Index Generator [By MUQWISHI]▋ INTRODUCTION :
The “Index Generator” simplifies the process of building a custom market index, allowing investors to enter a list of preferred holdings from global securities. It aims to serve as an approach for tracking performance, conducting research, and analyzing specific aspects of the global market. The output will include an index value, a table of holdings, and chart plotting, providing a deeper understanding of historical movement.
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▋ OVERVIEW:
The image can be taken as an example of building a custom index. I created this index and named it “My Oil & Gas Index”. The index comprises several global energy companies. Essentially, the indicator weights each company by collecting the number of shares and then computes the market capitalization before sorting them as seen in the table.
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▋ OUTPUTS:
The output can be divided into 3 sections:
1. Index Title (Name & Value).
2. Index Holdings.
3. Index Chart.
1. Index Title , displays the index name at the top, and at the bottom, it shows the index value, along with the daily change in points and percentage.
2. Index Holdings , displays list the holding securities inside a table that contains the ticker, price, daily change %, market cap, and weight %. Additionally, a tooltip appears when the user passes the cursor over a ticker's cell, showing brief information about the company, such as the company's name, exchange market, country, sector, and industry.
3. Index Chart , display a plot of the historical movement of the index in the form of a bar, candle, or line chart.
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▋ INDICATOR SETTINGS:
(1) Naming the index.
(2) Entering a currency. To unite all securities in one currency.
(3) Table location on the chart.
(4) Table’s cells size.
(5) Table’s colors.
(6) Sorting table. By securities’ (Market Cap, Change%, Price, or Ticker Alphabetical) order.
(7) Plotting formation (Candle, Bar, or Line)
(8) To show/hide any indicator’s components.
(9) There are 34 fields where user can fill them with symbols.
Please let me know if you have any questions.
Fundamental Analysis
ITG Scalper with Early SignalsThe TEMA-MACD Fusion Indicator combines the Triple Exponential Moving Average (TEMA) and Moving Average Convergence Divergence (MACD) to provide traders with a comprehensive insight into market momentum and trend direction.
TEMA is a powerful smoothing indicator that reduces lag and provides a clearer representation of price trends. By applying TEMA to MACD, this fusion indicator enhances the traditional MACD signals, offering more accurate and timely indications of trend changes and potential entry/exit points.
Key Features:
TEMA: The Triple Exponential Moving Average offers a unique perspective on price movements by providing a triple-smoothed average. It adapts more swiftly to changes in price compared to traditional moving averages, making it well-suited for capturing short to medium-term trends.
MACD: The Moving Average Convergence Divergence is a versatile momentum oscillator that depicts the relationship between two moving averages of an asset's price. It is widely used to identify trend direction, momentum strength, and potential reversal points.
Enhanced Signals: By integrating TEMA with MACD, this indicator generates enhanced signals that filter out noise and provide a clearer picture of market dynamics. It helps traders to identify trend reversals, confirm trend strength, and pinpoint potential entry and exit points with greater precision.
Customizable Parameters: Traders can customize the indicator's parameters according to their trading style and preferences, allowing for flexibility in signal generation and adaptability to various market conditions.
User-Friendly Interface: The indicator is designed with a user-friendly interface, making it accessible to traders of all levels of experience. Clear visual representations of signals and trend dynamics facilitate easy interpretation and decision-making.
Multi-Timeframe Compatibility: The TEMA-MACD Fusion Indicator is compatible with multiple timeframes, enabling traders to analyze trends and signals across different time horizons, from intraday to long-term perspectives.
Incorporate the TEMA-MACD Fusion Indicator into your trading strategy to gain deeper insights into market trends and make informed trading decisions with greater confidence.
True stock performance based on EY Bar divergenceI created this indicator to be used in conjunction with my other indicator "True stock performance based on Earnings Yield". I've detailed in that description how true performance is calculated. In short it measures how much EY is moving in relation to the stock price. The theory is that if stock price is moving heavily while EY isn't you have a sentiment driven trend and the stock isn't traded on fundamentals.
This indicators marks bars when stock performance divergences from true performance.
Green upward triangle = The true performance closes lower than previous while stock price is closing higher. This indicates a optimistic sentiment as stock price is pushed up even though price based on EY is moving down.
Red downward triangle = The true performance closes higher than previous while stock price closing lower. This indicates a negative sentiment as stock price is pushed down even though price based on EY is moving up.
How do I use it?
I use it to confirm when sentiment has taken taken over a stock. If you have a fair uptrend (when both stock performance and true performance are doing higher highs), optimistic divergencies are welcome. I've used NVIDIA from 2017 to 2019 to demonstrate.
But if true performance starts to make lower highs while stock performance keeps going up and you see optimistic divergencies, you can tell that the market is getting overoptimistic.
When the stock had crashed it eventually bottomed and started to make higher lows together with an uptrend of true performance, which I count as a fair uptrend. Regardless of that you start to see some negative divergencies indicating that people are scared that the stock will drop again and oversell. These opportunities can be good places to buy more.
But i don't care about earnings, I'm a technical trader. Do i have any use for it?
You possibly could, yes. If you want to follow the crowd, optimistic divergencies confirms that the market is still interested in the stock and may keep pushing the price up. But be careful. Negative divergencies almost never marks any tops, it often confirms downtrends and may indicate bottoms. Often the optimistic divergencies marks the top so don't buy more blindly using this indicator.
True stock performance based on Earnings YieldThe whole basis of the stock market is that you invest your money into a business that can use that money to increase it's earnings and pay you back for that investment with dividends and increased stock value. But because we are human the market often overbuy stocks that cant keep up their earnings with the current inflow of investments. We can also oversell a stock that is keeping up with earnings in regards to the stock price but we don't care because of the sentiment we have.
Earnings Yield is simply the percentage of Earnings Per Share in relation to the stock price. Alone, it's a great fundamental indicator to analyze a company. But I wanted to use it in another way and got tired of using the calculator all the time so that's why I made this indicator.
The goal is to see if the STOCK price is moving accordingly to the BUSINESS earnings. It works by calculating the difference of EY (TTM) previous close (1 bar) to the close thereafter. It then calculates the stock performance of the latest bar and divides that to get decimal form instead of percent. Then it divides the stock performance in decimal form with the difference of EY calculated before. The result shows how much the stock prices moves in relation to how much EY is moving. The theory is that if EY barely moves but the stock price moves heavily, you have a sentiment driven trend.
Example: Week 1 EY = 1.201. Week 2 EY = 1.105.
1.201 - 1.105 = 0.096
Week 2 performed a 11,2% increase in stock price. = 0.112 in decimal form.
0.112 / 0.096 = 1.67
1.67 is the multiple that plots this indicator.
Here is an good example of a stock that's currently in a highly sentiment driven trend, NVIDIA! (Posted 2024-03-30)
Here is an example of a Swedish stock that retail investors flocked to that have been blowned out completely.
When do I buy and sell?
This indicator is not meant to give exact entries or exits. The purpose is to scout the current and past sentiment, possible divergencies and see if a stock is over or under valued. I did add a 50 EMA to get some form of mean plotted. One could buy when true performance is low and sell when true performance drops below the 50 EMA. You could also just sell a part of your position and set a trailing exit with a ordinary 50 EMA or something like that. Often the sentiment will keep driving the price up. But if it last for 1 month or 1 year is impossible to tell.
Try it out and learn how it works and use it as you like. Cheers!
NVT Z-ScoreNVT Z-Score Script:
Data Source and Calculation: This script calculates the NVT ratio by dividing the market cap (assumed from QUANDL data) by a 90-day MA of the transaction volume (also from QUANDL), similar to the NVTS calculation. However, the adaptation lies in further analyzing the NVT ratio through a Z-score approach, not explicitly described in the original NVTS methodology.
Z-Score Analysis: The script calculates the mean and standard deviation of the NVT ratio over a user-defined period (daysForMean, defaulting to 180 days) and then computes the Z-score of the current NVT ratio relative to this historical data. This Z-score analysis introduces a standardized way of understanding the NVT ratio's deviation from its historical average, offering a nuanced view of market valuation states.
Visualization and Dynamic Zones: The visualization emphasizes Z-score-based dynamic zones (green, yellow, and red), determined by the stdDevMultiplier. These zones are plotted and filled on the chart, providing visual cues for interpreting the NVT ratio's current state in relation to its historical norm. This aspect significantly differs from the traditional NVTS approach by directly incorporating the concept of standard deviation and Z-scores into the analysis.
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.
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
Oster's Fair Index (OFI)Note : Excitingly, this indicator is optimized to work exclusively with weekly candles (1W) ! Because fundamental analyses, with their longer-term outlook, thrive on the broader perspective provided by weekly data.
Overview:
Oster's Fair Index (OFI) stands out as a sophisticated indicator to offer traders a comprehensive assessment of a stock's fundamental valuation. Unlike many conventional indicators that focus solely on technical analysis, OFI places a strong emphasis on fundamental metrics, providing traders with a deeper understanding of a stock's intrinsic worth. It applies Oster's method (explained below) to determine the fundamental fair price of a stock.
Innovative Approach to Fundamental Analysis:
OFI employs a unique approach to fundamental analysis, integrating multiple key metrics including Yield , P/S (Price-to-Sales) ratio , P/E (Price-to-Earnings) ratio , Debt/Asset ratio , and P/FCF (Price-to-Free-Cash-Flow) ratio . These metrics collectively offer a holistic view of a company's financial health, allowing traders to gauge its potential for growth and profitability. Notably, the fundamental metrics included in OFI are regarded as the most crucial indicators for fundamental stock evaluation according to Oster's method. Dividend yield and P/S ratio are prioritized as the most significant, followed by the P/E ratio, with supplementary consideration given to the debt-to-asset ratio and price-to-free cash flow ratio. This weighting reflects their importance in determining a stock's fair value according to the methodology, which is integrated into OFI's calculation process.
Customizable Parameters for Tailored Analysis:
One of OFI's standout features is its flexibility, allowing users to customize the fundamental parameters based on their specific investment strategy or preferences. Traders can selectively include or exclude metrics , adjust weighting factors , and set alarm thresholds to align with their unique trading objectives. This customization empowers traders to tailor OFI according to their individual preferences and market perspectives. Although a default value has been set for the weighting of the parameters, traders still have the option to customize it based on their own trading strategy and preference, ensuring that OFI remains adaptable to diverse trading styles and objectives.
Sophisticated Calculation Methodology:
Behind the scenes, OFI employs a sophisticated calculation methodology to derive its insights. It retrieves fundamental data for the selected stock, such as total revenue, earnings per share, debt-to-asset ratio, free cash flow per share, and dividend yield. However, these metrics are not viewed in isolation; rather, they are considered in relation to historical trends . For instance, while a low debt-to-asset ratio may indicate fundamental strength for a company, it must be interpreted in the context of its historical performance. If the debt-to-asset ratio has historically been consistently lower, it may suggest weaker performance despite the seemingly favorable current ratio. Furthermore, OFI goes beyond mere fundamental metrics by incorporating the stock price itself into its analysis . A low debt-to-asset ratio becomes even more attractive for the company if the stock price is also historically low, indicating undervaluation. OFI takes all these aspects into account, providing traders with a comprehensive and nuanced evaluation of a stock's fundamental attractiveness, considering all these aspects in relation to each of the fundamental metrics mentioned above.
Normalized Fairness Differentials for Standardized Comparison:
OFI employs a method where the aforementioned fundamental metrics interact as described earlier. These metrics are combined into a fundamental, normalized value using weighting factors. This value is then normalized by the moving price range of the last 12 months. The result provides insights not only into when the stock price was undervalued, overvalued, or fair, but also enables traders to estimate potential price movements based on the fundamental health of the company. Additionally, a dashed fair price line simply represents the sum of the current stock price and the OFI value. This line illustrates the fair price level of the stock derived from the methodology.
Interpretation:
A negative OFI indicates that the stock may be undervalued based on fundamental metrics. Conversely, a positive OFI suggests that the stock may be overvalued according to fundamental analysis. A zero OFI implies that the stock is trading at a fair price relative to its fundamentals, indicating a balanced valuation scenario. The values of OFI are not arbitrary; they represent the degree of overvaluation or undervaluation in the currency set in the chart settings. This means traders can discern, for example, how many USD the stock is undervalued or overvalued by . Additionally, a dashed fair price line simply represents the sum of the current stock price and the OFI value, illustrating the fair price levels of the stock derived from the methodology.
Dynamic Color Coding for Visual Clarity:
To enhance usability, OFI features dynamic color coding that visually highlights the fair price differentials. Green signifies potential undervaluation , red indicates potential overvaluation , and neutral colors represent fair valuation . This intuitive visual feedback enables traders to quickly identify opportunities and risks.
Alerts:
OFI generates alerts based on these interpretations to assist traders in making informed decisions. An Undervalued Signal (BUY) is triggered when the OFI is below zero and meets the buy threshold criteria. This indicates that the stock is fundamentally undervalued, prompting a BUY alert. Conversely, an Overvalued Signal (SELL) is generated when the OFI surpasses zero and meets the sell threshold criteria. This signals that the stock is fundamentally overvalued, prompting a SELL alert. When OFI hovers around zero, suggesting that the stock is trading at a fair price, a Fair Price Reached (FAIR) alert is generated. This encourages traders to consider profit-taking strategies given the balanced valuation.
Justification of Originality and Value:
In a landscape saturated with technical indicators, OFI distinguishes itself by offering traders a refreshingly simple yet powerful approach to fundamental analysis. While traditional methods often involve laborious scrutiny of financial metrics or even poring over entire company balance sheets, OFI streamlines this process, providing traders with a swift overview of a stock's fundamental health. Its strength lies in seamlessly integrating fundamental analysis with stock price movements, offering insights into how price correlates with fundamental metrics.
One could say we marry the simplicity of technical analysis with the depth of fundamental analysis. This unique combination empowers traders to make informed decisions with ease, leveraging the best of both worlds to navigate the markets effectively.
Conclusion:
In conclusion, Oster's Fair Index (OFI) represents a pioneering advancement in the realm of fundamental analysis, offering both sophisticated calculation methodologies and intuitive, user-friendly features. By marrying these elements with customizable parameters and intuitive visuals, OFI equips traders with a powerful tool for evaluating the fundamental valuation of stocks. Whether you're a seasoned investor or a novice trader, OFI offers invaluable insights that can inform and enrich your trading journey.
Trade-o-Scope: Highlight IntervalsTrade-o-Scope team presents the Highlight intervals indicator.
Highlight intervals is a simple yet powerful indicator that helps highlight adjustable time intervals on the chart.
Overview and functionality:
You can define time intervals at three different levels: Month, Day of the Week, and Day of the Month. The indicator will then highlight intervals at the intersection of these levels.
By default, the indicator will use the exchange timezone, but you can easily switch to any timezone that suits you best.
You can activate an info-panel about the type of asset displayed on the chart and the timezone used by the exchange.
You can select your preferred color for highlighting intervals.
With Highlight intervals, you're equipped to visualize and focus on the intervals that matter most to you while seeking alpha!
How it works:
You define which bars will be highlighted by configuring the indicator settings. These settings revolve around the time intervals and may combine:
Months: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec
Days of Month: 1 - 31
Days of Week: Mon, Tue, Wed, Thu, Fri, Sat, Sun
Example:
In the attached chart you can see an example where the time intervals are highlighted according to such a configuration:
Months: Feb, Mar
Days of Month: 1 - 15
Days of Week: Mon - Fri
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.
Buffett IndicatorThis is an open-source version of the Buffett indicator. The old version was code-protected and broken, so I created another version.
It's computed simply as the entire SPX 500 capitalization divided by the US GDP. Since TradingView does not have data for the SPX 500 capitalization, I used quarterly values of SPX devisors as a proxy.
I tried to create another version of the Buffett indicator for other countries/indexes, but I can't find the data. If you can help me find data for index divisors, I can add more choices to this indicator.
It's interesting to see how this indicator's behavior has changed in the last few years. Levels that looked crazy are not so crazy anymore.
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
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
Financial Ratio Analysis (with / without Competitors)What Is Financial Ratio Analysis?
Financial Ratio Analysis is a quantitative technique used to assess a company's liquidity, operational efficiency, and profitability by examining its financial statements, including the balance sheet, income statement, and cash flow statement. It provides valuable insights into a company's performance over time and allows for comparisons with other companies within the same industry or sector.
What Are the Uses of Financial Ratio Analysis?
Analysis of financial ratios serves two main purposes:
1. Track company performance
Determining individual financial ratios per period and tracking the change in their values over time is done to spot trends that may be developing in a company.
Current Ratio for Adobe Inc. NASDAQ:ADBE
2. Make comparative judgments regarding company performance
Comparing financial ratios with those of major competitors enables the identification of whether a company is performing better or worse than the industry average. This comparative analysis aids in understanding the company's competitive position and potential areas for improvement.
For comparison, the script would automatically select a maximum of 5 competitors from the US markets based on the ticker's industry. This ensures a relevant comparison with industry peers to evaluate performance and assess competitive positioning.
To compare the Free Cash Flow Margin of Apple Inc. NASDAQ:AAPL with its competitors.
To compare the Free Cash Flow Margin of Apple Inc. NASDAQ:AAPL with its competitors’ average.
Customized competitors list
To customize your own competitors list, you can specify the companies or tickers you want to include in the comparison. This allows for a tailored analysis based on your specific preferences and industry knowledge.
Example:
To compare PayPal NASDAQ:PYPL with NASDAQ:MELI , NASDAQ:DLO , and NYSE:PAY , users can input the following text into the competitors list:
NASDAQ:MELI,NASDAQ:DLO,NASDAQ:PYPL,NYSE:PAY;
This will ensure that the comparison includes these specific companies alongside PayPal.
Financial ratios are grouped into the following categories:
Liquidity ratios
Leverage ratios
Efficiency ratios
Profitability ratios
Market value ratios
Liquidity Ratios
Liquidity ratios are financial ratios that measure a company’s ability to repay both short-term and long-term obligations.
Current Ratio measures a company’s ability to pay off short-term liabilities with current assets:
Current ratio = Total current assets / Total current liabilities
Cash To Debt Ratio measures a company’s ability to pay off short-term liabilities with cash and cash equivalents. A high ratio indicates a company can pay off its debt and remain solvent into the foreseeable future. In addition, it also means that if necessary, the company can take on a larger amount of debt because it has the cash to support that.
Cash to debt ratio = Cash and Short Term Investments / Total debt
Leverage Financial Ratios
Leverage ratios measure the amount of capital that comes from debt. In other words, leverage financial ratios are used to evaluate a company’s debt levels.
Debt To Assets Ratio measures the relative amount of a company’s assets that are provided from debt. This indicator is a measure of assets that are growing at the expense of debt. Because of this, you can see how a company acquired its assets over time. It can be used to assess a company's ability to meet its current debt obligations.
Debt to assets ratio = Total debt / Total assets
Debt To Equity Ratio calculates the weight of total debt and financial liabilities against shareholders’ equity:
Debt to equity ratio = Total liabilities / Shareholder’s equity
Interest Coverage Ratio shows how easily a company can pay its interest expenses:
Interest coverage ratio = Operating income / Interest expense
Efficiency Ratios
Efficiency ratios, also known as activity financial ratios, are used to measure how well a company is utilizing its assets and resources.
Research & Development (R&D) Expense to Revenue Ratio measures the percentage of sales that is allocated to R&D expenditures.
R&D to revenue ratio = Research and development expense / Total revenue * 100%
Asset Turnover Ratio measures a company’s ability to generate sales from assets. The higher it is, the more efficient the company is, since higher ratios mean that the company generates more income per dollar of assets. Conversely, if the company has a low Asset turnover, this indicates that it is inefficiently using its assets.
Asset turnover ratio = Revenue / Average total assets for two periods
Inventory Turnover shows how quickly a company sells its stock. A low turnover can mean weak sales, while a high one can mean good sales or insufficient stock. Inventory turnover is an important indicator of a company's performance.
Inventory turnover = Cost of goods sold / Total inventories
Days Sales Outstanding measures the average number of days it takes for a company to collect cash from credit purchases.
Days sales outstanding = Average Accounts Receivable / Revenue x 365 Days
Days Inventory shows the time in days that is spent turning a company's inventory into sales. This metric is an indicator of a company's inventory management. Low values are preferred for Days Inventory, which means items are selling faster and there is a quick turnaround. Large values indicate that a company has invested too much in stocks and does not have time to sell them.
Days inventory = Average inventories / Cost of goods sold * Days in period
Profitability Ratios
Profitability ratios measure a company’s ability to generate income relative to revenue, balance sheet assets, operating costs, and equity.
Gross Margin compares the gross profit of a company to its net sales to show how much profit a company makes after paying its cost of goods sold:
Gross margin % = Gross income / Total revenue * 100
Operating Margin , sometimes known as the return on sales ratio, compares the operating income of a company to its net sales to determine operating efficiency:
Operating margin = Operating income / Revenue * 100%
Free Cash Flow Margin is a profitability ratio that compares a company's free cash flow to its revenue to understand the proportion of revenue that becomes free cash flow. The higher the percentage, the more cash is available from sales. A company that shows an increasing cash flow margin from year to year is certainly getting stronger with time. This is a good indicator of its probability for long-term success.
Free cash flow margin = Free Cash Flow / Total Revenue
Return On Assets measures how efficiently a company is using its assets to generate profit. A high ROA indicates that a company successfully converts invested money into income.
Return on assets = Net income before discontinued operations / Total average assets
Return On Equity measures how efficiently a company is using its equity to generate profit:
Return on equity = Net income / Shareholder’s equity
Revenue Growth refers to the increase in a company’s total revenue or income over a specific period
Revenue growth = (Current period revenue - previous period revenue) / Previous period revenue * 100%
Earnings Per Share Growth illustrates the growth of earnings per share over time.
Earnings per share growth = ( Current period EPS - previous period EPS ) / Previous period EPS * 100%
Operating Cash Flow Growth is the long term rate of growth of operating cash, the money that is actually coming into the bank from business operations.
Operating cash flow growth = ( Current period operating cash flow - previous period operating cash flow) / Previous period operating cash flow* 100%
Market Value Ratios
Market value ratios are used to evaluate the share price of a company’s stock.
Book Value Per Share calculates the per-share value of a company based on the equity available to shareholders. In case of the company liquidation, the book value per share shows the monetary value remaining for common shareholders after all assets are sold and all debt is paid. If a company’s Book value per share is higher than a market price of its share, then the stock may be considered undervalued.
Book value per share = Total common equity / Total common shares outstanding
Dividend Yield measures the amount of dividends attributed to shareholders relative to the market value per share:
Dividend yield = Dividends TTM for the primary issue excluding special dividends / Price of the primary issue
Diluted Earnings per Share (Diluted EPS)
EPS stands for earnings per share. Investors use EPS to measure how much money a company makes for every outstanding share the company has. Diluted EPS is slightly different in that it measures the earnings per share for a company if all convertible securities (such as preferred stocks, convertible debt instruments, stock options and warrants) were used to calculate the metric.
1995-Present - Inflation and Purchasing PowerGood day, everyone! Today, we're going to look at a chart that's a bit different from the usual price charts we analyse. This isn't just any chart; it's a lens into the past, adjusted for the reality of inflation—a concept we often hear about but seldom see directly applied to our trading charts.
What we have here is an 'Inflation Adjusted Price' indicator on TradingView, and it's doing something quite special. It's showing us the price of our asset, let's say the S&P 500, not just in today's dollars, but in the dollars of 1995. Why 1995, you ask? Well, it's the starting point we've chosen to measure how much actual buying power has changed since then.
So, every point on this red line we see represents what the S&P 500's value would be if we stripped away the effects of inflation. This is the price in terms of what your money could actually buy you back in 1995.
As traders and investors, we're always looking at prices going up and thinking, 'Great! My investment is growing!' But the real question we should ask is, 'Is my money growing in real terms? Can it buy me more than it did last year, or five, ten, or twenty-five years ago?'
This chart tells us exactly that. If the red line is above the actual price, it means that the S&P 500 has not just grown in nominal terms, but it has actually outpaced inflation. Your investment has grown in real terms; it can buy you more now than it could back in 1995.
On the flip side, if the red line is below the actual price, that's a sign that while the nominal price might be up, the real value, the purchasing power, hasn't grown as much or could even have fallen.
This view is crucial, especially for the long-term investors among us. It gives us a reality check on our investments and savings. Are we truly growing our wealth, or are we just keeping up with the cost of living? This indicator answers that.
Remember, the true measure of financial growth is not just the numbers on a chart. It's what you can do with those numbers—how much bread, or eggs, or yes, even houses, you can buy with your hard-earned money
Blockcircle Hard Forks & HalvingsThe Hard Forks & Halvings indicator simply displays the dates of system wide network upgrades being completed for Bitcoin and Ethereum.
Those upgrades are called hard forks and halvings.
In the screenshot you will see that March 13 marked for the system wide Ethereum network upgrade called "ETH Dencun", it is marked in blue.
HOW IT WORKS?
For example:
Bitcoin Halvings: Nov 28, 2012, Jul 9, 2016, May 11, 2020, etc..
Bitcoin Hard Forks: Aug 2015, Feb 2016, Mar 2016, Aug 2017, etc..
Ethereum Hard Forks: Jul 30, 2015, Mar 14, 2016, Mar 13, 2024, etc...
It's conveniently an indicator so it allows you to overlay it on top of any price chart, e.g. BTC/USD, ETH/USD, ARB/USD, MATIC/USD, OP/USD, RONIN/USD, STRK/USD, etc...so you can measure the exact impact each individual significant event had on the underlying asset price.
HOW TO USE IT?
You can apply this to examine price impact on competing Layer 1s and complimentary and key beneficiary Layer 2s like ARB/OP/MATIC/STRK, which are worth monitoring closely in light of the recent Ethereum Hard Fork Dencun Upgrade and Bitcoin Halving on April 18-19.
WHAT MAKES IT' USEFUL AND ORIGINAL?
I could not find an indicator that does anything remotely close to this, so decided to build it as it's so useful to track these key dates. You can plan ahead!
One of the key benefits is a sharp reduction in Layer 2 transaction processing fees, and will lay the ground work required for "Data Blobs", think of it as a form of transaction optimization to improve scalability for the entire Ethereum ecosystem.
This will strongly accelerate staking and retaking efforts. This indicator has already helped so much in being to forecast that we were going to experience a bit of a pull back post Dencun upgrade, because historically, we've generally reverted back to the mean post upgrade.
If you have any questions about it, please post it them! Thank you
Daily Close GAP Detector [Yosiet]User Manual for "Daily Close GAP Detector "
Overview
This script is designed to help traders identify and react to significant gaps in daily market prices. It plots daily open and close prices and highlights significant gaps with a cross. The script is particularly useful for identifying potential breakouts or reversals based on these gaps.
Configuration
GAP Close Threshold: This input allows you to set a threshold for the gap size that you consider significant. The default value is 0.001.
Timeframe Seeker: This input lets you choose the timeframe for the gap detection. The default is 'D' for daily.
Features
Daily Open and Close Lines: The script plots daily open and close prices. If the close price is lower than the open price, the line is colored red; otherwise, it's green.
Gap Detection: It calculates the difference between the current day's close and the previous day's close, both adjusted for the selected timeframe. If this difference exceeds the threshold, it's considered a significant gap.
Significant Gap Indicator: A cross is plotted on the chart to indicate significant gaps. The color of the cross indicates whether the gap is a short or long gap: red for short gaps and green for long gaps.
Alert Conditions: The script sets up alert conditions for short and long gap breakouts. You can customize the alert messages to include details like the ticker symbol, interval, price, and exchange.
How to Use
Add the Script to Your Chart: Copy the script into the Pine Script editor on TradingView and add it to your chart.
Configure Inputs: Adjust the "GAP Close Threshold" and "Timeframe Seeker" inputs as needed.
Review the Chart: The script will overlay daily open and close prices on your chart, along with crosses indicating significant gaps.
Set Alerts: Use the script's alert conditions to set up alerts for short and long gap breakouts. You can customize the alert messages to suit your trading strategy.
Extending the Code
To extend this script, you can modify the gap detection logic, add more indicators, or integrate it with other scripts for a more comprehensive trading strategy. Remember to test any changes thoroughly before using them in live trading.
Earnings Line+Growth stock investors are concerned with Earnings per share that is growing, Sales (Revenue) that is growing and Increasing gross margins. This indicator helps view each of these parameters.
On the chart is Tesla (TSLA) gross margin (blue line) on a 12 trailing months basis (TTM). As you can see, TSLA's margins appear to be eroding.
The user selects one of the following parameters to display from the input drop down menu:
"EARNINGS_PER_SHARE_BASIC", "TOTAL_REVENUE", or "GROSS_MARGIN".
The value axis for your selection will appear on the left side of the chart.
The user also selects one of the following periods: "FY", "FQ" or "TTM" (Fiscal year, fiscal quarter or 12-trailing months). You have an option to display the inputs by checking the box. This is useful as a reminder but can be removed if the label is in the way.
The chart will render on any chart time scale, however longer time scales will probably be of more value. Weekly charts work well.
It is not possible to display more than one line simultaneously because of axis incompatibilities. However, it is possible to load this indicator multiple times and select different items in each. In this case additional left-side scales will be shown as well as additional lines. Common pairings are Revenue (Sales) and Earnings, or, Revenue and Gross Margin.
@ jmikes
Bitcoin 5A Strategy@LilibtcIn our long-term strategy, we have deeply explored the key factors influencing the price of Bitcoin. By precisely calculating the correlation between these factors and the price of Bitcoin, we found that they are closely linked to the value of Bitcoin. To more effectively predict the fair price of Bitcoin, we have built a predictive model and adjusted our investment strategy accordingly based on this model. In practice, the prediction results of this model correspond quite high with actual values, fully demonstrating its reliability in predicting price fluctuations.
When the future is uncertain and the outlook is unclear, people often choose to hold back and avoid risks, or even abandon their original plans. However, the prediction of Bitcoin is full of challenges, but we have taken the first step in exploring.
Table of contents:
Usage Guide
Step 1: Identify the factors that have the greatest impact on Bitcoin price
Step 2: Build a Bitcoin price prediction model
Step 3: Find indicators for warning of bear market bottoms and bull market tops
Step 4: Predict Bitcoin Price in 2025
Step 5: Develop a Bitcoin 5A strategy
Step 6: Verify the performance of the Bitcoin 5A strategy
Usage Restrictions
🦮Usage Guide:
1. On the main interface, modify the code, find the BTCUSD trading pair, and select the BITSTAMP exchange for trading.
2. Set the time period to the daily chart.
3. Select a logarithmic chart in the chart type to better identify price trends.
4. In the strategy settings, adjust the options according to personal needs, including language, display indicators, display strategies, display performance, display optimizations, sell alerts, buy prompts, opening days, backtesting start year, backtesting start month, and backtesting start date.
🏃Step 1: Identify the factors that have the greatest impact on Bitcoin price
📖Correlation Coefficient: A mathematical concept for measuring influence
In order to predict the price trend of Bitcoin, we need to delve into the factors that have the greatest impact on its price. These factors or variables can be expressed in mathematical or statistical correlation coefficients. The correlation coefficient is an indicator of the degree of association between two variables, ranging from -1 to 1. A value of 1 indicates a perfect positive correlation, while a value of -1 indicates a perfect negative correlation.
For example, if the price of corn rises, the price of live pigs usually rises accordingly, because corn is the main feed source for pig breeding. In this case, the correlation coefficient between corn and live pig prices is approximately 0.3. This means that corn is a factor affecting the price of live pigs. On the other hand, if a shooter's performance improves while another shooter's performance deteriorates due to increased psychological pressure, we can say that the former is a factor affecting the latter's performance.
Therefore, in order to identify the factors that have the greatest impact on the price of Bitcoin, we need to find the factors with the highest correlation coefficients with the price of Bitcoin. If, through the analysis of the correlation between the price of Bitcoin and the data on the chain, we find that a certain data factor on the chain has the highest correlation coefficient with the price of Bitcoin, then this data factor on the chain can be identified as the factor that has the greatest impact on the price of Bitcoin. Through calculation, we found that the 🔵number of Bitcoin blocks is one of the factors that has the greatest impact on the price of Bitcoin. From historical data, it can be clearly seen that the growth rate of the 🔵number of Bitcoin blocks is basically consistent with the movement direction of the price of Bitcoin. By analyzing the past ten years of data, we obtained a daily correlation coefficient of 0.93 between the number of Bitcoin blocks and the price of Bitcoin.
🏃Step 2: Build a Bitcoin price prediction model
📖Predictive Model: What formula is used to predict the price of Bitcoin?
Among various prediction models, the linear function is the preferred model due to its high accuracy. Take the standard weight as an example, its linear function graph is a straight line, which is why we choose the linear function model. However, the growth rate of the price of Bitcoin and the number of blocks is extremely fast, which does not conform to the characteristics of the linear function. Therefore, in order to make them more in line with the characteristics of the linear function, we first take the logarithm of both. By observing the logarithmic graph of the price of Bitcoin and the number of blocks, we can find that after the logarithm transformation, the two are more in line with the characteristics of the linear function. Based on this feature, we choose the linear regression model to establish the prediction model.
From the graph below, we can see that the actual red and green K-line fluctuates around the predicted blue and 🟢green line. These predicted values are based on fundamental factors of Bitcoin, which support its value and reflect its reasonable value. This picture is consistent with the theory proposed by Marx in "Das Kapital" that "prices fluctuate around values."
The predicted logarithm of the market cap of Bitcoin is calculated through the model. The specific calculation formula of the Bitcoin price prediction value is as follows:
btc_predicted_marketcap = math.exp(btc_predicted_marketcap_log)
btc_predicted_price = btc_predicted_marketcap / btc_supply
🏃Step 3: Find indicators for early warning of bear market bottoms and bull market tops
📖Warning Indicator: How to Determine Whether the Bitcoin Price has Reached the Bear Market Bottom or the Bull Market Top?
By observing the Bitcoin price logarithmic prediction chart mentioned above, we notice that the actual price often falls below the predicted value at the bottom of a bear market; during the peak of a bull market, the actual price exceeds the predicted price. This pattern indicates that the deviation between the actual price and the predicted price can serve as an early warning signal. When the 🔴 Bitcoin price deviation is very low, as shown by the chart with 🟩green background, it usually means that we are at the bottom of the bear market; Conversely, when the 🔴 Bitcoin price deviation is very high, the chart with a 🟥red background indicates that we are at the peak of the bull market.
This pattern has been validated through six bull and bear markets, and the deviation value indeed serves as an early warning signal, which can be used as an important reference for us to judge market trends.
🏃Step 4:Predict Bitcoin Price in 2025
📖Price Upper Limit
According to the data calculated on February 25, 2024, the 🟠upper limit of the Bitcoin price is $194,287, which is the price ceiling of this bull market. The peak of the last bull market was on November 9, 2021, at $68,664. The bull-bear market cycle is 4 years, so the highest point of this bull market is expected in 2025. That is where you should sell the Bitcoin. and the upper limit of the Bitcoin price will exceed $190,000. The closing price of Bitcoin on February 25, 2024, was $51,729, with an expected increase of 2.7 times.
🏃Step 5: Bitcoin 5A Strategy Formulation
📖Strategy: When to buy or sell, and how many to choose?
We introduce the Bitcoin 5A strategy. This strategy requires us to generate trading signals based on the critical values of the warning indicators, simulate the trades, and collect performance data for evaluation. In the Bitcoin 5A strategy, there are three key parameters: buying warning indicator, batch trading days, and selling warning indicator. Batch trading days are set to ensure that we can make purchases in batches after the trading signal is sent, thus buying at a lower price, selling at a higher price, and reducing the trading impact cost.
In order to find the optimal warning indicator critical value and batch trading days, we need to adjust these parameters repeatedly and perform backtesting. Backtesting is a method established by observing historical data, which can help us better understand market trends and trading opportunities.
Specifically, we can find the key trading points by watching the Bitcoin price log and the Bitcoin price deviation chart. For example, on August 25, 2015, the 🔴 Bitcoin price deviation was at its lowest value of -1.11; on December 17, 2017, the 🔴 Bitcoin price deviation was at its highest value at the time, 1.69; on March 16, 2020, the 🔴 Bitcoin price deviation was at its lowest value at the time, -0.91; on March 13, 2021, the 🔴 Bitcoin price deviation was at its highest value at the time, 1.1; on December 31, 2022, the 🔴 Bitcoin price deviation was at its lowest value at the time, -1.
To ensure that all five key trading points generate trading signals, we set the warning indicator Bitcoin price deviation to the larger of the three lowest values, -0.9, and the smallest of the two highest values, 1. Then, we buy when the warning indicator Bitcoin price deviation is below -0.9, and sell when it is above 1.
In addition, we set the batch trading days as 25 days to implement a strategy that averages purchases and sales. Within these 25 days, we will invest all funds into the market evenly, buying once a day. At the same time, we also sell positions at the same pace, selling once a day.
📖Adjusting the threshold: a key step to optimizing trading strategy
Adjusting the threshold is an indispensable step for better performance. Here are some suggestions for adjusting the batch trading days and critical values of warning indicators:
• Batch trading days: Try different days like 25 to see how it affects overall performance.
• Buy and sell critical values for warning indicators: iteratively fine-tune the buy threshold value of -0.9 and the sell threshold value of 1 exhaustively to find the best combination of threshold values.
Through such careful adjustments, we may find an optimized approach with a lower maximum drawdown rate (e.g., 11%) and a higher cumulative return rate for closed trades (e.g., 474 times). The chart below is a backtest optimization chart for the Bitcoin 5A strategy, providing an intuitive display of strategy adjustments and optimizations.
In this way, we can better grasp market trends and trading opportunities, thereby achieving a more robust and efficient trading strategy.
🏃Step 6: Validating the performance of the Bitcoin 5A Strategy
📖Model interpretability validation: How to explain the Bitcoin price model?
The interpretability of the model is represented by the coefficient of determination R squared, which reflects the degree of match between the predicted value and the actual value. I divided all the historical data from August 18, 2015 into two groups, and used the data from August 18, 2011 to August 18, 2015 as training data to generate the model. The calculation result shows that the coefficient of determination R squared during the 2011-2015 training period is as high as 0.81, which shows that the interpretability of this model is quite high. From the Bitcoin price logarithmic prediction chart in the figure below, we can see that the deviation between the predicted value and the actual value is not far, which means that most of the predicted values can explain the actual value well.
The calculation formula for the coefficient of determination R squared is as follows:
residual = btc_close_log - btc_predicted_price_log
residual_square = residual * residual
train_residual_square_sum = math.sum(residual_square, train_days)
train_mse = train_residual_square_sum / train_days
train_r2 = 1 - train_mse / ta.variance(btc_close_log, train_days)
📖Model stability verification: How to affirm the stability of the Bitcoin price model when new data is available?
Model stability is achieved through model verification. I set the last day of the training period to February 2, 2024 as the "verification group" and used it as verification data to verify the stability of the model. This means that after generating the model if there is new data, I will use these new data together with the model for prediction, and then evaluate the interpretability of the model. If the coefficient of determination when using verification data is close to the previous training one and both remain at a high level, then we can consider this model as stability. The coefficient of determination calculated from the validation period data and model prediction results is as high as 0.83, which is close to the previous 0.81, further proving the stability of this model.
📖Performance evaluation: How to accurately evaluate historical backtesting results?
After detailed strategy testing, to ensure the accuracy and reliability of the results, we need to carry out a detailed performance evaluation on the backtest results. The key evaluation indices include:
• Net value curve: As shown in the rose line, it intuitively reflects the growth of the account net value. By observing the net value curve, we can understand the overall performance and profitability of the strategy.
The basic attributes of this strategy are as follows:
Trading range: 2015-8-19 to 2024-2-18, backtest range: 2011-8-18 to 2024-2-18
Initial capital: 1000USD, order size: 1 contract, pyramid: 50 orders, commission rate: 0.2%, slippage: 20 markers.
In the strategy tester overview chart, we also obtained the following key data:
• Net profit rate of closed trades: as high as 474 times, far exceeding the benchmark, as shown in the strategy tester performance summary chart, Bitcoin buys and holds 210 times.
• Number of closed trades and winning percentage: 100 trades were all profitable, showing the stability and reliability of the strategy.
• Drawdown rate & win-loose ratio: The maximum drawdown rate is only 11%, far lower than Bitcoin's 78%. Profit factor, or win-loose ratio, reached 500, further proving the advantage of the strategy.
Through these detailed evaluations, we can see clearly the excellent balance between risk and return of the Bitcoin 5A strategy.
⚠️Usage Restrictions: Strategy Application in Specific Situations
Please note that this strategy is designed specifically for Bitcoin and should not be applied to other assets or markets without authorization. In actual operations, we should make careful decisions according to our risk tolerance and investment goals.
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.
Bitcoin 5A Strategy - Price Upper & Lower Limit@LilibtcIn our long-term strategy, we have deeply explored the key factors influencing the price of Bitcoin. By precisely calculating the correlation between these factors and the price of Bitcoin, we found that they are closely linked to the value of Bitcoin. To more effectively predict the fair price of Bitcoin, we have built a predictive model and adjusted our investment strategy accordingly based on this model. In practice, the prediction results of this model correspond quite high with actual values, fully demonstrating its reliability in predicting price fluctuations.
When the future is uncertain and the outlook is unclear, people often choose to hold back and avoid risks, or even abandon their original plans. However, the prediction of Bitcoin is full of challenges, but we have taken the first step in exploring.
Table of contents:
Usage Guide
Step 1: Identify the factors that have the greatest impact on Bitcoin price
Step 2: Build a Bitcoin price prediction model
Step 3: Find indicators for warning of bear market bottoms and bull market tops
Step 4: Predict Bitcoin Price in 2025
Step 5: Develop a Bitcoin 5A strategy
Step 6: Verify the performance of the Bitcoin 5A strategy
Usage Restrictions
🦮Usage Guide:
1. On the main interface, modify the code, find the BTCUSD trading pair, and select the BITSTAMP exchange for trading.
2. Set the time period to the daily chart.
3. Select a logarithmic chart in the chart type to better identify price trends.
4. In the strategy settings, adjust the options according to personal needs, including language, display indicators, display strategies, display performance, display optimizations, sell alerts, buy prompts, opening days, backtesting start year, backtesting start month, and backtesting start date.
🏃Step 1: Identify the factors that have the greatest impact on Bitcoin price
📖Correlation Coefficient: A mathematical concept for measuring influence
In order to predict the price trend of Bitcoin, we need to delve into the factors that have the greatest impact on its price. These factors or variables can be expressed in mathematical or statistical correlation coefficients. The correlation coefficient is an indicator of the degree of association between two variables, ranging from -1 to 1. A value of 1 indicates a perfect positive correlation, while a value of -1 indicates a perfect negative correlation.
For example, if the price of corn rises, the price of live pigs usually rises accordingly, because corn is the main feed source for pig breeding. In this case, the correlation coefficient between corn and live pig prices is approximately 0.3. This means that corn is a factor affecting the price of live pigs. On the other hand, if a shooter's performance improves while another shooter's performance deteriorates due to increased psychological pressure, we can say that the former is a factor affecting the latter's performance.
Therefore, in order to identify the factors that have the greatest impact on the price of Bitcoin, we need to find the factors with the highest correlation coefficients with the price of Bitcoin. If, through the analysis of the correlation between the price of Bitcoin and the data on the chain, we find that a certain data factor on the chain has the highest correlation coefficient with the price of Bitcoin, then this data factor on the chain can be identified as the factor that has the greatest impact on the price of Bitcoin. Through calculation, we found that the 🔵 number of Bitcoin blocks is one of the factors that has the greatest impact on the price of Bitcoin. From historical data, it can be clearly seen that the growth rate of the 🔵 number of Bitcoin blocks is basically consistent with the movement direction of the price of Bitcoin. By analyzing the past ten years of data, we obtained a daily correlation coefficient of 0.93 between the number of Bitcoin blocks and the price of Bitcoin.
🏃Step 2: Build a Bitcoin price prediction model
📖Predictive Model: What formula is used to predict the price of Bitcoin?
Among various prediction models, the linear function is the preferred model due to its high accuracy. Take the standard weight as an example, its linear function graph is a straight line, which is why we choose the linear function model. However, the growth rate of the price of Bitcoin and the number of blocks is extremely fast, which does not conform to the characteristics of the linear function. Therefore, in order to make them more in line with the characteristics of the linear function, we first take the logarithm of both. By observing the logarithmic graph of the price of Bitcoin and the number of blocks, we can find that after the logarithm transformation, the two are more in line with the characteristics of the linear function. Based on this feature, we choose the linear regression model to establish the prediction model.
From the graph below, we can see that the actual red and green K-line fluctuates around the predicted blue and 🟢green line. These predicted values are based on fundamental factors of Bitcoin, which support its value and reflect its reasonable value. This picture is consistent with the theory proposed by Marx in "Das Kapital" that "prices fluctuate around values."
The predicted logarithm of the market cap of Bitcoin is calculated through the model. The specific calculation formula of the Bitcoin price prediction value is as follows:
btc_predicted_marketcap = math.exp(btc_predicted_marketcap_log)
btc_predicted_price = btc_predicted_marketcap / btc_supply
🏃Step 3: Find indicators for early warning of bear market bottoms and bull market tops
📖Warning Indicator: How to Determine Whether the Bitcoin Price has Reached the Bear Market Bottom or the Bull Market Top?
By observing the Bitcoin price logarithmic prediction chart mentioned above, we notice that the actual price often falls below the predicted value at the bottom of a bear market; during the peak of a bull market, the actual price exceeds the predicted price. This pattern indicates that the deviation between the actual price and the predicted price can serve as an early warning signal. When the 🔴 Bitcoin price deviation is very low, as shown by the chart with 🟩green background, it usually means that we are at the bottom of the bear market; Conversely, when the 🔴 Bitcoin price deviation is very high, the chart with a 🟥red background indicates that we are at the peak of the bull market.
This pattern has been validated through six bull and bear markets, and the deviation value indeed serves as an early warning signal, which can be used as an important reference for us to judge market trends.
🏃Step 4:Predict Bitcoin Price in 2025
📖Price Upper Limit
According to the data calculated on March 10, 2023(If you want to check latest data, please contact with author), the 🟠upper limit of the Bitcoin price is $132,453, which is the price ceiling of this bull market. The peak of the last bull market was on November 9, 2021, at $68,664. The bull-bear market cycle is 4 years, so the highest point of this bull market is expected in 2025, and the 🟠upper limit of the Bitcoin price will exceed $130,000. The closing price of Bitcoin on March 10, 2024, was $68,515, with an expected increase of 90%.
🏃Step 5: Bitcoin 5A Strategy Formulation
📖Strategy: When to buy or sell, and how many to choose?
We introduce the Bitcoin 5A strategy. This strategy requires us to generate trading signals based on the critical values of the warning indicators, simulate the trades, and collect performance data for evaluation. In the Bitcoin 5A strategy, there are three key parameters: buying warning indicator, batch trading days, and selling warning indicator. Batch trading days are set to ensure that we can make purchases in batches after the trading signal is sent, thus buying at a lower price, selling at a higher price, and reducing the trading impact cost.
In order to find the optimal warning indicator critical value and batch trading days, we need to adjust these parameters repeatedly and perform backtesting. Backtesting is a method established by observing historical data, which can help us better understand market trends and trading opportunities.
Specifically, we can find the key trading points by watching the Bitcoin price log and the Bitcoin price deviation chart. For example, on August 25, 2015, the 🔴 Bitcoin price deviation was at its lowest value of -1.11; on December 17, 2017, the 🔴 Bitcoin price deviation was at its highest value at the time, 1.69; on March 16, 2020, the 🔴 Bitcoin price deviation was at its lowest value at the time, -0.91; on March 13, 2021, the 🔴 Bitcoin price deviation was at its highest value at the time, 1.1; on December 31, 2022, the 🔴 Bitcoin price deviation was at its lowest value at the time, -1.
To ensure that all five key trading points generate trading signals, we set the warning indicator Bitcoin price deviation to the larger of the three lowest values, -0.9, and the smallest of the two highest values, 1. Then, we buy when the warning indicator Bitcoin price deviation is below -0.9, and sell when it is above 1.
In addition, we set the batch trading days as 25 days to implement a strategy that averages purchases and sales. Within these 25 days, we will invest all funds into the market evenly, buying once a day. At the same time, we also sell positions at the same pace, selling once a day.
📖Adjusting the threshold: a key step to optimizing trading strategy
Adjusting the threshold is an indispensable step for better performance. Here are some suggestions for adjusting the batch trading days and critical values of warning indicators:
• Batch trading days: Try different days like 25 to see how it affects overall performance.
• Buy and sell critical values for warning indicators: iteratively fine-tune the buy threshold value of -0.9 and the sell threshold value of 1 exhaustively to find the best combination of threshold values.
Through such careful adjustments, we may find an optimized approach with a lower maximum drawdown rate (e.g., 11%) and a higher cumulative return rate for closed trades (e.g., 474 times). The chart below is a backtest optimization chart for the Bitcoin 5A strategy, providing an intuitive display of strategy adjustments and optimizations.
In this way, we can better grasp market trends and trading opportunities, thereby achieving a more robust and efficient trading strategy.
🏃Step 6: Validating the performance of the Bitcoin 5A Strategy
📖Model accuracy validation: How to judge the accuracy of the Bitcoin price model?
The accuracy of the model is represented by the coefficient of determination R square, which reflects the degree of match between the predicted value and the actual value. I divided all the historical data from August 18, 2015 into two groups, and used the data from August 18, 2011 to August 18, 2015 as training data to generate the model. The calculation result shows that the coefficient of determination R squared during the 2011-2015 training period is as high as 0.81, which shows that the accuracy of this model is quite high. From the Bitcoin price logarithmic prediction chart in the figure below, we can see that the deviation between the predicted value and the actual value is not far, which means that most of the predicted values can explain the actual value well.
The calculation formula for the coefficient of determination R square is as follows:
residual = btc_close_log - btc_predicted_price_log
residual_square = residual * residual
train_residual_square_sum = math.sum(residual_square, train_days)
train_mse = train_residual_square_sum / train_days
train_r2 = 1 - train_mse / ta.variance(btc_close_log, train_days)
📖Model reliability verification: How to affirm the reliability of the Bitcoin price model when new data is available?
Model reliability is achieved through model verification. I set the last day of the training period to February 2, 2024 as the "verification group" and used it as verification data to verify the reliability of the model. This means that after generating the model if there is new data, I will use these new data together with the model for prediction, and then evaluate the accuracy of the model. If the coefficient of determination when using verification data is close to the previous training one and both remain at a high level, then we can consider this model as reliable. The coefficient of determination calculated from the validation period data and model prediction results is as high as 0.83, which is close to the previous 0.81, further proving the reliability of this model.
📖Performance evaluation: How to accurately evaluate historical backtesting results?
After detailed strategy testing, to ensure the accuracy and reliability of the results, we need to carry out a detailed performance evaluation on the backtest results. The key evaluation indices include:
• Net value curve: As shown in the rose line, it intuitively reflects the growth of the account net value. By observing the net value curve, we can understand the overall performance and profitability of the strategy.
The basic attributes of this strategy are as follows:
Trading range: 2015-8-19 to 2024-2-18, backtest range: 2011-8-18 to 2024-2-18
Initial capital: 1000USD, order size: 1 contract, pyramid: 50 orders, commission rate: 0.2%, slippage: 20 markers.
In the strategy tester overview chart, we also obtained the following key data:
• Net profit rate of closed trades: as high as 474 times, far exceeding the benchmark, as shown in the strategy tester performance summary chart, Bitcoin buys and holds 210 times.
• Number of closed trades and winning percentage: 100 trades were all profitable, showing the stability and reliability of the strategy.
• Drawdown rate & win-loose ratio: The maximum drawdown rate is only 11%, far lower than Bitcoin's 78%. Profit factor, or win-loose ratio, reached 500, further proving the advantage of the strategy.
Through these detailed evaluations, we can see clearly the excellent balance between risk and return of the Bitcoin 5A strategy.
⚠️Usage Restrictions: Strategy Application in Specific Situations
Please note that this strategy is designed specifically for Bitcoin and should not be applied to other assets or markets without authorization. In actual operations, we should make careful decisions according to our risk tolerance and investment goals.
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.
BTC Valuation
The BTC Valuation indicator
is a powerful tool designed to assist traders and analysts in evaluating the current state of Bitcoin's market valuation. By leveraging key moving averages and a logarithmic trendline, this indicator offers valuable insights into potential buying or selling opportunities based on historical price value.
Key Features:
200MA/P (200-day Moving Average to Price Ratio):
Provides a perspective on Bitcoin's long-term trend by comparing the current price to its 200-day Simple Moving Average (SMA).
A positive value suggests potential undervaluation, while a negative value may indicate overvaluation.
50MA/P (50-day Moving Average to Price Ratio):
Focuses on short-term trends, offering insights into the relationship between Bitcoin's current price and its 50-day SMA.
Helps traders identify potential bullish or bearish trends in the near term.
LTL/P (Logarithmic TrendLine to Price Ratio):
Incorporates a logarithmic trendline, considering Bitcoin's historical age in days.
Assists in evaluating whether the current price aligns with the long-term logarithmic trend, signaling potential overvaluation or undervaluation.
How to Use:
Z Score Indicator Integration:
The BTC Valuation indicator leverages the Z Score Indicator to score the ratios in a statistical way.
Statistical scoring provides a standardized measure of how far each ratio deviates from the mean, aiding in a more nuanced and objective evaluation.
Z Score Indicator
This BTC Valuation indicator provides a comprehensive view of Bitcoin's valuation dynamics, allowing traders to make informed decisions.
While indicators like BTC Valuation provide valuable insights, it's crucial to remember that no indicator guarantees market predictions.
Traders should use indicators as part of a comprehensive strategy and consider multiple factors before making trading decisions.
Historical performance is not indicative of future results. Exercise caution and continually refine your approach based on market dynamics.
