Stef's Money Supply IndicatorI have been fascinated by the growth in the Money Supply. Well, I think we ALL have been fascinated by this and the corresponding inflation that followed. That's why I created my Money Supply Indicator because I always wanted to chart and analyze my symbols based on the Money Supply. This indicator gives you that capability in a way that no other indicator in this field currently offers. Let me explain:
How does the indicator work?
Chart any symbol, turn on this indicator, and instantly it will factor in the M2 money supply on the asset's underlying price. Essentially, you are seeing the price of the asset normalized for the corresponding rise in the money supply. In some ways, this is a rather unique inflation-adjusted view of a symbol's price.
More importantly, you can compare and contrast the symbol's price adjusted for the rise in the Money Supply vs. the symbol's price without that adjustment by indexing all lines to 100. This is essential for understanding if the asset is at all-time highs, lows, or possibly undervalued or overvalued based on the current money supply situation.
Why does this matter?
This tool provides a deeper understanding of how the overall money supply influences the value of assets over time. By adjusting asset prices for changes in the money supply, traders can see the true value of assets relative to the amount of money in circulation.
What features can you access with this indicator?
The ability to normalize all lines to a starting point of 100 allows traders to compare the performance of the Money Supply, the symbol price, and the symbol price adjusted for the money supply all on one readable chart. This feature is particularly useful for spotting divergences and understanding relative performance over time with a rising or falling Money Supply.
What else can you do?
This is just version 1, and so I'll be adding more features rather soon, but there are two other important features in the settings menu including the following:
• Get the capability to quickly spot the highest and lowest points on the Money Supply adjusted price of your asset.
• Get the capability to change the gradient colors of the line when going up or down.
• Turn on the Brrrrrrr printer text as a reminder of our Fed Overlord Jerome Powell... lol
• Drag this indicator onto your main chart to combine it with your candlesticks or other charting techniques.
Stef's Money Supply Indicator! I look forward to hearing your feedback.
价值
Stock Value RainbowStock Valuation based on Book Value, Dividends, Cashflow, Earnings and Estimates and Money Multiplier
There are many ways to measure stock valuations: some methods are using book value, some are using dividends, some are using cashflow, some are using earnings and some using estimates data. Most of these valuation methods are based on multiplier effect which measure how many times the stock price could expand from their valuation base. This indicator attempts to unify all these measurements using just simple addition of all measurements such as: book value per share, dividend per share, cashflow per share, earning per share, and estimates of earning per share and then using multiplier effect to create beautiful rainbow to see how far the stock has growing up above or below their valuation base. The higher the stock price on rainbow spectrum means it is more expensive and the lower in the rainbow spectrum means it is cheaper. Here is the basic formula explanation:
SV = (BVPS + DPS + CFPS + EPS + EST) * MM
BVPS = Book Value Per Share (Asset - Liability)
DPS = Dividends Per Share
CFPS = Free Cash Flow Per Share
EPS = Earnings Per Share
EST = Estimates of EPS
MM = Money Multiplier (1x, 2x, ... ,10x)
- The gray line represents the stock value SV
- The rainbow above the gray line represents the multiplication factors from 1x, 2x, ..., 10x
- The rainbow below the gray line represents the division factors from 0.8x, 0.6x, ..., 0.2x
Check other script to value stock and index:
- Stock Value Rainbow: script to value stock based on book value, dividend, cash flow, earning and estimates.
- Index Value Rainbow: script to value index based on fed balance sheet and base money supply
- Gold Value Rainbow: script to value gold based on global money supply
- Stock Value US: script to check US stock value
- Stock Value EU: script to check EU stock value
- Stock Value JP: script to check JP stock value
- Stock Value CN: script to check CN stock value
Double FVG-BPR [QuantVue]The Double FVG BPR Indicator is a versatile tool that helps traders identify potential support and resistance levels through the concept of balanced price ranges.
A Balanced Price Range (BPR) is a zone on a price chart where the market has found equilibrium after a period of price imbalance.
It is identified by detecting a Fair Value Gap (FVG) in one direction, followed by an overlapping Fair Value Gap in the opposite direction.
Components of a Balanced Price Range
Fair Value Gap (FVG): A FVG occurs when there is a rapid price movement, creating a gap in the price chart where minimal trading occurs. This gap represents an imbalance between supply and demand.
Bullish FVG: A bullish FVG is identified when the low of a candle is higher than the high of a candle two periods ago, and the close of the previous candle is higher than the high of that same period.
Bearish FVG: A bearish FVG is identified when the high of a candle is lower than the low of a candle two periods ago, and the close of the previous candle is lower than the low of that same period.
Overlapping Fair Value Gap: For a BPR to be formed, an initial FVG must be followed by an overlapping FVG in the opposite direction. This creates a balanced zone where the price has moved up (or down) quickly and then moved down (or up) with similar intensity, suggesting a temporary equilibrium.
The area between the high and low points of these overlapping FVGs forms the BPR. This zone represents a temporary market equilibrium where supply and demand have balanced out after a period of significant price movement in both directions.
How to Use
Support and Resistance Levels: The upper and lower boundaries of the BPR act as dynamic support and resistance levels. Traders can use these levels to place buy and sell orders, anticipating that the price may find support or face resistance within these zones.
Trend Reversal and Continuation: The BPR can signal potential trend reversals or continuations.
If the price moves back into the BPR after a breakout, it may indicate a reversal. Conversely, if the price breaks out of the BPR with strong momentum, it may signal a trend continuation.
Sharpe RatioThe Sharpe Ratio Indicator is a widely-used tool designed to measure the risk-adjusted performance of an asset. The Sharpe Ratio helps investors understand how much excess return they are receiving for the extra volatility endured for holding a riskier asset. By comparing the return of an investment to the risk-free rate and accounting for the volatility of the investment's returns, the Sharpe Ratio provides a clear metric for evaluating the return on investment per unit of risk.
Features:
Calculation Period Input: Allows users to specify the period over which the Sharpe Ratio is calculated, making it flexible and adaptable to various trading strategies and time frames.
Risk-Free Rate Input: Users can set a specific risk-free rate, enabling a customized risk-return analysis based on prevailing market conditions or individual investment preferences.
Daily Returns Calculation: The indicator calculates daily returns, which are used to determine the mean return and standard deviation.
Sharpe Ratio Calculation: The indicator calculates the Sharpe Ratio by dividing the excess return (mean return minus risk-free rate) by the standard deviation of returns and annualizing the result (assuming 252 trading days in a year).
Visual Plot: The Sharpe Ratio is plotted on the chart, with a reference zero line to help users quickly assess whether the asset's returns are favorable relative to the risk-free rate.
How to Use:
Add the Indicator to Your Chart: Copy the script into the Pine Script editor on TradingView and add it to your chart.
Specify the Calculation Period: Adjust the Calculation Period input to match your desired time frame for the Sharpe Ratio calculation.
Set the Risk-Free Rate: Input the current risk-free rate (e.g., the yield on 10-year government bonds) to customize the risk-return analysis.
Interpret the Sharpe Ratio: A higher Sharpe Ratio indicates better risk-adjusted performance, while a ratio below zero suggests that the asset has underperformed relative to the risk-free rate.
Omega RatioThe Omega Ratio Indicator is a powerful tool designed to measure the risk-adjusted performance of an asset. Unlike traditional measures like the Sharpe or Sortino Ratios, the Omega Ratio considers both the frequency and magnitude of returns above and below a specified target return, providing a more comprehensive view of an asset's performance. This makes it an ideal choice for traders and investors seeking to evaluate the probability of achieving a desired return relative to the risk taken.
Features:
Calculation Period Input: Allows users to specify the period over which the Omega Ratio is calculated, making it flexible and adaptable to various trading strategies and time frames.
Target Return Input: Users can set a specific target return, enabling a customized risk-return analysis based on individual investment goals.
Daily Returns Calculation: The indicator calculates daily returns, which are used to determine the cumulative returns above and below the target return.
Omega Ratio Calculation: By dividing the cumulative returns above the target return by the cumulative returns below it, the indicator provides a clear measure of the likelihood of achieving the target return.
Visual Plot: The Omega Ratio is plotted on the chart, with a reference line at 1, helping users quickly assess whether the asset's returns are favorable relative to the target.
How to Use:
Add the Indicator to Your Chart: Copy the script into the Pine Script editor on TradingView and add it to your chart.
Specify the Calculation Period: Adjust the Calculation Period input to match your desired time frame for the Omega Ratio calculation.
Set the Target Return: Input your desired target return to customize the risk-return analysis.
Interpret the Omega Ratio: An Omega Ratio greater than 1 indicates that the asset's returns are more likely to exceed the target return than to fall short, whereas a ratio less than 1 suggests the opposite.
Sortino RatioThe Sortino Ratio Indicator is a custom script developed to measure the risk-adjusted performance of an asset. Unlike the Sharpe Ratio, which penalizes both upside and downside volatility equally, the Sortino Ratio specifically differentiates between harmful volatility (downside risk) and overall volatility. This makes it a preferred choice for traders and investors who are particularly concerned with downside risk.
Features:
Calculation Period Input: Users can specify the period over which the Sortino Ratio is calculated, making it adaptable to different time frames and trading strategies.
Daily Returns Calculation: The indicator calculates daily returns using logarithmic returns, which provides a more accurate reflection of percentage changes in price.
Mean Return and Downside Deviation: The script calculates the mean of daily returns and the downside deviation, focusing only on negative returns that fall below the mean.
Annualized Sortino Ratio: The Sortino Ratio is annualized assuming 252 trading days in a year, providing a standardized measure that can be easily compared across different assets and time periods.
Visual Plot: The indicator plots the Sortino Ratio on the chart, with a reference zero line to help users quickly assess the performance relative to the risk-free rate.
How to Use:
Add the Indicator to Your Chart: Copy the script into the Pine Script editor on TradingView and add it to your chart.
Specify the Calculation Period: Adjust the Calculation Period input to match your desired time frame for the Sortino Ratio calculation.
Interpret the Sortino Ratio: A higher Sortino Ratio indicates better risk-adjusted performance, while a ratio below zero suggests that the asset has underperformed relative to its downside risk.
Market Cap / Revenue RatioA variation of the P/S ratio, this script takes the future estimated revenue of the current stock versus it's Market Cap. It then compares the aforementioned Market Cap:Revenue ratio against a market bellwether's corresponding ratio (MSFT by default) to determine the following:
- Light green when the ratio is extremely low (Stock is very undervalued)
- Green when the ratio is low (Stock's multiple is lower by 20-50%)
- Blue when the ratio is close to the benchmark (Stock is fairly valued to benchmark multiple)
- Red when the ratio is high (Stock's mulitple is higher by 50% or more)
- Dark red when the ratio is extremely high (Stock is very overvalued)
CONFIGURABLE
- Full Table: Show the entire calculation table
- Minimalist: Go minimal and show only the ratio and color code
- Show Benchmark Multiple: Show the multiple ratio calculated between the current stock and the benchmark stock (MSFT by default)
NOTES
- When calculating the Market Cap, TradingView sometimes under-reports the number of shares and thus skews the Market Cap too low. This seems to happen for stocks with multiple share classes like GOOGL so be mindful that the data can be wrong for these kinds of stocks. You can check on this by going into the Indicator's Settings and select Show Full Table which will show the number of shares outstanding reported by TradingView.
- For certain stocks such as foreign ADRs, there won't be a future revenue estimate so the script will automatically use the Total Revenue for the most recent Quarter in these cases
Fair Value Calculator V 1.0Fair Value Calculator V 1.0
This indicator calculates the fair value of a stock based on the revenue growth rate and net profit margin of a company, providing a quick estimate of its intrinsic worth. The calculation takes into account:
Current Revenue: The company's current revenue
5-Year Growth Rate: Expected revenue annual growth rate (CAGR) over the next 5 years
Average PE Ratio: The average Price-to-Earnings ratio for the next 5 years
Average Profit Margin: The average profit margin for the next 5 years
Share Outstanding: The total number of shares outstanding
Yearly Share Buyback Rate: The percentage of shares bought back by the company each year
Discount Rate: The rate used to calculate the present value of the fair value
Using these inputs, the indicator estimates the fair value of the stock, providing a valuable tool for investors and traders to make informed decisions.
Note: all values can be adjusted by the user by entering the desired value and selecting the item in the setup menu.
How it works
The indicator calculates the future revenue based on the current revenue and the expected revenue annual growth rate (CAGR).
It then estimates the future earnings using the average profit margin.
The future price is calculated using the exit value of the PE ratio.
The present value of the fair value is calculated using the discount rate.
The indicator adjusts the fair value based on the yearly share buyback rate.
Benefits
Provides a quick but valuable estimate of a stock's fair value based on the revenue growth and the expected profit.
Helps investors and traders identify undervalued or overvalued stocks.
Allows users to adjust inputs to suit their own assumptions and scenarios.
Note
This indicator is for informational purposes only and should not be considered as investment advice. Always do your own research and consider multiple perspectives before making investment decisions.
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!
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
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.
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.
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.
Intrinsic Value Calculator - Earnings/Dividend Yield (%)
This Intrinsic Value Calculator is a stock valuation Calculator that uses proven and science-based valuation methods to automatically estimate the intrinsic value of stocks.
What Is Intrinsic Value?
Intrinsic value is a measure of what a company's stock is worth. Intrinsic value is different from the current market price of a stock. However, comparing it to that current price can give investors an idea of whether the stock is undervalued or overvalued.
How to Calculate Intrinsic Value
To calculate the intrinsic value of a stock, we use two valuation methods: Discounted Cash Flow (DCF) Valuation and Relative Valuation. We take the average of these two methods to estimate the intrinsic value as accurately as possible.
Using Discounted Cash Flow (DCF) analysis, cash flows are estimated based on how a business may perform in the future. Those cash flows are then discounted to today’s value to obtain the company's intrinsic value. The discount rate we used is a risk-free rate of return (Fixed Deposit Interest Rate).
While intrinsic valuation models see to value a business by looking only at the company on its own, relative valuation models seek to value a business by comparing the company to other Low-Risk investment opportunities, Fixed Deposit Return.
Line Graph : Earnings Yield vs Fixed Deposit Interest Rate vs Dividend Yield
Other than automatically estimating the intrinsic value of a stock, this script would plot the Earnings Yield, Fixed Deposit Interest, and Dividend Yield of a stock.
Investors should monitor Earnings Yield, Fixed Deposit Interest, and Dividend Yield of a stock for a few key reasons:
Earnings Yield:
Earnings Yield is a crucial metric that provides insight into a company's profitability. It is calculated by dividing the company's earnings per share (EPS) by the current stock price. A higher Earnings Yield indicates that the company is generating more profit for each dollar invested by shareholders. This metric is particularly useful when comparing a company's profitability against other investment options, such as fixed deposits, bonds, or other stocks.
Fixed Deposit Interest:
The Fixed Deposit Interest Rate, also known as the risk-free rate, is the return an investor can expect from investing in a risk-free asset such as a government bond or a fixed deposit. This rate serves as a benchmark for evaluating the returns offered by other investments, including stocks.
Dividend Yield:
Dividend Yield is a measure of the annual dividend income received by an investor relative to the stock price. It is calculated by dividing the annual dividend per share by the current stock price. Dividend-paying stocks often appeal to income-oriented investors seeking regular cash flow.
Monitoring these metrics can help investors make informed decisions about their investments, assess the relative attractiveness of different investment options, and manage their investment portfolios effectively.
Key Financial Ratio display
Key investment ratios play a crucial role in helping investors make informed investment decisions. By providing valuable insights into a company's financial health, ratios such as the Gross Margin, R&D Ratio, Net Margin, Return on Equity (ROE) Ratio allow investors to quickly assess a company's profitability, liquidity, and financial stability.
Gross margin is the percentage of a company's revenue that it retains after direct expenses, such as labor and materials, have been subtracted. Gross margin is an important profitability measure that looks at a company's gross profit compared to its revenue.
The Research & Development (R&D) to Sales Ratio is a measure to compare the effectiveness of R&D expenditures between companies in the same industry. It is calculated as R&D expenditure divided by Total Sales.
The net profit margin, or simply Net Margin , measures how much net income or profit is generated as a percentage of revenue. It is the ratio of net profits to revenues for a company or business segment.
The Return on Equity (ROE) Ratio is a measure of a company's profitability and efficiency in using its shareholders' investments to generate profits. It's calculated by dividing a company's net income by its shareholder's equity. This ratio is a reflection of how well a company is utilizing its shareholders' capital to generate returns.
The Operating Cash to Debt Ratio measures the percentage of a company's total debt that is covered by its operating cash flow for a given accounting period. If the company’s ratio were higher, it would indicate a strong fiscal position, considering its cash flow from operations is higher than its total debt.
Free Cash Flow Margin is a significant financial metric that measures a company's ability to generate cash from its operations after accounting for capital expenditures. It evaluates the percentage of free cash flow relative to total revenue. A high Free Cash Flow margin suggests that a company is efficient at converting its revenue into cash flow.
Neural Network Synthesis: Trend and Valuation [QuantraSystems]Neural Network Synthesis - Trend and Valuation
Introduction
The Neural Network Synthesis (𝓝𝓝𝒮𝔂𝓷𝓽𝓱) indicator is an innovative technical analysis tool which leverages neural network concepts to synthesize market trend and valuation insights.
This indicator uses a bespoke neural network model to process various technical indicator inputs, providing an improved view of market momentum and perceived value.
Legend
The main visual component of the 𝓝𝓝𝒮𝔂𝓷𝓽𝓱 indicator is the Neural Synthesis Line , which dynamically oscillates within the valuation chart, categorizing market conditions as both under or overvalued and trending up or down.
The synthesis line coloring can be set to trend analysis or valuation modes , which can be reflected in the bar coloring.
The sine wave valuation chart oscillates around a central, volatility normalized ‘fair value’ line, visually conveying the natural rhythm and cyclical nature of asset markets.
The positioning of the sine wave in relation to the central line can help traders to visualize transitions from one market phase to another - such as from an undervalued phase to fair value or an overvalued phase.
Case Study 1
The asset in question experiences a sharp, inefficient move upwards. Such movements suggest an overextension of price, and mean reversion is typically expected.
Here, a short position was initiated, but only after the Neural Synthesis line confirmed a negative trend - to mitigate the risk of shorting into a continuing uptrend.
Two take-profit levels were set:
The midline or ‘fair value’ line.
The lower boundary of the 𝓝𝓝𝒮𝔂𝓷𝓽𝓱 indicators valuation chart.
Although mean-reversion trades are typically closed when price returns to the mean, under circumstances of extreme overextension price often overcorrects from an overbought condition to an oversold condition.
Case Study 2
In the above study, the 𝓝𝓝𝒮𝔂𝓷𝓽𝓱 indicator is applied to the 1 Week Bitcoin chart in order to inform long term investment decisions.
Accumulation Zones - Investors can choose to dollar cost average (DCA) into long term positions when the 𝓝𝓝𝒮𝔂𝓷𝓽𝓱 indicates undervaluation
Distribution Zones - Conversely, when overvalued conditions are indicated, investors are able to incrementally sell holdings expecting the market peak to form around the distribution phase.
Note - It is prudent to pay close attention to any change in trend conditions when the market is in an accumulation/distribution phase, as this can increase the likelihood of a full-cycle market peak forming.
In summary, the 𝓝𝓝𝒮𝔂𝓷𝓽𝓱 indicator is also an effective tool for long term investing, especially for assets like Bitcoin which exhibit prolonged bull and bear cycles.
Special Note
It is prudent to note that because markets often undergo phases of extreme speculation, an asset's price can remain over or undervalued for long periods of time, defying mean-reversion expectations. In these scenarios it is important to use other forms of analysis in confluence, such as the trending component of the 𝓝𝓝𝒮𝔂𝓷𝓽𝓱 indicator to help inform trading decisions.
A special feature of Quantra’s indicators is that they are probabilistically built - therefore they work well as confluence and can easily be stacked to increase signal accuracy.
Example Settings
As used above.
Swing Trading
Smooth Length = 150
Timeframe = 12h
Long Term Investing
Smooth Length = 30
Timeframe = 1W
Methodology
The 𝓝𝓝𝒮𝔂𝓷𝓽𝓱 indicator draws upon the foundational principles of Neural Networks, particularly the concept of using a network of ‘neurons’ (in this case, various technical indicators). It uses their outputs as features, preprocesses this input data, runs an activation function and in the following creates a dynamic output.
The following features/inputs are used as ‘neurons’:
Relative Strength Index (RSI)
Moving Average Convergence-Divergence (MACD)
Bollinger Bands
Stochastic Momentum
Average True Range (ATR)
These base indicators were chosen for their diverse methodologies for capturing market momentum, volatility and trend strength - mirroring how neurons in a Neural Network capture and process varied aspects of the input data.
Preprocessing:
Each technical indicator’s output is normalized to remove bias. Normalization is a standard practice to preprocess data for Neural Networks, to scale input data and allow the model to train more effectively.
Activation Function:
The hyperbolic tangent function serves as the activation function for the neurons. In general, for complete neural networks, activation functions introduce non-linear properties to the models and enable them to learn complex patterns. The tanh() function specifically maps the inputs to a range between -1 and 1.
Dynamic Smoothing:
The composite signal is dynamically smoothed using the Arnaud Legoux Moving Average, which adjusts faster to recent price changes - enhancing the indicator's responsiveness. It mimics the learning rate in neural networks - in this case for the output in a single layer approach - which controls how much new information influences the model, or in this case, our output.
Signal Processing:
The signal line also undergoes processing to adapt to the selected assets volatility. This step ensures the indicator’s flexibility across assets which exhibit different behaviors - similar to how a Neural Network adjusts to various data distributions.
Notes:
While the indicator synthesizes complex market information using methods inspired by neural networks, it is important to note that it does not engage in predictive modeling through the use of backpropagation. Instead, it applies methodologies of neural networks for real-time market analysis that is both dynamic and adaptable to changing market conditions.
Crypto MVRV ZScore - Strategy [PresentTrading]█ Introduction and How it is Different
The "Crypto Valuation Extremes: MVRV ZScore - Strategy " represents a cutting-edge approach to cryptocurrency trading, leveraging the Market Value to Realized Value (MVRV) Z-Score. This metric is pivotal for identifying overvalued or undervalued conditions in the crypto market, particularly Bitcoin. It assesses the current market valuation against the realized capitalization, providing insights that are not apparent through conventional analysis.
BTCUSD 6h Long/Short Performance
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█ Strategy, How It Works: Detailed Explanation
The strategy leverages the Market Value to Realized Value (MVRV) Z-Score, specifically designed for cryptocurrencies, with a focus on Bitcoin. This metric is crucial for determining whether Bitcoin is currently undervalued or overvalued compared to its historical 'realized' price. Below is an in-depth explanation of the strategy's components and calculations.
🔶Conceptual Foundation
- Market Capitalization (MC): This represents the total dollar market value of Bitcoin's circulating supply. It is calculated as the current price of Bitcoin multiplied by the number of coins in circulation.
- Realized Capitalization (RC): Unlike MC, which values all coins at the current market price, RC is computed by valuing each coin at the price it was last moved or traded. Essentially, it is a summation of the value of all bitcoins, priced at the time they were last transacted.
- MVRV Ratio: This ratio is derived by dividing the Market Capitalization by the Realized Capitalization (The ratio of MC to RC (MVRV Ratio = MC / RC)). A ratio greater than 1 indicates that the current price is higher than the average price at which all bitcoins were purchased, suggesting potential overvaluation. Conversely, a ratio below 1 suggests undervaluation.
🔶 MVRV Z-Score Calculation
The Z-Score is a statistical measure that indicates the number of standard deviations an element is from the mean. For this strategy, the MVRV Z-Score is calculated as follows:
MVRV Z-Score = (MC - RC) / Standard Deviation of (MC - RC)
This formula quantifies Bitcoin's deviation from its 'normal' valuation range, offering insights into market sentiment and potential price reversals.
🔶 Spread Z-Score for Trading Signals
The strategy refines this approach by calculating a 'spread Z-Score', which adjusts the MVRV Z-Score over a specific period (default: 252 days). This is done to smooth out short-term market volatility and focus on longer-term valuation trends. The spread Z-Score is calculated as follows:
Spread Z-Score = (Market Z-Score - MVVR Ratio - SMA of Spread) / Standard Deviation of Spread
Where:
- SMA of Spread is the simple moving average of the spread over the specified period.
- Spread refers to the difference between the Market Z-Score and the MVRV Ratio.
🔶 Trading Signals
- Long Entry Condition: A long (buy) signal is generated when the spread Z-Score crosses above the long entry threshold, indicating that Bitcoin is potentially undervalued.
- Short Entry Condition: A short (sell) signal is triggered when the spread Z-Score falls below the short entry threshold, suggesting overvaluation.
These conditions are based on the premise that extreme deviations from the mean (as indicated by the Z-Score) are likely to revert to the mean over time, presenting opportunities for strategic entry and exit points.
█ Practical Application
Traders use these signals to make informed decisions about opening or closing positions in the Bitcoin market. By quantifying market valuation extremes, the strategy aims to capitalize on the cyclical nature of price movements, identifying high-probability entry and exit points based on historical valuation norms.
█ Trade Direction
A unique feature of this strategy is its configurable trade direction. Users can specify their preference for engaging in long positions, short positions, or both. This flexibility allows traders to tailor the strategy according to their risk tolerance, market outlook, or trading style, making it adaptable to various market conditions and trader objectives.
█ Usage
To implement this strategy, traders should first adjust the input parameters to align with their trading preferences and risk management practices. These parameters include the trade direction, Z-Score calculation period, and the thresholds for long and short entries. Once configured, the strategy automatically generates trading signals based on the calculated spread Z-Score, providing clear indications for potential entry and exit points.
It is advisable for traders to backtest the strategy under different market conditions to validate its effectiveness and adjust the settings as necessary. Continuous monitoring and adjustment are crucial, as market dynamics evolve over time.
█ Default Settings
- Trade Direction: Both (Allows for both long and short positions)
- Z-Score Calculation Period: 252 days (Approximately one trading year, capturing a comprehensive market cycle)
- Long Entry Threshold: 0.382 (Indicative of moderate undervaluation)
- Short Entry Threshold: -0.382 (Signifies moderate overvaluation)
These default settings are designed to balance sensitivity to market valuation extremes with a pragmatic approach to trade execution. They aim to filter out noise and focus on significant market movements, providing a solid foundation for both new and experienced traders looking to exploit the unique insights offered by the MVRV Z-Score in the cryptocurrency market.
LV Stock Valuation by Benjamin Graham's FormulaBenjamin Graham's stock valuation formula for growth companies is based on the principle that a stock is a part of a business, and that by analyzing the fundamentals of any company in the stock market, you should be able to derive its intrinsic value independent from its current stock price. Graham suggests that over the long-term, the stock price of a company and its intrinsic/fair value will converge towards each other until the stock price reflects the true value of the company. Finally, Graham recommends that after estimating the intrinsic value of a stock, investors should always purchase the stock with a "margin of safety," to protect oneself from assumptions and potential errors made in the valuation process.
Graham's stock valuation formula to calculate intrinsic value was originally shown in the 1962 edition of Security Analysis as follows:
V = EPS * (8.5 + 2g)
where:
V = intrinsic value per share (over the next 7-10 years)
EPS = earnings per share (over the trailing twelve months (TTM))
8.5 = price-to-earnings (P/E) base for a no-growth company
g = reasonably expected annual growth rate (over the next 7-10 years)
In 1974, Graham revised this formula, as published in The Intelligent Investor, to include a discount rate (aka required rate of return). This was after he concluded that the greatest contributing to stock values and prices over the past decade had been due to interest rates.
Graham's current stock valuation formula is shown below:
V = (EPS * (8.5 + 2g) * Z) / Y
where:
V = intrinsic value per share (over the next 7-10 years)
EPS = diluted earnings per share (over the trailing twelve months (TTM))
8.5 = price-to-earnings (P/E) base for a no-growth company (you can change it manually)
g = reasonably expected annual growth rate (calculated by 5-Yr EPS CAGR%) (you can change year period)
Z = average yield of XXX Bonds (4.4 is default on Graham's formula)
Y = current yield of XXX Bonds
Current bond yield values (Z and Y) are selected as an example from Turkey. You need to change it according to the country of stocks.
Buy price (BP) = Intrinsic value per share * (1 - Margin of safety %)
Margin of safety = selected 20% (you need to change it to 0, if you don’t want to use margin of safety and to see intrinsic value)
Buy price > Current market price: Consider buying the stock, as the current market price appears to be undervalued.
Buy price < Current market price: Consider selling or not buying the stock, as the current market price appears to be overvalued.
Keep in mind that this buy/sell recommendation is purely based on Graham's stock valuation formula and the current market price, and ignores all other fundamental, news, and market factors investors should examine as well before making an investment decision.
Buy price is calculated for 5 different P/E values in the script.
1. with fixed P/E
2. with current P/E
3. with forward P/E
4. with sector P/E (optional)
5. with index P/E (optional)
You can also do calculations by using different growth rate by selecting that option.
Different type of moving averages is also included in the script as an option.
ROCE with 3-Year EMAThis Pine Script indicator, "3-Year EMA of Return on Capital Employed (ROCE)," is designed for investors and traders who incorporate both fundamental and technical analysis in their market approach. ROCE is a crucial metric for evaluating the efficiency and profitability of a company's capital employment. Our script enhances this analysis by overlaying a 3-year Exponential Moving Average (EMA) on the ROCE, allowing users to compare current performance against a longer-term trend.
Key Features:
ROCE Calculation: The script calculates the Return on Capital Employed (ROCE) using EBIT (Earnings Before Interest and Taxes) for the Trailing Twelve Months (TTM) and Capital Employed (Total Assets minus Short Term Debt) for the Fiscal Year (FY). This calculation provides a snapshot of how effectively a company is using its capital to generate profits.
3-Year EMA Overlay: The script features a 3-year EMA of the ROCE, providing a smoothed, long-term trend line. This EMA helps in identifying broader trends in a company's operational efficiency and profitability, making it easier to spot deviations from the historical norm.
Customizable for Different Data Frequencies: Whether your data is quarterly, monthly, or weekly, the script is adaptable. The length of the EMA is adjustable to suit the data frequency, ensuring accurate representation over a 3-year period.
Visualization: The ROCE and its 3-year EMA are plotted with distinct colors for easy comparison and analysis. This visual representation aids in quickly assessing the company's current performance against its historical trend.
Customization: Users can adjust the EMA length to match the frequency of their data (e.g., 12 for quarterly, 36 for monthly, 156 for weekly data).
Usage Tips:
Best used on companies with stable and consistent reporting.
Combine with other fundamental and technical indicators fo
r comprehensive analysis.
Disclaimer: This script is provided for informational and educational purposes only and should not be construed as investment advice.
MicroStrategy / Bitcoin Market Cap RatioThis indicator offers a unique analytical perspective by comparing the market capitalization of MicroStrategy (MSTR) with that of Bitcoin (BTC) . Designed for investors and analysts interested in the correlation between MicroStrategy's financial performance and the Bitcoin market, the script calculates and visualizes the ratio of MSTR's market capitalization to Bitcoin's market capitalization.
Key Features:
Start Date: The script considers data starting from July 28, 2020, aligning with MicroStrategy's initial announcement to invest in Bitcoin.
Data Sources: It retrieves real-time data for MSTR's total shares outstanding, MSTR's stock price, and BTC's market capitalization.
Market Cap Calculations: The script calculates MicroStrategy's market cap by multiplying its stock price with the total shares outstanding. It then forms a ratio of MSTR's market cap to BTC's market cap.
Bollinger Bands: To add a layer of analysis, the script includes Bollinger Bands around the ratio, with customizable parameters for length and multiplier. These bands can help identify overbought or oversold conditions in the relationship between MSTR's and BTC's market values.
The indicator plots the MSTR/BTC market cap ratio and the Bollinger Bands, providing a clear visual representation of the relationship between these two market values over time.
This indicator is ideal for users who are tracking the impact of Bitcoin's market movements on MicroStrategy's valuation or vice versa. It provides a novel way to visualize and analyze the interconnectedness of a leading cryptocurrency asset and a major corporate investor in the space.
Financials - Quick OverviewThis unique indicator is designed to provide traders and investors with a concise yet comprehensive view of a company's financial health and sector classification. It features an intuitive table displayed prominently on the chart, offering a blend of essential company information and key financial metrics. This tool is ideal for those looking to integrate fundamental analysis into their technical trading strategy.
Key Features:
Company Sector Information: Get a quick glimpse of the company's industry sector, aiding in understanding its market position and comparative performance within its industry.
Financial Overview: The table includes vital financial data such as Earnings and Sales, providing insights into the company's revenue and profitability.
Growth Metrics: Track both quarter-over-quarter (QoQ) and year-over-year (YoY) growth, offering a dynamic view of the company's performance over time.
Operating Margin Percentage (OPM%): Understand the efficiency of the company's operations with the OPM%, which indicates the proportion of revenue that remains after paying for variable costs of production.
Price-to-Earnings (PE) Ratio: Assess the company's stock value relative to its earnings, an essential metric for valuation and comparative analysis within the sector.
Usage: This indicator is particularly useful for investors and traders who incorporate fundamental analysis into their decision-making process. By providing key financial data directly on the chart, it allows for a more integrated approach to technical and fundamental analysis. The indicator is designed to be straightforward and easy to interpret, making it suitable for both seasoned investors and those new to financial analysis.
Free cash flow yieldThis script shows
- FCF Yield Net based on enterprise value. See reference: www.investopedia.com
- FCF Yield Diluted: which reduced CFC net by dilution amount.
- FCF % change.
This should give you a good overview on how well the company is at growing FCF and how efficiently they are creating FCF.
V Shape Rebound - Valuation / Undervalued ZoneThe Indicator is a tool designed to assist value investors(short, middle, long) in identifying potential undervalued market opportunities.
How to Use:
The valuation level and valuation method can be adapted to individual risk management and capital management.
Observe Bottom Price: Using the system's historical data and V-bounce indicator, observe if a company is at the bottom of the price to show that it is undervalued.
The valuation levels are categorized into deep undervalue, light undervalue, and bullish retracement levels, and deep and light undervalue are usually used as buy positions.
Real-time Alerts: Users can set up real-time alert functionality to ensure they do not miss potential undervalued investment opportunities.
Combined with FIE Indicator: The indicator can be used in conjunction with the Financial Fundamental Intelligent Evaluation(FIE) indicator to provide investors with more comprehensive and accurate decision-making support.
**There are 8 key elements that must be adhered to before investing,
healthy financial position,
stable profitability,
stable cash flow,
good management quality,
no suspicion of accounting fraud,
undervalued price,
positive industry position and
strong competitive advantage
**It is important to ensure that the FIE's main indices are high average score and low score volatility.
The main indices include:
Comprehensive,
Compressive Strength,
Borrowing Capability,
Profitability,
Liquidity,
Leverage or debt,
Secondary indices include:
Quality of Earning,
Receivability,
Auxiliary indices greater than score 30~50 can indicate that Profitability is very solid.
Example 01
NYSE:TPL
Example 02
NASDAQ:AMAT
Example 03
NASDAQ:NVDA
Example 04
NASDAQ:USLM
Example 05
NASDAQ:CPRT
