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.
Forecasting
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.
Pro Momentum CalculatorThe Pro Momentum Calculator Indicator is a tool for traders seeking to gauge market momentum and predict future price movements. It achieves this by counting consecutive candle periods above or below a chosen Simple Moving Average (SMA) and then providing a percentage-based probability for the direction of the next candle.
Here's how this principle works:
1. Counting Consecutive Periods: The indicator continuously tracks whether the closing prices of candles are either above or below the chosen SMA.
- When closing prices are above the SMA, it counts consecutive periods as "green" or indicating potential upward momentum.
- When closing prices are below the SMA, it counts consecutive periods as "red" or suggesting potential downward momentum.
2. Assessing Momentum: By monitoring these consecutive periods, the indicator assesses the strength and duration of the current market trend.
This is important information for traders looking to understand the market's behavior.
3. Predicting the Next Candle: Based on the historical data of consecutive green and red periods, the indicator calculates a percentage probability for the direction of the next candle:
- If there have been more consecutive green periods, it suggests a higher likelihood of the next candle being green (indicating a potential upward movement).
- If there have been more consecutive red periods, it suggests a higher likelihood of the next candle being red (indicating a potential downward movement).
The Pro Momentum Calculator indicator's versatility makes it suitable for a wide range of financial markets, including stocks, Forex, indices, commodities, cryptocurrencies...
[SS] Linear ModelerHello everyone,
This is the linear modeler indicator.
It is a statistical based indicator that provides a likely price target and range based on a linear regression time series analysis.
To represent it visually, all the indicator does is it represents a linear regression channel and actually plots out the range at various points based on the current trend (see the chart below):
The indicator will perform the same assessment, but give you a working range and timeline for targets.
As well, the indicator will back-test the range and variables to see how it is performing and how reliable the results are likely to be.
General Functions:
In the chart above you can see all the various parameters and functions.
The indicator will display the most likely target (MLT) to be expected within the next pre-determined timeframe (by candles).
So for the first target, the indicator is saying within the next 10 candles, BA's MLT is 221.46 and based on BT results the reliability of this assessment is around 46%.
The indicator will also display the anticipated range at each designated timeframe.
In the chart above, we can see that at 20 candles, the likely range that BA should be trading in is 204 and 238 with a reliability of around 62% based on previous performance.
Plot Functions:
As this is performing a linear time series projection, you can have the indicator plot the projected ranges. Simply go to the settings menu and select the desired forecast length:
This will plot out the desired range and result over the specified time period. Here is an example of BA plotted over the next 50 candles on the hourly:
You can technically use this as an SMA/EMA type indicator, just keep in mind it may be a bit slower than a traditional EMA and SMA indicator, as it is processing a lot of data and plotting out forecasted data as opposed to an SMA or EMA.
If you wish to use it as an EMA or SMA, you can unselect the "Display Chart" Function to hide the table, and you can also select the "Plot Label" function. This will display the current projection analytics directly on your plotted line so you don't need to reference the table at all:
Tips on use:
I use this on the larger and smaller timeframes. On all timeframes, I will look to targets that display 90% to 100% in the BT results.
Bear in mind, this does not mean that we will 100% of the time hit this target, these targets can fail, it just means that there is a higher confidence of hitting this target than other, less reliable targets.
I will plot these targets out if they fall within the implied range of the timeframe I am looking at and will act on them according to the price action.
This is a great indicator to use in combination with other range based indicators. If you use the implied range from options to help guide your trading, you can see which targets are likely to be hit based on the current trend that fall within that implied range.
You can also assess the strength of the trends at various points in time and have an actionable range with a reliability reading at various points in time.
That is pretty much the bulk of the indicator.
Hopefully you find it helpful and useful.
As always, leave your questions and suggestions below.
Thanks for reading and checking it out!
Guassian Distribution Forecast [prediction intervals]The Indicator
The Indicator combines volatility and frequency distributions to forecast an area of possible price expansion with an approximate confidence interval / level and level of significance (significance level).
The Script Formula
Additional comments
To alter the models forecasting precision to reflect a given confidence interval, e.g the 90% confidence level (C.L.), use the 1.64 multiplier (toggle value in "Standard normal distribution sd" setting), to use a specific C.L., e.g. the 85th percentile either search for this on google, or calculate it yourself using a Standard Normal Distribution Probability table. Additionaly volatility may be changed by toggling the lookback period setting, this can be thought of as widening the distribution tails.
The look forward parameter is currently fixed at 20, this is because it does not currently work correctly with higher integers, I will try resolve this problem and any other bugs as soon as possible
Value At RiskThe Value at Risk Channel (VaR Channel) is a trading indicator designed to assist traders in managing their risk exposure effectively. By allowing users to select a specific time period and a probability value, this indicator generates upper and lower limits that the price might potentially attain within the chosen timeframe and probability range.
CONCEPTS
This indicator employs the concept of Value at Risk (VaR) calculation, a crucial metric in risk management. VaR quantifies the potential financial loss within a position, portfolio, or company over a defined time period. Financial institutions like banks and investment firms use VaR to estimate the extent and likelihood of potential losses in their portfolios.
The "historical method" is utilized to compute VaR within the indicator. This method analyzes the historical performance of returns and constructs a histogram representing the statistical distribution of past returns. Assuming returns adhere to a normal distribution, probabilities are assigned to different return values based on their position in the distribution percentile.
HOW TO USE
Suppose you wish to plot upper and lower price limits for a 4-hour period with a 5% probability. Access the indicator's Settings tab and set the Timeframe parameter to "4 hours" while configuring the Probability parameter to 5.0.
The indicator serves as a tool to determine appropriate Stop-Loss levels triggering with low probability. Additionally, it helps gauge the likelihood of triggering such levels.
Likewise, you can assess the probability of your desired Take-Profit level being reached within a specified time frame. For instance, if you anticipate your target to be achieved within a week, set the Timeframe parameter to "1 week" and adjust the Probability parameter to align the VaR channel's limits with your Take-Profit level. The resulting Probability parameter value reflects the likelihood of your target being met within the expected time frame.
This indicator proves valuable for evaluating and managing risk, as well as refining trading strategies. If you discover other applications for this indicator, feel free to share them in the comments!
SETTINGS
Timeframe: Designates the time period within which the price might touch the VaR channel's upper or lower boundary, considering the specified Probability parameter.
Probability: Defines the likelihood of the price reaching the VaR channel's upper or lower limit during the timeframe determined by the Timeframe parameter.
Window: Establishes the historical period (number of past bars) utilized for VaR calculation.
Custom SMA Plot It creates a custom indicator named "Custom SMA Plot (CSP)" that overlays on a price chart. The indicator fetches the closing prices and calculates a 14-period simple moving average (SMA) of these prices. This SMA is then visually represented as a blue line, which starts from the SMA value of the bar 100 candles ago and extends to the current bar's SMA value. The line has a thickness of 1 unit.
When price breaks over wave go long.
When price breaks below wave go short.
Gaussian Detrended ReversionThis strategy, titled "Gaussian Detrended Reversion Strategy," aims to identify potential price reversals using the customized Gaussian Detrended Price Oscillator (GDPO) in combination with smoothed price cycles.
Key Elements of the Strategy:
GDPO Calculation: The strategy first calculates the Detrended Price Oscillator (DPO) by comparing the close price to an Exponential Moving Average (EMA) of a specified period. This calculation helps identify short-term price cycles by detrending the price data.
Gaussian Smoothing: The DPO values are then smoothed using the Arnaud Legoux Moving Average (ALMA), applying a Gaussian smoothing technique. This smoothed version of the DPO is intended to filter out noise and provide a clearer picture of price trends.
Entry and Exit Conditions: The strategy defines conditions for both long and short entry points as well as exit points. It looks for specific crossover events between the smoothed GDPO and its lagged version. The strategy enters a long position when the smoothed GDPO crosses above the lag and is negative, and exits the long position when the smoothed GDPO crosses below the lag or the zero line. Similarly, the strategy enters a short position when the smoothed GDPO crosses below the lag and is positive, and exits the short position when the smoothed GDPO crosses above the lag or the zero line.
Visualization: The smoothed GDPO and its lag are plotted on the chart using distinct colors. The zero line is also displayed as a reference point. Additionally, the chart background changes color when the strategy enters a long or short position. Cross markers are also plotted at the crossover points as exit cues.
Overall, this strategy aims to capture potential price reversals using the GDPO and Gaussian smoothing, with specific entry and exit conditions to guide trading decisions.
Market Sessions and TPO (+Forecast)This indicator "Market Sessions and TPO (+Forecast)" shows various market sessions alongside a TPO profile (presented as the traditional lettering system or as bars) and price forecast for the duration of the session.
Additionally, numerous statistics for the session are shown.
Features
Session open and close times presented in boxes
Session pre market and post market shown
TPO profile generated for each session (normal market hours only)
A forecast for the remained of the session is projected forward
Forecast can be augmented by ATR
Naked POCs remain on the chart until violated
Volume delta for the session shown
OI Change for the session shown (Binance sourced)
Total volume for the session shown
Price range for the session shown
The image above shows processes of the indicator.
Volume delta, OI change, total volume and session range are calculated and presented for each session.
Additionally, a TPO profile for the most recent session is shown, and a forecast for the remainder of the active session is shown.
The image above shows an alternative display method for the session forecast and TPO profile!
Additionally, the pre-market and post-market times are denoted by dashed boxes.
The image above exemplifies additional capabilities.
That's all for now; further updates to come and thank you for checking this out!
And a special thank you to @TradingView of course, for making all of this possible!
