Machine Learning: SuperTrend Strategy TP/SL [YinYangAlgorithms]The SuperTrend is a very useful Indicator to display when trends have shifted based on the Average True Range (ATR). Its underlying ideology is to calculate the ATR using a fixed length and then multiply it by a factor to calculate the SuperTrend +/-. When the close crosses the SuperTrend it changes direction.
This Strategy features the Traditional SuperTrend Calculations with Machine Learning (ML) and Take Profit / Stop Loss applied to it. Using ML on the SuperTrend allows for the ability to sort data from previous SuperTrend calculations. We can filter the data so only previous SuperTrends that follow the same direction and are within the distance bounds of our k-Nearest Neighbour (KNN) will be added and then averaged. This average can either be achieved using a Mean or with an Exponential calculation which puts added weight on the initial source. Take Profits and Stop Losses are then added to the ML SuperTrend so it may capitalize on Momentum changes meanwhile remaining in the Trend during consolidation.
By applying Machine Learning logic and adding a Take Profit and Stop Loss to the Traditional SuperTrend, we may enhance its underlying calculations with potential to withhold the trend better. The main purpose of this Strategy is to minimize losses and false trend changes while maximizing gains. This may be achieved by quick reversals of trends where strategic small losses are taken before a large trend occurs with hopes of potentially occurring large gain. Due to this logic, the Win/Loss ratio of this Strategy may be quite poor as it may take many small marginal losses where there is consolidation. However, it may also take large gains and capitalize on strong momentum movements.
Tutorial:
In this example above, we can get an idea of what the default settings may achieve when there is momentum. It focuses on attempting to hit the Trailing Take Profit which moves in accord with the SuperTrend just with a multiplier added. When momentum occurs it helps push the SuperTrend within it, which on its own may act as a smaller Trailing Take Profit of its own accord.
We’ve highlighted some key points from the last example to better emphasize how it works. As you can see, the White Circle is where profit was taken from the ML SuperTrend simply from it attempting to switch to a Bullish (Buy) Trend. However, that was rejected almost immediately and we went back to our Bearish (Sell) Trend that ended up resulting in our Take Profit being hit (Yellow Circle). This Strategy aims to not only capitalize on the small profits from SuperTrend to SuperTrend but to also capitalize when the Momentum is so strong that the price moves X% away from the SuperTrend and is able to hit the Take Profit location. This Take Profit addition to this Strategy is crucial as momentum may change state shortly after such drastic price movements; and if we were to simply wait for it to come back to the SuperTrend, we may lose out on lots of potential profit.
If you refer to the Yellow Circle in this example, you’ll notice what was talked about in the Summary/Overview above. During periods of consolidation when there is little momentum and price movement and we don’t have any Stop Loss activated, you may see ‘Signal Flashing’. Signal Flashing is when there are Buy and Sell signals that keep switching back and forth. During this time you may be taking small losses. This is a normal part of this Strategy. When a signal has finally been confirmed by Momentum, is when this Strategy shines and may produce the profit you desire.
You may be wondering, what causes these jagged like patterns in the SuperTrend? It's due to the ML logic, and it may be a little confusing, but essentially what is happening is the Fast Moving SuperTrend and the Slow Moving SuperTrend are creating KNN Min and Max distances that are extreme due to (usually) parabolic movement. This causes fewer values to be added to and averaged within the ML and causes less smooth and more exponential drastic movements. This is completely normal, and one of the perks of using k-Nearest Neighbor for ML calculations. If you don’t know, the Min and Max Distance allowed is derived from the most recent(0 index of data array) to KNN Length. So only SuperTrend values that exhibit distances within these Min/Max will be allowed into the average.
Since the KNN ML logic can cause these exponential movements in the SuperTrend, they likewise affect its Take Profit. The Take Profit may benefit from this movement like displayed in the example above which helped it claim profit before then exhibiting upwards movement.
By default our Stop Loss Multiplier is kept quite low at 0.0000025. Keeping it low may help to reduce some Signal Flashing while not taking extra losses more so than not using it at all. However, if we increase it even more to say 0.005 like is shown in the example above. It can really help the trend keep momentum. Please note, although previous results don’t imply future results, at 0.0000025 Stop Loss we are currently exhibiting 69.27% profit while at 0.005 Stop Loss we are exhibiting 33.54% profit. This just goes to show that although there may be less Signal Flashing, it may not result in more profit.
We will conclude our Tutorial here. Hopefully this has given you some insight as to how Machine Learning, combined with Trailing Take Profit and Stop Loss may have positive effects on the SuperTrend when turned into a Strategy.
Settings:
SuperTrend:
ATR Length: ATR Length used to create the Original Supertrend.
Factor: Multiplier used to create the Original Supertrend.
Stop Loss Multiplier: 0 = Don't use Stop Loss. Stop loss can be useful for helping to prevent false signals but also may result in more loss when hit and less profit when switching trends.
Take Profit Multiplier: Take Profits can be useful within the Supertrend Strategy to stop the price reverting all the way to the Stop Loss once it's been profitable.
Machine Learning:
Only Factor Same Trend Direction: Very useful for ensuring that data used in KNN is not manipulated by different SuperTrend Directional data. Please note, it doesn't affect KNN Exponential.
Rationalized Source Type: Should we Rationalize only a specific source, All or None?
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
Machine Learning Smoothing Type: How should we smooth our Fast and Slow ML Datas to be used in our KNN Distance calculation? SMA, EMA or VWMA?
KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using.
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length?? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length?? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Forecasting
Euclidean Distance Predictive Candles [SS]Finally releasing this, its been in the works for the past 2 weeks and has undergone many iterations.
I am not sure if I am 100% happy with it yet, but I guess its best to release and get feedback to make improvements.
So this is the Euclidean distance predictive candle indicator and what it does is exactly what it sounds like, it uses Euclidean distance to identify similar candles and then plot the candles and range that immediately proceeded like candles.
While this is using a general machine learning/data science approach (Euclidean distance), I do not employ the KNN (Nearest Neighbors) algo into this. The reason being is it simply offered no predictive advantage than isolating for the last case. I tried it, I didn't like it, the results were not improve and, at times, acutally hindered so I ditched it. Perhaps it was my approach but using some other KNN indicators, I just don't really find them all that more advantageous to simply relying on the Law of Large Numbers and collecting more data rather than less data (which we will get into later in this explanation).
So using this indicator:
There is a lot of customizability here. And the reason is, not all settings are going to work the same for all tickers. To help you narrow down your parameters, I have included various backtest results that show you how the model is performing. You see in the AMZN chart above, with the current settings, it is performing optimally, with a cumulative range pass of 99% (meaning that, of all the cases, the indicator accurately predicted the next day high OR low range 99% of the time), and the ability to predict the candle slightly over 52%.
The recommended settings, from me, are as follows:
So these are generally my recommended settings.
Euclidian Tolerance: This will determine the parameters to look for similar candles. In general, the lower the tolerance, the greater the precision. I recommend keeping it between 0.5, for tickers with larger prices (like ES1! futures or NQ1!) or 0.05 for tickers with lower TPs, like SPY or QQQ.
If the ED Tolerance is too extreme that the indicator cannot find identical setups, it will alert you:
But in general, the more precise you can get it, the better.
Anchor Type: You will see the option to anchor by "Predicted Open" or by "Previous Close". I suggest sticking with anchoring by predicted open. All this means is, it is going to anchor your range, candle, high and low targets by the predicted open price. Anchoring by previous close will anchor by the close of yesterday. Both work okay, but in general the results from anchoring to predicted open have higher pass rates and more accurately depict the candle.
Euclidean Distance Measurement Type: You can choose to measure by candle body or from high to low wicks. I haven't played around with measuring from high to low wicks all that much, because candle body tends to do the job. But remember, ED is a neutral measurement. Which means, its not going to distinguish between a red or green candle, just the formation of the candle. Thus, I tend to recommend, pragmatically, not to necessarily rely on the candle being red or green, but one the formation of the candle (where are the wicks going, are there more bearish wicks or bullish wicks) etc. Examples will follow.
Range Prediction Type: You can filter the range prediction type by last instance (in which, it will pull the previous identical candle and plot the next candle that followed it, adjusted for the current ranges) or "Average of All Cases". So this is where we need to talk a little bit about the law of large numbers.
In general, in statistics, when you have a huge amount of random data, the law of large numbers stipulates that, within this randomness should be repeated events. This is why sometimes chart patterns work, sometimes they don't. When we filter by the average of all cases, we are relying on the law of large numbers. In general, if you are getting good Backtest readings from Last Instance, then you don't need to use this function. But it provides an alternative insight into potential candle formations next day. Its not a bad idea to compare between the two and look for similarities and differences.
So now that we have covered the boring details, let's get into how to use the indicator and some examples.
So the indicator is plotting the range and candle for the next day. As such, we are not looking at the current candle being plotted, but we are looking at the previous candle (see image below for example):
The green arrow shows the prediction for Friday, along with the corresponding result. The purple arrow shows the prediction for Monday which we have yet to realize.
So remember when you are using this, you need to look at the previous candle, and not the candle that it is currently plotting with realtime data, because it is plotting for the next candle.
If you are plotting by last instance, the indicator will tell you which day it is pulling its data from if you have opted to toggle on the demographic data:
You can see the green arrow pointing to the date where it is pulling from. This data serves as the example candle with the candle proceeding this date being the anchored candle (or the predicted candle).
Price Targets and Probability:
In the chart, you can see the green arrow pointing to the green portion of the table. In this table, it will give you the current TPs. These represent the current time target price, which means, the TPs shown here are for Friday. On Monday, the table will update with the TPs for Monday, etc. If you want to view the TPs in advance, you can view them from the actual candle itself.
Below the TPs, you see a bullish 7:6. It means, in a total of 13 cases, the next candle was bullish 7 times and bearish 6 times. Where do we see the number of cases? In the demographic table as well:
Auxiliary functions
Because you are using the previous candle, if you want to avoid confusion, you can have the indicator plot the price targets over the predicted candle, to anchor your attention so to speak. Simply select "Label" in the "Show Price Targets" section, which will look like this:
You can also ask the indicator to plot the demographic data of Higher High, Low, etc. information. What this does is simply looks at all the cases and plots how many times higher highs, lows, lower lows, highs etc. were made:
This will just count all of the cases identified and plot the number of times higher highs, lows, etc. were made.
Concluding Remarks
This is a kind of complex indicator and I can appreciate it may take some getting used to.
I will try to post a tutorial video at some point next week for it, so stay tuned for that.
But this isn't designed to make your life more complicated, just to help give you insights into potential outcomes for the next day or hour or 5 minute (it can be used on all timeframes).
If you find it helpful, great! If not, that's okay, too :-).
Please be aware, this is not my forte of indicators. I am not a data scientist or programmer. My background is in Epi and we don't use these types of data science approaches, so if you have any suggestions or critiques, feel free to share them below.
Otherwise, I hope you enjoy!
Take care everyone and safe trades!
Bullish vs. Bearish Candle CounterFollowing an exhaustive analysis of the most recent 50,000 candles within a given currency pair, a notable equilibrium between bearish and bullish candles has emerged as a persistent market phenomenon. This equilibrium, indicative of the market's continuous endeavor to establish parity, has spurred the development of the following indicator.
The indicator meticulously scrutinizes the preceding 100 candles, promptly triggering an on-chart marker when either bullish or bearish candle counts surpass the threshold of 60%. This marker serves as an invaluable tool, providing traders with a potential signal for the initiation of a trend reversal.
As such, this indicator serves as a valuable asset in a trader's toolkit, offering insights into shifts in market sentiment and the prospect of emerging trends.
Key Features:
- Customizable Candle Count: Traders can set the number of candlesticks to be analyzed in the input parameters, allowing flexibility in their analysis.
- Bullish and Bearish Percentage: Users can define their desired percentage for both bullish and bearish candles in the indicator's settings. The indicator calculates the percentage of each candle type within the specified range.
- Arrow Signals: The indicator plots arrows above or below the current candle, indicating bullish or bearish conditions based on the defined percentage thresholds. A green arrow signifies bullish sentiment, while a red arrow denotes bearish sentiment.
How to Use:
- Adjust Parameters: In the indicator settings, users can customize the number of candlesticks to be analyzed, as well as set their preferred percentages for both bullish and bearish conditions.
- Interpret Arrows: The indicator generates arrows above or below the current candle, reflecting the prevailing market sentiment. A green arrow suggests a bullish bias, while a red arrow indicates a bearish bias.
- Trade with Confidence: Traders can use this indicator as a tool to gauge market sentiment and make informed trading decisions. It helps identify potential entry and exit points based on the chosen percentage thresholds.
Multiperiod Volume Pressure Indicator
Description:
The Volume Pressure Indicator is a powerful tool designed to assess market sentiment based on a combination of price and volume data. By analyzing buy and sell pressure within specific lookback periods, this indicator provides valuable insights into the intensity of market buying and selling activities. Traders can use this information to make informed decisions, especially during periods of price consolidation or trend reversal.
Key Features:
- **Multi-Period Analysis:** Utilizes multiple lookback periods (1, 2, and 4) to calculate buy and sell pressures, offering a nuanced view of market dynamics over different timeframes.
- **Pressure Calculation:** Computes buy and sell pressures based on price range and closing values, providing a comprehensive understanding of market participant behavior.
- **Color-Coded Bars:** Visualizes market sentiment by coloring bars according to the number of positive (buy pressure > sell pressure) periods observed within the specified lookback periods.
How to Use:
- **Color Coding:** Green bars represent periods where buy pressure dominates, indicating potential buying interest. Yellow bars suggest a balance between buy and sell pressures. Red bars signal periods dominated by sell pressure, indicating potential selling interest.
- **Lookback Periods:** Shorter lookback periods (e.g., 1) offer insights into immediate market sentiment, while longer periods (e.g., 4) provide a broader perspective. Analyzing multiple periods can help traders confirm trends and anticipate reversals.
Customization:
- **Lookback Periods:** Adjust the length of the lookback periods (1, 2, and 4) to match your trading style and timeframe preferences.
Disclaimer:
Trading involves risk, and past performance is not indicative of future results. Always conduct thorough analysis and apply proper risk management techniques before making trading decisions.
Usage Scenarios:
- **Trend Confirmation:** Use the indicator to confirm the strength of an ongoing trend. Consistent green bars can validate a bullish trend, while red bars may confirm a bearish trend.
- **Reversal Signals:** Look for transitions in bar colors to identify potential trend reversals. A shift from green to yellow/red or vice versa can indicate changing market sentiment.
- **Divergence Analysis:** Compare price movements with the indicator's bar colors. Divergence between price trends and bar colors may signal upcoming price movements.
Machine Learning using Neural Networks | EducationalThe script provided is a comprehensive illustration of how to implement and execute a simplistic Neural Network (NN) on TradingView using PineScript.
It encompasses the entire workflow from data input, weight initialization, implicit neuron calculation, feedforward computation, backpropagation for weight adjustments, generating predictions, to visualizing the Mean Squared Error (MSE) Loss Curve for monitoring the training phase.
In the visual example above, you can see that the prediction is not aligned with the actual value. This is intentional for demonstrative purposes, and by incrementing the Epochs or Learning Rate, you will see these two values converge as the accuracy increases.
Hyperparameters:
Learning Rate, Epochs, and the choice between Simple Backpropagation and a verbose version are declared as script inputs, allowing users to tailor the training process.
Initialization:
Random initialization of weight matrices (w1, w2) is performed to ensure asymmetry, promoting effective gradient updates. A seed is added for reproducibility.
Utility Functions:
Functions for matrix randomization, sigmoid activation, MSE loss calculation, data normalization, and standardization are defined to streamline the computation process.
Neural Network Computation:
The feedforward function computes the hidden and output layer values given the input.
Two variants of the backpropagation function are provided for weight adjustment, with one offering a more verbose step-by-step computation of gradients.
A wrapper train_nn function iterates through epochs, performing feedforward, loss computation, and backpropagation in each epoch while logging and collecting loss values.
Training Invocation:
The input data is prepared by normalizing it to a value between 0 and 1 using the maximum standardized value, and the training process is invoked only on the last confirmed bar to preserve computational resources.
Output Forecasting and Visualization:
Post training, the NN's output (predicted price) is computed, standardized and visualized alongside the actual price on the chart.
The MSE loss between the predicted and actual prices is visualized, providing insight into the prediction accuracy.
Optionally, the MSE Loss Curve is plotted on the chart, illustrating the loss trajectory through epochs, assisting in understanding the training performance.
Customizable Visualization:
Various inputs control visualization aspects like Chart Scaling, Chart Horizontal Offset, and Chart Vertical Offset, allowing users to adapt the visualization to their preference.
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The following is this Neural Network structure, consisting of one hidden layer, with two hidden neurons.
Through understanding the steps outlined in my code, one should be able to scale the NN in any way they like, such as changing the input / output data and layers to fit their strategy ideas.
Additionally, one could forgo the backpropagation function, and load their own trained weights into the w1 and w2 matrices, to have this code run purely for inference.
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While this demonstration does create a “prediction”, it is on historical data. The purpose here is educational, rather than providing a ready tool for non-programmer consumers.
Normally in Machine Learning projects, the training process would be split into two segments, the Training and the Validation parts. For the purpose of conveying the core concept in a concise and non-repetitive way, I have foregone the Validation part. However, it is merely the application of your trained network on new data (feedforward), and monitoring the loss curve.
Essentially, checking the accuracy on “unseen” data, while training it on “seen” data.
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I hope that this code will help developers create interesting machine learning applications within the Tradingview ecosystem.
@tk · spectral█ OVERVIEW
This script is an indicator that helps traders to identify the price difference between spot and futures of the current crypto plotted into the chart. It works in both types of markets, when the chart is plotting the crypto in spot market, it will compare with its respective futures ticker and vice-versa. If the current asset isn't a crypt ticker, the indicator will not be plotted into the chart.
█ MOTIVATION
Since crypto's derivative market is based on spot market asset's price, to calculate the arbitrage mechanisms that attempts to balance the asset price, this indicator can help traders to identify some spot and futures price divergence that can create an anomaly of funding rate and can push it to an extreme negative — or positive — rate. So, easing to track the price difference between both markets will bring more evidences to identify an artificial price move, specially in crypto assets with low market cap.
█ CONCEPT
The trading concept to use this indicator is the concept of the arbitrage machamism created by exchanges that calculates the funding rate based on spot and futures price difference that will vary from exchange to exchange. This strategy don't works alone. It needs to be aligned together with others indicators like Exponential Moving Averages, Chart Patterns, Support and Resistance, and so on... Even more confluences that you have, bigger are your chances to increase the probability for a successful trade. So, don't use this indicator alone. Compose a trading strategy and use it to improve your analysis.
█ CUSTOMIZATION
This indicator allows the trader to customize the following settings:
GENERAL
Text size
Changes the font size of price difference table to improve accessibility.
Type: string
Options: `tiny`, `small`, `normal`, `large`.
Default: `small`
Position
Changes the position of price difference table.
Type: string
Options: `top_left`, `top_center`, `top_right`, `middle_left`, `middle_center`, `middle_right`, `bottom_left`, `bottom_center`, `bottom_right`.
Default: `bottom_right`
Pair Quote
The ticker quote symbol that will be used to base the ticker comparison from spot to futures (e.g. BTCUSDT which `USDT` is the quote. ETHBTC which `BTC` is the quote).
Type: string
Default: USDT
Spectrum Color
The color of the spectrum candles. Spectrum candles are the candles of the opposite market. If the current ticker is in the spot market, the spectrum candles will be the price of the futures market.
Type: color
Default: #434651
█ FUNCTIONS
The indicator contains the following functions:
stripStarts(src, str)
Strips a defined pattern from a string.
Parameters:
src: (string) Source string
str: (string) String pattern to be stripped from start of source string.
Returns: (string) Stripped string with matched regex pattern.
Golden Level Predictions v1.0Golden Level Predictions (GLP) Trading Indicator
This script introduces a custom trading indicator named "GLP" tailored for the TradingView platform. It offers various price levels derived from Fibonacci calculations and other mathematical models, assisting traders in pinpointing potential overpriced and discounted price levels.
Key Features:
User Inputs : Users have the flexibility to select their desired timeframe, with options ranging from Weekly, Daily, Monthly, and more. Additionally, they can opt to showcase Fibonacci lines and the associated prices within these levels.
Price Level Calculations :
- Employs constants such as the Golden Ratio (PHI) and Pi (PI) to extract various multipliers and factors.
- Assesses if the current asset is a cryptocurrency and tweaks calculations accordingly.
- Determines overpriced and discounted price levels, drawing from the current open price and past data.
Fibonacci Levels :
- For each overpriced and discounted level, the script computes intermediary Fibonacci levels, including 23.6%, 38.2%, 50%, 61.8%, and 78.6% (the 3rd level is excluded due to plot limitations).
- These levels are illustrated on the chart, granting traders a more detailed view of price targets.
Visual Elements :
- Projects horizontal lines to the subsequent selected indicator interval for every calculated price level.
- Exhibits potential percentage gains or losses at each tier, indicating the prospective price alteration upon reaching that level.
- Differentiates overpriced (green) and discounted (red) levels using color codes. A neutral price is depicted in yellow.
Anticipated Close Calculation : Offers a projected closing price for the current timeframe, based on a myriad of factors.
This indicator is particularly effective with cryptocurrencies due to their inherent volatility. It's also compatible with stocks and is most efficient with tickers that provide volume data.
Highlight BarHighlight bars in the past. I use this to show the start of moving average calculations - very helpful to anticipate the change in slope of moving averages. You can change color as well as how far back in time to highlight. The defaults are 20, 50 and 200.
I learned of the idea from Brian Shannon - thanks!
Seasonality and Presidential cycleAn incredibly useful indicator that shows seasonality and presidential cycles by indices, stocks and industries. Just type in a ticker and trade according to seasonal patterns
Blue line - seasonality excluding presidential cycles
Green line - seasonality taking into account presidential cycles
*Seasonal patterns over the last 10 years
This indicator uses the request.seed() function.
Requests data from a GitHub repository maintained by our team and returns it as a series.
Pine Seeds is a service to import custom data and access it via TradingView.
Use TradingView as frontend and use a GitHub repository as backend.
github.com
...
Rus: Невероятно полезный индикатор, который показывает сезонность и президентские циклы по индексам, акциям и отраслям. Просто вбейте тикер и торгуйте согласно сезонным паттернам
Синяя линия - сезонность без учета президентских циклов
Зеленая линия - сезонность с учетом президентских циклов
*Сезонные паттерны за последние 10 лет
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.
Intraday Volatility Bands [Honestcowboy]The Intraday Volatility Bands aims to provide a better alternative to ATR in the calculation of targets or reversal points.
How are they different from ATR based bands?
While ATR and other measures of volatility base their calculations on the previous bars on the chart (for example bars 1954 to 1968). The volatility used in these bands measure expected volatility during that time of the day.
Why would you take this approach?
Markets behave different during certain times of the day, also called sessions.
Here are a couple examples.
Asian Session (generally low volatility)
London Session (bigger volatility starts)
New York Session (overlap of New York with London creates huge volatility)
Generally when using bands or channel type indicators intraday they do not account for the upcoming sessions. On London open price will quickly spike through a bollinger band and it will take some time for the bands to adjust to new volatility.
This script will show expected volatility targets at the start of each new bar and will not adjust during the bar. It already knows what price is expected to do at this time of day.
Script also plots arrows when price breaches either the top or bottom of the bands. You can also set alerts for when this occurs. These are non repainting as the script knows the level at start of the bar and does not change.
🔷 CALCULATION
Think of this script like an ATR but instead it uses past days data instead of previous bars data. Charts below should visualise this more clearly:
The scripts measure of volatility is based on a simple high-low.
The script also counts the number of bars that exist in a day on your current timeframe chart. After knowing that number it creates the matrix used in it's calculations and data storage.
See how it works perfectly on a lower timeframe chart below:
Getting this right was the hardest part, check the coding if you are interested in this type of stuff. I commented every step in the coding process.
🔷 SETTINGS
Every setting of the script has a tooltip but I provided a breakdown here:
Some more examples of different charts:
Machine Learning: Trend Lines [YinYangAlgorithms]Trend lines have always been a key indicator that may help predict many different types of price movements. They have been well known to create different types of formations such as: Pennants, Channels, Flags and Wedges. The type of formation they create is based on how the formation was created and the angle it was created. For instance, if there was a strong price increase and then there is a Wedge where both end points meet, this is considered a Bull Pennant. The formations Trend Lines create may be powerful tools that can help predict current Support and Resistance and also Future Momentum changes. However, not all Trend Lines will create formations, and alone they may stand as strong Support and Resistance locations on the Vertical.
The purpose of this Indicator is to apply Machine Learning logic to a Traditional Trend Line Calculation, and therefore allowing a new approach to a modern indicator of high usage. The results of such are quite interesting and goes to show the impacts a simple KNN Machine Learning model can have on Traditional Indicators.
Tutorial:
There are a few different settings within this Indicator. Many will greatly impact the results and if any are changed, lots will need ‘Fine Tuning’. So let's discuss the main toggles that have great effects and what they do before discussing the lengths. Currently in this example above we have the Indicator at its Default Settings. In this example, you can see how the Trend Lines act as key Support and Resistance locations. Due note, Support and Resistance are a relative term, as is their color. What starts off as Support or Resistance may change when the price crosses over / under them.
In the example above we have zoomed in and circled locations that exhibited markers of Support and Resistance along the Trend Lines. These Trend Lines are all created using the Default Settings. As you can see from the example above; just because it is a Green Upwards Trend Line, doesn’t mean it’s a Support Line. Support and Resistance is always shifting on Trend Lines based on the prices location relative to them.
We won’t go through all the Formations Trend Lines make, but the example above, we can see the Trend Lines formed a Downward Channel. Channels are when there are two parallel downwards Trend Lines that are at a relatively similar angle. This means that they won’t ever meet. What may happen when the price is within these channels, is it may bounce between the upper and lower bounds. These Channels may drive the price upwards or downwards, depending on if it is in an Upwards or Downwards Channel.
If you refer to the example above, you’ll notice that the Trend Lines are formed like traditional Trend Lines. They don’t stem from current Highs and Lows but rather Machine Learning Highs and Lows. More often than not, the Machine Learning approach to Trend Lines cause their start point and angle to be quite different than a Traditional Trend Line. Due to this, it may help predict Support and Resistance locations at are more uncommon and therefore can be quite useful.
In the example above we have turned off the toggle in Settings ‘Use Exponential Data Average’. This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN. By Default it is enabled, but as you can see when it is disabled it may create some pretty strong lasting Trend Lines. This is why we advise you ZOOM OUT AS FAR AS YOU CAN. Trend Lines are only displayed when you’ve zoomed out far enough that their Start Point is visible.
As you can see in this example above, there were 3 major Upward Trend Lines created in 2020 that have had a major impact on Support and Resistance Locations within the last year. Lets zoom in and get a closer look.
We have zoomed in for this example above, and circled some of the major Support and Resistance locations that these Upward Trend Lines may have had a major impact on.
Please note, these Machine Learning Trend Lines aren’t a ‘One Size Fits All’ kind of thing. They are completely customizable within the Settings, so that you can get a tailored experience based on what Pair and Time Frame you are trading on.
When any values are changed within the Settings, you’ll likely need to ‘Fine Tune’ the rest of the settings until your desired result is met. By default the modifiable lengths within the Settings are:
Machine Learning Length: 50
KNN Length:5
Fast ML Data Length: 5
Slow ML Data Length: 30
For example, let's toggle ‘Use Exponential Data Averages’ back on and change ‘Fast ML Data Length’ from 5 to 20 and ‘Slow ML Data Length’ from 30 to 50.
As you can in the example above, all of the lines have changed. Although there are still some strong Support Locations created by the Upwards Trend Lines.
We will conclude our Tutorial here. Hopefully you’ve learned how to use Machine Learning Trend Lines and will be able to now see some more unorthodox Support and Resistance locations on the Vertical.
Settings:
Use Machine Learning Sources: If disabled Traditional Trend line sources (High and Low) will be used rather than Rational Quadratics.
Use KNN Distance Sorting: You can disable this if you wish to not have the Machine Learning Data sorted using KNN. If disabled trend line logic will be Traditional.
Use Exponential Data Average: This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN.
Machine Learning Length: How strong is our Machine Learning Memory? Please note, when this value is too high the data is almost 'too' much and can lead to poor results.
K-Nearest Neighbour (KNN) Length: How many K-Nearest Neighbours are allowed with our Distance Clustering? Please note, too high or too low may lead to poor results.
Fast ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 3/5/7 all seem to work well for Fast.
Slow ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 20 - 50 all seem to work well for Slow.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
RVI_HTFThe "RVI_HTF" indicator is a tool designed to assist traders in analyzing market trends using the Relative Vigor Index (RVI) across different timeframes. It enables users to customize various aspects of the indicator's appearance and behavior. By monitoring the RVI on different timeframes, tracking its relationship with the moving average, and paying attention to extreme arrows above the 80 or below the 20 line, traders can anticipate potential reversals, trends, or changes in market momentum.
Above 80 Line: When the RVI moves above the 80 line, it suggests that the market may be overbought. Extreme upward arrows (indicating potential sell signals) can be a sign that a bullish trend might be reaching an exhaustion point. Traders may anticipate a possible trend reversal or pullback.
Below 20 Line: When the RVI dips below the 20 line, it implies that the market might be oversold. Extreme downward arrows (indicating potential buy signals) can be an early signal of a potential bullish reversal. Traders may anticipate an upcoming uptrend or bounce.
Crossing Above Moving Average: When the RVI crosses above its moving average on the selected timeframe, it can serve as an early indication of potential bullish strength in the market. This suggests that buying pressure may be increasing.
Crossing Below Moving Average: Conversely, when the RVI crosses below its moving average, it can signal potential bearish momentum. This indicates that selling pressure may be gaining strength.
Variables:
Timeframe (TF) Selection:
The indicator allows you to select the timeframe for the RVI calculation. You can choose from various options such as 1 minute (1), 5 minutes (5), 15 minutes (15), 30 minutes (30), 60 minutes (60), 240 minutes (240), Daily (D), Weekly (W), Monthly (M), or use "Auto" to automatically select a higher timeframe based on your current chart's timeframe.
Moving Average Type (MA_Type):
Function: Allows users to select the type of moving average used in RVI calculations.
Options: You can select from various moving average types, including:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
SMMA (Smoothed Moving Average, also known as RMA)
WMA (Weighted Moving Average)
VWMA (Volume Weighted Moving Average)
DEMA (Double Exponential Moving Average)
Moving Average Length (MA_Length):
Function: Permits users to set the number of periods for the selected moving average type.
Purpose: Controls the sensitivity of the RVI indicator. Longer lengths provide smoother results, while shorter lengths react more quickly to price changes.
Up Arrow Color (upArrowColor):
Function: Enables users to customize the color of arrows that indicate potential Overbought areas. (Only shown when the TF is same as or lower than the chart TF)
Down Arrow Color (downArrowColor):
Function: Allows users to specify the color of downward-pointing arrows signaling potential Oversold areas. (Only shown when the TF is same as or lower than the chart TF)
RVI Up Color (firstColor):
Function: Defines the color of the RVI line when it indicates a bullish condition on the higher timeframe.
RVI Down Color (secondColor):
Function: Specifies the color of the RVI line when it suggests a bearish condition on the higher timeframe.
RVI-Based Moving Average Up Color (firstColorMA):
Function: Customizes the color of the RVI-based moving average line when it indicates a bullish condition.
RVI-Based Moving Average Down Color (secondColorMA):
Function: Defines the color of the RVI-based moving average line when it suggests a bearish condition.
Double AI Super Trend Trading - Strategy [PresentTrading]█ Introduction and How It is Different
The Double AI Super Trend Trading Strategy is a cutting-edge approach that leverages the power of not one, but two AI algorithms, in tandem with the SuperTrend technical indicator. The strategy aims to provide traders with enhanced precision in market entry and exit points. It is designed to adapt to market conditions dynamically, offering the flexibility to trade in both bullish and bearish markets.
*The KNN part is mainly referred from @Zeiierman.
BTCUSD 8hr performance
ETHUSD 8hr performance
█ Strategy, How It Works: Detailed Explanation
1. SuperTrend Calculation
The SuperTrend is a popular indicator that captures market trends through a combination of the Volume-Weighted Moving Average (VWMA) and the Average True Range (ATR). This strategy utilizes two sets of SuperTrend calculations with varying lengths and factors to capture both short-term and long-term market trends.
2. KNN Algorithm
The strategy employs k-Nearest Neighbors (KNN) algorithms, which are supervised machine learning models. Two sets of KNN algorithms are used, each focused on different lengths of historical data and number of neighbors. The KNN algorithms classify the current SuperTrend data point as bullish or bearish based on the weighted sum of the labels of the k closest historical data points.
3. Signal Generation
Based on the KNN classifications and the SuperTrend indicator, the strategy generates signals for the start of a new trend and the continuation of an existing trend.
4. Trading Logic
The strategy uses these signals to enter long or short positions. It also incorporates dynamic trailing stops for exit conditions.
Local picture
█ Trade Direction
The strategy allows traders to specify their trading direction: long, short, or both. This enables the strategy to be versatile and adapt to various market conditions.
█ Usage
ToolTips: Comprehensive tooltips are provided for each parameter to guide the user through the customization process.
Inputs: Traders can customize numerous parameters including the number of neighbors in KNN, ATR multiplier, and types of moving averages.
Plotting: The strategy also provides visual cues on the chart to indicate bullish or bearish trends.
Order Execution: Based on the generated signals, the strategy will execute buy or sell orders automatically.
█ Default Settings
The default settings are configured to offer a balanced approach suitable for most scenarios:
Initial Capital: $10,000
Default Quantity Type: 10% of equity
Commission: 0.1%
Slippage: 1
Currency: USD
These settings can be modified to suit various trading styles and asset classes.
Recession Indicator (Unemployment Rate)Unemployment rate
percentage of unemployed individuals in an economy among individuals currently in the labour force. It is calcuated as Unemployed IndividualsTotal Labour Force × 100 where unemployed individuals are those who are currently not working but are actively seeking work.
The unemployment rate is one of the primary economic indicators used to measure the health of an economy. It tends to fluctuate with the business cycle, increasing during recessions and decreasing during expansions. It is among the indicators most commonly watched by policy makers, investors, and the general public.
Policy makers and central banks consider how much the unemployment rate has increased during a particular recession to gauge the recession’s impact on the economy and to decide how to tailor fiscal and monetary policies to mitigate its adverse effects. In addition, central banks carefully try to predict the future trend of the unemployment rate to devise long-term strategies to lower it.
This indicator is a representation of yearly rate of change of Unemployment rate. Historically (not always) when ROC(Yearly) of Unemployment rate crossover zero line was a signal of recession or economic contraction.
IU SIP CALCULATORHow This Indicator Script Works:
1. This indicator script calculate the monthly SIP returns of any market over any user defined period.
2. SIP stands for Systematic Investment Plan. It is a way to invest in any asset by regularly investing a fixed amount of money at regular intervals for example Monthly, Weekly, Quarterly etc.
3. This indicator Calculate the following
# Average buy price
# Total quantity hold
# Yearly returns
# Monthly returns
# Total invested amount
# Total profits in amount
# Total portfolio value
# Total returns in per percentage term.
4. This script takes monthly SIP amount, starting month, starting year, ending year, ending month from the user and store the value for calculations.
5. After that it store the open price of every month into an array then it average the array and compare that price with the last month close price.
6. on the bases of this it performs all of the calculations.
7. The script plot every calculation into an table from.
8. It requires monthly chart timeframe for working.
9. The table is editable user can change the color and transparency.
How User Can Benefit From The Script:
1. User can get the past monthly SIP returns of any market he wants to invest this will give him an overview about what to expect from the market.
2. Once user understand the expected returns from the market he can adjust his investment strategy.
3. This help the user to Analyse various stocks and their past performance.
4. User can also short list the best performed stocks.
5. Over all this script will give complete SIP vision of any market.
