Earnings Line+Growth stock investors are concerned with Earnings per share that is growing, Sales (Revenue) that is growing and Increasing gross margins. This indicator helps view each of these parameters.
On the chart is Tesla (TSLA) gross margin (blue line) on a 12 trailing months basis (TTM). As you can see, TSLA's margins appear to be eroding.
The user selects one of the following parameters to display from the input drop down menu:
"EARNINGS_PER_SHARE_BASIC", "TOTAL_REVENUE", or "GROSS_MARGIN".
The value axis for your selection will appear on the left side of the chart.
The user also selects one of the following periods: "FY", "FQ" or "TTM" (Fiscal year, fiscal quarter or 12-trailing months). You have an option to display the inputs by checking the box. This is useful as a reminder but can be removed if the label is in the way.
The chart will render on any chart time scale, however longer time scales will probably be of more value. Weekly charts work well.
It is not possible to display more than one line simultaneously because of axis incompatibilities. However, it is possible to load this indicator multiple times and select different items in each. In this case additional left-side scales will be shown as well as additional lines. Common pairings are Revenue (Sales) and Earnings, or, Revenue and Gross Margin.
@ jmikes
Fundamental Analysis
Bitcoin 5A Strategy@LilibtcIn our long-term strategy, we have deeply explored the key factors influencing the price of Bitcoin. By precisely calculating the correlation between these factors and the price of Bitcoin, we found that they are closely linked to the value of Bitcoin. To more effectively predict the fair price of Bitcoin, we have built a predictive model and adjusted our investment strategy accordingly based on this model. In practice, the prediction results of this model correspond quite high with actual values, fully demonstrating its reliability in predicting price fluctuations.
When the future is uncertain and the outlook is unclear, people often choose to hold back and avoid risks, or even abandon their original plans. However, the prediction of Bitcoin is full of challenges, but we have taken the first step in exploring.
Table of contents:
Usage Guide
Step 1: Identify the factors that have the greatest impact on Bitcoin price
Step 2: Build a Bitcoin price prediction model
Step 3: Find indicators for warning of bear market bottoms and bull market tops
Step 4: Predict Bitcoin Price in 2025
Step 5: Develop a Bitcoin 5A strategy
Step 6: Verify the performance of the Bitcoin 5A strategy
Usage Restrictions
🦮Usage Guide:
1. On the main interface, modify the code, find the BTCUSD trading pair, and select the BITSTAMP exchange for trading.
2. Set the time period to the daily chart.
3. Select a logarithmic chart in the chart type to better identify price trends.
4. In the strategy settings, adjust the options according to personal needs, including language, display indicators, display strategies, display performance, display optimizations, sell alerts, buy prompts, opening days, backtesting start year, backtesting start month, and backtesting start date.
🏃Step 1: Identify the factors that have the greatest impact on Bitcoin price
📖Correlation Coefficient: A mathematical concept for measuring influence
In order to predict the price trend of Bitcoin, we need to delve into the factors that have the greatest impact on its price. These factors or variables can be expressed in mathematical or statistical correlation coefficients. The correlation coefficient is an indicator of the degree of association between two variables, ranging from -1 to 1. A value of 1 indicates a perfect positive correlation, while a value of -1 indicates a perfect negative correlation.
For example, if the price of corn rises, the price of live pigs usually rises accordingly, because corn is the main feed source for pig breeding. In this case, the correlation coefficient between corn and live pig prices is approximately 0.3. This means that corn is a factor affecting the price of live pigs. On the other hand, if a shooter's performance improves while another shooter's performance deteriorates due to increased psychological pressure, we can say that the former is a factor affecting the latter's performance.
Therefore, in order to identify the factors that have the greatest impact on the price of Bitcoin, we need to find the factors with the highest correlation coefficients with the price of Bitcoin. If, through the analysis of the correlation between the price of Bitcoin and the data on the chain, we find that a certain data factor on the chain has the highest correlation coefficient with the price of Bitcoin, then this data factor on the chain can be identified as the factor that has the greatest impact on the price of Bitcoin. Through calculation, we found that the 🔵number of Bitcoin blocks is one of the factors that has the greatest impact on the price of Bitcoin. From historical data, it can be clearly seen that the growth rate of the 🔵number of Bitcoin blocks is basically consistent with the movement direction of the price of Bitcoin. By analyzing the past ten years of data, we obtained a daily correlation coefficient of 0.93 between the number of Bitcoin blocks and the price of Bitcoin.
🏃Step 2: Build a Bitcoin price prediction model
📖Predictive Model: What formula is used to predict the price of Bitcoin?
Among various prediction models, the linear function is the preferred model due to its high accuracy. Take the standard weight as an example, its linear function graph is a straight line, which is why we choose the linear function model. However, the growth rate of the price of Bitcoin and the number of blocks is extremely fast, which does not conform to the characteristics of the linear function. Therefore, in order to make them more in line with the characteristics of the linear function, we first take the logarithm of both. By observing the logarithmic graph of the price of Bitcoin and the number of blocks, we can find that after the logarithm transformation, the two are more in line with the characteristics of the linear function. Based on this feature, we choose the linear regression model to establish the prediction model.
From the graph below, we can see that the actual red and green K-line fluctuates around the predicted blue and 🟢green line. These predicted values are based on fundamental factors of Bitcoin, which support its value and reflect its reasonable value. This picture is consistent with the theory proposed by Marx in "Das Kapital" that "prices fluctuate around values."
The predicted logarithm of the market cap of Bitcoin is calculated through the model. The specific calculation formula of the Bitcoin price prediction value is as follows:
btc_predicted_marketcap = math.exp(btc_predicted_marketcap_log)
btc_predicted_price = btc_predicted_marketcap / btc_supply
🏃Step 3: Find indicators for early warning of bear market bottoms and bull market tops
📖Warning Indicator: How to Determine Whether the Bitcoin Price has Reached the Bear Market Bottom or the Bull Market Top?
By observing the Bitcoin price logarithmic prediction chart mentioned above, we notice that the actual price often falls below the predicted value at the bottom of a bear market; during the peak of a bull market, the actual price exceeds the predicted price. This pattern indicates that the deviation between the actual price and the predicted price can serve as an early warning signal. When the 🔴 Bitcoin price deviation is very low, as shown by the chart with 🟩green background, it usually means that we are at the bottom of the bear market; Conversely, when the 🔴 Bitcoin price deviation is very high, the chart with a 🟥red background indicates that we are at the peak of the bull market.
This pattern has been validated through six bull and bear markets, and the deviation value indeed serves as an early warning signal, which can be used as an important reference for us to judge market trends.
🏃Step 4:Predict Bitcoin Price in 2025
📖Price Upper Limit
According to the data calculated on February 25, 2024, the 🟠upper limit of the Bitcoin price is $194,287, which is the price ceiling of this bull market. The peak of the last bull market was on November 9, 2021, at $68,664. The bull-bear market cycle is 4 years, so the highest point of this bull market is expected in 2025. That is where you should sell the Bitcoin. and the upper limit of the Bitcoin price will exceed $190,000. The closing price of Bitcoin on February 25, 2024, was $51,729, with an expected increase of 2.7 times.
🏃Step 5: Bitcoin 5A Strategy Formulation
📖Strategy: When to buy or sell, and how many to choose?
We introduce the Bitcoin 5A strategy. This strategy requires us to generate trading signals based on the critical values of the warning indicators, simulate the trades, and collect performance data for evaluation. In the Bitcoin 5A strategy, there are three key parameters: buying warning indicator, batch trading days, and selling warning indicator. Batch trading days are set to ensure that we can make purchases in batches after the trading signal is sent, thus buying at a lower price, selling at a higher price, and reducing the trading impact cost.
In order to find the optimal warning indicator critical value and batch trading days, we need to adjust these parameters repeatedly and perform backtesting. Backtesting is a method established by observing historical data, which can help us better understand market trends and trading opportunities.
Specifically, we can find the key trading points by watching the Bitcoin price log and the Bitcoin price deviation chart. For example, on August 25, 2015, the 🔴 Bitcoin price deviation was at its lowest value of -1.11; on December 17, 2017, the 🔴 Bitcoin price deviation was at its highest value at the time, 1.69; on March 16, 2020, the 🔴 Bitcoin price deviation was at its lowest value at the time, -0.91; on March 13, 2021, the 🔴 Bitcoin price deviation was at its highest value at the time, 1.1; on December 31, 2022, the 🔴 Bitcoin price deviation was at its lowest value at the time, -1.
To ensure that all five key trading points generate trading signals, we set the warning indicator Bitcoin price deviation to the larger of the three lowest values, -0.9, and the smallest of the two highest values, 1. Then, we buy when the warning indicator Bitcoin price deviation is below -0.9, and sell when it is above 1.
In addition, we set the batch trading days as 25 days to implement a strategy that averages purchases and sales. Within these 25 days, we will invest all funds into the market evenly, buying once a day. At the same time, we also sell positions at the same pace, selling once a day.
📖Adjusting the threshold: a key step to optimizing trading strategy
Adjusting the threshold is an indispensable step for better performance. Here are some suggestions for adjusting the batch trading days and critical values of warning indicators:
• Batch trading days: Try different days like 25 to see how it affects overall performance.
• Buy and sell critical values for warning indicators: iteratively fine-tune the buy threshold value of -0.9 and the sell threshold value of 1 exhaustively to find the best combination of threshold values.
Through such careful adjustments, we may find an optimized approach with a lower maximum drawdown rate (e.g., 11%) and a higher cumulative return rate for closed trades (e.g., 474 times). The chart below is a backtest optimization chart for the Bitcoin 5A strategy, providing an intuitive display of strategy adjustments and optimizations.
In this way, we can better grasp market trends and trading opportunities, thereby achieving a more robust and efficient trading strategy.
🏃Step 6: Validating the performance of the Bitcoin 5A Strategy
📖Model interpretability validation: How to explain the Bitcoin price model?
The interpretability of the model is represented by the coefficient of determination R squared, which reflects the degree of match between the predicted value and the actual value. I divided all the historical data from August 18, 2015 into two groups, and used the data from August 18, 2011 to August 18, 2015 as training data to generate the model. The calculation result shows that the coefficient of determination R squared during the 2011-2015 training period is as high as 0.81, which shows that the interpretability of this model is quite high. From the Bitcoin price logarithmic prediction chart in the figure below, we can see that the deviation between the predicted value and the actual value is not far, which means that most of the predicted values can explain the actual value well.
The calculation formula for the coefficient of determination R squared is as follows:
residual = btc_close_log - btc_predicted_price_log
residual_square = residual * residual
train_residual_square_sum = math.sum(residual_square, train_days)
train_mse = train_residual_square_sum / train_days
train_r2 = 1 - train_mse / ta.variance(btc_close_log, train_days)
📖Model stability verification: How to affirm the stability of the Bitcoin price model when new data is available?
Model stability is achieved through model verification. I set the last day of the training period to February 2, 2024 as the "verification group" and used it as verification data to verify the stability of the model. This means that after generating the model if there is new data, I will use these new data together with the model for prediction, and then evaluate the interpretability of the model. If the coefficient of determination when using verification data is close to the previous training one and both remain at a high level, then we can consider this model as stability. The coefficient of determination calculated from the validation period data and model prediction results is as high as 0.83, which is close to the previous 0.81, further proving the stability of this model.
📖Performance evaluation: How to accurately evaluate historical backtesting results?
After detailed strategy testing, to ensure the accuracy and reliability of the results, we need to carry out a detailed performance evaluation on the backtest results. The key evaluation indices include:
• Net value curve: As shown in the rose line, it intuitively reflects the growth of the account net value. By observing the net value curve, we can understand the overall performance and profitability of the strategy.
The basic attributes of this strategy are as follows:
Trading range: 2015-8-19 to 2024-2-18, backtest range: 2011-8-18 to 2024-2-18
Initial capital: 1000USD, order size: 1 contract, pyramid: 50 orders, commission rate: 0.2%, slippage: 20 markers.
In the strategy tester overview chart, we also obtained the following key data:
• Net profit rate of closed trades: as high as 474 times, far exceeding the benchmark, as shown in the strategy tester performance summary chart, Bitcoin buys and holds 210 times.
• Number of closed trades and winning percentage: 100 trades were all profitable, showing the stability and reliability of the strategy.
• Drawdown rate & win-loose ratio: The maximum drawdown rate is only 11%, far lower than Bitcoin's 78%. Profit factor, or win-loose ratio, reached 500, further proving the advantage of the strategy.
Through these detailed evaluations, we can see clearly the excellent balance between risk and return of the Bitcoin 5A strategy.
⚠️Usage Restrictions: Strategy Application in Specific Situations
Please note that this strategy is designed specifically for Bitcoin and should not be applied to other assets or markets without authorization. In actual operations, we should make careful decisions according to our risk tolerance and investment goals.
Blockunity US Market Liquidity (BML)Get a clear view of US market liquidity and monitor its status at a glance to anticipate movements on risky assets.
The Idea
The BML aggregates and analyzes total USD market liquidity in trillions of dollars. It is used to monitor the liquidity of the USD market. When liquidity is good, all is well. If liquidity is low, the US will maneuver and sell treasury bills (debt) to replenish its treasury, which can lead to bearish pressure on markets, particularly those considered risky, such as Bitcoin.
How to Use
The indicator is very easy to use, there's nothing special about it. This tool is mainly intended to be used as fundamental information, and not for active trading.
Elements
The US Market Liquidity has several distinct components:
FED Balance Sheet
The Fed credits member banks’ Fed accounts with money, and in return, banks sell the Fed US Treasuries and/or US Mortgage-Backed Securities. This is how the Fed “prints” money to juice the financial system.
US Treasury General Account
The US Treasury General Account (TGA) balances with the NY Fed. When it decreases, it means the US Treasury is injecting money into the economy directly and creating activity. When it increases, it means the US Treasury is saving money and not stimulating economic activity. The TGA also increases when the Treasury sells bonds. This action removes liquidity from the market as buyers must pay for their bonds with dollars.
Overnight Reverse Repurchase Agreements
A reverse repurchase agreement (known as Reverse Repo or RRP) is a transaction in which the New York Fed under the authorization and direction of the Federal Open Market Committee sells a security to an eligible counterparty with an agreement to repurchase that same security at a specified price at a specific time in the future.
Earnings Remittances Due to the Treasury
The Federal Reserve Banks remit residual net earnings to the US Treasury after providing for the costs of operations, payment of dividends, and the amount necessary to maintain each Federal Reserve Bank’s allotted surplus cap. Positive amounts represent the estimated weekly remittances due to the US Treasury. Negative amounts represent the cumulative deferred asset position, which is incurred during a period when earnings are not sufficient to provide for the cost of operations, payment of dividends, and maintaining surplus.
Settings
Several parameters can be defined in the indicator configuration. You can:
Choose the smoothing and timeframe to be used in the plot.
Set the EMA lookback period and display it or not. This affects the color of the main plot.
Set the period to be taken into account when calculating the variation rate in the table.
Select the data to be taken into account in the calculation.
Activate or not the barcolor.
Lastly, you can modify all table parameters.
Bitcoin 5A Strategy - Price Upper & Lower Limit@LilibtcIn our long-term strategy, we have deeply explored the key factors influencing the price of Bitcoin. By precisely calculating the correlation between these factors and the price of Bitcoin, we found that they are closely linked to the value of Bitcoin. To more effectively predict the fair price of Bitcoin, we have built a predictive model and adjusted our investment strategy accordingly based on this model. In practice, the prediction results of this model correspond quite high with actual values, fully demonstrating its reliability in predicting price fluctuations.
When the future is uncertain and the outlook is unclear, people often choose to hold back and avoid risks, or even abandon their original plans. However, the prediction of Bitcoin is full of challenges, but we have taken the first step in exploring.
Table of contents:
Usage Guide
Step 1: Identify the factors that have the greatest impact on Bitcoin price
Step 2: Build a Bitcoin price prediction model
Step 3: Find indicators for warning of bear market bottoms and bull market tops
Step 4: Predict Bitcoin Price in 2025
Step 5: Develop a Bitcoin 5A strategy
Step 6: Verify the performance of the Bitcoin 5A strategy
Usage Restrictions
🦮Usage Guide:
1. On the main interface, modify the code, find the BTCUSD trading pair, and select the BITSTAMP exchange for trading.
2. Set the time period to the daily chart.
3. Select a logarithmic chart in the chart type to better identify price trends.
4. In the strategy settings, adjust the options according to personal needs, including language, display indicators, display strategies, display performance, display optimizations, sell alerts, buy prompts, opening days, backtesting start year, backtesting start month, and backtesting start date.
🏃Step 1: Identify the factors that have the greatest impact on Bitcoin price
📖Correlation Coefficient: A mathematical concept for measuring influence
In order to predict the price trend of Bitcoin, we need to delve into the factors that have the greatest impact on its price. These factors or variables can be expressed in mathematical or statistical correlation coefficients. The correlation coefficient is an indicator of the degree of association between two variables, ranging from -1 to 1. A value of 1 indicates a perfect positive correlation, while a value of -1 indicates a perfect negative correlation.
For example, if the price of corn rises, the price of live pigs usually rises accordingly, because corn is the main feed source for pig breeding. In this case, the correlation coefficient between corn and live pig prices is approximately 0.3. This means that corn is a factor affecting the price of live pigs. On the other hand, if a shooter's performance improves while another shooter's performance deteriorates due to increased psychological pressure, we can say that the former is a factor affecting the latter's performance.
Therefore, in order to identify the factors that have the greatest impact on the price of Bitcoin, we need to find the factors with the highest correlation coefficients with the price of Bitcoin. If, through the analysis of the correlation between the price of Bitcoin and the data on the chain, we find that a certain data factor on the chain has the highest correlation coefficient with the price of Bitcoin, then this data factor on the chain can be identified as the factor that has the greatest impact on the price of Bitcoin. Through calculation, we found that the 🔵 number of Bitcoin blocks is one of the factors that has the greatest impact on the price of Bitcoin. From historical data, it can be clearly seen that the growth rate of the 🔵 number of Bitcoin blocks is basically consistent with the movement direction of the price of Bitcoin. By analyzing the past ten years of data, we obtained a daily correlation coefficient of 0.93 between the number of Bitcoin blocks and the price of Bitcoin.
🏃Step 2: Build a Bitcoin price prediction model
📖Predictive Model: What formula is used to predict the price of Bitcoin?
Among various prediction models, the linear function is the preferred model due to its high accuracy. Take the standard weight as an example, its linear function graph is a straight line, which is why we choose the linear function model. However, the growth rate of the price of Bitcoin and the number of blocks is extremely fast, which does not conform to the characteristics of the linear function. Therefore, in order to make them more in line with the characteristics of the linear function, we first take the logarithm of both. By observing the logarithmic graph of the price of Bitcoin and the number of blocks, we can find that after the logarithm transformation, the two are more in line with the characteristics of the linear function. Based on this feature, we choose the linear regression model to establish the prediction model.
From the graph below, we can see that the actual red and green K-line fluctuates around the predicted blue and 🟢green line. These predicted values are based on fundamental factors of Bitcoin, which support its value and reflect its reasonable value. This picture is consistent with the theory proposed by Marx in "Das Kapital" that "prices fluctuate around values."
The predicted logarithm of the market cap of Bitcoin is calculated through the model. The specific calculation formula of the Bitcoin price prediction value is as follows:
btc_predicted_marketcap = math.exp(btc_predicted_marketcap_log)
btc_predicted_price = btc_predicted_marketcap / btc_supply
🏃Step 3: Find indicators for early warning of bear market bottoms and bull market tops
📖Warning Indicator: How to Determine Whether the Bitcoin Price has Reached the Bear Market Bottom or the Bull Market Top?
By observing the Bitcoin price logarithmic prediction chart mentioned above, we notice that the actual price often falls below the predicted value at the bottom of a bear market; during the peak of a bull market, the actual price exceeds the predicted price. This pattern indicates that the deviation between the actual price and the predicted price can serve as an early warning signal. When the 🔴 Bitcoin price deviation is very low, as shown by the chart with 🟩green background, it usually means that we are at the bottom of the bear market; Conversely, when the 🔴 Bitcoin price deviation is very high, the chart with a 🟥red background indicates that we are at the peak of the bull market.
This pattern has been validated through six bull and bear markets, and the deviation value indeed serves as an early warning signal, which can be used as an important reference for us to judge market trends.
🏃Step 4:Predict Bitcoin Price in 2025
📖Price Upper Limit
According to the data calculated on March 10, 2023(If you want to check latest data, please contact with author), the 🟠upper limit of the Bitcoin price is $132,453, which is the price ceiling of this bull market. The peak of the last bull market was on November 9, 2021, at $68,664. The bull-bear market cycle is 4 years, so the highest point of this bull market is expected in 2025, and the 🟠upper limit of the Bitcoin price will exceed $130,000. The closing price of Bitcoin on March 10, 2024, was $68,515, with an expected increase of 90%.
🏃Step 5: Bitcoin 5A Strategy Formulation
📖Strategy: When to buy or sell, and how many to choose?
We introduce the Bitcoin 5A strategy. This strategy requires us to generate trading signals based on the critical values of the warning indicators, simulate the trades, and collect performance data for evaluation. In the Bitcoin 5A strategy, there are three key parameters: buying warning indicator, batch trading days, and selling warning indicator. Batch trading days are set to ensure that we can make purchases in batches after the trading signal is sent, thus buying at a lower price, selling at a higher price, and reducing the trading impact cost.
In order to find the optimal warning indicator critical value and batch trading days, we need to adjust these parameters repeatedly and perform backtesting. Backtesting is a method established by observing historical data, which can help us better understand market trends and trading opportunities.
Specifically, we can find the key trading points by watching the Bitcoin price log and the Bitcoin price deviation chart. For example, on August 25, 2015, the 🔴 Bitcoin price deviation was at its lowest value of -1.11; on December 17, 2017, the 🔴 Bitcoin price deviation was at its highest value at the time, 1.69; on March 16, 2020, the 🔴 Bitcoin price deviation was at its lowest value at the time, -0.91; on March 13, 2021, the 🔴 Bitcoin price deviation was at its highest value at the time, 1.1; on December 31, 2022, the 🔴 Bitcoin price deviation was at its lowest value at the time, -1.
To ensure that all five key trading points generate trading signals, we set the warning indicator Bitcoin price deviation to the larger of the three lowest values, -0.9, and the smallest of the two highest values, 1. Then, we buy when the warning indicator Bitcoin price deviation is below -0.9, and sell when it is above 1.
In addition, we set the batch trading days as 25 days to implement a strategy that averages purchases and sales. Within these 25 days, we will invest all funds into the market evenly, buying once a day. At the same time, we also sell positions at the same pace, selling once a day.
📖Adjusting the threshold: a key step to optimizing trading strategy
Adjusting the threshold is an indispensable step for better performance. Here are some suggestions for adjusting the batch trading days and critical values of warning indicators:
• Batch trading days: Try different days like 25 to see how it affects overall performance.
• Buy and sell critical values for warning indicators: iteratively fine-tune the buy threshold value of -0.9 and the sell threshold value of 1 exhaustively to find the best combination of threshold values.
Through such careful adjustments, we may find an optimized approach with a lower maximum drawdown rate (e.g., 11%) and a higher cumulative return rate for closed trades (e.g., 474 times). The chart below is a backtest optimization chart for the Bitcoin 5A strategy, providing an intuitive display of strategy adjustments and optimizations.
In this way, we can better grasp market trends and trading opportunities, thereby achieving a more robust and efficient trading strategy.
🏃Step 6: Validating the performance of the Bitcoin 5A Strategy
📖Model accuracy validation: How to judge the accuracy of the Bitcoin price model?
The accuracy of the model is represented by the coefficient of determination R square, which reflects the degree of match between the predicted value and the actual value. I divided all the historical data from August 18, 2015 into two groups, and used the data from August 18, 2011 to August 18, 2015 as training data to generate the model. The calculation result shows that the coefficient of determination R squared during the 2011-2015 training period is as high as 0.81, which shows that the accuracy of this model is quite high. From the Bitcoin price logarithmic prediction chart in the figure below, we can see that the deviation between the predicted value and the actual value is not far, which means that most of the predicted values can explain the actual value well.
The calculation formula for the coefficient of determination R square is as follows:
residual = btc_close_log - btc_predicted_price_log
residual_square = residual * residual
train_residual_square_sum = math.sum(residual_square, train_days)
train_mse = train_residual_square_sum / train_days
train_r2 = 1 - train_mse / ta.variance(btc_close_log, train_days)
📖Model reliability verification: How to affirm the reliability of the Bitcoin price model when new data is available?
Model reliability is achieved through model verification. I set the last day of the training period to February 2, 2024 as the "verification group" and used it as verification data to verify the reliability of the model. This means that after generating the model if there is new data, I will use these new data together with the model for prediction, and then evaluate the accuracy of the model. If the coefficient of determination when using verification data is close to the previous training one and both remain at a high level, then we can consider this model as reliable. The coefficient of determination calculated from the validation period data and model prediction results is as high as 0.83, which is close to the previous 0.81, further proving the reliability of this model.
📖Performance evaluation: How to accurately evaluate historical backtesting results?
After detailed strategy testing, to ensure the accuracy and reliability of the results, we need to carry out a detailed performance evaluation on the backtest results. The key evaluation indices include:
• Net value curve: As shown in the rose line, it intuitively reflects the growth of the account net value. By observing the net value curve, we can understand the overall performance and profitability of the strategy.
The basic attributes of this strategy are as follows:
Trading range: 2015-8-19 to 2024-2-18, backtest range: 2011-8-18 to 2024-2-18
Initial capital: 1000USD, order size: 1 contract, pyramid: 50 orders, commission rate: 0.2%, slippage: 20 markers.
In the strategy tester overview chart, we also obtained the following key data:
• Net profit rate of closed trades: as high as 474 times, far exceeding the benchmark, as shown in the strategy tester performance summary chart, Bitcoin buys and holds 210 times.
• Number of closed trades and winning percentage: 100 trades were all profitable, showing the stability and reliability of the strategy.
• Drawdown rate & win-loose ratio: The maximum drawdown rate is only 11%, far lower than Bitcoin's 78%. Profit factor, or win-loose ratio, reached 500, further proving the advantage of the strategy.
Through these detailed evaluations, we can see clearly the excellent balance between risk and return of the Bitcoin 5A strategy.
⚠️Usage Restrictions: Strategy Application in Specific Situations
Please note that this strategy is designed specifically for Bitcoin and should not be applied to other assets or markets without authorization. In actual operations, we should make careful decisions according to our risk tolerance and investment goals.
Optimal Buy Day (Zeiierman)█ Overview
The Optimal Buy Day (Zeiierman) indicator identifies optimal buying days based on historical price data, starting from a user-defined year. It simulates investing a fixed initial capital and making regular monthly contributions. The unique aspect of this indicator involves comparing systematic investment on specific days of the month against a randomized buying day each month, aiming to analyze which method might yield more shares or a better average price over time. By visualizing the potential outcomes of systematic versus randomized buying, traders can better understand the impact of market timing and how regular investments might accumulate over time.
These statistics are pivotal for traders and investors using the script to analyze historical performance and strategize future investments. By understanding which days offered more shares for their money or lower average prices, investors can tailor their buying strategies to potentially enhance returns.
█ Key Statistics
⚪ Shares
Definition: Represents the total number of shares acquired on a particular day of the month across the entire simulation period.
How It Works: The script calculates how many shares can be bought each day, given the available capital or monthly contribution. This calculation takes into account the day's opening price and accumulates the total shares bought on that day over the simulation period.
Interpretation: A higher number of shares indicates that the day consistently offered better buying opportunities, allowing the investor to acquire more shares for the same amount of money. This metric is crucial for understanding which days historically provided more value.
⚪ AVG Price
Definition: The average price paid per share on a particular day of the month, averaged over the simulation period.
How It Works: Each time shares are bought, the script calculates the average price per share, factoring in the new shares purchased at the current price. This average evolves over time as more shares are bought at varying prices.
Interpretation: The average price gives insight into the cost efficiency of buying shares on specific days. A lower average price suggests that buying on that day has historically led to better pricing, making it a potentially more attractive investment strategy.
⚪ Buys
Definition: The total number of transactions or buys executed on a particular day of the month throughout the simulation.
How It Works: This metric increments each time shares are bought on a specific day, providing a count of all buying actions taken.
Interpretation: The number of buys indicates the frequency of investment opportunities. A higher count could mean more consistent opportunities for investment, but it's important to consider this in conjunction with the average price and the total shares acquired to assess overall strategy effectiveness.
⚪ Most Shares
Definition: Identifies the day of the month on which the highest number of shares were bought, highlighting the specific day and the total shares acquired.
How It Works: After simulating purchases across all days of the month, the script identifies which day resulted in the highest total number of shares bought.
Interpretation: This metric points out the most opportune day for volume buying. It suggests that historically, this day provided conditions that allowed for maximizing the quantity of shares purchased, potentially due to lower prices or other factors.
⚪ Best Price
Definition: Highlights the day of the month that offered the lowest average price per share, indicating both the day and the price.
How It Works: The script calculates the average price per share for each day and identifies the day with the lowest average.
Interpretation: This metric is key for investors looking to minimize costs. The best price day suggests that historically, buying on this day led to acquiring shares at a more favorable average price, potentially maximizing long-term investment returns.
⚪ Randomized Shares
Definition: This metric represents the total number of shares acquired on a randomly selected day of the month, simulated across the entire period.
How It Works: At the beginning of each month within the simulation, the script selects a random day when the market is open and calculates how many shares can be purchased with the available capital or monthly contribution at that day's opening price. This process is repeated each month, and the total number of shares acquired through these random purchases is tallied.
Interpretation: Randomized shares offer a comparison point to systematic buying strategies. By comparing the total shares acquired through random selection against those bought on the best or worst days, investors can gauge the impact of timing and market fluctuations on their investment strategy. A higher total in randomized shares might indicate that over the long term, the specific days chosen for investment might matter less than consistent market participation. Conversely, if systematic strategies yield significantly more shares, it suggests that timing could indeed play a crucial role in maximizing investment returns.
⚪ Randomized Price
Definition: The average price paid per share for the shares acquired on the randomly selected days throughout the simulation period.
How It Works: Each time shares are bought on a randomly chosen day, the script calculates the average price paid for all shares bought through this randomized strategy. This average price is updated as the simulation progresses, reflecting the cost efficiency of random buying decisions.
Interpretation: The randomized price metric helps investors understand the cost implications of a non-systematic, random investment approach. Comparing this average price to those achieved through more deliberate, systematic strategies can reveal whether consistent investment timing strategies outperform random investment actions in terms of cost efficiency. A lower randomized price suggests that random buying might not necessarily result in higher costs, while a higher average price indicates that systematic strategies might provide better control over investment costs.
█ How to Use
Traders can use this tool to analyze historical data and simulate different investment strategies. By inputting their initial capital, regular contribution amount, and start year, they can visually assess which days might have been more advantageous for buying, based on historical price actions. This can inform future investment decisions, especially for those employing dollar-cost averaging strategies or looking to optimize entry points.
█ Settings
StartYear: This setting allows the user to specify the starting year for the investment simulation. Changing this value will either extend or shorten the period over which the simulation is run. If a user increases the value, the simulation begins later and covers a shorter historical period; decreasing the value starts the simulation earlier, encompassing a longer time frame.
Capital: Determines the initial amount of capital with which the simulation begins. Increasing this value simulates starting with more capital, which can affect the number of shares that can be initially bought. Decreasing this value simulates starting with less capital.
Contribution: Sets the monthly financial contribution added to the investment within the simulation. A higher contribution increases the investment each month and could lead to more shares being purchased over time. Lowering the contribution decreases the monthly investment amount.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Fundamental Analysis [TrendX_]__________xXx__________ INTRODUCTION __________xXx__________
Fundamental Analysis indicator employs a two-pronged approach to estimate the fair value of a security. This utilizes both relative valuation and intrinsic valuation methods, aiming to achieve a comprehensive understanding of the company's worth.
__________xXx__________ FEATURES AND USAGES __________xXx__________
1 - RELATIVE VALUATION:
Relative valuation takes a company's average financial ratios over a specific number of periods into account.
Price-to-Earnings Ratio (PE Ratio): This metric compares the company's current stock price to its earnings per share. A higher PE ratio indicates investors are willing to pay more for each dollar of earnings, potentially suggesting a growth expectation.
Price-to-Book Ratio (PB Ratio): This metric compares the company's current stock price to its book value per share. A higher PB ratio suggests the market values the company's assets more highly than their accounting book value.
Modified-PE-PB-Growth: This is the modified version for the PE and PB forward. Apply the company's average historical ROE growth rate to PE ratio. Similarly, apply the company's projected ROA growth rate to the industry average PB ratio to arrive at an adjusted PB ratio.
Enterprise Value (EV)/Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) Multiple: This metric compares the company's enterprise value (market capitalization + debt - cash) to its EBITDA. It provides a valuation measure that considers the company's capital structure.
2 - INTRINSIC VALUATION:
Intrinsic valuation attempts to estimate the inherent value of a company based on its future cash flow generation potential. This approach focuses on the company's long-term fundamentals rather than its current market price.
Discounted Cash Flow (DCF): This method discounts the company's projected future free cash flows to their present value. It requires forecasting future cash flows, a discount rate, and a terminal growth rate. The present value of these future cash flows represents the company's intrinsic value.
Dividend Discount Model (DDM): This method assumes the company's value is based on its ability to distribute future dividends to shareholders. It discounts the company's expected future dividends to their present value, providing another estimate of intrinsic value.
Graham Number: Developed by Benjamin Graham, this method utilizes a formula based on a company's earnings per share and book value per share to estimate its intrinsic value. The number 22.5, embedded within this formula, serves as a normalization factor, embodying an ‘ideal’ PE of 15 and PB of 1.5. This approach provides a conservative estimate of a company’s intrinsic value, offering a safety margin for investors.
Net-Nets: Net-Nets refer to micro-to-small companies trading at a price less than 67% of their net current asset value, which is calculated by subtracting current liabilities from current assets. This conservative approach, deeply rooted in the principles of value investing, essentially implies that these companies are undervalued to the extent that their market price is less than their liquidation value.
*** The color of each valuation toolkit’s background is determined UNDERVALUE (above current price) in Turquoise Green color and OVERVALUE (below acceptable rate) in Pink color.
3 - FINANCIAL METRICS
The financial metrics will provide a holistic view of company's financial health, efficiency, risk profile, and growth prospects
Efficiency Metrics:
Net Margin: This metric measures the percentage of each dollar of revenue remaining as profit after accounting for all operating expenses. A higher net margin indicates a company's efficiency in converting sales into profit.
Dividend Yield: This metric represents the annual dividend payment per share divided by the current stock price. It reflects the portion of a company's earnings distributed to shareholders as dividends. A higher dividend yield suggests a focus on shareholder returns.
Fraud Detection Metrics:
Beneish M-score (M-score): This metric is a statistical model used to identify potential accounting manipulations. A higher M-score indicates a greater likelihood of fraudulent activity. It's crucial to analyze the M-score along with other financial information.
Profitability and Growth Metrics:
Piotroski F-score (F-score): This metric assesses a company's financial health and profitability based on nine criteria. A higher F-score suggests a more robust and potentially higher-growth company.
Quick Ratio: This metric measures a company's ability to meet its short-term obligations (due within a year) using its most liquid assets (cash and equivalents, marketable securities, and accounts receivable). A higher quick ratio indicates a stronger short-term liquidity position.
Inventory Ratio: This metric measures how long it takes a company to sell its inventory on average. A lower inventory ratio suggests efficient inventory management and potentially lower holding costs.
Risk Metrics:
Risk-Free Rate (Risk-Free): This metric represents the theoretical rate of return on a risk-free investment, often approximated by the 10-year Treasury Constant Maturity Rate. It serves as a benchmark for evaluating the return required for riskier assets like stocks.
Beta: This metric measures a stock's volatility relative to the overall market (often represented by its market index). A beta of 1 indicates the stock's price movement mirrors the market. A beta greater than 1 suggests the stock is more volatile than the market, and vice versa.
Growth Metrics:
Capital Asset Pricing Model (CAPM): This model estimates the expected return on a stock based on its beta, the risk-free rate, and the market risk premium. CAPM helps determine if a stock is potentially overvalued or undervalued.
Weighted Average Cost of Capital (WACC): This metric represents the average cost of capital a company uses to finance its operations (equity and debt). A lower WACC suggests a company can access capital at a cheaper rate, potentially leading to higher profitability.
Compound Annual Growth Rate (CAGR): This metric calculates the average annual growth rate of a stock price over a specific period. It provides an indication of the historical price appreciation.
Additional:
Sustainable Growth Rate (Growth const.): This metric estimates the maximum long-term growth rate a company can sustain based on its internal resources (retained earnings) and industry growth.
Value at Risk (VaR): This metric estimates the maximum potential loss a stock price might experience over a given timeframe with a certain confidence level. It helps assess the downside risk associated with an investment.
*** The color of each metric’s background is determined above acceptable rate in Turquoise Green color and below acceptable rate in Pink color
__________xXx__________ CONCLUSION__________xXx__________
Fundamental analysis plays a critical role in empowering both investors and traders to navigate the dynamic stock markets. By delving deeper into a company's underlying financial health, future prospects, and competitive landscape, this approach fosters informed decision-making that leads to risk reduction and profit optimization. The Fundamental Analysis can serve as a cornerstone for investors and traders alike, offering a myriad of benefits.
For investors, it is instrumental in risk reduction, as it enables the assessment of a company’s fair value through financial statements, competitive advantages, and growth potential. This critical evaluation aids in avoiding overvalued stocks and spotting undervalued opportunities. Moreover, it fosters a long-term focus, steering investors towards decisions that reflect a company’s long-term prospects, thus supporting a buy-and-hold strategy that resonates with enduring investment objectives. Additionally, a profound comprehension of a company’s fundamentals bolsters investor confidence, ensuring that investment choices are grounded in solid data rather than speculative market noise.
Traders, on the other hand, can leverage fundamental analysis to pinpoint short-term opportunities by staying abreast of a company’s imminent catalysts such as financial health, efficiency, risk profile, or growth prospects. This knowledge allows them to anticipate market movements and seize fleeting chances for profit. It also provides informed insights for establishing entry and exit points, identifying companies poised for robust growth or those facing potential downturns, which is crucial for strategizing trades, including short selling. Importantly, by concentrating on fundamental data, traders can mitigate emotional decision-making, fostering a disciplined approach to trading that curtails the risks associated with impulsive, emotion-driven errors.
__________xXx__________ DISCLAIMER__________xXx__________
Past performance is not necessarily indicative of future results. Numerous factors and inherent uncertainties can influence the outcome of any endeavor, and predicting future events with certainty is impossible.
Trading and Investing inherently carries risk, and the majority of traders experience losses. This indicator is provided solely for informational and educational purposes and does not constitute financial advice.
Therefore, always exercise caution and independent judgment when making investment decisions based on any form of past performance analysis, including this indicator's results.
BTC Valuation
The BTC Valuation indicator
is a powerful tool designed to assist traders and analysts in evaluating the current state of Bitcoin's market valuation. By leveraging key moving averages and a logarithmic trendline, this indicator offers valuable insights into potential buying or selling opportunities based on historical price value.
Key Features:
200MA/P (200-day Moving Average to Price Ratio):
Provides a perspective on Bitcoin's long-term trend by comparing the current price to its 200-day Simple Moving Average (SMA).
A positive value suggests potential undervaluation, while a negative value may indicate overvaluation.
50MA/P (50-day Moving Average to Price Ratio):
Focuses on short-term trends, offering insights into the relationship between Bitcoin's current price and its 50-day SMA.
Helps traders identify potential bullish or bearish trends in the near term.
LTL/P (Logarithmic TrendLine to Price Ratio):
Incorporates a logarithmic trendline, considering Bitcoin's historical age in days.
Assists in evaluating whether the current price aligns with the long-term logarithmic trend, signaling potential overvaluation or undervaluation.
How to Use:
Z Score Indicator Integration:
The BTC Valuation indicator leverages the Z Score Indicator to score the ratios in a statistical way.
Statistical scoring provides a standardized measure of how far each ratio deviates from the mean, aiding in a more nuanced and objective evaluation.
Z Score Indicator
This BTC Valuation indicator provides a comprehensive view of Bitcoin's valuation dynamics, allowing traders to make informed decisions.
While indicators like BTC Valuation provide valuable insights, it's crucial to remember that no indicator guarantees market predictions.
Traders should use indicators as part of a comprehensive strategy and consider multiple factors before making trading decisions.
Historical performance is not indicative of future results. Exercise caution and continually refine your approach based on market dynamics.
LevelUp^ Earnings Line - Quarterly EPSThe LevelUp Earnings Line plots quarterly earning per share (EPS) data providing a visual representation of the earnings trend over time.
Earnings are a foundational concept that can have a significant impact on a stock's longer term performance. With the option to view earnings as a plot versus a table of statistics, you can quickly identify earnings acceleration or deceleration. A steep line upwards from one earnings release to another, or a series of progressively higher EPS values, indicates a strong earnings trajectory. The more pronounced the acceleration, the more likely the company is to outperform the market.
At each quarterly earnings release you can view the details for Reported (non-GAAP), Diluted and Basic EPS by hovering over the plotted symbols on the earnings line.
This indicator uses TradingView's financial functions to request the following EPS data:
▪ Reported (non-GAAP) : this is one of the most popular ways to view earnings information. With non-GAAP, companies often exclude nonrecurring charges such as acquisitions and restructuring costs as these items are often not indicative of a companies overall performance.
▪ Basic : net income minus preferred dividends divided by the average number of common shares outstanding.
▪ Diluted : net income minus preferred dividends divided by the average number of common shares outstanding & convertible preferred shares such as convertible debt, equity options and warrants.
Although the quarterly earnings data is the same across all timeframes, viewing the longer term trend versus the shorter term trend is relevant based on the objectives of the investor. For example, the earnings growth on a monthly chart provides the big picture view, which may span years. This can be helpful for investors interested in more of a buy and hold approach.
The earnings trend on weekly and daily charts has fewer data points simply based on the shorter timeframe. This information is helpful for investors who are more focused on trades that may be weeks or months in length. The momentum and direction of the current earnings trend is of great importance for those looking to ride the current trend.
Summary:
Historical models have shown the best-performing companies have consistent earnings growth. Whether you are looking short or long term, understanding the earnings trend is a key factor in determining the potential price direction.
Key Features:
▪ Choose the EPS to plot: Reported (non-GAAP), Basic or Diluted.
▪ View stats for all EPS types.
▪ Plot on daily, weekly and monthly timeframes.
Move Earnings Line To Main Chart
▪ Click on the indicator name on left side of the chart.
▪ Select the "..." option.
▪ Use the "Move-to" option to change the location of the earnings line.
▪ To hide the EPS scale on the left, select the "..." option.
▪ In "Pin to scale" select the "No scale (fullscreen)" option.
The LevelUp Earnings Line is included the LevelUp Tools suite of TradingView indicators for trend followers.
Commitments of Traders Report [Advanced]This indicator displays the Commitment of Traders (COT) report data in a clear, table format similar to an Excel spreadsheet, with additional functionalities to analyze open interest and position changes. The COT report, published weekly by the Commodity Futures Trading Commission (CFTC), provides valuable insights into market sentiment by revealing the positioning of various trader categories.
Display:
Release Date: When the data was released.
Open Interest: Shows the total number of open contracts for the underlying instrument held by selected trader category.
Net Contracts: Shows the difference between long and short positions for selected trader category.
Long/Short OI: Displays the long and short positions held by selected trader category.
Change in Long/Short OI: Displays the change in long and short positions since the previous reporting period. This can highlight buying or selling pressure.
Long & Short Percentage: Displays the percentage of total long and short positions held by each category.
Trader Categories (Configurable)
Commercials: Hedgers who use futures contracts to manage risk associated with their underlying business (e.g., producers, consumers).
Non-Commercials (Large Speculators): Speculative traders with large positions who aim to profit from price movements (e.g., hedge funds, investment banks).
Non-Reportable (Small Speculators/Retail Traders): Smaller traders with positions below the CFTC reporting thresholds.
CFTC Code: If the indicator fails to retrieve data, you can manually enter the CFTC code for the specific instrument. The code for instrument can be found on CFTC's website.
Using the Indicator Effectively
Market Sentiment Gauge: Analyze the positioning of each trader category to gauge overall market sentiment.
High net longs by commercials might indicate a bullish outlook, while high net shorts could suggest bearish sentiment.
Changes in open interest and long/short positions can provide additional insights into buying and selling pressure.
Trend Confirmation: Don't rely solely on COT data for trade signals. Use it alongside price action and other technical indicators for confirmation.
Identify Potential Turning Points: Extreme readings in COT data, combined with significant changes in open interest or positioning, might precede trend reversals, but exercise caution and combine with other analysis tools.
Disclaimer
Remember, the COT report is just one piece of the puzzle. It should not be used for making isolated trading decisions. Consider incorporating it into a comprehensive trading strategy that factors in other technical and fundamental analysis.
Credit
A big shoutout to Nick from Transparent FX ! His expertise and thoughtful analysis have been a major inspiration in developing this COT Report indicator. To know more about this indicator and how to use it, be sure to check out his work.
IDX Financials v2This indicator adds financial data, ratios, and valuations to your chart. The main objective is to present financial overview that can be glanced quickly to add to your considerations.
The visualization of the indicator consists of two parts:
A. Plots (lines alongside the candlestick)
B. Financial table on the right. Drag your candlestick to the left to provide blank area for the table.
Programatically, the financial data is obtained by using these Pine API:
request.earnings(...) API for the EPS values that are used by the price at average PER line , and
request.financial(..) API for the rest of financial data required by the indicator.
See What financial data is available in Pine for more info on getting financial data in Pine.
A. THE PLOTS
The plots produces two lines, price at average PER in blue and price at average PBV line in pink, calculated over some adjustable time period (the default is one year). By default, only price at average PER line is shown.
Note that PER stands for Price to Earning Ratio.
The price at average PER line shows the price level at the average PER. It is calculated using formula as follows:
line = AVGPER * EPSTTM
where AVGPER is the average PER over some time period (default is one year, adjustable) and EPSTTM is the standardized EPS TTM.
Note that the EPS is updated at the actual time of earning report publication , and not at standard quarter dates such as March 31st, Dec 31st, etc.. This approach is chosen to represent the actual PE at the time.
The price at average PBV line (PBV stands for Price to Book Value), which can be enabled in settings, shows the price at average PBV. It is calculated using formula as follows:
line = AVGPBV * BVPS
where AVGPBV is the average PBV over some period of time (default is one year, adjustable) and BVPS is the book value per share. Note that the PBV is clipped to range to avoid values that are too small/large.
Also note that unlike PER, the BVPS is updated at each quarterly date (such as March 31st, Dec 31st, etc.).
Apart from those lines, some values are written to the status line (i.e. the numbers next to indicator name), which represent the corresponding value at the currently hovered bar:
PER TTM
Average PER
Std value (zvalue) of PER TTM (equal to = (PERTTM - AVGPER)/STDPER)
PBV
The meaning for these abbreviations should be straightforward.
Using the price at average PER line
There are several ways to use the price at average PER line .
You can quickly get the sense of current valuation by seeing the price relative to the price at average PER line . If the price is above the line, the valuation is higher than the average valuation, and vice versa if the price is lower.
The distance between the price and the average is measured in unit of standard deviation. This is represented by the third number in the status line. Value zero indicates the price is exactly at the average PER line. Positive value indicates price is higher than average, and negative if price is lower than average. Usually people use value +2 and -2 to indicate extreme positions.
The second way to use the line is to see how the line jumps up or down at the earning report date . If the line jumps up, this indicates the increase of EPSTTM. And vice versa when the line jumps down.
When EPSTTM is trending up over several quarters, or if EPSTTM is expected to go up, usually the price is also trending up and the valuation is over the average. And vice versa when EPSTTM is trending down or expected to go down. Deviation from this pattern may present some buying or selling opportunity.
B. THE FINANCIAL TABLE
The second visual part is the financial table. The financial table contains financial information at the last bar . It has four sections:
1. Revenue
2. Income
3. Valuations
4. Ratios
Let's discuss them in detail.
1. Revenue and income sections
The revenue and income table are organized into rows and columns.
Each row shows the data at the specified time frame, as follows:
The first four rows shows quarterly revenue/income of the last four quarters.
Then followed by TTM data.
Then followed by forecast of next quarter revenue/income, if such forecast exists. Note the "(F)" notation next to the quarter name.
Then followed by forecast of TTM data of next quarter (only for income), if such forecast exists. Note the "(F)" notation next to the TTM name.
The columns of revenue and income sections show the following:
The time frame information (such as quarter name, TTM, etc.)
The revenue/income value, in billions or millions (configurable).
YoY (year on year) growth, i.e. comparing the value with the value one year earlier, if any.
QoQ (quarter on quarter) growth, i.e. comparing the value with previous quarter value, if any.
GPM/NPM (gross profit margin or net profit margin), i.e. the margin on the specified time period.
Using the Revenue and Income table
The table provides quick way to see the revenue and income trend. You can see the YoY growth as well as QoQ, if that is applicable (non seasonal stocks). You can also see how the margins change over the periods.
The values are also presented with relevant background color . Green indicates "good" value and red indicates "bad" value. The intensity represents how good/bad the value is. The limits of the good and bad values are currently hardcoded in the script.
2. Valuations section
The valuation shows current stock valuation. The section is organized in rows and columns. Each row contains one type of valuation criteria, as follows:
PER (Price Earning Ratio)
Next quarter PER forecast (marked by "(F)" notation) when available
PBV (Price to Book value)
For each valuation criteria, several values are presented as columns:
The current value of the criteria. By current, it means the value at the last bar.
The one year standard deviation position
The three years standard deviation position
3. Ratios Section
The ratios section contains the following useful financial ratios:
ROA (Return on Asset), equal to: NET_INCOME_TTM / TOTAL_ASSETS
ROE (Return on Equity), equal to: NET_INCOME_TTM / BOOK_VALUE_PER_SHARE
PEG (PER to Growth Ratio), equal to PER_TTM / (INCOME_TTM_GROWTH*100)
DER (Debt to Equity Ratio), taken from request.financial(syminfo.tickerid, "DEBT_TO_EQUITY", "FQ")
DPR (Dividend Payout Ratio), taken from request.financial(syminfo.tickerid, "DIVIDEND_PAYOUT_RATIO", "FY")
Dividend yield, equal to (DPR * (NET_INCOME_TTM / TOTAL_SHARES_OUTSTANDING)) / close
KNOWN BUGS
Currently does not handle when the financial quarter contains gap, i.e. there is missing quarter. This usually happens on newly IPO stocks.
RP - Realized Price for Bitcoin (BTC) [Logue]Realized Price (RP) - The RP is summation of the value of each BTC when it last moved divided by the total number of BTC in circulation. This gives an estimation of the average "purchase" price of BTC on the bitcoin network based on when it was last transacted. This indicator tells us if the average network participant is in a state of profit or loss. This indicator is normally used to detect BTC bottoms, but an extension can be used to detect when the bitcoin network is "highly" overvalued. Because the "strength" of the BTC tops has decreased over the cycles, a logarithmic function for the extension was created by fitting past cycles as log extension = slope * time + intercept. This indicator triggers when the BTC price is above the realized price extension. For the bottoms, the RP is shifted downwards at a default value of 80%. The slope, intercept, and RP bottom shift can all be modified in the script.
CVDD - Coin Value Days Destroyed for Bitcoin (BTC) [Logue]Cumulative Value Days Destroyed (CVDD) - The CVDD was created by Willy Woo and is the ratio of the cumulative value of Coin Days Destroyed in USD and the market age (in days). While this indicator is used to detect bottoms normally, an extension is used to allow detection of BTC tops. When the BTC price goes above the CVDD extension, BTC is generally considered to be overvalued. Because the "strength" of the BTC tops has decreased over the cycles, a logarithmic function for the extension was created by fitting past cycles as log extension = slope * time + intercept. This indicator is triggered for a top when the BTC price is above the CVDD extension. For the bottoms, the CVDD is shifted upwards at a default value of 120%. The slope, intercept, and CVDD bottom shift can all be modified in the script.
MVRVZ - MVRVZ Top and Bottom Indicator for BTC [Logue]Market Value-Realized Value Z-score (MVRVZ) - The MVRV-Z score measures the value of the bitcoin network by comparing the market cap to the realized value and dividing by the standard deviation of the market cap (market cap – realized cap) / std(market cap)). When the market value is significantly higher than the realized value, the bitcoin network is "overvalued". Very high values have signaled cycle tops in the past and low values have signaled bottoms. For tops, the default trigger value is above 6.85. For bottoms, the indicator is triggered when the MVRVZ is below -0.25 (default).
Bond Yield SpreadThe Bond Yield Spread Script is developed for forex traders, offering an automated tool to calculate the bond yield spread between two countries associated with the forex pair displayed on the chart.
Functionality:
The script starts by identifying the base and quote currencies of the current forex pair and aligns them with their corresponding national bond symbols based on user-selected maturity, with options ranging from 01Y to 30Y. It calculates the yield spread by subtracting the bond yield associated with the quote country from that of the base country, following the formula:
Yield Spread = Yield(Base Country) − Yield(Quote Country)
which is then displayed as a plot line on the chart.
This script relies solely on TradingView's internal yield symbols, with the following calculation:
"currency" => "first two letters" + maturity
And maturity, in this case, is the value that is configured in the indicator settings, for example:
"EUR" => "EU" + "02Y" will result in EU02Y -> which will be used in the formula, depending on the quote or base currency.
Application in Trading:
This indicator is invaluable for traders employing carry trading strategies or assessing currency strength based on traded interest rates as an indicator. A higher yield spread typically indicates a stronger currency, because the return obtained for holding the currency is higher.
Originality and Practicality:
This script is self-developed, aiming to fill the gap in automatic bond yield comparisons within the TradingView environment. It is particularly beneficial for traders focusing on macroeconomic factors affecting forex markets. Unlike other scripts, it integrates various bond maturities into one tool, enhancing its utility and application range.
Conclusion:
Designed for traders incorporating macroeconomics in their strategy, this script will be useful to calculate the bond yield differences automatically without having to enter a new formula for every new currency pair.
Compliance and Limitations:
The script complies with TradingView scripting standards, ensuring no lookahead bias and maintaining real-time data integrity. However, its utility depends on the comprehensive availability of bond yield data within TradingView. As not all countries issue bonds for each listed maturity, this may limit the script’s application for certain currency pairs or specific maturities.
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.
NUPL - Net Unrealized Profit-Loss BTC Tops/Bottoms [Logue]Net Unrealized Profit Loss (NUPL) - The NUPL measures the profit state of the bitcoin network to determine if past transfers of BTC are currently in an unrealized profit or loss state.
Values above zero indicate that the network is in overall profit, while values below zero indicate the network is in overall loss. Highly positive NUPL values indicate overvaluation of the BTC network and relatively negative NUPL values indicate an undervaluation of the BTC network.
For tops: The default setting for tops is based on decreasing "strength" of BTC tops. A decreasing linear function (trigger = slope * time + intercept) was fit to past cycle tops for this indicator and is used as the default to signal macro tops. The user can change the slope and intercept of the line by changing the slope and/or intercept factor. The user also has the option to indicate tops based on a horizontal line via a settings selection. This horizontal line default value is 73. This indicator is triggered for a top when the NUPL is above the trigger value.
For bottoms: Bottoms are displayed based on a horizontal line with a default setting of -13. The indicator is triggered for a bottom when the NUPL is below the bottom trigger value.
Blockunity Miners Synthesis (BMS)Track the status of Bitcoin and Ethereum Miners' Netflows and their asset reserves.
The Idea
The goal is to provide a simple tool for visualizing the changes in miners' flows and reserves.
How to Use
Analysing the behaviour of miners enables you to detect long-term opportunities, in particular with the state of reserves, but also in the shorter term with the visualization of Netflows.
Elements
Miners Reserves
Miners Reserves represent the balances of addresses belonging to mining pools (in BTC or ETH).
This data can also be displayed in USD via the indicator parameters:
Miners Netflow
The Netflow is calculated by subtracting the outflows from the inflows originating from addresses associated with mining pools. When this result is negative, it indicates that more funds are exiting the miners' accounts than the funds they are receiving. Consequently, negative miner netflows suggests selling activity.
This data can also be displayed in USD via the indicator parameters. You can also choose the timeframe. For example, selecting "Yearly" will give a Netflow daily average taking into account the last 365 days:
Settings
In the settings, you can first choose which asset to view, between Bitcoin and Ethereum. Here are the reserves of Ethereum miners:
As with Bitcoin, Netflow can also be displayed in the timeframe of your choice. Here you can see the average daily netflow of Ethereum miners in USD over the last 30 days:
Here are all the parameters:
Asset Selector: Choose between Bitcoin or Ethereum miner data.
Get values in USD: Displays values in USD instead of assets.
Switch between Netflow and Reserve : If checked, displays Miners' Reserves data. If unchecked, displays Miners' Netflow data.
Display timeframe: Allows you to select the timeframe for displaying the Netflow plot.
Period Lookback (in days): Select the period to be taken into account when calculating the variation percentage of Miners' Reserves.
Lastly, you can modify all table and labels parameters.
PUELL - PUELL Top and Bottom Indicator for BTC [Logue]Puell Multiple Indicator (PUELL) - The Puell multiple is the ratio between the daily coin issuance in USD and its 365-day moving average. This multiple helps to measure miner profitability. The PUELL indicator smooths the Puell multiple using a 14-day simple moving average. When the PUELL goes to high values relative to historical values, it indicates the profitability of the miners is high and a top may be near. When the PUELL is low relative to historical values, it indicates the profitability of the minors is low and a bottom may be near. The default trigger values are PUELL values above 3.0 for a "top" and below 0.5 for a "bottom".
TFS - Bitcoin (BTC) Transaction Fee Spike Top Indicator [Logue]Transaction Fee Spike (TFS) - For bitcoin (BTC), transaction fees on the bitcoin network can signal a mania phase when they increase well above historical values. This mania phase may indicate we are near a top in the BTC price. The transaction fee in USD is directly retrieved from Glassnode. The default trigger for this indicator fires when the transaction fees increase above $44/transaction.
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.
Greenblatts Magic Formula - A multiple approachThis indicator is supposed to help find undervalued stocks. Inspired by Joel Greenblatt's strategy where he ranks stocks with the lowest EV/EBIT and the highest ROC. Inspired by the ERP5 strategy I have added Earnings Yield together with ROC.
My approach and how I use the indicator is to see Magic Formula score as a multiple, rather than ranking the numbers between different stocks. Like P/E for comparison. Different kinds of companies trades at different multiples so you have to compare the current MF Score in relation to historical MF Score to get an idea if it truly is undervalued. You also want to see that price actually reacts to a low MF Score.
As i general rule for myself I stay away from companies with EV/EBIT above 13 and generally want to see MF Score below 6-7. A company trading at a negative MF Score indicates that the company may be heavily undervalued.
Red line = EV/EBIT
Green line = ROC + EY / 2
Yellow line = "MF Score" EVEBIT - (ROC+EY/2)
Blue line = The 50 EMA of MF score
The strategy is simple. Look for companies which might be undervalued. Compare the current MF score to it's history. If it's trading near a previous bottom it indicates that the company might be undervalued. You can also use the MF EMA to see a more smooth curve to interpret the multiple.
Historical PE ratio vs medianThe Trailing Twelve-Month Price-to-Earnings (TTM P/E) Ratio vs. Median Value Indicator is a financial analytical tool designed to assess the current valuation of a stock or index in comparison to its historical norm. This is achieved by calculating the P/E ratio using the sum of the entity's earnings per share (EPS) over the past twelve months and dividing it by its current share price. The resulting TTM P/E ratio is then compared against the median P/E ratio calculated over a specified historical period.
The median P/E ratio serves as a benchmark, representing the midpoint of the entity's valuation over the selected timeframe, thus smoothing out short-term volatility and anomalies. By comparing the current TTM P/E ratio to this median, the indicator provides a relative measure of whether the stock or index is currently overvalued, undervalued, or trading at its historical valuation norms.
VEMA_LTFVEMA indicator is based on lower time frame volume data and it has 3 lines.
20, 50, 100 moving averages of the close price in each candle with the highest volume.
Effectively working fine and hence sharing.
Will Add more information with examples in next update
Weekend Analysis
There are always discussions about how the weekend affects Crypto-Coins.
It seems that on Monday, the price usually returns to Friday's level.
To make a qualified statement, I wrote this script that tests exactly that
and provides an evaluation.
It displays a candle for Saturday and Sunday.
Either green or red, but also blue if there was hardly any movement.
This threshold is set at 2%, but can be changed in the settings.
If the relative distance from Saturday's open to Friday's close is less than this value,
it counts as the same.
The timeframe should be between day and hour so that Tradingview goes back far enough in the past.
The output (here for BTC)
Total: 477
Lower: 20%
Equal: 55%
Higher: 25%
is displayed in the chart, but also output via the log function.
