Martingale + Grid DCA Strategy [YinYangAlgorithms]This Strategy focuses on strategically Martingaling when the price has dropped X% from your current Dollar Cost Average (DCA). When it does Martingale, it will create a Purchase Grid around this location to likewise attempt to get you a better DCA. Likewise following the Martingale strategy, it will sell when your Profit has hit your target of X%.
Martingale may be an effective way to lower your DCA. This is due to the fact that if your initial purchase; or in our case, initial Grid, all went through and the price kept going down afterwards, that you may purchase more to help lower your DCA even more. By doing so, you may bring your DCA down and effectively may make it easier and quicker to reach your target profit %.
Grid trading may be an effective way of reducing risk and lowering your DCA as you are spreading your purchases out over multiple different locations. Likewise we offer the ability to ‘Stack Grids’. What this means, is that if a single bar was to go through 20 grids, the purchase amount would be 20x what each grid is valued at. This may help get you a lower DCA as rather than creating 20 purchase orders at each grid location, we create a single purchase order at the lowest grid location, but for 20x the amount.
By combining both Martingale and Grid DCA techniques we attempt to lower your DCA strategically until you have reached your target profit %.
Before we start, we just want to make it known that first off, this Strategy features 8% Commission Fees, you may change this in the Settings to better reflect the Commission Fees of your exchange. On a similar note, due to Commission Fees being one of the number one profit killers in fast swing trade strategies, this strategy doesn’t focus on low trades, but the ideology of it may result in low amounts of trades. Please keep in mind this is not a bad thing. Since it has the ability to ‘Stack Grid Purchases’ it may purchase more for less and result in more profit, less commission fees, and likewise less # of trades.
Tutorial:
In this example above, we have it set so we Martingale twice, and we use 100 grids between the upper and lower level of each martingale; for a total of 200 Grids. This strategy will take total capital (initial capital + net profit) and divide it by the amount of grids. This will result in the $ amount purchased per grid. For instance, say you started with $10,000 and you’ve made $2000 from this Strategy so far, your total capital is $12,000. If you likewise are implementing 200 grids within your Strategy, this will result in $12,000 / 200 = $60 per grid. However, please note, that the further down the grid / martingale is, the more volume it is able to purchase for $60.
The white line within the Strategy represents your DCA. As the Strategy makes purchases, this will continue to get lower as will your Target Profit price (Blue Line). When the Close goes above your Target Profit price, the Strategy will close all open positions and claim the profit. This profit is then reinvested back into the Strategy, which may exponentially help the Strategy become more profitable the longer it runs for.
In the example above, we’ve zoomed in on the first example. In this we want to focus on how the Strategy got back into the trades shortly after it sold. Currently within the Settings we have it set so our entry is when the Lowest with a length of 3 is less than the previous Lowest with a length of 3. This is 100% customizable and there are multiple different entry options you can choose from and customize such as:
EMA 7 Crossover EMA 21
EMA 7 Crossunder EMA 21
RSI 14 Crossover RSI MA 14
RSI 14 Crossunder RSI MA 14
MFI 14 Crossover MFI MA 14
MFI 14 Crossunder MFI MA 14
Lowest of X Length < Previous Lowest of X Length
Highest of X Length > Previous Highest of X Length
All of these entry options may be tailored to be checked for on a different Time Frame than the one you are currently using the Strategy on. For instance, you may be running the Strategy on the 15 minute Time Frame yet decide you want the RSI to cross over the RSI MA on the 1 Day to be a valid entry location.
Please keep in mind, this Strategy focuses on DCA, this means you may not want the initial purchase to be the best location. You may want to buy when others think it is a good time to sell. This is because there may be strong bearish momentum which drives the price down drastically and potentially getting you a good DCA before it corrects back up.
We will continue to add more Entry options as time goes on, and if you have any in mind please don’t hesitate to let us know.
Now, back to the example above, if we refer to the Yellow circle, you may see that the Lowest of a length of 3 was less than its previous lowest, this triggered the martingales to create their grids. Only a few bars later, the price went into the first grid and went a little lower than its midpoint (Yellow line). This caused about 60% of the first grid to be purchased. Shortly after the price went even lower into this grid and caused the entire first martingale grid to be purchased. However, if you notice, the white line (your DCA) is lower than the midpoint of the first grid. This is due to the fact that we have ‘Stack Grid Purchases’ enabled. This allows the Strategy to purchase more when a single bar crosses through multiple grid locations; and effectively may lower your average more than if it simply executed a purchase order at each grid.
Still looking at the same location within our next example, if we simply increase the Martingale amount from 2 to 3 we can see something strange happens. What happened is our Target Profit price was reached, then our entry condition was met, which caused all of the martingale grids to be formed; however, the price continued to increase afterwards. This may not be a good thing, sure the price could correct back down to these grid locations, but what if it didn’t and it just kept increasing? This would result in this Strategy being stuck and unable to make any trades. For this reason we have implemented a Failsafe in the Settings called ‘Reset Grids if no purchase happens after X bars’.
We have enabled our Failsafe ‘Reset Grids if no purchase happens after X bars’ in this example above. By default it is set to 100 bars, but you can change this to whatever works best for you. If you set it to 0, this Failsafe will be disabled and act like the example prior where it is possible to be stuck with no trades executing.
This Failsafe may be an important way to ensure the Strategy is able to make purchases, however it may also mean the Grids increase in price when it is used, and if a massive correction were to occur afterwards, you may lose out on potential profit.
This Strategy was designed with WebHooks in mind. WebHooks allow you to send signals from the Strategy to your exchange. Simply set up a Custom TradingView Bot within the OKX exchange or 3Commas platform (which has your exchange API), enter the data required from the bot into the settings here, select your bot type in ‘Webhook Alert Type’, and then set up the alert. After that you’re good to go and this Strategy will fully automate all of its trades within your exchange for you. You need to format the Alert a certain way for it to work, which we will go over in the next example.
Add an alert for this Strategy and simply modify the alert message so all it says is:
{{strategy.order.alert_message}}
Likewise change from the Alert ‘Settings’ to Alert ‘Notifications’ at the top of the alert popup. Within the Notifications we will enable ‘Webhook URL’ and then we will pass the URL we are sending the Webhook to. In this example we’ve put OKX exchange Webhook URL, however if you are using 3Commas you’ll need to change this to theirs.
OKX Webhook URL:
www.okx.com
3Commas Webhook URL:
app.3commas.io
Make sure you click ‘Create’ to actually create this alert. After that you’re all set! There are many Tutorials videos you can watch if you are still a little confused as to how Webhook trading works.
Due to the nature of this Strategy and how it is designed to work, it has the ability to never sell unless there it will make profit. However, because of this it also may be stuck waiting in trades for quite a long period of time (usually a few months); especially when your Target Profit % is 15% like in the example above. However, this example above may be a good indication that it may maintain profitability for a long period of time; considering this ‘Deep Backtest’ is from 2017-8-17.
We will conclude the tutorial here. Hopefully you understand how this Strategy has the potential to make calculated and strategic DCA Grid purchases for you and then based on a traditional Martingale fashion, bulk sell at the desired Target Profit Percent.
Settings:
Purchase Settings:
Only Purchase if its lower than DCA: Generally speaking, we want to lower our Average, and therefore it makes sense to only buy when the close is lower than our current DCA and a Purchase Condition is met.
Purchase Condition: When creating the initial buy location you must remember, you want to Buy when others are Fearful and Sell when others are Greedy. Therefore, many of the Buy conditions involve times many would likewise Sell. This is one of the bonuses to using a Strategy like this as it will attempt to get you a good entry location at times people are selling.
Lower / Upper Change Length: This Lower / Upper Length is only used if the Purchase Condition is set to 'Lower Changed' or 'Upper Changed'. This is when the Lowest or Highest of this length changes. Lowest would become lower or Highest would become higher.
Purchase Resolution: Purchase Resolution is the Time Frame that the Purchase Condition is calculated on. For instance, you may only want to start a new Purchase Order when the RSI Crosses RSI MA on the 1 Day, but yet you run this Strategy on the 15 minutes.
Sell Settings:
Trailing Take Profit: Trailing Take Profit is where once your Target Profit Percent has been hit, this will trail up to attempt to claim even more profit.
Target Profit Percent: What is your Target Profit Percent? The Strategy will close all positions when the close price is greater than your DCA * this Target Profit Percent.
Grid Settings:
Stack Grid Purchases: If a close goes through multiple Buy Grids in one bar, should we amplify its purchase amount based on how many grids it went through?
Reset Grids if no purchase happens after X Bars: Set this to 0 if you never want to reset. This is very useful in case the price is very bullish and continues to increase after our Target Profit location is hit. What may happen is, Target Profit location is hit, then the Entry condition is met but the price just keeps increasing afterwards. We may not want to be sitting waiting for the price to drop, which may never happen. This is more of a failsafe if anything. You may set it very large, like 500+ if you only want to use it in extreme situations.
Grid % Less than Initial Purchase Price: How big should our Buy Grid be? For instance if we bought at 0.25 and this value is set to 20%, that means our Buy Grid spans from 0.2 - 0.25.
Grid Amounts: How many Grids should we create within our Buy location?
Martingale Settings:
Amount of Times 'Planned' to Martingale: The more Grids + the More Martingales = the less $ spent per grid, however the less risk. Remember it may be better to be right and take your time than risk too much and be stuck too long.
Martingale Percent: When the current price is this percent less than our DCA, lets create another Buy Grid so we can lower our average more. This will make our profit location less.
Webhook Alerts:
Webhook Alert Type: How should we format this Alert? 3Commas and OKX take their alerts differently, so please select the proper one or your webhooks won't work.
3Commas Webhook Alerts:
3Commas Bot ID: The 3Commas Bot ID is needed so we know which BOT ID we are sending this webhook too.
3Commas Email Token: The 3Commas Email Token is needed for your webhooks to work properly as it is linked to your account.
OKX Webhook Alerts:
OKX Signal Token: This Signal Token is attached to your OKX bot and will be used to access it within OKX.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Statistics
TrendX Earning-Approach Valuation (Stock)TrendX Earning-Approach Valuation (Stock) indicator is a Fundamental Analysis tool that only focus on the Earnings of the company.
USAGE
This Earning-Approach Valuation is easy to use and customize. TrendX valuates a company's Fair Value based on all the earnings multiples and its average with a little interference of users' risk capacity. Technical Analysis is also included as an additional basis for investment decisions.
Valuation tool
The strategy projects the future value of the company based on its Fiscal Quarter operating income, net income and diluted total shares outstanding. Operating income is the income from the core business operations, before interest and taxes. Net income is the income after interest, taxes and other expenses. The strategy assumes that the operating income and net income will grow at the same rate as their historical values.
The strategy also adjusts the diluted total shares outstanding, which may change due to dilutive securities, to calculate the projected EPS. It then uses the price-to-earnings (P/E) as a multiple in future valuation approach.
Value classification
TrendX classifies 2 phases between Under-value and Over-value, which are represented in green and red, respectively. This toolkit can work well with other indicators of technical analysis, but it can also stand on its own because of its built-in Technical Analysis plugins, which are explained below.
Display potential Support and Resistance levels
TrendX shows support and resistance levels based on the company's past and present Fair Values, which is colored in white. It also draws a current Fair Value line with green coloring.
Potential Entry and Exit zone
By combining the Breakout and retesting technique in both Lagging and Leading's perpective, with the Earning-based valuation, traders can optimize not only the entry-level at the Undervalued zone but also the exit-level at the potential “Bear” area.
Margin of Safety
TrendX also incorporates the margin of safety, which is shown in Risk Ability for customs.
CONCLUSION
The strategy is useful for valuing companies that have positive and stable earnings, and a predictable growth rate. Accordingly, it can also be helpful for traders to use alongside other forms of Technical Analysis.
Many traders fail to realize that indicators are not enough to achieve success, and they end up getting confused and frustrated by trying to find a perfect solution. TrendX aims to avoid this problem by providing clear and concise signals that can be easily followed
Disclaimer
The results achieved in the past are not all reliable sources of what will happen in the future. There are many factors and uncertainties that can affect the outcome of any endeavor, and no one can guarantee or predict with certainty what will occur.
The strategy also relies on assumptions that may not be accurate or realistic, which can vary depending on the market conditions and investor sentiment.
If you notice significant changes in the Valuation over time, it is due to revisions in the company’s reported financials, changes in accounting standards, or corrections of previous errors.
Therefore, you should always exercise caution and judgment when making decisions based on past performance.
Expected Move by Option's Implied Volatility High Liquidity
This script plots boxes to reflect weekly, monthly and yearly expected moves based on "At The Money" put and call option's implied volatility.
Symbols in range: This script will display Expected Move data for Symbols with high option liquidity.
Weekly Updates: Each weekend, the script is updated with fresh expected move data, a job that takes place every Saturday following the close of the markets on Friday.
In the provided script, several boxes are created and plotted on a price chart to represent the expected price moves for various timeframes.
These boxes serve as visual indicators to help traders and analysts understand the expected price volatility.
Definition of Expected Move: Expected Move refers to the anticipated range within which the price of an underlying asset is expected to move over a specific time frame, based on the current implied volatility of its options. Calculation: Expected Move is typically calculated by taking the current stock price and applying a multiple of the implied volatility. The most commonly used multiple is the one-standard-deviation move, which encompasses approximately 68% of potential price outcomes.
Example: Suppose a stock is trading at $100, and the implied volatility of its options is 20%. The one-standard-deviation expected move would be $100 * 0.20 = $20.
This suggests that there is a 68% probability that the stock's price will stay within a range of $80 to $120 over the specified time frame. Usage: Traders and investors use the expected move as a guideline for setting trading strategies and managing risk. It helps them gauge the potential price swings and make informed decisions about buying or selling options.There is a 68% chance that the underlying asset stock or ETF price will be within the boxed area at option expiry. The data on this script is updating weekly at the close of Friday, calculating the implied volatility for the week/month/year based on the "at the money" put and call options with the relevant expiry. This script will display Expected Move data for Symbols within the range of JBL-NOTE in alphabetical order.
In summary, implied volatility reflects market expectations about future price volatility, especially in the context of options. Expected Move is a practical application of implied volatility, helping traders estimate the likely price range for an asset over a given period. Both concepts play a vital role in assessing risk and devising trading strategies in the options and stock markets.
StrategyDashboardLibrary ”StrategyDashboard”
Hey, everybody!
I haven’t done anything here for a long time, I need to get better ^^.
In my strategies, so far private, but not about that, I constantly use dashboards, which clearly show how my strategy is working out.
Of course, you can also find a number of these parameters in the standard strategy window, but I prefer to display everything on the screen, rather than digging through a bunch of boxes and dropdowns.
At the moment I am using 2 dashboards, which I would like to share with you.
1. monthly(isShow)
this is a dashboard with the breakdown of profit by month in per cent. That is, it displays how much percentage you made or lost in a particular month, as well as for the year as a whole.
Parameters:
isShow (bool) - determine allowance to display or not.
2. total(isShow)
The second dashboard displays more of the standard strategy information, but in a table format. Information from the series “number of consecutive losers, number of consecutive wins, amount of earnings per day, etc.”.
Parameters:
isShow (bool) - determine allowance to display or not.
Since I prefer the dark theme of the interface, now they are adapted to it, but in the near future for general convenience I will add the ability to adapt to light.
The same goes for the colour scheme, now it is adapted to the one I use in my strategies (because the library with more is made by cutting these dashboards from my strategies), but will also make customisable part.
If you have any wishes, feel free to write in the comments, maybe I can implement and add them in the next versions.
Statistics TableStrategy Statistics
This library will add a table with statistics from your strategy. With this library, you won't have to switch to your strategy tester tab to view your results and positions.
Usage:
You can choose whether to set the table by input fields by adding the below code to your strategy or replace the parameters with the ones you would like to use manually.
// Statistics table options.
statistics_table_enabled = input.string(title='Show a table with statistics', defval='YES', options= , group='STATISTICS')
statistics_table_position = input.string(title='Position', defval='RIGHT', options= , group='STATISTICS')
statistics_table_margin = input.int(title='Table Margin', defval=10, minval=0, maxval=100, step=1, group='STATISTICS')
statistics_table_transparency = input.int(title='Cell Transparency', defval=20, minval=1, maxval=100, step=1, group='STATISTICS')
statistics_table_text_color = input.color(title='Text Color', defval=color.new(color.white, 0), group='STATISTICS')
statistics_table_title_cell_color = input.color(title='Title Cell Color', defval=color.new(color.gray, 80), group='STATISTICS')
statistics_table_cell_color = input.color(title='Cell Color', defval=color.new(color.purple, 0), group='STATISTICS')
// Statistics table init.
statistics.table(strategy.initial_capital, close, statistics_table_enabled, statistics_table_position, statistics_table_margin, statistics_table_transparency, statistics_table_text_color, statistics_table_title_cell_color, statistics_table_cell_color)
Sample:
If you are interested in the strategy used for this statistics table, you can browse the strategies on my profile.
Backtest Strategy Optimizer AdapterBacktest Strategy Optimizer Adapter
With this library, you will be able to run one or multiple backtests with different variables (combinations). For example, you can run 100 backtests of Supertrend at once with an increment factor of 0.1. This way, you can easily fetch the most profitable settings and apply them to your strategy.
To get a better understanding of the code, you can check the code below.
Single backtest results
= backtest.results(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Add backtest results to a table
backtest.table(initial_capital, profit_and_loss, open_balance, winrate, entries, exits, wins, losses, backtest_table_position, backtest_table_margin, backtest_table_transparency, backtest_table_cell_color, backtest_table_title_cell_color, backtest_table_text_color)
Backtest result without chart labels
= backtest.run(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Backtest result profit
profit = backtest.profit(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Backtest result winrate
winrate = backtest.winrate(date_start, date_end, long_entry, long_exit, take_profit_percentage, stop_loss_percentage, atr_length, initial_capital, order_size, commission)
Start Date
You can set the start date either by using a timestamp or a number that refers to the number of bars back.
Stop Loss / Take Profit Issue
Unfortunately, I did not manage to achieve 100% accuracy for the take profit and stop loss. The original TradingView backtest can stop at the correct position within a bar using the strategy.exit stop and limit variables. However, it seems unachievable with a crossunder/crossover function in PineScript unless it is calculated on every tick (which would make the backtesting results invalid). So far, I have not found a workaround, and I would be grateful if someone could solve this issue, if it is even possible. If you have any solutions or fixes, please let me know!
Multiple Backtest Results / Optimizer
You can run multiple backtests in a single strategy or indicator, but there are certain requirements for placing the correct code in the right way. To view examples of running multiple backtests, you can refer to the links provided in the updates I posted below. In the samples I have also explained how you can auto-generate code for your backtest strategy.
Financial PlusFinancial Plus is an indicator designed to provide users with the flexibility to select up to 10 different financial metrics within four key categories: Statistics, Income Statements, Balance Sheets, and Cash Flow. Powered by Pine Script's request.financial() function, this library offers access to over 200 financial metrics.
You can choose from multiple frequency options, such as FQ (quarterly) , FY (yearly) , TTM (trailing twelve months) , and FH (semiannual) , depending on the availability of each metric. For detailed information regarding specific metrics and their supported frequencies, please consult Financial IDs .
BarChangeDeltaThe "BarChangeDelta" indicator, facilitates the calculation of price delta or percent changes between user-defined start and end points within the current or between preceding and current bars. It offers several customizable options to fit various trading strategies.
// ================================================== INFO ==================================================
This indicator provides the following key functionalities:
- Two Modes:
* PreviousToCurrentBarDelta: Compares user-selected start points from the previous bar to the end points of the current bar.
* CurrentBarDelta: Compares user-selected start and end points within the current bar.
- Start Point/End Point Customization: Allows users to define the source for start and end points used in the delta calculations.
- ABS Mode: Option to display only absolute values, reflected on the histogram drawn.
- Show delta in percents: Enables users to calculate delta in percentage changes instead of price delta.
- Moving Average (MA) Plot: A plot of the MA of the last user-defined number of delta prices or percents.
// ================================================== NOTES ==================================================
The "BarChangeDelta" indicator finds practical application in various trading scenarios. It can be particularly useful for assessing daily price changes between open/close or high/low for determining strike prices, especially for 0DTE trading.
Z-ScoreThe "Z-Score" indicator is a unique and powerful tool designed to help traders identify overbought and oversold conditions in the market. Below is an explanation of its features, usefulness, and what makes it special:
Features:
Z-Score Calculation: The indicator calculates the Z-Score, a statistical measure that represents how far the current price is from the moving average (MA) in terms of standard deviations. It helps identify extreme price movements.
Customizable Parameters: Traders can adjust key parameters such as the Z-Score threshold, the type of MA (e.g., SMA, EMA), and the length of the moving average to suit their trading preferences.
Signal Options: The indicator offers flexibility in terms of signaling. Traders can choose whether to trigger signals when the Z-Score crosses the specified threshold or when it moves away from the threshold.
Visual Signals : Z-Score conditions are represented visually on the chart with color-coded background highlights. Overbought conditions are marked with a red background, while oversold conditions are indicated with a green background.
Information Table: A dynamic information table displays essential details, including the MA type, MA length, MA value, standard deviation, current price, and Z-Score. This information table helps traders make informed decisions.
Usefulness:
Overbought and Oversold Signals: Z-Score is particularly valuable for identifying overbought and oversold market conditions. Traders can use this information to potentially enter or exit positions.
Statistical Analysis: The Z-Score provides a statistical measure of price deviation, offering a data-driven approach to market analysis.
Customization: Traders can customize the indicator to match their trading strategies and preferences, enhancing its adaptability to different trading styles.
Visual Clarity: The visual signals make it easy for traders to quickly spot potential trade opportunities on the price chart.
In summary, the Z-Score indicator is a valuable tool for traders looking to incorporate statistical analysis into their trading strategies. Its customizability, visual signals, and unique statistical approach make it an exceptional choice for identifying overbought and oversold market conditions and potential trading opportunities.
Expected Move by Option's Implied Volatility Symbols: EAT - GBDC
This script plots boxes to reflect weekly, monthly and yearly expected moves based on "At The Money" put and call option's implied volatility.
Symbols in range: This script will display Expected Move data for Symbols within the range of EAT-GDBC in alphabetical order.
Weekly Updates: Each weekend, the script is updated with fresh expected move data, a job that takes place every Saturday following the close of the markets on Friday.
In the provided script, several boxes are created and plotted on a price chart to represent the expected price moves for various timeframes.
These boxes serve as visual indicators to help traders and analysts understand the expected price volatility.
Definition of Expected Move: Expected Move refers to the anticipated range within which the price of an underlying asset is expected to move over a specific time frame, based on the current implied volatility of its options. Calculation: Expected Move is typically calculated by taking the current stock price and applying a multiple of the implied volatility. The most commonly used multiple is the one-standard-deviation move, which encompasses approximately 68% of potential price outcomes.
Example: Suppose a stock is trading at $100, and the implied volatility of its options is 20%. The one-standard-deviation expected move would be $100 * 0.20 = $20.
This suggests that there is a 68% probability that the stock's price will stay within a range of $80 to $120 over the specified time frame. Usage: Traders and investors use the expected move as a guideline for setting trading strategies and managing risk. It helps them gauge the potential price swings and make informed decisions about buying or selling options. There is a 68% chance that the underlying asset stock or ETF price will be within the boxed area at option expiry. The data on this script is updating weekly at the close of Friday, calculating the implied volatility for the week/month/year based on the "at the money" put and call options with the relevant expiry.
In summary, implied volatility reflects market expectations about future price volatility, especially in the context of options. Expected Move is a practical application of implied volatility, helping traders estimate the likely price range for an asset over a given period. Both concepts play a vital role in assessing risk and devising trading strategies in the options and stock markets.
Expected Move by Option's Implied Volatility Symbols: CLFD-EARN This script plots boxes to reflect weekly, monthly and yearly expected moves based on "At The Money" put and call option's implied volatility.
Symbols in range: This script will display Expected Move data for Symbols within the range of CLFD - EARN in alphabetical order.
Weekly Updates: Each weekend, the script is updated with fresh expected move data, a job that takes place every Saturday following the close of the markets on Friday.
In the provided script, several boxes are created and plotted on a price chart to represent the expected price moves for various timeframes.
These boxes serve as visual indicators to help traders and analysts understand the expected price volatility.
Definition of Expected Move: Expected Move refers to the anticipated range within which the price of an underlying asset is expected to move over a specific time frame, based on the current implied volatility of its options. Calculation: Expected Move is typically calculated by taking the current stock price and applying a multiple of the implied volatility. The most commonly used multiple is the one-standard-deviation move, which encompasses approximately 68% of potential price outcomes.
Example: Suppose a stock is trading at $100, and the implied volatility of its options is 20%. The one-standard-deviation expected move would be $100 * 0.20 = $20.
This suggests that there is a 68% probability that the stock's price will stay within a range of $80 to $120 over the specified time frame. Usage: Traders and investors use the expected move as a guideline for setting trading strategies and managing risk. It helps them gauge the potential price swings and make informed decisions about buying or selling options. There is a 68% chance that the underlying asset stock or ETF price will be within the boxed area at option expiry. The data on this script is updating weekly at the close of Friday, calculating the implied volatility for the week/month/year based on the "at the money" put and call options with the relevant expiry.
In summary, implied volatility reflects market expectations about future price volatility, especially in the context of options. Expected Move is a practical application of implied volatility, helping traders estimate the likely price range for an asset over a given period. Both concepts play a vital role in assessing risk and devising trading strategies in the options and stock markets.
Expected Move by Option's Implied Volatility Symbols: B - CLF
This script plots boxes to reflect weekly, monthly and yearly expected moves based on "At The Money" put and call option's implied volatility.
Symbols in range: This script will display Expected Move data for Symbols within the range of B - CLF in alphabetical order.
Weekly Updates: Each weekend, the script is updated with fresh expected move data, a job that takes place every Saturday following the close of the markets on Friday.
In the provided script, several boxes are created and plotted on a price chart to represent the expected price moves for various timeframes.
These boxes serve as visual indicators to help traders and analysts understand the expected price volatility.
Definition of Expected Move: Expected Move refers to the anticipated range within which the price of an underlying asset is expected to move over a specific time frame, based on the current implied volatility of its options. Calculation: Expected Move is typically calculated by taking the current stock price and applying a multiple of the implied volatility. The most commonly used multiple is the one-standard-deviation move, which encompasses approximately 68% of potential price outcomes.
Example: Suppose a stock is trading at $100, and the implied volatility of its options is 20%. The one-standard-deviation expected move would be $100 * 0.20 = $20.
This suggests that there is a 68% probability that the stock's price will stay within a range of $80 to $120 over the specified time frame. Usage: Traders and investors use the expected move as a guideline for setting trading strategies and managing risk. It helps them gauge the potential price swings and make informed decisions about buying or selling options. There is a 68% chance that the underlying asset stock or ETF price will be within the boxed area at option expiry. The data on this script is updating weekly at the close of Friday, calculating the implied volatility for the week/month/year based on the "at the money" put and call options with the relevant expiry.
In summary, implied volatility reflects market expectations about future price volatility, especially in the context of options. Expected Move is a practical application of implied volatility, helping traders estimate the likely price range for an asset over a given period. Both concepts play a vital role in assessing risk and devising trading strategies in the options and stock markets.
TrendX Financial Modelling (Stock)TrendX Financial Modelling (Stock) indicator is a comprehensive tool that takes full advantage of both financial modelling and technical analysis to estimate the Intrinsic Value of any security. There are 2 main Fundamental methods for Intrinsic valuation: Discounted Cash Flow (DCF) and Basic Valuation.
USAGE
This Intrinsic Value Indicator is easy to use and customize. TrendX enables adjusting the parameters such as the type of basic valuation, market expected growth rate, the earnings multiple, and the margin of safety level according to your own assumptions and preferences. You can also apply different filters and alerts to get notified when a buy or sell signal is generated.
Valuation tool
DCF model will calculate the Present Value of all expected future cash flows, discounted at an appropriate rate, and compare it with the current market condition. In addition, Basic Valuation consists of 6 types of approaches depending on the industry of the target company. Combining these, the chart will show the potential target value from the current price.
Value classification
TrendX classifies 2 phases between Under-value and Fair-value, which are represented in Purple and grey, respectively.
Display potential targets
TrendX spot key target levels based on TrendX’s Valuation toolkit.
Optimal valued entry-exit
By combining the Breakout structure and divergences with the TrendX financial model, investors can optimize not only the entry-level at the Undervalued zone but also the exit-level at the potential “Bear” area.
Margin of safety
TrendX also incorporates the margin of safety principle, which is a key concept in value investing. The margin of safety is the secured zone between the intrinsic value and the market price, expressed as a percentage. The higher the margin of safety, the lower the risk of loss and the higher the potential return, which is customizable based on your preferences.
CONCLUSION
The Intrinsic Financial Model Indicator is very practical for any investor who wants to make informed and rational decisions based on Fundamental Analysis. It will help find undervalued gems in any market and avoid overpaying for overhyped stocks. Accordingly, it can also be helpful for traders to use alongside other forms of Technical Analysis.
Many traders fail to realize that indicators are not enough to achieve success, and they end up getting confused and frustrated by trying to find a perfect solution. TrendX aims to avoid this problem by providing clear and concise signals that can be easily followed
DISCLAIMER
The results achieved in the past are not all reliable sources of what will happen in the future. There are many factors and uncertainties that can affect the outcome of any endeavor, and no one can guarantee or predict with certainty what will occur.
If you notice significant changes in the Intrinsic Valuation over time, it is due to revisions in the company’s reported financials, changes in accounting standards, or corrections of previous errors.
Therefore, you should always exercise caution and judgment when making decisions based on past performance.
Lite Trading Diary : equity curveDynamic trading journal with equity curve display. Detailed results with prop firm objectives, editable, $/month estimation, possibility to compare two strategies.
one line in parameter = one trade.
For each trade, specify : RR (Win, or "-1" for a stoploss), type of trade, and a comment.
The bottom left table summarizes the overall performance with some key information. RA return => Risk adjusted performance.
there is the possibility to define a "Type" : type 1, 2 or 3. It allows to split the equity curve. You can thus distinguish the different sub-strategies of your strategy, visually see their effectiveness, and be able to adjust your risk exposure accordingly.
Learn from your backtests. Identify your strengths, your weaknesses, and improve!
All the conditions to succeed in the challenge are adjustable in the parameters. Please note : drawdown on the equity curve is max drawdown. On the table => static drawdown.
Use "A random day trading" indicator to spice up your training.
I hope this will be useful for you to track your performance !
OI Visible Range Ladder [Kioseff Trading]Hello!
This Script “OI Visible Range Ladder” calculates open interest profiles for the visible range alongside an OI ladder for the visible period!
Features
OI Profile Anchored to Visible Range
OI Ladder Anchored to Visible Range
Standard POC and Value Area Lines, in Addition to Separated POCs and Value Area Lines for each category of OI x Price
Configurable Value Area Targets
Curved Profiles
Up to 9999 Profile Rows per Visible Range
Stylistic Options for Profiles
Up to 9999 volume profile levels (Price levels) can be calculated for each profile, thanks to the new polyline feature, allowing for less aggregation / more precision of open interest at price.
The image above shows primary functionality!
Green profiles = Up OI / Up Price
Yellow profiles = Down OI / Up Price
Purple profiles = Up OI / Down Price
Red profiles = Down OI / Down Price
The image above shows POCs for each OI x Price category!
Profiles can be anchored on the left side for a more traditional look.
The indicator is robust enough to calculate on “small price periods”, or for a price period spanning your entire chart fully zoomed out!
That’s about it :D
This indicator is Part of a series titled “Bull vs. Bear” - a suite of profile-like indicators.
Thanks for checking this out!
If you have any suggestions please feel free to share!
SPTS_StatsPakLibFinally getting around to releasing the library component to the SPTS indicator!
This library is packed with a ton of great statistics functions to supplement SPTS, these functions add to the capabilities of SPTS including a forecast function.
The library includes the following functions
1. Linear Regression (single independent and single dependent)
2. Multiple Regression (2 independent variables, 1 dependent)
3. Standard Error of Residual Assessment
4. Z-Score
5. Effect Size
6. Confidence Interval
7. Paired Sample Test
8. Two Tailed T-Test
9. Qualitative assessment of T-Test
10. T-test table and p value assigner
11. Correlation of two arrays
12. Quadratic correlation (curvlinear)
13. R Squared value of 2 arrays
14. R Squared value of 2 floats
15. Test of normality
16. Forecast function which will push the desired forecasted variables into an array.
One of the biggest added functionalities of this library is the forecasting function.
This function provides an autoregressive, trainable model that will export forecasted values to 3 arrays, one contains the autoregressed forecasted results, the other two contain the upper confidence forecast and the lower confidence forecast.
Hope you enjoy and find use for this!
Library "SPTS_StatsPakLib"
f_linear_regression(independent, dependent, len, variable)
TODO: creates a simple linear regression model between two variables.
Parameters:
independent (float)
dependent (float)
len (int)
variable (float)
Returns: TODO: returns 6 float variables
result: The result of the regression model
pear_cor: The pearson correlation of the regresion model
rsqrd: the R2 of the regression model
std_err: the error of residuals
slope: the slope of the model (coefficient)
intercept: the intercept of the model (y = mx + b is y = slope x + intercept)
f_multiple_regression(y, x1, x2, input1, input2, len)
TODO: creates a multiple regression model between two independent variables and 1 dependent variable.
Parameters:
y (float)
x1 (float)
x2 (float)
input1 (float)
input2 (float)
len (int)
Returns: TODO: returns 7 float variables
result: The result of the regression model
pear_cor: The pearson correlation of the regresion model
rsqrd: the R2 of the regression model
std_err: the error of residuals
b1 & b2: the slopes of the model (coefficients)
intercept: the intercept of the model (y = mx + b is y = b1 x + b2 x + intercept)
f_stanard_error(result, dependent, length)
x TODO: performs an assessment on the error of residuals, can be used with any variable in which there are residual values (such as moving averages or more comlpex models)
param x TODO: result is the output, for example, if you are calculating the residuals of a 200 EMA, the result would be the 200 EMA
dependent: is the dependent variable. In the above example with the 200 EMA, your dependent would be the source for your 200 EMA
Parameters:
result (float)
dependent (float)
length (int)
Returns: x TODO: the standard error of the residual, which can then be multiplied by standard deviations or used as is.
f_zscore(variable, length)
TODO: Calculates the z-score
Parameters:
variable (float)
length (int)
Returns: TODO: returns float z-score
f_effect_size(array1, array2)
TODO: Calculates the effect size between two arrays of equal scale.
Parameters:
array1 (float )
array2 (float )
Returns: TODO: returns the effect size (float)
f_confidence_interval(array1, array2, ci_input)
TODO: Calculates the confidence interval between two arrays
Parameters:
array1 (float )
array2 (float )
ci_input (float)
Returns: TODO: returns the upper_bound and lower_bound cofidence interval as float values
paired_sample_t(src1, src2, len)
TODO: Performs a paired sample t-test
Parameters:
src1 (float)
src2 (float)
len (int)
Returns: TODO: Returns the t-statistic and degrees of freedom of a paired sample t-test
two_tail_t_test(array1, array2)
TODO: Perofrms a two tailed t-test
Parameters:
array1 (float )
array2 (float )
Returns: TODO: Returns the t-statistic and degrees of freedom of a two_tail_t_test sample t-test
t_table_analysis(t_stat, df)
TODO: This is to make a qualitative assessment of your paired and single sample t-test
Parameters:
t_stat (float)
df (float)
Returns: TODO: the function will return 2 string variables and 1 colour variable. The 2 string variables indicate whether the results are significant or not and the colour will
output red for insigificant and green for significant
t_table_p_value(df, t_stat)
TODO: This performs a quantaitive assessment on your t-tests to determine the statistical significance p value
Parameters:
df (float)
t_stat (float)
Returns: TODO: The function will return 1 float variable, the p value of the t-test.
cor_of_array(array1, array2)
TODO: This performs a pearson correlation assessment of two arrays. They need to be of equal size!
Parameters:
array1 (float )
array2 (float )
Returns: TODO: The function will return the pearson correlation.
quadratic_correlation(src1, src2, len)
TODO: This performs a quadratic (curvlinear) pearson correlation between two values.
Parameters:
src1 (float)
src2 (float)
len (int)
Returns: TODO: The function will return the pearson correlation (quadratic based).
f_r2_array(array1, array2)
TODO: Calculates the r2 of two arrays
Parameters:
array1 (float )
array2 (float )
Returns: TODO: returns the R2 value
f_rsqrd(src1, src2, len)
TODO: Calculates the r2 of two float variables
Parameters:
src1 (float)
src2 (float)
len (int)
Returns: TODO: returns the R2 value
test_of_normality(array, src)
TODO: tests the normal distribution hypothesis
Parameters:
array (float )
src (float)
Returns: TODO: returns 4 variables, 2 float and 2 string
Skew: the skewness of the dataset
Kurt: the kurtosis of the dataset
dist = the distribution type (recognizes 7 different distribution types)
implication = a string assessment of the implication of the distribution (qualitative)
f_forecast(output, input, train_len, forecast_length, output_array, upper_array, lower_array)
TODO: This performs a simple forecast function on a single dependent variable. It will autoregress this based on the train time, to the desired length of output,
then it will push the forecasted values to 3 float arrays, one that contains the forecasted result, 1 that contains the Upper Confidence Result and one with the lower confidence
result.
Parameters:
output (float)
input (float)
train_len (int)
forecast_length (int)
output_array (float )
upper_array (float )
lower_array (float )
Returns: TODO: Will return 3 arrays, one with the forecasted results, one with the upper confidence results, and a final with the lower confidence results. Example is given below.
Global Leaders M2Introducing the Global Leaders M2 Indicator
The Global Leaders M2 indicator is a comprehensive tool designed to provide you with crucial insights into the money supply (M2) of the world's top 10 economic powerhouses. This powerful indicator offers a wealth of information to help you make informed decisions in the financial markets.
Key Features:
Multi-Country M2 Data: Access M2 data for the world's top 10 economic leaders, including China, the United States, Japan, Germany, the United Kingdom, France, Italy, Canada, Russia, and India.
Rate of Change Analysis: Understand the rate of change in M2 data for each country and the overall global aggregate, allowing you to gauge the momentum of monetary supply.
Customizable Display: Tailor your chart to display the data of specific countries, or focus on the total global M2 value based on your preferences.
Currency Selection: Choose your preferred currency for displaying the M2 data, making it easier to work with data in your currency of choice.
Interactive Overview Table: Get an overview of M2 data for each country and the global total in an interactive table, complete with color-coded indicators to help you quickly spot trends.
Precision and Clarity: The indicator provides precision to two decimal places and uses color coding to differentiate between positive and negative rate of change.
Whether you're a seasoned investor or a newcomer to the world of finance, the Global Leaders M2 indicator equips you with valuable data and insights to guide your financial decisions. Stay on top of global monetary supply trends, and trade with confidence using this user-friendly and informative tool.
Gap Statistics (Zeiierman)█ Overview
The Gap Statistics (Zeiierman) indicator is crafted to monitor, analyze, and visually present price gaps on a trading chart. Price gaps are areas on a chart where the price jumps up or down from the previous close to the next open, creating a "gap" in the normal price pattern. This script delivers an extensive range of statistics related to these gaps, encompassing their size, direction (whether bullish or bearish), frequency of getting filled, as well as the average number of bars it takes for a gap to be filled. The indicator also visually represents the gaps, making it easier for traders to spot and analyze them.
█ How It Works
Gap Identification: The script identifies gaps by comparing the open price of a bar to the close price of the previous bar. If there is a discrepancy between the two, it is recognized as a gap.
Gap Classification: Once a gap is identified, it is classified based on its size (as a percentage of the previous close price) and direction (bullish or bearish). The gap is then added to a specific category based on its size.
Gap Tracking: The script keeps track of all identified gaps using arrays and user-defined types, storing details like their size, direction, and whether they have been filled.
Gap Filling: The script continuously monitors the price to check if any previously identified gaps get filled. A gap is considered filled if the price moves back into the gap area.
Statistics and Alerts: The script calculates various statistics like the total number of gaps, the number of filled gaps, the average number of bars it takes for a gap to fill, and the percentage of gaps that get filled. It also generates alerts when a new gap is identified or an existing gap gets filled.
█ How to Use
Gaps are often classified into four main types:
Common Gaps: These are not associated with any major news and are likely to get filled quickly.
Breakaway Gaps: These occur at the end of a price pattern and signal the beginning of a new trend.
Runaway Gaps: Also known as continuation gaps, these occur in the middle of a trend and signal a surge in interest in the stock.
Exhaustion Gaps: These occur near the end of a price pattern and signal a final attempt to hit new highs or lows.
The Gap Statistics (Zeiierman) indicator enhances a trader's ability to use gaps in their trading strategy in several ways:
Statistical Analysis: Traders get comprehensive statistics on gaps, such as their size, direction, and how often they get filled.
Performance Tracking: The indicator tracks how many bars it typically takes for a gap to fill, providing traders with an average timeframe for gap closure.
█ Settings
Display Gaps: Choose to display "All Gaps," "Active Gaps," or "None."
Show Gap Size: Toggle on/off the display of the gap size.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
RSI Heatmap Screener [ChartPrime]The RSI Heatmap Screener is a versatile trading indicator designed to provide traders and investors with a deep understanding of their selected assets' market dynamics. It offers several key features to facilitate informed decision-making:
█ Custom Asset Selection:
The user can choose up to 30 assets that you want to analyze, allowing for a tailored experience.
█ Adjustable RSI Length:
Customize your analysis by adjusting the RSI length to align with your trading strategy.
█ RSI Heatmap:
The heatmap feature uses various colors to represent RSI values:
█ Color coding for labels:
Grey: Signifies a neutral RSI, indicating a balanced market.
Yellow: Suggests overbought conditions, advising caution.
Pale Red: Indicates mild overbought conditions in a strong area.
Bright Red: Represents strong overbought conditions, hinting at a potential downturn.
Pale Green: Signals mild oversold conditions with signs of recovery.
Dark Green: Denotes full oversold conditions, with potential for a bounce.
Purple: Highlights extremely oversold conditions, pointing to an opportunity for a relief bounce.
█ Levels:
Central Plot and Zones: The central plot displays the average RSI of the selected assets, offering an overview of market sentiment. Overbought and oversold zones in red and green provide clear reference points.
█ Hover Labels:
Hover over an asset to access details on various indicators like VWAP, Stochastic, SMA, TradingView ranking, and Volume Rating. Bullish and bearish indicators are marked with ticks and crosses, and a fire emoji denotes heavily overextended assets.
█ TradingView Ranking:
Utilize the TradingView ranking metric to assess an asset's performance and popularity.
Thank you to @tradingview for this ranking metric.
█ Volume Rating:
Gain insights into trading volumes for more informed decision-making.
█ Oscillator at the Bottom:
The RSI average for the entire market, presented in a normalized format, offers a broader market perspective. Green indicates a favorable buying area, while red suggests market overextension and potential short or sell opportunities.
█ Heatmap Visualization:
Historical RSI values for each selected asset are displayed. Red indicates overbought conditions, while green signals oversold conditions, helping you spot trends and potential turning points.
This screener is designed to make entering the market simpler and more comprehensive for all traders and investors.
Intraday Volume Rating [Honestcowboy]The Intraday Volume Rating aims to provide a clearer picture of what volume is telling you on intraday charts.
What is different to other forms of volume analysis
While Volume averages and other measures of volume base their calculations on the previous bars on the chart (for example bars 1954 to 1968). The average volume in this indicator measures expected volume during that time of the day.
🔷 Why would you take this approach?
Markets behave different during certain times of the day, also called sessions.
Here are a couple examples.
Asian Session (generally low volatility)
London Session (bigger volatility starts)
New York Session (overlap of New York with London creates huge volatility)
Generally when using other types of volume averaging it does not take into account these sessions and that the market has a pattern for volume in an intraday chart.
🔷 CALCULATION
Think of this script like an average of volume but instead it uses past days data instead of previous bars data.
This is a chart explaining the indicator this script is based on The same principle applies but instead we measure volume at each bar of the day.
The script also counts the number of bars that exist in a day on your current timeframe chart. After knowing that number it creates the matrix used in it's calculations and data storage.
See how it works perfectly on a lower timeframe chart below:
Getting this right was the hardest part, check the coding if you are interested in this type of stuff. I commented every step in the coding process.
Every setting of the script is commented so no need for further explanation, enjoy!
Frequency Distribution Overlay [SS]Hello all,
This is the frequency distribution indicator. It does as the name suggests,
It breaks down the frequency distribution of any stock over a user defined lookback period and shows where the accumulations rest by percantage.
This is a function that I used to have to export to Excel or SPSS to do, but now its possible in Pinescript!
Essentially, it breaks down the areas a stock has closed in over a defined period and gives you the accumulation for each area.
What it is used for:
It is used to see where the higher areas of price accumulation rest. This helps us to identify potentially likely retracement areas and pullback areas.
It colour coordinates based on distribution and lists the composition of each zone in a label in each box.
The zones are divided by standard deviation, which means that the top and bottom of each range act as substantial areas of support/resistance (as it falls outside the normal variance of a stock).
Customizability:
The indicator is pretty straight forward, you select your desired lookback period and it will adjust accordingly.
Additionally, you can adjust for close, high, low, etc. if you want to see the accumulation and distribution of hights vs, lows vs closes.
You can toggle off the text labels if you don't want them.
The green boxes represent the areas of highest accumulation, the red box the areas of lower accumulation.
You can use it on any timeframe you wish. Above is an example of the daily, but you can also use it on the smaller TFs as well:
TSLA on the 5 minute:
And that is the indicator!
Let me know if you have questions or suggestions.
Safe trades everyone!
Old Tradability by Kiersten & HajiIntroduction:
The "Old Tradability" is a meticulously crafted indicator designed exclusively for TradingView users. It brings together the power of various well-respected indicators, offering traders a comprehensive tool to gauge market conditions and make informed decisions. Whether you're a novice trader looking for a reliable indicator or a seasoned professional seeking to add another layer to your analytical toolbox, Old Tradability is tailored to provide actionable insights.
Core Features:
Dual Level Analysis:
Long-Term Trend Analysis: At its core, Old Tradability emphasizes the identification of prevailing long-term market trends. To achieve this, it leverages the capabilities of some of the most recognized indicators in the trading world, such as:
MACD (Moving Average Convergence Divergence): Known for its reliability in spotting trend changes and momentum.
MFI (Money Flow Index): A valuable tool to evaluate the flow of money into and out of an asset, often used to predict overbought or oversold conditions.
Heikin Ashi: A unique form of candlestick charting that filters market noise, helping traders understand the market sentiment and trend direction more clearly.
Short-Term Analysis Using MinMax Normalization: The indicator doesn't stop at just identifying the long-term trend. Recognizing the importance of short-term price movements, Old Tradability applies MinMax Normalization on shorter time frames. This technique adjusts the scale of data, making it easier to spot potential reversals or continuation patterns.
Strategic Trading Recommendations:
The principle is simple yet effective. When the long-term trend is bullish and the short-term analysis places the asset in the bottom 20%, it presents a potential buying opportunity. Conversely, if the long-term trend is bearish and the short-term places the asset in the top 20%, traders might consider it as a selling signal.
Integrated Risk Management Alerts:
One of the standout features of Old Tradability is its built-in risk management system. This feature ensures that traders are not only informed about potential trade setups but also about the inherent risks associated.
The system sends out timely alerts for what it deems as "perfect setups," allowing traders to act swiftly and decisively. This minimizes the chance of missing out on lucrative trades while also providing an extra layer of security by notifying users about unfavorable conditions.
Conclusion:
The Old Tradability Indicator is more than just a tool; it's a comprehensive trading companion. Its dual-level analysis ensures that traders have a holistic view of the market, while its integrated risk management alerts keep them one step ahead. If you're looking for a dependable, detailed, and actionable indicator on TradingView, Old Tradability might just be the perfect addition to your trading strategy. Happy trading!
Supertrend Multiasset Correlation - vanAmsen Hello traders!
I am elated to introduce the "Supertrend Multiasset Correlation" , a groundbreaking fusion of the trusted Supertrend with multi-asset correlation insights. This approach offers traders a nuanced, multi-layered perspective of the market.
The Underlying Concept:
Ever pondered over the term Multiasset Correlation?
In the intricate tapestry of financial markets, assets do not operate in silos. Their movements are frequently intertwined, sometimes palpably so, and at other times more covertly. Understanding these correlations can unlock deeper insights into overarching market narratives and directional trends.
By melding the Supertrend with multi-asset correlations, we craft a holistic narrative. This allows traders to fathom not merely the trend of a lone asset but to appreciate its dynamics within a broader market tableau.
Strategy Insights:
At the core of this indicator is its strategic approach. For every asset, a signal is generated based on the Supertrend parameters you've configured. Subsequently, the correlation of daily price changes is assessed. The ultimate signal on the selected asset emerges from the average of the squared correlations, factoring in their direction. This indicator not only accounts for the asset under scrutiny (hence a correlation of 1) but also integrates 12 additional assets. By default, these span U.S. growth ETFs, value ETFs, sector ETFs, bonds, and gold.
Indicator Highlights:
The "Supertrend Multiasset Correlation" isn't your run-of-the-mill Supertrend adaptation. It's a bespoke concoction, tailored to arm traders with an all-encompassing view of market intricacies, fortified with robust correlation metrics.
Key Features:
- Supertrend Line : A crystal-clear visual depiction of the prevailing market trajectory.
- Multiasset Correlation : Delve into the intricate interplay of various assets and their correlation with your primary instrument.
- Interactive Correlation Table : Nestled at the top right, this table offers a succinct overview of correlation metrics.
- Predictive Insights : Leveraging correlations to proffer predictive pointers, adding another layer of conviction to your trades.
Usage Nuances:
- The bullish Supertrend line radiates in a rejuvenating green hue, indicative of potential upward swings.
- On the flip side, the bearish trajectory stands out in a striking red, signaling possible downtrends.
- A rich suite of customization tools ensures that the chart resonates with your trading ethos.
Parting Words:
While the "Supertrend Multiasset Correlation" bestows traders with a rejuvenated perspective, it's paramount to embed it within a comprehensive trading blueprint. This would include blending it with other technical tools and adhering to stringent risk management practices. And remember, before plunging into live trades, always backtest to fine-tune your strategies.
