Machine Learning : Cosine Similarity & Euclidean DistanceIntroduction:
This script implements a comprehensive trading strategy that adheres to the established rules and guidelines of housing trading. It leverages advanced machine learning techniques and incorporates customised moving averages, including the Conceptive Price Moving Average (CPMA), to provide accurate signals for informed trading decisions in the housing market. Additionally, signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation are utilised to enhance the signal quality and improve trading accuracy.
Features:
Market Analysis: The script utilizes advanced machine learning methods such as Lorentzian, Euclidean distance, and Cosine similarity to analyse market conditions. These techniques measure the similarity and distance between data points, enabling more precise signal identification and enhancing trading decisions.
Cosine similarity:
Cosine similarity is a measure used to determine the similarity between two vectors, typically in a high-dimensional space. It calculates the cosine of the angle between the vectors, indicating the degree of similarity or dissimilarity.
In the context of trading or signal processing, cosine similarity can be employed to compare the similarity between different data points or signals. The vectors in this case represent the numerical representations of the data points or signals.
Cosine similarity ranges from -1 to 1, with 1 indicating perfect similarity, 0 indicating no similarity, and -1 indicating perfect dissimilarity. A higher cosine similarity value suggests a closer match between the vectors, implying that the signals or data points share similar characteristics.
Lorentzian Classification:
Lorentzian classification is a machine learning algorithm used for classification tasks. It is based on the Lorentzian distance metric, which measures the similarity or dissimilarity between two data points. The Lorentzian distance takes into account the shape of the data distribution and can handle outliers better than other distance metrics.
Euclidean Distance:
Euclidean distance is a distance metric widely used in mathematics and machine learning. It calculates the straight-line distance between two points in Euclidean space. In two-dimensional space, the Euclidean distance between two points (x1, y1) and (x2, y2) is calculated using the formula sqrt((x2 - x1)^2 + (y2 - y1)^2).
Dynamic Time Windows: The script incorporates a dynamic time window function that allows users to define specific time ranges for trading. It checks if the current time falls within the specified window to execute the relevant trading signals.
Custom Moving Averages: The script includes the CPMA, a powerful moving average calculation. Unlike traditional moving averages, the CPMA provides improved support and resistance levels by considering multiple price types and employing a combination of Exponential Moving Averages (EMAs) and Simple Moving Averages (SMAs). Its adaptive nature ensures responsiveness to changes in price trends.
Signal Processing Techniques: The script applies signal processing techniques such as Know sure thing, Rational Quadratic, and sigmoid transformation to enhance the quality of the generated signals. These techniques improve the accuracy and reliability of the trading signals, aiding in making well-informed trading decisions.
Trade Statistics and Metrics: The script provides comprehensive trade statistics and metrics, including total wins, losses, win rate, win-loss ratio, and early signal flips. These metrics offer valuable insights into the performance and effectiveness of the trading strategy.
Usage:
Configuring Time Windows: Users can customize the time windows by specifying the start and finish time ranges according to their trading preferences and local market conditions.
Signal Interpretation: The script generates long and short signals based on the analysis, custom moving averages, and signal processing techniques. Users should pay attention to these signals and take appropriate action, such as entering or exiting trades, depending on their trading strategies.
Trade Statistics: The script continuously tracks and updates trade statistics, providing users with a clear overview of their trading performance. These statistics help users assess the effectiveness of the strategy and make informed decisions.
Conclusion:
With its adherence to housing trading rules, advanced machine learning methods, customized moving averages like the CPMA, and signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation, this script offers users a powerful tool for housing market analysis and trading. By leveraging the provided signals, time windows, and trade statistics, users can enhance their trading strategies and improve their overall trading performance.
Disclaimer:
Please note that while this script incorporates established tradingview housing rules, advanced machine learning techniques, customized moving averages, and signal processing techniques, it should be used for informational purposes only. Users are advised to conduct their own analysis and exercise caution when making trading decisions. The script's performance may vary based on market conditions, user settings, and the accuracy of the machine learning methods and signal processing techniques. The trading platform and developers are not responsible for any financial losses incurred while using this script.
By publishing this script on the platform, traders can benefit from its professional presentation, clear instructions, and the utilisation of advanced machine learning techniques, customised moving averages, and signal processing techniques for enhanced trading signals and accuracy.
I extend my gratitude to TradingView, LUX ALGO, and JDEHORTY for their invaluable contributions to the trading community. Their innovative scripts, meticulous coding patterns, and insightful ideas have profoundly enriched traders' strategies, including my own.
Cerca negli script per "the script"
Trend Reversal System with SR levelsHello All,
This is the Trend Reversal System with Support/Resistance levels script. long time ago I published it as closed source but now I upgraded it and and published as open-source with a different name. I hope it would be useful for you all while trading/analyzing.
The script has some parts in it: Setup, Count, SR levels, Risk levels & Targets . Now lets check them:
Setup Part: it has two part, Buy or Sell Setup. one of them can be active only. Buy setup: if current close checks if current is lower/equal than the close of the 5. bar. if yes then the script increases number of buy setup. and if it reaches 9 then the script checks if current low is lower/equal than the lows of last 3. and 4. bars, or if the low of the last bar is lower/equal than the lows of last 3. and 4. bars. if yes then the script increases the buy setup by 1. if these conditions met then it puts the label 'S' , same for Sell setup. S labels on both setup are potential reversals.
Count Part: If buy or sell setup reaches the 9 then Count part starts from 1. lets see buy count: If current close is lower/equal than the low of the 3. bar and buy count is lower than 12 or low of the bar 13 is less than or equal to the close of bar 8 then buy count increase or it's completed. if it's completed then the script puts C label, and it's potential reversal. of course there are some conditions that can cancel the count buy/sell or recycle/restart.
By using Setup and Count levels the script can show Support/Resistance Levels, Risk levels & Targets. SR levels are potential reversal levels.
Lets see some example screenshots:
Support/Resistance levels:
Potential Reversal levels and how setup/counts are shown:
Count part can recycle and the script shows it as 'R' , ( you can see the conditions for Recycle in the script ):
Count can be cancelled and and it's shown as 'x'
If the scripts find 9 on Setup or 13 on Count then it checks if it's a good level to buy/sell and if it decides it's good level then it shows TRSSetup Buy/Sell or TRSCount Buy/Sell and also shows the target. in following example the script checks and decide it's a good level to take long position. it can be aggressive or conservative, Conservative is recommended.
Enjoy!
Volume FootprintThe Volume Footprint chart is analyzing volume data from inside the candle and split them into Up and Down Volume in the same way as Volume Profile analyzes the volume data from a fragment of the chart.
The visualization is little different:
Down Volume (sells) are shown on the left side of a candle.
Up Volume (Buys) are shown on the right side of a candle.
User can pick data precision used by Volume Footprint. We recomend to use the highest possible precision.
Unfortunatelly Trading View has many limitations.
If after adding script nothing is visible with error: "'The study references too many candles in history'" you need to use lower precision - It can be changed in script settings.
This script is a part of a toolkit called "Volume Footprint", containing few tools:
Volume Footprint - Scripts drawing Volume Footprint chart.
Volume Footprint Statistics - Script showing table with basic statistics about Up and Down volume inside the candles.
Volume Delta In Candle - Chart showing history of delta (difference between Up and Down volume) changes inside the current candle.
Volume Cumulative Delta - Chart showing history of cumulative delta (sum of difference between Up and Down volume in trading period equal to chart interval).
This script can be used by any user. You do not need to have PRO or PREMIUM account to use it.
Script with limited access, contact author to get authorization
User Interface:
Script is grouping Up and Down Volume into slots based on price. Slots height is controled by "Slot height" param in settings.
On left side of a candle Down Volume is shown and on right side Up Volume is shown.
Before Down Volume may appear imbalance symbols:
⠀↓ - 3 times
⠀↡ - 5 times
⠀⇊ - 10 times
After Up Volume may appear imbalance symbols:
⠀↑ - 3 time
⠀↟ - 5 times
⠀⇈ - 10 times
Above the candle we can show some basic statistics of that candle:
"V:" - Row with volume statistics:
⠀∑ - Total volume,
⠀Δ - Difference between Up and Down Volume.
⠀min Δ - Smallest difference between Up and Down Volume in that candle
⠀max Δ - Biggest difference between Up and Down Volume in that candle
Script settings:
Slot height = 10^ - Price slot height on the chart:
⠀ 0 - 1$
⠀ 1 - 10$
⠀ 2 - 100$
⠀ 3 - 1000$
⠀-1 - 0.1$
⠀-2 - 0.01$
⠀-3 - 0.001$
Data precision - One of 6 levels of data precision: ▉▇▆▅▃▁, where ▉ means the highest precision and ▁ the lowest available precision. On 15 minute chart highest precision should be available, but on 1D it will probably hit TradingView limitations and script will not be even launched by the platform with error: "'The study references too many candles in history'". The general recommendation is to use the highest available precision for a given instrument and interval.
Precise warnings - Option to show precise warnings about missing volume in candle footprint (warning connected with one of TradingView limitations).
Draw candles - Option of drawing candles fiting to volume labels and 2 fields for picking colors of up and down candles. The general recommendation is to hide chart candles and turn on this option.
Show stats - Showing stats over the candle: ∑, Δ, min Δ, max Δ. You can use 'Volume Footprint Statistics' script instead
Font size - Used to draw all the data over the chart: T(iny), S(mall), N(ormal), L(arge)
Centered - If checked volume labels are stick to candle (centered).
Color values - Option to draw labels with use of Up or Down color, depending which value (Volume Up or Volume Down) is bigger in the price slot.
Filter - Filtering option than allow hinding labels with small values:
⠀0 - filter turned off.
⠀1-5 - filtering with transparency
⠀6-10 - Filtering with hiding values.
Show zeros - It can show zeros or leave empty places
Highlight biggest slot - Option to highlight price slot with biggest volume in the candle.
Imbalances - Showing imbalance symbols before Down or after Up Volume
Only over average - Showing imbalances symbols only for volume not smaller than the average value.
Value area - Option to identify group of slots with biggest volume in each candle. A group is a smallest set of neighboring slots that have at least n(param) % of candle volume .
⠀ Value Area Minimal Volume (%) - Value area size as % of candle volume .
⠀ Color - Color of the Value area.
⠀ Show borders - Showing border lines of value areas over the candle.
⠀ Track - Option to track value areas. Potencial Support-Resistance zones.
⠀ Only active - Hide areas that were crossed by the price.
Show Values - Show volume value over tracked value areas.
Troubleshooting:
In case of any problems, send error details to the author of the script.
Known issues:
"The study references too many candles in history" - Change "Data precision" settings to some lower value.
Trend Line Adam Moradi v1 (Tutorial Content)
The Pine Script strategy that plots pivot points and trend lines on a chart. The strategy allows the user to specify the period for calculating pivot points and the number of pivot points to be used for generating trend lines. The user can also specify different colors for the up and down trend lines.
The script starts by defining the input parameters for the strategy and then calculates the pivot high and pivot low values using the pivothigh() and pivotlow() functions. It then stores the pivot points in two arrays called trend_top_values and trend_bottom_values. The script also has two arrays called trend_top_position and trend_bottom_position which store the positions of the pivot points.
The script then defines a function called add_to_array() which takes in three arguments: apointer1, apointer2, and val. This function adds val to the beginning of the array pointed to by apointer1, and adds bar_index to the beginning of the array pointed to by apointer2. It then removes the last element from both arrays.
The script then checks if a pivot high or pivot low value has been calculated, and if so, it adds the value and its position to the appropriate arrays using the add_to_array() function.
Next, the script defines two arrays called bottom_lines and top_lines which will be used to store trend lines. It also defines a variable called starttime which is set to the current time.
The script then enters a loop to calculate and plot the trend lines. It first deletes any existing trend lines from the chart. It then enters two nested loops which iterate over the pivot points stored in the trend_bottom_values and trend_top_values arrays. For each pair of pivot points, the script calculates the slope of the line connecting them and checks if the line is a valid trend line by iterating over the price bars between the two pivot points and checking if the line is above or below the close price of each bar. If the line is found to be a valid trend line, it is plotted on the chart using the line.new() function.
Finally, the script colors the trend lines using the colors specified by the user.
Tutorial Content
'PivotPointNumber' is an input parameter for the script that specifies the number of pivot points to consider when calculating the trend lines. The value of 'PivotPointNumber' is set by the user when they configure the script. It is used to determine the size of the arrays that store the values and positions of the pivot points, as well as the number of pivot points to loop through when calculating the trend lines.
'up_trend_color' is an input parameter for the script that specifies the color to use for drawing the trend lines that are determined to be upward trends. The value of 'up_trend_color' is set by the user when they configure the script and is passed to the color parameter of the line.new() function when drawing the upward trend lines. It determines the visual appearance of the upward trend lines on the chart.
'down_trend_color' is an input parameter for the script that specifies the color to use for drawing the trend lines that are determined to be downward trends. The value of 'down_trend_color' is set by the user when they configure the script and is passed to the color parameter of the line.new() function when drawing the downward trend lines. It determines the visual appearance of the downward trend lines on the chart.
'pivothigh' is a variable in the script that stores the value of the pivot high point. It is calculated using the pivothigh() function, which returns the highest high over a specified number of bars. The value of 'pivothigh' is used in the calculation of the trend lines.
'pivotlow' is a variable in the script that stores the value of the pivot low point. It is calculated using the pivotlow() function, which returns the lowest low over a specified number of bars. The value of 'pivotlow' is used in the calculation of the trend lines.
'trend_top_values' is an array in the script that stores the values of the pivot points that are determined to be at the top of the trend. These are the pivot points that are used to calculate the upward trend lines.
'trend_top_position' is an array in the script that stores the positions (i.e., bar indices) of the pivot points that are stored in the 'trend_top_values' array. These positions correspond to the locations of the pivot points on the chart.
'trend_bottom_values' is an array in the script that stores the values of the pivot points that are determined to be at the bottom of the trend. These are the pivot points that are used to calculate the downward trend lines.
'trend_bottom_position' is an array in the script that stores the positions (i.e., bar indices) of the pivot points that are stored in the 'trend_bottom_values' array. These positions correspond to the locations of the pivot points on the chart.
apointer1 and apointer2 are variables used in the add_to_array() function, which is defined in the script. They are both pointers to arrays, meaning that they hold the memory addresses of the arrays rather than the arrays themselves. They are used to manipulate the arrays by adding new elements to the beginning of the arrays and removing elements from the end of the arrays.
apointer1 is a pointer to an array of floating-point values, while apointer2 is a pointer to an array of integers. The specific arrays that they point to depend on the arguments passed to the add_to_array() function when it is called. For example, if add_to_array(trend_top_values, trend_top_posisiton, pivothigh) is called, then apointer1 would point to the tval array and apointer2 would point to the tpos array.
'bottom_lines' (short for "Bottom Lines") is an array in the script that stores the line objects for the downward trend lines that are drawn on the chart. Each element of the array corresponds to a different trend line.
'top_lines' (short for "Top Lines") is an array in the script that stores the line objects for the upward trend lines that are drawn on the chart. Each element of the array corresponds to a different trend line.
Both 'bottom_lines' and 'top_lines' are arrays of type "line", which is a data type in PineScript that represents a line drawn on a chart. The line objects are created using the line.new() function and are used to draw the trend lines on the chart. The variables are used to store the line objects so that they can be manipulated and deleted later in the script.
Loops
maxline is a variable in the script that specifies the maximum number of trend lines that can be drawn on the chart. It is used to determine the size of the bottom_lines and top_lines arrays, which store the line objects for the trend lines.
The value of maxline is set to 3 at the beginning of the script, meaning that at most 3 trend lines can be drawn on the chart at a time. This value can be changed by the user if desired by modifying the assignment statement "maxline = 3".
'count_line_low' (short for "Count Line Low") is a variable in the script that keeps track of the number of downward trend lines that have been drawn on the chart. It is used to ensure that the maximum number of trend lines (as specified by the maxline variable) is not exceeded.
'count_line_high' (short for "Count Line High") is a variable in the script that keeps track of the number of upward trend lines that have been drawn on the chart. It is used to ensure that the maximum number of trend lines (as specified by the maxline variable) is not exceeded.
Both 'count_line_low' and 'count_line_high' are initialized to 0 at the beginning of the script and are incremented each time a new trend line is drawn. If either variable exceeds the value of maxline, then no more trend lines are drawn.
'pivot1', 'up_val1', 'up_val2', up1, and up2 are variables used in the loop that calculates the downward trend lines in the script. They are used to store intermediate values during the calculation process.
'pivot1' is a loop variable that is used to iterate through the pivot points (stored in the trend_bottom_values and trend_bottom_position arrays) that are being considered for use in the trend line calculation.
'up_val1' and 'up_val2' are variables that store the values of the pivot points that are used to calculate the downward trend line.
up1 and up2 are variables that store the positions (i.e., bar indices) of the pivot points that are stored in 'up_val1' and 'up_val2', respectively. These positions correspond to the locations of the pivot points on the chart.
'value1' and 'value2' are variables that are used to store the values of the pivot points that are being compared in the loop that calculates the trend lines in the script. They are used to determine whether a trend line can be drawn between the two pivot points.
For example, if 'value1' is the value of a pivot point at the top of the trend and 'value2' is the value of a pivot point at the bottom of the trend, then a trend line can be drawn between the two points if 'value1' is greater than 'value2'. The values of 'value1' and 'value2' are used in the calculation of the slope and intercept of the trend line.
'position1' and 'position2' are variables that are used to store the positions (i.e., bar indices) of the pivot points that are being compared in the loop that calculates the trend lines in the script. They are used to determine the distance between the pivot points, which is necessary for calculating the slope of the trend line.
For example, if 'position1' is the position of a pivot point at the top of the trend and 'position2' is the position of a pivot point at the bottom of the trend, then the distance between the two points is given by 'position1' - 'position2'. This distance is used in the calculation of the slope of the trend line.
'different', 'high_line', 'low_location', 'low_value', and 'valid' are variables that are used in the loop that calculates the downward trend lines in the script. They are used to store intermediate values during the calculation process.
'different' is a variable that stores the slope of the downward trend line being calculated. It is calculated as the difference in value between the two pivot points (stored in up_val1 and up_val2) divided by the distance between the pivot points (calculated using their positions, stored in up1 and up2).
'high_line' is a variable that stores the current value of the trend line being calculated at a given point in the loop. It is initialized to the value of the second pivot point (stored in up_val2) and is updated on each iteration of the loop using the value of different.
'low_location' is a variable that stores the position (i.e., bar_index) on the chart of the point where the trend line being calculated first touches the low price. It is initialized to the position of the second pivot point (stored in up2) and is updated on each iteration of the loop if the trend line touches a lower low.
'low_value' is a variable that stores the value of the trend line at the point where it first touches the low price. It is initialized to the value of the second pivot point (stored in up_val2) and is updated on each iteration of the loop if the trend line touches a lower low.
'valid' is a Boolean variable that is used to indicate whether the trend line being calculated is valid. It is initialized to true and is set to false if the trend line does not pass through all the lows between the pivot points. If valid is still true after the loop has completed, then the trend line is considered valid and is drawn on the chart.
d_value1, d_value2, d_position1, and d_position2 are variables that are used in the loop that calculates the upward trend lines in the script. They are used to store intermediate values during the calculation process.
d_value1 and d_value2 are variables that store the values of the pivot points that are used to calculate the upward trend line.
d_position1 and d_position2 are variables that store the positions (i.e., bar indices) of the pivot points that are stored in d_value1 and d_value2, respectively. These positions correspond to the locations of the pivot points on the chart.
The variables d_value1, d_value2, d_position1, and d_position2 have the same function as the variables uv1, uv2, up1, and up2, respectively, but for the calculation of the upward trend lines rather than the downward trend lines. They are used in a similar way to store intermediate values during the calculation process.
thank you.
MTFindicatorsQuite recently TradingView added the possibility to create and use Libraries in PineScript. With this feature PineScript became higher quality of coding language overnight. Libraries enable splitting your code into multiple files, providing easier access to code reusability.
I was working on a script which included 3000 lines of code, which was recompiling 1:30 min, and recalculating over 1 minute as well. So I split it into 2 parts: main part + library containing "main logic", which I reuse in variety of scripts, but don't change too often. Result? Now recompilation of my main script takes 10 and recalculation 8 seconds!!!. I instantly fell in love with libraries.
Having said that, and being dedicated hater of security() calls, I have decided to publish a library of MTF indicators created with my own approach: "dig into formula". I have explained reasons for such approach in desription to this script:
So this library script will be a set of indicators reaching to higher timeframes. Just include one line at the beginning of the script you are creating:
import Peter_O/MTFindicators/1 as LIB
and then somewhere is the code add this line:
rsimtf=LIB.rsi_mtf(close,5,14)
All of a sudden you have access to rsimtf from 5x higher timeframe without any hassle :)
I start with RSI MTF, next ones will be ADX, Stochastic and some more. I will update this library with them here as well. Feel free to request particular indicators in comments. Maybe PSAR? Maybe Bollinger Bands?
PriceCatch Crypto Screener IOHi,
TradingView community and Crypto traders. Warm Greetings.
PriceCatch Crypto Screener.
I am sharing a script that screens the following Crypto.
ADAUSD, ALGOUSD, AMPUSD, ATOMUSD, AUCTIONUSD, AVAXUSD, BTCUSD, BTRSTUSD, CHZUSD, DAIUSD, DOGEUSD, DOTUSD,
EOSUSD, ETHUSD, FETUSD, FXUSD, JASMYUSD, LINKUSD, LRCUSD, MANAUSD, MATICUSD, NUUSD, OMGUSD, OXTUSD,
SHIBUSD, SKLUSD, SNXUSD, SOLUSD, TRIBEUSD, USTUSD, XLMUSD, XTZUSD, YFIUSD, ZRXUSD
Once this script is added to your chart and the resolution is set, it will automatically keep watching for any breakout on the above Crypto and display the results on chart for your attention. You may use the settings option of the script to screen on different time-frames. For example, you may manually select 30Min, 1H, etc., to scan on the respective time-frames. This way, you can quickly spot any opportunities emerging on different time-frames. The Screener runs independently of any symbol displayed on your chart.
NOTE - PRIOR TO USING THIS SCRIPT:
Please remember that the script is shared with absolutely no assurances about usability and any warranties whatsoever and as a responsible trader, please satisfy yourselves thoroughly and use it only if you are convinced it works for you. Remember, you are 100% responsible for your actions. If you understand and accept that, you may use the script.
QUERIES/FEEDBACK
Please PM me or leave comments.
Regards to all and wish everyone all the best with trading.
Exponential MA Channel, Daily Timeframe (Crypto)Moving averages are some of the most common tools for traders. Some of the most widely used ones are simple moving averages (e.g. 20SMA, 50 SMA, 100 SMA, 200SMA,...). There are endless combinations of moving averages that can be used. I prefer to use exponential moving averages because they react more quickly to price data (essentially they filter back through the data over a discrete number of timesteps, with more recent history receiving the highest weighting in the final calculation).
This script uses a combination of the 21EMA, 53 EMA, and 100EMA. The idea of this script is to provide insight into when an asset might be close to a local top/bottom by monitoring price within the middle channel (yellow, blue, and orange lines), as well as identifying longer timeframe opportunities to buy/sell by examining the upper (green) and lower (red) bands. Disclaimer: this is not a guarantee that if price enters a region, that it will be a top or bottom, it is simply an indicator to get an idea based on price history.
As far as I know, this particular combination of exponential moving averages has not yet been published. I do not have an infinite amount of time to check through the entire library of published scripts. If someone else has already done this, I was unaware. Numerical computations were performed on ETHBTC price data in order to find the coefficients used in this script. Essentially, each EMA has a multiplier of either 1, a fraction of 1, or a number larger than 1 (these are the numbers in the script being multiplied by 'out1', 'out2', 'out3'; feel free to change these and see how this changes the indicator). I have found it to be useful for myself, and hope other people can tinker with this idea. My only wish is to allow other people to use this starting point to explore for themselves. I hope that I am allowed to publish this script without it being taken down so that others can freely use it.
Recommendations: although this was fit specifically for ETHBTC, it appears useful for many crypto pairs, specifically alt-BTC pairs and crypto-USD pairs. For example, I have found it useful for BTCUSD, ETHUSD, LINKUSD, LINKBTC, ETHBTC, ADABTC, etc. Only use on the DAILY timeframe.
How to use Leverage and Margin in PineScriptEn route to being absolutely the best and most complete trading platform out there, TradingView has just closed 2 gaps in their PineScript language.
It is now possible to create and backtest a strategy for trading with leverage.
Backtester now produces Margin Calls - so recognizes mid-trade drawdown and if it is too big for the broker to maintain your trade, some part of if will be instantly closed.
New additions were announced in official blogpost , but it lacked code examples, so I have decided to publish this script. Having said that - this is purely educational stuff.
█ LEVERAGE
Let's start with the Leverage. I will discuss this assuming we are always entering trades with some percentage of our equity balance (default_qty_type = strategy.percent_of_equity), not fixed order quantity.
If you want to trade with 1:1 leverage (so no leverage) and enter a trade with all money in your trading account, then first line of your strategy script must include this parameter:
default_qty_value = 100 // which stands for 100%
Now, if you want to trade with 30:1 leverage, you need to multipy the quantity by 30x, so you'd get 30 x 100 = 3000:
default_qty_value = 3000 // which stands for 3000%
And you can play around with this value as you wish, so if you want to enter each trade with 10% equity on 15:1 leverage you'd get default_qty_value = 150.
That's easy. Of course you can modify this quantity value not only in the script, but also afterwards in Script Settings popup, "Properties" tab.
█ MARGIN
Second newly released feature is Margin calculation together with Margin Calls. If the market goes against your trades and your trading account cannot maintain mid-trade drawdown - those trades will be closed in full or partly. Also, if your trading account cannot afford to open more trades (pyramiding those trades), Margin mechanism will prevent them from being entered.
I will not go into details about how Margin calculation works, it was all explainged in above mentioned blogpost and documentation .
All you need to do is to add two parameters to the opening line of your script:
margin_long = 1./30*50, margin_short = 1./30*50
Whereas "30" is a leverage scale as in 30:1, and "50" stands for 50% of Margin required by your broker. Personally the Required Margin number I've met most often is 50%, so I'm using value 50 here, but there are literally 1000+ brokers in this world and this is individual decision by each of them, so you'd better ask yourself.
--------------------
Please note, that if you ever encounter a strategy which triggers Margin Call at least once, then it is probably a very bad strategy. Margin Call is a last resort, last security measure - all the risks should be calculated by the strategy algorithm before it is ever hit. So if you see a Margin Call being triggred, then something is wrong with risk management of the strategy. Therefore - don't use it!
Tape [LucF]█ OVERVIEW
This script prints an ersatz of a trading console's "tape" section to the right of your chart. It displays the time, price and volume of each update of the chart's feed. It also calculates volume delta for the bar. As it calculates from realtime information, it will not display information on historical bars.
█ FEATURES
Calculations
Each new line in the tape displays the last price/volume update from the TradingView feed that's building your chart. These updates do not necessarily correspond to ticks from the originating broker/exchange's matching engine. Multiple broker/exchange ticks are often aggregated in one chart update.
The script first determines if price has moved up or down since the last update. The polarity of the price change, in turn, determines the polarity of the volume for that specific update. If price does not move between consecutive updates, then the last known polarity is used. Using this method, we can calculate a running volume delta accumulation for the bar, which becomes the bar's final volume delta value when the bar closes (you can inspect values of elapsed realtime bars in the Data Window or the indicator's values). Note that these values will all reset if the script re-executes because of a change in inputs or a chart refresh.
While this method of calculating volume delta is not perfect, it is currently the most precise way of calculating volume delta available on TradingView at the moment. Calculating more precise results would require scripts to have access to bid/ask levels from any chart timeframe. Charts at seconds timeframes do use exchange/broker ticks when the feeds you are using allow for it, and this indicator will run on them, but tick data is not yet available from higher timeframes, for now. Also note that the method used in this script is far superior to the intrabar inspection technique used on historical bars in my other "Delta Volume" indicators. This is because volume delta here is calculated from many more realtime updates than the available intrabars in history.
Inputs
You can use the script's inputs to configure:
• The number of lines displayed in the tape.
• If new lines appear at the top or bottom.
• If you want to hide lines with low volume.
• The precision of volume values.
• The size of the text and the colors used to highlight either the tape's text or background.
• The position where you want the tape on your chart.
• Conditions triggering three different markers.
Display
Deltas are shown at the bottom of the tape. They are reset on each bar. Time delta displays the time elapsed since the beginning of the bar, on intraday timeframes only. Contrary to the price change display by TradingView at the top left of charts, which is calculated from the close of the previous bar, the price delta in the tape is calculated from the bar's open, because that's the information used in the calculation of volume delta. The time will become orange when volume delta's polarity diverges from that of the bar. The volume delta value represents the current, cumulative value for the bar. Its color reflects its polarity.
When new realtime bars appear on the chart, a ↻ symbol will appear before the volume value in tape lines.
Markers
There are three types of markers you can choose to display:
• Marker 1 on volume bumps. A bump is defined as two consecutive and increasing/decreasing plus/minus delta volume values,
when no divergence between the polarity of delta volume and the bar occurs on the second bar.
• Marker 2 on volume delta for the bar exceeding a limit of your choice when there is no divergence between the polarity of delta volume and the bar. These trigger at the bar's close.
• Marker 3 on tape lines with volume exceeding a threshold. These trigger in realtime. Be sure to set a threshold high enough so that it doesn't generate too many alerts.
These markers will only display briefly under the bar, but another marker appears next to the relevant line in the tape.
The marker conditions are used to trigger alerts configured on the script. Alert messages will mention the marker(s) that triggered the specific alert event, along with the relevant volume value that triggered the marker. If more than one marker triggers a single alert, they will overprint under the bar, which can make it difficult to distinguish them.
For more detailed on-chart analysis of realtime volume delta, see my Delta Volume Realtime Action .
█ NOTES FOR CODERS
This script showcases two new Pine features:
• Tables, which allow Pine programmers to display tabular information in fixed locations of the chart. The tape uses this feature.
See the Pine User Manual's page on Tables for more information.
• varip -type variables which we can use to save values between realtime updates.
See the " Using `varip` variables " publication by PineCoders for more information.
Risk Management: Position Size & Risk RewardHere is a Risk Management Indicator that calculates stop loss and position sizing based on the volatility of the stock. Most traders use a basic 1 or 2% Risk Rule, where they will not risk more than 1 or 2% of their capital on any one trade. I went further and applied four levels of risk: 0.25%, 0.50%, 1% and 2%. How you apply these different levels of risk is what makes this indicator extremely useful. Here are some common ways to apply this script:
• If the stock is extremely volatile and has a better than 50% chance of hitting the stop loss, then risk only 0.25% of your capital on that trade.
• If a stock has low volatility and has less than 20% change of hitting the stop loss, then risk 2% of your capital on that trade.
• Risking anywhere between 0.25% and 2% is purely based on your intuition and assessment of the market.
• If you are on a losing streak and you want to cut back on your position sizing, then lowering the Risk % can help you weather the storm.
• If you are on a winning streak and your entries are experiencing a higher level of success, then gradually increase the Risk % to reap bigger profits.
• If you want to trade outside the noise of the market or take on more noise/risk, you can adjust the ATR Factor.
• … and whatever else you can imagine using it to benefit your trading.
The position size is calculated using the Capital and Risk % fields, which is the percentage of your total trading capital (a.k.a net liquidity or Capital at Risk). If you instead want to calculate the position size based on a specific amount of money, then enter the amount in the Custom Risk Amt input box. Any amount greater than 0 in the Custom Risk Amt field will override the values in the Capital and Risk % fields.
The stop loss is calculated by using the ATR. The default setting is the 14 RMA, but you can change the length and smoothing of the true range moving average to your liking. Selecting a different length and smoothing affects the stop loss and position size, so choose these values very carefully.
The ATR Factor is a multiplier of the ATR. The ATR Factor can be used to adjust the stop loss and move it outside of the market noise. For the more volatile stock, increase the factor to lower the stop loss and reduce the chance of getting stopped out. For stocks with less volatility , you can lower the factor to raise the stop loss and increase position size. Adjusting the ATR Factor can also be useful when you want the stop loss to be at or below key levels of support.
The Market Session is the hours the market is open. The Market Session only affects the Opening Range Breakout (ORB) option, so it’s important to change these values if you’re trading the ORB and you’re outside of Eastern Standard Time or you’re trading in a foreign exchange.
The ORB is a bonus to the script. When enabled, the indicator will only appear in the first green candle of the day (09:30:00 or 09:30 AM EST or the start time specified in Market Session). When using the ORB, the stop loss is based on the spread of the first candle at the Open. The spread is the difference between the High and Low of the green candle. On 1-day or higher timeframes, the indicator will be the spread of the last (or current) candle.
The output of the indicator is a label overlaying the chart:
1. ATR (14 RMA x2) – This indicated that the stop loss is determined by the ATR. The x2 is the ATR Factor. If ORB is selected, then the first line will show SPREAD, instead of ATR.
2. Capital – This is your total capital or capital at risk.
3. Risk X% of Capital – The amount you’re risking on a % of the Capital. If a Custom Risk Amt is entered, then Risk Amount will be shown in place of Capital and Risk % of Capital.
4. Entry – The current price.
5. Stop Loss – The stop loss price.
6. -1R – The stop loss price and the amount that will be lost of the stop loss is hit.
7. – These are the target prices, or levels where you will want to take profit.
This script is primarily meant for people who are new to active trading and who are looking for a sound risk management strategy based on market volatility . This script can also be used by the more experienced trader who is using a similar system, but also wants to see it applied as an indicator on TradingView. I’m looking forward to maintaining this script and making it better in future revisions. If you want to include or change anything you believe will be a good change or feature, then please contact me in TradingView.
Daily GAP StatsI did not write the script from scratch but rather started editing code of an existing one. The original code came from a script called GAP DETECTOR by @Asch-
First up: I am a trader, not a programmer and therefore my code most likely is inefficient. If someone with more expertise would like to help and optimize it - feel free to get in touch, I am always happy to learn some new tricks. :)
This script does 2 things:
- It shows daily gaps stats based on user inputs
- It shows color coded labels on gap days with additional information in tooltips ( important: make sure to read 'known issues/limitations' at the end )
User Inputs
==========
Although the input dialog is pretty straight forward, I do a quick rundown:
- Length: max lookback time
- Gap Direction: self explanatory
- Show All Gaps | Cont Only | Reversal Only | Off:
This refers to the way labels are displayed on gap days (again: make sure to read known issues/limitations!)
- Show All Gaps: does what it says
- Cont Only: only shows gaps where price continued in the gap direction. If you filter for gap ups and chose 'Cont only' you will only see labels on gap days where price closed above the open (and vice versa if you scan for gap downs).
- Reversal Only: you will only see labels for closes below the open on gap up days (and the opposite on gap down days)
- Off: self explanatory
- Gap Measure in ATR/PCT: self explanatory, ATR is calculated over a 10d period
- Gap Size (Abs Values): no negative values allowed here. If you filter for gap downs and enter 3 it means it will show gaps where the stock fell more than 3 ATR/PCT on the open.
- RVOL Factor: along with significant gaps should come significant volume. RVOL = volume of the gap day / 20d average volume
- Viewing Options: Placing the stats label in the window is a bit tricky (see knonw issues/limitations) and I was not sure which way I liked better. See for yourself what works best for you.
Known Isusses/Limitations:
=======================
- Positioning of the stats table:
As to my knowledge, Tradingview only allows label positioning relative to price and not relative to the chart window. I tried to always display the gap stats table in the upper right corner, using 52wk high as y-coordinate. This works ok most of the time, but is not pretty. If anybody has some fancy way to tag the label in a fixed position, please get in touch.
- Max number of labels per script:
TradingView has a limitation that allows a maxium of ~50 labels per script. If there are more labels, TradingView will automatically cut the oldest ones, without any notification. I have found this behaviour to be rather inconsistent - sometimes it'll dump labels even if there are a lot fewer than 50. Hopefully TradingView will drop this limitation at one point in the future.
Important: The inconsistent display of the gap day labels has NO INFLUENCE on the calculations in the gap stats table - the count and the calculations are complete and correct!
Polynomial Regression Bands + Channel [DW]This is an experimental study designed to calculate polynomial regression for any order polynomial that TV is able to support.
This study aims to educate users on polynomial curve fitting, and the derivation process of Least Squares Moving Averages (LSMAs).
I also designed this study with the intent of showcasing some of the capabilities and potential applications of TV's fantastic new array functions.
Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as a polynomial of nth degree (order).
For clarification, linear regression can also be described as a first order polynomial regression. The process of deriving linear, quadratic, cubic, and higher order polynomial relationships is all the same.
In addition, although deriving a polynomial regression equation results in a nonlinear output, the process of solving for polynomials by least squares is actually a special case of multiple linear regression.
So, just like in multiple linear regression, polynomial regression can be solved in essentially the same way through a system of linear equations.
In this study, you are first given the option to smooth the input data using the 2 pole Super Smoother Filter from John Ehlers.
I chose this specific filter because I find it provides superior smoothing with low lag and fairly clean cutoff. You can, of course, implement your own filter functions to see how they compare if you feel like experimenting.
Filtering noise prior to regression calculation can be useful for providing a more stable estimation since least squares regression can be rather sensitive to noise.
This is especially true on lower sampling lengths and higher degree polynomials since the regression output becomes more "overfit" to the sample data.
Next, data arrays are populated for the x-axis and y-axis values. These are the main datasets utilized in the rest of the calculations.
To keep the calculations more numerically stable for higher periods and orders, the x array is filled with integers 1 through the sampling period rather than using current bar numbers.
This process can be thought of as shifting the origin of the x-axis as new data emerges.
This keeps the axis values significantly lower than the 10k+ bar values, thus maintaining more numerical stability at higher orders and sample lengths.
The data arrays are then used to create a pseudo 2D matrix of x power sums, and a vector of x power*y sums.
These matrices are a representation the system of equations that need to be solved in order to find the regression coefficients.
Below, you'll see some examples of the pattern of equations used to solve for our coefficients represented in augmented matrix form.
For example, the augmented matrix for the system equations required to solve a second order (quadratic) polynomial regression by least squares is formed like this:
(∑x^0 ∑x^1 ∑x^2 | ∑(x^0)y)
(∑x^1 ∑x^2 ∑x^3 | ∑(x^1)y)
(∑x^2 ∑x^3 ∑x^4 | ∑(x^2)y)
The augmented matrix for the third order (cubic) system is formed like this:
(∑x^0 ∑x^1 ∑x^2 ∑x^3 | ∑(x^0)y)
(∑x^1 ∑x^2 ∑x^3 ∑x^4 | ∑(x^1)y)
(∑x^2 ∑x^3 ∑x^4 ∑x^5 | ∑(x^2)y)
(∑x^3 ∑x^4 ∑x^5 ∑x^6 | ∑(x^3)y)
This pattern continues for any n ordered polynomial regression, in which the coefficient matrix is a n + 1 wide square matrix with the last term being ∑x^2n, and the last term of the result vector being ∑(x^n)y.
Thanks to this pattern, it's rather convenient to solve the for our regression coefficients of any nth degree polynomial by a number of different methods.
In this script, I utilize a process known as LU Decomposition to solve for the regression coefficients.
Lower-upper (LU) Decomposition is a neat form of matrix manipulation that expresses a 2D matrix as the product of lower and upper triangular matrices.
This decomposition method is incredibly handy for solving systems of equations, calculating determinants, and inverting matrices.
For a linear system Ax=b, where A is our coefficient matrix, x is our vector of unknowns, and b is our vector of results, LU Decomposition turns our system into LUx=b.
We can then factor this into two separate matrix equations and solve the system using these two simple steps:
1. Solve Ly=b for y, where y is a new vector of unknowns that satisfies the equation, using forward substitution.
2. Solve Ux=y for x using backward substitution. This gives us the values of our original unknowns - in this case, the coefficients for our regression equation.
After solving for the regression coefficients, the values are then plugged into our regression equation:
Y = a0 + a1*x + a1*x^2 + ... + an*x^n, where a() is the ()th coefficient in ascending order and n is the polynomial degree.
From here, an array of curve values for the period based on the current equation is populated, and standard deviation is added to and subtracted from the equation to calculate the channel high and low levels.
The calculated curve values can also be shifted to the left or right using the "Regression Offset" input
Changing the offset parameter will move the curve left for negative values, and right for positive values.
This offset parameter shifts the curve points within our window while using the same equation, allowing you to use offset datapoints on the regression curve to calculate the LSMA and bands.
The curve and channel's appearance is optionally approximated using Pine's v4 line tools to draw segments.
Since there is a limitation on how many lines can be displayed per script, each curve consists of 10 segments with lengths determined by a user defined step size. In total, there are 30 lines displayed at once when active.
By default, the step size is 10, meaning each segment is 10 bars long. This is because the default sampling period is 100, so this step size will show the approximate curve for the entire period.
When adjusting your sampling period, be sure to adjust your step size accordingly when curve drawing is active if you want to see the full approximate curve for the period.
Note that when you have a larger step size, you will see more seemingly "sharp" turning points on the polynomial curve, especially on higher degree polynomials.
The polynomial functions that are calculated are continuous and differentiable across all points. The perceived sharpness is simply due to our limitation on available lines to draw them.
The approximate channel drawings also come equipped with style inputs, so you can control the type, color, and width of the regression, channel high, and channel low curves.
I also included an input to determine if the curves are updated continuously, or only upon the closing of a bar for reduced runtime demands. More about why this is important in the notes below.
For additional reference, I also included the option to display the current regression equation.
This allows you to easily track the polynomial function you're using, and to confirm that the polynomial is properly supported within Pine.
There are some cases that aren't supported properly due to Pine's limitations. More about this in the notes on the bottom.
In addition, I included a line of text beneath the equation to indicate how many bars left or right the calculated curve data is currently shifted.
The display label comes equipped with style editing inputs, so you can control the size, background color, and text color of the equation display.
The Polynomial LSMA, high band, and low band in this script are generated by tracking the current endpoints of the regression, channel high, and channel low curves respectively.
The output of these bands is similar in nature to Bollinger Bands, but with an obviously different derivation process.
By displaying the LSMA and bands in tandem with the polynomial channel, it's easy to visualize how LSMAs are derived, and how the process that goes into them is drastically different from a typical moving average.
The main difference between LSMA and other MAs is that LSMA is showing the value of the regression curve on the current bar, which is the result of a modelled relationship between x and the expected value of y.
With other MA / filter types, they are typically just averaging or frequency filtering the samples. This is an important distinction in interpretation. However, both can be applied similarly when trading.
An important distinction with the LSMA in this script is that since we can model higher degree polynomial relationships, the LSMA here is not limited to only linear as it is in TV's built in LSMA.
Bar colors are also included in this script. The color scheme is based on disparity between source and the LSMA.
This script is a great study for educating yourself on the process that goes into polynomial regression, as well as one of the many processes computers utilize to solve systems of equations.
Also, the Polynomial LSMA and bands are great components to try implementing into your own analysis setup.
I hope you all enjoy it!
--------------------------------------------------------
NOTES:
- Even though the algorithm used in this script can be implemented to find any order polynomial relationship, TV has a limit on the significant figures for its floating point outputs.
This means that as you increase your sampling period and / or polynomial order, some higher order coefficients will be output as 0 due to floating point round-off.
There is currently no viable workaround for this issue since there isn't a way to calculate more significant figures than the limit.
However, in my humble opinion, fitting a polynomial higher than cubic to most time series data is "overkill" due to bias-variance tradeoff.
Although, this tradeoff is also dependent on the sampling period. Keep that in mind. A good rule of thumb is to aim for a nice "middle ground" between bias and variance.
If TV ever chooses to expand its significant figure limits, then it will be possible to accurately calculate even higher order polynomials and periods if you feel the desire to do so.
To test if your polynomial is properly supported within Pine's constraints, check the equation label.
If you see a coefficient value of 0 in front of any of the x values, reduce your period and / or polynomial order.
- Although this algorithm has less computational complexity than most other linear system solving methods, this script itself can still be rather demanding on runtime resources - especially when drawing the curves.
In the event you find your current configuration is throwing back an error saying that the calculation takes too long, there are a few things you can try:
-> Refresh your chart or hide and unhide the indicator.
The runtime environment on TV is very dynamic and the allocation of available memory varies with collective server usage.
By refreshing, you can often get it to process since you're basically just waiting for your allotment to increase. This method works well in a lot of cases.
-> Change the curve update frequency to "Close Only".
If you've tried refreshing multiple times and still have the error, your configuration may simply be too demanding of resources.
v4 drawing objects, most notably lines, can be highly taxing on the servers. That's why Pine has a limit on how many can be displayed in the first place.
By limiting the curve updates to only bar closes, this will significantly reduce the runtime needs of the lines since they will only be calculated once per bar.
Note that doing this will only limit the visual output of the curve segments. It has no impact on regression calculation, equation display, or LSMA and band displays.
-> Uncheck the display boxes for the drawing objects.
If you still have troubles after trying the above options, then simply stop displaying the curve - unless it's important to you.
As I mentioned, v4 drawing objects can be rather resource intensive. So a simple fix that often works when other things fail is to just stop them from being displayed.
-> Reduce sampling period, polynomial order, or curve drawing step size.
If you're having runtime errors and don't want to sacrifice the curve drawings, then you'll need to reduce the calculation complexity.
If you're using a large sampling period, or high order polynomial, the operational complexity becomes significantly higher than lower periods and orders.
When you have larger step sizes, more historical referencing is used for x-axis locations, which does have an impact as well.
By reducing these parameters, the runtime issue will often be solved.
Another important detail to note with this is that you may have configurations that work just fine in real time, but struggle to load properly in replay mode.
This is because the replay framework also requires its own allotment of runtime, so that must be taken into consideration as well.
- Please note that the line and label objects are reprinted as new data emerges. That's simply the nature of drawing objects vs standard plots.
I do not recommend or endorse basing your trading decisions based on the drawn curve. That component is merely to serve as a visual reference of the current polynomial relationship.
No repainting occurs with the Polynomial LSMA and bands though. Once the bar is closed, that bar's calculated values are set.
So when using the LSMA and bands for trading purposes, you can rest easy knowing that history won't change on you when you come back to view them.
- For those who intend on utilizing or modifying the functions and calculations in this script for their own scripts, I included debug dialogues in the script for all of the arrays to make the process easier.
To use the debugs, see the "Debugs" section at the bottom. All dialogues are commented out by default.
The debugs are displayed using label objects. By default, I have them all located to the right of current price.
If you wish to display multiple debugs at once, it will be up to you to decide on display locations at your leisure.
When using the debugs, I recommend commenting out the other drawing objects (or even all plots) in the script to prevent runtime issues and overlapping displays.
Accumulation/Distribution Open Interest Money Flow Hi, this script is the version of Accumulation / Distribution Money Flow (ADMF) that uses Open Interes ts in the required markets instead of Volume.
Can be set from the menu. (Futures/Others)
NOTE: I only modified this script.
The original script belongs to cl8DH.
Original of the script:
I think it will make a difference in the future and commodity markets.
Since the system uses CFTC data, use only for 1W timeframe.
With my best regards..
BullTrading 15 Min Daily Retracement Channel V2.0Hi Traders, I have been received tons of private messages asking me access to my previous scripts. Please let me apologize for not being able to answer all messages. Once you publish a script in Tradingview you can manage individual access to the script but there is no option to open the script after being published.
For that reason I decided to open this script for you, this indicator implements many of the best features contained in previous "invite only scripts". It is designed to display alerts for day trading and short term swings using the 15m Timeframe.
How to use "BullTrading 15 Min Daily Retracement Channel V2.0" indicator?
This indicator is experimental but works if the trader applies good judgment and risk management. Neither myself or BullTrading Asset Management is or will be responsible for any live trading loss using this script, so please use it on Demo.
ALWAYS USE PENDING ORDERS!! It is always much better to wait price using your Fibonacci Retracement tool or the indicator reference lines.
Set Buy signals AT THE CLOSE of yellow candlesticks.
Set Sell signals AT THE CLOSE of fuchsia candlesticks.
Most of the time Buy/Sell signals will react with too much anticipation so you can wait for the price to form enough fractals in order to analyze your entry levels, risk management, SL's and TP's.
AVOID AT ALL COSTS to enter the market immediately after a signal. WAIT for fractal formations to confirm reversals. I will say it AGAIN: Use only pending orders and AVOID market execution orders!
If your order is already filled and the moving average changes color or the price consolidates in your entry level it is better to trail tight your SL, exit the trade with small loss or Break Even instead of waiting for a full Stop Loss exit (this conditions usually indicate trend continuations against the indicator signals).
If the market goes against the signal, THINK and use the indicator lines and channels to match a level using your Fib retracement tool. Applying this criteria will lead for better entry levels in the opposite direction.
Feel free to comment suggestions.
Best Regards
GustavoRubi
KK_Intraday MAsHey guys,
today I was browsing through intraday Charts looking at some moving averages. Basically what I wanted to see was whether the currency pair was trading below or above the moving average of the day/week/month. For a better understanding: The daily MA on a 15 minute Forex Chart would be the 96 MA.
I encountered the problem that i always had to change the settings for my MAs when changing the Time Interval, so I coded this here up. It is pretty simple but maybe somebody else has the same problem and can put it to use.
The script has some settings as listed below:
Choice which MAs to plot, (Daily, Weekly, Monthly)
Choice which type of MA to use (Simple, Exponential, Weighted)
Neccesary Settings for the correct calculation (e.g. Number of trading hours per day). These settings depend on the instrument you are using and should always be checked before using this script.
There are a few things to Note when using this script:
This script works for intraday charts only.
The monthly MA doesn't work on any Time Interval smaller than 15 minutes. Can't do anything about it unfortunately.
This is my first published Script, use it with caution and let me know what you think about it!
Nef33 Forex & Crypto Trading Signals PRO
1. Understanding the Indicator's Context
The indicator generates signals based on confluence (trend, volume, key zones, etc.), but it does not include predefined SL or TP levels. To establish them, we must:
Use dynamic or static support/resistance levels already present in the script.
Incorporate volatility (such as ATR) to adjust the levels based on market conditions.
Define a risk/reward ratio (e.g., 1:2).
2. Options for Determining SL and TP
Below, I provide several ideas based on the tools available in the script:
Stop Loss (SL)
The SL should protect you from adverse movements. You can base it on:
ATR (Volatility): Use the smoothed ATR (atr_smooth) multiplied by a factor (e.g., 1.5 or 2) to set a dynamic SL.
Buy: SL = Entry Price - (atr_smooth * atr_mult).
Sell: SL = Entry Price + (atr_smooth * atr_mult).
Key Zones: Place the SL below a support (for buys) or above a resistance (for sells), using Order Blocks, Fair Value Gaps, or Liquidity Zones.
Buy: SL below the nearest ob_lows or fvg_lows.
Sell: SL above the nearest ob_highs or fvg_highs.
VWAP: Use the daily VWAP (vwap_day) as a critical level.
Buy: SL below vwap_day.
Sell: SL above vwap_day.
Take Profit (TP)
The TP should maximize profits. You can base it on:
Risk/Reward Ratio: Multiply the SL distance by a factor (e.g., 2 or 3).
Buy: TP = Entry Price + (SL Distance * 2).
Sell: TP = Entry Price - (SL Distance * 2).
Key Zones: Target the next resistance (for buys) or support (for sells).
Buy: TP at the next ob_highs, fvg_highs, or liq_zone_high.
Sell: TP at the next ob_lows, fvg_lows, or liq_zone_low.
Ichimoku: Use the cloud levels (Senkou Span A/B) as targets.
Buy: TP at senkou_span_a or senkou_span_b (whichever is higher).
Sell: TP at senkou_span_a or senkou_span_b (whichever is lower).
3. Practical Implementation
Since the script does not automatically draw SL/TP, you can:
Calculate them manually: Observe the chart and use the levels mentioned.
Modify the code: Add SL/TP as labels (label.new) at the moment of the signal.
Here’s an example of how to modify the code to display SL and TP based on ATR with a 1:2 risk/reward ratio:
Modified Code (Signals Section)
Find the lines where the signals (trade_buy and trade_sell) are generated and add the following:
pinescript
// Calculate SL and TP based on ATR
atr_sl_mult = 1.5 // Multiplier for SL
atr_tp_mult = 3.0 // Multiplier for TP (1:2 ratio)
sl_distance = atr_smooth * atr_sl_mult
tp_distance = atr_smooth * atr_tp_mult
if trade_buy
entry_price = close
sl_price = entry_price - sl_distance
tp_price = entry_price + tp_distance
label.new(bar_index, low, "Buy: " + str.tostring(math.round(bull_conditions, 1)), color=color.green, textcolor=color.white, style=label.style_label_up, size=size.tiny)
label.new(bar_index, sl_price, "SL: " + str.tostring(math.round(sl_price, 2)), color=color.red, textcolor=color.white, style=label.style_label_down, size=size.tiny)
label.new(bar_index, tp_price, "TP: " + str.tostring(math.round(tp_price, 2)), color=color.blue, textcolor=color.white, style=label.style_label_up, size=size.tiny)
if trade_sell
entry_price = close
sl_price = entry_price + sl_distance
tp_price = entry_price - tp_distance
label.new(bar_index, high, "Sell: " + str.tostring(math.round(bear_conditions, 1)), color=color.red, textcolor=color.white, style=label.style_label_down, size=size.tiny)
label.new(bar_index, sl_price, "SL: " + str.tostring(math.round(sl_price, 2)), color=color.red, textcolor=color.white, style=label.style_label_up, size=size.tiny)
label.new(bar_index, tp_price, "TP: " + str.tostring(math.round(tp_price, 2)), color=color.blue, textcolor=color.white, style=label.style_label_down, size=size.tiny)
Code Explanation
SL: Calculated by subtracting/adding sl_distance to the entry price (close) depending on whether it’s a buy or sell.
TP: Calculated with a double distance (tp_distance) for a 1:2 risk/reward ratio.
Visualization: Labels are added to the chart to display SL (red) and TP (blue).
4. Practical Strategy Without Modifying the Code
If you don’t want to modify the script, follow these steps manually:
Entry: Take the trade_buy or trade_sell signal.
SL: Check the smoothed ATR (atr_smooth) on the chart or calculate a fixed level (e.g., 1.5 times the ATR). Also, review nearby key zones (OB, FVG, VWAP).
TP: Define a target based on the next key zone or multiply the SL distance by 2 or 3.
Example:
Buy at 100, ATR = 2.
SL = 100 - (2 * 1.5) = 97.
TP = 100 + (2 * 3) = 106.
5. Recommendations
Test in Demo: Apply this logic in a demo account to adjust the multipliers (atr_sl_mult, atr_tp_mult) based on the market (forex or crypto).
Combine with Zones: If the ATR-based SL is too wide, use the nearest OB or FVG as a reference.
Risk/Reward Ratio: Adjust the TP based on your tolerance (1:1, 1:2, 1:3)
VPSA-VTDDear Sir/Madam,
I am pleased to present the next iteration of my indicator concept, which, in my opinion, serves as a highly useful tool for analyzing markets using the Volume Spread Analysis (VSA) method or the Wyckoff methodology.
The VPSA (Volume-Price Spread Analysis), the latest version in the family of scripts I’ve developed, appears to perform its task effectively. The combination of visualizing normalized data alongside their significance, achieved through the application of Z-Score standardization, proved to be a sound solution. Therefore, I decided to take it a step further and expand my project with a complementary approach to the existing one.
Theory
At the outset, I want to acknowledge that I’m aware of the existence of other probabilistic models used in financial markets, which may describe these phenomena more accurately. However, in line with Occam's Razor, I aimed to maintain simplicity in the analysis and interpretation of the concepts below. For this reason, I focused on describing the data using the Gaussian distribution.
The data I read from the chart — primarily the closing price, the high-low price difference (spread), and volume — exhibit cyclical patterns. These cycles are described by Wyckoff's methodology, while VSA complements and presents them from a different perspective. I will refrain from explaining these methods in depth due to their complexity and broad scope. What matters is that within these cycles, various events occur, described by candles or bars in distinct ways, characterized by different spreads and volumes. When observing the chart, I notice periods of lower volatility, often accompanied by lower volumes, as well as periods of high volatility and significant volumes. It’s important to find harmony within this apparent chaos. I think that chart interpretation cannot happen without considering the broader context, but the more variables I include in the analytical process, the more challenges arise. For instance, how can I determine if something is large (wide) or small (narrow)? For elements like volume or spread, my script provides a partial answer to this question. Now, let’s get to the point.
Technical Overview
The first technique I applied is Min-Max Normalization. With its help, the script adjusts volume and spread values to a range between 0 and 1. This allows for a comparable bar chart, where a wide bar represents volume, and a narrow one represents spread. Without normalization, visually comparing values that differ by several orders of magnitude would be inconvenient. If the indicator shows that one bar has a unit spread value while another has half that value, it means the first bar is twice as large. The ratio is preserved.
The second technique I used is Z-Score Standardization. This concept is based on the normal distribution, characterized by variables such as the mean and standard deviation, which measures data dispersion around the mean. The Z-Score indicates how many standard deviations a given value deviates from the population mean. The higher the Z-Score, the more the examined object deviates from the mean. If an object has a Z-Score of 3, it falls within 0.1% of the population, making it a rare occurrence or even an anomaly. In the context of chart analysis, such strong deviations are events like climaxes, which often signal the end of a trend, though not always. In my script, I assigned specific colors to frequently occurring Z-Score values:
Below 1 – Blue
Above 1 – Green
Above 2 – Red
Above 3 – Fuchsia
These colors are applied to both spread and volume, allowing for quick visual interpretation of data.
Volume Trend Detector (VTD)
The above forms the foundation of VPSA. However, I have extended the script with a Volume Trend Detector (VTD). The idea is that when I consider market structure - by market structure, I mean the overall chart, support and resistance levels, candles, and patterns typical of spread and volume analysis as well as Wyckoff patterns - I look for price ranges where there is a lack of supply, demand, or clues left behind by Smart Money or the market's enigmatic identity known as the Composite Man. This is essential because, as these clues and behaviors of market participants — expressed through the chart’s dynamics - reflect the actions, decisions, and emotions of all players. These behaviors can help interpret the bull-bear battle and estimate the probability of their next moves, which is one of the key factors for a trader relying on technical analysis to make a trade decision.
I enhanced the script with a Volume Trend Detector, which operates in two modes:
Step-by-Step Logic
The detector identifies expected volume dynamics. For instance, when looking for signs of a lack of bullish interest, I focus on setups with decreasing volatility and volume, particularly for bullish candles. These setups are referred to as No Demand patterns, according to Tom Williams' methodology.
Simple Moving Average (SMA)
The detector can also operate based on a simple moving average, helping to identify systematic trends in declining volume, indicating potential imbalances in market forces.
I’ve designed the program to allow the selection of candle types and volume characteristics to which the script will pay particular attention and notify me of specific market conditions.
Advantages and Disadvantages
Advantages:
Unified visualization of normalized spread and volume, saving time and improving efficiency.
The use of Z-Score as a consistent and repeatable relative mechanism for marking examined values.
The use of colors in visualization as a reference to Z-Score values.
The possibility to set up a continuous alert system that monitors the market in real time.
The use of EMA (Exponential Moving Average) as a moving average for Z-Score.
The goal of these features is to save my time, which is the only truly invaluable resource.
Disadvantages:
The assumption that the data follows a normal distribution, which may lead to inaccurate interpretations.
A fixed analysis period, which may not be perfectly suited to changing market conditions.
The use of EMA as a moving average for Z-Score, listed both as an advantage and a disadvantage depending on market context.
I have included comments within the code to explain the logic behind each part. For those who seek detailed mathematical formulas, I invite you to explore the code itself.
Defining Program Parameters:
Numerical Conditions:
VPSA Period for Analysis – The number of candles analyzed.
Normalized Spread Alert Threshold – The expected normalized spread value; defines how large or small the spread should be, with a range of 0-1.00.
Normalized Volume Alert Threshold – The expected normalized volume value; defines how large or small the volume should be, with a range of 0-1.00.
Spread Z-SCORE Alert Threshold – The Z-SCORE value for the spread; determines how much the spread deviates from the average, with a range of 0-4 (a higher value can be entered, but from a logical standpoint, exceeding 4 is unnecessary).
Volume Z-SCORE Alert Threshold – The Z-SCORE value for volume; determines how much the volume deviates from the average, with a range of 0-4 (the same logical note as above applies).
Logical Conditions:
Logical conditions describe whether the expected value should be less than or equal to or greater than or equal to the numerical condition.
All four parameters accept two possibilities and are analogous to the numerical conditions.
Volume Trend Detector:
Volume Trend Detector Period for Analysis – The analysis period, indicating the number of candles examined.
Method of Trend Determination – The method used to determine the trend. Possible values: Step by Step or SMA.
Trend Direction – The expected trend direction. Possible values: Upward or Downward.
Candle Type – The type of candle taken into account. Possible values: Bullish, Bearish, or Any.
The last available setting is the option to enable a joint alert for VPSA and VTD.
When enabled, VPSA will trigger on the last closed candle, regardless of the VTD analysis period.
Example Use Cases (Labels Visible in the Script Window Indicate Triggered Alerts):
The provided labels in the chart window mark where specific conditions were met and alerts were triggered.
Summary and Reflections
The program I present is a strong tool in the ongoing "game" with the Composite Man.
However, it requires familiarity and understanding of the underlying methodologies to fully utilize its potential.
Of course, like any technical analysis tool, it is not without flaws. There is no indicator that serves as a perfect Grail, accurately signaling Buy or Sell in every case.
I would like to thank those who have read through my thoughts to the end and are willing to take a closer look at my work by using this script.
If you encounter any errors or have suggestions for improvement, please feel free to contact me.
I wish you good health and accurately interpreted market structures, leading to successful trades!
CatTheTrader
M Farm Scalper v4"M Farm Scalper v2" Trading Indicator on TradingView
Overview
This script uses a combination of indicators to help attempt the best view of when to exit and enter markets. The author has seen that usage of multiple indicators combined provided value and create profit.
1. Improved Signal Reliability
Combining swing highs and lows with Swing Failure Patterns (SFP) increases the reliability of the signals. Each indicator contributes different insights into market behavior:
Swing Highs and Lows: These help identify key support and resistance levels.
Swing Failure Patterns: These provide early warning signs of potential trend reversals when price fails to maintain new highs or lows.
2. Comprehensive Market Analysis
Using multiple indicators allows for a more comprehensive analysis of market conditions:
Trend Analysis: Swing highs and lows can indicate the overall trend direction.
Reversal Signals: SFPs highlight potential reversal points where the current trend might be weakening.
3. Enhanced Signal Strength
The script not only detects basic SFPs but also evaluates their strength by considering the number of failures within a specified range:
Strength of SFPs: By quantifying the strength of SFPs, the script can distinguish between weak and strong reversal signals. This helps traders prioritize stronger signals, reducing false positives.
4. Visual and Alert-based Trading
The combined use of these indicators improves both visual analysis and automated alert systems:
Visual Representation: Plotting different characters for swing points and SFPs makes it easier for traders to quickly interpret the chart.
Alerts: Automated alerts for specific conditions (like swing high/low failures) enable traders to respond promptly to significant market movements without constantly monitoring the charts.
5. Flexibility and Customization
The script includes parameters that allow traders to customize the behavior of the indicators based on their trading preferences:
Customization of Lookback Period (swingHistory): Traders can adjust this to fine-tune the sensitivity of swing point detection.
Selective Plotting (plotSwings, plotFirstSFPOnly, plotStrongerSFPs): These options provide flexibility in how much information is displayed on the chart, preventing clutter and focusing on relevant signals.
6. Minimized Noise and False Signals
By using a combination of indicators, the strategy aims to filter out market noise and reduce the likelihood of false signals:
Confluence of Signals: When multiple indicators align to provide a signal, it generally indicates a higher probability setup, thus reducing the chances of acting on false or less significant market moves.
7. Contextual Market Understanding
Combining indicators offers a more contextual understanding of market dynamics:
Market Context: Identifying both support/resistance levels (via swing points) and potential trend reversals (via SFPs) provides a fuller picture of market conditions, allowing traders to make more informed decisions.
Conclusion
Combining multiple indicators in the "M Farm Scalper v2" script is a strategic choice designed to enhance the robustness, reliability, and actionable quality of the trading signals. This approach leverages the strengths of each indicator to provide a well-rounded, comprehensive trading tool that aids traders in identifying high-probability trade setups and minimizing the risk of false signals.
ChatGPT can make mistakes. Check important info.
Introducing "M Farm Scalper v2" – an advanced proprietary trading indicator designed exclusively for the TradingView platform. This tool excels in identifying key swing points and Swing Failure Patterns (SFPs), offering traders unique visual and auditory cues to enhance decision-making. It's particularly tailored for the 5-minute timeframe but adaptable to suit a variety of trading styles.
Unique Features
Advanced Swing Point Detection: Leverages a sophisticated algorithm to detect swing highs and lows, integrating predictive analytics to forecast potential market reversals.
Dynamic Swing Failure Pattern Analysis: Employs a real-time analysis combining price action and volume data to pinpoint bullish and bearish reversal opportunities with high precision.
Innovative Visual and Auditory Cues: Features unique, easy-to-understand icons such as animals and fruits to represent market signals, simplifying complex market data into actionable insights.
Functionality
"M Farm Scalper v2" is crafted to deliver:
Configurable Parameters: Users can adjust settings including Swing History, visibility of swing points, and sensitivity for detecting stronger SFPs, making it highly customizable to fit individual trading strategies.
Clear, Actionable Outputs: Designed to offer straightforward visual signals directly on the trading chart, facilitating quick and effective decision-making.
Compliance and Originality
Original Integration of Features: This script combines several analytical techniques into a cohesive unit that surpasses the capabilities of existing open-source scripts in both originality and functionality.
Justification for Closed-Source: The proprietary nature of the algorithms and the unique method of data presentation are maintained as closed-source to protect the integrity and effectiveness of the tool, providing users with a reliable competitive advantage.
Application Instructions
To apply "M Farm Scalper v2," add it from the TradingView "Indicators" menu by searching for our script. Adjust the customizable settings as per your trading requirements and observe how the indicator’s outputs make market dynamics easy to interpret and act upon.
Chart Presentation
The accompanying chart is presented cleanly, focusing solely on the outputs of "M Farm Scalper v2." Each visual cue is annotated to demonstrate its relevance, ensuring that traders can easily understand and utilize the information provided without distraction.
Conclusion
"M Farm Scalper v2" is not just an indicator but an essential trading tool for those seeking precision and efficiency in their trading operations. Its advanced features and user-friendly design make it a valuable addition to any trader’s arsenal, especially for those involved in scalping and short-term trading.
Protected script
This script is published closed-source but you may use it freely. You can favorite it to use it on a chart. You cannot view or modify its source code.
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.
Previous 5 Day Market CloseThis indicator can be used with a strategy known as gap close reversal. Gap close reversal is a trading strategy based on the idea that when a market experiences a gap (a significant difference between the previous day's close and the current day's open), there's a tendency for the price to fill or "close" the gap by moving back to the previous day's closing price. Traders often look for such opportunities as potential entry or exit points.
Here's how you can use this script for gap close reversal trading:
Identify Gaps: Look for instances where the current day's open price significantly deviates from the previous day's close, resulting in a gap on the chart. This could be a gap-up (where the open is higher than the previous close) or a gap-down (where the open is lower than the previous close).
Plot the Script: Apply the "Past 5 Days Close" script to your chart. This will plot the closing prices of the past five trading days as lines on the chart. These lines will serve as reference points for potential gap close levels.
Look for Reversal Signals: Monitor the price action as the market opens and observe how it behaves in relation to the previous day's close and the lines plotted by the script. If the price starts to move towards one of the past closing prices after a gap, it could indicate a potential reversal.
Confirm with Other Indicators: Use additional technical indicators or chart patterns to confirm the potential reversal signal. For example, you might look for bullish or bearish candlestick patterns, support or resistance levels, or momentum indicators aligning with the reversal.
Gorb WallIntroduction:
Gorb Wall is a trading tool that offers a unique approach to market trend analysis. It extends the capabilities of the Gorb Algo indicator by presenting a multi-ticker, multi-timeframe dashboard, enabling traders to capture crucial market movements across various financial instruments without flipping through charts.
Overview:
Multi-Ticker Analysis: Monitor and analyze multiple financial instruments simultaneously.
Customizable Timeframes: Tailor the script to various timeframes to suit your trading strategy.
Gorb Algo Market Trend: An algorithm that adapts to market conditions, providing insights into trend changes.
User-Friendly Dashboard: Easily configure and customize the dashboard placement on your chart.
Color-Coded Trend Indicators: Visual cues to quickly assess bullish or bearish trends.
Optimized for Performance: Efficiently coded to ensure smooth running on TradingView without overloading resources.
How Gorb Wall Works:
The script utilizes Gorb Algo's market trend algorithm to process price and volume data across selected tickers and timeframes.
It applies a complex calculation to identify trends, using a combination of volatility analysis, momentum measurements, and trend strength indicators.
The output is a simplified visual representation on the dashboard, where colored circles indicate the trend direction, providing an at-a-glance market overview.
Unique Features:
Proprietary Algorithm: The heart of Gorb Wall lies in its unique Gorb Algo Market Trend algorithm. Unlike standard trend-following indicators, this proprietary algorithm integrates multiple technical analysis concepts (e.g., moving averages, volume data, price action, and oscillators) to provide a more comprehensive market trend analysis.
Multi-Dimensional Analysis: The script analyzes market trends by simultaneously processing data across multiple tickers and timeframes, offering a broader view of market movements than traditional single-ticker indicators.
We recommend exploring & choosing which tickers/timeframes best suits your needs and style of trading, and use that to combine with our suite of indicators.
Settings:
All skill-level friendly presets, easy to enable features with one-click
Dashboard Placement: Choose from top/bottom left/right for dashboard positioning.
Trend Speed Mode: Select the algorithm speed - Fast, Medium, Slow, Slowest.
Bullish/Bearish Trend Colors: Customize colors for trend indicators.
Additional Tickers: Input options for monitoring multiple financial instruments.
Timeframe Selection: Choose from a range of timeframes for each ticker.
How to Use
In the image below, we can see a basic example of how this indicator functions.
The dashboard displays up to three different tickers per the user's choice, with 4 different timeframes the user can choose. It that runs the algorithm line on the specified ticker & timeframe and plots a colored circle that identifies that tickers trend on the specified timeframes.
There are two colors, white for bullish trend and purple for bearish trend. These are the two consistent colors across our suit of indicators to help simplify trading by using simple color matching for confluence. Below is a continued breakdown on using this indicator:
Dynamic Trend Visualization in Real-Time Updates
The dashboard dynamically updates trend colors (white for bullish, purple for bearish) based on real-time market data, offering immediate insights into market sentiment. The next three images below these the live change in data as price action begins developing over multiple timeframes.
In the image above, we are on the 5min AAPL chart, we have SPY, QQQ, and VIX as our tickers on the dashboard with 1min, 2min, 3min, and 10min timeframes chosen. We begin to see VIX flip bullish, which can usually mean down side for indices.
We then see as AAPL's price begins to slow and reverse, we see SPY's trend following on the smaller timeframes first with VIX still leading the way indicating possible bearish change.
In the image above, we can see that price dips down and SPY & QQQ market trends have flipped bearish on all timeframes, while VIX continues to be bullish(validating the downwards price action)
Customizable Settings
Users can adjust settings such as dashboard placement, trend speed mode, and color themes to suit individual trading styles.
In the image below, we can see the dashboard placement setting offers four different locations the user can move the dashboard. Just like in Gorb Algo , the user can choose which trendline speed they want to use to best fit their trading strategy.
In the image below, we can see the "bullish trend" & "bearish trend" colors setting. These colors by default match the rest of our suite of indicators, white is bullish and purple is bearish. Users can change these color settings to meet their preferences.
In the image below, we can see there are three market ticker options that the user can change. This allows users to monitor their favorite tickers across or easily flip through multiple tickers in order to gauge their current market trends without having to change their chart
In the image below, we can see the 4 timeframes that are on the dashboard. The user has the ability to change each of those four, to whatever timeframe best suits their trading needs. There are 12 different timeframe options to choose from.
Quick Dashboard Review
Using color-coded trend detection, this quickly gauges market trends and provides a visual to easily identify these changes in real-time across multiple timeframes. When a circle changes color, this means that price has flipped that direction, causing a change in the Gorb Algo market trendline. As stated above, white is for bullish trend and purple is for bearish trend, but these colors can be changed to fit the users trading strategy and style. Each timeframe the user chooses will be updated in real-time, including the higher time frames like the daily & weekly. They have been modified to pull data a same speed the lower timeframes are.
This helps provide quick visual identification of real market trend changes as price action develops. It is best used in conjunction with other forms of technical analysis for a holistic trading approach.
Conclusion:
This indicator is designed to streamline market trend analysis, offering traders an innovative, efficient, and easy-to-use tool for making informed trading decisions. This tool complements our suite of indicators, providing unique market insights that are not typically available in traditional open-source scripts.
How to get access:
You can see the Author's instructions to get access to this indicator
RISK DISCLAIMER
All content, tools, scripts & education provided by Gorb Algo are for informational & educational purposes only. Trading is risky and most lose their money, past performance does not guarantee future results.
Advanced Dynamic Threshold RSI [Elysian_Mind]Advanced Dynamic Threshold RSI Indicator
Overview
The Advanced Dynamic Threshold RSI Indicator is a powerful tool designed for traders seeking a unique approach to RSI-based signals. This indicator combines traditional RSI analysis with dynamic threshold calculation and optional Bollinger Bands to generate weighted buy and sell signals.
Features
Dynamic Thresholds: The indicator calculates dynamic thresholds based on market volatility, providing more adaptive signal generation.
Performance Analysis: Users can evaluate recent price performance to further refine signals. The script calculates the percentage change over a specified lookback period.
Bollinger Bands Integration: Optional integration of Bollinger Bands for additional confirmation and visualization of potential overbought or oversold conditions.
Customizable Settings: Traders can easily customize key parameters, including RSI length, SMA length, lookback bars, threshold multiplier, and Bollinger Bands parameters.
Weighted Signals: The script introduces a unique weighting mechanism for signals, reducing false positives and improving overall reliability.
Underlying Calculations and Methods
1. Dynamic Threshold Calculation:
The heart of the Advanced Dynamic Threshold RSI Indicator lies in its ability to dynamically calculate thresholds based on multiple timeframes. Let's delve into the technical details:
RSI Calculation:
For each specified timeframe (1-hour, 4-hour, 1-day, 1-week), the Relative Strength Index (RSI) is calculated using the standard 14-period formula.
SMA of RSI:
The Simple Moving Average (SMA) is applied to each RSI, resulting in the smoothing of RSI values. This smoothed RSI becomes the basis for dynamic threshold calculations.
Dynamic Adjustment:
The dynamically adjusted threshold for each timeframe is computed by adding a constant value (5 in this case) to the respective SMA of RSI. This dynamic adjustment ensures that the threshold reflects changing market conditions.
2. Weighted Signal System:
To enhance the precision of buy and sell signals, the script introduces a weighted signal system. Here's how it works technically:
Signal Weighting:
The script assigns weights to buy and sell signals based on the crossover and crossunder events between RSI and the dynamically adjusted thresholds. If a crossover event occurs, the weight is set to 2; otherwise, it remains at 1.
Signal Combination:
The weighted buy and sell signals from different timeframes are combined using logical operations. A buy signal is generated if the product of weights from all timeframes is equal to 2, indicating alignment across timeframe.
3. Experimental Enhancements:
The Advanced Dynamic Threshold RSI Indicator incorporates experimental features for educational exploration. While not intended as proven strategies, these features aim to offer users a glimpse into unconventional analysis. Some of these features include Performance Calculation, Volatility Calculation, Dynamic Threshold Calculation Using Volatility, Bollinger Bands Module, Weighted Signal System Incorporating New Features.
3.1 Performance Calculation:
The script calculates the percentage change in the price over a specified lookback period (variable lookbackBars). This provides a measure of recent performance.
pctChange(src, length) =>
change = src - src
pctChange = (change / src ) * 100
recentPerformance1H = pctChange(close, lookbackBars)
recentPerformance4H = pctChange(request.security(syminfo.tickerid, "240", close), lookbackBars)
recentPerformance1D = pctChange(request.security(syminfo.tickerid, "1D", close), lookbackBars)
3.2 Volatility Calculation:
The script computes the standard deviation of the closing price to measure volatility.
volatility1H = ta.stdev(close, 20)
volatility4H = ta.stdev(request.security(syminfo.tickerid, "240", close), 20)
volatility1D = ta.stdev(request.security(syminfo.tickerid, "1D", close), 20)
3.3 Dynamic Threshold Calculation Using Volatility:
The dynamic thresholds for RSI are calculated by adding a multiplier of volatility to 50.
dynamicThreshold1H = 50 + thresholdMultiplier * volatility1H
dynamicThreshold4H = 50 + thresholdMultiplier * volatility4H
dynamicThreshold1D = 50 + thresholdMultiplier * volatility1D
3.4 Bollinger Bands Module:
An additional module for Bollinger Bands is introduced, providing an option to enable or disable it.
// Additional Module: Bollinger Bands
bbLength = input(20, title="Bollinger Bands Length")
bbMultiplier = input(2.0, title="Bollinger Bands Multiplier")
upperBand = ta.sma(close, bbLength) + bbMultiplier * ta.stdev(close, bbLength)
lowerBand = ta.sma(close, bbLength) - bbMultiplier * ta.stdev(close, bbLength)
3.5 Weighted Signal System Incorporating New Features:
Buy and sell signals are generated based on the dynamic threshold, recent performance, and Bollinger Bands.
weightedBuySignal = rsi1H > dynamicThreshold1H and rsi4H > dynamicThreshold4H and rsi1D > dynamicThreshold1D and crossOver1H
weightedSellSignal = rsi1H < dynamicThreshold1H and rsi4H < dynamicThreshold4H and rsi1D < dynamicThreshold1D and crossUnder1H
These features collectively aim to provide users with a more comprehensive view of market dynamics by incorporating recent performance and volatility considerations into the RSI analysis. Users can experiment with these features to explore their impact on signal accuracy and overall indicator performance.
Indicator Placement for Enhanced Visibility
Overview
The design choice to position the "Advanced Dynamic Threshold RSI" indicator both on the main chart and beneath it has been carefully considered to address specific challenges related to visibility and scaling, providing users with an improved analytical experience.
Challenges Faced
1. Differing Scaling of RSI Results:
RSI values for different timeframes (1-hour, 4-hour, and 1-day) often exhibit different scales, especially in markets like gold.
Attempting to display these RSIs on the same chart can lead to visibility issues, as the scaling differences may cause certain RSI lines to appear compressed or nearly invisible.
2. Candlestick Visibility vs. RSI Scaling:
Balancing the visibility of candlestick patterns with that of RSI values posed a unique challenge.
A single pane for both candlesticks and RSIs may compromise the clarity of either, particularly when dealing with assets that exhibit distinct volatility patterns.
Design Solution
Placing the buy/sell signals above/below the candles helps to maintain a clear association between the signals and price movements.
By allocating RSIs beneath the main chart, users can better distinguish and analyze the RSI values without interference from candlestick scaling.
Doubling the scaling of the 1-hour RSI (displayed in blue) addresses visibility concerns and ensures that it remains discernible even when compared to the other two RSIs: 4-hour RSI (orange) and 1-day RSI (green).
Bollinger Bands Module is optional, but is turned on as default. When the module is turned on, the users can see the upper Bollinger Band (green) and lower Bollinger Band (red) on the main chart to gain more insight into price actions of the candles.
User Flexibility
This dual-placement approach offers users the flexibility to choose their preferred visualization:
The main chart provides a comprehensive view of buy/sell signals in relation to candlestick patterns.
The area beneath the chart accommodates a detailed examination of RSI values, each in its own timeframe, without compromising visibility.
The chosen design optimizes visibility and usability, addressing the unique challenges posed by differing RSI scales and ensuring users can make informed decisions based on both price action and RSI dynamics.
Usage
Installation
To ensure you receive updates and enhancements seamlessly, follow these steps:
Open the TradingView platform.
Navigate to the "Indicators" tab in the top menu.
Click on "Community Scripts" and search for "Advanced Dynamic Threshold RSI Indicator."
Select the indicator from the search results and click on it to add to your chart.
This ensures that any future updates to the indicator can be easily applied, keeping you up-to-date with the latest features and improvements.
Review Code
Open TradingView and navigate to the Pine Editor.
Copy the provided script.
Paste the script into the Pine Editor.
Click "Add to Chart."
Configuration
The indicator offers several customizable settings:
RSI Length: Defines the length of the RSI calculation.
SMA Length: Sets the length of the SMA applied to the RSI.
Lookback Bars: Determines the number of bars used for recent performance analysis.
Threshold Multiplier: Adjusts the multiplier for dynamic threshold calculation.
Enable Bollinger Bands: Allows users to enable or disable Bollinger Bands integration.
Interpreting Signals
Buy Signal: Generated when RSI values are above dynamic thresholds and a crossover occurs.
Sell Signal: Generated when RSI values are below dynamic thresholds and a crossunder occurs.
Additional Information
The indicator plots scaled RSI lines for 1-hour, 4-hour, and 1-day timeframes.
Users can experiment with additional modules, such as machine-learning simulation, dynamic real-life improvements, or experimental signal filtering, depending on personal preferences.
Conclusion
The Advanced Dynamic Threshold RSI Indicator provides traders with a sophisticated tool for RSI-based analysis, offering a unique combination of dynamic thresholds, performance analysis, and optional Bollinger Bands integration. Traders can customize settings and experiment with additional modules to tailor the indicator to their trading strategy.
Disclaimer: Use of the Advanced Dynamic Threshold RSI Indicator
The Advanced Dynamic Threshold RSI Indicator is provided for educational and experimental purposes only. The indicator is not intended to be used as financial or investment advice. Trading and investing in financial markets involve risk, and past performance is not indicative of future results.
The creator of this indicator is not a financial advisor, and the use of this indicator does not guarantee profitability or specific trading outcomes. Users are encouraged to conduct their own research and analysis and, if necessary, consult with a qualified financial professional before making any investment decisions.
It is important to recognize that all trading involves risk, and users should only trade with capital that they can afford to lose. The Advanced Dynamic Threshold RSI Indicator is an experimental tool that may not be suitable for all individuals, and its effectiveness may vary under different market conditions.
By using this indicator, you acknowledge that you are doing so at your own risk and discretion. The creator of this indicator shall not be held responsible for any financial losses or damages incurred as a result of using the indicator.
Kind regards,
Ely
Tick StatisticsTick Statistics:
I have seen many questions/queries related to tick data in TV telegram channels. This script will help pine scripts to understand how ticks work, how to capture and process tick data.
This is an educational indicator script for pine scripters.
The indicator shall work only on real time candles. Tick data capture is initiated as soon as indicator is loaded on the chart. You might not get correct statistics on 1st candle in case indicator is loaded when real time candle is in progress, in such case you can monitor the statistics generated for subsequent candles.
Generated statistics is shown on the chart by placing 2 diamond shapes above and below the candle.
Diamond shape below the candle will have candles ‘tick data’ listed in a table. This can be view by placing mouse pointer on the diamond shape. Refer to point 1 below for more details.
Diamond shape above the candle will have statistics as mentioned in point no 2 onwards. To view the statistics place the mouse point on the diamond shape. The shape will appear in green color when both tick price and tick volume are both moving in the same direction. The diamond shape in red color means tick price and tick volume are moving in opposite direction.
The script captures tick by tick data and generate statistics below:
1. List of tick data with details below: (this is stored in the diamond shape placed below the candle)
a. Tick no
b. Tick type – Up tick (Up), Down tick (Dn), No change (--)
c. Tick price
d. Volume
e. Price difference (as compared to previous tick price)
f. Volume difference (as compared to previous tick volume)
2. Tick statistics
a. Total ticks
b. Number of up ticks
c. Number of down ticks
d. Number of No change ticks
3. Volume Statistics
a. Total volume
b. Up tick volume
c. Down tick volume
d. Volume associated with ticks where there is no change
e. Candle volume (just for reconciliation purpose)
4. Max-min statistics
a. Max volume = <> at price = <> at tick no = <>
b. Min volume = <> at price = <> at tick no = <>
c. Max price = <> at volume = <> at tick no = <>
d. Min price = <> at volume = <> at tick no = <>
5. Candle summary
a. Price << Up >> (if price is up as compared to 1st tick <> otherwise
b. Volume <> (if up tick volume is more than down tick volume <> otherwise
Fair Value Gap [LuxAlgo]Fair value gaps (FVG) highlight imbalances areas between market participants and have become popular amongst technical analysts. The following script aims to display fair value gaps alongside the percentage of filled gaps and the average duration (in bars) before gaps are filled.
Users can be alerted when an FVG is filled using the alerts built into this script.
🔶 USAGE
In practice, FVG's highlight areas of support (bullish FVG) and resistances (bearish FVG). Once a gap is filled, suggesting the end of the imbalance, we can expect the price to reverse.
This approach is more contrarian in nature, users wishing to use a more trend-following approach can use the identification of FVG as direct signals, going long with the identification of a bullish FVG, and short with a bearish FVG.
🔹 Mitigation
By default, the script highlights the areas of only unmitigated FVG's. Users can however highlight the mitigation level of mitigated FVG's, that is the lower extremity of bullish FVG's and the upper extremity of bearish FVG's.
The user can track the evolution of a mitigated FVG's using the "Dynamic" setting.
🔹 Threshold
The gap height can be used to determine the degree of imbalance between buying and selling market participants. Users can filter fair value gaps based on the gap height using the "Threshold %" setting. Using the "Auto" will make use of an automatic threshold, only keeping more volatile FVG's.
🔶 DETAILS
We use the following rules for detecting FVG's in this script:
Bullish FVG
low > high(t-2)
close(t-1) > high(t-2)
(low - high(t-2)) / high(t-2) > threshold
Upper Bullish FVG = low
Lower Bullish FVG = high(t-2)
Bearish FVG
high < low(t-2)
close(t-1) < low(t-2)
(low(t-2) - high) / high < -threshold
Upper Bearish FVG = low(t-2)
Lower Bearish FVG = high
🔶 SETTINGS
Threshold %: Threshold percentage used to filter our FVG's based on their height.
Auto Threshold: Use the cumulative mean of relative FVG heights as threshold.
Unmitigatted Levels: Extent the mitigation level of the number of unmitigated FVG's set by the user.
Mitigation Levels: Show the mitigation levels of mitigated FVG's.
Timeframe : Timeframe of the price data used to detect FVG's.
