Machine Learning using Neural Networks | EducationalThe script provided is a comprehensive illustration of how to implement and execute a simplistic Neural Network (NN) on TradingView using PineScript.
It encompasses the entire workflow from data input, weight initialization, implicit neuron calculation, feedforward computation, backpropagation for weight adjustments, generating predictions, to visualizing the Mean Squared Error (MSE) Loss Curve for monitoring the training phase.
In the visual example above, you can see that the prediction is not aligned with the actual value. This is intentional for demonstrative purposes, and by incrementing the Epochs or Learning Rate, you will see these two values converge as the accuracy increases.
Hyperparameters:
Learning Rate, Epochs, and the choice between Simple Backpropagation and a verbose version are declared as script inputs, allowing users to tailor the training process.
Initialization:
Random initialization of weight matrices (w1, w2) is performed to ensure asymmetry, promoting effective gradient updates. A seed is added for reproducibility.
Utility Functions:
Functions for matrix randomization, sigmoid activation, MSE loss calculation, data normalization, and standardization are defined to streamline the computation process.
Neural Network Computation:
The feedforward function computes the hidden and output layer values given the input.
Two variants of the backpropagation function are provided for weight adjustment, with one offering a more verbose step-by-step computation of gradients.
A wrapper train_nn function iterates through epochs, performing feedforward, loss computation, and backpropagation in each epoch while logging and collecting loss values.
Training Invocation:
The input data is prepared by normalizing it to a value between 0 and 1 using the maximum standardized value, and the training process is invoked only on the last confirmed bar to preserve computational resources.
Output Forecasting and Visualization:
Post training, the NN's output (predicted price) is computed, standardized and visualized alongside the actual price on the chart.
The MSE loss between the predicted and actual prices is visualized, providing insight into the prediction accuracy.
Optionally, the MSE Loss Curve is plotted on the chart, illustrating the loss trajectory through epochs, assisting in understanding the training performance.
Customizable Visualization:
Various inputs control visualization aspects like Chart Scaling, Chart Horizontal Offset, and Chart Vertical Offset, allowing users to adapt the visualization to their preference.
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The following is this Neural Network structure, consisting of one hidden layer, with two hidden neurons.
Through understanding the steps outlined in my code, one should be able to scale the NN in any way they like, such as changing the input / output data and layers to fit their strategy ideas.
Additionally, one could forgo the backpropagation function, and load their own trained weights into the w1 and w2 matrices, to have this code run purely for inference.
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While this demonstration does create a “prediction”, it is on historical data. The purpose here is educational, rather than providing a ready tool for non-programmer consumers.
Normally in Machine Learning projects, the training process would be split into two segments, the Training and the Validation parts. For the purpose of conveying the core concept in a concise and non-repetitive way, I have foregone the Validation part. However, it is merely the application of your trained network on new data (feedforward), and monitoring the loss curve.
Essentially, checking the accuracy on “unseen” data, while training it on “seen” data.
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I hope that this code will help developers create interesting machine learning applications within the Tradingview ecosystem.
Statistics
Treasury Yields Heatmap [By MUQWISHI]▋ INTRODUCTION :
The “Treasury Yields Heatmap” generates a dynamic heat map table, showing treasury yield bond values corresponding with dates. In the last column, it presents the status of the yield curve, discerning whether it’s in a normal, flat, or inverted configuration, which determined by using Pearson's linear regression coefficient. This tool is built to offer traders essential insights for effectively tracking bond values and monitoring yield curve status, featuring the flexibility to input a starting period, timeframe, and select from a range of major countries' bond data.
_______________________
▋ OVERVIEW:
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▋ YIELD CURVE:
It is determined through Pearson's linear regression coefficient and considered…
R ≥ 0.7 → Normal
0.7 > R ≥ 0.35 → Slight Normal
0.35 > R > -0.35 → Flat
-0.35 ≥ R > -0.7 → Slight Inverted
-0.7 ≥ R → Inverted
_______________________
▋ INDICATOR SETTINGS:
#Section One: Table Setting
#Section Two: Technical Setting
(1) Country: Select country’s treasury yields data
(2) Timeframe: Time interval.
(3) Fetch By:
(3A) Date: Retrieve data by beginning of date.
(3B) Period: Retrieve data by specifying the number of time series back.
Enjoy. Please let me know if you have any questions.
Thank you.
Bankruptcy Risk: Altman Z-Score, Zmijewski Score, Grover GThis custom indicator calculates three common bankruptcy risk scores:
Altman Z-Score
Zmijewski Score
Grover G-Score
Altman Z-Score
Companies are in healthy condition if the Z-Score > 2.6.
Companies are in vulnerable conditions and need improvement (grey area) if the score is between 1.1 - 2.6.
Companies have the potential to lead to serious bankruptcy if the Z-Score < 1.1.
Zmijewski Score
The company has the potential to go bankrupt if the value of X Score > 0.
The company is healthy if the value of X Score < 0.
Grover Model: G-Score
The company has the potential to go bankrupt if the G Score ≤ -0.02.
The company is in good health if the value of G Score ≥ 0.001.
The indicator pulls key financial metrics and calculates each score, then displays the results in a table with color-coding based on the level of bankruptcy risk.
Users can toggle between FQ and FY periods and view details on the underlying metrics. This provides an easy way to visualize bankruptcy risk for a company and compare across different scoring models.
Useful for fundamental analysis and assessing financial health.
The financial ratios and methodology are based on research described in
"Analysis of Bankruptcy Prediction Models in Determining Bankruptcy of Consumer Goods Companies in Indonesia" (Thomas et al., 2020).
Market Performance TableThe Market Performance Table displays the performance of multiple tickers (up to 5) in a table format. The tickers can be customized by selecting them through the indicator settings.
The indicator calculates various metrics for each ticker, including the 1-day change percentage, whether the price is above the 50, 20, and 10-day simple moving averages (SMA), as well as the relative strength compared to the 10/20 SMA and 20/50 SMA crossovers. It also calculates the price deviation from the 50-day SMA.
The table is displayed on the chart and can be positioned in different locations.
Credits for the idea to @Alex_PrimeTrading ;)
PCA-Risk IndicatorOBJECTIVE:
The objective of this indicator is to synthesize, via PCA (Principal Component Analysis), several of the most used indicators with in order to simplify the reading of any asset on any timeframe.
It is based on my Bitcoin Risk Long Term indicator, and is the evolution of another indicator that I have not published 'Average Risk Indicator'.
The idea of this indicator is to use statistics, in this case the PCA, to reduce the number of dimensions (indicator) to aggregate them in some synthetic indicators (PCX)
I invite you to dig deeper into the PCA, but that is to try to keep as much information as possible from the raw data. The signal minus the noise.
I realized this indicator a year ago, but I publish it now because I do not see the interest to keep it private.
USAGE:
Unlike the Bitcoin Risk Long Term indicator, it does not make sense to change or disable the input indicators unless you use the 'Average Indicator' function. Because each input is weighted to generate the outputs, the PCX.
I extracted several courses (Bitcoin, Gold, S&P, CAC40) on several timeframes (W, D, 4h, 1h) of Trading view and use the Excel generated for the data on which I played the PCA analysis.
The results:
explained_variance_ratio: 0.55540809 / 0.13021972 / 0.07303142 / 0.03760925
explained_variance: 11.6639671 / 2.73470717 / 1.53371209 / 0.7898212
Interpretation:
Simply put, 55% of the information contained in each indicator can be represented with PC1, +13% with PC2, +7% with PC3, +3% with PC4.
What is important to understand is that PC1, which serves as a thermometer in a way, gives a simple indication of over-buying or over-selling area better than any other indicator.
PC2, difficult to interpret, is more reactive because precedes PC1, but can give false signals.
PC3 and PC4 do not seem relevant to me.
The way I use it is to take PC1 for Risk indicator, and display PC2 with 'Area'. When PC2 turns around and PC1 arrives on extremes, it can be good points to act.
NOTES :
- It is surprising that a simple average of all the indicators gives a fairly relevant result
- With Average indicator as Risk indicator, you can combine the indicators of your choice and see the predictive power with the staining of bars.
- You can add alerts on the levels of your choice on the Risk Indicator
- If you have any idea of adding an indicator, modification, criticism, bug found: share them, it’s appreciated!
---- FR ----
OBJECTIF :
L'objectif de cet indicateur est de synthétiser, via l'ACP (Analyse en Composantes Principales), plusieurs indicateurs parmi les plus utilisés avec afin de simplifier la lecture de n'importe quel actif sur n'importe quel timeframe.
Il est inspiré de mon indicateur 'Bitcoin Risk Long Term indicator', et est l'évolution d'un autre indicateur que je n'ai pas publié 'Average Risk Indicator'.
L'idée de cet indicateur est d'utiliser les statistiques, en l'occurence l'ACP, pour réduire le nombre de dimensions (indicateur) pour les agréger dans quelques indicateurs synthétiques (PCX)
Je vous invite à creuser l'ACP, mais c'est chercher à conserver un maximum d'informations à partir de la donnée brute. Le signal moins le bruit.
J'ai réalisé cet indicateur il y a un an, mais je le publie maintenant car je ne vois pas l'intérêt de le garder privé.
UTILISATION :
Contrairement à 'Bitcoin Risk Long Term indicator', il ne fait pas sens de modifier ou désactiver les indicateurs inputs, sauf si vous utiliser la fonction 'Average Indicator'. Car chaque input est pondéré pour générer les outputs, les PCX.
J'ai extrait plusieurs cours (Bitcoin, Gold, S&P, CAC40) sur plusieurs timeframes (W, D, 4h, 1h) de Trading view et utiliser les Excel généré pour la data sur laquelle j'ai joué l'analyse ACP.
Les résultats :
explained_variance_ratio : 0.55540809 / 0.13021972 / 0.07303142 / 0.03760925
explained_variance : 11.6639671 / 2.73470717 / 1.53371209 / 0.7898212
Interprétation :
Pour faire simple, 55% de l'information contenu dans chaque indicateur peut être représenté avec PC1, +13% avec PC2, +7% avec PC3, +3% avec PC4.
Ce qui faut y comprendre c'est que le PC1, qui sert de thermomètre en quelque sorte, donne une indication simple de zone de sur-achat ou sur-vente mieux que n'importe quel autre indicateur.
PC2, difficile à interpréter, est plus réactif car précède PC1, mais peut donner des faux signaux.
PC3 et PC4 ne me semble pas pertinent.
La manière dont je m'en sert c'est de prendre PC1 pour Risk indicator, et d'afficher PC2 avec 'Region'. Lorsque PC2 se retourne et que PC1 arrive sur des extrêmes, cela peut être des bons points pour agir.
NOTES :
- Il est étonnant de constater qu'une simple moyenne de tous les indicateurs donne un résultat assez pertinent
- Avec Average indicator comme Risk indicator, vous pouvez combiner les indicateurs de vos choix et voir la force prédictive avec la coloration des bars.
- Vous pouvez ajouter des alertes sur les niveaux de votre choix sur le Risk Indicator
- Si vous avez la moindre idée d'ajout d'indicateur, modification, critique, bug trouvé : partagez-les, c'est apprécié !
IU SIP CALCULATORHow This Indicator Script Works:
1. This indicator script calculate the monthly SIP returns of any market over any user defined period.
2. SIP stands for Systematic Investment Plan. It is a way to invest in any asset by regularly investing a fixed amount of money at regular intervals for example Monthly, Weekly, Quarterly etc.
3. This indicator Calculate the following
# Average buy price
# Total quantity hold
# Yearly returns
# Monthly returns
# Total invested amount
# Total profits in amount
# Total portfolio value
# Total returns in per percentage term.
4. This script takes monthly SIP amount, starting month, starting year, ending year, ending month from the user and store the value for calculations.
5. After that it store the open price of every month into an array then it average the array and compare that price with the last month close price.
6. on the bases of this it performs all of the calculations.
7. The script plot every calculation into an table from.
8. It requires monthly chart timeframe for working.
9. The table is editable user can change the color and transparency.
How User Can Benefit From The Script:
1. User can get the past monthly SIP returns of any market he wants to invest this will give him an overview about what to expect from the market.
2. Once user understand the expected returns from the market he can adjust his investment strategy.
3. This help the user to Analyse various stocks and their past performance.
4. User can also short list the best performed stocks.
5. Over all this script will give complete SIP vision of any market.
Sync Frame (MTF Charts) [Kioseff Trading]Hello!
This indicator "Sync Frame" displays various lower timeframe charts for the asset on your screen!
5 lower timeframe candle charts shown
Timeframes auto-calculated using the new timeframe.from_seconds() function
Heikin-Ashi candles available
Baseline chart type available
Dynamic Scaling for ease of use
User customizable timeframes
Simple script (:
The image above shows the baseline chart type.
Time image above shows a traditional candlestick chart.
The image above shows a hekin-ashi chart.
The image above shows the indicator when nearly zoomed in as much as possible. The lower timeframe charts adjust to my chart positioning.
The image above shows my screen fully zoomed out; the lower timeframe charts adjust in both height and width to accommodate my chart positioning!
Thank you for checking this out (:
Blockchain FundamentalThis indicator is made for traders to harness fundamental blockchain data for better decision-making. Unlike traditional tools, this indicator doesn't depend on standard technical indicators. It offers a novel perspective by focusing on core blockchain metrics like capitalization, miner activity, and other intrinsic data elements. I've designed a distinct scoring logic, exclusive to BF, ensuring it's user-friendly and provides actionable insights for traders at all levels.
Mainly created for Bitcoin , but can be applied to any other crypto assets in cost of losing some metrics in the analysis.
Ethereum chart:
Features:
Customizable Moving Averages:
Choose from an array of moving averages, with the flexibility to adjust the length for a tailored analysis, aiding in pinpointing asset trends.
Blockchain Metrics Integration:
Incorporates a range of blockchain metrics such as Market Cap to Realised Cap ratio, Spent Output Profit Ratio, ATH Drawdown, and more.
Blockchain Metrics Evaluation:
Each metric can be toggled on/off to customize the analysis. Using default settings, traders can use all of the metrics combined.
Every metric is essentially evaluated on a scale from -100 to 100 and then combined with others. If any metric is uncertain about its direction (equals to 0), then the score of it is not accounted in a final calculation.
Kalman Filter:
This indicator offers the option to apply a Kalman filter to the signals, enhancing the smoothness and accuracy of the indicator’s output. This is my approach to mitigate the noise in the final output.
Signal Oscillator:
Displays the aggregated score of all selected blockchain metrics.
Offers visual signals with adjustable upper and lower bounds for easy interpretation based on particular asset observation.
Visual Elements:
Signal Oscillator:
A visual representation of the aggregated blockchain fundamental score.
(White line for a raw calculation, orange line for kalman-filtered one)
Signal Counter:
Displays the count of metrics currently being considered in the fundamental score calculation. (grey line at the middle of an indicator)
Buy/Sell Signal Coloring:
The background color changes to indicate potential buying or selling opportunities based on user-defined bounds.
Usage:
Analysis:
Use the signal oscillator to identify potential market tops and bottoms based on blockchain fundamental data.
Adjust the bounds to customize the sensitivity of buy/sell signals.
Customization:
Enable/disable specific blockchain metrics to tailor the indicator to your analytical needs.
Adjust the moving average type and length for better analysis.
Integration:
Combine with other technical indicators to create a comprehensive trading strategy.
Utilize in conjunction with volume and price action analysis for enhanced decision-making. Every output could be used in traders custom strategies and indicators.
IU Probability CalculatorHow This Script Works:
1. This script calculate the probability of price reaching a user-defined price level within one candle with the help Normal Distribution Probability Table.
2. Normal Distribution Probability Table is use for calculating probability of events, it's very powerful for calculation of probability and this script is fully based on that table.
3. It takes the Average True Range value or Standard Deviation value of past user-defined length bar.
4. After that it take this formula z = ( price_level - close ) / (ATR or Standard Deviation) and return the value for z, for the bearish side it take z = (close - price level) / (ATR or Standard Deviation ) formula.
5. Once we have the z it look into Normal Distribution Probability Table and match the value.
6. Now the value of z is multiple buy 100 in order to make it look in percentage term.
7. After that this script subtract the final value with 100 because probability always comes under 100%
8. finally we plot the probability at the bottom of the chart the red line indicates "The probability of price not reaching that price level", While the green line indicates "Probability of price Reaching that level " .
9. This script will work fine for both of the directions
How This Is Useful For The User:
1. With this script user can know the probability of price reaching the certain level within one candle for both Directions .
2. This is useful while creating options hedging strategies
3. This can be helpful for deciding stop loss level.
4. It's useful for scalpers for managing their traders and it can be use by binary option traders.
IU Average move How The Script Works :
1. This script calculate the average movement of the price in a user defined custom session and plot the data in a table from on top left corner of the chart.
2. The script takes highest and lowest value of that custom session and store their difference into an array.
3. Then the script average the array thus gets the average price.
4. Addition to that the script converter the price pip change into percentage in order to calculate the value in percentage form.
5. This script is pure price action based the script only take price value and doesn't take any indicator for calculation.
6. The script works on every type of market.
7. If the session is invalid it returns nothing
8. The background color, text color and transparency is changeable.
How User Can Benefit From This Script:
1. User can understand the volatility of any session that he/she wish to trade.
2. It can be helpful for understanding the average price moment of any tradeble asset.
3. It will give the average price movement both in percentage and points bases.
4. By understanding the volatility user can adjust his stop loss or take profit with respect his risk management.
Quadratic & Linear Time Series Regression [SS]Hey everyone,
Releasing the Quadratic/Linear Time Series regression indicator.
About the indicator:
Most of you will be familiar with the conventional linear regression trend boxes (see below):
This is an awesome feature in Tradingview and there are quite a few indicators that follow this same principle.
However, because of the exponential and cyclical nature of stocks, linear regression tends to not be the best fit for stock time series data. From my experience, stocks tend to fit better with quadratic (or curvlinear) regression, which there really isn't a lot of resources for.
To put it into perspective, let's take SPX on the 1 month timeframe and plot a linear regression trend from 1930 till now:
You can see that its not really a great fit because of the exponential growth that SPX has endured since the 1930s. However, if we take a quadratic approach to the time series data, this is what we get:
This is a quadratic time series version, extended by up to 3 standard deviations. You can see that it is a bit more fitting.
Quadratic regression can also be helpful for looking at cycle patterns. For example, if we wanted to plot out how the S&P has performed from its COVID crash till now, this is how it would look using a linear regression approach:
But this is how it would look using the quadratic approach:
So which is better?
Both linear regression and quadratic regression are pivotal and important tools for traders. Sometimes, linear regression is more appropriate and others quadratic regression is more appropriate.
In general, if you are long dating your analysis and you want to see the trajectory of a ticker further back (over the course of say, 10 or 15 years), quadratic regression is likely going to be better for most stocks.
If you are looking for short term trades and short term trend assessments, linear regression is going to be the most appropriate.
The indicator will do both and it will fit the linear regression model to the data, which is different from other linreg indicators. Most will only find the start of the strongest trend and draw from there, this will fit the model to whatever period of time you wish, it just may not be that significant.
But, to keep it easy, the indicator will actually tell you which model will work better for the data you are selecting. You can see it in the example in the main chart, and here:
Here we see that the indicator indicates a better fit on the quadratic model.
And SPY during its recent uptrend:
For that, let's take a look at the Quadratic Vs the Linear, to see how they compare:
Quadratic:
Linear:
Functions:
You will see that you have 2 optional tables. The statistics table which shows you:
The R Squared to assess for Variance.
The Correlation to assess for the strength of the trend.
The Confidence interval which is set at a default of 1.96 but can be toggled to adjust for the confidence reading in the settings menu. (The confidence interval gives us a range of values that is likely to contain the true value of the coefficient with a certain level of confidence).
The strongest relationship (quadratic or linear).
Then there is the range table, which shows you the anticipated price ranges based on the distance in standard deviations from the mean.
The range table will also display to you how often a ticker has spent in each corresponding range, whether that be within the anticipated range, within 1 SD, 2 SD or 3 SD.
You can select up to 3 additional standard deviations to plot on the chart and you can manually select the 3 standard deviations you want to plot. Whether that be 1, 2, 3, or 1.5, 2.5 or 3.5, or any combination, you just enter the standard deviations in the settings menu and the indicator will adjust the price targets and plotted bands according to your preferences. It will also count the amount of time the ticker spent in that range based on your own selected standard deviation inputs.
Tips on Use:
This works best on the larger timeframes (1 hour and up), with RTH enabled.
The max lookback is 5,000 candles.
If you want to ascertain a longer term trend (over years to months), its best to adjust your chart timeframe to the weekly and/or monthly perspective.
And that's the indicator! Hopefully you all find it helpful.
Let me know your questions and suggestions below!
Safe trades to all!
[dharmatech] Area Under Yield Curve : USThis indicator displays the area under the U.S. Treasury Securities yield curve.
If you compare this to SP:SPX , you'll see that there are large periods where they are inversely related. Other times, they track together. When the move together, watch out for the expected and eventual divergence.
By default, this indicator will show up in a separate pane. If you move it to an existing pane (e.g. along side SP:SPX ) you'll need to move it to a different price scale.
The area under the yield curve is a quick way to see if the overall yield curve moved up or down. Generally speaking, increasing yields isn't good for markets, unless there is some other stimulus going on simultaneously.
The following treasury securities are used in this calculation:
FRED:DGS1MO (1 month)
FRED:DGS3MO (3 month)
FRED:DGS6MO (6 month)
FRED:DGS1 (1 year)
FRED:DGS2 (2 year)
FRED:DGS3 (3 year)
FRED:DGS5 (5 year)
FRED:DGS7 (7 year)
FRED:DGS10 (10 year)
FRED:DGS20 (20 year)
FRED:DGS30 (30 year)
Tops & Bottoms - Time of Day Report█ OVERVIEW
The indicator tracks and reports the percentage of occurrence of daily tops and bottoms by the time of the day.
█ CONCEPTS
At certain times during the trading day, the market reverses and marks the high or low of the day. Tops and bottoms are vital when entering a trade, as they will decide if you are catching the train or being straight offside. They are equally crucial when exiting a position, as they will determine if you are closing at the optimal price or seeing your unrealized profits vanish.
This indicator is before all for educational purposes. It aims to make the knowledge available to all traders, facilitate understanding of the various markets, and ultimately get to know your trading pairs by heart.
Tops and bottoms percentage of occurrence on EURGBP (London time).
Up days versus down days on EURUSD (London time).
█ FEATURES
Selectable time zones
Present the column chart in your local time zone (or other market participants).
Configurable time range filter
Select the period to report from.
Day type filter
Analyze all days, or filter only up days or down days.
█ HOW TO USE
Plot the indicator and visit the 1-hour or 30-minute timeframe.
█ NOTES
Timeframe choice
The 1-hour timeframe produces a higher number of days sampled. Prefer the usage of the 30-minute timeframe when your market starts at 9:30 AM.
Daylight Saving Time (DST)
The exchange time and geographical time zone options may observe Daylight Saving Time, unlike UTC+0.
Monte Carlo Simulation - Your Strategy [Kioseff Trading]Hello!
This script “Monte Carlo Simulation - Your Strategy” uses Monte Carlo simulations for your inputted strategy returns or the asset on your chart!
Features
Monte Carlo Simulation: Performs Monte Carlo simulation to generate multiple future paths.
Asset Price or Strategy: Can simulate either future asset prices based on historical log returns or a specific trading strategy's future performance.
User-Defined Input: Allows you to input your own historical returns for simulation.
Statistical Methods: Offers two simulation methods—Gaussian (Normal) distribution and Bootstrapping.
Graphical Display: Provides options for graphical representation, including line plots and histograms.
Cumulative Probability Target: Enables setting a user-defined cumulative probability target to quantify simulation results.
Adjustable Parameters: Offers numerous user-adjustable settings like number of simulations, forecast length, and more.
Historical Data Points: Option to specify the amount of historical data to be used in the simulation (price).
Custom Binning: Allows you to select the binning method for histograms, with options like Sturges, Rice, and Square Root.
Best/Worst Case: Allows you to show only the best case / worst case outcome (range) for all simulations!
Scatterplot: allows you to show up to 1000 potential outcomes for a specified trade number (or bars forward price endpoint) using a scatter plot.
The image above shows the primary components of the indicator!
The image above shows the best/worst case outcome feature in action!
The image above shows a "fun feature" where 1000 simulated end points for a 15-bar price trajectory are shown as a scatter plot!
How To Perform a Monte Carlo Simulation On Your Strategy
Really, you can input any data into the indicator it will perform a Monte Carlo Simulation on it :D
The following instructions show how to export your strategy results from TradingView to an Excel File, copy the data, and input it into the indicator.
However , you are not limited to following this method!
Wherever your strategy results are stored, simply copy and paste them into the indicator text area in the settings and simulations will begin.
Returns Should Follow This Format
1
3
-3
2
-5
The numbers are presented as a single column. No commas or separators used.
The numbers above are in sequential order. A return of "1" for the first trade and a return of "-5" for the last trade. Your strategy returns will likely be in sequential order already so don't worry too much about this (:
How To Perform a Monte Carlo Simulation On Your TradingView Strategy With Excel Data
Export your strategy returns to an excel file using TradingView
Navigate to your downloads folder to column G "Profit"
Click the column and press CTRL + SPACE to highlight the entire column
Press CTRL + C to copy the entire column
Open this indicator's settings and paste the returns into the text area
The image above illustrates the process!
Notes on Inputting Returns
*Must input your returns without a separate as a vertical list
*The initial text area can only hold so many return values. If your list of trades is large you can input additional returns into two additional text areas at the bottom of the indicator settings.
That should be it; thank you for checking this out!
Xeeder - US Government Bonds AnalysisXeeder - US Government Bonds Analysis (USBA)
The "Xeeder - US Government Bonds Analysis" (USBA) is a comprehensive tool designed to assist traders in analyzing the spread, historical volatility, and correlation between two different U.S. Government Bonds. This indicator is crucial for understanding the relative performance and risk factors between two bond assets.
Details of the Indicator:
Bond Input Settings: This feature allows traders to select two different U.S. Government Bonds from a dropdown list. The bonds range from 1-month to 30-year maturities.
Timeframe Settings: Traders can choose the timeframe for the analysis, such as Daily, Weekly, etc.
Moving Average (MA) Settings: The indicator offers various types of moving averages like SMA, EMA, WMA, etc., for calculating the spread's moving average. Traders can also specify the length of the moving average.
Spread Calculation: The indicator calculates the spread between the selected bonds and plots it on the chart.
Historical Volatility: The indicator calculates and plots the historical volatility of the spread, which is useful for risk assessment.
Correlation Coefficient: This feature calculates the correlation between the two selected bonds over a specified period.
How to Use the Indicator:
Select Bonds: Choose two U.S. Government Bonds from the dropdown list that you are interested in analyzing.
Choose Timeframe: Select the timeframe that aligns with your trading or investment strategy.
Configure MA Settings: Adjust the type and length of the moving average according to your needs.
Analyze Plots: Observe the plotted spread, its moving average, historical volatility, and correlation coefficient to gain insights into the bonds' relative performance and risk factors.
Interpret Data: Use the plotted data to make informed decisions about bond trading or hedging strategies.
Example of Usage:
As a trader focused on swing trading and strategy development, you can use the USBA indicator to:
Select Bonds: Choose bonds that you believe will show significant spread changes based on your macroeconomic and geopolitical analysis.
Adjust Settings: Configure the MA settings to suit your trading strategy.
Analysis and Comparison: Examine the spread, historical volatility, and correlation to identify potential trading opportunities or hedging strategies.
Content Creation: Use the insights gained to write compelling articles on bond market trends, risks, and opportunities, enriching your financial journalism portfolio.
Remember, the USBA indicator is a versatile tool that provides a multi-faceted analysis of U.S. Government Bonds. Always consider your broader trading strategy and market conditions when using this tool.
Seasonal Trend by LogReturnSeasonal trend in terms of stocks refers to typical and recurring patterns in stock prices that happen at a specific time of the year. There are many theories and beliefs regarding seasonal trends in the financial markets, and some traders use these patterns to guide their investment decisions.
This indicator calculates the trend by "Daily" logarithmic returns of the past years.
So, you should use this indicator with a "Daily" mainchart.
Note: If you select more years in the past than data is available, the line turns red.
Statistical Package for the Trading Sciences [SS]
This is SPTS.
It stands for Statistical Package for the Trading Sciences.
Its a play on SPSS (Statistical Package for the Social Sciences) by IBM (software that, prior to Pinescript, I would use on a daily basis for trading).
Let's preface this indicator first:
This isn't so much an indicator as it is a project. A passion project really.
This has been in the works for months and I still feel like its incomplete. But the plan here is to continue to add functionality to it and actually have the Pinecoding and Tradingview community contribute to it.
As a math based trader, I relied on Excel, SPSS and R constantly to plan my trades. Since learning a functional amount of Pinescript and coding a lot of what I do and what I relied on SPSS, Excel and R for, I use it perhaps maybe a few times a week.
This indicator, or package, has some of the key things I used Excel and SPSS for on a daily and weekly basis. This also adds a lot of, I would say, fairly complex math functionality to Pinescript. Because this is adding functionality not necessarily native to Pinescript, I have placed most, if not all, of the functionality into actual exportable functions. I have also set it up as a kind of library, with explanations and tips on how other coders can take these functions and implement them into other scripts.
The hope here is that other coders will take it, build upon it, improve it and hopefully share additional functionality that can be added into this package. Hence why I call it a project. Okay, let's get into an overview:
Current Functions of SPTS:
SPTS currently has the following functionality (further explanations will be offered below):
Ability to Perform a One-Tailed, Two-Tailed and Paired Sample T-Test, with corresponding P value.
Standard Pearson Correlation (with functionality to be able to calculate the Pearson Correlation between 2 arrays).
Quadratic (or Curvlinear) correlation assessments.
R squared Assessments.
Standard Linear Regression.
Multiple Regression of 2 independent variables.
Tests of Normality (with Kurtosis and Skewness) and recognition of up to 7 Different Distributions.
ARIMA Modeller (Sort of, more details below)
Okay, so let's go over each of them!
T-Tests
So traditionally, most correlation assessments on Pinescript are done with a generic Pearson Correlation using the "ta.correlation" argument. However, this is not always the best test to be used for correlations and determine effects. One approach to correlation assessments used frequently in economics is the T-Test assessment.
The t-test is a statistical hypothesis test used to determine if there is a significant difference between the means of two groups. It assesses whether the sample means are likely to have come from populations with the same mean. The test produces a t-statistic, which is then compared to a critical value from the t-distribution to determine statistical significance. Lower p-values indicate stronger evidence against the null hypothesis of equal means.
A significant t-test result, indicating the rejection of the null hypothesis, suggests that there is statistical evidence to support that there is a significant difference between the means of the two groups being compared. In practical terms, it means that the observed difference in sample means is unlikely to have occurred by random chance alone. Researchers typically interpret this as evidence that there is a real, meaningful difference between the groups being studied.
Some uses of the T-Test in finance include:
Risk Assessment: The t-test can be used to compare the risk profiles of different financial assets or portfolios. It helps investors assess whether the differences in returns or volatility are statistically significant.
Pairs Trading: Traders often apply the t-test when engaging in pairs trading, a strategy that involves trading two correlated securities. It helps determine when the price spread between the two assets is statistically significant and may revert to the mean.
Volatility Analysis: Traders and risk managers use t-tests to compare the volatility of different assets or portfolios, assessing whether one is significantly more or less volatile than another.
Market Efficiency Tests: Financial researchers use t-tests to test the Efficient Market Hypothesis by assessing whether stock price movements follow a random walk or if there are statistically significant deviations from it.
Value at Risk (VaR) Calculation: Risk managers use t-tests to calculate VaR, a measure of potential losses in a portfolio. It helps assess whether a portfolio's value is likely to fall below a certain threshold.
There are many other applications, but these are a few of the highlights. SPTS permits 3 different types of T-Test analyses, these being the One Tailed T-Test (if you want to test a single direction), two tailed T-Test (if you are unsure of which direction is significant) and a paired sample t-test.
Which T is the Right T?
Generally, a one-tailed t-test is used to determine if a sample mean is significantly greater than or less than a specified population mean, whereas a two-tailed t-test assesses if the sample mean is significantly different (either greater or less) from the population mean. In contrast, a paired sample t-test compares two sets of paired observations (e.g., before and after treatment) to assess if there's a significant difference in their means, typically used when the data points in each pair are related or dependent.
So which do you use? Well, it depends on what you want to know. As a general rule a one tailed t-test is sufficient and will help you pinpoint directionality of the relationship (that one ticker or economic indicator has a significant affect on another in a linear way).
A two tailed is more broad and looks for significance in either direction.
A paired sample t-test usually looks at identical groups to see if one group has a statistically different outcome. This is usually used in clinical trials to compare treatment interventions in identical groups. It's use in finance is somewhat limited, but it is invaluable when you want to compare equities that track the same thing (for example SPX vs SPY vs ES1!) or you want to test a hypothesis about an index and a leveraged share (for example, the relationship between FNGU and, say, MSFT or NVDA).
Statistical Significance
In general, with a t-test you would need to reference a T-Table to determine the statistical significance of the degree of Freedom and the T-Statistic.
However, because I wanted Pinescript to full fledge replace SPSS and Excel, I went ahead and threw the T-Table into an array, so that Pinescript can make the determination itself of the actual P value for a t-test, no cross referencing required :-).
Left tail (Significant):
Both tails (Significant):
Distributed throughout (insignificant):
As you can see in the images above, the t-test will also display a bell-curve analysis of where the significance falls (left tail, both tails or insignificant, distributed throughout).
That said, I have not included this function for the paired sample t-test because that is a bit more nuanced. But for the one and two tailed assessments, the indicator will provide you the P value.
Pearson Correlation Assessment
I don't think I need to go into too much detail on this one.
I have put in functionality to quickly calculate the Pearson Correlation of two array's, which is not currently possible with the "ta.correlation" function.
Quadratic (Curvlinear) Correlation
Not everything in life is linear, sometimes things are curved!
The Pearson Correlation is great for linear assessments, but tends to under-estimate the degree of the relationship in curved relationships. There currently is no native function to t-test for quadratic/curvlinear relationships, so I went ahead and created one.
You can see an example of how Quadratic and Pearson Correlations vary when you look at CME_MINI:ES1! against AMEX:DIA for the past 10 ish months:
Pearson Correlation:
Quadratic Correlation:
One or the other is not always the best, so it is important to check both!
R-Squared Assessments:
The R-squared value, or the square of the Pearson correlation coefficient (r), is used to measure the proportion of variance in one variable that can be explained by the linear relationship with another variable. It represents the goodness-of-fit of a linear regression model with a single predictor variable.
R-Squared is offered in 3 separate forms within this indicator. First, there is the generic R squared which is taking the square root of a Pearson Correlation assessment to assess the variance.
The next is the R-Squared which is calculated from an actual linear regression model done within the indicator.
The first is the R-Squared which is calculated from a multiple regression model done within the indicator.
Regardless of which R-Squared value you are using, the meaning is the same. R-Square assesses the variance between the variables under assessment and can offer an insight into the goodness of fit and the ability of the model to account for the degree of variance.
Here is the R Squared assessment of the SPX against the US Money Supply:
Standard Linear Regression
The indicator contains the ability to do a standard linear regression model. You can convert one ticker or economic indicator into a stock, ticker or other economic indicator. The indicator will provide you with all of the expected information from a linear regression model, including the coefficients, intercept, error assessments, correlation and R2 value.
Here is AAPL and MSFT as an example:
Multiple Regression
Oh man, this was something I really wanted in Pinescript, and now we have it!
I have created a function for multiple regression, which, if you export the function, will permit you to perform multiple regression on any variables available in Pinescript!
Using this functionality in the indicator, you will need to select 2, dependent variables and a single independent variable.
Here is an example of multiple regression for NASDAQ:AAPL using NASDAQ:MSFT and NASDAQ:NVDA :
And an example of SPX using the US Money Supply (M2) and AMEX:GLD :
Tests of Normality:
Many indicators perform a lot of functions on the assumption of normality, yet there are no indicators that actually test that assumption!
So, I have inputted a function to assess for normality. It uses the Kurtosis and Skewness to determine up to 7 different distribution types and it will explain the implication of the distribution. Here is an example of SP:SPX on the Monthly Perspective since 2010:
And NYSE:BA since the 60s:
And NVDA since 2015:
ARIMA Modeller
Okay, so let me disclose, this isn't a full fledge ARIMA modeller. I took some shortcuts.
True ARIMA modelling would involve decomposing the seasonality from the trend. I omitted this step for simplicity sake. Instead, you can select between using an EMA or SMA based approach, and it will perform an autogressive type analysis on the EMA or SMA.
I have tested it on lookback with results provided by SPSS and this actually works better than SPSS' ARIMA function. So I am actually kind of impressed.
You will need to input your parameters for the ARIMA model, I usually would do a 14, 21 and 50 day EMA of the close price, and it will forecast out that range over the length of the EMA.
So for example, if you select the EMA 50 on the daily, it will plot out the forecast for the next 50 days based on an autoregressive model created on the EMA 50. Here is how it looks on AMEX:SPY :
You can also elect to plot the upper and lower confidence bands:
Closing Remarks
So that is the indicator/package.
I do hope to continue expanding its functionality, but as of now, it does already have quite a lot of functionality.
I really hope you enjoy it and find it helpful. This. Has. Taken. AGES! No joke. Between referencing my old statistics textbooks, trying to remember how to calculate some of these things, and wanting to throw my computer against the wall because of errors in the code, this was a task, that's for sure. So I really hope you find some usefulness in it all and enjoy the ability to be able to do functions that previously could really only be done in external software.
As always, leave your comments, suggestions and feedback below!
Take care!
Correlation Coefficient based on Log ReturnsMeasuring correlations based on log returns, rather than raw prices or simple returns, offers several advantages:
- stationarity: Log returns are more stationary, resulting in more meaningful and reliable results
- volatility: Log returns give a consistent measure of relative changes of assets with different volatility
Log returns are time-additive and often more stationary than simple returns, making them statistically more reliable for analyses in financial contexts. Additionally, they provide a consistent measure of relative price changes and align more closely with the assumptions of many statistical models, including normal distribution.
K's Reversal Indicator IIIK's Reversal Indicator III is based on the concept of autocorrelation of returns. The main theory is that extreme autocorrelation (trending) that coincide with a technical signals such as one from the RSI, may result in a powerful short-term signal that can be exploited.
The indicator is calculated as follows:
1. Calculate the price differential (returns) as the current price minus the previous price.
2. the correlation between the current return and the return from 14 periods ago using a lookback of 14 periods.
3. Calculate a 14-period RSI on the close prices.
To generate the signals, use the following rules:
* A bullish signal is generated whenever the correlation is above 0.60 while the RSI is below 40.
* A bearish signal is generated whenever the correlation is above 0.60 while the RSI is above 60.
Position Cost DistributionThe Position Cost Distribution indicator (also known as the Market Position Overview, Chip Distribution, or CYQ Algorithm) provides an estimate of how shares are distributed across different price levels. Visually, it resembles the Volume Profile indicator, though they rely on distinct computational approaches.
🟠 Principle
The Position Cost Distribution algorithm is based on the principle that a security's total shares outstanding usually remains constant, except under conditions like stock splits, reverse splits, or new share issuance. It views all trading activity as simply exchanging share positions between holders at different price points.
By analyzing daily trade volume and the prior day's distribution, the algorithm infers the resulting share distribution after each day. By tracking these inferred transpositions over time, the indicator builds up an aggregate view of the estimated share concentration at each price level. This provides insights into potential buying and selling pressure zones that could form support or resistance areas.
Together with the Volume Profile, the Position Cost Distribution gives traders multiple lenses for examining market structure from both a volume and positional standpoint. Both can help identify meaningful technical price levels.
🟠 Algorithm
The algorithm initializes by allocating all shares to the price range encompassed by the first bar displayed on the chart. Preferably, the chart window should include the stock's IPO date, allowing the model to distribute shares specifically to the IPO price.
For subsequent trading sessions, the indicator performs the following calculations:
1. The daily turnover ratio is calculated by dividing the bar's trading volume by total outstanding shares.
2. For each price level (bucket), the number of shares is reduced by the turnover amount to represent shares transferring from existing holders.
3. The bar's total volume is then added to buckets corresponding to that period's price range.
Currently, the model assumes each share has an equal probability of being exchanged, regardless of how long ago it was acquired or at what price. Potential optimizations could incorporate factors like making shares held longer face a smaller chance of transfer compared to more recently purchased shares.
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中文介绍：该指标为“筹码分布”的一个 TradingView 实现 :)
WIPFunctionLyaponovLibrary "WIPFunctionLyaponov"
Lyapunov exponents are mathematical measures used to describe the behavior of a system over
time. They are named after Russian mathematician Alexei Lyapunov, who first introduced the concept in the
late 19th century. The exponent is defined as the rate at which a particular function or variable changes
over time, and can be positive, negative, or zero.
Positive exponents indicate that a system tends to grow or expand over time, while negative exponents
indicate that a system tends to shrink or decay. Zero exponents indicate that the system does not change
significantly over time. Lyapunov exponents are used in various fields of science and engineering, including
physics, economics, and biology, to study the long-term behavior of complex systems.
~ generated description from vicuna13b
---
To calculate the Lyapunov Exponent (LE) of a given Time Series, we need to follow these steps:
1. Firstly, you should have access to your data in some format like CSV or Excel file. If not, then you can collect it manually using tools such as stopwatches and measuring tapes.
2. Once the data is collected, clean it up by removing any outliers that may skew results. This step involves checking for inconsistencies within your dataset (e.g., extremely large or small values) and either discarding them entirely or replacing with more reasonable estimates based on surrounding values.
3. Next, you need to determine the dimension of your time series data. In most cases, this will be equal to the number of variables being measured in each observation period (e.g., temperature, humidity, wind speed).
4. Now that we have a clean dataset with known dimensions, we can calculate the LE for our Time Series using the following formula:
λ = log(||M^T * M - I||)/log(||v||)
where:
λ (Lyapunov Exponent) is the quantity that will be calculated.
||...|| denotes an Euclidean norm of a vector or matrix, which essentially means taking the square root of the sum of squares for each element in the vector/matrix.
M represents our Jacobian Matrix whose elements are given by:
J_ij = (∂fj / ∂xj) where fj is the jth variable and xj is the ith component of the initial condition vector x(t). In other words, each element in this matrix represents how much a small change in one variable affects another.
I denotes an identity matrix whose elements are all equal to 1 (or any constant value if you prefer). This term essentially acts as a baseline for comparison purposes since we want our Jacobian Matrix M^T * M to be close to it when the system is stable and far away from it when the system is unstable.
v represents an arbitrary vector whose Euclidean norm ||v|| will serve as a scaling factor in our calculation. The choice of this particular vector does not matter since we are only interested in its magnitude (i.e., length) for purposes of normalization. However, if you want to ensure that your results are accurate and consistent across different datasets or scenarios, it is recommended to use the same initial condition vector x(t) as used earlier when calculating our Jacobian Matrix M.
5. Finally, once we have calculated λ using the formula above, we can interpret its value in terms of stability/instability for our Time Series data:
- If λ < 0, then this indicates that the system is stable (i.e., nearby trajectories will converge towards each other over time).
- On the other hand, if λ > 0, then this implies that the system is unstable (i.e., nearby trajectories will diverge away from one another over time).
~ generated description from airoboros33b
---
Reference:
en.wikipedia.org
www.collimator.ai
blog.abhranil.net
www.researchgate.net
physics.stackexchange.com
---
This is a work in progress, it may contain errors so use with caution.
If you find flaws or suggest something new, please leave a comment bellow.
_measure_function(i)
helper function to get the name of distance function by a index (0 -> 13).\
Functions: SSD, Euclidean, Manhattan, Minkowski, Chebyshev, Correlation, Cosine, Camberra, MAE, MSE, Lorentzian, Intersection, Penrose Shape, Meehl.
Parameters:
i (int)
_test(L)
Helper function to test the output exponents state system and outputs description into a string.
Parameters:
L (float )
estimate(X, initial_distance, distance_function)
Estimate the Lyaponov Exponents for multiple series in a row matrix.
Parameters:
X (map)
initial_distance (float) : Initial distance limit.
distance_function (string) : Name of the distance function to be used, default:`ssd`.
Returns: List of Lyaponov exponents.
max(L)
Maximal Lyaponov Exponent.
Parameters:
L (float ) : List of Lyapunov exponents.
Returns: Highest exponent.
Median of Means Estimator Median of Means (MoM) is a measure of central tendency like mean (average) and median. However, it could be a better and robust estimator of central tendency when the data is not normal, asymmetric, have fat tails (like stock price data) and have outliers. The MoM can be used as a robust trend following tool and in other derived indicators.
Median of means (MoM) is calculated as follows, the MoM estimator shuffles the "n" data points and then splits them into k groups of m data points (n= k*m). It then computes the Arithmetic Mean of each group (k). Finally, it calculate the median over the resulting k Arithmetic Means. This technique diminishes the effect that outliers have on the final estimation by splitting the data and only considering the median of the resulting sub-estimations. This preserves the overall trend despite the data shuffle.
Below is an example to illustrate the advantages of MoM
Set A Set B Set C
3 4 4
3 4 4
3 5 5
3 5 5
4 5 5
4 5 5
5 5 5
5 5 5
6 6 8
6 6 8
7 7 10
7 7 15
8 8 40
9 9 50
10 100 100
Median 5 5 5
Mean 5.5 12.1 17.9
MoM 5.7 6.0 17.3
For all three sets the median is the same, though set A and B are the same except for one outlier in set B (100) it skews the mean but the median is resilient. However, in set C the group has several high values despite that the median is not responsive and still give 5 as the central tendency of the group, but the median of means is a value of 17.3 which is very close to the group mean 17.9. In all three cases (set A, B and C) the MoM provides a better snapshot of the central tendency of the group. Note: The MoM is dependent on the way we split the data initially and the value might slightly vary when the randomization is done sevral time and the resulting value can give the confidence interval of the MoM estimator.
Cumulative Distribution of a Dataset [SS]This is the Cumulative Distribution of a Dataset indicator that also calculates the Kurtosis and Skewness for a selected dataset and determines the normality and distribution type.
What it does, in pragmatic terms?
In the most simplest terms, it calculates the cumulative distribution function (or CDF) of user-defined dataset.
The cumulative distribution function (CDF) is a concept used in statistics and probability to describe how the probability of a random variable taking on a certain value or less is distributed across the entire range of possible values. In simpler terms, you can conceptualize the CDF as this:
Imagine you have a list of data, such as test scores of students in a class. The CDF helps you answer questions like, "What's the probability that a randomly chosen student scored 80 or less on the test?"
Or in our case, say we are in a strong up or downtrend on a stock. The CDF can help us answer questions like "Based on this current xyz trend, what is the probability that a ticker will fall above X price or below Y price".
Within the indicator, you can manually assess a price of interest. Let's say, for NVDA, we want to know the probability NVDA goes above or below $450. We can enter $450 into the indicator and get this result:
Other functions:
Kurtosis and Skewness Functions:
In addition to calculating and plotting the CDF, we can also plot the kurtosis & Skewness.
This can help you look for outlier periods where the distribution of your dataset changed. It can potentially alert you to when a stock is behaving abnormally and when it is more stable and evenly distributed.
Tests of normality
The indicator will use the kurtosis and skewness to determine the normality of the dataset. The indicator is programmed to recognize up to 7 different distribution types and alert you to them and the implications they have in your overall assessment.
e.g. #1 AMC during short squeeze:
e.g. #2: BA during the COVID crash:
Plotting the standardized Z-Score of the Distribution Dataset
You can also standardize the dataset by converting it into Z-Score format:
Plot the raw, CDF results
Two values are plotting, the green and the red. The green represents the probability of a ticker going higher than the current value. The red represents the probability of a ticker going lower than the current value.
Limitations
There are some limitations of the indicator which I think are important to point out. They are:
The indicator cannot tell you timelines, it can only tell you the general probability that data within the dataset will fall above or below a certain value.
The indicator cannot take into account projected periods of consolidation. It is possible a ticker can remain in a consolidation phase for a very long time. This would have the effect of stabilizing the probability in one direction (if there was a lot of downside room, it can normalize the data out so that the extent of the downside probability is mitigated). Thus, its important to use judgement and other methods to assess the likelihood that a stock will pullback or continue up, based on the overall probability.
The indicator is only looking at an individual dataset.
Using this indicator, you have to omit a large amount of data and look at solely a confined dataset. In a way, this actually improves the accuracy, but can also be misleading, depending on the size and strength of the dataset being chosen. It is important to balance your choice of dataset time with such things as:
a) The strength of the uptrend or downtrend.
b) The length of the uptrend or downtrend.
c) The overall performance of the stock leading into the dataset time period
And that is the indicator in a nutshell.
Hopefully you find it helpful and interesting. Feel free to leave questions, comments and suggestions below.
Safe trades everyone and take care!
