that the future is fundamentally uncertain and that past results are by no means a guarantee of future performance.
Based on this, this indicator uses historical trading data from a year, a week or a day ago and compares price fluctuations in the past with current conditions.
"Bars to predict" can be used to indicate how far into the future the indicator is looking.
"Amount of bars to show" determines how many bars are generally displayed. A high value allows you to see how accurate the method was in the past.
Whalemap [BigBeluga]The Whalemap indicator aims to spot big buying and selling activity represented as big orders for a possible bottom or top formation on the chart.
🔶 CALCULATION
The indicator uses volume to spot big volume activity represented as big orders in the market.
for i = 0 to len - 1
blV.vol += (close > close ? volume : 0)
brV.vol += (close < close ? volume : 0)
When volume exceeds its own threshold, it is a sign that volume is exceeding its normal value and is considered as a "Whale order" or "Whale activity," which is then plotted on the chart as circles.
🔶 DETAILS
The indicator plots Bubbles on the chart with different sizes indicating the buying or selling activity. The bigger the circle, the more impact it will have on the market.
On each circle is also plotted a line, and its own weight is also determined by the strength of its own circle; the bigger the circle, the bigger the line.
Old buying/selling activity can also be used for future support and resistance to spot interesting areas.
The more price enters old buying/selling activity and starts producing orders of the same direction, it might be an interesting point to take a closer look.
🔶 EXAMPLES
The chart above is showing us price reacting to big orders, finding good bottoms in price and good tops in confluence with old activity.
🔶 SETTINGS
Users will have the options to:
Filter options to adjust buying and selling sensitivity.
Display/Hide Lines
Display/Hide Bubbles
Choose which orders to display (from smallest to biggest)
GKD-C Chande Forecast Oscillator [Loxx]The Giga Kaleidoscope GKD-C Chande Forecast Oscillator is a confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ GKD-C Chande Forecast Oscillator
The Chande Forecast Oscillator (CFO) is a technical analysis tool developed by Tushar Chande. It operates by plotting the percentage difference between the closing price and a linear regression forecasted price over a specified number of periods, often referred to as 'n-periods' or 'x-periods'. The essence of this oscillator is to compare actual prices with forecasted ones, thereby providing insights into the momentum and potential trend direction of a financial instrument.
The calculation involves taking the current closing price, subtracting it from the n-period simple moving average, and then dividing this number by the total of the absolute differences between the closing price and the moving average over the same period. The CFO value is positive (above zero) when the forecast price is greater than the closing price, indicating a potential upward trend. Conversely, it is negative (below zero) when the forecast price is less than the closing price, suggesting a downward trend.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chande Forecast Oscillator.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker CC Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Advance Trend Pressure as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
? Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
MA Sabres [LuxAlgo]The "MA Sabres" indicator highlights potential trend reversals based on a moving average direction. Detected reversals are accompanied by an extrapolated "Sabre" looking shape that can be used as support/resistance and as a source of breakouts.
🔶 USAGE
If a selected moving average (MA) continues in the same direction for a certain time, a change in that direction could signify a potential reversal.
In this publication, when a trend change occurs, a sabre-shaped figure is drawn which can be used as support/resistance:
A sabre can be indicative of a direction, however, it can also act as a stop-loss when the price should go in the opposite direction:
Or show potential areas of interest:
🔶 DETAILS
This publication will look for a change in direction after the MA went in the same direction during x consecutive bars (settings: " Reversal after x bars in the same direction ").
Then a circle-shaped drawing will be drawn 1 bar back, at the previous high/low, dependable of the previous direction.
From there originates a sabre-shaped figure where the tip lies as far as the user-set MA length.
The angle of the "sabre" relies on the ATR of the previous 14 bars.
Less volatility will create a flatter sabre while the opposite is true when there is more volatility in the previous 14 bars.
The sabre is created by the latest feature, polylines , which enables us to connect several 'points', resulting in a polyline.new() object.
Do note that sabres are offset by one bar to the past to align their locations.
🔶 SETTINGS
MA Type: SMA, EMA, SMMA (RMA), HullMA, WMA, VWMA, DEMA, TEMA, NONE (off)
Length: this sets the length of MA, and the length of the sabre shape
Previous Trend Duration: After the MA direction is the same for x consecutive bars, the first time the direction changes, a sabre is drawn
Machine Learning: Gaussian Process Regression [LuxAlgo]We provide an implementation of the Gaussian Process Regression (GPR), a popular machine-learning method capable of estimating underlying trends in prices as well as forecasting them.
While this implementation is adapted to real-time usage, do remember that forecasting trends in the market is challenging, do not use this tool as a standalone for your trading decisions.
🔶 USAGE
The main goal of our implementation of GPR is to forecast trends. The method is applied to a subset of the most recent prices, with the Training Window determining the size of this subset.
Two user settings controlling the trend estimate are available, Smooth and Sigma . Smooth determines the smoothness of our estimate, with higher values returning smoother results suitable for longer-term trend estimates.
Sigma controls the amplitude of the forecast, with values closer to 0 returning results with a higher amplitude. Do note that due to the calculation of the method, lower values of sigma can return errors with higher values of the training window.
🔹 Updating Mechanisms
The script includes three methods to update a forecast. By default a forecast will not update for new bars (Lock Forecast).
The forecast can be re-estimated once the price reaches the end of the forecasting window when using the "Update Once Reached" method.
Finally "Continuously Update" will update the whole forecast on any new bar.
🔹 Estimating Trends
Gaussian Process Regression can be used to estimate past underlying local trends in the price, allowing for a noise-free interpretation of trends.
This can be useful for performing descriptive analysis, such as highlighting patterns more easily.
🔶 SETTINGS
Training Window: Number of most recent price observations used to fit the model
Forecasting Length: Forecasting horizon, determines how many bars in the future are forecasted.
Smooth: Controls the degree of smoothness of the model fit.
Sigma: Noise variance. Controls the amplitude of the forecast, lower values will make it more sensitive to outliers.
Update: Determines when the forecast is updated, by default the forecast is not updated for new bars.
Rug Pull DetectorOverview
Have you ever wondered why tickers have such erratic movements that seemingly come from nowhere? These "rug pull" events happen quite often and can catch even the most seasoned traders off-guard.
Unlike most other indicators which rely on historical data to make inferences about future price movements, the Rug Pull Detector (RPD) enables you to take a glimpse into market makers' delta-neutral hedging in real-time.
Market makers by nature must be delta-neutral which means that they cannot position themselves to profit from providing liquidity (either long or short). Liquidity provided to the short or long side must end up in a stock purchase or sale to neutralize the trade.
Volatile movements in a ticker's price movement most often result directly after a period of extremely low volatility. These volatile movements are very often "rug pulled" which ends up reverting the ticker back to the price at which the event first occurred. RPD shows these events in real-time. This knowledge can be used to help determine the most probable near-future direction a ticker will gravitate towards after a rug pull event occurs.
Usage
RPD works on any ticker and on any timeframe and can be used as a tool in determining an exit price for a trade. Vertical shading on the chart indicates a warning signal that a rug pull event may be about to kick-off. Once a rug pull event has occurred and is confirmed, a blue label will appear on the chart with a price. A line is then drawn from the bar at which the event occurred and is extended to each subsequent bar until the price is reached once more; thus concluding the event. Furthermore, red or green shading will be present to easily visually identify rug pull events on the chart and whether they are risks to the downside (red) or upside (green). RPD is broken down into 2 main types of events:
Active Event - These events are characterized by a red or green shading and a blue price line.
Dormant Event - These events do not have shading but are still identifiable via a blue price line. Active events that are superseded by newer events will become dormant.
Active events tend to have a higher chance to return to the initial price point and tend to arrive there quicker.
Dormant events have a slightly lower chance to return to the initial price point and may take longer to arrive there.
Please note:
This indicator has no way of telling the exact amount of time that will pass before the ticker returns to the identified price; however, in more cases than not - the ticker will return to that price within a reasonable amount of time relative to the timeframe you are viewing.
There is a small chance any single event will never conclude. These are anomalies and do occur on occasion.
Using RPD alongside tools such as the RSI, Anchored VWAP, or other trend-based indicators will help determine when the ticker's price might be about to pivot and head back towards the identified price point.
Seeing is Believing:
SPY 1D downside rug-pull
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AAPL 15s downside and upside rug-pulls
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AMD 2D downside rug-pull
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VIX 1h downside and upside rug-pulls
Want to see more? Check out my recent Ideas for more examples of the Rug Pull Detector in action.
Disclaimer:
Any information in relation to the Rug Pull Detector does not constitute any financial, investment, or trading advice. Trade or invest at your own risk.
Kaschko's Seasonal TrendThis script calculates the average price moves (using each bar's close minus the previous bar's close) for the trading days, weeks or months (depending on the timeframe it is applied to) of a number of past calendar years (up to 30) to construct a seasonal trend which is then drawn as a seasonal chart (overlay) onto the price chart. Supported are the 1D,1W,1M timeframes.
The seasonal chart is adjusted to the price chart (so that both occupy the same height on the overall chart) and it is also de-trended, which means that the seasonal chart's starting value is the same in each year and the progression during the year is adjusted so that no abrupt gap occurs between years and the highs and lows of consecutive years of the seasonal chart (if projected over more than one year) are also at the same level. Of course, this also means that the absolute value of the seasonal chart has no meaning at all.
You can configure the number of bars the seasonal chart is drawn into the future. This projection shows how price could move in the future if the market shows the same seasonal tendencies like in the past. On the daily chart, the trading week of year (TWOY), trading day of month (TDOM) and trading day of year (TDOY) are shown in the status line.
Caution is advised as seasonality is based on the past. It is not a reliable prediction of the future. But it can still be used as an additional confirmation or contradiction of an otherwise recognized possible impending trend.
I have used a virtually identical indicator for a long time in a commercial software package popular among futures traders, but have not found anything comparable here. Therefore I implemented it myself. I hope you find it useful.
SFC Valuation Model - Fair ValueValuation is the analytical process of determining the current (or projected) worth of an asset or a company. There are many techniques used for doing a valuation. An analyst placing a value on a company looks at the business's management, the composition of its capital structure, the prospect of future earnings, and the market value of its assets, among other metrics.
Fundamental analysis is often employed in valuation, although several other methods may be employed such as the capital asset pricing model (CAPM) or the dividend discount model (DDM), Discounted Cash Flow (DCF) and many others.
A valuation can be useful when trying to determine the fair value of a security, which is determined by what a buyer is willing to pay a seller, assuming both parties enter the transaction willingly. When a security trades on an exchange, buyers and sellers determine the market value of a stock or bond.
There is no universal standard for calculating the intrinsic value of a company or stock. Financial analysts attempt to determine an asset's intrinsic value by using fundamental and technical analyses to gauge its actual financial performance.
Intrinsic value is useful because it can help an investor understand whether a potential investment is overvalued or undervalued.
This indicator allows investors to simulate different scenarios depending on their view of the stock's value. It calculates different models automatically, but users can define the fair value manually by changing the settings.
For example: change the weight of the model; choose how conservatively want to evaluate the stock; use different growth rate or discount rate and so on.
The indicator shows other useful metrics in order to help investors to evaluate the stock.
This indicator can save users hours of searching financial data and calculating fair value.
There are few valuation methods/steps
- Macroeconomics - analyse the current economic;
- Define how the sector is performing;
- Relative valuation method - compare few stocks and find the Outlier;
- Absolute valuation method historically- define how the stock performed in the past;
- Absolute valuation method - define how the stock is performed now and find the fair value;
- Technical analysis
How to use:
1. Once you have completed the initial evaluation steps, simply load the indicator.
2. Check the default settings and see if they suit you.
3. Find the fair value and wait for the stock to reach it.
