Real Dominance//Due to incompliance with TV rules, I re-publish this indicator once again. Hope this time it's complaint.
Indicator shows dominance of main coin (BTC by default) after deduction of all stablecoins marketcaps and compares it to dominance that provides TradingView (BTC.D by default). The reason of writing this indicator is to deduct all stablecoins' caps from bitcoin dominance and show dominance without impact of other stablecoins. It means, that if crypto cap equals to, let's say 100, stablecoins' cap will be part of it (something between 10 and 20), but generally stablecoins are not crypto and it's caps are generally not limited, so we can't clearly see what is real dominance of BTC in compare with altcoins.
Notes:
1. dominance for timeframes lower than 1D could be calculated only on tariffs Pro+ or Premium (TV limitation)
2. you may change any and all tickers in indicator's setup menu
3. at the moment of publication (03.06.2023), TV doesn't offer market cap tickers for all stablecoins. Therefore in case it will be added in the future you may add it in the setup menu. There are placeholders for stablecoins that has market cap in amount of more than 5mil USD as of today.
Индикатор показывает доминацию главной монеты (по умолчанию BTC) за вычетом доли всех стейблкоинов в сравнении к доминации, которую показывает TradingView (по умолчанию BTC.D). Причиной написания данного индикатора является необходимость вычесть влияние стейблов на доминацию, так как важно смотреть доминацию именно в сравнении BTC/altcoins, и не учитывать стейблкойны, объем которых по большому счету не ограничен.
Особенности работы:
1. на тарифах кроме Pro+ и Premium, доминация может быть рассчитана только на дневном таймфрейме и выше (ограничения TradingView).
2. все тикеры, включая главную и сравниваемую монеты можно менять по желанию в настройках. Стиль линий настраивается на соответствующей вкладке в настройках.
3. к сожалению, на момент публикации индикатора (03.06.2023), TradingView предоставляет данные капитализации для ограниченного количества стейблкойнов. В настройки добавлены заглушки для последующего добавления других стейблкойнов. В список внесены монеты, капитализация которых на момент публикации индикатора составляла более 5 млн долларов.
펀더멘털 어낼리시스
Liquidity Proxy : ChinaThis is based on the 'Global Liquidity Proxy' as defined by Darius Dale.
GLP is comprised of:
* Central bank balance sheet
* Narrow money supply
* Foreign exchange reserves minus gold
This is an approximation based on the description above.
This indicator shows the global liquidity proxy for China.
The model, in terms of TradingView symbols is:
YoY change % of
CNCBBS + CNM1 + CNFER - CNGRES
The chart doesn't exactly match what Darius shows so his model is likely somewhat different.
RED : China liquidity index
GREEN : SSE composite index YoY change %
Sector/IndustryThis is a simple script that displays a symbol's sector and industry in a table at bottom right area of the chart.
Central Bank LiquidityCentral Bank Liquidity = Total value of the assets of all Federal Reserve Banks - Overnight Reverse Repurchase Agreements (RRP) - The Treasury General Account (TGA)
TradingView ticker arithmetic: FRED:WALCL-FRED:WTREGEN-FRED:RRPONTSYD
Valuation Metrics Table (P/S, P/E, etc.)This table gives the user a very easy way of seeing many valuation metrics. I also included the 5 year median of the price to sales and price to earnings ratios. Then I calculated the percent difference between the median and the current ratio. This gives a sense of whether or not a stock is over valued or under valued based on historical data. The other ratios are well known and don't require any explanation. You can turn off the ones you don't want in the settings of the indicator. Another thing to mention is that diluted EPS is used in calculations
US Market SentimentThe "US Market Sentiment" indicator is designed to provide insights into the sentiment of the US market. It is based on the calculation of an oscillator using data from the High Yield Ratio. This indicator can be helpful in assessing the overall sentiment and potential market trends.
Key Features:
Trend Direction: The indicator helps identify the general trend direction of market sentiment. Positive values indicate a bullish sentiment, while negative values indicate a bearish sentiment. Traders and investors can use this information to understand the prevailing market sentiment.
Overbought and Oversold Levels: The indicator can highlight overbought and oversold conditions in the market. When the oscillator reaches high positive levels, it suggests excessive optimism and a potential downside correction. Conversely, high negative levels indicate excessive pessimism and the possibility of an upside rebound.
Divergence Analysis: The indicator can reveal divergences between the sentiment oscillator and price movements. Divergences occur when the price reaches new highs or lows, but the sentiment oscillator fails to confirm the move. This can signal a potential trend reversal or weakening of the current trend.
Confirmation of Trading Signals: The "US Market Sentiment" indicator can be used to confirm other trading signals or indicators. For instance, if a momentum indicator generates a bullish signal, a positive reversal in the sentiment oscillator can provide additional confirmation for the trade.
Usage and Interpretation:
Positive values of the "US Market Sentiment" indicate a bullish sentiment, suggesting potential buying opportunities.
Negative values suggest a bearish sentiment, indicating potential selling or shorting opportunities.
Extreme positive or negative values may signal overbought or oversold conditions, respectively, and could precede a market reversal.
Divergences between the sentiment oscillator and price trends may suggest a potential change in the current market direction.
Traders and investors can combine the "US Market Sentiment" indicator with other technical analysis tools to enhance their decision-making process and gain deeper insights into the US market sentiment.
Rolling Risk-Adjusted Performance RatiosThis simple indicator calculates and provides insights into different performance metrics of an asset - Sharpe, Sortino and Omega Ratios in particular. It allows users to customize the lookback period and select their preferred data source for evaluation of an asset.
Sharpe Ratio:
The Sharpe Ratio measures the risk-adjusted return of an asset by considering both the average return and the volatility or riskiness of the investment. A higher Sharpe Ratio indicates better risk-adjusted performance. It allows investors to compare different assets or portfolios and assess whether the returns adequately compensate for the associated risks. A higher Sharpe Ratio implies that the asset generates more return per unit of risk taken.
Sortino Ratio:
The Sortino Ratio is a variation of the Sharpe Ratio that focuses specifically on the downside risk or volatility of an asset. It takes into account only the negative deviations from the average return (downside deviation). By considering downside risk, the Sortino Ratio provides a more refined measure of risk-adjusted performance, particularly for investors who are more concerned with minimizing losses. A higher Sortino Ratio suggests that the asset has superior risk-adjusted returns when considering downside volatility.
Omega Ratio:
The Omega Ratio measures the probability-weighted ratio of gains to losses beyond a certain threshold or target return. It assesses the skewed nature of an asset's returns by differentiating between positive and negative returns and assigning more weight to extreme gains or losses. The Omega Ratio provides insights into the potential asymmetry of returns, highlighting the potential for significant positive or negative outliers. A higher Omega Ratio indicates a higher probability of achieving large positive returns compared to large negative returns.
Utility:
Performance Evaluation: Provides assessment of an asset's performance, considering both returns and risk factors.
Risk Comparison: Allows for comparing the risk-adjusted returns of different assets or portfolios. Helps identify investments with better risk-reward trade-offs.
Risk Management: Assists in managing risk exposure by evaluating downside risks and volatility.
TTP NVT StudioNVT Studio is an indicator that aims to find areas of reversal of the Bitcoin price based on the extreme areas of Network Value Transaction.
Instructions:
- We recommend using it on INDEX:BTCUSD
- Use the daily or weekly timeframe
The indicator works as an oscillator and offers to visualisation modes.
1) Showing the short term oscillations of NVT showing signals in potential areas of reversal.
2) The actual value of NVT displayed. When in green is an area of value and in red when its overextended.
This indicator can be used based on the signals or based on breakouts of trend lines drawn in the oscillator mode.
Red/green dots: signal type 1 - extremes with confirmation, these might trigger late
Yellow/Orange: signal type 2 - extremes without confirmation, might trigger too soon
P/E RatioPlots the P/E Ratio with highest, lowest and average, as well as two ranges, 25-20 & 20-0 considered as the regular P/E Range
Range BreakerStrategy Description: Range Breaker
The Range Breaker strategy is a breakout trading strategy that aims to capture profits when the price of a financial instrument moves out of a defined range. The strategy identifies swing highs and swing lows over a specified lookback period and enters long or short positions when the price breaks above the swing high or below the swing low, respectively. It also employs stop targets based on a percentage to manage risk and protect profits.
Beginner's Guide:
Understand the concepts:
a. Swing High: A swing high is a local peak in price where the price is higher than the surrounding prices.
b. Swing Low: A swing low is a local trough in price where the price is lower than the surrounding prices.
c. Lookback Period: The number of bars or periods the strategy analyzes to determine swing highs and swing lows.
d. Stop Target: A predetermined price level at which the strategy will exit the position to manage risk and protect profits.
Configure the strategy:
a. Set the initial capital, order size, commission, and pyramiding as needed for your specific trading account.
b. Choose the desired lookback period to identify the swing highs and lows.
c. Set the stop target multiplier and stop target percentage as desired to manage risk and protect profits.
Backtest the strategy:
a. Set the backtest start date to analyze the strategy's historical performance.
b. Observe the backtesting results to evaluate the strategy's effectiveness and adjust the parameters if necessary.
Implement the strategy:
a. Apply the strategy to your preferred financial instrument on the TradingView platform.
b. Monitor the strategy's performance and adjust the parameters as needed to optimize its effectiveness.
Risk management:
a. Always use a stop target to protect your trading capital and manage risk.
b. Don't risk more than a small percentage of your trading capital on a single trade.
c. Be prepared to adjust the strategy or stop trading it if the market conditions change significantly.
Adjusting the Lookback Period and Timeframes for Optimal Strategy Performance
The Range Breaker strategy uses a lookback period to identify swing highs and lows, which serve as the basis for determining entry and exit points for long and short positions. By adjusting the lookback period and analyzing different timeframes, you can potentially find the best strategy configuration for each specific asset.
Adjusting the lookback period:
The lookback period is a critical parameter that affects the sensitivity of the strategy to price movements. A shorter lookback period will make the strategy more sensitive to smaller price fluctuations, resulting in more frequent trading signals. On the other hand, a longer lookback period will make the strategy less sensitive, generating fewer signals but potentially capturing larger price movements.
To optimize the lookback period for a specific asset, you can test different lookback values and compare their performance in terms of risk-adjusted returns, win rate, and other relevant metrics. Keep in mind that using an overly short lookback period may lead to overtrading and increased transaction costs, while an overly long lookback period may cause the strategy to miss profitable trading opportunities.
Analyzing different timeframes:
Timeframes refer to the duration of each bar or candlestick on the chart. Shorter timeframes (e.g., 5-minute, 15-minute, or 30-minute) focus on intraday price movements, while longer timeframes (e.g., daily, weekly, or monthly) capture longer-term trends. The choice of timeframe affects the number of trading signals generated by the strategy and the length of time each position is held.
To find the best strategy for each asset, you can test the Range Breaker strategy on different timeframes and analyze its performance. Keep in mind that shorter timeframes may require more active monitoring and management due to the increased frequency of trading signals. Longer timeframes, on the other hand, may require more patience as positions are held for extended periods.
Finding the best strategy for each asset:
Every asset has unique price characteristics that may affect the performance of a trading strategy. To find the best strategy for each asset, you should:
a. Test various lookback periods and timeframes, observing the strategy's performance in terms of profitability, risk-adjusted returns, and win rate.
b. Consider the asset's historical price behavior, such as its volatility, liquidity, and trend-following or mean-reverting tendencies.
c. Evaluate the strategy's performance during different market conditions, such as bullish, bearish, or sideways markets, to ensure its robustness.
d. Keep in mind that each asset may require a unique set of strategy parameters for optimal performance, and there may be no one-size-fits-all solution.
By experimenting with different lookback periods and timeframes, you can fine-tune the Range Breaker strategy for each specific asset, potentially improving its overall performance and adaptability to changing market conditions. Always practice proper risk management and be prepared to make adjustments as needed.
Remember that trading strategies carry inherent risk, and past performance is not indicative of future results. Always practice proper risk management and consider your own risk tolerance before trading with real money.
Fierytrading: Volatility DepthDear Tradingview community,
I'd like to share one of my staple indicators with you. The volatility depth indicator calculates the volatility over a 7-day period and plots it on your chart.
This indicator only works for the DAILY chart on BTC/USD.
Colors
I've color coded the indicator as follows:
- Red: Extreme Volatility
- Orange: High Volatility
- Yellow: Normal Volatility
- Green: Low Volatility
Red: extreme changes in price. Often during local tops and bottoms.
Orange: higher than average moves in price. Often before or after a "red" period. Often seen in the middle of bear or bull markets.
Yellow: normal price action. Often seen during early stage bull-markets and late stage bear-markets.
Green: very low price movement. Often during times of indecision. Once this indicator becomes green, you can expect a big move in either direction. Low volatility is always followed by high volatility.
In a long-term uptrend, a green period often signals a bullish break out. In a long-term downtrend it often signals a bearish break out.
How to use
Save the indicator and apply it to your chart. You can change the length in the settings, but it's optimized for 7 days, so no need to change it.
I've build in alerts for all 4 different volatility periods. In most cases, the low volatility alert is enough.
Good luck!
Rule of 40The rule of 40 is a popular metric for measuring the quality of SaaS companies. It measures growth and profitability. Companies are considered good if this sum is above 40.
It is the sum of the year over year sales growth and profit margin.
Rule of 40 = YoY sales growth + profit margin
Cobra's CryptoMarket VisualizerCobra's Crypto Market Screener is designed to provide a comprehensive overview of the top 40 marketcap cryptocurrencies in a table\heatmap format. This indicator incorporates essential metrics such as Beta, Alpha, Sharpe Ratio, Sortino Ratio, Omega Ratio, Z-Score, and Average Daily Range (ADR). The table utilizes cell coloring resembling a heatmap, allowing for quick visual analysis and comparison of multiple cryptocurrencies.
The indicator also includes a shortened explanation tooltip of each metric when hovering over it's respected cell. I shall elaborate on each here for anyone interested.
Metric Descriptions:
1. Beta: measures the sensitivity of an asset's returns to the overall market returns. It indicates how much the asset's price is likely to move in relation to a benchmark index. A beta of 1 suggests the asset moves in line with the market, while a beta greater than 1 implies the asset is more volatile, and a beta less than 1 suggests lower volatility.
2. Alpha: is a measure of the excess return generated by an investment compared to its expected return, given its risk (as indicated by its beta). It assesses the performance of an investment after adjusting for market risk. Positive alpha indicates outperformance, while negative alpha suggests underperformance.
3. Sharpe Ratio: measures the risk-adjusted return of an investment or portfolio. It evaluates the excess return earned per unit of risk taken. A higher Sharpe ratio indicates better risk-adjusted performance, as it reflects a higher return for each unit of volatility or risk.
4. Sortino Ratio: is a risk-adjusted measure similar to the Sharpe ratio but focuses only on downside risk. It considers the excess return per unit of downside volatility. The Sortino ratio emphasizes the risk associated with below-target returns and is particularly useful for assessing investments with asymmetric risk profiles.
5. Omega Ratio: measures the ratio of the cumulative average positive returns to the cumulative average negative returns. It assesses the reward-to-risk ratio by considering both upside and downside performance. A higher Omega ratio indicates a higher reward relative to the risk taken.
6. Z-Score: is a statistical measure that represents the number of standard deviations a data point is from the mean of a dataset. In finance, the Z-score is commonly used to assess the financial health or risk of a company. It quantifies the distance of a company's financial ratios from the average and provides insight into its relative position.
7. Average Daily Range: ADR represents the average range of price movement of an asset during a trading day. It measures the average difference between the high and low prices over a specific period. Traders use ADR to gauge the potential price range within which an asset might fluctuate during a typical trading session.
Utility:
Comprehensive Overview: The indicator allows for monitoring up to 40 cryptocurrencies simultaneously, providing a consolidated view of essential metrics in a single table.
Efficient Comparison: The heatmap-like coloring of the cells enables easy visual comparison of different cryptocurrencies, helping identify relative strengths and weaknesses.
Risk Assessment: Metrics such as Beta, Alpha, Sharpe Ratio, Sortino Ratio, and Omega Ratio offer insights into the risk associated with each cryptocurrency, aiding risk assessment and portfolio management decisions.
Performance Evaluation: The Alpha, Sharpe Ratio, and Sortino Ratio provide measures of a cryptocurrency's performance adjusted for risk. This helps assess investment performance over time and across different assets.
Market Analysis: By considering the Z-Score and Average Daily Range (ADR), traders can evaluate the financial health and potential price volatility of cryptocurrencies, aiding in trade selection and risk management.
Features:
Reference period optimization, alpha and ADR in particular
Source calculation
Table sizing and positioning options to fit the user's screen size.
Tooltips
Important Notes -
1. The Sharpe, Sortino and Omega ratios cell coloring threshold might be subjective, I did the best I can to gauge the median value of each to provide more accurate coloring sentiment, it may change in the future.
The median values are : Sharpe -1, Sortino - 1.5, Omega - 20.
2. Limitations - Some cryptos have a Z-Score value of NaN due to their short lifetime, I tried to overcome this issue as with the rest of the metrics as best I can. Moreover, it limits the time horizon for replay mode to somewhere around Q3 of 2021 and that's with using the split option of the top half, to remain with the older cryptos.
3. For the beginner Pine enthusiasts, I recommend scimming through the script as it serves as a prime example of using key features, to name a few : Arrays, User Defined Functions, User Defined Types, For loops, Switches and Tables.
4. Beta and Alpha's benchmark instrument is BTC, due to cryptos volatility I saw no reason to use SPY or any other asset for that matter.
Financial Radar Chart by zdmreRadar chart is often used when you want to display data across several unique dimensions. Although there are exceptions, these dimensions are usually quantitative, and typically range from zero to a maximum value. Each dimension’s range is normalized to one another, so that when we draw our spider chart, the length of a line from zero to a dimension’s maximum value will be the similar for every dimension.
This Charts are useful for seeing which variables are scoring high or low within a dataset, making them ideal for displaying performance.
How is the score formed?
Debt Paying Ability
if Debt_to_Equity < %10 : 100
elif < 20% : 90
elif < 30% : 80
elif < 40% : 70
elif < 50% : 60
elif < 60% : 50
elif < 70% : 40
elif < 80% : 30
elif < 90% : 20
elif < 100% : 10
else: 0
ROIC
if Return_on_Invested_Capital > %50 : 100
elif > 40% : 90
elif > 30% : 80
elif > 20% : 70
elif > 10% : 50
elif > 5% : 20
else: 0
ROE
if Return_on_Equity > %50 : 100
elif > 40% : 90
elif > 30% : 80
elif > 20% : 70
elif > 10% : 50
elif > 5% : 20
else: 0
Operating Ability
if Operating_Margin > %50 : 100
elif > 30% : 90
elif > 20% : 80
elif > 15% : 60
elif > 10% : 40
elif > 0 : 20
else: 0
EV/EBITDA
if Enterprise_Value_to_EBITDA < 3 : 100
elif < 5 : 80
elif < 7 : 70
elif < 8 : 60
elif < 10 : 40
elif < 12 : 20
else: 0
FREE CASH Ability
if Price_to_Free_Cash_Flow < 5 : 100
elif < 7 : 90
elif < 10 : 80
elif < 16 : 60
elif < 18 : 50
elif < 20 : 40
elif < 22 : 30
elif < 30 : 20
elif < 40 : 15
elif < 50 : 10
elif < 60 : 5
else: 0
GROWTH Ability
if Revenue_One_Year_Growth > %20 : 100
elif > 16% : 90
elif > 14% : 80
elif > 12% : 70
elif > 10% : 50
elif > 7% : 40
elif > 4% : 30
elif > 2% : 20
elif > 0 : 10
else: 0
Top12/Bottom88 Weighted Ratio I multiplied the price of each of the top QQQ holdings by their percentage weight, and the bottom 88 holdings for a total of 100. I divide the top 12 weighted price by the bottom 88 weighted price. So I can see when money is flowing in and out of the megacaps. It needs to be updated every quarter, which I may need to do now....
Custom Group Financials [Technimentals]This script allows the user to build custom groups and combine the same financial data from 40 different symbols simultaneously and plot it data as a total or as an average.
By default, the top 40 symbols in the QQQ are used. Between them they account for the majority of the index. This is a good workaround for the lack of ETF financial data in TradingView.
This functions much like any other financial indicator. You choose the financial data and period:
FY = Financial Year
FQ = Financial Quarter
TTM = Trailing Twelve Months
Bare in mind that some data only exists in FY data.
Thanks to @LucF for writing most of this code!
Enjoy!
Joel Greenblatt Magic FormulaJoel Greenblatt Magic Formula. I always wanted to make this.
The Indicator shows 3 values.
ROC,EY,SUM.
ROC= Return On Capital.
EY=Earnings Yield
SUM= Addition of Two.
Formula:
ROC=EBIT / (Net Working Capital + Net Fixed Assets).
EY = EBIT / Enterprise value
Enterprise Value=(Market value of equity + Net Interest-bearing debt)
To implement the strategy, investors start by identifying a universe of stocks, typically large-cap or mid-cap companies that trade on a major stock exchange. Next, they rank the stocks based on their ROC and EY. The companies with the best combination of these two metrics are considered the best investments (based on this ranking).
For example, a stock that ranks 10th on EY and 99th on ROIC gets a value of 109. The two ranks are simply added together and all stocks are ranked on the sum of the two ranks. The stocks with the lowest values are best.
All credits to "The Little Book That Beats The Market" by Joel Greenblatt
The Magic Formula strategy is a stock selection method popularized by Joel Greenblatt’s book The Little Book That Beats the Market.
It involves ranking companies based on Two factors:
A high return on capital and A high Earnings Yield.
The companies with the best combination of these two metrics are considered the best investments. The strategy aims to find undervalued companies with strong financials that have the potential for high returns over the long term.
DOTS [CHE]This indicator is a must-have for every trader as it provides a practical tool to quickly evaluate the current price of a security. Designed specifically for manual trading, this indicator is based on "The Forbidden RSI " indicator and provides an easy way to identify overbought and oversold conditions in the market. By using this indicator, traders can make informed decisions about when to enter or exit a trade, maximizing their potential profits and minimizing their risks. Its simple yet effective design makes it an ideal choice for traders of all experience levels. Whether you are a seasoned professional or just starting out, this indicator can help you take your trading to the next level.
Description:
This is a Pine Script code designed to create an indicator that identifies overbought and oversold conditions in a security. The code first defines a function named "func" that takes three arguments - "close", "length", and "tr". It then calculates a value "k" based on the "close" and "length" arguments using this function.
The code then checks if "k" is greater than a variable named "OverBought" and assigns the resulting Boolean value to "OverboughtCond". It also checks if "k" is less than a variable named "OverSold" and assigns the resulting Boolean value to "OversoldCond".
The code then plots a small circle above the bar if "OverboughtCond" is true and below the bar if "OversoldCond" is true. The circles are colored green for "Overbought" and red for "Oversold". The code also creates a label with the name "Overbought" above the bar and a label with the name "Oversold" below the bar if the respective conditions are met.
Finally, the code sets up alert conditions for both the "Overbought" and "Oversold" cases, with a custom message that includes the name of the security, the current price, and the indicator's name.
I've tested the script for weeks and I hope it brings you as much success as it did me
best regards
Chervolino
Reversal Points [CC]This original script was created based on a suggestion from @kerpiciwuasile. My original Reversal Points script was removed because I modeled it after an indicator by Demark, but this script will have no such issues. Reversal points are an exciting concept for me because it is such a useful tool when placing trades. This is my first attempt at a new overall layout for my script and I included a bunch of customization so let me know what you think.
My script works by finding lows that are surrounded by bars that have higher lows and highs that are surrounded by lower highs. I use this logic to find short term lows or highs and I use the same concept to find mid-term lows or highs but a mid-term high is a short term high surrounded by lower short term highs and a mid-term low is a short term low surrounded by higher short term lows. And of course this means that long term highs or lows use the same logic to find highs or lows that are surrounded by mid-term highs or lows. I would recommend to buy at the long term low points or sell at the long term high points.
Keep in mind of course that short term highs and lows are very common and reversal points will get rarer as you look for longer term reversal points. I would recommend to experiment and see which reversal points work best for you and of course, know that there is no magical formula to use for all stocks.
Also there are a few scenarios where you want to enable or disable the inside bar setting. You would want to ignore inside bars if the market is currently very volatile or if you are using this indicator on a crypto chart. This is not an exact science but more of a recommendation, so feel free to experiment with it.
Reversal points are crucial for traders as they signal a potential change in the market trend, providing opportunities for entry or exit.
In summary, this code snippet is a powerful tool for traders to detect and visualize reversal points on a trading chart, providing valuable insights into potential trend changes and facilitating more informed trading decisions.
Let me know if you would like me to publish other scripts or if you want me to do something custom for you!
Stock Comparison to S&P 500This indicator, "Stock Comparison to S&P 500," is designed to help traders compare the financial health and valuation of a chosen stock to the S&P 500 index. It compares several key financial metrics of the stock to the corresponding metrics of the S&P 500, including earnings growth, price-to-earnings ratio, price-to-book ratio, and price-to-sales ratio.
The indicator calculates the differences between each metric of the selected stock and the S&P 500, and then weights them using a formula that takes into account the importance of each metric. The resulting value represents the overall comparison between the stock and the S&P 500.
The indicator also displays the differences between the individual metrics in separate plots, allowing traders to see how each metric contributes to the overall comparison. Additionally, it colors the plots green if the selected stock is performing better than the S&P 500 in a particular metric and red if it's performing worse.
Traders can use this indicator to gain insight into the relative financial health and valuation of a selected stock compared to the S&P 500 index, which can help inform their trading decisions.
BTCUSD Price prediction based on central bank liquidityIn recent months the idea that Bitcoin prices are increasingly linked to liquidity provided by central banks has gained strength. Multiple opinion leaders in the bitcoin space have shared their thoughts to explain why this is happening and why it makes sense. Some of these people I'm talking about are Preston Pysh, Dr. Jeff Ross, Steven McClurg, Lynn Alden among others.
The reality is that the correlation between market liquidity, measured as Assets held by the Federal Reserve, Bank of Japan and European Central bank, and Bitcoin prices is high. This made me wonder whether a regression between "market liquidity" and BTCUSD prices made sense in order to understand where Bitcoin prices are in relation to the liquidity in the market. After several trials I ended up fitting a polynomial regression of degree 5 between Market Liquidity and BTCUSD prices since 2013. This regression resulted in r-squared value of 90.93%. I initially visualized the results in python notebooks but then I thought it would be cool to be able to see them in real-time in tradingview.
That's where this script comes handy...
This script takes the coefficients and intercept from the polynomial regression I built and applies them to the "market_liquidity" index. In addition, it adds upper and lower bound lines to the prediction based on a 95% confidence interval. As you will see, particularly since 2020, the price of bitcoin has rarely been above or below the lines representing the 95% confidence interval. When price has actually crossed these lines it's been in moments where Bitcoin was highly overbought or oversold. Therefore this indicator could be used to understand when it's a good moment to enter or exit the market based on central bank fundamentals.
Here's the detailed step-by-step description of what the script does
1) It defines the coefficients obtained from running the regression betweeen "market liquidity" and BTCUSD. Market liquidity is defined as:
Market liquidity = FRED:WALCL + FX_IDX:JPYUSD*FRED:JPNASSETS + FX:EURUSD*FRED:ECBASSETSW - FRED:RRPONTSYD - FRED:WTREGEN
2) It defines a scale factor. The reason for this is that coefficients from the regression are very small numbers, given the huge numbers of the value of assets held by central banks. Pinescript doesn't support numbers with many decimals and rounds them to 0, so the coefficients had to be scaled up in order to be able to calculate the regression results.
3) It calculates market liquity with the formula defined above. Market liquidity is calculated in US Dollars.
4) It calculates the predicted BTCUSD price based on the coefficients and the market liquidity values.
5) It scales down the values by the same factor used to scale the coefficients up
6) It defines the standard deviation of the "potential_btcusd_price_scaled" and the actual BTCUSD prices.
7) It defines upper and lower bounds to the BTCUSD price prediction using a z-score of 1.96, which is equivalent to 95% confidence interval.
8) Lastly it plots the BTCUSD price prediction (orange) and the upper (red) and lower(green) confidence intervals.
The script can be updated as the correlation of BTCUSD to central bank assets changes (the slope values can be updated).
How to use it:
When actual BTCUSD price (blue line in the chart) crosses over the red line (upper bound) or crosses under the green line (lower bound) it should be taken as a sign that the price of BTCUSD may be overvalued or undervalued based on the value of assets held by major central banks.
Global LiquidityThe "Global Liquidity" script is an indicator that calculates and displays the global liquidity value using a formula that takes into account the money supply of several major economies. The script utilizes data from various sources, such as the Federal Reserve Economic Data (FRED), Economics, and FX_IDC.
The indicator plots the global liquidity value as a candlestick chart and breaks it down into two categories: the Euro-Atlantic region (West) and the rest of the world (East). The values are denominated both in inflation-adjusted dollars and in trillions of dollars. The script also calculates the spread between the Euro-Atlantic region and the rest of the world.
Traders and investors can use this indicator to gauge the overall liquidity of the global economy and to identify potential investment opportunities or risks. By breaking down the liquidity value into different regions, traders can also gain insights into regional economic trends and dynamics.
Note that this script is subject to the terms of the Mozilla Public License 2.0 and was created by rodopacapital.
Ema Short Long Indicator[CHE]█ CONCEPTS
This Pine Script is an EMA Short Long indicator that displays the crossing EMA lines on the chart. The indicator uses three exponential moving averages (EMAs) to generate the buy and sell signals. The EMA lines are plotted as green (uptrend) and red (downtrend) lines. When the green line is above the white signal line, the indicator generates a buy signal, when the green line is below the white signal line, the indicator generates a sell signal. Arrows are also displayed marking the buy and sell signals. There is also an option to allow indicator repainting or not. Finally, users can also set alerts to be alerted to potential trading opportunities.
Note: please do not disable "time frame gaps". Allows to calculate the indicator on a Timeframe (TF) different from that of the chart Time window. The TF should ideally be higher than the charts to provide a broader perspective than
the TF of the chart. Using TFs lower than the chart's will deliver fragmentary results, since only the last value of intrabar is displayed (multiple values cannot be displayed for a single chart bar). The Gaps setting determines the behavior when the TF is higher than the TF of the chart. If 'gaps' is checked, higher TF values only come in and are interconnected on the diagram when the higher TF completed. This has the advantage of avoidance Real-time epainting. If Gaps is not enabled, Gaps are filled with the last higher TF value calculated, which will not produce a repaint Values on historical bars but repaint values realtime.
█ HOW TO USE IT
Load the indicator on an active chart (see the Help Center if you don't know how).
Time period
By default, the script uses an auto-stepping mechanism to adjust the time period of its moving window to the chart's timeframe. The following table shows chart timeframes and the corresponding time period used by the script. When the chart's timeframe is less than or equal to the timeframe in the first column, the second column's time period is used to calculate the Ema Short Long Indicator :
Chart Time
timeframe period
1min 🠆 1H
5min 🠆 4H
1H 🠆 1D
4H 🠆 3D
12H 🠆 1W
1D 🠆 1M
1W 🠆 3M
█ DESCRIPTION
The script begins by setting up the chart indicator with a short title, "ESLI", and enabling it as an overlay. It then initializes several variables for time conversions, to be used later in the script.
The timeStep_translate() function converts the timeframe of the chart into a string representing a larger time interval, based on the number of seconds in the timeframe. The resulting string is used to label the horizontal axis of the chart.
Next, the script defines several input variables that can be modified by the user. These include the colors of the EMA lines and the signals, whether or not the indicator is allowed to repaint (i.e. update past values based on future data), and the number of periods used to calculate the EMA and signal lines.
The f_security() function calls the request.security() function to fetch data from the specified security and timeframe, and is used to calculate the EMA and signal lines using the ta.ema() function. The clo variable is assigned the closing price data, adjusted for repainting and timeframe.
The EMA line is calculated using a weighted average of the EMA over the specified period and two times that period, as well as three times that period, divided by six. The signal line is calculated as the EMA of the EMA line over the specified period.
The col_css variable sets the color of the EMA line based on whether it is currently above or below the signal line. The script then plots the EMA and signal lines, and uses the plotshape() function to indicate long and short signals based on the crossovers and crossunders of the EMA and signal lines.
Finally, the script sets up alert conditions using the alertcondition() function to notify the user when a long or short signal is generated, including information about the symbol and closing price.
█ SPECIAL THANKS
Special thanks to LOXX, I wanted to take a moment to express my gratitude for his valuable input in the EMA calculation. His insights and expertise have greatly helped me in improving my Pine Script coding skills. Thanks to his suggestion, I was able to better understand the EMA formula and implement it effectively in my script.
Your generosity in sharing your knowledge and experience is truly appreciated. It is through collaboration and exchanging ideas that we can all grow and become better in our craft.
This script provides exact signals that, with suitable additional indicators, provide very good results.
Best regards
Chervolino
