K's Reversal Indicator IIIK's Reversal Indicator III is based on the concept of autocorrelation of returns. The main theory is that extreme autocorrelation (trending) that coincide with a technical signals such as one from the RSI, may result in a powerful short-term signal that can be exploited.
The indicator is calculated as follows:
1. Calculate the price differential (returns) as the current price minus the previous price.
2. the correlation between the current return and the return from 14 periods ago using a lookback of 14 periods.
3. Calculate a 14-period RSI on the close prices.
To generate the signals, use the following rules:
* A bullish signal is generated whenever the correlation is above 0.60 while the RSI is below 40.
* A bearish signal is generated whenever the correlation is above 0.60 while the RSI is above 60.
Forecasting
YinYang Bar ForecastOverview:
YinYang Bar Forecast is a prediction indicator. It predicts the movement for High, Low, Open and Close for up to 13 bars into the future. We created this Indicator as we felt the TradingView community could benefit from a bar forecast as there wasn’t any currently available.
Our YinYang Bar Forecast is something we plan on continuously working on to better improve it, but at its current state it is still very useful and decently accurate. It features many calculations to derive what it thinks the future bars will hold. Let’s discuss some of the logic behind it:
Each bar has its High, Low, Open and Close calculated individually for highest accuracy. Within these calculations we first check which bar it is we are calculating and base our span back length that we are getting our data from based on the bar index we are generating. This helps us get a Moving Average for this bar index.
We take this MA and we apply our Custom Volume Filter calculation on it, which is essentially us dividing the current bars volume over the average volume in the last ‘Filtered Length’ (Setting) length. We take this decimal and multiply it on our MA and smooth it out with a VWMA.
We take the new Volume Filtered MA and apply a RSI Filter calculation on it. RSI Filter is where we take the difference between the high and low of this bar and we multiply it with an RSI calculation using our Volume Filtered MA. We take the result of that multiplication and either add or subtract it from the Volume Filtered MA based on if close > open. This makes our RSI Filtered MA.
Next, we do an EMA Strength Calculation which is where we check if close > ema(close, ‘EMA Averaged Length’) (Setting). Based on this condition we assign a multiplier that is applied to our RSI Filtered MA. We divide by how many bars we are predicting and add a bit to each predictive bar so that the further we go into the future the stronger the strength is.
Next we check RSI and RSI MA levels and apply multiplications based on its RSI levels and if it is greater than or less than the MA. Also it is affected by if the RSI is <= 30 and >= 70.
Finally we check the MFI and MFI MA levels and like RSI we apply multiplications based on its MFI levels and if it is greater than or less than the MA. It is also affected by if the MFI is <= 30 and >= 70.
Please note the way we calculate this may change in the future, this is just currently what we deemed works best for forecasting the future bars. Also note this script uses MA calculations out of scope for efficiency but there is potential for inconsistencies.
Innately it’s main use is the projection it provides. It only draws the bars for realtime bars and not historical ones, so the best way to backtest it is with TradingView’s Replay Tool.
Well, enough of the logic behind it, let's get to understanding how to use it:
Tutorial:
So unfortunately we aren’t able to plot legit bars/candles into the future so we’ve had to do a bit of a work around using lines and fills. As you can see here we have 4 Lines and 3 Zones:
Lines:
Green: Represents the High
Orange: Represents the Open
Teal: Represents the Close
Red: Represents the Low
Zones:
High Zone: This zone is from either Open or Close to the High and is ALWAYS filled with Green.
Open/Close Zone: This zone is from the Open to the Close and is filled with either Green or Red based on if it's greater than the previous bar (real or forecasted).
Low Zone: This zone is from either Open or Close to the Low and is ALWAYS filled with Red.
As you can see generally the Forecasted bars are generally within strong pivot locations and are a good estimation of what will likely go on. Please note, the WHOLE structure of the prediction can change based on the current bars movements and the way it affects the calculations.
Let's look 1 bar back from the current bar just so we can see what it used to Forecast:
As you can see it has changed quite a bit from the previous bar, but if you look close, we drew horizontal lines around where its projecting the next bar to be (our current realtime bar), if we go back to the live chart:
Its projections were pretty close for the high and low. Generally, right now at least, it does a much better job at predicting the high and low than it does the open and close, however we will do our best to fine tune that in future updates.
Remember, this indicator is not meant to base your trades on, but rather give you a Forecast towards the general direction of the next few bars. Somewhat like weather, the farther the bar (or day for weather), the harder it is to predict. For this reason we recommend you focusing on the first few bars as they are more accurate, but review the further ones as they may help show the trend and the way that pair will move.
We will conclude this tutorial here, hopefully this Predictive Indicator can be of some help and use to you. If you have any questions, comments, ideas or concerns please let us know.
Settings:
Forecast Length: How many bars should we predict into the Future? Max 13
Each Bar Length Multiplier: For each new Forecast bar, how many more bars are averaged? Min 2
VWMA Averaged Length: All Forecast bars are put into a VWMA, what length should we use?
EMA Averaged Length: All Forecast bars are put into a EMA, what length should we use?
Filtered Length: What length should we use for Filtered Volume and RSI?
EMA Strength Length: What length should we use for the EMA Strength
HAPPY TRADING!
Buy/Sell BoxThis indicator tries to identify the points where the price exceeds or falls below a rectangle based on the opening and closing prices of the previous period, the creation of the boxes occurs when a doji is detected therefore it will calculate the coordinates of the rectangle that will be drawn around it, therefore the indicator offers buy or sell signals based on this logic. Specifically, the buy signal is generated if the closing price is above the top of the rectangle and satisfies some previous price conditions while the sell signal is generated if the closing price is below the bottom of the rectangle and satisfies some conditions of previous prices within a further threshold based on the Ema 150.
Lines are then drawn on the graph to visually display the extreme price levels, which can be useful for any confirmation of buy and sell signals, Stop Loss and Take Profit, Trend Filter (to visually understand if the trend is bullish or bearish)
A potentially effective trading strategy could involve identifying buy and sell signals near the extreme price level lines drawn by the indicator. This approach can be used to try to improve the accuracy of your trading signals and make more informed decisions. For example:
When you receive a buy or sell signal based on the dojis and rectangles generated by the indicator, check whether the price is also near one of the extreme price level lines. If you are receiving a buy signal and notice that the current price is near a low of the lower level line, this may further confirm the buying opportunity, as the price is near a significant resistance level. On the contrary, if the sell signal was close to a maximum price level it could confirm an excellent short entry.
It is also possible to use the boxes as reference points to set the stop loss and take profit levels. If you are entering a buy position, you might consider setting your stop loss just below an upper line of the last box. Additionally, you may want to set your take profit near a higher price level if you are looking to maximize profits. This will help manage risks and protect your capital.
Ruth Buy/Sell Signal for Day Trade and Swing TradeRuth is based on the most known technical indicators and designed for intraday traders. Ruth's aim is to find the best Buy/Sell points and decide to stop loss point with minimum Loss also Ruth tries to find multiple Profit points as TP1/TP2/TP3/TP4/TP5. Ruth was designed based on the heat map colors to be user-friendly and easy to read. While cold color preferred for Short positions, warm colors preferred for Long positions. The most important feature of Ruth is that after the signal is generated, the candles in which the profitable levels are painted one by one with their own special color codes, so that even the most inexperienced users can understand where they should close their positions.
There are two types of signal Ruth can produce for fast trade.
Short Signal: These signals means market tends to be move to down.
Short Stop Loss Point: This is the maximum risk for the position. Shown with single red line inside of the signal.
Short Entry Point: This is the best entry price for short side position. Shown with single baby blue line inside of the signal.
Short Take Profit (TP1): This level represents the profit level the signal is most likely to reach. Shown with single blue line inside of the signal.
Short Take Profit (TP2): This level represents the profit level with a high probability of the signal occurring. Shown with single light purple line inside of the signal.
Short Take Profit (TP3): This level represents the profit level with an intermediate probability of the signal occurring. Shown with single dark purple line inside of the signal.
Short Take Profit (TP4): This level represents the profit level with a low probability of the signal occurring. Shown with single light lilac line inside of the signal.
Short Take Profit (TP5): This level represents the profit level with a tight probability of the signal occurring. Shown with single dark lilac line inside of the signal.
Long Signal: These signals means market tends to be move to up.
Long Stop Loss Point: This is the maximum risk for the position. Shown with single red line inside of the signal.
Long Entry Point: This is the best entry price for short side position. Shown with single baby blue line inside of the signal.
Long Take Profit (TP1): This level represents the profit level the signal is most likely to reach. Shown with single greenish yellow line inside of the signal.
Long Take Profit (TP2): This level represents the profit level with a high probability of the signal occurring. Shown with yellow purple line inside of the signal.
Long Take Profit (TP3): This level represents the profit level with an intermediate probability of the signal occurring. Shown with single dark yellow line inside of the signal.
Long Take Profit (TP4): This level represents the profit level with a low probability of the signal occurring. Shown with single orange line inside of the signal.
Long Take Profit (TP5): This level represents the profit level with a tight probability of the signal occurring. Shown with single dark orange line inside of the signal.
Timeframe: In general best and fastest results occurred in shorter timeframes like 1 min / 5 mins / 15 mins but feel free to try higher timeframes.
Tips & Tricks:
1) Gray line drawn ot the graph represents Dema, we suggests you to go on Short Singals under gray line and go on Long Signals upper gray line.
2) Mostly, Signals easily reach their TP2 / TP3 levels and then generally there is reaction or take profit desire so commodity price turns the opposite direction. If in short time price won't turn to Signal direction close position.
3) Don't forget, every positions has own risks and profits but trade in main trend is crucial.
GP - Long Short ScannerThis script is made to predict the point at which price-time charts will rise or fall. The script was inspired by the RSI and TSI formulas. The formula is simply; Calculates the RSI and TSI values of open, high, low and close. Calculated values are converted to an array. The maximum and minimum values in the array are taken for the candles included in the calculation. These values calculate the time when the "Long" label will be seen on the chart of the candle that will increase the price. At the same time, it calculates the time when the "Short" label will appear on the chart of the candle that will decrease the price. Although these calculations are not precise; Seeing the “Long” label means that the price will rise at that candle, and seeing the “Short” label means that the price will decrease at that candle. The “Long”, “Short” tags from this script alone should not be used to determine the direction of the price. It can be used on all price-time charts.
Bursa Malaysia Index SeriesBursa Malaysia Index Series. The index computation is as follows:-
Current aggregate Market Capitalisation/Base Aggregate Market Capitalisation x 100.
The Bursa Malaysia Index Series is calculated and disseminated on a real-time basis at 60-second intervals during Bursa’s trading hours.
Buy/Sell EMA CrossoverThe indicator identifies potential trading opportunities within the market. It is entirely based on the combination of exponential moving averages by drawing triangles on the chart that identify buy or sell signals combined with vertical bars that create areas of interest.
Specifically, when a buy signal occurs, the indicator draws a vertical bar with an azure background, indicating a possible buy area. Similarly, a sell signal is represented by a vertical bar with a fuchsia background, indicating a possible sell area.
These areas represent the main point of the indicator which uses exponential moving averages which, based on the direction of prices, identify the trend and color the background of the graph in order to visually highlight the predominant trend.
The green triangles above the bars of the chart suggest possible upside opportunities (good bullish entry points) when the 21 ema crosses the 200 ema.
While on the contrary the red triangles, 21 ema lower than the 200 ema, can indicate possible bearish trends (good bearish entry points).
While the white and purple triangles reveal moments of potential indecision or market change.
We can think of them as situations of uncertain trend in which it is possible to place a long or short order near some conditions that we are going to see.
The white triangles below, which are created when the 13 ema is higher than the 21 ema, indicate a possible bullish zone while the purple triangles above (13 ema lower than the 21) could suggest a bearish reflex
Colored lines represent moving averages blue = 200, 21= fuchsia and 13 = white. If the price is above the 200 period line then it could be a bullish opportunity, otherwise it could be a bearish one.
An interesting strategy to adopt is to evaluate, for example, the inputs near the vertical bars (azure - long) (fuchsia - short) when a white or purple triangle appears.
The more prominent green triangle indicates that the trend is going in a long direction.
On the contrary, the red (short) triangles are the opposite of the green ones and have the same importance as input logic.
The white triangle instead present more often inside the indicator identifies interesting buying areas of short duration, it is important to consider that the closer the triangles are to the vertical blue bars the stronger the entry signal.
Finally, the purple triangles are the short-term bearish trends whose entry near the fuchsia vertical bars defines a short.
HL 930 by JPThe "High and Low of 9:30 Candle" strategy is a simple trading strategy commonly used in the stock market and other financial markets. It involves using the price range (high and low) of the first candlestick that forms at the opening of a trading session, typically at 9:30 AM, as a basis for making trading decisions. Here's a description of this strategy:
1. Timeframe: This strategy is often applied to intraday trading, where traders focus on short-term price movements within a single trading day.
2. 9:30 AM Candle: The strategy begins by observing the first candlestick that forms at 9:30 AM, which is the opening time for many stock markets, including the New York Stock Exchange (NYSE). This candle represents the price action during the first few minutes of trading.
3. High and Low: Identify the highest price (the candle's high) and the lowest price (the candle's low) during the 9:30 AM candle's time period. These price levels are critical for the strategy.
4. Trading Decisions:
Long (Buy) Signal: If the current market price breaks above the high of the 9:30 AM candle, it may trigger a bullish signal. Traders may consider entering a long (buy) position, anticipating further upward momentum.
Short (Sell) Signal: Conversely, if the market price breaks below the low of the 9:30 AM candle, it may trigger a bearish signal. Traders may consider entering a short (sell) position, anticipating further downward movement.
5. Stop-Loss and Take-Profit: To manage risk, traders often set stop-loss orders just below the low (for long positions) or just above the high (for short positions) of the 9:30 AM candle. They may also establish take-profit levels based on their risk-reward preferences.
6. Time Frame: This strategy is typically used for short-term trading and may be effective in capturing quick price movements that often occur at the market open. Traders often close their positions before the end of the trading day.
7. Caution: While the "High and Low of 9:30 Candle" strategy can be straightforward, it should not be used in isolation. Traders should consider other technical and fundamental factors, such as volume, market sentiment, news events, and overall market trends, when making trading decisions.
Remember that trading strategies always carry risks, and it's essential to have a well-thought-out risk management plan in place. Additionally, backtesting and practice are crucial before implementing any trading strategy in a live market to evaluate its historical performance and suitability for your trading style.
Cross Correlation [Kioseff Trading]Hello!
This script "Cross Correlation" calculates up to ~10,000 lag-symbol pair cross correlation values simultaneously!
Cross correlation calculation for 20 symbols simultaneously
+/- Lag Range is theoretically infinite (configurable min/max)
Practically, calculate up to 10000 lag-symbol pairs
Results can be sorted by greatest absolute difference or greatest sum
Ability to "isolate" the symbol on your chart and check for cross correlation against a list of symbols
Script defaults to stock pairs when on a stock, Forex pairs when on a Forex pair, crypto when on a crypto coin, futures when on a futures contract.
A custom symbol list can be used for cross correlation checking
Can check any number of available historical data points for cross correlation
Practical Assessment
Ideally, we can calculate cross correlation to determine if, in a list of assets, any of the assets frequently lead or lag one another.
Example
Say we are comparing the log returns for the previous 10 days for SPY and XLU.
*A single time-interval corresponds to the timeframe of your chart i.e. 1-minute chart = 1-minute time interval. We're using days for this example.
(Example Results)
A lag value (k) +/-3 is used.
The cross correlation (normalized) for k = +3 is -0.787
The cross correlation (normalized) for k = -3 is 0.216
A positive "k" value indicates the correlation when Asset A (SPY) leads Asset B (XLU)
A negative "k" value indicates the correlation when Asset B (XLU) leads Asset A (SPY)
A normalized cross correlation of -0.787 for k = +3 indicates an "adequately strong" negative relationship when SPY leads XLU by 3 days.
When SPY increases or decreases - XLU frequently moves in the opposite direction 3 days later.
A cross correlation value of 0.216 at k = −3 indicates a "weak" positive correlation when XLU leads SPY by 3 days.
There's a slight tendency for SPY to move in the same direction as XLU 3 days later.
After the cross-correlation score is normalized it will fall between -1 and 1.
A cross-correlation score of 1 indicates a perfect directional relationship between asset A and asset B at the corresponding lag (k).
A cross correlation of -1 indicates a perfect inverse relationship between asset A and asset B at the corresponding lag (k).
A cross correlation of 0 indicates no correlation at the corresponding lag (k).
The image above shows the primary usage for the script!
The image above further explains the data points located in the table!
The image above shows the script "isolating" the symbol on my chart and checking the cross correlation between the symbol and a list of symbols!
Wrapping Up
With this information, hopefully you can find some meaningful lead-lag relationships amongst assets!
Thank you for checking this out (:
Bitcoin Market Cap wave model weeklyThis Bitcoin Market Cap wave model indicator is rooted in the foundation of my previously developed tool, the : Bitcoin wave model
To derive the Total Market Cap from the Bitcoin wave price model, I employed a straightforward estimation for the Total Market Supply (TMS). This estimation relies on the formula:
TMS <= (1 - 2^(-h)) for any h.This equation holds true for any value of h, which will be elaborated upon shortly. It is important to note that this inequality becomes the equality at the dates of halvings, diverging only slightly during other periods.
Bitcoin wave model is based on the logarithmic regression model and the sinusoidal waves, induced by the halving events.
This chart presents the outcome of an in-depth analysis of the complete set of Bitcoin price data available from October 2009 to August 2023.
The central concept is that the logarithm of the Bitcoin price closely adheres to the logarithmic regression model. If we plot the logarithm of the price against the logarithm of time, it forms a nearly straight line.
The parameters of this model are provided in the script as follows: log(BTCUSD) = 1.48 + 5.44log(h).
The secondary concept involves employing the inherent time unit of Bitcoin instead of days:
'h' denotes a slightly adjusted time measurement intrinsic to the Bitcoin blockchain. It can be approximated as (days since the genesis block) * 0.0007. Precisely, 'h' is defined as follows: h = 0 at the genesis block, h = 1 at the first halving block, and so forth. In general, h = block height / 210,000.
Adjustments are made to account for variations in block creation time.
The third concept revolves around investigating halving waves triggered by supply shock events resulting from the halvings. These halvings occur at regular intervals in Bitcoin's native time 'h'. All halvings transpire when 'h' is an integer. These events induce waves with intervals denoted as h = 1.
Consequently, we can model these waves using a sin(2pih - a) function. The parameter determining the time shift is assessed as 'a = 0.4', aligning with earlier expectations for halving events and their subsequent outcomes.
The fourth concept introduces the notion that the waves gradually diminish in amplitude over the progression of "time h," diminishing at a rate of 0.7^h.
Lastly, we can create bands around the modeled sinusoidal waves. The upper band is derived by multiplying the sine wave by a factor of 3.1*(1-0.16)^h, while the lower band is obtained by dividing the sine wave by the same factor, 3.1*(1-0.16)^h.
The current bandwidth is 2.5x. That means that the upper band is 2.5 times the lower band. These bands are forming an exceptionally narrow predictive channel for Bitcoin. Consequently, a highly accurate estimation of the peak of the next cycle can be derived.
The prediction indicates that the zenith past the fourth halving, expected around the summer of 2025, could result in Total Bitcoin Market Cap ranging between 4B and 5B USD.
The projections to the future works well only for weekly timeframe.
Enjoy the mathematical insights!
Bitcoin wave modelBitcoin wave model is based on the logarithmic regression model and the sinusoidal waves, induced by the halving events.
This chart presents the outcome of an in-depth analysis of the complete set of Bitcoin price data available from October 2009 to August 2023.
The central concept is that the logarithm of the Bitcoin price closely adheres to the logarithmic regression model. If we plot the logarithm of the price against the logarithm of time, it forms a nearly straight line.
The parameters of this model are provided in the script as follows: log (BTCUSD) = 1.48 + 5.44log(h).
The secondary concept involves employing the inherent time unit of Bitcoin instead of days:
'h' denotes a slightly adjusted time measurement intrinsic to the Bitcoin blockchain. It can be approximated as (days since the genesis block) * 0.0007. Precisely, 'h' is defined as follows: h = 0 at the genesis block, h = 1 at the first halving block, and so forth. In general, h = block height / 210,000.
Adjustments are made to account for variations in block creation time.
The third concept revolves around investigating halving waves triggered by supply shock events resulting from the halvings. These halvings occur at regular intervals in Bitcoin's native time 'h'. All halvings transpire when 'h' is an integer. These events induce waves with intervals denoted as h = 1.
Consequently, we can model these waves using a sin(2pih - a) function. The parameter determining the time shift is assessed as 'a = 0.4', aligning with earlier expectations for halving events and their subsequent outcomes.
The fourth concept introduces the notion that the waves gradually diminish in amplitude over the progression of "time h," diminishing at a rate of 0.7^h.
Lastly, we can create bands around the modeled sinusoidal waves. The upper band is derived by multiplying the sine wave by a factor of 3.1*(1-0.16)^h, while the lower band is obtained by dividing the sine wave by the same factor, 3.1*(1-0.16)^h.
The current bandwidth is 2.5x. That means that the upper band is 2.5 times the lower band. These bands are forming an exceptionally narrow predictive channel for Bitcoin. Consequently, a highly accurate estimation of the peak of the next cycle can be derived.
The prediction indicates that the zenith past the fourth halving, expected around the summer of 2025, could result in prices ranging between 200,000 and 240,000 USD.
Enjoy the mathematical insights!
Pro Bollinger Bands CalculatorThe "Pro Bollinger Bands Calculator" indicator joins our suite of custom trading tools, which includes the "Pro Supertrend Calculator", the "Pro RSI Calculator" and the "Pro Momentum Calculator."
Expanding on this series, the "Pro Bollinger Bands Calculator" is tailored to offer traders deeper insights into market dynamics by harnessing the power of the Bollinger Bands indicator.
Its core mission remains unchanged: to scrutinize historical price data and provide informed predictions about future price movements, with a specific focus on detecting potential bullish (green) or bearish (red) candlestick patterns.
1. Bollinger Bands Calculation:
The indicator kicks off by computing the Bollinger Bands, a well-known volatility indicator. It calculates two pivotal Bollinger Bands parameters:
- Bollinger Bands Length: This parameter sets the lookback period for Bollinger Bands calculations.
- Bollinger Bands Deviation: It determines the deviation multiplier for the upper and lower bands, typically set at 2.0.
2. Visualizing Bollinger Bands:
The Bollinger Bands derived from the calculations are skillfully plotted on the price chart:
- Red Line: Represents the upper Bollinger Band during bearish trends, suggesting potential price declines.
- Teal Line: Represents the lower Bollinger Band in bullish market conditions, signaling the possibility of price increases.
3.Analyzing Consecutive Candlesticks:
The indicator's core functionality revolves around tracking consecutive candlestick patterns based on their relationship with the Bollinger Bands lines. To be considered for analysis, a candlestick must consistently close either above (green candles) or below (red candles) the Bollinger Bands lines for multiple consecutive periods.
4. Labeling and Enumeration:
To convey the count of consecutive candles displaying consistent trend behavior, the indicator meticulously assigns labels to the price chart. The position of these labels varies depending on the direction of the trend, appearing either below (for bullish patterns) or above (for bearish patterns) the candlesticks. The label colors match the candle colors: green labels for bullish candles and red labels for bearish ones.
5. Tabular Data Presentation:
The indicator complements its graphical analysis with a customizable table that prominently displays comprehensive statistical insights. Key data points within the table encompass:
- Consecutive Candles: The count of consecutive candles displaying consistent trend characteristics.
- Candles Above Upper BB: The number of candles closing above the upper Bollinger Band during the consecutive period.
- Candles Below Lower BB: The number of candles closing below the lower Bollinger Band during the consecutive period.
- Upcoming Green Candle: An estimated probability of the next candlestick being bullish, derived from historical data.
- Upcoming Red Candle: An estimated probability of the next candlestick being bearish, also based on historical data.
6. Custom Configuration:
To cater to diverse trading strategies and preferences, the indicator offers extensive customization options. Traders can fine-tune parameters such as Bollinger Bands length, upper and lower band deviations, label and table placement, and table size to align with their unique trading approaches.
Pro RSI CalculatorThe "Pro RSI Calculator" indicator is the latest addition to a series of custom trading tools that includes the "Pro Supertrend Calculator" and the "Pro Momentum Calculator."
Building upon this series, the "Pro RSI Calculator" is designed to provide traders with further insights into market trends by leveraging the Relative Strength Index (RSI) indicator.
Its primary objective remains consistent: to analyze historical price data and make informed predictions about future price movements, with a specific focus on identifying potential bullish (green) or bearish (red) candlestick patterns.
1. RSI Calculation:
The indicator begins by computing the RSI, a widely used momentum oscillator. It calculates two crucial RSI parameters:
RSI Length: This parameter determines the lookback period for RSI calculations.
RSI Upper and Lower Bands: These thresholds define overbought and oversold conditions, typically set at 70 and 30, respectively.
2. RSI Bands Visualization:
The RSI values obtained from the calculation are skillfully plotted on the price chart, appearing as two distinct lines:
Red Line: Represents the RSI when indicating a bearish trend, anticipating potential price declines.
Teal Line: Represents the RSI in bullish market conditions, signaling the possibility of price increases.
3. Consecutive Candlestick Analysis:
The indicator's core functionality revolves around tracking consecutive candlestick patterns based on their relationship with the RSI lines.
To be included in the analysis, a candlestick must consistently close either above (green candles) or below (red candles) the RSI lines for multiple consecutive periods.
4. Labeling and Enumeration:
To communicate the count of consecutive candles displaying consistent trend behavior, the indicator meticulously assigns labels to the price chart.
Label positioning varies depending on the trend's direction, appearing either below (for bullish patterns) or above (for bearish patterns) the candlesticks.
The color scheme aligns with the candle colors: green labels for bullish candles and red labels for bearish ones.
5. Tabular Data Presentation:
The indicator enhances its graphical analysis with a customizable table that prominently displays comprehensive statistical insights.
Key data points in the table include:
- Consecutive Candles: The count of consecutive candles displaying consistent trend characteristics.
- Candles Above Upper RSI: The number of candles closing above the upper RSI threshold during the consecutive period.
- Candles Below Lower RSI: The number of candles closing below the lower RSI threshold during the consecutive period.
- Upcoming Green Candle: An estimated probability of the next candlestick being bullish, derived from historical data.
- Upcoming Red Candle: An estimated probability of the next candlestick being bearish, also based on historical data.
6. Custom Configuration:
To cater to various trading strategies and preferences, the indicator offers extensive customization options.
Traders can fine-tune parameters like RSI length, upper, and lower bands, label and table placement, and table size to align with their unique trading approaches.
The Next Pivot [Kioseff Trading]Hello!
This script "The Next Pivot" uses various similarity measures to compare historical price sequences to the current price sequence!
Features
Find the most similar price sequence up to 100 bars from the current bar
Forecast price path up to 250 bars
Forecast ZigZag up to 250 bars
Spearmen
Pearson
Absolute Difference
Cosine Similarity
Mean Squared Error
Kendall
Forecasted linear regression channel
The image above shows/explains some of the indicator's capabilities!
The image above highlights the projected zig zag (pivots) pattern!
Colors are customizable (:
Additionally, you can plot a forecasted LinReg channel.
Should load times permit it, the script can search all bar history for a correlating sequence. This won't always be possible, contingent on the forecast length, correlation length, and the number of bars on the chart.
Reasonable Assessment
The script uses various similarity measures to find the "most similar" price sequence to what's currently happening. Once found, the subsequent price move (to the most similar sequence) is recorded and projected forward.
So,
1: Script finds most similar price sequence
2: Script takes what happened after and projects forward
While this may be useful, the projection is simply the reaction to a possible one-off "similarity" to what's currently happening. Random fluctuations are likely and, if occurring, similarities between the current price sequence and the "most similar" sequence are plausibly coincidental.
That said, if you have any ideas on cool features to add please let me know!
Thank you (:
Machine Learning: Trend Pulse⚠️❗ Important Limitations: Due to the way this script is designed, it operates specifically under certain conditions:
Stocks & Forex : Only compatible with timeframes of 8 hours and above ⏰
Crypto : Only works with timeframes starting from 4 hours and higher ⏰
❗Please note that the script will not work on lower timeframes.❗
Feature Extraction : It begins by identifying a window of past price changes. Think of this as capturing the "mood" of the market over a certain period.
Distance Calculation : For each historical data point, it computes a distance to the current window. This distance measures how similar past and present market conditions are. The smaller the distance, the more similar they are.
Neighbor Selection : From these, it selects 'k' closest neighbors. The variable 'k' is a user-defined parameter indicating how many of the closest historical points to consider.
Price Estimation : It then takes the average price of these 'k' neighbors to generate a forecast for the next stock price.
Z-Score Scaling: Lastly, this forecast is normalized using the Z-score to make it more robust and comparable over time.
Inputs:
histCap (Historical Cap) : histCap limits the number of past bars the script will consider. Think of it as setting the "memory" of model—how far back in time it should look.
sampleSpeed (Sampling Rate) : sampleSpeed is like a time-saving shortcut, allowing the script to skip bars and only sample data points at certain intervals. This makes the process faster but could potentially miss some nuances in the data.
winSpan (Window Size) : This is the size of the "snapshot" of market data the script will look at each time. The window size sets how many bars the algorithm will include when it's measuring how "similar" the current market conditions are to past conditions.
All these variables help to simplify and streamline the k-NN model, making it workable within limitations. You could see them as tuning knobs, letting you balance between computational efficiency and predictive accuracy.
Price Variation and Projection IndicatorThis indicator calculates and visualizes various aspects of price variation and projection based on certain parameters such as rate of change, time interval, constant value, and more. It helps traders understand potential price movements and provides insights into potential support and resistance levels.
The indicator displays the following information:
Resistance and support levels based on the highest and lowest prices over a specified period.
∆P (Price Variation) calculated between two high oscillations.
∆t (Time Variation) calculated between two high oscillations.
Price variation rate.
Price projections based on rate of change and the most occurred variation.
Additionally, parallel lines are drawn to illustrate projected price ranges, and the most frequent ∆P value is shown for reference.
in short the indicator does it projects possible support and resistance for you to add a mark for example you see that it gave a projection you mark it on the chart with horizontal line or horizontal ray you can configure it by Period or by ∆t calculation limit au increase the period it will increase the projection of all targets interesting periods to use 20 50 80 120 200 since the ∆t calculation limit au decrease increases the projection in the Price projection that is showing the information in blue color when increasing it decreases the projection target ∆t calculation interesting limit to use 3 4 6 7 8 9
it works for all timeframes can be used for Swing trade or day trade
use I like to use it with a closed market that helps me to trace possible support and resistance can be used with open market as well
Choose your preferred language to display the information
Please note that this indicator is designed for educational and informational purposes. Always conduct your own analysis and consider risk management strategies before making trading decisions.
Crypto/DXY ScoringHi!
This indicator "Crypto/DXY Scoring", a multi-purpose script, consists of various comparison statistics (including an alternative RS/RSMOM model) to show the strength of a currency against the DXY.
Features
"Contrived" RS/RSMOM alternative model
Compare the strength of the crypto currency on your chart to any asset (DXY default)
Glass's ∆
Z-comparison
Hedges' g
Cliff's Delta
Z-score for log returns
RRG graph (with adjusted dimensions) Traditional RRG graph coming soon (:
Let's go over some simplified interpretations of what's shown on the chart!
The image above provides generalized interpretations for the three of the data series plotted by the indicator.
The image above further explains the other plots for the indicator!
The image above shows the final result!
Underlying Theory
"When the dollar is strong as indicated by the DXY, it usually means that investors are seeking safety in traditional assets. Bitcoin (crypto) is often considered a "risk-on" asset, meaning investors might sell BTC in favor of holding dollars, thus driving BTC prices down."
Given the complexities associated with this relationship, including its contentious implications and a variable correlation between crypto and the DXY, this theory is one within a plethora.
That said, regardless of accuracy, this indicator adheres to the theory outlined above (:
The image above shows the purpose of the red/lime columns and the corresponding red/green lines.
Should the crypto on your chart and the DXY (or comparison symbol) exhibit negative correlation, and should the performance of DXY (or comparison symbol) hold any predictive utility for the subsequent performance of the crypto on your chart, the red columns violating the red line might indicate an upcoming "dump" for the crypto on your chart.
Lime green columns violating the green line may indicator an inverse response.
Alternative Relative Rotation Graph
In its current state, the alternated dimensions for the Relative Rotation Graph cause it to function more as a "Relative Performance Graph".
Fear not; a traditional RRG graph is coming soon!
The image above shows our alternative RRG!
Interpretation
With this model, you can quickly/intuitively assess the relative performance of the display cryptos against an index of their performance.
The image above shows generalized interpretations of the model!
That's it for this indicator! Thank you for checking it out; more to come (:
Intraday Volatility BarsThis script produce a volatility histrogram by bar with the current volatility overlayed.
The histogram shows cumulative average volatility over n days.
And the dots are todays cumulative volatility.
In other words, it calculates the True Range of each bar and adds it to todays value.
This script is build for intraday timeframes between one and 1440 minutes only.
I use this to show me when volatility is above/below/equal to the average volatility.
When the dots are above the histogram then it is a more volatile day, and vice versa.
Recognizing a more volatile day as early as possible can be an advantage for daytrader.
Days that start with higher volatility seems to continue to increase relative to the past few days. Or when midday volatility rises it seems to continue as well.
Happy Trading!
Volatility Price RangeThe Volatility Price Range is an overlay which estimates a price range for the next seven days and next day, based on historical volatility (already available in TradingView). The upper and lower bands are calculated as follows:
The Volatility for one week is calculated using the formula: WV = HV * √t where:
WV: one-week volatility
HV: annual volatility
√: square root
t: the time factor expressed in years
From this formula we can deduce the weekly volatility WV = HV * √(1 / 52) = HV / 7.2 where 52: weeks in a year.
The daily volatility DV = HV * √(1 / 365) = HV / 19.1 where 365: days in a year.
To calculate the lower and upper value of the bands, the weekly/daily volatility value obtained will be subtracted/added from/to the current price.
Liquidity Heatmap [BigBeluga]The Liquidity Heatmap is an indicator designed to spot possible resting liquidity or potential stop loss using volume or Open interest.
The Open interest is the total number of outstanding derivative contracts for an asset—such as options or futures—that have not been settled. Open interest keeps track of every open position in a particular contract rather than tracking the total volume traded.
The Volume is the total quantity of shares or contracts traded for the current timeframe.
🔶 HOW IT WORKS
Based on the user choice between Volume or OI, the idea is the same for both.
On each candle, we add the data (volume or OI) below or above (long or short) that should be the hypothetical liquidation levels; More color of the liquidity level = more reaction when the price goes through it.
Gradient color is calculated between an average of 2 points that the user can select. For example: 500, and the script will take the average of the highest data between 500 and 250 (half of the user's choice), and the gradient will be based on that.
If we take volume as an example, a big volume spike will mean a lot of long or short activity in that candle. A liquidity level will be displayed below/above the set leverage (4.5 = 20x leverage as an example) so when the price revisits that zone, all the 20x leverage should be liquidated.
Huge volume = a lot of activity
Huge OI = a lot of positions opened
More volume / OI will result in a stronger color that will generate a stronger reaction.
🔶 ROUTE
Here's an example of a route for long liquidity:
Enable the filter = consider only green candles.
Set the leverage to 4.5 (20x).
Choose Data = Volume.
Process:
A green candle is formed.
A liquidity level is established.
The level is placed below to simulate the 20x leverage.
Color is applied, considering the average volume within the chosen area.
Route completed.
🔶 FEATURE
Possibility to change the color of both long and short liquidity
Manual opacity value
Manual opacity average
Leverage
Autopilot - set a good average automatically of the opacity value
Enable both long or short liquidity visualization
Filtering - grab only red/green candle of the corresponding side or grab every candle
Data - nzVolume - Volume - nzOI - OI
🔶 TIPS
Since the limit of the line is 500, it's best to plot 2 scripts: one with only long and another with only short.
🔶 CONCLUSION
The liquidity levels are an interesting way to think about possible levels, and those are not real levels.
ROC Based Buy/Sell SignalsIndicator Explanation:
The "Consolidation Identifier (ROC) with Buy/Sell Signals" indicator is designed to help traders identify potential consolidation zones in the market using the Rate of Change (ROC) indicator. It plots both the positive and negative ROC values, providing insights into price momentum changes. The indicator also includes buy and sell signals that are generated when the positive ROC crosses above the negative ROC (buy signal) or when the negative ROC crosses above the positive ROC (sell signal).
How It Works:
The indicator calculates the ROC of the closing price over a specified period. ROC measures the percentage change in price over a given period. Positive ROC values indicate price increases, while negative ROC values indicate price decreases.
The positive and negative ROC values are plotted on the chart using different colors. The key feature of this indicator is the buy and sell signals that occur when the positive ROC crosses above the negative ROC (buy signal) or when the negative ROC crosses above the positive ROC (sell signal). These signals can help traders identify potential shifts in momentum and potential consolidation zones.
Why It's Useful:
Consolidation Detection: The indicator helps identify periods of potential consolidation in the market. Consolidation zones often precede significant price movements, making them valuable for traders looking to anticipate trends.
Momentum Shifts: The ROC crossovers provide insights into momentum changes. Buy and sell signals can indicate shifts in the market sentiment, helping traders make more informed decisions.
Pairs Well With:
Volume Analysis: Combining this indicator with volume analysis can provide a more comprehensive view of market activity during consolidation zones.
Trend Confirmation Indicators: Pairing with trend-following indicators can help confirm the direction of potential breakout moves following consolidations.
Warnings:
False Signals: Like any technical indicator, false signals can occur, especially in choppy or low-volume markets. Always use additional indicators or analysis to confirm signals.
Market Conditions: The effectiveness of the indicator can vary based on market conditions. It may work better during ranging or consolidation periods rather than strong trending phases.
Parameter Optimization: Adjusting the indicator's parameters (ROC period, SMA period, ROC threshold) may be necessary to fine-tune its performance for specific assets or timeframes.
Machine Learning Regression Trend [LuxAlgo]The Machine Learning Regression Trend tool uses random sample consensus (RANSAC) to fit and extrapolate a linear model by discarding potential outliers, resulting in a more robust fit.
🔶 USAGE
The proposed tool can be used like a regular linear regression, providing support/resistance as well as forecasting an estimated underlying trend.
Using RANSAC allows filtering out outliers from the input data of our final fit, by outliers we are referring to values deviating from the underlying trend whose influence on a fitted model is undesired. For financial prices and under the assumptions of segmented linear trends, these outliers can be caused by volatile moves and/or periodic variations within an underlying trend.
Adjusting the "Allowed Error" numerical setting will determine how sensitive the model is to outliers, with higher values returning a more sensitive model. The blue margin displayed shows the allowed error area.
The number of outliers in the calculation window (represented by red dots) can also be indicative of the amount of noise added to an underlying linear trend in the price, with more outliers suggesting more noise.
Compared to a regular linear regression which does not discriminate against any point in the calculation window, we see that the model using RANSAC is more conservative, giving more importance to detecting a higher number of inliners.
🔶 DETAILS
RANSAC is a general approach to fitting more robust models in the presence of outliers in a dataset and as such does not limit itself to a linear regression model.
This iterative approach can be summarized as follow for the case of our script:
Step 1: Obtain a subset of our dataset by randomly selecting 2 unique samples
Step 2: Fit a linear regression to our subset
Step 3: Get the error between the value within our dataset and the fitted model at time t , if the absolute error is lower than our tolerance threshold then that value is an inlier
Step 4: If the amount of detected inliers is greater than a user-set amount save the model
Repeat steps 1 to 4 until the set number of iterations is reached and use the model that maximizes the number of inliers
🔶 SETTINGS
Length: Calculation window of the linear regression.
Width: Linear regression channel width.
Source: Input data for the linear regression calculation.
🔹 RANSAC
Minimum Inliers: Minimum number of inliers required to return an appropriate model.
Allowed Error: Determine the tolerance threshold used to detect potential inliers. "Auto" will automatically determine the tolerance threshold and will allow the user to multiply it through the numerical input setting at the side. "Fixed" will use the user-set value as the tolerance threshold.
Maximum Iterations Steps: Maximum number of allowed iterations.
Pro Supertrend CalculatorThis indicator is an adapted version of Julien_Eche's 'Pro Momentum Calculator' tailored specifically for TradingView's 'Supertrend indicator'.
The "Pro Supertrend Calculator" indicator has been developed to provide traders with a data-driven perspective on price movements in financial markets. Its primary objective is to analyze historical price data and make probabilistic predictions about the future direction of price movements, specifically in terms of whether the next candlestick will be bullish (green) or bearish (red). Here's a deeper technical insight into how it accomplishes this task:
1. Supertrend Computation:
The indicator initiates by computing the Supertrend indicator, a sophisticated technical analysis tool. This calculation involves two essential parameters:
- ATR Length (Average True Range Length): This parameter determines the sensitivity of the Supertrend to price fluctuations.
- Factor: This multiplier plays a pivotal role in establishing the distance between the Supertrend line and prevailing market prices. A higher factor value results in a more significant separation.
2. Supertrend Visualization:
The Supertrend values derived from the calculation are meticulously plotted on the price chart, manifesting as two distinct lines:
- Green Line: This line represents the Supertrend when it indicates a bullish trend, signifying an anticipation of rising prices.
- Red Line: This line signifies the Supertrend in bearish market conditions, indicating an expectation of falling prices.
3. Consecutive Candle Analysis:
- The core function of the indicator revolves around tracking successive candlestick patterns concerning their relationship with the Supertrend line.
- To be included in the analysis, a candlestick must consistently close either above (green candles) or below (red candles) the Supertrend line for multiple consecutive periods.
4.Labeling and Enumeration:
- To communicate the count of consecutive candles displaying uniform trend behavior, the indicator meticulously applies labels to the price chart.
- The positioning of these labels varies based on the direction of the trend, residing either below (for bullish patterns) or above (for bearish patterns) the candlestick.
- The color scheme employed aligns with the color of the candle, using green labels for bullish candles and red labels for bearish ones.
5. Tabular Data Presentation:
- The indicator augments its graphical analysis with a customizable table prominently displayed on the chart. This table delivers comprehensive statistical insights.
- The tabular data comprises the following key elements for each consecutive period:
a. Consecutive Candles: A tally of the number of consecutive candles displaying identical trend characteristics.
b. Candles Above Supertrend: A count of candles that remained above the Supertrend during the sequential period.
3. Candles Below Supertrend: A count of candles that remained below the Supertrend during the sequential period.
4. Upcoming Green Candle: An estimation of the probability that the next candlestick will be bullish, grounded in historical data.
5. Upcoming Red Candle: An estimation of the probability that the next candlestick will be bearish, based on historical data.
6. Tailored Configuration:
To accommodate diverse trading strategies and preferences, the indicator offers extensive customization options. Traders can fine-tune parameters such as ATR length, factor, label and table placement, and table size to align with their unique trading approaches.
In summation, the "Pro Supertrend Calculator" indicator is an intricately designed tool that leverages the Supertrend indicator in conjunction with historical price data to furnish traders with an informed outlook on potential future price dynamics, with a particular emphasis on the likelihood of specific bullish or bearish candlestick patterns stemming from consecutive price behavior.
