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.
Z-VALUE
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
Local
█ 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
TASC 2023.12 Growth and Value Switching System█ OVERVIEW
This script implements a rotation system for trading value and growth ETFs, as developed by Markos Katsanos and detailed in the article titled 'Growth Or Value?' in TASC's December 2023 edition of Traders' Tips . The purpose of this script is to demonstrate how short-term momentum can be employed to track market trends and provide clarity on when to switch between value and growth.
█ CONCEPTS
The central concept of the presented rotation strategy is based on the observation that the stock market undergoes cycles favoring either growth or value stocks. Consequently, the script introduces a momentum trading system that is designed to switch between value and growth equities based on prevailing market conditions. Specifically tailored for long-term index investors, the system focuses on trading Vanguard's value and growth ETFs ( VTV and VUG ) on a weekly timeframe.
To identify the ETF likely to outperform, the script uses a custom relative strength indicator applied to both VTV and VUG in comparison with an index ( SPY ). To minimize risk and drawdowns during bear markets, when both value and growth experience downtrends, the script employs the author's custom volume flow indicator (VFI) and blocks trades when its reading indicates money outflow . Positions are closed if the relative strength of the current open trade ETF falls below that of the other ETF for two consecutive weeks and is also below its moving average. Additionally, the script implements a stop-loss when the ETF is trading below its 40-week moving average, but only during bear markets.
The script plots the relative strengths of the value and growth equities along with the signals triggered by the aforementioned rules. Information about the current readings of the relative strength and volume flow indicators, along with the current open position, is displayed in a table.
█ CALCULATIONS
The script uses the request.security() function to gather price data for both equities and the reference index. Custom relative strength and volume flow indicators are calculated based on the formulas presented in the original article. By default, the script employs the same parameters for these indicators as proposed in the original article for VTV and VUG on a weekly timeframe.
CAPM Model with Returns TableThe given Pine Script is designed to implement the Capital Asset Pricing Model (CAPM) to calculate the expected return for a specified asset over various user-defined periods and compare it with the asset's historical mean return. The core features and functionalities of the script include:
Inputs:
Benchmark Symbol: Defaulted to "CRYPTOCAP:TOTAL". This serves as a comparison metric.
Risk-free Rate: Represents the return on an investment that is considered risk-free.
Benchmark Period: Used for plotting purposes. It doesn't affect table calculations.
Period Settings: Allows users to specify four different time periods for calculations.
Functionalities:
Computes daily returns for the benchmark and asset.
Calculates beta, which represents the volatility of the asset as compared to the volatility of the benchmark.
Uses CAPM to estimate expected returns over user-defined periods.
Generates a table displaying the expected return and asset's mean return for each period.
Provides implications based on the comparison between the expected returns and the asset's historical returns. This is showcased through a mutable label that is updated with each bar.
Visualization:
Plots expected return and asset's mean return over the benchmark period.
Provides a horizontal line to represent zero return.
Use Case:
This script can be helpful for traders or analysts looking to gauge the potential return of an asset compared to its historical performance using the CAPM. The implications provided by the script can serve as useful insights for making investment decisions. It's especially beneficial for those trading or analyzing assets in the cryptocurrency market, given the default benchmark setting.
Note: Before relying on this script for trading decisions, ensure a thorough understanding of its methodology and validate its assumptions against your research.
BETA Benchmark - Tables!The indicator measures and plots the average beta of the defined periods of the selected asset, benchmarked with TOTAL.
Stablecoin Supply Ratio Oscillator
The Stablecoin Supply Ratio Oscillator (SSRO) is a cryptocurrency indicator designed for mean reversion analysis and sentiment assessment. It calculates the ratio of CRYPTO:BTCUSD 's market capitalization to the sum of stablecoins' market capitalization and z-scores the result, offering insights into market sentiment and potential turning points.
Methodology:
The SSRO is calculated as follows-
method ssro(float src, array stblsrc, int len) =>
float ssr = src / stblsrc.sum() // Source of the underlying divided by the sum of stablecoin sources
(ssr - ta.sma(ssr, len)) / ta.stdev(ssr, len) // Z-Score Transformed
This ratio is Z-Scored to provide a standardized measure, allowing users to identify periods of market fear or greed based on the allocation of capital between the underlying and Stablecoins ( CRYPTOCAP:USDT , CRYPTOCAP:USDC , CRYPTO:TUSD , CRYPTOCAP:BUSD , CRYPTOCAP:DAI , CRYPTOCAP:USDD , CRYPTOCAP:FRAX ). The z-scored values indicate potential areas of discount (buying opportunities) or premium (selling opportunities) relative to historical patterns.
Customization:
Underlying Asset: SSRO is customizable to different underlying assets, offering a versatile tool for various cryptocurrencies.
Calculation Length: Users can adjust the length of the calculation, tailoring the indicator to short or long-term analysis.
Visualization: SSRO can be displayed as candles, providing a visual representation of premium and discount areas.
Interpretation:
Market Sentiment: Lower SSRO values may indicate market fear, suggesting a preference for stablecoins as a relatively safer haven for capital. Conversely, higher values may suggest market greed, as more capital is allocated to the underlying asset.
Utility and Use Cases:
1. Mean Reversion Analysis: SSRO identifies potential mean reversion opportunities, guiding traders on optimal entry and exit points.
2. Sentiment Analysis: The indicator provides insights into market sentiment, aiding traders in understanding market dynamics.
3. Macro Analysis: The majority of cryptos follow \ correlate to CRYPTO:BTCUSD , Therefore by assessing premium and discount areas of CRYPTO:BTCUSD relative to the chosen underlying asset, users gain insights into potential market tops and bottoms.
4. Divergence Analysis: SSRO divergence from price trends can signal potential reversals, providing traders with additional confirmation for their decisions.
The Stablecoin Supply Ratio Oscillator is a valuable tool for cryptocurrency traders, offering a nuanced perspective on market sentiment and mean reversion opportunities. Its customization options and visual representation make it a versatile and powerful addition to the crypto analyst's toolkit.
Fair Value by MMEnglish
IMPORTANT NOTICE
This indicator is used to find fair value based on historical data. Past growth data may not be sustainable, which will cause the price targets given by the indicator to be inaccurate. Any price on this indicator cannot be considered as investment advice. Trading decisions are the responsibility of the person using the indicator.
What is the Fair Value by MM indicator?
This is an indicator that tries to find the fair value of a stock by looking at its historical data and growth over a certain period of time. By analyzing a stock's historical growth data, it generates a fair value and potential price estimate.
The indicator presents the financial data of a stock with 3 different data sets.
1. Summary and Valuation
2. Average Quarterly Growth
3. Profit margins
** Number of Lookback Periods for Quarters **
The first input of the indicator is where you specify how many quarters back to value the stock. By default, it is based on the last 12 quarters, i.e. 3 years. Since there is not enough historical data for newly listed companies, you can change this figure according to the company you are analyzing.
** Show Summary **
The Indicator starts in this mode by default. This mode gives you data such as sales, EBITDA, EBIT, net profit and free cash flow in PER SHARE and TTM values. The reason for using per share values is that a company's price is per share, and it saves you time to look at all other metrics on a per share basis. For example, if a company with a share price of $10 has sales per share of $5, we can say that this company has generated half of its market capitalization in sales revenue in the last 1 year.
In the indicator's default mode (Show Summary);
1. Sales per share TTM (Red)
2. EBITDA per share TTM (Orange)
3. EBIT per share TTM (Yellow)
4. Net Income per share TTM (Blue)
5. Free Cash Flow per share TTM (Green)
6. Share close price (White)
7. Fair value of the share (Green if price is below fair value, Red if price is above fair value)
8. Price target for the next 12 months (Yellow)
** Show AVG Growth QoQ **
When this option is selected, you can see the average quarterly growth in sales, EBITDA, EBIT, net profit and free cash flow, respectively, over the period you have selected (e.g. the last 12 quarters). This data gives an idea about the company's growth and the pace of its growth.
** Show Profit Margins **
When this option is selected, you can see gross profit margin, EBITDA margin, EBIT margin, net profit margin and free cash flow margin data respectively. It provides a quick overview to determine whether the company is increasing revenue by narrowing profit margins or increasing both revenue growth and profit margins.
** Include Sales **
When this option is selected, sales revenues are included in the company's valuation.
** Include Ebitda **
When this option is selected, EBITDA is included in the valuation of the company.
** Include Ebit **
When this option is selected, EBIT is included in the valuation of the company.
** Include Net Profit **
When this option is selected, net profit is included in the valuation of the company.
** Include FCF **
When this option is selected, free cash flow is included in the valuation of the company.
By default, the valuation is based on sales, EBITDA and EBIT. Net profit and free cash flow can be optionally selected. Or the metrics you do not want can be excluded from the valuation calculation.
What do the colors mean?
** Red **
Represents the company's data related to the company's sales.
** Orange **
Represents the company's data related to the company's EBITDA.
** Yellow **
Represents the company's data related to the company's EBIT.
** Blue **
Represents the company's data related to the company's Net Income.
** Green **
Represents the company's data related to the company's Free Cash Flow.
Turkish
ÖNEMLİ UYARI
Bu indikatör geçmiş verileri baz alarak adil değer bulmaya yarar. Geçmişte oluşan büyüme verileri sürdürelebilir olmayabilir, bu da indikatörün verdiği fiyat hedeflerinin yanılmasına sebep olacaktır. Bu indikatör üzerinde yer alan herhangi bir fiyat, yatırım tavsiyesi kapsamında değerlendirilemez. Alım/satım kararları indikatörü kullanan kişinin sorumluluğundadır.
Fair Value by MM indikatörü nedir?
Bu bir hissenin belirli bir periyotu kapsayan geçmiş verilerine ve gelişimlerine bakarak adil değerini bulmaya çalışan bir indikatördür. Bir hissenin geçmiş büyüme verilerini analiz ederek adil değer ve potansiyel fiyat tahmini oluşturur.
İndikatör bir hissenin finansal datasını 3 farklı veri seti ile sunmaktadır.
1. Özet ve Değerleme
2. Ortalama Çeyreklik Büyümeler
3. Kar marjları
** Number of Lookback Periods for Quarters **
İndikatörün ilk input’u, hisseyi değerlemek için kaç çeyrek geriye bakacağınızı belirttiğiniz kısımdır. Varsayılan olarak son 12 çeyrek, yani 3 yılı baz alır. Yeni arz olmuş şirketlerde yeterli geçmiş veri bulunmadığı için bu rakamı incelediğiniz şirkete göre değiştirebilirsiniz.
** Show Summary **
İndikatör varsayılan olarak bu modda başlar. Bu mod, satışlar, favök, esas faaliyet karı, net kar ve serbest nakit akışı gibi verileri HİSSE BAŞINA ve YILLIKLANDIRILMIŞ değerleri ile size verir. Hisse başına değerlerin kullanılmasındaki sebep, bir şirketin fiyatı hisse başınadır, ve diğer tüm metriklere hisse başına bakmak size zaman kazandırır. Örneğin, hisse fiyatı $10 olan bir şirketin, hisse başına satışları $5 ise, bu şirket son 1 yılda piyasa değerinin yarısı kadar satış geliri elde etmiş diyebiliriz.
İndikatörün varsayılan modunda (Show Summary);
1. Hisse başına yıllıklandırılmış Satışlar (Kırmızı)
2. Hisse başına yıllıklandırılmış FAVÖK (Turuncu)
3. Hisse başına yıllıklandırılmış Esas Faaliyet Karı (Sarı)
4. Hisse başına yıllıklandırılmış Net Kar (Mavi)
5. Hisse başına yıllıklandırılmış Serbest Nakit Akışı (Yeşil)
6. Hisse kapanış fiyatı (Beyaz)
7. Hissenin adil değeri (Fiyat Adil değerin altında ise Yeşil, Üstünde ise Kırmızı)
8. Önümüzdeki 12 aylık fiyat hedefi (Sarı)
** Show AVG Growth QoQ **
Bu seçenek seçildiğinde, sırası ile satışlar, favök, esas faaliyet karı, net kar ve serbest nakit akışının, seçmiş olduğunuz periyotta (örneğin son 12 çeyrek), çeyreklik olarak ortalama % kaç büyüdüğünü görebilirsiniz. Bu veri, şirketin gelişimi ve gelişim hızı hakkında fikir vermektedir.
** Show Profit Margings **
Bu seçenek seçildiğinde, sırası ile brüt kar marjı, favök marjı, esas faaliyet kar marjı, net kar marjı ve serbest nakit akışı marjı verilerini görebilirsiniz. Şirketin karlılık marjlarını daraltarak mı gelirini arttırdığını yoksa hem gelir artışı hem de kar marjlarını arttırdığını tespit etmek için hızlı bir bakış sunar.
** Include Sales **
Bu seçenek seçildiğinde, şirketin değerlemesine satış gelirleri dahil edilir.
** Include Ebitda **
Bu seçenek seçildiğinde, şirketin değerlemesine favök dahil edilir.
** Include Ebit **
Bu seçenek seçildiğinde, şirketin değerlemesine esas faaliyet karları dahil edilir.
** Include Net Profit **
Bu seçenek seçildiğinde, şirketin değerlemesine net kar dahil edilir.
** Include FCF **
Bu seçenek seçildiğinde, şirketin değerlemesine serbest nakit akışı dahil edilir.
Varsayılan olarak, satışlar, favök ve esas faaliyet karı üzerinden değerleme yapılır. Net kar ve serbest nakit akışı isteğe göre seçilebilir. Ya da istemediğiniz metrikler değerleme hesaplamasından çıkarılabilir.
Renkler ne anlama geliyor?
** Kırmızı **
Şirketin satışları ile ilgili verilerini temsil eder.
** Turuncu **
Şirketin favök’ü ile ilgili verilerini temsil eder.
** Sarı **
Şirketin esas faaliyet karı ile ilgili verilerini temsil eder.
** Mavi **
Şirketin net karı ile ilgili verileri temsil eder.
** Yeşil **
Şirketin serbest nakit akışı ile ilgili verilerini temsil eder.
FVG w/ Fibs [QuantVue]The "FVG w/ Fibs" indicator is a trading tool designed to identify and visualize Fair Value Gaps (FVGs) while overlaying two Fibonacci retracement levels.
• Bullish FVG: Occurs when the low of the current bar is higher than the high of two bars ago, and the previous close is higher than the high of two bars ago.
• Bearish FVG: Occurs when the high of the current bar is lower than the low of two bars ago, and the previous close is lower than the low of two bars ago.
The indicator filters these gaps based on user-defined criteria such as the minimum percentage size of the gap.
Once identified, these FVGs are highlighted on the chart using customizable boxes and the 50% and 61.8% (default settings) Fibonacci retracement levels are calculated and drawn based on the size of the identified FVG.
• Dynamically updates and extends the boxes as the price evolves.
• Alerts / visual changes for FVGs that get filled.
• User option for fills by Wicks or Close
• User-customizable settings for box colors, styles, and Fibonacci level appearances
Give this indicator a BOOST and COMMENT your thoughts!
We hope you enjoy.
Cheers!
Inflation-adjusted performanceOVERVIEW
The Inflation-adjusted performance indicator plots an adjusted closing price for the asset
on the main chart by multiplying the asset price by an inflation factor which is derived from CPI-U. The indicator has a `lookback` length, which is used to lookup the CPI-U index value from `lookback` years ago.
The inflation adjusted price is then calculated as `inflationAdjustedPrice = CPIToday / CPIBackThen * currentPrice`
CONCEPTS
This can be a useful tool to assess how an asset has performed as a store of value and inflation hedge over a given period.
The following are the key concepts and user inputs for the oscillator:
Input: The user can specify the lookback period, in years, using the `lookback` attribute on the settings widget. Defult is 13.
CPI Data: The indicator uses CPI data from tradingview's BLS feed.
Inflation Factor: An inflation factor is calculated by dividing today's CPI by the CPI from the lookback period. This factor represents the increase in prices due to inflation over the lookback period.
Inflation-adjusted Price: The offer price of the asset from `lookback` years ago is adjusted for inflation using the calculated inflation factor. This adjusted price represents what the offer price would be today if it had kept up with inflation.
Earnings Yield SpreadThe Earnings Yield Spread might offer an investor some insight into areas of value.
Earnings yield is the ratio of Diluted earnings per share over the trailing twelve months (TTM) to the company’s share price. Earnings yield shows how much the company has earned per share as a percentage of its share price. It shows investors how much yield they are getting in earnings in return for owning the stock at its current share price. (Thank you, TradingView)
One might wonder how the earnings yield on their investment compares to the yield on a US 10 year treasury bond. The Earnings Yield Spread indicator will read above zero if the stock in question earnings yield is higher than US10Y and will read below zero if the stock in question earnings yield is lower than the US10Y.
Earnings yield is relative to the stock in question, so comparisons should be drawn to its own historical reading and not to other symbols.
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
Fundamental ScreenerThis indicator is designed to compare the year-over-year earnings and sales growth, as well as the P/E ratio of up to 10 stocks simultaneously .
This provides valuable insights into the fundamental performance of multiple stocks at the same time, allowing traders to quickly identify which stocks are outperforming or underperforming their peers.
The earnings and sales growth figures are calculated on a year-over-year basis , comparing the most recent quarter to the same quarter 1 year ago.
The P/E ratio is a valuation metric that measures a company's stock price relative to its trailing twelve months earnings per share.
By comparing these three key metrics across multiple stocks, traders can quickly identify which stock in a group has superior fundamentals.
Customization
Chose to compare 5 or 10 symbols
Table position, color, and size
Median Value TradedThe Median Value Traded script is an indicator that allows traders to visualize the median value traded for a particular asset. The median value traded is an important metric as it provides a clearer understanding of the trading activity for an asset, which can be used to inform trading strategies.
To use this script, simply add it to your chart and adjust the "Lookback Period" input as desired. The "Lookback Period" input determines the number of bars used in the median calculation, with a default value of 20.
The median value traded is calculated by taking the product of the volume and closing price for each bar in the lookback period, calculates the median value, and then divides by one million for easier readability.
The script also includes color inputs for the positive and negative columns, allowing traders to customize the appearance of the plot to their liking.
Overall, the Median Value Traded script is a useful tool for traders looking to gain a deeper understanding of trading activity for a particular asset.
Dividend Percentile RankWhat it does
The Indicator plots the percentile rank of the current dividend yield within the configured lookback timeframe. A percentile rank of 80 means that in 80% of the time within the lookback period the dividend yield was below the current yield. And in the remaining 20% of the time it was above the current yield. While a percentile rank of 0 means that the current dividend yield is below all historical dividend yields within the lookback timeframe.
You can configure three values:
1. Lookback Trading Days: This is the timeframe of past trading days that are used to calculate the percentile rank. The default value is 765 days (~3years) while one trading year is about 252 days.
2. Buy threshold in %: Configures the green zone of the Dividend Percentile Chart
3. Buy threshold in %: Configures the red zone of the Dividend Percentile Chart
How it works
First, the Dividend Yield is calculated. After that, the dividend Yield is used to calculate the percentage rank within the configured lookback period. The result is plotted depending on the configured threshold zones.
What you can use it for
The Dividend Percentile Rank is plotted as an oscillator that oscillates between 0 and 100.
High values, e.g. above 80, can be considered as a buy signal as the current dividend yield is high in comparison to past dividend yields.
Low values, e.g. below 20, can be considered as a sell signal as the current dividend yield is lower in comparison to past dividend yields.
(PS: Always take into consideration to evaluate the companies ability to continue paying dividends in the future. The yield alone is no guarantee for high returns)
