Source CorrelationIn this small indicator I make it possible for the user to set two different input sources. Then, the indicator displays the correlation of these two input sources. It's a very small script, but I think it could be helpful to somebody to find uncorrelated indicators for his trading strategy. To use uncorrelated indicators is in general recommended.
Enjoy this small, but powerful tool. 🧙♂️
Statistics
ATR Daily BandThis indicator draws an upper and lower band for each day. It uses the Average True Range calculation (with configurable lookback) and places the band at 1ATR above and 1ATR below the daily open.
I use this indicator as a simple gauge to tell how significant price movement is, and get a feel for the daily volatility. Due to the fractal nature of price action, it can be difficult to determine if a price movement is significant while zoomed in on a single intraday chart. Using this indicator, I can tell if the price action is approaching the ATR or if it's just staying within the band.
Strategies: Useful for both mean reversion and momentum strategies. It's up to you to decide how this metric will fit into your trading strategy. I currently use this indicator to look for mean reversion setups, but that is due to the current market conditions and my personal trading style.
Initial Balance Panel Strategy for BitcoinInitial Balance Strategy
Initial Balance Strategy uses a source code of "Initial Balance Monitoring Panel" that build from "Initial Balance Markets Time Zones - Overall Highest and Lowest".
Initial Balance is based on the highest and lowest price action within the first 60 minutes of trading. Reading online this can depict which way the market can trend for the session. More information about Initial Balance Panel you can read at the end of the article.
Strategy idea
The main idea is to catch the trend move when most of the 16 Crypto pairs break the Low or High levels together. I found good results when 15 of 16 pairs is break that levels and after we manage the trade within some trail stop indicator, I choose Volatility Stop for this strategy.
Additional Strategy idea
The second one idea that was not made is to catch the pullback after fully green/red zones in Initial Balance Panel become white. That mean the main trend can be finished and we can try to catch good pullback in opposite direction.
Binance Crypto pairs
The strategy use the 16 default Crypto currencies pairs from the Binance. As additional variations of the strategy can be changing the currencies pairs and their number.
List of default pairs:
BINANCE:BTCUSDT, BINANCE:ETHUSDT, BINANCE:EOSUSDT, BINANCE:LTCUSDT, BINANCE:XRPUSDT, BINANCE:DASHUSDT, BINANCE:IOTAUSDT, BINANCE:NEOUSDT, BINANCE:QTUMUSDT, BINANCE:XMRUSDT, BINANCE:ZECUSDT, BINANCE:ETCUSDT, BINANCE:ADAUSDT, BINANCE:XTZUSDT, BINANCE:LINKUSDT, BINANCE:DOTUSDT
Summary
The strategy works very well for a buy trades with settings 15 crypto pairs of 16 that follow the trend with breaking the long initial balance level.
Initial Balance Monitoring Panel
Allows you to have an instant view of 16 Crypto pairs within a monitoring panel, monitoring Initial Balance (Asia, London, New York Stock Exchanges).
The code can easily be changed to suit the crypto pairs you are trading.
The setup of my chart would also include this indicator and the "Initial Balance Markets Time Zones - Overall Highest and Lowest" (with all IBs enabled) as shown above.
Initial Balance is based on the highest and lowest price action within the first 60 minutes of trading. Reading online this can depict which way the market can trend for the session.
The indicator has been coded for Crypto (so other symbols may not work as expected).
Though Initial Balance is based off the first 60 minutes of the trading markets opening, but Crypto is 24/7, this indicator looks at how Asia, London and New York Stock Exchanges opening trading can affect Crypto price action.
Source: Initial Balance Monitoring Panel
Position and Profit/LossHelps users track their position and profit/loss in real-time.
Instructions :
Open the indicator settings
Input your Quantity, Buy Price, Fee, and Target Price
This indicator is designed to provide users with simple real-time tracking of their positions and profit/loss within a trading session. It offers clear and concise information that enables users to understand their current position's profitability, making it easier for them to manage their trades effectively.
Input parameters
qty : Quantity of the position (default value: 100.0). The target label is represented by a green cross
buy_price : Buy price of the position (default value: 1.0).
fee : Fee percentage for the transaction (default value: 0.0016). note that this is not a percentage, but rather a decimal. So 0.0016 is 0.16%
target : Target price for the position (default value: 1.0). This is an extra label to show you where your target is on the chart. The target label is represented by a green cross
In addition to the main profit/loss label, the script also displays two auxiliary labels. The "BuyPrice" label presents the buy price of the position as a red cross symbol on the chart, allowing users to easily identify their entry point. The "targetSell" label displays the target sell price as green cross symbol, indicating the desired exit point for the position. These visual markers help users visualize their trading strategy.
The script takes into account that users may only need this information displayed on the last bar, as continuous updates might not be necessary. By checking if the current bar is the last one, the script ensures that the labels are only displayed when relevant.
Limitations
The script assumes that trading is done using the same quantity; which is not always the case. This will change with subsequent updates.
Statistics TableThis script display some useful Statistics data that can be useful in making trading decision.
Here the list of information this script is display in table format.
You can change each and every single ema and rs length as per your need from setting.
1) close difference from first ema
2) close difference from second ema
3) close difference from third ema
4) close difference from fourth ema
5) difference between first and second ema
6) difference between second and third ema
7) difference between first and third ema
8) volume up down ratio
9) ATR/ADR %
10) volume pocket pivot count
11) daily closing range
12) weekly closing range
13) close difference from 52week high
14) close difference from 52week low
15) close difference from All time high
16) close difference from All time low
17) rs line above or below first rs ema
18) rs line above or below second rs ema
19) rs line above or below third rs ema
20) rs line above or below fourth rs ema
21) first rs value
22) second rs value
23) third rs value
24) fourth rs value
25) difference between previous first rs length days change % and current first rs length days change %
26) difference between previous second rs length days change % and current second rs length days change %
27) difference between previous third rs length days change % and current third rs length days change %
Time Series Model IndicatorHello,
I am releasing this time series modelling indicator.
Brief overview of the indicator's functionality:
The Time Series Model indicator is a technical analysis tool that calculates and visualizes a linear regression line based on historical price data. It assesses the trend direction and provides an outer band around the regression line to indicate potential support and resistance levels. The indicator also detects outliers in the price data and calculates correlations between the time variable and the closing price. It offers various customization options such as input length, user-defined hours in advance, display settings for tables and fills, and the ability to show variable correlations. Overall, this indicator aims to help traders identify trends, potential reversals, and price extremes in a given time series.
Specific Functions:
Slope Calculations: The indicator calculates the slope and intercept of the regression line using the specified length of assessment (user defined). It also computes the residuals, standard error of the regression, and the upper and lower bounds of the standard error region. Additionally, it calculates multiple standard deviation bands around the regression line. The slope will change to green if the stock is in an uptrend and to red if the stock is in a downtrend.
Outliers: This feature detects extreme positive and negative outliers based on the z-score calculated from the price data. It highlights the outliers with a red background color to red if this option is selected.
Correlation to Time Assessments: This feature performs trend assessments based on the correlation between time and price data. It identifies uptrends, downtrends, falling trends, rising trends, etc.
Outerband Plots: This feature plots the regression line, standard error bands, and multiple standard deviation bands around the regression line. It also fills the areas between these lines.
Trend Assessment: This feature further assesses the trend based on the strength of the correlation. It identifies strong up or down trends, moderate trends, weak trends, no trend, etc.
Linear Regression Time Data: This section retrieves price data (close, high, low, open) for the specified timeframe and stores them in arrays for a linear regression analysis.
Define LinReg Variables: This section calculates linear regression lines and their upper and lower control limits for the close, low and high prices. It also calculates the correlation between close price and time.
Manual assessments: This feature allows for the manual assessment of time series data. The user can input a look forward for hours in the future and get the predicted price range based on the current time relationship. See image below:
Calculating model "fit": The indicator will display the amount of time the stock closes within and outside its respective bands to ascertain the degree of "fit" (see image below):
Explanations:
The outer cloud: The outer, tealish green cloud represents the regression line + 1.5 standard deviations from the regression line.
The inner cloud: The inner, white coloured cloud represents the immediate time series range calculated through regression of the open, high and low price of the ticker.
Correlations:
The ability of the indicator to calculate correlations on both the smaller and larger timeframes are its strongest feature. You can see the formation of trends by tracking the correlation over the length of the time series model's assessment. You can also track the degree of change. The image below shows the correlation table:
In this image, we can see that the stock is in a moderate downtrend manifested by a correlation of -0.73 (purple arrow).
This downtrend is weakening as manifested by a positive change of 0.05 on the shorter timeframe.
If we scroll down on the table and see the Close, High and Low, we can see that the larger trend over time is a downtrend and that this downtrend is actually strengthening. We know this by the negative change (negative change = significant inverse relationship to time is increasing. i.e. as time increases, the stock price decreases proportionately).
So what does negative correlation to time mean?
If a stock's price exhibits a negative correlation to time, it implies that there is a systematic relationship between the passage of time and the stock's price movement in the opposite direction. This finding could have several potential implications for traders and investors. Firstly, it suggests that the stock's price tends to decrease as time progresses, indicating a downward trend or bearish sentiment. This information might be useful for traders looking to capitalize on short-selling or hedging strategies. Secondly, it could indicate a potential opportunity to predict future price movements based on the timing of negative correlations. By understanding the relationship between time and price, investors may be able to make more informed decisions about when to buy or sell the stock. Lastly, a negative correlation to time may also suggest the influence of external factors or market conditions that systematically impact the stock's performance over time. Therefore, monitoring this correlation can provide insights into broader market dynamics and help investors better understand the stock's behavior.
What about a positive correlation to time?
If a stock's price demonstrates a positive correlation to time, it means that there is a consistent relationship between the passage of time and the stock's price movement in the same direction. This positive correlation to time can have significant implications for traders and investors. Firstly, it indicates a potential upward trend or bullish sentiment, suggesting that the stock's price tends to increase as time progresses. This information can be valuable for investors seeking long-term growth opportunities or looking to capitalize on upward price movements. Secondly, a positive correlation to time may provide insights into the stock's historical performance patterns and help identify potential buying or selling opportunities based on the timing of positive correlations. Additionally, understanding this correlation can aid in assessing the stock's overall trajectory and identifying potential market trends. It's important to note that positive correlation to time does not guarantee future performance, but it can offer valuable information to inform investment decisions.
Because this indicator is pretty big, I have done an overview and tutorial video which I will link below:
As always, please leave your comments and suggestions below.
I thank you for taking the time to read and check out this indicator.
Safe trades everyone and enjoy your weekend!
Cobra's CryptoMarket VisualizerCobra's Crypto Market Screener is designed to provide a comprehensive overview of the top 40 marketcap cryptocurrencies in a table\heatmap format. This indicator incorporates essential metrics such as Beta, Alpha, Sharpe Ratio, Sortino Ratio, Omega Ratio, Z-Score, and Average Daily Range (ADR). The table utilizes cell coloring resembling a heatmap, allowing for quick visual analysis and comparison of multiple cryptocurrencies.
The indicator also includes a shortened explanation tooltip of each metric when hovering over it's respected cell. I shall elaborate on each here for anyone interested.
Metric Descriptions:
1. Beta: measures the sensitivity of an asset's returns to the overall market returns. It indicates how much the asset's price is likely to move in relation to a benchmark index. A beta of 1 suggests the asset moves in line with the market, while a beta greater than 1 implies the asset is more volatile, and a beta less than 1 suggests lower volatility.
2. Alpha: is a measure of the excess return generated by an investment compared to its expected return, given its risk (as indicated by its beta). It assesses the performance of an investment after adjusting for market risk. Positive alpha indicates outperformance, while negative alpha suggests underperformance.
3. Sharpe Ratio: measures the risk-adjusted return of an investment or portfolio. It evaluates the excess return earned per unit of risk taken. A higher Sharpe ratio indicates better risk-adjusted performance, as it reflects a higher return for each unit of volatility or risk.
4. Sortino Ratio: is a risk-adjusted measure similar to the Sharpe ratio but focuses only on downside risk. It considers the excess return per unit of downside volatility. The Sortino ratio emphasizes the risk associated with below-target returns and is particularly useful for assessing investments with asymmetric risk profiles.
5. Omega Ratio: measures the ratio of the cumulative average positive returns to the cumulative average negative returns. It assesses the reward-to-risk ratio by considering both upside and downside performance. A higher Omega ratio indicates a higher reward relative to the risk taken.
6. Z-Score: is a statistical measure that represents the number of standard deviations a data point is from the mean of a dataset. In finance, the Z-score is commonly used to assess the financial health or risk of a company. It quantifies the distance of a company's financial ratios from the average and provides insight into its relative position.
7. Average Daily Range: ADR represents the average range of price movement of an asset during a trading day. It measures the average difference between the high and low prices over a specific period. Traders use ADR to gauge the potential price range within which an asset might fluctuate during a typical trading session.
Utility:
Comprehensive Overview: The indicator allows for monitoring up to 40 cryptocurrencies simultaneously, providing a consolidated view of essential metrics in a single table.
Efficient Comparison: The heatmap-like coloring of the cells enables easy visual comparison of different cryptocurrencies, helping identify relative strengths and weaknesses.
Risk Assessment: Metrics such as Beta, Alpha, Sharpe Ratio, Sortino Ratio, and Omega Ratio offer insights into the risk associated with each cryptocurrency, aiding risk assessment and portfolio management decisions.
Performance Evaluation: The Alpha, Sharpe Ratio, and Sortino Ratio provide measures of a cryptocurrency's performance adjusted for risk. This helps assess investment performance over time and across different assets.
Market Analysis: By considering the Z-Score and Average Daily Range (ADR), traders can evaluate the financial health and potential price volatility of cryptocurrencies, aiding in trade selection and risk management.
Features:
Reference period optimization, alpha and ADR in particular
Source calculation
Table sizing and positioning options to fit the user's screen size.
Tooltips
Important Notes -
1. The Sharpe, Sortino and Omega ratios cell coloring threshold might be subjective, I did the best I can to gauge the median value of each to provide more accurate coloring sentiment, it may change in the future.
The median values are : Sharpe -1, Sortino - 1.5, Omega - 20.
2. Limitations - Some cryptos have a Z-Score value of NaN due to their short lifetime, I tried to overcome this issue as with the rest of the metrics as best I can. Moreover, it limits the time horizon for replay mode to somewhere around Q3 of 2021 and that's with using the split option of the top half, to remain with the older cryptos.
3. For the beginner Pine enthusiasts, I recommend scimming through the script as it serves as a prime example of using key features, to name a few : Arrays, User Defined Functions, User Defined Types, For loops, Switches and Tables.
4. Beta and Alpha's benchmark instrument is BTC, due to cryptos volatility I saw no reason to use SPY or any other asset for that matter.
Autoregressive CloudHello,
I am releasing this indicator called the Autoregressive Cloud Indicator.
What it does:
The indicator performs an autoregression analysis on 3 price variables of a ticker, those being the High, the Low and the Close. It uses a 1-lag system and looks back at the previous close, high and low’s effect on the proceeding high, low and close. It then plots out the anticipated range for the ticker based on the autoregression analysis, as well as displays the lag-correlation (autocorrelation) in a table.
What is Autoregression analysis?
Autoregression is a modelling technique used to describe a time series based on its own past values. It assumes that the current value of a variable is a linear combination of its previous values and a random error term.
And what is autocorrelation?
Autocorrelation measures the correlation between a time series and its lagged values. It quantifies the degree to which the current value of a series is related to its past values at different lags, indicating any patterns or dependencies in the data over time. Autoregression and autocorrelation are closely related concepts used to analyze and model time series data.
So how does it work?
The indicator calculates autoregressive values for the close, high, and low prices of a security based on the specified lookback length (which is defaulted to 50). It then plots three sets of clouds representing the smoothed autoregressive values for each price component (done using the SMA function). The transparency of the clouds can be adjusted using the "Transparency" input. Additionally, the code includes a correlation table that displays the correlation coefficients between the lagged values of the close, high, and low prices. The table's position can be customized using the "Position" input.
The indicator defaults to the chart timeframe; however, you can manually adjust the indicator to display the range for whatever timeframe you would like. You can view the 30 minute, 15 or even hourly range on the 1 minute or 5 minute chart if you want.
The indicator will show the anticipated “true trading range” of the stock based on the autoregression and autocorrelation of all 3 variables:
Above is SPY on the 5 minute timeframe with 15 minute levels overlayed. Here, you can see the anticipated trading range for that 15 minute time period.
Using the Correlation Table:
The correlation table displays the Pearson Coefficient for all 3 autoregressions.
A positive correlation: A positive autocorrelation indicates a positive relationship between past and current values of a time series variable. It suggests that when the variable has a high value at a certain time, it is more likely to have a high value in the future, and when it has a low value, it is more likely to have a low value in the future. This positive autocorrelation can imply persistence or trend in the data, indicating that past values can provide useful information for predicting future values. The rule of thumb is anything over 0.5 is considered significant.
A positive correlation among all 3 variables also indicates an uptrend. If you see a strong positive (i.e. the values are all greater than 0.8), it indicates an incredibly decisive and strong uptrend.
A negative correlation: A negative autocorrelation indicates an inverse relationship between past and current values of a time series variable. It suggests that when the variable has a high value at a certain time, it is more likely to have a low value in the future, and vice versa. This negative autocorrelation can imply mean reversion or oscillatory behavior in the data, where extreme values tend to be followed by values closer to the average. It indicates that past values can provide useful information for predicting future values by anticipating a reversal in the direction of the variable. The rule of thumb is anything below or equal to -0.5 is considered significant.
A negative correlation among all 3 variables also indicates a downtrend. If you see a strong negative (i.e. the values are all less than or equal to -0.8), it indicates an incredibly decisive and strong downtrend.
Uses of the Indicator:
The indicator can be used for the following functions:
1. Day trading and scalping within an expected range;
2. Determining the strength or weakness of an uptrend or downtrend on various timeframes;
3. Determining the relationship between previous values and past performance and its effect on future performance;
4. Can alert to changes in trend direction in advance (you may see high, low or close turn negative before others, signifying that weakness is beginning to materialize in an uptrend, or inverse in a downtrend (value changes positive)).
Customizability:
SMA: The autoregression data is smoothed by a 3 period lookback. You can change this if you want, but in order for the indicator to present the true trading range, it is recommended to leave it at <= 3.
Lookback Length: This is the length of the lookback period for the autoregression and autocorrelation functions.
Transparency settings: You can adjust the transparency of the clouds manually.
Timeframe: You can adjust the timeframe, as explained above, to display the timeframe of interest. When you adjust the timeframe, the data will all reflect that timeframe and not necessarily the current TF you have open (i.e. you select 30 minutes while viewing it on the 5 minute, it will show the data for the 30 minute TF period).
Video Tutorial:
I have prepared a video outlining the indicator and also explaining the theory of autoregression/correlation. You can find it below:
Let me know any comments, questions or suggestions below.
Thank you for taking the time to read/watch and check out this indicator.
Safe trades everyone!
MTF Stationary Extreme IndicatorThe Multiple Timeframe Stationary Extreme Indicator is designed to help traders identify extreme price movements across different timeframes. By analyzing extremes in price action, this indicator aims to provide valuable insights into potential overbought and oversold conditions, offering opportunities for trading decisions.
The indicator operates by calculating the difference between the latest high/low and the high/low a specified number of periods back. This difference is expressed as a percentage, allowing for easy comparison and interpretation. Positive values indicate an increase in the extreme, while negative values suggest a decrease.
One of the unique features of this indicator is its ability to incorporate multiple timeframes. Traders can choose a higher timeframe to analyze alongside the current timeframe, providing a broader perspective on market dynamics. This feature enables a comprehensive assessment of extreme price movements, considering both short-term and longer-term trends.
By observing extreme movements on different timeframes, traders can gain deeper insights into market conditions. This can help in identifying potential areas of confluence or divergence, supporting more informed trading decisions. For example, when extreme movements align across multiple timeframes, it may indicate a higher probability of a significant price reversal or continuation.
To use the Multiple Timeframe Stationary Extreme Indicator effectively, traders should consider a few key points:
- Choose the Timeframes : Select the appropriate timeframes based on your trading strategy and objectives. The current timeframe represents the focus of your analysis, while the higher timeframe provides a broader context. Ensure the chosen timeframes align with your trading style and the asset you are trading.
- Interpret Extreme Movements : Pay attention to extreme movements that breach certain levels. Values above zero indicate a rise in the extreme, potentially signaling overbought conditions. Conversely, values below zero suggest a decrease, potentially indicating oversold conditions. Use these extreme movements as potential entry or exit signals, in conjunction with other indicators or confirmation signals.
- Validate with Price Action : Confirm the extreme movements observed on the indicator with price action. Look for confluence between the indicator's extreme levels and key support or resistance levels, trendlines, or chart patterns. This can provide added confirmation and increase the reliability of the signals generated by the indicator.
- Consider Volatility Filters : The indicator can be enhanced by incorporating volatility filters. By adjusting the sensitivity of the extreme differences calculation based on market volatility, traders can adapt the indicator to different market conditions. Higher volatility may require a longer lookback period, while lower volatility may call for a shorter one. Experiment with volatility filters to fine-tune the indicator's performance.
- Combine with Other Analysis Techniques : The Multiple Timeframe Stationary Extreme Indicator is most effective when used as part of a comprehensive trading strategy. Combine it with other technical analysis tools, such as trend indicators, oscillators, or chart patterns, to form a well-rounded approach. Consider risk management techniques and money management principles to optimize your trading strategy.
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Remember that trading indicators, including the Multiple Timeframe Stationary Extreme Indicator, should not be used in isolation. They serve as tools to assist in decision-making, but they require proper context, analysis, and confirmation. Always conduct thorough analysis and consider market conditions, news events, and other relevant factors before making trading decisions.
It's recommended to backtest the indicator on historical data to assess its performance and effectiveness for your trading approach. This will help you understand its strengths and limitations, allowing you to refine and optimize your usage of the indicator.
Correlation Coefficient - DXY & XAUPublishing my first indicator on TradingView. Essentially a modification of the Correlation Coefficient indicator, that displays a 2 ticker symbols' correlation coefficient vs, the chart presently loaded.. You can modify the symbols, but the default uses DXY and XAU, which have been displaying strong negative correlation.
As with the built-in CC (Correlation Coefficient) indicator, readings are taken the same way:
Positive Correlation = anything above 0 | stronger as it moves up towards 1 | weaker as it moves back down towards 0
Negative Correlation = anything below 0 | stronger moving down towards -1 | weaker moving back up towards 0
This is primarily created to work with the Bitcoin weekly chart, for comparing DXY and Gold (XAU) price correlations (in advance, when possible). If you change the chart timeframe to something other than weekly, consider playing with the Length input, which is set to 35 by default where I think it best represents correlations with Bitcoin's weekly timeframe for DXY and Gold.
The intention is that you might be able to determine future direction of Bitcoin based on positive or negative correlations of Gold and/or the US Dollar Index. DXY has been making peaks and valleys prior to Bitcoin since after March 2020 black swan event, where it peaked just after instead. In the future, it may flip over again and Bitcoin may hit major highs or lows prior to DXY, again. So, keep an eye on the charts for all 3, as well as the indicator correlations.
Currently, we've moved back into negative correlation between Bitcoin and DXY, and positive correlation with Bitcoin and Gold:
Negative Correlation b/w Bitcoin and DXY - if DXY moves up, Bitcoin likely moves down, or if DXY moves down, Bitcoin likely moves up (or if Bitcoin were to move first before DXY, as it did on March 2020, instead)
Positive Correlation b/w Bitcoin and Gold - Bitcoin and Gold will likely move up or down with each other.
DXY is represented by the green histogram and label, Gold is represented by the yellow histogram and label. Again, you can modify the tickers you want to check against, and you can modify the colors for their histograms / labels.
The inspiration from came from noticing areas of same date or delayed negative correlation between Bitcoin and DXY, here is one of my most recent posts about that:
Please let me know if you have any questions, or would like to see updates to the indicator to make it easier to use or add more useful features to it.
I hope this becomes useful to you in some way. Thank you for your support!
Cheers,
dudebruhwhoa :)
MA Correlation CoefficientThis script helps you visualize the correlation between the price of an asset and 4 moving averages of your choice. This indicator can help you identify trendy markets as well as trend-shifts.
Disclaimer
Bear in mind that there is always some lag when using Moving-Averages, hence the purpose of this indicator is as a trend identification tool rather than an entry-exit strategy.
Working Principle
The basic idea behind this indicator is the following:
In a trendy market you will find high correlation between price and all kinds of Moving-Averages. This works both ways, no matter bull or bear trend.
In sideways markets you might find a mix of correlations accross timeframes (2018) or high correlation with Low-Timeframe averages and low correlation with High-Timeframe averages (2021/2022).
Trend shifts might be characterised by a 'staircase' type of correlation (yellow), where the asset regains correlation with higher timeframe averages
Indicator Options
1. Source : data used for indicator calculation
1. Correlation Window : size of moving window for correlation calculation
2. Average Type :
Simple-Moving-Average (SMA)
Exponential-Moving-Average (EMA)
Hull-Moving-Average (HMA)
Volume-Weighted-Moving-Average (VWMA)
3. Lookback : number of past candles to calculate average
4. Gradient : modify gradient colors. colors relate to correlation values.
Plot Explanation
The indicator plots, using colors, the correlation of the asset with 4 averages. For every candle, 4 correlation values are generated, corresponding to 4 colors. These 4 colors are stacked one on top of the other generating the patterns explained above. These patterns may help you identify what kind of market you're in.
Session KillZones [7Bridges]Session Killzones by 7Bridges indicator display the killzones of asian, LND and NY sessions. There is also a custom session of your choice.
The times of each killzone are GMT time and you can adjust it in the settings.
You have also the beginning of the day, GMT and EST timezones.
By default the killzones are set like that on the GMT/UTC timezone :
-> Asia : 00:00 - 06:00
-> Pre London : 06:00 - 07:00
-> London : 07:00 - 10:00
-> New York : 12:00 - 15:00
-> Custom session : choose your own time
What makes the indicator very different is that the session is not overlapping the price but you have bars below and above the price.
Settings:
-> you can chose to display the Killzones (Asia, pre LND, LND and NY)
-> you can manages the time of the sessions
-> you can chose to display the start of the day (GMT/UTC and EST )
The indicator is displayed by default only for all the timeframes below 60min.
Variance WindowsJust a quick trial at using statistical variance/standard deviation as an indicator. The general idea is that higher variance in the short term tends to indicate more volatility/movement. The other thing is that it can help set probabilistic boundaries for movements (e.g., if you set the bars to be 2 standard deviations, you are visualizing a range that denotes a 95% probability window).
I haven't really tried forming any sort of strategies around this indicator, but there are a few potential possibilities for its usability.
Generally speaking, the magnitude of the standard deviation (relative to the price) is small when the market is consolidating. It is larger when the market is trending up or own.
If the long term variance and the short-term variance are close to each other in scale, the trend is strong. Otherwise, the trend is weak. Note that I am only saying that the "trend" is strong , not that it is necessarily positive. this could be an up-trend, down-trend, or a sideways trend.
When the magnitudes of the variances are changing from very similar to very different (usually it's the long-term variance getting much larger than the short-term one), that's an indication that the previous trend is coming to an end.
Typically, it's the long-term variance that is bigger than the short-term. However, when you see them cross where the short-term is bigger or even much bigger than the long-term, it's indicative of a spike event (more often than not, one that is not favorable if you are holding any position on a given security).
Because you have probabilistic windows based on some n standard deviations from the midline (which in this version, I've used a ZLEMA as that midline), those boundaries could possibly be used to set stop-loss limits and the like.
There's nothing too complicated or deep about this particular indicator. All I'm really doing is assuming that we are dealing with a Gaussian random process. I am actually using EMA as my mean computation, even though for a proper Gaussian variance calculation, I should be using SMA. When I used SMA, though, it felt a lot more sensitive to noise, which made it feel less usable. In any case, it's just a simple first trial in many years after not having even looked at Pine Script to finally messing around with it again. Open to a litany of criticisms as I'm sure there will be many that are rightly deserved. Otherwise, happy scalping to thee.
Crypto Correlation MatrixA crypto correlation matrix or table is a tool that displays the correlation between different cryptocurrencies and other financial assets. The matrix provides an overview of the degree to which various cryptocurrencies move in tandem or independently of each other. Each cell represents the correlation between the row and column assets respectively.
The correlation matrix can be useful for traders and investors in several ways:
First, it allows them to identify trends and patterns in the behavior of different cryptocurrencies. By looking at the correlations between different assets, traders can gain insight into the intra-relationships of the crypto market and make more informed trading decisions. For example, if two cryptocurrencies have a high positive correlation, meaning that they tend to move in the same direction, a trader may want to diversify their portfolio by choosing to invest in only one of the two assets.
Additionally, the correlation matrix can help traders and investors to manage risk. By analyzing the correlations between different assets, traders can identify opportunities to hedge their positions or limit their exposure to particular risks. For example, if a trader holds a portfolio of cryptocurrencies that are highly correlated with each other, they may be at greater risk of losses if the market moves against them. By diversifying their portfolio with assets that are less correlated with each other, they can reduce their overall risk.
Some of the unique properties for this specific script are the correlation strength levels in conjunction with the color gradient of cells, intended for clearer readability.
Features:
Supports up to 64 different crypto assets.
Dark/Light mode.
Correlation strength levels and cell coloring.
Adjustable positioning on the chart.
Alerts at the close of a bar. (Daily timeframe or higher recommended)
market sessions by sellstreetIndicator of trading sessions:
Indicator created to track the opening of trading sessions:
Asia, Frankfurt, London, New York.
Tracking the opening of these key levels
- Day Opening (DO), Week Opening (WO), Month Opening (MO).
- New York (NYM) openings display.
- Highs and lows of the previous day (PDH/PDL).
- Day of the week display.
- Formation of the Сentral Bank Dealers Range (CBDR).
Indicator settings.
The open source code will help traders to understand the technical part of the script.
Flexible visual and technical setup of the indicator:
- Ability to enable/disable the display of trading sessions on the history.
- Enabling/disabling the display of the key opening levels on the chart history for a convenient backtest.
- Automatically switch to summer/winter time.
To use this indicator, add it to your favorites after the chart
TradingView must be overloaded to work correctly.
Reverse Repo CorrelationReverse Repo Correlation Indicator
This TradingView indicator calculates the correlation between the current stock's close price and the value of the Reverse Repo Rate (`RRPONTSYD`). It uses the Pearson correlation coefficient to measure the strength and direction of the relationship.
Inputs
- **Correlation Length**: The number of bars used to calculate the correlation.
- **Background Transparency**: The transparency level (0-100) for the background color indicating positive or negative correlation.
### How it works
1. The indicator retrieves the close price of the current stock and assigns it to the `stockClose` variable.
2. The **Correlation Length** input determines the number of bars used to calculate the correlation.
3. The `pearson_corr` function calculates the Pearson correlation between the `stockClose` and `rrpontsydValue` variables over the specified length.
4. The `rrpontsydValue` is retrieved using the `request.economic` function, which fetches the Reverse Repo Rate value (`RRPONTSYD`) for the "US" economic calendar.
5. The correlation value is plotted on the chart as a line, with positive correlations displayed in green and negative correlations in red.
6. The **Background Transparency** input determines the transparency level of the background color, which changes based on the correlation value. Positive correlations have a green background, while negative correlations have a red background.
Adjust the `correlationLength` and `transparency` inputs as needed.
Cross Period Comparison IndicatorReally excited to be sharing this indicator!
This is the cross-period comparison indicator, AKA the comparison indicator.
What does it do?
The cross-period comparison indicator permits for the qualitative assessment of two points in time on a particular equity.
What is its use?
At first, I was looking for a way to determine the degree of similarity between two points, such as using Cosine similarity values, Euclidean distances, etc. However, these tend to trigger a lot of similarities but without really any context. Context matters in trading and thus what I wanted really was a qualitative assessment tool to see what exactly was happening at two points in time (i.e. How many buyers were there? What was short interest like? What was volume like? What was the volatility like? RSI? Etc.)
This indicator permits that qualitative assessment, displaying things like total buying volume during each period, total selling volume, short interest via Put to Call ratio activity, technical information such as Stochastics and RSI, etc.
How to use it?
The indicator is fairly self explanatory, but some things require a little more in-depth discussion.
The indicator will display the Max and Min technical values of a period, as well as a breakdown in the volume information and put to call information. The user can then make the qualitative determination of degrees of similarity. However, I have included some key things to help ascertain similarity in a more quantitative way. These include:
1. Adding average period Z-Score
2. Adding CDF probability distributions for each respective period
3. Adding Pearson correlations for each respective period over time
4. Providing the linear regression equation for each period
So let us discuss these 4 quantitative measures a bit more in-depth.
Adding Period Z-Score
For those who do not know, Z-Score is a measure of the distance from a mean. It generally spans 0 (at the mean) to 3 (3 standard deviations away from the mean). Z-Score in the stock market is very powerful because it is actually our indicator of volatility. Z-Score forms the basis of IV for option traders and it generally is the go to, to see where the market is in relation to its overall mean.
Adding Z-Score lets the user make 2 big determinations. First and foremost, it’s a measure of overall volatility during the period. If you are getting a Z-Score that is crazy high (1.5 or greater), you know there was a lot of volatility in that period marked by frequent deviations from its mean (since on average it was trading 1.5 standard deviations away from its mean).
The other thing it tells you is the overall sentiment of that time. If the average Z Score was 1.5 for example, we know that buying interest was high and the sentiment was somewhat optimistic, as the stock was trading, on average, + 1.5 SDs away from its mean.
If, on the other hand, the average was, say, - 1.2, then we know the sentiment was overall pessimistic. There was frequent selling and the stock was frequently being pushed below its mean with heavy selling pressure.
We can also check these assumptions of buying / selling buy verifying the volume information. The indicator will list the Buy to Sell Ratio (number of Buyers to Sellers), as well as the total selling volume and total buying volume. Thus, the user can see, objectively, whether sellers or buyers led a particular period.
Adding CDF Probability
CDF probabilities simply mean the extent a stock traded above or below its normal distribution levels.
To help you understand this, the indicator lists the average close price for a period. Directly below that, it lists the CDF probabilities. What this is telling you, is how often and how likely, during that period, the stock was trading below its average. For example, in the main chart, the average close price for BTC in Period A is 29869. The CDF probability is 0.51. This means, during Period A, 51% of the time, BTC was trading BELOW 29869. Thus, the other 49% of the time it was trading ABOVE 29869.
CDF probabilities also help us to assess volatility, similar to Z-Score. Generally speaking, the CDF should consistently be reading about 0.50 to 0.51. This is the point of an average value, half the values should be above the average and half the values should be below. But in times of heightened volatility, you may actually see the CDF creep up to 0.54 or higher, or 0.48 or lower. This means that there was extremely extensive volatility and is very indicative of true “whipsaw” type price action history where a stock refuses to average itself out in one general area and frequently jumps up and down.
Adding Pearson Correlation
Most know what this is, but just in case, the Pearson correlation is a measure of statistical significance. It ranges from 0 (not significant) to 1 (very significant). It can be positive or negative. A positive signifies a positive relationship (i.e. as one value increases so too does the other value being compared). If it is a negative value, it means an inverse relationship (i.e. one value increases proportionately to the other’s decline).
In this indicator, the Pearson correlation is measured against time. A strong positive relationship (a value of 0.5 or greater) indicates that the stock is trading positive to time. As time goes by, the stock goes up. This is a normal relationship and signifies a healthy uptrend.
Inversely, if the Pearson correlation is negative, it means that as time increases, the stock is going down proportionately. This signifies a strong downtrend.
This is another way for the user to interpret sentiment during a specific period.
IF the Pearson correlation is less than 0.5 or -0.5, this signifies an area of indecision. No real trend formed and there was no real strong relationship to time.
Adding Linear Regression Equation
A linear regression equation is simply the slope and the intercept. It is expressed with the formula y= mx + b.
The indicator does a regression analysis on each period and presents this formula accordingly. The user can see the slope and intercept.
Generally speaking, when two periods share the same slope (m value) but different intercept (b value), it can be said that the relationship to time is identical but the starting point is different.
If the slope and intercept are different, as you see in the BTC chart above, it represents a completely different relationship to time and trajectory.
Indicator Specific Information:
The indicator retains the customizability you would expect. You can customize all of your lengths for technical, change and Z-Score. You can toggle on or off Period data, if you want to focus on a single period. You can also toggle on a difference table that directly compares the % difference between Period A to Period B (see image below):
You will also see on the input menu a input for “Threshold” assessments. This simply modifies the threshold parameters for the technical readings. It is defaulted to 3, which means when two technical (for example Max Stochastics) are within +/- 3 of each other, the indicator will light these up as green to indicate similarities. They just clue the user visually to areas where there are similarities amongst the qualitative technical data.
Timeframes
This is best used on the daily timeframe. You can use it on the smaller timeframe but the processing time may take a bit longer. I personally like it for the Daily, Weekly and 4 hour charts.
And this is the indicator in a nutshell!
I will provide a tutorial video in the coming day on how to use it, so check back later!
As always, leave your comments/questions and suggestions below. I have been slowly modifying stuff based on user suggestions so please keep them coming but be patient as it does take some time and I am by no means a coder or expert on this stuff.
Safe trades to all!
Rate of DeviationThe Rate of Deviation indicator calculates and displays the amount the current price varies above or below the average price over Length bars. A deviation value greater than the base level indicates that the current price is higher than the price average while a deviation less than the base level indicates that the current price is lower than the price average.
StatBox📊 StatBox: A Comprehensive Trading Indicator for RSI, Volume Percent, and ADD 📈💼
Introducing StatBox, the ultimate trading indicator designed to provide traders with a powerful analytical toolset for making informed trading decisions. With StatBox, you gain access to real-time data on Relative Strength Index (RSI), Volume Percent, and ADD (Advance/Decline Differential). This dynamic combination of indicators empowers you to navigate the market with greater precision and confidence. 📊🔍
Key Features of StatBox:
1️⃣ RSI (Relative Strength Index): RSI is a widely recognized momentum oscillator that measures the speed and change of price movements. StatBox displays RSI as a numerical value, ranging from 0 to 100, allowing you to quickly assess whether a security is overbought or oversold. This information is invaluable for identifying potential reversal points and optimizing entry or exit strategies.
2️⃣ Volume Percent: StatBox provides a visual representation of the Volume Percent, which reflects the relative trading volume compared to a specified period. By monitoring volume dynamics, you gain insights into market sentiment and potential price trends. A higher volume percentage often indicates stronger market participation, suggesting increased interest in a particular security.
3️⃣ ADD (Advance/Decline Differential): ADD is a breadth indicator that calculates the difference between advancing (upward moving) and declining (downward moving) securities. StatBox presents ADD as a histogram, enabling you to assess the overall strength or weakness of the market. Positive values indicate bullish sentiment, while negative values suggest bearish sentiment. By tracking ADD, you can identify potential market reversals or confirm existing trends.
With StatBox, you can:
✅ Quickly gauge the overbought or oversold conditions of a security using RSI.
✅ Monitor volume dynamics to assess market sentiment and potential price trends.
✅ Analyze the breadth of the market and identify bullish or bearish signals with ADD.
✅ Make well-informed trading decisions based on a comprehensive view of multiple indicators.
StatBox provides a user-friendly interface, allowing you to seamlessly integrate it into your preferred trading platform or charting software. Its intuitive design and real-time data updates ensure you have the most accurate and up-to-date information at your fingertips.
Upgrade your trading arsenal and unlock the potential of RSI, Volume Percent, and ADD with StatBox. Experience the power of multiple indicators in a single comprehensive tool. Download StatBox today and gain a competitive edge in the dynamic world of trading! 🚀📈
Grid Spot Trading Algorithm V2 - The Quant ScienceGrid Spot Trading Algorithm V2 is the last grid trading algorithm made by our developer team.
Grid Spot Trading Algorithm V2 is a fixed 10-level grid trading algorithm. The grid is divided into an accumulation area (red) and a selling area (green).
In the accumulation area, the algorithm will place new buy orders, selling the long positions on the top of the grid.
BUYING AND SELLING LOGIC
The algorithm places up to 5 limit orders on the accumulation section of the grid, each time the price cross through the middle grid. Each single order uses 20% of the equity.
Positions are closed at the top of the grid by default, with the algorithm closing all orders at the first sell level. The exit level can be adjusted using the user interface, from the first level up to the fifth level above.
CONFIGURING THE ALGORITHM
1) Add it to the chart: Add the script to the current chart that you want to analyze.
2) Select the top of the grid: Confirm a price level with the mouse on which to fix the top of the grid.
3) Select the bottom of the grid: Confirm a price level with the mouse on which to fix the bottom of the grid.
4) Wait for the automatic creation of the grid.
USING THE ALGORITHM
Once the grid configuration process is completed, the algorithm will generate automatic backtesting.
You can add a stop loss that destroys the grid by setting the destruction price and activating the feature from the user interface. When the stop loss is activated, you can view it on the chart.
[MAD] Position starter & calculatorThe tool you're using is a financial instrument trading planner and analyzer.
Here is how to use it:
Trade Planning: You can plan your trade entries and exits, calculating potential profits, losses, and their ratio (P/L ratio).
You can define up to five target closing prices with varying volumes, which can be individually activated or deactivated (volume set to 0%).
Risk Management: There's a stop-loss function to calculate and limit potential losses.
Additionally, it includes a liquidation pre-calculation for adjustable leverages and position maintenance(subject to exchange variation).
Customization: You can customize the tool's appearance with five adjustable color schemes, light and dark.
-----------------
Initiation: This tool functions as an indicator.
To start, add it as an indicator.
Once added, you can close the indicator window.
Now wait, till you'll see a blue box at the bottom of the input window.
Parameter Input:
Enter your parameters (SL, box left, box right, TP1, TP2, TP3, TP4, TP5) in the direction of the desired trade.
Click from top to bottom for a short trade or bottom to top for a long trade.
Adjustment: If you want to move the box in the future, adjust the times in the indicator settings directly as click input is not yet platform-supported.
This tool functions as a ruler and doesn't offer alerts (for now).
Here is another examples of how to set up a Position-calculation but here for a short:
Have fun trading
Position_controlLibrary "Position_control"
This is a library for defining positions and working with them.
f_calculateLeverage(_Leverage, _maintenance, _value, _direction)
Calculate the leverage used in a trade.
@description This function calculates the leverage used in a trade, based on the value of the trade, the maintenance margin, and the direction of the trade.
Parameters:
_Leverage (float) : The leverage used in the trade, as a floating point number.
_maintenance (float) : The maintenance margin percentage, as a floating point number.
_value (float) : The value of the trade, as a floating point number.
_direction (string) : The direction of the trade, either "long" or "short".
Returns: The leverage used in the trade, as a floating point number.
f_calculate_PL(_Position, _max_TP, _Position_index, _show_profit, _i_decimals_contracts, _i_decimals_prercent)
Calculate the profit or loss for a given trade.
@description This function calculates the profit or loss for a given trade, based on the position type, maximum take profit, position index, and whether to show the profit as a percentage or a value.
Parameters:
_Position (t_Position_type ) : An array of position types for the trade.
_max_TP (int) : The maximum take profit for the trade, as an integer value.
_Position_index (int) : The index of the position in the array, as an integer value.
_show_profit (bool) : A boolean value indicating whether to show the profit as a percentage or a value.
_i_decimals_contracts (int)
_i_decimals_prercent (int)
Returns: The profit or loss for the trade, as a floating point number.
f_drawposition(_Position, _Parameters, _Position_index)
draws a position on the chart
@description via sending in a typo of Position this function is able to drawout Stoploss, Entrybox, Takeprofits and the required labels with information
Parameters:
_Position (t_Position_type ) : array of type t_Position_type containing the position information.
_Parameters (t_drawing_parameters)
_Position_index (int) : the index of the current position.
Returns: None but boxes / lines / labels on the chart itself
t_TP_Variant
Fields:
TP_Type (series__string)
TP_Parameter_1 (series__integer)
TP_Parameter_2 (series__integer)
TP_Parameter_3 (series__float)
TP_Parameter_4 (series__float)
t_TPs
Fields:
TP_Price (series__float)
TP_Lot (series__float)
TP_Variant (|t_TP_Variant|#OBJ)
TP_Active (series__bool)
t_SLs
Fields:
SL_Price (series__float)
SL_Lot (series__float)
SL_Active (series__bool)
t_Position_type
Fields:
Lot (series__float)
Leverage (series__float)
Maintenance (series__float)
Starttime (series__integer)
Entry_Start (series__float)
Stoptime (series__integer)
Entry_Stop (series__float)
Entryprice (series__float)
TPs (array__|t_TPs|#OBJ)
SLs (array__|t_SLs|#OBJ)
t_drawing_parameters
Fields:
ShowPos (series__bool)
ShowLIQ (series__bool)
A_Colors (array__color)
Prolong_lines (series__bool)
Str_fontsize (series__string)
Textshift (series__integer)
Decimals_contracts (series__integer)
Decimals_price (series__integer)
Decimals_percent (series__integer)
bartime (series__integer)
Metrics using Alternative Portfolio TheoryLibrary "APT_Metrics"
Portfolio metrics using alternative portfolio theory
metrics(init, cur, start, end, alpha)
Calculates APT metrics
Parameters:
init (float) : Starting Equity (strategy.initial)
cur (float)
start (int) : Start date (UNIX)
end (int) : End Date (UNIX)
alpha (float) : Confidence interval for DaR/CDaR. Defval = 0.05
Returns: Plots table with APT metrics
The metrics are shown in the bottom pane being applied to a buy-and-hold strategy.
PLEASE NOTE: This is the first draft of the library. Some calculations may be incorrect. If you spot any mistakes then please let me know and I will correct them as soon as possible. I am also open to suggestions on how to improve this.
At the moment this only works on the daily timeframe until I can find a way to universally calculate annualized volatility.
