Trade-o-Scope: Plot Custom DataTrade-o-Scope team presents the "Plot Custom Data" indicator.
"Plot Custom Data" is designed to help you bring the custom timeseries data and plot it on the chart.
Motivation:
In the fast-evolving landscape of markets, traders often find themselves analyzing a plethora of data sources, specialized market information, various metrics, etc. While TradingView offers a rich catalog of instruments and market data, there are cases where traders rely on unique data sources, custom calculations, bespoke metrics, or refined analytics that demand visualization.
In some cases, traditional visualization approaches for custom data like plotting in Excel or other platforms may suffice for some. still, we really adore all the great features TradingView supercharts have - native scaling and scrolling, drawing, combining data, applying indicators and strategies on top of any series, etc. The ability to play with custom data on TradingView charts is just a whole new level compared to any alternatives.
With this indicator, we aim to empower traders to effortlessly bring their custom data to the familiar and friendly interface of the TradingView chart.
Basic Features:
Easy Data Input : Simply paste your numerical timeseries data, whether it's a column from Excel, Google Sheets, CSV file, or multiline text from any text editor.
Flexible Configuration : Define the order of values, starting date, and timeframe to match the specifications of your data.
Custom appearance : Personalize your chart by assigning a title to your data and selecting colors for the plot.
Advanced Features:
Value Multiplier : Set a multiplier for the provided values to adjust their scale as needed.
Formatting Options : Customize the display format of values on the chart, whether as price, percentage, or volume.
Conditional Coloring : Define conditions for changing the plot color based on a specific threshold.
Plot Style Selection : Choose from various plot styles such as line, histogram, area, columns, and more, to best visualize your data.
Additional Visual References : Enhance data analysis by activating fixed horizontal lines to aid in visual interpretation.
Leverage TradingView Capabilities : Seamlessly apply various indicators from the TradingView catalog, such as Bollinger Bands or RSI, onto your custom data chart for comprehensive analysis.
Chart Example:
On the chart above, you can observe several "Plot Custom Data" indicators added to demonstrate what your custom data can look like and how multiple indicators can be combined, as well as the indicator configuration screen.
How it works:
The "Plot Custom Data" indicator processes the list of values provided on the configuration form and converts it into data points to be plotted on the chart. To determine the timing of plotting each value, the indicator uses the configured 'starting date' and 'timeframe'. This means the indicator will always plot each subsequent value from your list at the distance of one timeframe from the previous one.
Limitations and Best Practices:
1) The way timeseries data is inputted on the "Plot Custom Data" indicator configuration page, leverages a standard input text field. This input type has a limited character capacity, approximately 4000-4400 symbols. Therefore, the timeseries data cannot exceed this limit.
Let's illustrate this with an example.
Suppose you have a column in Excel that you wish to plot using the indicator. Consider the first 5 rows of values in your column:
| -123.45
| -43.5
| 3.12
| 9.6
| 146.78
To calculate the total symbols used, we need to account for the minus sign, numbers, decimal point, and two invisible symbols at the end of each line, which helps structure the text into multiple lines. So, in total, these 5 rows will use:
row_1 + row_2 + row_3 + row_4 + row_5 = 9 + 7 + 6 + 5 + 8 = 35 symbols or 7 symbols per row on avarage.
If we assume the remaining data follows a similar format, we can estimate the number of rows that can be inputted on the indicator configuration page:
from ~4000 / 7 = 571 rows
to ~4400 / 7 = 628 rows
The right approach to overcome this limitation is to reduce the number of symbols representing each value as much as possible. In our example, consider dropping all decimals, which would lead to an average of 5.4 symbols per row and allow for up to ~800 rows to be accepted by the input field.
If your data primarily consists of large values such as thousands or millions, consider dividing it by a multiplier like x1000 and activating the same multiplier in the indicator settings.
For example, instead of storing values like 5,620,000 in your list, store the value 562 and activate an x10,000 multiplier in the configuration to display the original value on the chart.
It is also possible to combine multiple indicators on one chart and split a larger list of values between them. If you need to display significantly more custom data on charts, you may contact us in DM to find\develop a proper solution.
2) Another important consideration is to ensure that all your data aligns with the selected timeframe in the indicator configuration.
Remember, you provide only the values to the indicator without corresponding dates. Dates are calculated automatically by the indicator based on the configured starting date and chosen timeframe. This means the indicator will always plot each subsequent value from your list at the distance of one timeframe from the previous one.
Here's an example:
Suppose you have a timeseries with 'date' and 'value' columns and a timeframe of 30 minutes:
# | date | value
--|---------------------------|---------
1 | 2024-01-01 11:30:00 | -241.45
2 | 2024-01-01 12:02:13 | -11.56
3 | 2024-01-01 13:00:00 | 21.87
..
..
6 | 2024-01-01 14:30:00 | 19.6
7 | 2024-01-01 15:00:00 |
8 | 2024-01-01 15:30:00 | 46.78
Rows 1-3 represent bad practice:
Row 2 is not precisely aligned with the timeframe, as it has a time of 12:02:13 instead of the expected 12:00:00. This won't cause errors, but the plot will show this value at 12:00:00.
Row 3 has a 1-hour difference from Row 2, which poses a real problem. As the indicator doesn't know the actual dates, it expects Row 3 to be 30 minutes later than Row 2, so it will plot 21.87 at 12:30:00 instead of the actual 13:00:00. And the rest of the values from your list will have offset on the chart as well.
Rows 6-7 represent the correct approach to overcome gaps in your data:
If, for some reason, there is no value for 15:00:00 in the original dataset, adding an empty Row 7 ensures each subsequent row is located 30 minutes from the previous one. The indicator will treat the empty row correctly, not plotting anything at this date on the chart, but will plot a line connecting the value of 19.6 from Row 6 with the value of 46.78 from Row 8.
How to use:
1) Add the "Plot Custom Data" indicator to the chart.
Voila! You'll see the plot based on the default timeseries values list and settings.
2) Paste your numerical timeseries data.
Define the order of values, starting date, and timeframe.
Assign a title to your data and select colors for the plot.
Voila! You'll see the plot based on your timeseries values list and settings.
3) Customize the indicator's settings:
Set a values multiplier if needed.
Specify the appropriate format for the values (price, percentage, or volume).
Choose whether to change the plot color based on conditions.
Select the plot style.
Add horizontal lines.
4) Explore your data, apply additional indicators and strategies from the TradingView catalog, or develop your own scripts using Pine.
Statistics
TradeDots - Buy Sell SignalsThe TradeDots Buy Sell Indicator is a sophisticated multi-strategy analytics tool designed to discern market direction and identify potential trading opportunities.
The TradeDots indicator utilizes a trend-following strategy that initially identifies the long-term market trend, whether bullish or bearish, at a macro level. It then zooms in on pullback and reversal price action patterns within this broader trend. These patterns are confirmed by a micro-level candlestick analysis, which leads to the issuance of a "buy" alert. This process ensures both macro and micro elements of the market are considered before entering a trade.
How Does It Work?
In more detail, the procedure begins at the macro level where the advanced indicator applies an Exponential Moving Average (EMA) within the current timeframe. This EMA is then cross-verified with another EMA of the same length from a higher timeframe, ensuring a reliable assessment of long-term market trends. This approach helps establish whether the market situation favors long or short positions.
Following this, a complex mathematical model uses a designated window of candlesticks to calculate price action changes, storing all significant reversal patterns for subsequent comparison. The algorithm then identifies similar repeated reversal patterns in the chart, pinpointing potential market turning points.
For precision and reliability, the confirmation of these reversal patterns involves further refinement and filtering at a micro level. By calculating market momentum in tandem with an exhaustive analysis, e.g. Average True Value (ATR), candlestick body and wick data, the algorithm can affirm the reversal. Finally, the buy or sell signals are plotted on the chart in real-time.
The indicator includes 4 distinct entry strategies for both "Long" and "Short" orders. Each strategy represents different levels of rigorousness in their analysis rules. For instance, a "Weak Buy" signal represents a lighter pullback strength compared to a "Strong Buy" signal, with "Reversal Buy" exhibiting the robust pullback strength based on the change in price action value.
Each type of order comes with its minimum threshold and conditions for profit-taking to prevent excessive trading activity that could lead to high commission costs. Once these conditions are met and coupled with a reversal signal — generated with the same concepts as mentioned earlier but in the opposite direction — a sell signal is then triggered in real time. It's a systematic process that ensures an optimal balance between timely entries and exits in the market.
Generating Trading Ideas Catering To Traders Of All Kinds
TradeDots Buy Sell Indicator includes multiple strategies and many features:
4+ Types of Trading Alerts: Strong, Weak, Reversal, and Breakout for different market conditions (Should not be followed blindly).
2+ Trading Styles: Buy and Hold, Swing Trading (Should not be followed blindly).
Facilitates both "Long" and "Short" trades.
+ more. (Check the changelog below for current features)
HOW TO USE
⭐️ TRADING STRATEGY
Buy and Hold: An approach suitable for long-term investments or as an alternative to a dollar-cost averaging strategy by identifying only the undervalued positions in markets with long-term growth potential like stocks and indices.
Note: This strategy does not provide an exit strategy.
Swing Trading: This method targets buying low and selling high, adapted for traders looking to make the most of short to mid-term market volatility.
⭐️ ORDER DIRECTION
Order direction is for “Swing trading” strategy and other strategies that comes with an exit strategy. It is to choose the direction of the market that you wish to place your order on.
Long: Primarily targeting markets that exhibit a left-skewed trend (more often it rises than falls), this strategy focuses on "long" trading opportunities, avoiding "short" market actions.
Short: Apt for markets displaying a right-skewed trend (more often it falls than rises), this approach targets "short" opportunities exclusively, refraining from "long" market actions.
Long and Short: This comprehensive strategy identifies trading opportunities for both "long" and "short" market actions, facilitating increased opportunities for volatile assets.
⭐️ ALERT TYPES
Strong: These alerts designate high risk/reward return opportunities with a reasonable win rate. They tend to appear near previous support pivots where a Change in Character (CoCh) may often occur, typically coupled with a tight stop-loss strategy.
Weak: Indicative of opportunities balancing risk/reward return and win-rate, these alerts often appear during strong momentum markets.
Reversal: These signals identify potential reversals by highlighting extreme oversold or overbought states, thus revealing markets that are underpriced or overpriced for swift trading actions.
Breakout: They are to identify a change in trend and market breakout by gaps created post earnings or significant economic events, purposed for "Buy high, sell higher" strategies.
⭐️ STOP LOSS
The stop-loss feature offers customization options, enabling users to close a position upon reaching a predefined percentage drawdown. As volatility varies across different timeframes and markets, tuning this feature in accordance with the market allows optimal usage of this indicator.
CONCLUSION
While technical indicators are certainly vital in trading analysis, they are just one part of the equation. The individual trader's style and mindset significantly influence their trading outcomes, making them equally crucial in the process. Therefore, relying solely on indicators for a successful trading outcome may not be the most effective strategy.
Understanding and leveraging these indicators requires substantial time and significant effort from traders. They need to deeply engage with these tools to truly grasp their behavior and functionality. Taking this into consideration, our aim is to create highly advanced, customizable, and user-friendly technical indicators. This tool is designed to illuminate the fundamental role that technical indicators play as a supportive decision-making apparatus, aiding traders to more swiftly embark on their journey towards successful trading.
See Author's instructions below to get instant access to this indicator.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Trade-o-Scope: Multi-Asset Price TrackerTrade-o-Scope team presents the "Multi-Asset Price Tracker" indicator.
"Multi-Asset Price Tracker" is designed to help analyze price changes across multiple assets within time intervals you define.
Motivation:
Most screeners on the market track price changes over predefined periods, like 1H\4H\12H\1D\3D\7D\etc. Typically, they compare initial and current prices.
But what if you want to analyze assets' performance over arbitrary historical intervals?
Perhaps you want to spot which assets dropped most within 7 hours after a specific industry announcement? Or do you want to check which asset grew the most in January last year?
This is where the "Multi-Asset Price Tracker" shines. It lets you define arbitrary intervals and track price changes for selected assets.
Overview and functionality:
Define arbitrary time intervals in history for analyzing price changes.
Create up to 10 lists, each with up to 40 symbols to track. Populate lists with data from text files, Excel, or CSV. Lists are just multiline text strings, each line representing an individual symbol.
Switch between lists, with one active list at a time.
Specify up to 3 fixed symbols for additional analysis alongside active list symbols. You may use them as reference points to compare price changes of the symbols in the List.
Customize table appearance and position.
Set sorting criteria and direction for displayed values.
View the results in a table on the chart.
How it works:
"Multi-Asset Price Tracker" will track the symbol's price during the interval you define.
The indicator identifies each symbol's Start, Max, Min, and End prices during the interval. It then calculates relative changes: Start-to-Max, Start-to-Min, and Start-to-End.
The list of relative changes is ordered by the column chosen in settings and displayed on the chart.
List example (up to 10 Lists):
BINANCE:WLDUSDT.P
BINANCE:WOOUSDT.P
BINANCE:XEMUSDT.P
...
...
(up to 40 symbols per List)
Calculation example:
Symbol Start-price = $100
Symbol Max-price = $150
Symbol Min-price = $75
Symbol End-price = $110
Start-to-Max = 100% * (Max-price - Start-price) / Start-price = 100% * ($150 - $100) / $100 = 50%
Start-to-Min = 100% * (Min-price - Start-price) / Start-price = 100% * ($75 - $100) / $100 = -25%
Start-to-End = 100% * (End-price - Start-price) / Start-price = 100% * ($110 - $100) / $100 = 10%
Chart example:
In the provided chart, you can observe an example with a table generated by the indicator, along with manually added arrows and labels explaining the calculation process for an individual symbol from the list.
How to use:
1) Add "Multi-Asset Price Tracker" indicator to the chart
2) The indicator will ask you to define the start and the end of the interval using the mouse on the chart.
Voila! You'll see the table with sorted relative price changes based on default list values and settings.
3) Customize the indicator's settings:
Define the interval Start and End datetime.
Optionally, change the interval Start and End by clicking on the indicator name and dragging the vertical mark at the edges of the interval to a new position.
Define the Start and End price sources.
Choose a color to highlight the interval on the chart's background.
Set the table position, size, text size, and border size.
Specify the column for sorting and its direction.
Choose whether to sort chart symbols and fixed symbols together with list symbols or display them at the top of the table irrespective of their relative price change.
Enable fixed symbols if needed, and define up to 3 fixed symbols.
Define symbol lists, with up to 10 lists and 40 symbols each.
Select which List to make active - symbols from the active list will be calculated and displayed.
Inflation CorrelationHeyo fellas,
In today’s dynamic economic landscape, understanding the relationship of market prices to other economical factors like inflation rate is crucial. The Inflation Correlation Indicator is designed to provide traders with a clear visualization of this relationship. By correlating average inflation rates from selected countries with market closing prices, this indicator offers a unique perspective on potential market movements influenced by inflationary trends.
Features:
Country Selection: Choose from the European Union (EU), Germany (DE), or the United States (US) to tailor the correlation analysis to your specific market interest.
Correlation Length Customization: Adjust the correlation length to refine the sensitivity of the indicator to recent inflation data.
Visual Clarity: The correlation histogram changes color based on the direction of the correlation, providing an intuitive understanding of the inflation correlation.
Whether you’re a fundamental analyst seeking to incorporate macroeconomic indicators into your strategy or a trader looking for an edge in inflation-sensitive markets, the Inflation Correlation Indicator is an indispensable tool in your TradingView arsenal.
Thanks for checking this out!
Best regards,
simwai
LSMA Z-Score [BackQuant]LSMA Z-Score
Main Features and Use in the Trading Strategy
- The indicator normalizes the LSMA into a detrended Z-Score, creating an oscillator with standard deviation levels to indicate trend strength.
- Adaptive coloring highlights the rate of change and potential reversals, with different colors for positive and negative changes above and below the midline.
- Extreme levels with adaptive coloring indicate the probability of a reversion, providing strategic entry or exit points.
- Alert conditions for crossing the midline or significant shifts in trend direction enhance its utility within a trading strategy.
1. What is an LSMA?
The Least Squares Moving Average (LSMA) is a technical indicator that smoothens price data to help identify trends. It uses the least squares regression method to fit a straight line through the selected price points over a specified period. This approach minimizes the sum of the squares of the distances between the line and the price points, providing a more statistically grounded moving average that can adapt more smoothly to price changes.
2. What is a Z-Score?
A Z-Score is a statistical measurement that describes a value's relationship to the mean of a group of values, 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. A Z-Score helps in understanding if a data point is typical for a given data set or if it is atypical. In finance, a Z-Score is often used to measure how far a piece of data is from the average of a set, which can be helpful in identifying outliers or unusual data points.
3. Why Turning LSMA into a Z-Score is Innovative and Its Benefits
Converting LSMA into a Z-Score is innovative because it combines the trend identification capabilities of the LSMA with the statistical significance testing of Z-Scores. This transformation normalizes the LSMA, creating a detrended oscillator that oscillates around a mean (zero line), with standard deviation levels to show trend strength. This method offers several benefits:
Enhanced Trend Detection:
- By normalizing the LSMA, traders can more easily identify when the price is deviating significantly from its trend, which can signal potential trading opportunities.
Standardization:
- The Z-Score transformation allows for comparisons across different assets or time frames, as the score is standardized.
Objective Measurement of Trend Strength:
- The use of standard deviation levels provides an objective measure of trend strength and volatility.
4. How It Can Be Used in the Context of a Trading System
This indicator can serve as a versatile tool within a trading system for a range of things:
Trend Confirmation:
- A positive Z-Score can confirm an uptrend, while a negative Z-Score can confirm a downtrend, providing traders with signals to enter or exit trades.
Oversold/Overbought Conditions:
- Extreme Z-Score levels can indicate overbought or oversold conditions, suggesting potential reversals or pullbacks.
Volatility Assessment:
- The standard deviation levels can help traders assess market volatility, with wider bands indicating higher volatility.
5. How It Can Be Used for Trend Following
For trend following strategies, this indicator can be particularly useful:
Trend Strength Indicator:
- By monitoring the Z-Score's distance from zero, traders can gauge the strength of the current trend, with larger absolute values indicating stronger trends.
Directional Bias:
- Positive Z-Scores can be used to establish a bullish bias, while negative Z-Scores can establish a bearish bias, guiding trend following entries and exits.
Color-Coding for Trend Changes :
- The adaptive coloring of the indicator based on the rate of change and extreme levels provides visual cues for potential trend reversals or continuations.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
This is using the Midline Crossover:
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Fractional Differentiation█ Description
This Pine Script indicator implements fractional differentiation, a mathematical operation that extends the concept of differentiation to non-integer orders. Fractional differentiation is particularly significant in financial analysis, as it enables analysts to uncover underlying patterns in price series that are not evident with traditional integer-order differentiation. The motivation behind fractional differencing lies in its ability to balance the trade-off between retaining data/feature memory and ensuring stationarity.
█ Significance
Fractional differentiation offers a nuanced view of market data, allowing for the adjustment of the differentiation order to balance between signal clarity and noise reduction. This is especially useful in financial markets, where the choice of differentiation order can highlight long-term trends or short-term price movements without completely smoothing out the valuable market noise.
█ Approximations Used
The implementation relies on the Gamma function for the computation of coefficients in the fractional differentiation formula. Given the complexity of the Gamma function, this script uses an approximation method based on the Lanczos approximation for the logarithm of the Gamma function, as detailed in "An Analysis Of The Lanczos Gamma Approximation" by Glendon Ralph Pugh (2004). This approximation strikes a balance between computational efficiency and accuracy, making it suitable for real-time market analysis in Pine Script.
█ Limitations
While this script opens new avenues for market analysis, it comes with inherent limitations:
- The approximation of the Gamma function, although accurate, is not exact. The precision of the fractional differentiation result may vary slightly, especially for higher-order differentiations.
- The script's performance is subject to Pine Script's execution environment, with a default loop limit set to 100 iterations for practicality. Users might need to adjust this limit based on their specific use case, balancing between computational load and the desired depth of historical data analysis.
█ Credits
This script makes use of the `MathSpecialFunctionsGamma` library, authored by Ricardo Santos . This library provides essential mathematical functions, including an approximation of the Gamma function, which is crucial for the fractional differentiation calculation.
I also extend my sincere gratitude to
Dr. Marcos López de Prado for his seminal work, Advances in Financial Machine Learning (2018). Dr. López de Prado's insights have significantly influenced our approach to developing sophisticated analytical tools.
Dr. Ernie Chan for his freely and generously sharing valuable insights via discourse on quantitative trading strategies through his talks and publications.
Z Score & Trend FollowingIntroduction:
The Z Score & Trend Following indicator is a tool used in financial markets to assess the standard deviation of a data point from its mean value over a specified period. It calculates the Z score, which is a measure of how many standard deviations an element is from the mean. This indicator also incorporates trend-following characteristics, allowing traders to visualize trends based on the Z score.
Indicator Parameters:
Standard Deviation Length: Determines the length of the standard deviation calculation.
Average Length: Specifies the length of the moving average used for the mean calculation.
Calculation Type: Allows users to choose between different types of moving averages (SMA, EMA, WMA, VMA, TMA).
Standard Deviations: Sets the number of standard deviations to be used for trend analysis.
Bar Color: Option to enable or disable bar coloring based on trend conditions.
Calculations:
Z Score Calculation: The Z score is calculated as the difference between the source data point and the moving average divided by the standard deviation.
zscore = (src - (getMA(src, length))) / ta.stdev(src, slength)
Plots:
Z Score Plot: Plots the Z score values, typically in green.
Inverted Z Score Plot: Plots the inverted Z score values (multiplied by -1), typically in red.
Lines:
Zero Line: A horizontal dotted line indicating zero.
Upper Threshold Line: A dotted line representing the upper threshold defined by the number of standard deviations.
Lower Threshold Line: A dotted line representing the lower threshold defined by the negative number of standard deviations.
Bar Color:
The bar color changes based on the Z score values and the predefined standard deviation thresholds. Green bars indicate values above the upper threshold, red bars indicate values below the lower threshold, lime bars indicate positive Z scores, and maroon bars indicate negative Z scores. Neutral values are represented by black bars.
Conclusion:
The Z Score & Trend Following indicator combines the statistical concept of Z score with trend-following characteristics to provide traders with insights into market trends and potential reversal points. By visualizing Z scores alongside trend analysis, traders can make more informed decisions regarding market entry and exit points.
AWR - Chris - V3Calculates the AWR based on Dr. Coles formula. Takes the last 12 weeks (not including the current one) highs and lows, adds them and then divides by twelve, give the average weekly range. Then takes the AWR and show the playing field based on the high and low of the current weekly candle. Must be used on the weekly chart to get the data needed then manually plot your lines on the required charts.
Bitcoin Regression Price BoundariesTLDR
DCA into BTC at or below the blue line. DCA out of BTC when price approaches the red line. There's a setting to toggle the future extrapolation off/on.
INTRODUCTION
Regression analysis is a fundamental and powerful data science tool, when applied CORRECTLY . All Bitcoin regressions I've seen (Rainbow Log, Stock-to-flow, and non-linear models), have glaring flaws ... Namely, that they have huge drift from one cycle to the next.
Presented here, is a canonical application of this statistical tool. "Canonical" meaning that any trained analyst applying the established methodology, would arrive at the same result. We model 3 lines:
Upper price boundary (red) - Predicted the April 2021 top to within 1%
Lower price boundary (green)- Predicted the Dec 2022 bottom within 10%
Non-bubble best fit line (blue) - Last update was performed on Feb 28 2024.
NOTE: The red/green lines were calculated using solely data from BEFORE 2021.
"I'M INTRUIGED, BUT WHAT EXACTLY IS REGRESSION ANALYSIS?"
Quite simply, it attempts to draw a best-fit line over some set of data. As you can imagine, there are endless forms of equations that we might try. So we need objective means of determining which equations are better than others. This is where statistical rigor is crucial.
We check p-values to ensure that a proposed model is better than chance. When comparing two different equations, we check R-squared and Residual Standard Error, to determine which equation is modeling the data better. We check residuals to ensure the equation is sufficiently complex to model all the available signal. We check adjusted R-squared to ensure the equation is not *overly* complex and merely modeling random noise.
While most people probably won't entirely understand the above paragraph, there's enough key terminology in for the intellectually curious to research.
DIVING DEEPER INTO THE 3 REGRESSION LINES ABOVE
WARNING! THIS IS TECHNICAL, AND VERY ABBREVIATED
We prefer a linear regression, as the statistical checks it allows are convenient and powerful. However, the BTCUSD dataset is decidedly non-linear. Thus, we must log transform both the x-axis and y-axis. At the end of this process, we'll use e^ to transform back to natural scale.
Plotting the log transformed data reveals a crucial visual insight. The best fit line for the blowoff tops is different than for the lower price boundary. This is why other models have failed. They attempt to model ALL the data with just one equation. This causes drift in both the upper and lower boundaries. Here we calculate these boundaries as separate equations.
Upper Boundary (in red) = e^(3.24*ln(x)-15.8)
Lower Boundary (green) = e^(0.602*ln^2(x) - 4.78*ln(x) + 7.17)
Non-Bubble best fit (blue) = e^(0.633*ln^2(x) - 5.09*ln(x) +8.12)
* (x) = The number of days since July 18 2010
Anyone familiar with Bitcoin, knows it goes in cycles where price goes stratospheric, typically measured in months; and then a lengthy cool-off period measured in years. The non-bubble best fit line methodically removes the extreme upward deviations until the residuals have the closest statistical semblance to normal data (bell curve shaped data).
Whereas the upper/lower boundary only gets re-calculated in hindsight (well after a blowoff or capitulation occur), the Non-Bubble line changes ever so slightly with each new datapoint. The last update to this line was made on Feb 28, 2024.
ENOUGH NERD TALK! HOW CAN I APPLY THIS?
In the simplest terms, anything below the blue line is a statistical buying opportunity. The closer you approach the green line (the lower boundary) the more statistically strong that opportunity is. As price approaches the red line, is a growing statistical likelyhood/danger of an imminent blowoff top.
So a wise trader would DCA (dollar cost average) into Bitcoin below the blue line; and would DCA out of Bitcoin as it approaches the red line. Historically, you may or may not have a large time-window during points of maximum opportunity. So be vigilant! Anything within 10-20% of the boundary should be regarded as extreme opportunity.
Note: You can toggle the future extrapolation of these lines in the settings (default on).
CLOSING REMARKS
Keep in mind this is a pure statistical analysis. It's likely that this model is probing a complex, real economic process underlying the Bitcoin price. Statistical models like this are most accurate during steady state conditions, where the prevailing fundamentals are stable. (The astute observer will note, that the regression boundaries held despite the economic disruption of 2020).
Thus, it cannot be understated: Should some drastic fundamental change occur in the underlying economic landscape of cryptocurrency, Bitcoin itself, or the broader economy, this model could drastically deviate, and become significantly less accurate.
Furthermore, the upper/lower boundaries cross in the year 2037. THIS MODEL WILL EVENTUALLY BREAK DOWN. But for now, given that Bitcoin price moves on the order of 2000% from bottom to top, it's truly remarkable that, using SOLELY pre-2021 data, this model was able to nail the top/bottom within 10%.
NSE Option Straddle Candle Chart
'NSE Option Straddle Candle Chart' plot a straddle chart of the mentioned strike.
Straddle means combine price of a call price and a put price.
User has 4 inputs :
1 : Spot Symbol
2 : Expiry date
3 : Straddle Strikes
4 : Ema Length
5 : Supertrend Inputs
How to use :
1 : Trade need to know first what is a straddle. If ATM straddle price is 405, than it means market is likely to close within 405 points up or down at the expiry.
2 : Straddle is traded on pairs only
3 : If trader sells a straddle than , straddle price should move down. For there reference supertrend and moving average is plotted on chart
4 : Both this indicators helps trade to identify the trend , hence predict market.
5 : Options are dying assite , so is straddle , so prefer selling straddle instead of buying.
Prepare Targets, Stop Loss, Position Size and calculate PnL You are watching the price action of your favorite coin. Then the price changes quickly and you know you could start a good trade now.
But how much should you buy, where should you set your Target for Profit Taking and your Stop Loss? How much money do you want to risk, how much money would you win if the trade is succesfull?
This indicator helps you to set up your trade in a quick way, no need to do some calculations by hand.
How does it work?
Just enter the prices where you want to take Profit and where your Stopp Loss should be.
Enter the number of coins and wether you buy or sell/go long or short.
These targets are then shown in the chart, move them around to see if your stopp loss is positioned well. See directly what your profit or loss would be.
See some Screenshots with more explanations for what is possible and how to set up everything.
General Overview:
How to set up the Trade:
Formatting and Extras:
Let me know if you like it!
Price Scenarios - The Quant ScienceGENERAL OVERVIEW
Price Scenarios - The Quant Science is a quantitative statistical indicator that provides a forecast probability about future prices moving using the mathematical-statistical formula of statistical probability and expected value.
HOW TO USE
The indicator displays arrow-shaped signals that represent the probable future price movement calculated by the indicator, including the current percentage probability. Additionally, the candlesticks are colored based on the predicted direction to facilitate visual analysis. By default, green is used for bullish movements and red for bearish movements. The trader can set the analysis period (default value is 200) and the percentage threshold of probability to consider (default value is greater than 0.50 or 50%) through the user interface.
USER INTERFACE
Lenght analysis: with this features you can handle the length of the dataset to be used for estimating statistical probabilities.
Expected value: with this feature you can handle the threshold of the expected value to filter, only probabilities greater than this threshold will be considered by the model. By default, it is set to 0.50, which is equivalent to 50%.
Design Settings: modify the colors of your indicator with just a few clicks by managing this function.
We recommend disabling 'Wick' and 'Border' from the settings panel for a smoother and more efficient user experience.
NSE Option Chain
This Indicator show Options Data on signal dashboard , that help trader to analyse the market.
Options data consist of two things , Call and Put.
Every Strike has its Call and Put price.
So if user Opens any chart which is traded in options , dashboard will show total 16 Call and 16 Put strikes
8 Above from ATM and 8 Below from ATM.
On left hand side of dashboard there is Call data and on right side there is Put data.
Call side datas are , Call LTP which is latest price of that call strike , Call Chg which is change in points from previous day close and third is Call % which is % change from previous day close.
Same is on put side.
Color code is done based on positive or negative of data. If change or % is negative then color is red else green.
ATM strike data is plotted in bold
Inputs :
Spot Symbol Input for Option dashboard
Expiry date of that option contract
Strike interval between 2 strikes
Reference ATM strike ( user should keep this input as current ATM strike )
How to Use :
If dashboard shows call side is negative and put side is positive then that means market Bearish , because falling market leads to falling price of call and increase in price of Put.
Similarly if put is negative and call is positive then market is bullish.
This dashboard give trend conformation , trader should take other conformation also before taking trade.
Statistics • Chi Square • P-value • SignificanceThe Statistics • Chi Square • P-value • Significance publication aims to provide a tool for combining different conditions and checking whether the outcome is significant using the Chi-Square Test and P-value.
🔶 USAGE
The basic principle is to compare two or more groups and check the results of a query test, such as asking men and women whether they want to see a romantic or non-romantic movie.
–––––––––––––––––––––––––––––––––––––––––––––
| | ROMANTIC | NON-ROMANTIC | ⬅︎ MOVIE |
–––––––––––––––––––––––––––––––––––––––––––––
| MEN | 2 | 8 | 10 |
–––––––––––––––––––––––––––––––––––––––––––––
| WOMEN | 7 | 3 | 10 |
–––––––––––––––––––––––––––––––––––––––––––––
|⬆︎ SEX | 10 | 10 | 20 |
–––––––––––––––––––––––––––––––––––––––––––––
We calculate the Chi-Square Formula, which is:
Χ² = Σ ( (Observed Value − Expected Value)² / Expected Value )
In this publication, this is:
chiSquare = 0.
for i = 0 to rows -1
for j = 0 to colums -1
observedValue = aBin.get(i).aFloat.get(j)
expectedValue = math.max(1e-12, aBin.get(i).aFloat.get(colums) * aBin.get(rows).aFloat.get(j) / sumT) //Division by 0 protection
chiSquare += math.pow(observedValue - expectedValue, 2) / expectedValue
Together with the 'Degree of Freedom', which is (rows − 1) × (columns − 1) , the P-value can be calculated.
In this case it is P-value: 0.02462
A P-value lower than 0.05 is considered to be significant. Statistically, women tend to choose a romantic movie more, while men prefer a non-romantic one.
Users have the option to choose a P-value, calculated from a standard table or through a math.ucla.edu - Javascript-based function (see references below).
Note that the population (10 men + 10 women = 20) is small, something to consider.
Either way, this principle is applied in the script, where conditions can be chosen like rsi, close, high, ...
🔹 CONDITION
Conditions are added to the left column ('CONDITION')
For example, previous rsi values (rsi ) between 0-100, divided in separate groups
🔹 CLOSE
Then, the movement of the last close is evaluated
UP when close is higher then previous close (close )
DOWN when close is lower then previous close
EQUAL when close is equal then previous close
It is also possible to use only 2 columns by adding EQUAL to UP or DOWN
UP
DOWN/EQUAL
or
UP/EQUAL
DOWN
In other words, when previous rsi value was between 80 and 90, this resulted in:
19 times a current close higher than previous close
14 times a current close lower than previous close
0 times a current close equal than previous close
However, the P-value tells us it is not statistical significant.
NOTE: Always keep in mind that past behaviour gives no certainty about future behaviour.
A vertical line is drawn at the beginning of the chosen population (max 4990)
Here, the results seem significant.
🔹 GROUPS
It is important to ensure that the groups are formed correctly. All possibilities should be present, and conditions should only be part of 1 group.
In the example above, the two top situations are acceptable; close against close can only be higher, lower or equal.
The two examples at the bottom, however, are very poorly constructed.
Several conditions can be placed in more than 1 group, and some conditions are not integrated into a group. Even if the results are significant, they are useless because of the group formation.
A population count is added as an aid to spot errors in group formation.
In this example, there is a discrepancy between the population and total count due to the absence of a condition.
The results when rsi was between 5-25 are not included, resulting in unreliable results.
🔹 PRACTICAL EXAMPLES
In this example, we have specific groups where the condition only applies to that group.
For example, the condition rsi > 55 and rsi <= 65 isn't true in another group.
Also, every possible rsi value (0 - 100) is present in 1 of the groups.
rsi > 15 and rsi <= 25 28 times UP, 19 times DOWN and 2 times EQUAL. P-value: 0.01171
When looking in detail and examining the area 15-25 RSI, we see this:
The population is now not representative (only checking for RSI between 15-25; all other RSI values are not included), so we can ignore the P-value in this case. It is merely to check in detail. In this case, the RSI values 23 and 24 seem promising.
NOTE: We should check what the close price did without any condition.
If, for example, the close price had risen 100 times out of 100, this would make things very relative.
In this case (at least two conditions need to be present), we set 1 condition at 'always true' and another at 'always false' so we'll get only the close values without any condition:
Changing the population or the conditions will change the P-value.
In the following example, the outcome is evaluated when:
close value from 1 bar back is higher than the close value from 2 bars back
close value from 1 bar back is lower/equal than the close value from 2 bars back
Or:
close value from 1 bar back is higher than the close value from 2 bars back
close value from 1 bar back is equal than the close value from 2 bars back
close value from 1 bar back is lower than the close value from 2 bars back
In both examples, all possibilities of close against close are included in the calculations. close can only by higher, equal or lower than close
Both examples have the results without a condition included (5 = 5 and 5 < 5) so one can compare the direction of current close.
🔶 NOTES
• Always keep in mind that:
Past behaviour gives no certainty about future behaviour.
Everything depends on time, cycles, events, fundamentals, technicals, ...
• This test only works for categorical data (data in categories), such as Gender {Men, Women} or color {Red, Yellow, Green, Blue} etc., but not numerical data such as height or weight. One might argue that such tests shouldn't use rsi, close, ... values.
• Consider what you're measuring
For example rsi of the current bar will always lead to a close higher than the previous close, since this is inherent to the rsi calculations.
• Be careful; often, there are na -values at the beginning of the series, which are not included in the calculations!
• Always keep in mind considering what the close price did without any condition
• The numbers must be large enough. Each entry must be five or more. In other words, it is vital to make the 'population' large enough.
• The code can be developed further, for example, by splitting UP, DOWN in close UP 1-2%, close UP 2-3%, close UP 3-4%, ...
• rsi can be supplemented with stochRSI, MFI, sma, ema, ...
🔶 SETTINGS
🔹 Population
• Choose the population size; in other words, how many bars you want to go back to. If fewer bars are available than set, this will be automatically adjusted.
🔹 Inputs
At least two conditions need to be chosen.
• Users can add up to 11 conditions, where each condition can contain two different conditions.
🔹 RSI
• Length
🔹 Levels
• Set the used levels as desired.
🔹 Levels
• P-value: P-value retrieved using a standard table method or a function.
• Used function, derived from Chi-Square Distribution Function; JavaScript
LogGamma(Z) =>
S = 1
+ 76.18009173 / Z
- 86.50532033 / (Z+1)
+ 24.01409822 / (Z+2)
- 1.231739516 / (Z+3)
+ 0.00120858003 / (Z+4)
- 0.00000536382 / (Z+5)
(Z-.5) * math.log(Z+4.5) - (Z+4.5) + math.log(S * 2.50662827465)
Gcf(float X, A) => // Good for X > A +1
A0=0., B0=1., A1=1., B1=X, AOLD=0., N=0
while (math.abs((A1-AOLD)/A1) > .00001)
AOLD := A1
N += 1
A0 := A1+(N-A)*A0
B0 := B1+(N-A)*B0
A1 := X*A0+N*A1
B1 := X*B0+N*B1
A0 := A0/B1
B0 := B0/B1
A1 := A1/B1
B1 := 1
Prob = math.exp(A * math.log(X) - X - LogGamma(A)) * A1
1 - Prob
Gser(X, A) => // Good for X < A +1
T9 = 1. / A
G = T9
I = 1
while (T9 > G* 0.00001)
T9 := T9 * X / (A + I)
G := G + T9
I += 1
G *= math.exp(A * math.log(X) - X - LogGamma(A))
Gammacdf(x, a) =>
GI = 0.
if (x<=0)
GI := 0
else if (x
Chisqcdf = Gammacdf(Z/2, DF/2)
Chisqcdf := math.round(Chisqcdf * 100000) / 100000
pValue = 1 - Chisqcdf
🔶 REFERENCES
mathsisfun.com, Chi-Square Test
Chi-Square Distribution Function
Simple SSRThis indicator shows "SSR" on the chart when SSR "Short Sale Restriction" is activated on the ticker.
SSR "Short Sale Restriction" or "alternative uptick rule" is a rule introduced by the SEC that prohibits shorting on the bid when a stock has dropped more than 10% from the prior days close in the regular trading hours.
It will stay activated for the day it has triggered and the following day through regular and extended market hours.
Since this rule only applies to the US stock market it checks for the exchange and only displays it for US stocks.
Fourier Adjusted Volume Zone Oscillator [BackQuant]Fourier Adjusted Volume Zone Oscillator
Welcome to BackQuant's FSVZO, Primarily we decided to combine the Fourier analysis to a leading indicator concept. Since in concept it can be beneficial.
We also decided to add in the momentum velocity indicator as a point of confluence.
Which will be discussed later in how it can be used in a trading system. For now onto the boring stuff, please read all of this and enjoy!
Fourier ? What and Why:
Fourier transforms are a mathematical technique used for transforming signals between time and frequency domains. In the context of financial markets, this allows analysts to deconstruct price movements into constituent sinusoidal waves. By isolating these waves, traders can identify the dominant market cycles and trends hidden within the 'noise' of short-term price fluctuations.
Empirical Evidence and Benefits:
Cycle Identification: Empirical studies have shown that markets exhibit cyclical behaviors due to various economic, geopolitical, and psychological factors. Fourier filtering helps in pinpointing these cycles, even in seemingly random market movements.
Trend Detection: By highlighting dominant frequencies, traders can more accurately determine the prevailing trend direction, aiding in trend-following or contrarian strategies.
Volatility Clarity: Filtering out noise enhances the visibility of true market volatility, crucial for risk management and strategy adjustment.
Why the Volume Zone Oscillator (VZO) and Origins + Advantages:
The VZO was developed by Walid Khalil and David Steckler and introduced in the "Stocks & Commodities" magazine in 2009. It integrates volume with price movements to gauge the flow of buying and selling pressure. Unlike traditional volume indicators that solely quantify trading volume, the VZO interprets volume's impact on price direction, offering insights into the strength or weakness of a price trend.
Empirical Evidence and Benefits:
Market Sentiment: Volume is a key indicator of market sentiment. High volume accompanying price movements indicates strong sentiment, whereas low volume suggests a lack of conviction. The VZO makes this analysis quantifiable.
Overbought/Oversold Conditions: By quantifying where the current volume-weighted price is within its range, the VZO helps identify potential reversals, providing actionable signals for entering or exiting trades.
Trend Confirmation: The VZO's ability to confirm price trends with volume adds an extra layer of validation to trading signals, reducing the likelihood of false breakouts or breakdowns.
Why we Decided to Combine Them
The integration of Fourier filtering with the VZO offers a comprehensive view of the market by combining the geometric clarity of price movements with the psychological insights provided by volume analysis. This synergy allows for a more nuanced understanding of market dynamics.
Enhanced Signal Accuracy: The combination reduces the chances of false signals. Fourier filtering's trend and cycle identification, combined with the VZO's volume-based confirmation, can significantly enhance trading decision accuracy.
Market Turns and Continuations: Fourier analysis can indicate potential turning points or continuation patterns, which, when confirmed with volume analysis through the VZO, provides a robust signal for traders to act upon.
Adaptability: Both tools adapt well to various market conditions, making this combination versatile across different trading instruments and timeframes.
Empirical Evidence:
While specific empirical studies directly analyzing the combined effectiveness of Fourier filtering and VZO might be scarce, the foundational research supporting each method individually provides strong evidence of their validity. Academic and practical applications in financial markets have demonstrated the value of both Fourier analysis for cycle detection and volume-based oscillators like the VZO for assessing market strength and sentiment. Together, they offer a compelling toolkit for traders aiming to refine their market analysis and strategy execution.
USER INPUTS
Momentum Velocity Group
Show Confluence Momentum Velocity?: This toggle allows users to decide whether they want to display the momentum velocity indicator on their chart. It's designed to show the momentum of price movements, potentially indicating acceleration or deceleration in price trends.
Calculation Source: This setting lets users select the price data used for calculating the momentum velocity. Common options include the close, open, high, low, or an average of these prices. The choice depends on what aspect of price action the trader wishes to analyze.
Lookback Period: Determines the number of bars used to calculate the momentum. A longer period may smooth out the indicator, reducing sensitivity to recent price changes, while a shorter period may make the indicator more responsive to new information.
Use Adaptive Filtering?: Enables the use of adaptive filtering for the momentum calculation. This feature adjusts the indicator's sensitivity based on recent market volatility, potentially improving the indicator's responsiveness to market changes.
Adaptive Lookback Period: Specifies the period for the adaptive filter. This setting fine-tunes how rapidly the filter adjusts to changes in market conditions.
FSVZO Group
Show FSVZO?: This input controls whether the Fourier Smoothed Volume Zone Oscillator is displayed on the chart. It's the main feature of the script, combining Fourier analysis with volume data to provide insights into market dynamics.
Calculation Source for FSVZO: Similar to the momentum velocity calculation source, this setting allows users to choose the price data (close, open, high, low, or an average) that will be used for FSVZO calculations.
Calculation Period: Defines the length of the window for Fourier analysis and VZO calculation. This period can affect the sensitivity and smoothing of the indicator.
Show FSVZO Band Filler? (Ribbon): When enabled, this feature displays a filled area or ribbon on the chart, making it easier to visualize the oscillator's movement and trends.
Show FSVZO Moving Average (Ema)?: This toggle allows the display of an Exponential Moving Average (EMA) of the FSVZO, which can help smooth out its movements and provide a clearer trend direction.
MA Period: Specifies the length of the moving average applied to the FSVZO. Adjusting this period can affect the smoothness and lag of the trend indication.
Smooth VZO (Reduces noise, but increases its accuracy): Enables smoothing of the Volume Zone Oscillator to reduce noise and potentially increase the accuracy of its signals.
Smooth Period: Defines the smoothing period for the VZO, affecting how much noise reduction is applied.
UI Settings Group
Show Static Overbought and Oversold Levels?: Enables the display of predetermined levels that indicate overbought or oversold conditions, helping traders identify potential reversal points.
Show Adaptive Levels?: Allows the use of dynamic, market-condition-adjusted levels for overbought and oversold indicators, offering a more nuanced view of market extremes.
Show Detected Trend Shifts?: This setting controls the display of markers or indications when the script detects potential shifts in market trends, based on the oscillator's movements.
Trendshift Shader?: When enabled, this feature visually highlights areas on the chart where trend shifts are detected, improving the visibility of these important signals.
DIVERGENCES Group
Show Detected Divergences?: This option toggles the display of divergences between price action and the oscillator, which can signal potential reversals.
Use extra filtering when detecting divergences?: Enables additional criteria for identifying divergences, potentially improving the reliability of these signals.
Paint bars when Divergences are detected?: This feature changes the color of price bars when divergences are identified, making them stand out on the chart.
How to calculate divergences: Allows users to choose the method for calculating divergences, affecting the sensitivity and types of divergences that are identified.
Only calculate divergences on values absolutely greater than this: Sets a threshold for divergence calculation, focusing on more significant divergences and reducing noise.
Each input is designed to offer flexibility and control to the user, enabling a highly customizable experience tailored to individual trading strategies and market conditions.
How Can it Be Used in a Trading System
There are a few key ways it can be used, the main way is going to be the trend of the band/ ribbon. As that denotes the primary trend. Thus, if it were to trend up and reach the static overbought zone, there is a high probability of a reversion. This will also work well when it is in an extreme zone and there is a divergence.
Other ways of using it, it taking profit when there is an extreme background hue. Or potentially starting to get ready to buy on a higher timeframe if there is a extreme oversold background hue.
For more clear trends out of the FSVZO you may choose to use the moving average crossing the midline in confluence with the momentum velocity.
Please use with caution, nothing BackQuant or associated entities do are financial advice. please do not use this or any other indicator alone, they are not meant to be used in isolation.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
This is using the Midline Crossover of the FSVZO:
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Historical Correlation [LuxAlgo]The Historical Correlation tool aims to provide the historical correlation coefficients of up to 10 pairs of user-defined tickers starting from a user-defined point in time.
Users can choose to display the historical values as lines or the most recent correlation values as a heat map.
🔶 USAGE
This tool provides historical correlation coefficients, the correlation coefficient between two assets highlight their linear relationship and is always within the range (-1, 1).
It is a simple and easy to use statistical tool, with the following interpretation:
Positive correlation (values close to +1.0): the two assets move in sync, they rise and fall at the same time.
Negative correlation (values close to -1.0): the two assets move in opposite directions: when one goes up, the other goes down and vice versa.
No correlation (values close to 0): the two assets move independently.
The user must confirm the selection of the anchor point in order for the tool to be executed; this can be done directly on the chart by clicking on any bar, or via the date field in the settings panel.
For the parameter Anchor period , the user can choose between the following values NONE, HOURLY, DAILY, WEEKLY, MONTHLY, QUARTERLY and YEARLY. If NONE is selected, there will be no resetting of the calculations, otherwise the calculations will start from the first bar of the new period.
There is a wide range of trading strategies that make use of correlation coefficients between assets, some examples are:
Pair Trading: Traders may wish to take advantage of divergences in the price movements of highly positively correlated assets; even highly positively correlated assets do not always move in the same direction; when assets with a correlation close to +1.0 diverge in their behavior, traders may see this as an opportunity to buy one and sell the other in the expectation that the assets will return to the likely same price behavior.
Sector rotation: Traders may want to favor some sectors that are expected to perform in the next cycle, tracking the correlation between different sectors and between the sector and the overall market.
Diversification: Traders can aim to have a diversified portfolio of uncorrelated assets. From a risk management perspective, it is useful to know the correlation between the assets in your portfolio, if you hold equal positions in positively correlated assets, your risk is tilted in the same direction, so if the assets move against you, your risk is doubled. You can avoid this increased risk by choosing uncorrelated assets so that they move independently.
Hedging: Traders may want to hedge positions with correlated assets, from a hedging perspective, if you are long an asset, you can hedge going long a negative correlated asset or going short a positive correlated asset.
Traders generally need to develop awareness, a key point is to be aware of the relationships between the assets we hold or trade, the historical correlation is an invaluable tool in our arsenal which allows us to make better informed decisions.
On this chart we have an example of historical correlations for several futures markets.
We can clearly see how positively correlated the Nasdaq100 and Dow30 are with the SP500 over the whole period, or how the correlation between the Euro and the SP500 falls from almost +85% to almost -4% since 2021.
As we can see, correlations, like everything else in the market, are not static and vary over time depending on many factors, from macro to technical and everything in between.
🔹 Heatmap
The chart above shows the tool with the default settings and the Drawing Mode set to 'HEATMAP'.
We can see the current correlation between the assets, in this case the FX pairs.
The highest positive correlation is +90% (+0.90) between EURUSD and GBPUSD.
The highest negative correlation is -78% (-0.78) between EURUSD and USDJPY.
The pair with no correlation is AUDUSD and EURCAD with 1% (0.01)
On the above chart we can see the current correlations for the futures markets.
Currently, the assets that are less correlated to the SP500 are NaturalGas and the Euro, the more positive correlations are Nasdaq100 and Dow20, and the more negative correlations are the Yen, Treasury Bonds and 10-Year Notes.
🔶 DETAILS
🔹 Anchor Period
This chart shows the standard FX correlations with the Anchor Period set to `MONTHLY`.
We can clearly see how the calculations restart with the new month, in this case we can clearly see the differences between the correlations from month to month.
Let us look at the correlation coefficient between GBPUSD and USDJPY
In January, their correlation started at close to -100%, rose to close to +50%, only to fall to close to 0% and remain there for the second half of the month.
In February it was -90% in the first few days of the month and is now around -57%.
And between AUDUSD and EURCAD
Last month their correlation was negative for most of the month, reaching -70% and ending around -14%.
This month their correlation has never gone below +21% and at the time of writing is close to +53%.
🔶 SETTINGS
Anchor point: Starting point from which the tool is executed
Anchor period: At the beginning of each new period, the tool will reset the calculations
Pairs from 1 to 10: For each pair of tickers, you can: enable/disable the pair, select the color and specify the two tickers from which you wish to obtain the correlation
🔹 Style
Drawing Mode: Output style, `LINES` will show the historical correlations as lines, `HEATMAP` will show the current correlations with a color gradient from green for correlations near 1 to red for correlations near -1.
Kalman Price Filter [BackQuant]Kalman Price Filter
The Kalman Filter, named after Rudolf E. Kálmán, is a algorithm used for estimating the state of a linear dynamic system from a series of noisy measurements. Originally developed for aerospace applications in the early 1960s, such as guiding Apollo spacecraft to the moon, it has since been applied across numerous fields including robotics, economics, and, notably, financial markets. Its ability to efficiently process noisy data in real-time and adapt to new measurements has made it a valuable tool in these areas.
Use Cases in Financial Markets
1. Trend Identification:
The Kalman Filter can smooth out market price data, helping to identify the underlying trend amidst the noise. This is particularly useful in algorithmic trading, where identifying the direction and strength of a trend can inform trade entry and exit decisions.
2. Market Prediction:
While no filter can predict the future with certainty, the Kalman Filter can be used to forecast short-term market movements based on current and historical data. It does this by estimating the current state of the market (e.g., the "true" price) and projecting it forward under certain model assumptions.
3. Risk Management:
The Kalman Filter's ability to estimate the volatility (or noise) of the market can be used for risk management. By dynamically adjusting to changes in market conditions, it can help traders adjust their position sizes and stop-loss orders to better manage risk.
4. Pair Trading and Arbitrage:
In pair trading, where the goal is to capitalize on the price difference between two correlated securities, the Kalman Filter can be used to estimate the spread between the pair and identify when the spread deviates significantly from its historical average, indicating a trading opportunity.
5. Optimal Asset Allocation:
The filter can also be applied in portfolio management to dynamically adjust the weights of different assets in a portfolio based on their estimated risks and returns, optimizing the portfolio's performance over time.
Advantages in Financial Applications
Adaptability: The Kalman Filter continuously updates its estimates with each new data point, making it well-suited to markets that are constantly changing.
Efficiency: It processes data and updates estimates in real-time, which is crucial for high-frequency trading strategies.
Handling Noise: Its ability to distinguish between the signal (e.g., the true price trend) and noise (e.g., random fluctuations) is particularly valuable in financial markets, where price data can be highly volatile.
Challenges and Considerations
Model Assumptions: The effectiveness of the Kalman Filter in financial applications depends on the accuracy of the model used to describe market dynamics. Financial markets are complex and influenced by numerous factors, making model selection critical.
Parameter Sensitivity: The filter's performance can be sensitive to the choice of parameters, such as the process and measurement noise values. These need to be carefully selected and potentially adjusted over time.
Despite these challenges, the Kalman Filter remains a potent tool in the quantitative trader's arsenal, offering a sophisticated method to extract useful information from noisy financial data. Its use in trading strategies should, however, be complemented with sound risk management practices and an awareness of the limitations inherent in any model-based approach to trading.
Open Intrest / Volume / Liquidations (Suite) [BigBeluga]This indicator is a suite of tools that aims to provide traders with efficient metrics to analyze the market in a different way, such as various types of Open Interest, Intraday Volume, and Liquidations.
This indicator can both save time and also provide a different approach to the usual price action trading style.
🔶 FEATURES
The indicator contains the following features:
Open Interest Suite
- Delta OI
- Net longs and shorts
- OI Relative Strength Index
Intraday Volume Suite
- Bullish and Bearish LTF Volume
- CVD
- Delta Volume
Liquidations Suite
- Long and Short Liquidations
- Cumulative Liquidations
🔶 EXAMPLE OF SUITE
In the example above, we can see how we can plot long and short positions, both opening and closing out.
This can give a unique way to view which side is the strongest but also which side has the most resting liquidity.
For example, if more longs are entering the market, it also means more liquidity for longs and vice versa.
Or, for example, plotting the delta OI will allow the user to see big percentages in change and spot big areas of position closing out.
This presents a fascinating method for observing numerous positions closing out in conjunction with a surge of liquidations, which could indicate a potential reversal in price.
Here, we can see a basic example of using intraday volume on a 1m LTF.
With this, we are able to see both bullish and bearish volume of the same candle, very useful to see both volumes traded in the same candle.
Using the CVD to see the overall direction based purely on the volume and spot divergence, for example, the price in an uptrend but CVD going down, indicating weak shorts in the market or trapped shorts.
Or simply view liquidations happening in the market in a very different way, both long and short liquidation at the same time + the option to use multi-timeframe liquidations.
🔶 CONCLUSION
The idea of this script is to provide a set of tools in a unique script to optimize time and analyze the market in both a quick way and in a different way than usual.
Kernels©2024, GoemonYae; copied from @jdehorty's "KernelFunctions" on 2024-03-09 to ensure future dependency compatibility. Will also add more functions to this script.
Library "KernelFunctions"
This library provides non-repainting kernel functions for Nadaraya-Watson estimator implementations. This allows for easy substition/comparison of different kernel functions for one another in indicators. Furthermore, kernels can easily be combined with other kernels to create newer, more customized kernels.
rationalQuadratic(_src, _lookback, _relativeWeight, startAtBar)
Rational Quadratic Kernel - An infinite sum of Gaussian Kernels of different length scales.
Parameters:
_src (float) : The source series.
_lookback (simple int) : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
_relativeWeight (simple float) : Relative weighting of time frames. Smaller values resut in a more stretched out curve and larger values will result in a more wiggly curve. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel.
startAtBar (simple int)
Returns: yhat The estimated values according to the Rational Quadratic Kernel.
gaussian(_src, _lookback, startAtBar)
Gaussian Kernel - A weighted average of the source series. The weights are determined by the Radial Basis Function (RBF).
Parameters:
_src (float) : The source series.
_lookback (simple int) : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
startAtBar (simple int)
Returns: yhat The estimated values according to the Gaussian Kernel.
periodic(_src, _lookback, _period, startAtBar)
Periodic Kernel - The periodic kernel (derived by David Mackay) allows one to model functions which repeat themselves exactly.
Parameters:
_src (float) : The source series.
_lookback (simple int) : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
_period (simple int) : The distance between repititions of the function.
startAtBar (simple int)
Returns: yhat The estimated values according to the Periodic Kernel.
locallyPeriodic(_src, _lookback, _period, startAtBar)
Locally Periodic Kernel - The locally periodic kernel is a periodic function that slowly varies with time. It is the product of the Periodic Kernel and the Gaussian Kernel.
Parameters:
_src (float) : The source series.
_lookback (simple int) : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
_period (simple int) : The distance between repititions of the function.
startAtBar (simple int)
Returns: yhat The estimated values according to the Locally Periodic Kernel.
FreedX Grid Backtest█ FreedX Grid Backtest is an open-source tool that offers accurate GRID calculations for GRID trading strategies. This advanced tool allows users to backtest GRID trading parameters with precision, accurately reflecting exchange functionalities. We are committed to enhancing trading strategies through precise backtesting solutions and address the issue of unreliable backtesting practices observed on GRID trading strategies. FreedX Grid Backtest is designed for optimal calculation speed and plotting efficiency, ensuring users to achieve fastest calculations during their analysis.
█ GRID TRADING STRATEGY SETTINGS
The core of the FreedX Grid Backtest tool lies in its ability to simulate grid trading strategies. Grid trading involves placing orders at regular intervals within a predefined price range, creating a grid of orders that capitalize on market volatility.
Features:
⚙️ Backtest Range:
→ Purpose: Allows users to specify the backtesting range of GRID strategy. Closes all positions at the end of this range.
→ How to Use: Drag the dates to fit the desired backtesting range.
⚙️ Investment & Compounding:
→ Purpose: Allows users to specify the total investment amount and select between fixed and compound investment strategies. Compounding adjusts trade quantities based on performance, enhancing the grid strategy's adaptability to market changes.
→ How to Use: Set the desired investment amount and choose between "Fixed" or "Compound" for the investment method.
⚙️ Leverage & Grid Levels:
→ Purpose: Leverage amplifies the investment amount, increasing potential returns (and risks). Users can define the number of grid levels, which determines how the investment is distributed across the grid.
→ How to Use: Input the desired leverage and number of grids. The tool automatically calculates the distribution of funds across each grid level.
⚙️ Distribution Type & Mode:
→ Purpose: Users can select the distribution type (Arithmetic or Geometric) to set how grid levels are determined. The mode (Neutral, Long, Short) dictates the direction of trades within the grid.
→ How to Use: Choose the distribution type and mode based on the desired trading strategy and market outlook.
⚙️ Enable LONG/SHORT Grids exclusively:
█ MANUAL LEVELS AND STOP TRIGGERS
Beyond automated settings, the tool offers manual adjustments for traders seeking finer control over their grid strategies.
Features:
⚙️ Manual Level Adjustment:
→ Purpose: Enables traders to manually set the top, reference, and bottom levels of the grid, offering precision control over the trading range.
→ How to Use: Activate manual levels and adjust the top, reference, and bottom levels as needed to define the grid's scope.
⚙️ Stop Triggers:
→ Purpose: Provides an option to set upper and lower price limits, acting as stop triggers to close or terminate trades. This feature safeguards investments against significant market movements outside the anticipated range.
→ How to Use: Enable stop triggers and specify the upper and lower limits. The tool will automatically manage positions based on these parameters.
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This guide gives you a quick and clear overview of the FreedX Grid Backtest tool, explaining how you can use this cutting-edge tool to improve your trading strategies.
Blockunity US Market Liquidity (BML)Get a clear view of US market liquidity and monitor its status at a glance to anticipate movements on risky assets.
The Idea
The BML aggregates and analyzes total USD market liquidity in trillions of dollars. It is used to monitor the liquidity of the USD market. When liquidity is good, all is well. If liquidity is low, the US will maneuver and sell treasury bills (debt) to replenish its treasury, which can lead to bearish pressure on markets, particularly those considered risky, such as Bitcoin.
How to Use
The indicator is very easy to use, there's nothing special about it. This tool is mainly intended to be used as fundamental information, and not for active trading.
Elements
The US Market Liquidity has several distinct components:
FED Balance Sheet
The Fed credits member banks’ Fed accounts with money, and in return, banks sell the Fed US Treasuries and/or US Mortgage-Backed Securities. This is how the Fed “prints” money to juice the financial system.
US Treasury General Account
The US Treasury General Account (TGA) balances with the NY Fed. When it decreases, it means the US Treasury is injecting money into the economy directly and creating activity. When it increases, it means the US Treasury is saving money and not stimulating economic activity. The TGA also increases when the Treasury sells bonds. This action removes liquidity from the market as buyers must pay for their bonds with dollars.
Overnight Reverse Repurchase Agreements
A reverse repurchase agreement (known as Reverse Repo or RRP) is a transaction in which the New York Fed under the authorization and direction of the Federal Open Market Committee sells a security to an eligible counterparty with an agreement to repurchase that same security at a specified price at a specific time in the future.
Earnings Remittances Due to the Treasury
The Federal Reserve Banks remit residual net earnings to the US Treasury after providing for the costs of operations, payment of dividends, and the amount necessary to maintain each Federal Reserve Bank’s allotted surplus cap. Positive amounts represent the estimated weekly remittances due to the US Treasury. Negative amounts represent the cumulative deferred asset position, which is incurred during a period when earnings are not sufficient to provide for the cost of operations, payment of dividends, and maintaining surplus.
Settings
Several parameters can be defined in the indicator configuration. You can:
Choose the smoothing and timeframe to be used in the plot.
Set the EMA lookback period and display it or not. This affects the color of the main plot.
Set the period to be taken into account when calculating the variation rate in the table.
Select the data to be taken into account in the calculation.
Activate or not the barcolor.
Lastly, you can modify all table parameters.
Fundamental Analysis [TrendX_]__________xXx__________ INTRODUCTION __________xXx__________
Fundamental Analysis indicator employs a two-pronged approach to estimate the fair value of a security. This utilizes both relative valuation and intrinsic valuation methods, aiming to achieve a comprehensive understanding of the company's worth.
__________xXx__________ FEATURES AND USAGES __________xXx__________
1 - RELATIVE VALUATION:
Relative valuation takes a company's average financial ratios over a specific number of periods into account.
Price-to-Earnings Ratio (PE Ratio): This metric compares the company's current stock price to its earnings per share. A higher PE ratio indicates investors are willing to pay more for each dollar of earnings, potentially suggesting a growth expectation.
Price-to-Book Ratio (PB Ratio): This metric compares the company's current stock price to its book value per share. A higher PB ratio suggests the market values the company's assets more highly than their accounting book value.
Modified-PE-PB-Growth: This is the modified version for the PE and PB forward. Apply the company's average historical ROE growth rate to PE ratio. Similarly, apply the company's projected ROA growth rate to the industry average PB ratio to arrive at an adjusted PB ratio.
Enterprise Value (EV)/Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) Multiple: This metric compares the company's enterprise value (market capitalization + debt - cash) to its EBITDA. It provides a valuation measure that considers the company's capital structure.
2 - INTRINSIC VALUATION:
Intrinsic valuation attempts to estimate the inherent value of a company based on its future cash flow generation potential. This approach focuses on the company's long-term fundamentals rather than its current market price.
Discounted Cash Flow (DCF): This method discounts the company's projected future free cash flows to their present value. It requires forecasting future cash flows, a discount rate, and a terminal growth rate. The present value of these future cash flows represents the company's intrinsic value.
Dividend Discount Model (DDM): This method assumes the company's value is based on its ability to distribute future dividends to shareholders. It discounts the company's expected future dividends to their present value, providing another estimate of intrinsic value.
Graham Number: Developed by Benjamin Graham, this method utilizes a formula based on a company's earnings per share and book value per share to estimate its intrinsic value. The number 22.5, embedded within this formula, serves as a normalization factor, embodying an ‘ideal’ PE of 15 and PB of 1.5. This approach provides a conservative estimate of a company’s intrinsic value, offering a safety margin for investors.
Net-Nets: Net-Nets refer to micro-to-small companies trading at a price less than 67% of their net current asset value, which is calculated by subtracting current liabilities from current assets. This conservative approach, deeply rooted in the principles of value investing, essentially implies that these companies are undervalued to the extent that their market price is less than their liquidation value.
*** The color of each valuation toolkit’s background is determined UNDERVALUE (above current price) in Turquoise Green color and OVERVALUE (below acceptable rate) in Pink color.
3 - FINANCIAL METRICS
The financial metrics will provide a holistic view of company's financial health, efficiency, risk profile, and growth prospects
Efficiency Metrics:
Net Margin: This metric measures the percentage of each dollar of revenue remaining as profit after accounting for all operating expenses. A higher net margin indicates a company's efficiency in converting sales into profit.
Dividend Yield: This metric represents the annual dividend payment per share divided by the current stock price. It reflects the portion of a company's earnings distributed to shareholders as dividends. A higher dividend yield suggests a focus on shareholder returns.
Fraud Detection Metrics:
Beneish M-score (M-score): This metric is a statistical model used to identify potential accounting manipulations. A higher M-score indicates a greater likelihood of fraudulent activity. It's crucial to analyze the M-score along with other financial information.
Profitability and Growth Metrics:
Piotroski F-score (F-score): This metric assesses a company's financial health and profitability based on nine criteria. A higher F-score suggests a more robust and potentially higher-growth company.
Quick Ratio: This metric measures a company's ability to meet its short-term obligations (due within a year) using its most liquid assets (cash and equivalents, marketable securities, and accounts receivable). A higher quick ratio indicates a stronger short-term liquidity position.
Inventory Ratio: This metric measures how long it takes a company to sell its inventory on average. A lower inventory ratio suggests efficient inventory management and potentially lower holding costs.
Risk Metrics:
Risk-Free Rate (Risk-Free): This metric represents the theoretical rate of return on a risk-free investment, often approximated by the 10-year Treasury Constant Maturity Rate. It serves as a benchmark for evaluating the return required for riskier assets like stocks.
Beta: This metric measures a stock's volatility relative to the overall market (often represented by its market index). A beta of 1 indicates the stock's price movement mirrors the market. A beta greater than 1 suggests the stock is more volatile than the market, and vice versa.
Growth Metrics:
Capital Asset Pricing Model (CAPM): This model estimates the expected return on a stock based on its beta, the risk-free rate, and the market risk premium. CAPM helps determine if a stock is potentially overvalued or undervalued.
Weighted Average Cost of Capital (WACC): This metric represents the average cost of capital a company uses to finance its operations (equity and debt). A lower WACC suggests a company can access capital at a cheaper rate, potentially leading to higher profitability.
Compound Annual Growth Rate (CAGR): This metric calculates the average annual growth rate of a stock price over a specific period. It provides an indication of the historical price appreciation.
Additional:
Sustainable Growth Rate (Growth const.): This metric estimates the maximum long-term growth rate a company can sustain based on its internal resources (retained earnings) and industry growth.
Value at Risk (VaR): This metric estimates the maximum potential loss a stock price might experience over a given timeframe with a certain confidence level. It helps assess the downside risk associated with an investment.
*** The color of each metric’s background is determined above acceptable rate in Turquoise Green color and below acceptable rate in Pink color
__________xXx__________ CONCLUSION__________xXx__________
Fundamental analysis plays a critical role in empowering both investors and traders to navigate the dynamic stock markets. By delving deeper into a company's underlying financial health, future prospects, and competitive landscape, this approach fosters informed decision-making that leads to risk reduction and profit optimization. The Fundamental Analysis can serve as a cornerstone for investors and traders alike, offering a myriad of benefits.
For investors, it is instrumental in risk reduction, as it enables the assessment of a company’s fair value through financial statements, competitive advantages, and growth potential. This critical evaluation aids in avoiding overvalued stocks and spotting undervalued opportunities. Moreover, it fosters a long-term focus, steering investors towards decisions that reflect a company’s long-term prospects, thus supporting a buy-and-hold strategy that resonates with enduring investment objectives. Additionally, a profound comprehension of a company’s fundamentals bolsters investor confidence, ensuring that investment choices are grounded in solid data rather than speculative market noise.
Traders, on the other hand, can leverage fundamental analysis to pinpoint short-term opportunities by staying abreast of a company’s imminent catalysts such as financial health, efficiency, risk profile, or growth prospects. This knowledge allows them to anticipate market movements and seize fleeting chances for profit. It also provides informed insights for establishing entry and exit points, identifying companies poised for robust growth or those facing potential downturns, which is crucial for strategizing trades, including short selling. Importantly, by concentrating on fundamental data, traders can mitigate emotional decision-making, fostering a disciplined approach to trading that curtails the risks associated with impulsive, emotion-driven errors.
__________xXx__________ DISCLAIMER__________xXx__________
Past performance is not necessarily indicative of future results. Numerous factors and inherent uncertainties can influence the outcome of any endeavor, and predicting future events with certainty is impossible.
Trading and Investing inherently carries risk, and the majority of traders experience losses. This indicator is provided solely for informational and educational purposes and does not constitute financial advice.
Therefore, always exercise caution and independent judgment when making investment decisions based on any form of past performance analysis, including this indicator's results.
