Pivot Points Level [TradingFinder] 4 Methods + Reversal lines🔵 Introduction
"Pivot Points" are places on the price chart where buyers and sellers are most active. Pivot points are calculated based on the previous day's price data and serve as reference points for traders to make decisions.
Types of Pivot Points :
Floor
Woodie
Camarilla
Fibonacci
🟣 Floor Pivot Points
Floor pivot points are widely used in technical analysis. The central pivot point (PP) serves as the main level of support or resistance, indicating the potential direction of the trend.
The first to third levels of resistance (R1, R2, R3) and support (S1, S2, S3) provide additional signals for potential trend reversals or continuations.
Floor Pivot Points Formula :
Pivot Point (PP): (H + L + C) / 3
First Resistance (R1): (2 * P) - L
Second Resistance (R2): P + H - L
Third Resistance (R3): H + 2 * (P - L)
First Support (S1): (2 * P) - H
Second Support (S2): P - H + L
Third Support (S3): L - 2 * (H - P)
🟣 Camarilla Pivot Points
Camarilla pivot points include eight levels that closely align with support and resistance. These points are particularly useful for setting stop-loss and profit targets.
Camarilla Pivot Points Formula :
Fourth Resistance (R4): (H - L) * 1.1 / 2 + C
Third Resistance (R3): (H - L) * 1.1 / 4 + C
Second Resistance (R2): (H - L) * 1.1 / 6 + C
First Resistance (R1): (H - L) * 1.1 / 12 + C
First Support (S1): C - (H - L) * 1.1 / 12
Second Support (S2): C - (H - L) * 1.1 / 6
Third Support (S3): C - (H - L) * 1.1 / 4
Fourth Support (S4): C - (H - L) * 1.1 / 2
🟣 Woodie Pivot Points
Woodie pivot points are similar to floor pivot points but place more emphasis on the closing price. This method often results in different pivot levels than the floor method.
Woodie Pivot Points Formula :
Pivot Point (PP): (H + L + 2 * C) / 4
First Resistance (R1): (2 * P) - L
Second Resistance (R2): P + H - L
First Support (S1): (2 * P) - H
Second Support (S2): P - H + L
🟣 Fibonacci Pivot Points
Fibonacci pivot points use the standard floor pivot points and then apply Fibonacci retracement levels to the range of the previous trading period. The common retracement levels used are 38.2%, 61.8%, and 100%.
Fibonacci Pivot Points Formula :
Pivot Point (PP): (H + L + C) / 3
Third Resistance (R3): PP + ((H - L) * 1.000)
Second Resistance (R2): PP + ((H - L) * 0.618)
First Resistance (R1): PP + ((H - L) * 0.382)
First Support (S1): PP - ((H - L) * 0.382)
Second Support (S2): PP - ((H - L) * 0.618)
Third Support (S3): PP - ((H - L) * 1.000)
These pivot point calculations help traders identify potential support and resistance levels, enabling more informed decision-making in their trading strategies.
🔵 How to Use
🟣 Two Methods for Trading Pivot Points
There are two primary methods for trading pivot points: trading with "pivot point breakouts" and trading with "price reversals."
🟣 Pivot Point Breakout
A breakout through pivot lines provides a significant signal to the trader, indicating a change in market sentiment. When an upward breakout occurs and the price crosses these lines, a trader can enter a long position and place their stop-loss below the pivot point (P).
Similarly, if a downward breakout happens, a short order can be placed below the pivot point.
When trading with pivot point breakouts, if the upward trend breaks, the first and second support levels can be the trader's profit targets. In a downward trend, the first and second resistance levels will serve this role.
🟣 Price Reversal
Another method for trading pivot points is waiting for the price to reverse from the support and resistance levels. To execute this strategy, one should trade in the opposite direction of the trend as the price reverses from the pivot point.
It's worth noting that although traders use this tool in higher time frames, it yields better results in shorter time frames such as one-hour, 30-minute, and 15-minute intervals.
Cerca negli script per "跨境通12月4日地天板"
Daye's Quarterly TheoryDaye's Quarterly Theory Indicator
Description
The Daye's Quarterly Theory Indicator divides trading time into smaller units to help traders identify potential accumulation, manipulation, distribution, and reversal/continuation phases within a day. It applies these time divisions to your charts, offering visual guidance aligned with ICT's PO3 concept:
Accumulation (A): The phase where positions are accumulated.
Manipulation (M): The phase where the market moves against the prevailing trend to trap traders.
Distribution (D): The phase where accumulated positions are distributed.
Reversal/Continuation (X): The phase indicating either a reversal or continuation of the trend.
This indicator breaks down time into quarters at different levels:
Daily Quarters:
Q1: 18:00 - 00:00 (Asia)
Q2: 00:00 - 06:00 (London)
Q3: 06:00 - 12:00 (NY AM)
Q4: 12:00 - 18:00 (NY PM)
90-Minute Quarters:
Q1: 18:00 - 19:30
Q2: 19:30 - 21:00
Q3: 21:00 - 22:30
Q4: 22:30 - 00:00
Micro Quarters (22.5 minutes) (Displayed on 7-minute TF or lower):
Q1: 18:00 - 18:22:30
Q2: 18:22:30 - 18:45
Q3: 18:45 - 19:07:30
Q4: 19:07:30 - 19:30
Features
Time Box Visualization: Highlights different quarters of the trading day to help visualize market phases.
Customizable Colors: Allows users to set different colors for daily, 90-minute, and micro quarters.
Flexible Settings: Designed to work out-of-the-box on both light and dark background charts.
ICT PO3 Alignment: Helps traders align their strategies with ICT's Accumulation, Manipulation, Distribution, and Reversal/Continuation phases.
Usage
Apply this indicator to your NQ1! or ES1! charts and observe the confluence with ICT's macro times. Use it to predict potential market phases and optimize your trading strategy by buying after manipulation down or selling after manipulation up.
Note: The indicator's display may vary based on the timeframe viewed and broker feeds. Back-test and research for best results on your preferred assets.
Get intraday extended dataIf you have interacted with Pine for some time, you probably noticed that if you are using DWM resolutions, you will not be able to obtain complete data from the extended intraday ticker using the usual functions request.security() and request.security_lower_tf(). This is quite logical if you understand the principle of mapping data from the secure context to the main one. The main reason is the different opening and closing times of the intraday data with extended clocks and DWM.
This script visualizes one of the approaches to solving this problem. I will briefly describe the principle of operation:
For example, take the symbol NASDAQ:AAPL.
Our main resolution is 1D, but we want to receive extended data from a 4-hour interval. The daytime bar opens at 09:30 and closes at 16:00. The same period at a resolution of 4 hours covers 4 bars:
04:00 - 08:00
08:00 - 12:00
12:00 - 16:00
16:00 - 20:00
So, if we use the request.security_lower_tf() function, we will not get the bars 04:00 - 08:00 and 16:00 - 20:00 because their closing times are not within the range of the main context (09:30 - 16:00).
If we use the request.security() function, we will get the bar 04:00 - 08:00, but we will not get the bar 16:00 - 20:00 because its closing time will be in the future, and it is impossible to get values from the future.
So, what I propose is to use the upgraded request.security() function, inside which another function will be executed, storing all the bars in a var array and putting the post-market bars in the array of the next day. Next, all we have to do is isolate these bars, place them in the previous array, and remove them from the current one.
I visualized the received data simply as text, but you can do it differently using the proposed mechanism.
In order for everything to work, you need to fill in the inputs correctly:
"Symbol for calculate" - This is the symbol from which we will receive extended data.
"Intraday data period" - The period from which we will receive extended data.
"Specify your chart timeframe here" - This is an input that allows you to operate with data from the main context while being inside the secure one. Enter your current chart timeframe here. If there are problems, a warning will appear informing you about this.
If you want to use these developments, take the get_data() function, it will return:
1. the number of past items - it is useful for outputting values in real time, because it is not possible to simply delete them there, because they will always arrive and it is easier to make a slice with an indentation for this number
2. cleared object of type Inner_data containing arrays of open, high, low, close, volume, time, time_close intraday data
3. its same value from the previous bar
RiskMetrics█ OVERVIEW
This library is a tool for Pine programmers that provides functions for calculating risk-adjusted performance metrics on periodic price returns. The calculations used by this library's functions closely mirror those the Broker Emulator uses to calculate strategy performance metrics (e.g., Sharpe and Sortino ratios) without depending on strategy-specific functionality.
█ CONCEPTS
Returns, risk, and volatility
The return on an investment is the relative gain or loss over a period, often expressed as a percentage. Investment returns can originate from several sources, including capital gains, dividends, and interest income. Many investors seek the highest returns possible in the quest for profit. However, prudent investing and trading entails evaluating such returns against the associated risks (i.e., the uncertainty of returns and the potential for financial losses) for a clearer perspective on overall performance and sustainability.
One way investors and analysts assess the risk of an investment is by analyzing its volatility , i.e., the statistical dispersion of historical returns. Investors often use volatility in risk estimation because it provides a quantifiable way to gauge the expected extent of fluctuation in returns. Elevated volatility implies heightened uncertainty in the market, which suggests higher expected risk. Conversely, low volatility implies relatively stable returns with relatively minimal fluctuations, thus suggesting lower expected risk. Several risk-adjusted performance metrics utilize volatility in their calculations for this reason.
Risk-free rate
The risk-free rate represents the rate of return on a hypothetical investment carrying no risk of financial loss. This theoretical rate provides a benchmark for comparing the returns on a risky investment and evaluating whether its excess returns justify the risks. If an investment's returns are at or below the theoretical risk-free rate or the risk premium is below a desired amount, it may suggest that the returns do not compensate for the extra risk, which might be a call to reassess the investment.
Since the risk-free rate is a theoretical concept, investors often utilize proxies for the rate in practice, such as Treasury bills and other government bonds. Conventionally, analysts consider such instruments "risk-free" for a domestic holder, as they are a form of government obligation with a low perceived likelihood of default.
The average yield on short-term Treasury bills, influenced by economic conditions, monetary policies, and inflation expectations, has historically hovered around 2-3% over the long term. This range also aligns with central banks' inflation targets. As such, one may interpret a value within this range as a minimum proxy for the risk-free rate, as it may correspond to the minimum rate required to maintain purchasing power over time.
The built-in Sharpe and Sortino ratios that strategies calculate and display in the Performance Summary tab use a default risk-free rate of 2%, and the metrics in this library's example code use the same default rate. Users can adjust this value to fit their analysis needs.
Risk-adjusted performance
Risk-adjusted performance metrics gauge the effectiveness of an investment by considering its returns relative to the perceived risk. They aim to provide a more well-rounded picture of performance by factoring in the level of risk taken to achieve returns. Investors can utilize such metrics to help determine whether the returns from an investment justify the risks and make informed decisions.
The two most commonly used risk-adjusted performance metrics are the Sharpe ratio and the Sortino ratio.
1. Sharpe ratio
The Sharpe ratio , developed by Nobel laureate William F. Sharpe, measures the performance of an investment compared to a theoretically risk-free asset, adjusted for the investment risk. The ratio uses the following formula:
Sharpe Ratio = (𝑅𝑎 − 𝑅𝑓) / 𝜎𝑎
Where:
• 𝑅𝑎 = Average return of the investment
• 𝑅𝑓 = Theoretical risk-free rate of return
• 𝜎𝑎 = Standard deviation of the investment's returns (volatility)
A higher Sharpe ratio indicates a more favorable risk-adjusted return, as it signifies that the investment produced higher excess returns per unit of increase in total perceived risk.
2. Sortino ratio
The Sortino ratio is a modified form of the Sharpe ratio that only considers downside volatility , i.e., the volatility of returns below the theoretical risk-free benchmark. Although it shares close similarities with the Sharpe ratio, it can produce very different values, especially when the returns do not have a symmetrical distribution, since it does not penalize upside and downside volatility equally. The ratio uses the following formula:
Sortino Ratio = (𝑅𝑎 − 𝑅𝑓) / 𝜎𝑑
Where:
• 𝑅𝑎 = Average return of the investment
• 𝑅𝑓 = Theoretical risk-free rate of return
• 𝜎𝑑 = Downside deviation (standard deviation of negative excess returns, or downside volatility)
The Sortino ratio offers an alternative perspective on an investment's return-generating efficiency since it does not consider upside volatility in its calculation. A higher Sortino ratio signifies that the investment produced higher excess returns per unit of increase in perceived downside risk.
█ CALCULATIONS
Return period detection
Calculating risk-adjusted performance metrics requires collecting returns across several periods of a given size. Analysts may use different period sizes based on the context and their preferences. However, two widely used standards are monthly or daily periods, depending on the available data and the investment's duration. The built-in ratios displayed in the Strategy Tester utilize returns from either monthly or daily periods in their calculations based on the following logic:
• Use monthly returns if the history of closed trades spans at least two months.
• Use daily returns if the trades span at least two days but less than two months.
• Do not calculate the ratios if the trade data spans fewer than two days.
This library's `detectPeriod()` function applies related logic to available chart data rather than trade data to determine which period is appropriate:
• It returns true if the chart's data spans at least two months, indicating that it's sufficient to use monthly periods.
• It returns false if the chart's data spans at least two days but not two months, suggesting the use of daily periods.
• It returns na if the length of the chart's data covers less than two days, signifying that the data is insufficient for meaningful ratio calculations.
It's important to note that programmers should only call `detectPeriod()` from a script's global scope or within the outermost scope of a function called from the global scope, as it requires the time value from the first bar to accurately measure the amount of time covered by the chart's data.
Collecting periodic returns
This library's `getPeriodicReturns()` function tracks price return data within monthly or daily periods and stores the periodic values in an array . It uses a `detectPeriod()` call as the condition to determine whether each element in the array represents the return over a monthly or daily period.
The `getPeriodicReturns()` function has two overloads. The first overload requires two arguments and outputs an array of monthly or daily returns for use in the `sharpe()` and `sortino()` methods. To calculate these returns:
1. The `percentChange` argument should be a series that represents percentage gains or losses. The values can be bar-to-bar return percentages on the chart timeframe or percentages requested from a higher timeframe.
2. The function compounds all non-na `percentChange` values within each monthly or daily period to calculate the period's total return percentage. When the `percentChange` represents returns from a higher timeframe, ensure the requested data includes gaps to avoid compounding redundant values.
3. After a period ends, the function queues the compounded return into the array , removing the oldest element from the array when its size exceeds the `maxPeriods` argument.
The resulting array represents the sequence of closed returns over up to `maxPeriods` months or days, depending on the available data.
The second overload of the function includes an additional `benchmark` parameter. Unlike the first overload, this version tracks and collects differences between the `percentChange` and the specified `benchmark` values. The resulting array represents the sequence of excess returns over up to `maxPeriods` months or days. Passing this array to the `sharpe()` and `sortino()` methods calculates generalized Information ratios , which represent the risk-adjustment performance of a sequence of returns compared to a risky benchmark instead of a risk-free rate. For consistency, ensure the non-na times of the `benchmark` values align with the times of the `percentChange` values.
Ratio methods
This library's `sharpe()` and `sortino()` methods respectively calculate the Sharpe and Sortino ratios based on an array of returns compared to a specified annual benchmark. Both methods adjust the annual benchmark based on the number of periods per year to suit the frequency of the returns:
• If the method call does not include a `periodsPerYear` argument, it uses `detectPeriod()` to determine whether the returns represent monthly or daily values based on the chart's history. If monthly, the method divides the `annualBenchmark` value by 12. If daily, it divides the value by 365.
• If the method call does specify a `periodsPerYear` argument, the argument's value supersedes the automatic calculation, facilitating custom benchmark adjustments, such as dividing by 252 when analyzing collected daily stock returns.
When the array passed to these methods represents a sequence of excess returns , such as the result from the second overload of `getPeriodicReturns()`, use an `annualBenchmark` value of 0 to avoid comparing those excess returns to a separate rate.
By default, these methods only calculate the ratios on the last available bar to minimize their resource usage. Users can override this behavior with the `forceCalc` parameter. When the value is true , the method calculates the ratio on each call if sufficient data is available, regardless of the bar index.
Look first. Then leap.
█ FUNCTIONS & METHODS
This library contains the following functions:
detectPeriod()
Determines whether the chart data has sufficient coverage to use monthly or daily returns
for risk metric calculations.
Returns: (bool) `true` if the period spans more than two months, `false` if it otherwise spans more
than two days, and `na` if the data is insufficient.
getPeriodicReturns(percentChange, maxPeriods)
(Overload 1 of 2) Tracks periodic return percentages and queues them into an array for ratio
calculations. The span of the chart's historical data determines whether the function uses
daily or monthly periods in its calculations. If the chart spans more than two months,
it uses "1M" periods. Otherwise, if the chart spans more than two days, it uses "1D"
periods. If the chart covers less than two days, it does not store changes.
Parameters:
percentChange (float) : (series float) The change percentage. The function compounds non-na values from each
chart bar within monthly or daily periods to calculate the periodic changes.
maxPeriods (simple int) : (simple int) The maximum number of periodic returns to store in the returned array.
Returns: (array) An array containing the overall percentage changes for each period, limited
to the maximum specified by `maxPeriods`.
getPeriodicReturns(percentChange, benchmark, maxPeriods)
(Overload 2 of 2) Tracks periodic excess return percentages and queues the values into an
array. The span of the chart's historical data determines whether the function uses
daily or monthly periods in its calculations. If the chart spans more than two months,
it uses "1M" periods. Otherwise, if the chart spans more than two days, it uses "1D"
periods. If the chart covers less than two days, it does not store changes.
Parameters:
percentChange (float) : (series float) The change percentage. The function compounds non-na values from each
chart bar within monthly or daily periods to calculate the periodic changes.
benchmark (float) : (series float) The benchmark percentage to compare against `percentChange` values.
The function compounds non-na values from each bar within monthly or
daily periods and subtracts the results from the compounded `percentChange` values to
calculate the excess returns. For consistency, ensure this series has a similar history
length to the `percentChange` with aligned non-na value times.
maxPeriods (simple int) : (simple int) The maximum number of periodic excess returns to store in the returned array.
Returns: (array) An array containing monthly or daily excess returns, limited
to the maximum specified by `maxPeriods`.
method sharpeRatio(returnsArray, annualBenchmark, forceCalc, periodsPerYear)
Calculates the Sharpe ratio for an array of periodic returns.
Callable as a method or a function.
Namespace types: array
Parameters:
returnsArray (array) : (array) An array of periodic return percentages, e.g., returns over monthly or
daily periods.
annualBenchmark (float) : (series float) The annual rate of return to compare against `returnsArray` values. When
`periodsPerYear` is `na`, the function divides this value by 12 to calculate a
monthly benchmark if the chart's data spans at least two months or 365 for a daily
benchmark if the data otherwise spans at least two days. If `periodsPerYear`
has a specified value, the function divides the rate by that value instead.
forceCalc (bool) : (series bool) If `true`, calculates the ratio on every call. Otherwise, ratio calculation
only occurs on the last available bar. Optional. The default is `false`.
periodsPerYear (simple int) : (simple int) If specified, divides the annual rate by this value instead of the value
determined by the time span of the chart's data.
Returns: (float) The Sharpe ratio, which estimates the excess return per unit of total volatility.
method sortinoRatio(returnsArray, annualBenchmark, forceCalc, periodsPerYear)
Calculates the Sortino ratio for an array of periodic returns.
Callable as a method or a function.
Namespace types: array
Parameters:
returnsArray (array) : (array) An array of periodic return percentages, e.g., returns over monthly or
daily periods.
annualBenchmark (float) : (series float) The annual rate of return to compare against `returnsArray` values. When
`periodsPerYear` is `na`, the function divides this value by 12 to calculate a
monthly benchmark if the chart's data spans at least two months or 365 for a daily
benchmark if the data otherwise spans at least two days. If `periodsPerYear`
has a specified value, the function divides the rate by that value instead.
forceCalc (bool) : (series bool) If `true`, calculates the ratio on every call. Otherwise, ratio calculation
only occurs on the last available bar. Optional. The default is `false`.
periodsPerYear (simple int) : (simple int) If specified, divides the annual rate by this value instead of the value
determined by the time span of the chart's data.
Returns: (float) The Sortino ratio, which estimates the excess return per unit of downside
volatility.
Hybrid Overbought/Oversold OverlayIntroduction
This is a new representation of my well-known oscillator Hybrid Overbought/Oversold Detector overlaid on the chart. The script utilizes the following 12 different oscillators to bring forth a new indicator which I call it Hybrid OB/OS .
Utilized Oscillators
The utilized oscillators here are:
Bollinger Bands %B
Chaikin Money Flow (CMF)
Chande Momentum Oscillator (CMO)
Commodity Channel Index (CCI)
Disparity Index (DIX)
Keltner Channel %K
Money Flow Index (MFI)
Rate Of Change (ROC)
Relative Strength Index (RSI)
Relative Vigor Index (RVI/RVGI)
Stochastic
Twiggs Money Flow (TMF)
The challenging part of utilizing mentioned oscillators was that some of their formulas range are not similar and some of them does not have a mathematical range at all. So I used a normalization function to normalize all their output values to (0, 100) interval.
Overbought/Oversold Levels Calculation
I noticed that the levels which considered as OB/OS level by various traders for each of the utilized oscillators are so different, e.g., many traders consider 30 as OS level and 70 as OB level for RSI and some others take 20 and 80 as the levels, or some traders consider 20 and 80 as OS/OB levels for Stochastic oscillator. Also these levels could be different on different assets, e.g., OB/OS levels for CCI on EURUSD chart might be 80 and 20 while the levels on BTCUSDT chart might be 75 and 25, and so on.
So I decided to make a routine to automate the calculation of these levels using historical data. By this feature, my indicator would calculate the corresponding levels for the oscillators on current chart and then decide about the overbought/oversold situation of each one, which leads to a more accurate Hybrid OB/OS indication.
As the result, if all 12 individual oscillators say it's overbought/oversold, the Hybrid OB/OS shows 100% overbought/oversold, vice versa, if none of them say it's overbought/oversold, the Hybrid OB/OS shows 0, and so on.
The Overlaying Oscillator Problem!
A programming-related challenge here was that Pine Script assigns two separate spaces to the oscillators and the overlaid indicators, and the programmers are limited to use just one of them in each of their codes.
Knowing this, I was forced to simulate the oscillator space on the chart and display my oscillator as a diagram somehow. Of course it won't be as nice as the oscillator itself, because the relation between the main chart bars and the oscillator bars could not be obtained, but it's better than nothing!
Settings and Usage
The indicator settings contain some options about the calculations, the diagram display and the signals appearance. By default they are fine, but you could change them as you prefer.
This indicator is better to be used alongside other indicators as a confirmation (specially in counter-trend strategies I believe). Also it generates an external signal which you could use it in your own designed indicators as well.
Feel free to test it and also the former form of the Hybrid OB/OS . Good Luck!
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
LMACD - Logarithmic MACD Weekly BTC Index [Logue]Logarithmic Moving Average Convergence Divergence (LMACD) Weekly Indicator - The LMACD is a momentum indicator that measures the strength of a trend using 12-period and 26-period moving averages. The weekly LMACD for this indicator is calculated by determining the difference between the log (base 10) of the 12-week and 26-week exponential moving averages. Larger positive numbers indicate a larger positive momentum.
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 line default value is 0.125. This indicator is triggered for a top when the LMACD is above the trigger value.
For bottoms: Bottoms are displayed based on a horizontal line with a default setting of -0.07. The indicator is triggered for a bottom when the LMACD is below the bottom trigger value.
Moving Average PropertiesThis indicator calculates and visualizes the Relative Smoothness (RS) and Relative Lag (RL) or call it accuracy of a selected moving average (MA) in comparison to the SMA of length 2 (the lowest possible length for a moving average and also the one closest to the price).
Median RS (Relative Smoothness):
Interpretation: The median RS represents the median value of the Relative Smoothness calculated for the selected moving average across a specified look-back period (max bar lookback is set at 3000).
Significance: A more negative (larger) median RS suggests that the chosen moving average has exhibited smoother price behavior compared to a simple moving average over the analyzed period. A less negative value indicates a relatively choppier price movement.
Median RL (Relative Lag):
Interpretation: The median RL represents the median value of the Relative Lag calculated for the selected moving average compared to a simple moving average of length 2.
Significance: A higher median RL indicates that the chosen moving average tends to lag more compared to a simple moving average. Conversely, lower values suggest less lag in the selected moving average.
Ratio of Median RS to Median RL:
Interpretation: This ratio is calculated by dividing the median RS by the median RL.
Significance: Traders might use this ratio to assess the balance between smoothness and lag in the chosen moving average. This a measure of for every % of lag what is the smoothness achieved. This can be used a benchmark to decide what length to choose for a MA to get an equivalent value between two stocks. For example a TESLA stock on a 15 minute time frame with a length of 12 has a value (ratio of RS/RL) of -150 , where as APPLE stock of length 35 on a 15 minute chart also has a value (ratio of RS/RL) of -150.
I imply that a MA of length 12 working on TESLA stock is equivalent to MA of length 35 on a APPLE stock. (THIS IS A EXAMPLE).
My assumption is that finding the right moving average length for a stock isn't a one-size-fits-all situation. It's not just about using a fixed length; it's about adapting to the unique characteristics of each stock. I believe that what works for one stock might not work for another because they have different levels of smoothness or lag in their price movements. So, instead of applying the same length to all stocks, I suggest adjusting the length of the moving average to match the values that we know work best for achieving the desired smoothness or lag or its ratio (RS/RL). This way, we're customizing the indicator for each stock, tailoring it to their individual behaviors rather than sticking to a one-size-fits-all approach.
Users can choose from various types of moving averages (EMA, SMA, WMA, VWMA, HMA) and customize the length of the moving average. RS measures the smoothness of the MA, while RL measures its lag compared to a simple moving average. The script plots the median RS and RL values, the selected MA, and the ratio of median RS to median RL on the price chart. Traders can use this information to assess the performance of different moving averages and potentially inform their trading decisions.
Clustered Asset Moving Average @shrilssThe Clustered Asset Moving Average script is designed to provide traders with a unique perspective on a cluster of multiple assets. By combining the closing prices and volumes of 12 specified assets, this indicator calculates a Clustered Moving Average to reveal potential trends and market sentiment within this asset cluster.
Key Features:
- Asset Cluster Analysis:
The script considers 12 assets, including well-known names such as Google (GOOG), Microsoft (MSFT), Apple (AAPL), Tesla (TSLA), and others.
It calculates the price and volume of each asset to form a comprehensive view of the asset cluster.
- Clustered Moving Average Calculation:
The Asset Price and Volume are combined to calculate the Clustered Moving Average
This moving average reflects the relationship between the aggregated price and volume of the specified assets.
- Multiple Exponential Moving Averages (EMA):
The script includes three EMAs (10, 25, and 100) applied to the Clustered Moving Average, providing different time perspectives.
Users can customize the visibility of each EMA based on their trading preferences.
- Visual Representation:
The indicator offers a visual representation of the Clustered Moving Average, allowing traders to quickly identify trends and potential reversal points.
Different EMAs are color-coded, enhancing visual clarity.
HolidayLibrary "Holiday"
- Full Control over Holidays and Daylight Savings Time (DLS)
The Holiday Library is an essential tool for traders and analysts who engage in backtesting and live trading . This comprehensive library enables the incorporation of crucial calendar elements - specifically Daylight Savings Time (DLS) adjustments and public holidays - into trading strategies and backtesting environments.
Key Features:
- DLS Adjustments: The library takes into account the shifts in time due to Daylight Savings. This feature is particularly vital for backtesting strategies, as DLS can impact trading hours, which in turn affects the volatility and liquidity in the market. Accurate DLS adjustments ensure that backtesting scenarios are as close to real-life conditions as possible.
- Comprehensive Holiday Metadata: The library includes a rich set of holiday metadata, allowing for the detailed scheduling of trading activities around public holidays. This feature is crucial for avoiding skewed results in backtesting, where holiday trading sessions might differ significantly in terms of volume and price movement.
- Customizable Holiday Schedules: Users can add or remove specific holidays, tailoring the library to fit various regional market schedules or specific trading requirements.
- Visualization Aids: The library supports on-chart labels, making it visually intuitive to identify holidays and DLS shifts directly on trading charts.
Use Cases:
1. Strategy Development: When developing trading strategies, it’s important to account for non-trading days and altered trading hours due to holidays and DLS. This library enables a realistic and accurate representation of these factors.
2. Risk Management: Trading around holidays can be riskier due to thinner liquidity and greater volatility. By integrating holiday data, traders can better manage their risk exposure.
3. Backtesting Accuracy: For backtesting to be effective, it must simulate the actual market conditions as closely as possible. Incorporating holidays and DLS adjustments contributes to more reliable and realistic backtesting results.
4. Global Trading: For traders active in multiple global markets, this library provides an easy way to handle different holiday schedules and DLS shifts across regions.
The Holiday Library is a versatile tool that enhances the precision and realism of trading simulations and strategy development . Its integration into the trading workflow is straightforward and beneficial for both novice and experienced traders.
EasterAlgo(_year)
Calculates the date of Easter Sunday for a given year using the Anonymous Gregorian algorithm.
`Gauss Algorithm for Easter Sunday` was developed by the mathematician Carl Friedrich Gauss
This algorithm is based on the cycles of the moon and the fact that Easter always falls on the first Sunday after the first ecclesiastical full moon that occurs on or after March 21.
While it's not considered to be 100% accurate due to rare exceptions, it does give the correct date in most cases.
It's important to note that Gauss's formula has been found to be inaccurate for some 21st-century years in the Gregorian calendar. Specifically, the next suggested failure years are 2038, 2051.
This function can be used for Good Friday (Friday before Easter), Easter Sunday, and Easter Monday (following Monday).
en.wikipedia.org
Parameters:
_year (int) : `int` - The year for which to calculate the date of Easter Sunday. This should be a four-digit year (YYYY).
Returns: tuple - The month (1-12) and day (1-31) of Easter Sunday for the given year.
easterInit()
Inits the date of Easter Sunday and Good Friday for a given year.
Returns: tuple - The month (1-12) and day (1-31) of Easter Sunday and Good Friday for the given year.
isLeapYear(_year)
Determine if a year is a leap year.
Parameters:
_year (int) : `int` - 4 digit year to check => YYYY
Returns: `bool` - true if input year is a leap year
method timezoneHelper(utc)
Helper function to convert UTC time.
Namespace types: series int, simple int, input int, const int
Parameters:
utc (int) : `int` - UTC time shift in hours.
Returns: `string`- UTC time string with shift applied.
weekofmonth()
Function to find the week of the month of a given Unix Time.
Returns: number - The week of the month of the specified UTC time.
dayLightSavingsAdjustedUTC(utc, adjustForDLS)
dayLightSavingsAdjustedUTC
Parameters:
utc (int) : `int` - The normal UTC timestamp to be used for reference.
adjustForDLS (bool) : `bool` - Flag indicating whether to adjust for daylight savings time (DLS).
Returns: `int` - The adjusted UTC timestamp for the given normal UTC timestamp.
getDayOfYear(monthOfYear, dayOfMonth, weekOfMonth, dayOfWeek, lastOccurrenceInMonth, holiday)
Function gets the day of the year of a given holiday (1-366)
Parameters:
monthOfYear (int)
dayOfMonth (int)
weekOfMonth (int)
dayOfWeek (int)
lastOccurrenceInMonth (bool)
holiday (string)
Returns: `int` - The day of the year of the holiday 1-366.
method buildMap(holidayMap, holiday, monthOfYear, weekOfMonth, dayOfWeek, dayOfMonth, lastOccurrenceInMonth, closingTime)
Function to build the `holidaysMap`.
Namespace types: map
Parameters:
holidayMap (map) : `map` - The map of holidays.
holiday (string) : `string` - The name of the holiday.
monthOfYear (int) : `int` - The month of the year of the holiday.
weekOfMonth (int) : `int` - The week of the month of the holiday.
dayOfWeek (int) : `int` - The day of the week of the holiday.
dayOfMonth (int) : `int` - The day of the month of the holiday.
lastOccurrenceInMonth (bool) : `bool` - Flag indicating whether the holiday is the last occurrence of the day in the month.
closingTime (int) : `int` - The closing time of the holiday.
Returns: `map` - The updated map of holidays
holidayInit(addHolidaysArray, removeHolidaysArray, defaultHolidays)
Initializes a HolidayStorage object with predefined US holidays.
Parameters:
addHolidaysArray (array) : `array` - The array of additional holidays to be added.
removeHolidaysArray (array) : `array` - The array of holidays to be removed.
defaultHolidays (bool) : `bool` - Flag indicating whether to include the default holidays.
Returns: `map` - The map of holidays.
Holidays(utc, addHolidaysArray, removeHolidaysArray, adjustForDLS, displayLabel, defaultHolidays)
Main function to build the holidays object, this is the only function from this library that should be needed. \
all functionality should be available through this function. \
With the exception of initializing a `HolidayMetaData` object to add a holiday or early close. \
\
**Default Holidays:** \
`DLS begin`, `DLS end`, `New Year's Day`, `MLK Jr. Day`, \
`Washington Day`, `Memorial Day`, `Independence Day`, `Labor Day`, \
`Columbus Day`, `Veterans Day`, `Thanksgiving Day`, `Christmas Day` \
\
**Example**
```
HolidayMetaData valentinesDay = HolidayMetaData.new(holiday="Valentine's Day", monthOfYear=2, dayOfMonth=14)
HolidayMetaData stPatricksDay = HolidayMetaData.new(holiday="St. Patrick's Day", monthOfYear=3, dayOfMonth=17)
HolidayMetaData addHolidaysArray = array.from(valentinesDay, stPatricksDay)
string removeHolidaysArray = array.from("DLS begin", "DLS end")
܂Holidays = Holidays(
܂ utc=-6,
܂ addHolidaysArray=addHolidaysArray,
܂ removeHolidaysArray=removeHolidaysArray,
܂ adjustForDLS=true,
܂ displayLabel=true,
܂ defaultHolidays=true,
܂ )
plot(Holidays.newHoliday ? open : na, title="newHoliday", color=color.red, linewidth=4, style=plot.style_circles)
```
Parameters:
utc (int) : `int` - The UTC time shift in hours
addHolidaysArray (array) : `array` - The array of additional holidays to be added
removeHolidaysArray (array) : `array` - The array of holidays to be removed
adjustForDLS (bool) : `bool` - Flag indicating whether to adjust for daylight savings time (DLS)
displayLabel (bool) : `bool` - Flag indicating whether to display a label on the chart
defaultHolidays (bool) : `bool` - Flag indicating whether to include the default holidays
Returns: `HolidayObject` - The holidays object | Holidays = (holidaysMap: map, newHoliday: bool, holiday: string, dayString: string)
HolidayMetaData
HolidayMetaData
Fields:
holiday (series string) : `string` - The name of the holiday.
dayOfYear (series int) : `int` - The day of the year of the holiday.
monthOfYear (series int) : `int` - The month of the year of the holiday.
dayOfMonth (series int) : `int` - The day of the month of the holiday.
weekOfMonth (series int) : `int` - The week of the month of the holiday.
dayOfWeek (series int) : `int` - The day of the week of the holiday.
lastOccurrenceInMonth (series bool)
closingTime (series int) : `int` - The closing time of the holiday.
utc (series int) : `int` - The UTC time shift in hours.
HolidayObject
HolidayObject
Fields:
holidaysMap (map) : `map` - The map of holidays.
newHoliday (series bool) : `bool` - Flag indicating whether today is a new holiday.
activeHoliday (series bool) : `bool` - Flag indicating whether today is an active holiday.
holiday (series string) : `string` - The name of the holiday.
dayString (series string) : `string` - The day of the week of the holiday.
Buy and hold visualiserThis indicator shows the historical performance of a buy and hold portfolio. The purpose of the indicator is to show
1. the effect of the hold time (time between buying and selling a number of instruments) and
2. the effect of investing all capital at once (lump sum) versus dividing the investment over a number of months or years (cost averaging).
The indicator shows four lines:
- a dotted line at 0 (dollar or any other currency),
- a dotted line at the level of initial investment,
- a blue line that shows the amount of capital after selling at the end of the investment period after a lump sum investment,
- a green line that shows the amount of capital after selling at the end of the investment period after an investment that was done in chunks (cost averaging)
When 'chunks' is set to 1, the green line will match the blue line.
When 'investment' is set to 1, the blue and green lines will show the factor by which the initial investment was multiplied at the end of the investment period.
The effect of the hold time can be easily seen in the following example: Choose SPX (CBOE) as the active instrument, set 'chunks' to 1 and 'months' to 12. Depending on when you bought your portfolio, selling it a year later is like tossing a coin. Set 'months' to 360 and it becomes clear that it doesn't matter when you buy, the value of your portfolio will likely multiply considerably in 30 years, even if you bought everything all at once just before a bear market. It shows that with a long time horizon, you don't have to worry about timing the market.
Continue the example above and set 'chunks' to 12, thus spreading the initial investment over 12 months. The green line shows the cost averaging performance. The blue lump sum line is above the green line most of the time. Increase the chunks to 60 and the difference increases.
Megabar Breakout (Range & Volume & RSI)Hey there,
This strategy is based on the idea that certain events lead to what are called Megabars. Megabars are bars that have a very large range and volume. I wanted to verify whether these bars indicate the start of a trend and whether one should follow the trend.
Summary of the Code:
The code is based on three indicators: the range of the bar, the volume of the bar, and the RSI. When certain values of these indicators are met, a Megabar is identified. The direction of the Megabar indicates the direction in which we should trade.
Why do I combine these indicators?
I want to identify special bars that have the potential to mark the beginning of a breakout. Therefore, a bar needs to exhibit high volume, have a large range (huge price movement), and we also use the Relative Strength Index (RSI) to assess potential momentum. Only if all three criteria are met within one candle, do we use this as an identifier for a megabar.
Explanation of Drawings on the Chart:
As you can see, there is a green background on my chart. The green background symbolizes the time when I'm entering a trade. Only if a Megabar happens during that time, I'm ready to enter a trade. The time is between 6 AM and 4 PM CET. It's just because I prefer that time. Also, the strategy draws an error every time a Megabar happens based on VOL and Range only (not on the RSI). That makes it pretty easy to go through your chart and check the biggest bars manually. You can activate or deactivate these settings via the input data of the strategy.
When Do We Enter a Trade?
We wait for a Megabar to happen during our trading session. If the Megabar is bullish, we open a LONG trade at the opening price of the next candle. If the Megabar is bearish, we open a SHORT trade at the opening price of the next candle.
Where Do We Put Our Take Profit & Stop Loss?
The default setting is TP = 40 Pips and SL = 30 Pips. In that case, we are always trading with a risk-reward ratio of 1.33 by default. You can easily change these settings via the input data of the strategy.
Strategy Results
The criteria for Megabars were chosen by me in a way that makes Megabars something special. They are not intended to occur too frequently, as the fundamental idea of this strategy would otherwise not hold. This results in only 37 closed trades within the last 12 months. If you change the criterias for a megabar to a milder one, you will create more Megabars and therefore more trades. It's up to you. I have adapted this strategy to the 30-minute chart of the EURUSD. In the evaluation, we consider a period of 12 months, which I believe is sufficient.
My default settings for the indicators look like this:
Avg Length Vol 20
Avg Multiplier Vol 3
Avg Length Range 20
Avg Multiplier Range 4
Value SMA RSI for Long Trades 50
Value SMA RSI for Short Trades 70
IMPORTANT: The current performance overview does not display the results of these settings. Please change the settings to my default ones so that you can see how I use this strategy.
I do not recommend trading this strategy without further testing. The script is meant to reflect a basic idea and be used as a tool to identify Megabars. I have made this strategy completely public so that it can be further developed. One can take this framework and test it on different timeframes and different markets.
Stock WatchOverview
Watch list are very common in trading, but most of them simply provide the means of tracking a list of symbols and their current price. Then, you click through the list and perform some additional analysis individually from a chart setup. What this indicator is designed to do is provide a watch list that employs a high/low price range analysis in a table view across multiple time ranges for a much faster analysis of the symbols you are watching.
Discussion
The concept of this Stock Watch indicator is best understood when you think in terms of a 52 Week Range indication on many financial web sites. Taken a given symbol, what is the high and the low over a 52 week range and then determine where current price is within that range from a percentage perspective between 0% and 100%.
With this concept in mind, let's see how this Stock Watch indicator is meant to benefit.
There are four different H/L ranges relative to the chart's setting and a Scope property. Let's use a three month (3M) chart as our example and set the indicator's Scope = 4. A 3M chart provides three months of data in a single candle, now when we set the Scope = 4 we are stating that 1X is going to look over four candles for the high/low range.
The Scope property is used to determine how many candles it is to scan to determine the high/low range for the corresponding 1X, 3X, 5X and 10X periods. This is how different time ranges are put into perspective. Using a 3M chart with Scope = 4 would represent the following time windows:
- 1X = 3M * 4 is a 12 Months or 1 Year High/Low Range
- 3X = 3M * 4 * 3 is a 36 Months or 3 Years High/Low Range
- 5X = 3M * 4 * 5 is a 60 Months or 5 Years High/Low Range
- 10X = 3M * 4 * 10 is a 120 Months or 10 Years High/Low Range.
With these calculations, the indicator then determines where current price is within each of these High/Low ranges from a percentage perspective between 0% and 100%.
Once the 0% to 100% value is calculated, it then will shade the value according to a color gradient from red to green (or any other two colors you set the indictor to). This color shading really helps to interpret current price quickly.
The greater power to this range and color shading comes when you are able to see where price is according to price history across the multiple time windows. In this example, there is quick analysis across 1 Year, 3 Year, 5 Year and 10 Year windows.
Now let's further improve this quick analysis over 15 different stocks for which the indicator allows you to watch up to at any one time.
For value traders this is huge, because we're always looking for the bargains and we wait for price to be in the value range. Using this indicator helps to instantly see if price has entered a value range before we decide to do further analysis with other charting and fundamental tools.
The Code
The heart of all this is really very simple as you can see in the following code snippet. We're simply looking for the highest high and lowest low across the different scopes and calculating the percentage of the range where current price is for each symbol being watched.
scope = baseScope
watch1X = math.round(((watchClose - ta.lowest(watchLow, scope)) / (ta.highest(watchHigh, scope) - ta.lowest(watchLow, scope))) * 100, 0)
table.cell(tblWatch, columnId, 2, str.format("{0, number, #}%", watch1X), text_size = size.small, text_color = colorText, bgcolor = getBackColor(watch1X))
//3X Lookback
scope := baseScope * 3
watch3X = math.round(((watchClose - ta.lowest(watchLow, scope)) / (ta.highest(watchHigh, scope) - ta.lowest(watchLow, scope))) * 100, 0)
table.cell(tblWatch, columnId, 3, str.format("{0, number, #}%", watch3X), text_size = size.small, text_color = colorText, bgcolor = getBackColor(watch3X))
Conclusion
The example I've laid out here are for large time windows, because I'm a long term investor. However, keep in mind that this can work on any chart setting, you just need to remember that your chart's time period and scope work together to determine what 1X, 3X, 5X and 10X represent.
Let me try and give you one last scenario on this. Consider your chart is set for a 60 minute chart, meaning each candle represents 60 minutes of time and you set the Stock Watch indicator to a scope = 4. These settings would now represent the following and you would be watching up to 15 different stocks across these windows at one time.
1X = 60 minutes * 4 is 240 minutes or 4 hours of time.
3X = 60 minutes * 4 * 3 = 720 minutes or 12 hours of time.
5X = 60 minutes * 4 * 5 = 1200 minutes or 20 hours of time.
10X = 60 minutes * 4 * 10 = 2400 minutes or 40 hours of time.
I hope you find value in my contribution to the cause of trading, and if you have any comments or critiques, I would love to here from you in the comments.
Back Week For BacktestIt is Backtest Calculator For Essential and Plus plan holders, the length of available intraday data is calculated as follows: from now to 6 weeks back multiplied by timeframe(in minutes), i.e. you can go 6 weeks back on the 1-minute chart, 12 weeks back on the 2-minute chart, 30 weeks back on the 5-minute chart, 90 weeks back on the 15-minute chart and so on. The higher timeframe is selected, the more intraday data is available.
This show creates a weekday label based on the data in the plans allowed by TradingView. This show creates a weekday label based on the data in the plans allowed by TradingView. How much data is available for Bar Replay? According to the article, we can replay 6 weeks backwards for a 1-minute chart. This indicator is a label that shows how far we can go back, consisting of multiplying each minute by 6 between 1 minute and 60 minutes.
1 minute => 6 week backtest
2 minutes => 12 week backtest
.....
15 minutes => 90 week backtest
...
59 minutes => 354 week backtest
Stock's Intrinsic Value| DCF modelScript Description
This pine script is based on a YouTube video titled: Warren Buffett: How to Calculate the Intrinsic Value of a Stock. Warren Buffett is a famous value investor who follows the principles of his mentor Benjamin Graham. He looks for companies that have strong competitive advantages, consistent earnings, and low debt. He also considers the intrinsic value of a company, which is the present value of its future cash flows, and compares it to the market price. He prefers to buy stocks that are trading below their intrinsic value and hold them for a long time.
One of the methods that Buffett uses to estimate the intrinsic value of a company is the discounted cash flow (DCF) model. This involves projecting the free cash flow (FCF) of the company for several years and then discounting it back to the present using an appropriate discount rate. The discount rate is usually the weighted average cost of capital (WACC) of the company, which reflects its cost of equity and debt. The sum of the discounted FCFs and terminal value is the intrinsic value of the company.
Lastly, a margin of safety is included when using the DCF method for stock valuation because of uncertainty and error in estimating future cash flows and the intrinsic value of the company.
When the current price is below margin of safety, it means that the stock is currently undervalued and being price at significantly below its intrinsic value.
Guideline for determining each variable in this script
FCF growth rate: This is the annual rate at which the free cash flow (FCF) of the company is expected to grow over a forecast 10-year period. You can use historical FCF growth rates, industry averages, analyst estimates, or your assumptions to project the FCF growth rate. The higher the FCF growth rate, the higher the intrinsic value will be.
Discount rate: This is the rate of return that you require to invest in the company. It reflects the risk and opportunity cost of investing in the company. You can use the weighted average cost of capital (WACC) of the company, capital pricing model (CAPM), hurdle rate, or market rate as the discount rate. The lower the discount rate, the higher the intrinsic value.
The margin of safety: Provides a cushion against errors in the valuation or adverse events that may affect the company. The margin of safety depends on your personal preference and risk tolerance. Normally is at 15% - 30%, the higher the margin of safety you set, the lower the chance that the stock will hit that level.
How to use this script
Step 1: This script only works for stocks that have financial data of free cash flow and total common shares outstanding
Step 2: Please use a yearly chart (12-month chart)
Step 3: You are required to determine a growth rate that will grow the free cash flow 10 years into the future
Step 4: You are required to determine a discount rate for the calculations
Step 5: You are required to add a margin of safety (Accounting for uncertainty)
Step 6: The rest of the calculations will be done automatically.
Disclaimer when using this script
I'm not a financial advisor
This script is for education purposes only
There are risks involved with stock market investing and investors should not act upon the content or information found here without first seeking advice from an accountant, financial planner, lawyer or other professional.
I can’t guarantee that this script will be error-free as I still consider myself a Pinescript beginner
Before making any decisions, investors should always research companies individually
I'll not be liable for any loss incurred, arising from the use of, or reliance on, this script
Limitations of this script
This script only works on the yearly chart (12 monthly charts)
The intrinsic value of a company will be negative if the company have a negative forecasted free cash flow
You need to make an educated guess about the growth rate, discount rate and margin of safety
This script uses free cash flow instead of owner's earnings (Operating cash flow - Maintenance capital expenditure), therefore it can't accurately estimate the maintenance capital expenditure.
Need at least 6 years’ worth of financial data
Market capitalisation uses total common shares outstanding multiplied by the closing price instead of using company-level total outstanding shares multiplied by the closing price
Bitcoin Google Trends OverlayThis indicator overlays Bitcoin Google trends data starting from 16/12/2018 until 10/12/2023. To have more recent data, you will need to update the data points manually.
If it is not showing properly, you need to plot the indicator to a new scale. Try also to use a logarithmic scale to better correlate the Bitcoin Google Trends data.
Interpretation:
Google Trends data and the Bitcoin price are very correlated. Google Trends data is a good indicator of market sentiment, but it usually lags.
Monthly Price Insights V1 [PINESCRIPTLABS]This indicator displays a chart table that compares the current price of the asset with the closing prices of the last 12 months. This table includes columns for the month, the current price, the previous price, and the percentage change.
Monthly Color Visualization: The indicator uses unique colors for each month, facilitating quick identification of the current and previous periods on the trading chart.
Calculation of Percentage Changes: The indicator calculates the percentage change in price from the previous month, providing a clear perspective on the price trend over time.
Dynamic Data Update: It automatically updates to reflect changes in the asset's price.
Español:
Este indicador muestra una tabla en el gráfico que compara el precio actual del activo con los precios de cierre de los últimos 12 meses. Esta tabla incluye columnas para el mes, el precio actual, el precio anterior y el cambio porcentual.
Visualización por Colores Mensuales: El indicador utiliza colores únicos para cada mes, facilitando la identificación rápida del periodo actual y los anteriores en el gráfico de trading.
Cálculo de Cambios Porcentuales: El indicador calcula el cambio porcentual en el precio desde el mes anterior, proporcionando una perspectiva clara de la tendencia del precio a lo largo del tiempo.
Actualización Dinámica de Datos: Se actualiza automáticamente para reflejar los cambios en el precio del activo.
AMDX-XAMDGuided by ICT tutoring and also inspired by the teaching of
Daye', I create this versatile "AMDX" indicator.
A = Accumulation
M = Manipulation
D = Distribution
X = Continuation Or Reversal
This indicator shows a different way of viewing all the Timeframes by dividing them into Quarters, in this context the Trading sessions are divided into a 90m cycle, dividing each time range into Q1-Q2-Q3-Q4, in this way you have a clear vision of what the price is likely to do
True Open Times =
Opening Week - Monday at 6pm
Opening Day - 00:00
Asia -7.30pm
London -01.30
New York -07:30
PM -1.30pm
Session Times =
Q1 Asia 18:00-00:00
Q2 London 00:00-06:00
Q3 New York 06:00-12:00
Q4 PM 12:00-18:00
The user has the possibility to:
- Choose whether to display AMDX W
- Choose whether to display AMDX D
- Choose whether to display AMDX Session
- Choose to show the text in the Box
- Choose to show open levels
The indicator should be used as ICT and 'Daye' show in their concepts.
The indicator divides everything into Quarter ranges and classifies them into Q1-Q2-Q3-Q4 (as in the example above), and each Quarter has its own specific function, and can be used in this way:
If Q1 does an expansion it is likely that Q2 will do a consolidation, Q3 will do a Manipulation and Q4 will do a reversal returning to Q1
-If we are Bullish we buy under Open Session
-If we are Bearish we buy above open session
As in the example below:
If something is not clear, comment below and I will reply as soon as possible.
Ultimate Seasonality Indicator [SS]Hello everyone,
This is my seasonality indicator. I have been working on it for like 2 months, so hope you like it!
What it does?
The Ultimate Seasonality indicator is designed to provide you, the trader, an in-depth look at seasonality. The indicator gives you the ability to do the following functions:
View the most bearish and bullish months over a user defined amount of years back.
View the average daily change for each respective months over a user defined amount of years back.
See the most closely correlated month to the current month to give potential insights of likely trend.
Plot out areas of High and Low Seasonality.
Create a manual seasonal forecast model by selecting the desired month you would like to model the current month data after.
Have the indicator develop an autoregressive seasonal model based on seasonally lagged variables, using principles of machine learning.
I will go over these functions 1 by 1, its a whopper of an indicator so I will try to be as clear and concise as possible.
Viewing Bullish vs Bearish Months, Average Daily Change & Correlation to Current Month
The indicator will break down the average change, as well as the number of bullish and bearish days by month. See the image below as an example:
In the table to the right, you will see a breakdown of each month over the past 3 years.
In the first column, you will see the average daily change. A negative value, means it was a particularly bearish month, a positive value means it was a particularly bullish month.
The next column over shows the correlation to the current dataset. How this works is the indicator takes the size of the monthly data for each month, and compares it to the last X number of days up until the last trading day. It will then perform a correlation assessment to see how closely similar the past X number of trading days are to the various monthly data.
The last 2 columns break down the number of Bullish and Bearish days, so you can see how many red vs green candles happened in each respective month over your set timeframe. In the example above, it is over the pats 3 years.
Plot areas of High and Low Seasonality
In the chart above, you will see red and green highlighted zones.
Red represents areas of HIGH Seasonality .
Green represents areas of LOW Seasonality .
For this function, seasonality is measured by the autocorrelation function at various lags (12 lags). When there is an average autocorrelation of greater than 0.85 across all seasonal lags, it is considered likely the result of high seasonality/trend.
If the lag is less than or equal to 0.05, it is indicative of very low seasonality, as there is no predominate trend that can be found by the autocorrelation functions over the seasonally lagged variables.
Create Manual Seasonal Forecasts
If you find a month that has a particularly high correlation to the current month, you can have the indicator create a seasonal model from this month, and fit it onto the current dataset (past X days of trading).
If we look at the example below:
We can see that the most similar month to the current data is September. So, we can ask the indicator to create a seasonal forecast model from only September data and fit it to our current month. This is the result:
You will see, using September data, our most likely close price for this month is 450 and our model is y= 1.4305x + -171.67.
We can accept the 450 value but we can use the equation to model the data ourselves manually.
For example, say we have a target price on the month of 455 based on our own analysis. We can calculate the likely close price, should this target be reached, by substituting this target for x. So y = 1.4305x + -171.67 becomes
y = 1.4305(455) +- 171.67
y = 479.20
So the likely close price would be 479.20. No likely, and thus its not likely we are to see 455.
HOWEVER, in this current example, the model is far too statistically insignificant to be used. We can see the correlation is only 0.21 and the R squared is 0.04. Not a model you would want to use!
You want to see a correlation of at least 0.5 or higher and an R2 of 0.5 or higher.
We can improve the accuracy by reducing the number of years we look back. This is what happens when we reduce the lookback years to 1:
You can see reducing to 1 year gives December as the most similar month. However, our R2 value is still far too low to really rely on this data whole-heartedly. But it is a good reference point.
Automatic Autoregressive Model
So this is my first attempt at using some machine learning principles to guide statistical analysis.
In the main chart above, you will see the indicator making an autoregressive model of seasonally lagged variables. It does this in steps. The steps include:
1) Differencing the data over 12, seasonally lagged variables.
2) Determining stationarity using DF test.
3) Determining the highest, autocorrelated lags that fall within a significant stationary result.
4) Creating a quadratic model of the two identified lags that best represents a stationary model with a high autocorrelation.
What are seasonally lagged variables?
Seasonally lagged variables are variables that represent trading months. So a lag of 25 would be 1 month, 50, 2 months, 75, 3 months, etc.
When it displays this model, it will show us what the results of the t-statistic are for the DF test, whether the data is stationary, and the result of the autocorrelation assessment.
It will then display the model detail in the tip table, which includes the equation, the current lags being used, the R2 and the correlation value.
Concluding Remarks
That's the indicator in a nutshell!
Hope you like it!
One final thing, you MUST have your chart set to daily, otherwise you will get a runtime error. This can ONLY be used on the daily timeframe!
Feel free to leave your questions, comments and suggestions below.
Note:
My "ultimate" indicators are made to give the functionality of multiple indicators in one. If you like this one, you may like some of my others:
Ultimate P&L Indicator
Ultimate Customizable EMA/SMA
Thanks for checking out the indicator!
[blackcat] L3 MACD and RSI Fusion The MACD and RSI fusion is a popular technical analysis strategy used by traders to identify buy and sell signals in the market. The strategy makes use of two popular technical indicators, the Moving Average Convergence Divergence (MACD) and the Relative Strength Index (RSI), and combines them to create a powerful trading signal.
The MACD and RSI fusion was originally developed for the Chinese stock market and is commonly used by traders all over the world. The strategy is based on the idea that the MACD and RSI indicators can be used together to provide a more accurate and reliable signal.
To use the MACD and RSI fusion , traders need to follow a few simple steps. The following code is the TradingView Pine script v4 indicator equivalent of the original MACD and RSI fusion code:
```
//@version=4
study(" MACD and RSI fusion ", overlay=false)
// Define the simple fusion indicator
simple_fusion = (ema(close, 12) - ema(close, 26)) * 1.2 + rsi(close, 14) / 50
// Define the simple fusion lag indicator
simple_fusion_lag = nz(simple_fusion )
// Plot the simple fusion and simple fusion lag indicators
plot(simple_fusion, color=color.blue, title="simple fusion")
plot(simple_fusion_lag, color=color.red, title="simple fusion Lag")
```
This code defines the simple fusion and simple fusion Lag indicators and plots them on the chart. The simple fusion indicator is the sum of the 12- and 26-period exponential moving averages of the closing price, multiplied by 1.2, and added to the 14-period relative strength index of the closing price, divided by 50. The simple fusion Lag indicator is the value of the simple fusion indicator from the previous period.
Traders can use the simple fusion and simple fusion Lag indicators to identify buy and sell signals. When the simple fusion indicator crosses above the simple fusion Lag indicator, it is a buy signal, and when the simple fusion indicator crosses below the simple fusion Lag indicator, it is a sell signal.
In conclusion, the MACD and RSI fusion is a simple but powerful technical analysis strategy that combines two popular technical indicators to identify buy and sell signals in the market.
Yesterday's High v.17.07Yesterday’s High Breakout it is a trading system based on the analysis of yesterday's highs, it works in trend-following mode therefore it opens a long position at the breakout of yesterday's highs even if they occur several times in one day.
There are several methods for exiting a trade, each with its own unique strategy. The first method involves setting Take-Profit and Stop-Loss percentages, while the second utilizes a trailing-stop with a specified offset value. The third method calls for a conditional exit when the candle closes below a reference EMA.
Additionally, operational filters can be applied based on the volatility of the currency pair, such as calculating the percentage change from the opening or incorporating a gap to the previous day's high levels. These filters help to anticipate or delay entry into the market, mitigating the risk of false breakouts.
In the specific case of INJ, a 12% Take-Profit and a 1.5% Stop-Loss were set, with an activated trailing-stop percentage, TRL 1 and OFF 0.5.
To postpone entry and avoid false breakouts, a 1% gap was added to the price of yesterday's highs.
Name: Yesterday's High Breakout - Trend Follower Strategy
Author: @tumiza999
Category: Trend Follower, Breakout of Yesterday's High.
Operating mode: Spot or Futures (only long).
Trade duration: Intraday.
Timeframe: 30M, 1H, 2H, 4H
Market: Crypto
Suggested usage: Short-term trading, when the market is in trend and it is showing high volatility.
Entry: When there is a breakout of Yesterday's High.
Exit: Profit target or Trailing stop, Stop loss or Crossunder EMA.
Configuration:
- Gap to anticipate or postpone the entry before or after the identified level
- Rate of Change for Entry Condition
- Take Profit, Stop Loss and Trailing Stop
- EMA length
Backtesting:
⁃ Exchange: BINANCE
⁃ Pair: INJUSDT
⁃ Timeframe: 4H
- Treshold: 1
- Gap%: 1
- SL: 1.5
- TP:12
- TRL: 1
- OFF-TRL: 0.5
⁃ Fee: 0.075%
⁃ Slippage: 1
- Initial Capital: 10000 USDT
- Position sizing: 10% of Equity
- Start : 2018-07-26 (Out Of Sample from 2022-12-23)
- Bar magnifier: on
Credits: LucF for Pine Coders (f_security function to avoid repainting using security)
Disclaimer: Risk Management is crucial, so adjust stop loss to your comfort level. A tight stop loss can help minimise potential losses. Use at your own risk.
How you or we can improve? Source code is open so share your ideas!
Leave a comment and smash the boost button!
Thanks for your attention, happy to support the TradingView community.
ICT Sessions_One Setup for Life [MK]The script plots the High/Low of the following trading sessions:
London - 02:00-05:00
NY AM - 09:30-12:00
New York Lunch - 12:00-13:30
New York PM - 13:30-16:00
Due to the high level of liquidity (resting orders), highs and lows of these sessions may be used as buy/sell areas and also as profit target areas. Typically, buy orders would be initiated below a session low and sell orders would be initiated above a
session high.
The script also plots 'RTH (Regular Trading Hours) Opening Gaps'. The RTH gaps are drawn from the closing price of regular trading at 16:15 (EST) to the open price of regular trading at 09:30 (EST). Gaps can be areas that traders might anticipate to be filled at some time in the future. A gap 'midline' is available if needed and yesterday RTH close line can be shown and extended to the current bar.
This script is simply a means to draw boxes around certain areas/periods on the charts. It is in no way a trading strategy and users should spend much time to study the concept and should also perform extensive back-testing before taking any trades.
By setting the lookback value to a much higher value then the default of 6, users can utilise the script to perform their own backtesting studies.
The above chart shows the default setup of the indicator. Note that the user has to choose how far (in days) to lookback and draw the sessions/gaps.
It is also possible to show the session high//low lines and extend them to the current bar time. If this is used it is advised to keep the lookback period as low as possible to ensure charts stay clean/uncluttered.
All boxes/lines styles/colors are fully customisable.
CoffeeShopCrypto 3pl MAThe CoffeeShopCrypto 3pl MA indicator is a technical analysis tool that uses three different moving averages to identify trends in the price of an asset. The three moving averages have lengths of 12, 26, and 50. If these numbers sound familiar its because they are based off the standard of the MACD indicator, and can be either simple moving averages (SMA) or exponential moving averages (EMA), depending on user preference.
The following is plotted on the chart
The fast EMA/SMA (based on the 12-period length) in yellow.
The mid EMA/SMA (based on the 26-period length) in gray.
The slow EMA/SMA (based on the 50-period length) in either green or red, depending on whether the current close price is above or below the Overall Trend MA.
In addition to the moving averages, the indicator also calculates the MACD (Moving Average Convergence Divergence), and uses it to color the bars based on the momentum of the asset.
The MACD is calculated using two user-defined lengths (fast and slow), as well as a user-defined smoothing length for the signal line. The oscillator and signal line can be either SMA or EMA, and the colors of the MACD bars are based on whether the histogram is growing or falling, and whether it is above or below the zero line.
Overall, this indicator provides traders with a comprehensive tool for understanding the trend of an asset, as well as the momentum behind that trend. The moving averages provide a clear visual representation of the trend, while the MACD bars give insight into the strength of that trend and potential shifts in momentum.
---------------LONG ENTRY----------------
MA1 above MA2 and Overall trend = Green
IF RSI is above its midline you are confirmed for a long entry
-----------Short Entry--------------
MA1 below MA2 and Overall trend = Red
IF RSI is below its midline you are confirmed for a short entry
