The Z-score The Z-score, also known as the standard score, is a statistical measurement that describes a value's relationship to the mean of a group of values. It's measured in terms of standard deviations from the mean. If a Z-score is 0, it indicates that the data point's score is identical to the mean score. Z-scores may be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean.
The concept of Z-score was introduced by statistician Carl Friedrich Gauss as part of his "method of the least squares," which was an important step in the development of the normal distribution and Z-score tables. It's a key concept in statistics and is used in various statistical tests.
In financial analysis, Z-scores are used to determine whether a data point is usual or unusual. You can think of it as a measure of how many standard deviations an element is from the mean. For instance, a Z-score of 1.0 would denote a value that is one standard deviation from the mean. Z-scores are also used to predict probabilities, with Z-scores having a distribution that is expected to be normal.
In trading, a Z-score is used to determine how often a trading system may produce a string of winners or losers. It can help a trader to understand whether the losses or profits they see are something that the system would most likely produce, or if it's a once in a blue moon situation. This helps traders make decisions about when to start or stop a system.
I just wanted to play a bit with the Z-score I guess.
Feel free to share your findings if you discover additional applications for this strategy or identify timeframes where it appears to perform more optimally.
How it works:
This strategy is based on a statistical concept called Z-score, which measures the number of standard deviations a data point is from the mean. In other words, it helps determine how unusual or usual a data point is.
In the context of this strategy, Z-score is applied to a 10-period EMA (Exponential Moving Average) of Heikin-Ashi candlestick close prices. The Z-score is calculated over a look-back period of 25 bars.
The EMA of the Z-score is then calculated over a 20-bar period, and the upper and lower thresholds (bounds for buy and sell signals) are defined using the 90th and 10th percentiles of this EMA score.
Long positions are taken when the Z-score crosses above the lower threshold or crosses above the mid-line (50th percentile). An additional long entry is made when the Z-score crosses above the highest value the EMA has been in the past 100 periods.
Short positions are initiated when the EMA crosses below the upper threshold, lower threshold or the highest value the EMA has been in the past 100 periods.
Positions are closed when opposing entry conditions are met, for example, a long position is closed when the short entry condition is true, and vice versa.
Set your desired start date for the strategy. This can be modified in the timestamp("YYYY MM DD") function at the top of the script.
Statistics
Machine Learning : Torben's Moving Median KNN BandsWhat is Median Filtering ?
Median filtering is a non-linear digital filtering technique, often used to remove noise from an image or signal. Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image). Median filtering is very widely used in digital image processing because, under certain conditions, it preserves edges while removing noise (but see the discussion below), also having applications in signal processing.
The main idea of the median filter is to run through the signal entry by entry, replacing each entry with the median of neighboring entries. The pattern of neighbors is called the "window", which slides, entry by entry, over the entire signal. For one-dimensional signals, the most obvious window is just the first few preceding and following entries, whereas for two-dimensional (or higher-dimensional) data the window must include all entries within a given radius or ellipsoidal region (i.e. the median filter is not a separable filter).
The median filter works by taking the median of all the pixels in a neighborhood around the current pixel. The median is the middle value in a sorted list of numbers. This means that the median filter is not sensitive to the order of the pixels in the neighborhood, and it is not affected by outliers (very high or very low values).
The median filter is a very effective way to remove noise from images. It can remove both salt and pepper noise (random white and black pixels) and Gaussian noise (randomly distributed pixels with a Gaussian distribution). The median filter is also very good at preserving edges, which is why it is often used as a pre-processing step for edge detection.
However, the median filter can also blur images. This is because the median filter replaces each pixel with the value of the median of its neighbors. This can cause the edges of objects in the image to be smoothed out. The amount of blurring depends on the size of the window used by the median filter. A larger window will blur more than a smaller window.
The median filter is a very versatile tool that can be used for a variety of tasks in image processing. It is a good choice for removing noise and preserving edges, but it can also blur images. The best way to use the median filter is to experiment with different window sizes to find the setting that produces the desired results.
What is this Indicator ?
K-nearest neighbors (KNN) is a simple, non-parametric machine learning algorithm that can be used for both classification and regression tasks. The basic idea behind KNN is to find the K most similar data points to a new data point and then use the labels of those K data points to predict the label of the new data point.
Torben's moving median is a variation of the median filter that is used to remove noise from images. The median filter works by replacing each pixel in an image with the median of its neighbors. Torben's moving median works in a similar way, but it also averages the values of the neighbors. This helps to reduce the amount of blurring that can occur with the median filter.
KNN over Torben's moving median is a hybrid algorithm that combines the strengths of both KNN and Torben's moving median. KNN is able to learn the underlying distribution of the data, while Torben's moving median is able to remove noise from the data. This combination can lead to better performance than either algorithm on its own.
To implement KNN over Torben's moving median, we first need to choose a value for K. The value of K controls how many neighbors are used to predict the label of a new data point. A larger value of K will make the algorithm more robust to noise, but it will also make the algorithm less sensitive to local variations in the data.
Once we have chosen a value for K, we need to train the algorithm on a dataset of labeled data points. The training dataset will be used to learn the underlying distribution of the data.
Once the algorithm is trained, we can use it to predict the labels of new data points. To do this, we first need to find the K most similar data points to the new data point. We can then use the labels of those K data points to predict the label of the new data point.
KNN over Torben's moving median is a simple, yet powerful algorithm that can be used for a variety of tasks. It is particularly well-suited for tasks where the data is noisy or where the underlying distribution of the data is unknown.
Here are some of the advantages of using KNN over Torben's moving median:
KNN is able to learn the underlying distribution of the data.
KNN is robust to noise.
KNN is not sensitive to local variations in the data.
Here are some of the disadvantages of using KNN over Torben's moving median:
KNN can be computationally expensive for large datasets.
KNN can be sensitive to the choice of K.
KNN can be slow to train.
Ratio To Average - The Quant ScienceRatio To Average - The Quant Science is a quantitative indicator that calculates the percentage ratio of the market price in relation to a reference average. The indicator allows the calculation of the ratio using four different types of averages: SMA, EMA, WMA, and HMA. The ratio is represented by a series of histograms that highlight periods when the ratio is positive (in green) and periods when the ratio is negative (in red).
What is the Ratio to Average?
The Ratio to Average is a measure that tracks the price movements with one of its averages, calculating how much the price is above or below its own average, in percentage terms.
USER INTERFACE
Lenght: it adjusts the number of bars to include in the calculation of the average.
Moving Average: it allows you to choose the type of average to use.
Color Up/Color Down : it allows you to choose the color of the indicator for positive and negative ratios.
Currency ConverterWhat it is?
It's a Currency converter/Exchange rate Viewer.
How it works?
You put the main currency and the other currency in setting then it will show the converted rate in chart.
Daily data infoThis indicator can:
- Indicate on which day of the week created the High or Low
- Based on the bars you set, sum the times in which (The day of the week you want to test makes the Weekly High or Low)
- Has the option to Sum or calculate the %
Position Size Calculator (EzAlgo)Upon adding the indicator to the chart, you will be prompted to place entry price lines, stop loss price line, and multiple take profit price lines by clicking at the desired price level on the chart.
Section Summaries
Table Settings: Allows users to select position and font size from drop-down menus. Displays current settings and potential profit/loss values.
Price Points: Users can set their Entry and select whether they want to include a DCA entry, Stop Loss price, Liquidation Buffer %, Take Profit levels and the amount of position to close at each level.
Risk Management: Users fill out their Account Size, set their Risk % (or fixed $ amount) for each Entry, set Manual Leverage, or allow the indicator to automatically choose the leverage based on the Stop Loss price distance from Entry and the Risk % per Entry.
User-Input Descriptions
DCA Price: The price at which users initiate their second, equally sized and leveraged position when using a Dollar-Cost Averaging (DCA) strategy. Upon reaching the DCA Price, the Entry Price adjusts to the Avg Price, calculated as the midpoint between initial and DCA entries.
Liquidation Buffer: A pre-set percentage that determines how close to the Stop Loss a position can get before it's liquidated. This assists the Auto Leverage feature in optimizing the leverage amount according to risk tolerance.
Risk per Entry: The proportion of the account, in % or a fixed dollar amount, that users are willing to risk for each trading position. If DCA is checked, this will assume users are entering with half of the total position size per entry.
Automatic Leverage: Auto Leverage automatically determines the optimal leverage level for a trade based on the user's Stop Loss price distance from the Entry point and the user-defined risk percentage per Entry. It also considers a user-defined Liquidation Buffer, which is a preset percentage determining how close to the Stop Loss a position can get before it's liquidated. This tool allows traders to optimize their leverage amount according to their risk tolerance.
Max Leverage: The highest leverage level users are willing to use, even if the exchange permits higher. This limit applies when the Auto Leverage feature is enabled.
Seasonal Performance for Stocks & CryptoThe Seasonal Performance indicator quickly allows you to see if you are in a bullish or bearish time of year for an underlying security and where the current performance stacks up compared to the same time of year historically. Table is fully customizable from colors to what data to see.
Table Displays
Average Performance
Best Performance
Worst Performance
Last Performance
Current Performance
Note this indicator will only work with Stocks, ETF's, Index's or Crypto.
Central Bank Balance SheetsDisplay FED, China, Europe and Japan Central Bank Assets over time in USD.
Display FED, China, Europe and Japan Central Bank Assets as % of their country's GDP over time.
Display the sum of all of the central bank assets over time.
Autocorrelation - The Quant ScienceAutocorrelation - The Quant Science it is an indicator developed to quickly calculate the autocorrelation of a historical series. The objective of this indicator is to plot the autocorrelation values and highlight market moments where the value is positive and exceeds the attention threshold.
This indicator can be used for manual analysis when a trader needs to search for new price patterns within the historical series or to create complex formulas in estimating future prices.
What is autocorrelation?
Autocorrelation in trading is a statistical measure used to determine the presence of a relationship or pattern of dependence between values in a financial time series over time. It represents the correlation of past values in a series with its future values. In other words, autocorrelation in trading aims to identify if there are systematic relationships between the past prices or returns of a security or market and its future prices or returns. This analysis can be helpful in identifying patterns or trends that can be leveraged for informed trading decisions. The presence of autocorrelation may suggest that market prices or returns follow a certain pattern or trend over time.
Limitations of the model
It is important to note that autocorrelation does not necessarily imply a causal relationship between past and future values. Other variables or market factors may influence the dynamics of prices or returns, and therefore autocorrelation could be merely a random coincidence. Therefore, it is essential to carefully evaluate the results of autocorrelation analysis along with other information and trading strategies to make informed decisions.
How to use
The usage is very simple, you just need to add it to the current chart to activate the indicator.
From the user interface, you can manage two important features:
1. Lenght: the delay period applied to the historical series during the autocorrelation calculation can be managed from the user interface. By default, it is set to 20, which means that the autocorrelation ratio within the historical series is calculated with a delay of 20 bars.
2. Threshold: the threshold value that the autocorrelation level must meet can be managed from the user interface. By default, it is set to 0.50, which means that the autocorrelation value must be higher than this threshold to be considered valid and displayed on the chart.
3. Bar color: the color used to display the autocorrelation data and highlight the bars when autocorrelation is valid can be managed from the user interface.
To set up the chart
We recommend disabling the 'wick' and 'border' of the candlesticks from the chart settings for a high-quality user experience.
Z-Score Retracement Indicator Releasing the Z-Score Retracement Indicator
What it does:
The Z-Score retracement indicator operates similarly to Fibonacci retracements. It calculates the average, standard deviation and Z-Scores of a user defined period and will plot out the various Standard Deviation levels and price targets based on the period under analysis.
It will visually display the distance from the mean and which price targets correspond to which standard deviation. It is intended to aid traders in determining where the mean is and to calculate the probability of price targets and the probability of a regression to the mean.
How do you use it?
When you launch the indicator, it will ask you to identify the start period. I always try to identify periods where the most recent uptrend or downtrend has started, and then start the analysis from that point. When you plot it, the indicator will display the current strength of the trend in the same way that a regression or log-linear regression indicator would, by displaying the Pearson Correlation Coefficient:
Unlike with linear regression or log-linear regression, you don't necessarily need to have a strong up or downtrend to use the indicator, however it is ideal to find an area with a clear trend.
From there, you can clearly see the standard deviation zones:
The indicator will also display the current Standard Deviation of the last close price.
The centre line (a Z-Score of 0) corresponds to absolute neutrality and would be a regression to the mean. The price target for this area is also listed.
If you want to calculate the price of a specific Standard Deviation, you can! Launch the settings and select the input you would like:
Simply select whether you want to convert a Standard Deviation Z-Score to price or price to Z-Score. Then input the variable into the appropriate field. The indicator will then display the results in a table at the top right of the chart:
In the above image, you can see that a Z-Score of 0 (indicating absolute neutrality) on QQQ is equivalent to a price of 299.
Calculating Probability:
You can calculate probability by reference a Z-Score Table. I have discussed this in my previous Z-Score based indicators, but essentially, the rule of thumb is this:
Anything over + 2 standard deviations corresponds with roughly less than a 5 to 10% chance of continuation higher. I encourage you to check out the writeup on my Z-Score Probability Indicator for more information on the price use of Z-Score for probability determinations.
Concluding remarks:
And that is the indicator! Its pretty straight forward and its intended to be an added tool for regression and log-linear regression based traders to help visualize the actual distance from the mean a ticker is currently trading at.
Another thing to keep in mind is you can use this on the very small time frames as well. I have used this for day trading on the 5 and 1 minute timeframe to determine "regressions to the mean" on a much smaller level. Like here:
That's the indicator!
Hope you enjoy, leave your questions, comments and suggestions below, safe trades everyone!
Annualized Spot-Future DifferenceThe "Annualized Spot-Future Difference" indicator (ASFD) compares the closing prices of a futures contract and its underlying spot asset. It calculates the price difference between the two instruments and annualizes this difference to provide a standardized measure for comparison.
The indicator takes inputs for the futures ticker symbol and the spot ticker symbol, allowing flexibility in selecting the specific assets for analysis. Additionally, it allows the user to input the contract date, which represents the expiration date of the futures contract.
The ASFD indicator plots the annualized difference between the futures and spot prices. It calculates the price difference by subtracting the spot price from the futures price. To annualize this difference, it considers the remaining days to the contract expiration and scales the difference accordingly.
The annualized difference can provide insights into market expectations, as it reflects the market's perception of the future price movement of the underlying asset. A positive value indicates that the futures price is higher than the spot price, potentially suggesting bullish sentiment. Conversely, a negative value suggests bearish sentiment, with the futures price lower than the spot price.
Traders and analysts can utilize the ASFD indicator to identify potential opportunities for arbitrage or evaluate market sentiment regarding the underlying asset. By monitoring changes in the annualized difference over time, they can gain insights into market dynamics and make informed trading decisions.
It's important to note that the ASFD indicator relies on accurate and up-to-date pricing data for both the futures and spot assets. Traders should verify that the selected ticker symbols correspond to the desired instruments and ensure that the contract date aligns with the relevant futures contract expiration.
Overall, the ASFD indicator provides a quantitative measure of the annualized price difference between futures and spot assets, enabling traders and analysts to assess market expectations and identify potential trading opportunities.
Z-Score Candles with ReversalsIn the process of releasing some of my Z-Score based indicators. This is the Z-Score Candle indicator.
What it does:
This converts the current candles into a z-score based candle over a 14 period lookback (adjustable but recommended to leave at 14).
It plots out the overbought/oversold areas using colours and will lookback over a user defined period of time to identify previous areas of bullish and bearish reversals.
Why Z-Score Candles?
Before we get into how to use it, I think its important to discuss why converting candles to a Z-Score is advantageous.
When we convert candlesticks to Z-Score, we have the ability to view areas of natural mathematical support and resistance (I want to clarify, when I saw mathematical support and resistance, it is kind of a misnomer, it is not the same as technical support and resistance. Its a measure of the natural tendency of things to revert to their mean and not deviate to extreme poles of their mean for prolonged period of time, I use the term mathematical support and resistance as it is something most traders are familiar with and operates similarly).
This is particularly helpful during trends. For example, if we take a look at the following BA chart:
In the chart above, you can see that despite BA not being on technical support (that red line), the indicator identified math support (the support was identified by the indicator looking at BA's natural deviations from its mean and seeing that, at that particular point in time, BA had deviated to an area that traditionally leads to reversals to the upside).
If we look at another example:
We can see in the chart above that, despite BA making a new high on the day and "breaking out" of previous resistance, BA was at math resistance being 3.0 Standard Deviations from its trading mean at the time. Thus, necessitating the pullback you see in the chart.
How to use it:
The indicator can be used similar to RSI and Stochastics or any other oscillator based indicator. The difference is, you can actually see the price action in terms of its relationship to its mean. What the means, is the indicator displays the current price action in terms of the ticker's relationship to its current mean and average. This permits us to see areas of rejection and support in relation to its current distance from neutrality. We can also see the various positions of each of the ticker's values from the mean. For example, we can see where the open is in relation to the average, the high and the low vs simply looking at a single variable (usually the close price).
The indicator will also highlight areas where the ticker has deviated to extreme ends of its mean (defined at a Z-Score of +/- 3.0). The picture below is an example of a bearish extreme:
And a bullish extreme:
You can see in both cases a reversal resulted almost immediately.
Inputs:
In the chart above, you can see the 3 main input sections.
Z-Score Lookback: This determines the lookback length for the Z-Score. The recommendation is to leave at 14, especially if you are a day trader.
SMA Inputs: The SMA (The white line) can be toggled off and on. You can also change the source to the High, Low, Close and Open Z-Score. You can adjust the lookback length of the SMA to your liking to assess trends. It does not need to be the same input as the Z-Score.
Reversal Inputs: The reversal inputs determines the length of lookback for the indicator to determine the most extreme bearish and bullish deviation from its mean. It is defaulted at 75 but can be adjusted based on preference. For more frequent signals, you can reduce the lookback length but be prepared for false signals in that case. You can also toggle off the reversal labels if you do not want them.
Concluding remarks:
And that is the Z-Score Candle indicator in a nutshell. Pretty self explanatory otherwise. It is more tailored to day traders. It is not a tool I would necessarily use for longer-term outlooks. I would use a simple Z-Score based indicator for that. But for active day trading, this is very helpful. That said, it can be used to look at longer term outlooks as well, but there are more powerful Z-Score based indicators for that (you can check out my own Z-Score indicator or my recently released Z-Score Probability Indicator which is more tailored for bigger picture outlooks).
Hope you enjoy, as always leave your comments, suggestions and questions below!
Safe trades to all!
Z-Score Probability IndicatorThis is the Z-Score Probability indicator. As many people like my original Z-Score indicator and have expressed more interest in the powers of the Z, I decided to make this indicator which shows additional powers of the Z-Score.
Z-Score is not only useful for measuring a ticker or any other variable’s distance from the mean, it is also useful to calculate general probability in a normal distribution set. Not only can it calculate probability in a dataset, but it can also calculate the variables within said dataset by using the Standard Deviation and the Mean of the dataset.
Using these 2 aspects of the Z-Score, you can, In principle, have an indicator that operates similar to Fibonacci retracement levels with the added bonus of being able to actually ascertain the realistic probability of said retracement.
Let’s take a look at an example:
This is a chart showing SPY on the daily timeframe. If we look at the current Z-Score level, we can see that SPY is pushing into the 2 to 3 Z-Score range. We can see two things from this:
1. We can see that a retracement to a Z-Score of 2 would correspond to a price of 425.26 based on the current dataset. And
2. We can see that the probability that SPY retraces to a Z-Score of 2 is around 0.9800 or 98%.
To take it one step further, we can look at the various other variables in the distribution. If we were to bet on SPY retracing back to -1 SDs, that would correspond to a price of around 397.15, with a probability of around 0.1600 or 16% (see image below):
Let’s say, we thought SPY would go to $440. Well, we can see that the probability SPY goes to 434.64 currently is pretty low. How do we know? Because the Z-Score table shows us the probability of values falling BELOW that Z-score level in the current distribution. So if we look at this example below:
We can see that 0.9998 or roughly 99% of values in the current SPY distribution will fall below 434.64. Thus, it may be unrealistic, at this point in time, to target said value.
So what is a Z-Score Table?
Well, I need to disclose/clarify that the Z-Score Table being displayed in this indicator does Z-Score probability a HUGE injustice. However, with the constraints what is realistic to fit into an indicator, I had to make it far more succinct. Let’s take a look at an actual Z-Score Table below:
Above is a look an the actual Z-Score table. How it works is you first identify you’re Z-Score and then find the corresponding value that relates to your score. The number displayed in the dataset represents the number of variables in the dataset/density distribution that fall BELOW that particular Z-score.
So, for example, if we have a Z-Score of -2.31, we can consult that table, go to the -2.3 then scroll across to the 0.01 to represent -2.31. We would see that this Z-Score corresponds to a 0.0104 probability zone (or essentially 1%) indicating that the majority of the variables in the distribution fall below that mean Z-score. In terms of tickers and stocks, that would mean it would theoretically be “overbought”.
So what does the indicator Z-Table tell us?
I have averaged out the data for the purposes of this indicator. However, you can also reference a manual Z-Table to get the exact probability for the current precise Z-Score. However, the reality is it doesn’t necessarily matter to be exact when it comes to tickers. The reason being, ticker’s are in constant flux, and by the time you identify that probability, the ticker will already be at a different level. So generalizations are okay in these circumstances, you just need to get the “gist” of where the distribution lies.
So how do I use the indicator?
Using the indicator is pretty straightforward. Once launched, you will see the current Z-Score of the ticker, the current levels based on the distribution and the summarized Z-Table.
The Z-Table will turn gray to indicate the zone the ticker is currently in. In this case, we can see that SPY currently is in the 2 SD Zone, meaning that 0.98 or 98% of the current dataset being shown falls below the price we are at:
When we launch the settings, we can see a few inputs.
Lookback Length: This determines the number of candles back we want to calculate the distribution for. It is defaulted to 75, but you can adjust it to whichever length you want.
SMA Length: The SMA is optional but defaults to on. If you want to see the smoothed trend of the Z-Score, this will do the trick. It does not need to be set to the same
length as the Z-Score lookback. Thus, if you want a more or less responsive SMA with, say, a larger dataset, then you can reduce the SMA length yourself.
Distribution Probability Fills: This simply colour codes the distribution zones / probability zones on the indicator.
Show Z-Table: This will display the summarized Z-Table.
Show SMA: As I indicated, the SMA is optional, you can toggle it on or off to see the overall Z-Score trend.
Concluding Remarks:
And that my friends is the Z-Score Probability Indicator.
I hope you all enjoy it and find it helpful. As always leave your comments, questions and suggestions below.
Safe trades to all and take care!
Risk-Adjusted Return OscillatorThe Risk-Adjusted Return Oscillator (RAR) is designed to aid traders in predicting future price action by analysing the risk-adjusted performance of an asset. This oscillator is displayed directly on the price chart, unlike other oscillators.
By considering the risk-return relationship, the indicator helps identify periods of overvaluation or undervaluation, allowing traders to anticipate potential price reversals or trend accelerations.
HOW TO USE
The Risk-Adjusted Return Oscillator analyses the risk-adjusted performance of an asset to detect price reversals and accelerations. Here's how to interpret its signals:
Ranging Market:
Overbought Signal: When the RAR curve reaches the overbought level (upper red line), it suggests a potential reversal signal. It indicates that the asset may be overvalued, and a price correction or trend reversal could occur.
Oversold Signal: When the RAR curve reaches the oversold level (lower red line), it indicates a potential reversal signal. It suggests that the asset may be undervalued, and a price correction or trend reversal could take place.
Trending Market:
Overbought Signal: In a trending market, an overbought signal (RAR curve reaching upper red line) suggests trend acceleration. It indicates that the existing trend is gaining strength, and buying pressure is increasing.
Oversold Signal: In a trending market, an oversold signal (RAR curve reaching lower red line) also signifies trend acceleration. It suggests that the prevailing trend is intensifying, and selling pressure is increasing.
Thus, it's important to consider the market context when interpreting overbought and oversold signals. In ranging markets, these signals act as potential reversal points. However, in trending markets, they indicate trend acceleration, reinforcing the current price direction.
SETTINGS
Period Length: Adjust the number of bars used to calculate returns and standard deviation.
Smoothing: Define the smoothing period for the RAR curve.
Show Overbought/Oversold Signals: Choose whether to display triangular shapes for overbought and oversold conditions.
Correlation TrackerCorrelation Tracker Indicator
The Correlation Tracker indicator calculates and visualizes the correlation between two symbols on a chart. It helps traders and investors understand the relationship and strength of correlation between the selected symbol and another symbol of their choice.
Indicator Features:
- Correlation Calculation: The indicator calculates the correlation between two symbols based on the provided lookback period.
- Correlation Scale: The correlation value is normalized to a scale ranging from 0 to 1 for easy interpretation.
- Table Display: A table is displayed on the chart showing the correlation value and a descriptive label indicating the strength of the correlation.
- Customization Options: Users can customize the text color, table background color, and choose whether to display the Pearson correlation value.
- The Correlation Tracker indicator utilizes a logarithmic scale calculation, making it particularly suitable for longer timeframes such as weekly charts, thereby providing a more accurate and balanced measure of correlations across a wide range of values.
How to Use:
1. Select the symbol for which you want to track the correlation (default symbol is "SPX").
2. Adjust the lookback period to define the historical data range for correlation calculation.
3. Customize the text color and table background color according to your preference.
4. Choose whether to display the Pearson correlation value or a descriptive label for correlation strength.
5. Observe the correlation line on the chart, which changes color based on the strength of the correlation.
6. Refer to the correlation table for the exact correlation value or the descriptive label indicating the correlation strength.
Note: The indicator can be applied to any time frame chart and is not limited to logarithmic scale.
Script TimerWanna know how long your script takes to execute.
Just put this function at the end of your code and it will tell you how much time it takes to run your algo from start to end.
Data will show in the data window panel measured in seconds
AlgebraLibLibrary "AlgebraLib"
f_signaldraw(_side, _date)
: Draw a simple label with Buy or Sell signal
Parameters:
_side (string)
_date (int)
Returns: : VOID, it draws a new label
Rolling Risk-Adjusted Performance RatiosThis simple indicator calculates and provides insights into different performance metrics of an asset - Sharpe, Sortino and Omega Ratios in particular. It allows users to customize the lookback period and select their preferred data source for evaluation of an asset.
Sharpe Ratio:
The Sharpe Ratio measures the risk-adjusted return of an asset by considering both the average return and the volatility or riskiness of the investment. A higher Sharpe Ratio indicates better risk-adjusted performance. It allows investors to compare different assets or portfolios and assess whether the returns adequately compensate for the associated risks. A higher Sharpe Ratio implies that the asset generates more return per unit of risk taken.
Sortino Ratio:
The Sortino Ratio is a variation of the Sharpe Ratio that focuses specifically on the downside risk or volatility of an asset. It takes into account only the negative deviations from the average return (downside deviation). By considering downside risk, the Sortino Ratio provides a more refined measure of risk-adjusted performance, particularly for investors who are more concerned with minimizing losses. A higher Sortino Ratio suggests that the asset has superior risk-adjusted returns when considering downside volatility.
Omega Ratio:
The Omega Ratio measures the probability-weighted ratio of gains to losses beyond a certain threshold or target return. It assesses the skewed nature of an asset's returns by differentiating between positive and negative returns and assigning more weight to extreme gains or losses. The Omega Ratio provides insights into the potential asymmetry of returns, highlighting the potential for significant positive or negative outliers. A higher Omega Ratio indicates a higher probability of achieving large positive returns compared to large negative returns.
Utility:
Performance Evaluation: Provides assessment of an asset's performance, considering both returns and risk factors.
Risk Comparison: Allows for comparing the risk-adjusted returns of different assets or portfolios. Helps identify investments with better risk-reward trade-offs.
Risk Management: Assists in managing risk exposure by evaluating downside risks and volatility.
Monthly Gain in percentageThis scripts calculates the percentage gain in a month (previous 22 trade days).
Firstly, it calculates number of sample days
-> minimum no. of sampling duration = 22
-> maximum no. of sampling duration = increased from 22 until 0day_low > 22_day_high
Example if sampling_duration = 100
Monthly percentage gain = ((Current_Day_High_Price - 0_Day_Low_Price) * 100 / 0_Day_Low_Price) * 22 / sampling_duration
Monthly Strategy Performance TableWhat Is This?
This script code adds a Monthly Strategy Performance Table to your Pine Script strategy scripts so you can see a month-by-month and year-by-year breakdown of your P&L as a percentage of your account balance.
The table is based on realized equity rather than open equity, so it only updates the metrics when a trade is closed.
That's why some numbers will not match the Strategy Tester metrics (such as max drawdown), as the Strategy Tester bases metrics like max drawdown on open trade equity and not realized equity (closed trades).
The script is still a work-in-progress, so make sure to read the disclaimer below. But I think it's ready to release the code for others to play around with.
How To Use It
The script code includes one of my strategies as an example strategy. You need to replace my strategy code with your own. To do that just copy the source code below into a blank script, delete lines 11 -> 60 and paste your strategy code in there instead of mine. The script should work with most systems, but make sure to read the disclaimer below.
It works best with a significant amount of historical data, so it may not work very effectively on intraday timeframes as there is a severe limitation of available bars on TradingView. I recommend using it on 4HR timeframes and above, as anything less will produce very little usable data. Having a premium TradingView plan will also help boost the number of available bars.
You can hover your mouse over a table cell to get more information in the form of tooltips (such as the Long and Short win rate if you hover over your total return cell).
Credit
The code in this script is based on open-source code originally written by QuantNomad, I've made significant changes and additions to the original script but all credit for the idea and especially the display table code goes to them - I just built on top of it:
Why Did I Make This?
None of this is trading or investment advice, just my personal opinion based on my experience as a trader and systems developer these past 6+ years:
The TradingView Strategy Tester is severely limited in some important ways. And unless you use complex Excel formulas on exported test data, you can't see a granular perspective of your system's historical performance.
There is much more to creating profitable and tradeable systems than developing a strategy with a good win rate and a good return with a reasonable drawdown.
Some additional questions we need to ask ourselves are:
What did the system's worst drawdown look like?
How long did it last?
How often do drawdowns occur, and how quickly are they typically recovered?
How often do we have a break-even or losing month or year?
What is our expected compounded annual growth rate, and how does that growth rate compare to our max drawdown?
And many more questions that are too long to list and take a lifetime of trading experience to answer.
Without answering these kinds of questions, we run the risk of developing systems that look good on paper, but when it comes to live trading, we are uncomfortable or incapable of enduring the system's granular characteristics.
This Monthly Performance Table script code is intended to help bridge some of that gap with the Strategy Tester's limited default performance data.
Disclaimer
I've done my best to ensure the numbers this code outputs are accurate, and according to my testing with my personal strategy scripts it appears to work fine. But there is always a good chance I've missed something, or that this code will not work with your particular system.
The majority of my TradingView systems are extremely simple single-target systems that operate on a closed-candle basis to minimize many of the data reliability issues with the Strategy Tester, so I was unable to do much testing with multiple targets and pyramiding etc.
I've included a Debug option in the script that will display important data and information on a label each time a trade is closed. I recommend using the Debug option to confirm that the numbers you see in the table are accurate and match what your strategy is actually doing.
Always do your own due diligence, verify all claims as best you can, and never take anyone's word for anything.
Take care, and best of luck with your trading :)
Kind regards,
Matt.
PS. If you're interested in learning how this script works, I have a free hour-long video lesson breaking down the source code - just check out the links below this script or in my profile.
[Mad] Liquidation LevelsThe Liquidation Lines Technical Indicator is a trading tool designed to assist traders in identifying potential liquidation levels. This indicator generates virtual positions, known as "liquidation lines", which mark the points at which these positions would be liquidated under specified conditions.
Key Features:
Quantity of Lines: The indicator can create up to 125 liquidation lines, evenly distributed between long and short positions. This limit is derived from a maximum of 500 lines, divided by four to account for two types of leverage (long and short).
Customizable Liquidation Levels: Users are given the ability to set liquidation levels according to their individual trading strategies and the current market conditions.
Customizable Visuals: The color and thickness of the liquidation lines can be adjusted to suit personal preferences, providing a clear visual representation on the trading chart for ease of analysis.
Selectable Signal Sources: The indicator provides the flexibility to choose the signal source for creating the liquidation lines. Users can select from a range of popular technical analysis tools such as Bollinger Bands, MACD crosses, EMA crosses, or SMA crosses. This feature allows traders to customize the formation of liquidation lines based on their preferred technical indicators, adding to the comprehensiveness and versatility of the tool.
Two selectable Leverages: The indicator accommodates both long and short leverages, offering a comprehensive understanding of potential liquidation points for various trading scenarios.
Selectable Exchange Maintenance: The indicator allows users to select their specific cryptocurrency exchange. This feature ensures that the liquidation lines are accurately calculated according to the maintenance margin requirements of the chosen exchange, adding precision and customization to the trading analysis.
Ultimate Correlation CoefficientIt contains the Correlations for SP:SPX , TVC:DXY , CURRENCYCOM:GOLD , TVC:US10Y and TVC:VIX and is intended for INDEX:BTCUSD , but works fine for most other charts as well.
Don't worry about the colored mess, what you want is to export your chart ->
TradingView: How can I export chart data?
and then use the last line in the csv file to copy your values into a correlation table.
Order is:
SPX
DXY
GOLD
US10Y
VIX
Your last exported line should look like this:
2023-05-25T02:00:00+02:00 26329.56 26389.12 25873.34 26184.07 0 0.255895534 -0.177543633 0.011944815 0.613678565 0.387705043 0.696003298 0.566425278 0.877838156 0.721872645 0 -0.593674719 -0.839538073 -0.662553817 -0.873684242 -0.695764534 -0.682759656 -0.54393749 -0.858188808 -0.498548691 0 0.416552489 0.424444345 0.387084882 0.887054782 0.869918437 0.88455388 0.694720993 0.192263269 -0.138439783 0 -0.39773255 -0.679121698 -0.429927048 -0.780313396 -0.661460134 -0.346525721 -0.270364046 -0.877208139 -0.367313687 0 -0.615415111 -0.226501775 -0.094827955 -0.475553396 -0.408924242 -0.521943234 -0.426649404 -0.266035908 -0.424316191
The zeros are thought as a demarcation for ease of application :
2023-05-25T02:00:00+02:00 26329.56 26389.12 25873.34 26184.07 0 -> unused
// 15D 30D 60D 90D 120D 180D 360D 600D 1000D
0.255895534 -0.177543633 0.011944815 0.613678565 0.387705043 0.696003298 0.566425278 0.877838156 0.721872645 -> SPX
0
-0.593674719 -0.839538073 -0.662553817 -0.873684242 -0.695764534 -0.682759656 -0.54393749 -0.858188808 -0.498548691 -> DXY
0
0.416552489 0.424444345 0.387084882 0.887054782 0.869918437 0.88455388 0.694720993 0.192263269 -0.138439783 -> GOLD
0
-0.39773255 -0.679121698 -0.429927048 -0.780313396 -0.661460134 -0.346525721 -0.270364046 -0.877208139 -0.367313687 -> US10Y
0
-0.615415111 -0.226501775 -0.094827955 -0.475553396 -0.408924242 -0.521943234 -0.426649404 -0.266035908 -0.424316191 -> VIX
KernelFunctionsFiltersLibrary "KernelFunctionsFilters"
This library provides filters for non-repainting kernel functions for Nadaraya-Watson estimator implementations made by @jdehorty. Filters include a smoothing formula and zero lag formula. You can find examples in the code. For more information check out the original library KernelFunctions.
rationalQuadratic(_src, _lookback, _relativeWeight, startAtBar, _filter)
Parameters:
_src (float)
_lookback (simple int)
_relativeWeight (simple float)
startAtBar (simple int)
_filter (simple string)
gaussian(_src, _lookback, startAtBar, _filter)
Parameters:
_src (float)
_lookback (simple int)
startAtBar (simple int)
_filter (simple string)
periodic(_src, _lookback, _period, startAtBar, _filter)
Parameters:
_src (float)
_lookback (simple int)
_period (simple int)
startAtBar (simple int)
_filter (simple string)
locallyPeriodic(_src, _lookback, _period, startAtBar, _filter)
Parameters:
_src (float)
_lookback (simple int)
_period (simple int)
startAtBar (simple int)
_filter (simple string)
j(line1, line2)
Parameters:
line1 (float)
line2 (float)
